AES/TG/14-02 An integrated study of Permian Rotliegend ...

AES/TG/14-02 An integrated study of Permian Rotliegend reservoir rocks in the Greater Ameland Area

21-02-2014 N.E. Clerx

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Title : An integrated study of Permian Rotliegend reservoir rocks in the

Greater Ameland Area

Author(s) : N.E. Clerx

Date : February 21st, 2014

Professor(s) : Dr. M.E. Donselaar Supervisor(s) : F.R. Pardoel, D.J. van Leverink, M.W. Ecclestone TA Report number : AES/TG/14-02

Postal Address : Section for Applied Geology Department of Geoscience & Engineering Delft University of Technology

P.O. Box 5028 The Netherlands Telephone : (31) 15 2781328 (secretary)

Telefax : (31) 15 2781189

Copyright © 2014 Section for Applied Geology

All rights reserved. No parts of this publication may be reproduced, Stored in a retrieval system, or transmitted, In any form or by any means, electronic, Mechanical, photocopying, recording, or otherwise, Without the prior written permission of the Section for Applied Geology

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Rotliegend reservoir rocks in the Greater Ameland Area January 2014

An integrated study of Permian Rotliegend reservoir rocks in the Greater Ameland Area A proposed refined sedimentological model of distal Upper Rotliegend deposits

based on core analysis, and its relation to the Base Permian Unconformity

AES2006 – Graduation thesis

February 21st, 2014

Nicole Clerx

Abstract Despite extensive research that has been conducted during the past twenty years, the exact

geological history of the Greater Ameland Area remains enigmatic. Sediment distribution and diagenetic

processes cause marked and unexpected variation in well productivity in the study area. However, the

exact causes for this are not fully understood.

The goal of this thesis is to build on current understanding so as to improve geological understanding of

reservoir quality distribution, specifically in terms of sedimentology and structural evolution so as to

better explain production behaviour in the Greater Ameland Area.

During reservoir rock deposition the Greater Ameland Area was situated in the distal part of the

Southern Permian Basin, an elongate E-W trending land-locked basin extending from Poland to the UK.

Gas is currently produced from distal Rotliegend aeolian sandstones that exhibit relatively poor porosity

and permeability properties.

Literature research, extensive core study, well correlation, seismic interpretation and various

techniques for heterogeneity quantification are the main methods that were used for the research

described in this thesis.

This thesis presents a newly devised facies classification, based on soft sediment deformation

intensity, which provides increased insight into specific details of the sedimentary environment

prevailing during the deposition of Upper Rotliegend reservoir sandstones. Climatic variations are

dominant in governing sedimentary processes by means of water table fluctuations, seconded by

palaeotopographic variation that locally leads to differences in base level. Soft sediment deformation

complicates the relationship between depositional processes and reservoir quality distribution. The

study has not identified any conclusive findings in terms of how this complication can be incorporated in

reservoir quality modelling. Heterogeneity quantification is instrumental for understanding and

predicting reservoir quality distribution, but the various methods that were applied provide unequivocal

results. Sediment distribution, and hence the areal distribution of reservoir quality was influenced by

the pre-existing Variscan structural framework and associated palaeotopography. This is expressed by

onlap of Permian sediments on Carboniferous deposits, and by distinct thickness variations within the

Upper Rotliegend.

The study results provide better insight into sedimentological processes during deposition of

Permian Rotliegend reservoir rocks and the resulting reservoir quality distribution in a distal aeolian-

fluvial setting.

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Contents

Topic Section-Page

Front page i

Abstract ii

Contents iii

List of Figures iv

List of Tables v

1. Introduction 1-1

2. Geological Setting 2-4

3. Available data 3-12

4. Methods 4-16

5. Project results 5-22

a) Sedimentology 5-22

b) Reservoir quality distribution 5-49

c) Seismic interpretation 5-72

6. Discussion 6-82

a) Sedimentology 6-82

b) Reservoir quality distribution 6-83

c) Seismic interpretation 6-84

7. Conclusions & recommendations 7-86

Acknowledgements 88

References 89

Appendices 93

iv


List of figures Figure Section-Page

Figure 1.1 – Overview of the study area 1-2

Figure 2.1 – Structural elements in the Dutch subsurface 2-4

Figure 2.2 – Conceptual model of the sedimentary environment during Permian deposition 2-6

Figure 2.3 – Thickness distribution of Rotliegend sediments in the SPB 2-7

Figure 2.4 – Thickness of the Zechstein Group throughout the SPB 2-8

Figure 2.5 - Conceptual facies distribution for the Slochteren Formation in the Netherlands 2-9

Figure 2.6 – Schematic diagram of lake level variation in the sedimentary environment 2-10

Figure 2.7 – Schematic overview of the sedimentary succession in the Dutch part of the SPB 2-11

Figure 3.1 – Overview of the available data 3-13

Figure 3.2 – Well locations in the study area 3-14

Figure 3.3 – Location overview of the seismic data used 3-15

Figure 4.1 – Schematic overview of incorporiation of available data 4-17

Figure 4.2 – Schematic drawing of a Lorenz curve 4-19

Figure 5.1 - Schematic overview of dominant depositional environments in the SPB 5-23

Figure 5.2 – Reservoir unit subdivision scheme as used in the GAA 5-23

Figure 5.3 – Overview of well locations of which core material was studied 5-25

Figure 5.4 – Examples of various types of SSD-structures 5-28

Figure 5.5 – Observed soft sediment deformation features 5-29

Figure 5.6 – Core photographs showing SSD-facies types 5-33

Figure 5.7 – Conceptual sedimentological model 5-34

Figure 5.8 – Overview of the study area with SSD-well correlations 5-35

Figure 5.9 – N—S well correlation 5-37

Figure 5.10 – W—E well correlation 5-38

Figure 5.11 – W—E well correlation 5-39

Figure 5.12 – Interpolated ‘average SSD-facies’ maps 5-40

Figure 5.13 – Bar chart of (lag-)facies occurrence 5-41

Figure 5.14 – Bar chart of transition probability matrix 5-43

Figure 5.15 – Bar chart of lag-transition probability matrix 5-44

Figure 5.16 – Bar chart of the second order facies transition probability matrix 5-45

Figure 5.17 – Bar chart of the second order lag-facies transition probability matrix 5-46

Figure 5.18 – Proposed refined conceptual sedimentological model 5-48

Figure 5.19 – Mineralogical trend map of diagenetic clays throughout the GAA 5-51

Figure 5.20 – Facies distribution in and around the GAA 5-53

Figure 5.21 – Shale content distribution map for all ROSLU flow units 5-54

Figure 5.22 – Porosity distribution maps 5-56

Figure 5.23 – Permeability distribution maps 5-57

Figure 5.24 – Permeability characteristics of aeolian dune and interdune deposits 5-58

Figure 5.25 – Location map of wells AME-107, AMN- 1, MGT- 1B and TRN- 1 5-59

Figure 5.26 – SMLP for well AME-107 5-61

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Figure 5.27 – SMLP with shifted depth interval for the plug data points 5-62

Figure 5.28 – SMLP with shifted depth interval for plug data points and an adapted log depth range 5-63

Figure 5.29 – SMLP with shifted depth interval for the plug data and stretch-corrected log data 5-65

Figure 5.30 – Map view of Gini coefficients averaged for the complete ROSLU-interval 5-66

Figure 5.31 – Map view of Gini coefficients for ROSLU4 5-67

Figure 5.32 – Section of ROSLU4 in well AME-203 5-69

Figure 5.33 – Porosity-permeability relationship per SSD-facies 5-70

Figure 5.34 – Map indicating the location of wells that have penetrated Carboniferous strata 5-74

Figure 5.35 – Overview of interpretation grid after first-pass interpretation 5-74

Figure 5.36 – Seismic W-E trending cross-section through multiple well locations 5-75

Figure 5.37 – N-S seismic section (inline 6244) 5-76

Figure 5.38 – Overview of lateral extent of seismic cubes 5-77

Figure 5.39– Thickness map of the Rotliegend Group in the GAA 5-78

Figure 5.40 – N—S and E—W well correlation 5-79

Figure 5.41 – N—S and E—W well correlation for the lower part of the Rotliegend Group 5-80

List of tables Table Section-Page

Table 5.1 – Lithofacies classification scheme used within NAM 5-22

Table 5.2 – Table listing core intervals studied during the core study 5-25

Table 5.3 – Definition of the SSD -facies classification and their characteristics 5-32

Table 5.4 – Facies occurrence for the SSD-facies log and the lag-distance SSD-facies 5-41

Table 5.5 – Average difference between facies occurrence and lag facies occurrence 5-42

Table 5.6 – Transition probability matrix 5-42

Table 5.7 – Lag-transition probability matrix 5-44

Table 5.8 – Second order facies transition probability matrix 5-45

Table 5.9 – Second order lag- facies transition probability matrix 5-45

Table 5.10 – Scales of available data and necessary model input 5-55

Table 5.11– Depth of inflection points in Figure 5.26 calculated based on (a) plug data; (b) log data 5-60

Table 5.12– Depth of inflection points in Figure 5.27 calculated based on (a) plug data; (b) log data 5-61

Table 5.13 – Depth of inflection points in Figure 5.28 calculated based on (a) plug data; (b) log data 5-63

Table 5.14 – Depth of inflection points in Figure 5.29 calculated based on (a) plug data; (b) log data 5-64

Table 5.15 – Gini coefficients for every ROSLU-flow unit 5-65

Table 5.16 – Dykstra Parsons-coefficients for every ROSLU-flow unit 5-66

Table 5.17 – Number of data points per facies for porosity- permeability cross plot 5-68

Table 5.18 – Average mini-permeameter values per facies and associated deviations 5-68

Table 5.19 – Polarity of seismic reflectors indicated by the top Carboniferous well tops 5-76

chapter 1-1 page


1. Introduction The Greater Ameland Area (GAA) is situated on the northern coast of the Netherlands with gas

production facilities located on and around the island of Ameland. Gas production started in 1986, via

wells targeting Permian Rotliegend sandstone reservoirs. Since the production start-up some 44 billion

cubic meters of gas have been produced and the GAA has therefore made a major contribution to the

gas production history of the Netherlands.

For this thesis an extensive study was done on the sedimentology of the Rotliegend red beds in the GAA,

their reservoir quality characteristics and –distribution.

a) Project description This thesis aims to further improve the understanding of the hydrocarbon habitat of- and reservoir

quality of Permian Rotliegend sandstone reservoirs within Greater Ameland Area (GAA), that is located

in the shallow offshore Dutch subsurface.

Study work was carried out as part of an internship with Shell, in the production geology-team of the

ONEgas asset located at NAM, Assen.

The main topic that will be addressed in this thesis is two-fold. Firstly, this thesis will try to improve the

geological understanding of the sedimentology and structural setting of distal Rotliegend deposits. As a

second objective, the knowledge gained through this study will support de-risking undeveloped prospect

in the vicinity of existing hydrocarbon fields and therefore this thesis also has business relevance.

The sedimentological study aims at devising a core description method suitable for better prediction of

reservoir quality distribution. Seismic interpretation of the top of Carboniferous deposits focuses on

assessing the possible presence of palaeotopography prior to sediment deposition and its role with

respect to the (resulting) facies distribution.

Since the early days of hydrocarbon production in the GAA, wells demonstrate unexpected behaviour in

terms of productivity and pressure decline. As stated in one of the older reports, “marked variation

[exists] in well test productivity over relatively short distance” [Cohen et al., 1989]. In spite of a wide

gamma of later studies and reports, so far no satisfactory explanation for this unexpected production

behaviour has been found.

A detailed conceptual sedimentological model was devised based on core analysis. The reservoir quality

properties and their distribution were studied extensively, with particular focus on the characterization

of heterogeneity.

Furthermore, seismic interpretation was carried out to investigate the basal geometry of Rotliegend

deposits and to determine the relation between sediment thickness and the refined sedimentological

model.

chapter 1-2 page


b) Study area The Greater Ameland Area is situated on and around Ameland, one of the Dutch Wadden islands in the

southern North Sea. Figure 1.1 indicates the location of the island, including the main fields.

The area is subdivided into three main fields with varying reservoir quality (see Figure 1.1). In order of

decreasing reservoir quality the Ameland Oost fields (AME) are located in the south west, the Ameland

Westgat domain (AWG) covers the central part of the study area and the Ameland Noord fields (AMN)

bound the GAA in the north.

Figure 1.1 – Overview of the study area, including the main fields (in green) and prospects (in white-green dashes)

The dominant reservoir formations are part of the Permian Rotliegend Group and are aeolian and fluvial

siliciclastics with a thickness of approximately 100m in the south becoming thicker towards the north.

On top of the reservoirs, Permian Zechstein evaporites and carbonates form an effective seal that is

locally up to 800m thick but that has undergone severe halokinesis. The source rock is represented by

Westphalian coals and Namurian carbonaceous shale that belong to the Carboniferous Limburg Group.

Horst blocks represent the main trapping mechanism, and charge occurred from the Jurassic until

present, only interrupted during a period of Late Jurassic uplift. [Grötsch et al., 2011]

The Rotliegend reservoir sediments have been deposited throughout a large part of the Southern North

Sea, but are within the GAA relatively fine-grained. Porosity and permeability is limited, and hence the

Ameland fields are characterized as “tight gas fields”.

Compartmentalization of the reservoir is significant, related to the structural development of the area

and associated tectonic activity. Fault throws of up to 100m are present and this has caused significant

sealing capacity of several inter- and intra-field faults. Therefore pressure communication between

certain reservoirs is limited [Gupta, 2013].

chapter 1-3 page


The Nederlandse Aardolie Maatschappij B.V. (NAM) and Energie Beheer Nederland B.V. (EBN) both hold

a share of 40% of the acreage in the Greater Ameland Area, whereas Exxon Mobil is the third party in

the joint venture with a 20% participation in the revenues derived from gas production in the area.

The prospectivity of the GAA was identified in the 1960’s, when the first exploration wells were drilled

and encountered gas accumulations. Appraisal drilling began in 1973, in the AWG- and AMN-area. Gas

production is confined to the AME- and AWG-fields and started in 1986 and 1993, respectively.

In total, some 52 billion standard cubic meters of connected gas have been identified in place, which

rank the Ameland Oost and –Westgat blocks as the second largest Dutch offshore gas field [Hoetz et al.,

2007].

Within the AMN-area undeveloped accumulations of gas are present, but due to challenging

development- and operating boundary conditions these reservoirs have not been exploited to date.

Identification of opportunities for further development is currently ongoing in the AMN- and northern

part of the AWG-area. Also, attempts to optimize facilities and production activities are done to increase

and postpone ultimate recovery in the GAA.

c) Thesis contents Detailed core study lead to a new facies classification scheme based on soft sediment deformation

intensity. Soft sediment deformation is ubiquitous in all the core material that was studied, and imposes

certain sedimentological mechanisms that provide more insight to the geological development of the

Greater Ameland Area.

A variety of heterogeneity measures have been investigated and provide a link between the updated

sedimentological model and reservoir quality behaviour. However, some of these measures provide

contradictory results. All in all, heterogeneity plays a significant role in determining reservoir quality but

no firm predictive value was established based on the data analysis that was carried out.

Seismic interpretation of the top Carboniferous surface was carried out and confirmed the hypothesis of

an onlap geometry of basal Permian deposits. Interpretation of the surface was challenging, mainly

related to limited data quality and –availability. Thickness maps indicate that the pre-existing structural

grain plays a significant role in sediment- and facies distribution.

chapter 2-4 page


2. Geological Setting This section covers the regional geological history of significance for the evolution of the hydrocarbon

potential in the Greater Ameland Area (GAA). The tectonic framework and regional stratigraphy will be

addressed followed by the associated depositional environment, and lastly the petroleum system will be

set out briefly.

Due to their economic importance, the most

important and intensely studied rocks in the

subsurface of the GAA are Permian red beds

and associated sediments of the Rotliegend

Formation. These rocks have been

deposited throughout a large part of the

Southern Permian Basin (SPB) in an arid

desert environment. The SPB is a large

intracratonic basin which was located at a

palaeolatitude similar to those of the

present-day North African and Arabian

deserts [Glennie, 1998].

a) Tectonic framework Deformation styles and intensity vary

between different regions of the

Netherlands, both on- and offshore.

However, many of the deformational and

tectonostratigraphic features present in the

Dutch subsurface follow basement fault

trends that are deemed to originate from

the Silurian-Early Devonian Caledonian

Orogeny [Ziegler, 1990].

The NW-SE strike of the structural features

caused by the Caledonian structural grain is

recognized in all the major Dutch grabens.

Figure 2.1 provides an overview of the

major structural elements in the Dutch

subsurface and indicates the area of

interest for this study.

Figure 2.1 – Structural elements in the Dutch subsurface during the Late Jurassic to Early Cretaceous. Shaded in white are basins whereas darker colours indicate progressively higher areas. The red circle indicates the location of the study area [Geluk, 2005]

chapter 2-5 page


Three major tectonic events have influenced the evolution of the GAA into becoming a prolific

hydrocarbon province [de Jager, 2007; Ligtenberg et al., 2011]. The first of these occurred in the

Carboniferous, and was related to the Variscan orogeny; secondly there was the break-up of Pangaea in

the Mesozoic; and lastly in the Late Cretaceous-Early Tertiary the period of Alpine Inversion played a

major role for the regional tectonostratigraphic development.

The Variscan Orogeny, associated to the closure of the southern proto-Tethys Ocean and the

agglomeration of Pangaea, took place in the Carboniferous. The Netherlands were located in the

foreland basin of this fold-and-thrust belt, and have been significantly affected by post-orogenic events.

The tectonic activity ceased towards the end of the Carboniferous. This all occurred while the

Netherlands were situated at a palaeolatitude of approximately 20°N [Pharaoh et al., 2010].

During the Permian the E-W trending Southern Permian Basin developed, due to regional subsidence

and early rifting. Sedimentation took place coeval with normal faulting as small pull-apart basins and

tilted fault blocks formed. Subsidence rates were higher than sedimentation rates, invoking catastrophic

flooding and cyclic evaporation of the saline sea in the central part of the basin [Ligtenberg et al., 2011].

The break-up of Pangaea occurred in three distinct Kimmerian rifting ‘phases’.

The first of these, the Early Kimmerian, occurred during the Early Triassic-Early Jurassic and is associated

with the initiation of continental break-up, reflected by the opening of a sea between Norway and

Greenland [Ziegler, 1990].

In the Middle Triassic, the Mid-Kimmerian uplift was expressed by major thermal doming in the Central

North Sea and locally by related volcanism and erosion [Ligtenberg et al., 2011].

The last phase of the break-up of Pangaea, the Late Kimmerian rifting event, was the most significant for

the evolution of the study area, as the tectonic structures currently present in the subsurface developed

at that time. The Late Kimmerian phase spans the Late Jurassic-Early Cretaceous, and during this period

the break-up of Laurasia induced the opening of the Atlantic Ocean. This created transtensional basins

by reactivation of the ancient (Silurian-Early Devonian) structural grain, and is characterized by a rapid

decrease in extension rate in the North Sea [Geluk, 2005].

The stage of Alpine Inversion covers the Late Cretaceous-Early Tertiary. During this period, a

compressional regime reigned by which existing basins were inversed in multiple phases. The intensity

of deformation varies per location, but general features include local overthrusts and pop-ups,

especially along major pre-existing reactivated faults [de Jager, 2007].

b) Stratigraphy Within the tectonic framework described above, a variety of sediments was deposited. However, only

the stratigraphy of interest for the petroleum system in the Southern Permian Basin will be expanded

upon here.

During the Carboniferous, sediment deposition took place in what is generally regarded as the precursor

of the SPB. The Dinantian, the first part of the Carboniferous (ca. 360 – 326 Ma), is characterized by

carbonate deposition in a sediment-starved system in the Old Red Group and the Farne or

chapter 2-6 page


Carboniferous Limestone Group. Open-marine shelf deposits grade into shallow marine fines towards

the basin centre in the north [Kombrink et al., 2010].

The Silesian era is discussed according to its subdivision into the Namurian, Westphalian and Stephanian

stages. All formations deposited in the Silesian belong to what is referred to as the Limburg Group in the

Netherlands with various terminology attached to individual formations. Namurian deposits represent

the transition from a carbonate to siliciclastic depositional regime, and are generally prodelta sediments

and deltaics. During the Westphalian a well-drained fluvial environment existed, with resulting deltaic to

fluvio-lacustrine deposits in which numerous coal layers are present. The Stephanian stage is a

continuation of the progressively more arid setting associated with well-drained alluvial-fan systems

sourced from the Variscan mountain range [Kombrink et al., 2010].

By the end of the Carboniferous, the SPB had become entirely land-locked and was situated at

approximately 10°N latitude [Glennie and Hurst, 2007]. Erosion and non-deposition of Carboniferous

strata in certain locations in the basin gives rise to the widespread presence of the ‘Base Permian

Unconformity’ (BPU). This unconformity represents a hiatus of up to as much as 60 Ma [Geluk, 1999a],

and is an amalgamation of several smaller unconformities [Glennie, 1998].

The lowermost Permian stratigraphic group is the Lower Rotliegend, which consists of successive or

alternating ephemeral fluvial systems and basaltic lava flows related to late Hercynian rifting. The

igneous rocks belong to the Emmen Volcanic Formation, which is not present everywhere.

The Lower Rotliegend Group is only present in Drenthe and in parts of the West Netherlands Basins. In

the northern Dutch offshore, a few occurrences of this group, which are not quite as characteristic since

they lack clear volcanics sediments, have been ascribed to the Lower Rotliegend [Geluk, 2005].

On top of the Lower Rotliegend Group lays the Upper Rotliegend. The Upper Rotliegend Group

comprises coarse- and fine-grained, clastic sediments of predominantly of red-bed type, as well as

evaporites. The Slochteren Formation (ROSL) represents sandy-conglomeratic deposits of the more

proximal part of the sedimentary environment, whereas the claystone-evaporite formation closer to/in

the centre of the basin is called the

Silverpit Formation (ROCL). In general,

the Slochteren Formation is regarded as

fluvial/aeolian whereas the Silverpit

Formation is primarily lacustrine. These

formations are lateral equivalents of

each other and grade over a relatively

narrow transition zone in the present-

day Dutch offshore [McKie, 2011]. The

GAA is located right in this transitional

zone.

A conceptual model illustrating the

sedimentary environment during the

deposition of the Rotliegend Group is

shown in Figure 2.2. Figure 2.2 – Conceptual model of the sedimentary environment during deposition of the Permian Rotliegend; the red circle indicates the location of the study area in this depositional setting; dashed box indicates transition from Slochteren to Silverpit formation [McKie, 2011]

chapter 2-7 page


The top of the Upper Rotliegend Group is represented by its contact with the Zechstein Group. The base

of the Upper Rotliegend is located on top of the unconformable contact with the Carboniferous

deposits, or locally on top of the volcanics of the Lower Rotliegend Group.

The Silverpit and Slochteren Formations interfinger a number of times, which is interpreted to be as a

result of climatic variations [Trusheim, 1971; Hedemann et al., 1984]. The three most prominent Silverpit

intercalations are related to longer periods of lake expansion. A clear proximal-distal basin trend from

south to north can be seen in the thickness of the deposits: the Silverpit tongues gradually grade into

the Slochteren formation towards the south. Regional log correlation suggests that lake transgression

and retrogradation was a gradual, multiphase process, whereas subsequent progradations of the

Slochteren Formation (perhaps tectonically related) into the basin occurred relatively rapidly [van

Adrichem Boogaert and Kouwe, 1993-1997a].

The Silverpit Formation consists of dominantly red to brown silty shales, with frequent anhydrite

nodules and cement, and local sandstone layers. Towards the centre of the basin, anhydrite becomes

more ubiquitous and even forms intercalations with the claystones [Fryberger et al., 2011].

As the distal equivalent of the Slochteren Formation, the Silverpit Formation is present throughout

almost the entire part of the Southern

Permian Basin. Its maximum thickness

can be found in the eastern offshore,

where total vertical thickness is up to

approximately 325 meters. Figure 2.3

illustrates the combined thickness of

both Slochteren Members throughout

the basin [Gast et al., 2010].

Three different members have been

identified in the Silverpit Formation,

which extend southwards and cover

(parts of) the Slochteren Formation

near the basin fringes. The Ameland,

Hollum & Ten Boer Members all

intercalate with the more proximal

deposits. The Ten Boer Member

extends further south than the

Ameland Member, which in turn

protrudes much further than the

Hollum Member [Gast et al., 2010]

Figure 2.3 – Thickness distribution of Rotliegend sediments in the Southern Permian Basin [Gast et al., 2010]

chapter 2-8 page


The most important reservoir rocks in the Greater Ameland Area belong to the Slochteren Formation,

which is named after the village where this formation was first encountered to be gas-bearing. The

Slochteren Formation is generally light red/pink to yellow/grey sandstones and conglomerates, with

subordinate intercalations of dark red to green/grey silty claystones [van Adrichem Boogaert and

Kouwe, 1993-1997a].

The Slochteren Formation is generally subdivided into the Lower and Upper Slochteren Members. The

Lower Slochteren is a sandstone-dominated succession with considerable local intervals of

conglomerate. It is covered by the Ameland Member towards the centre of the basin. The Upper

Slochteren Member lies on top of the Ameland Member, and its upper limit is marked by either the red

to brown claystones of the Ten Boer Member or by the black bituminous shales of the Coppershale

Member of the Zechstein Formation. The Upper Slochteren Member has a smaller areal distribution

than the Lower Slochteren Member, as it is restricted to the transitional area between the basin and the

fringes of the SPB [Fryberger et al., 2011].

The top of the Rotliegend Group is

marked by an angular unconformity

or a disconformity. The stratigraphic

group is overlain by the black shales

of the Zechstein Group and in

specific by the Coppershale

Member, which marks the rapid

transition to fully marine conditions

by catastrophic flooding. The

Zechstein formations satisfy the

classical model of cyclic chemical

precipitation in a giant basin: of

transgressional carbonates and

mudstones followed by evaporites.

Zechstein stratigraphy is well-

defined and the individual cycles are

clearly correlatable across the whole

SPB [Peryt et al., 2010]. Figure 2.4

gives an overview of the thickness

distribution of the Zechstein Group

throughout the SPB.

Figure 2.4 – Thickness of the Zechstein Group throughout the Southern Permian Basin [Peryt et al., 2010]

chapter 2-9 page


c) Depositional environment The reservoir rocks of the Greater Ameland Area studied during this project are of Permian age, and

were deposited in the Southern Permian Basin (SPB). The areal extent of the Southern Permian Basin

ranged from the east of England to the eastern border of Poland in E-W direction, and from the

southern Baltic Sea to the upland areas of Belgium (N-S). The basin depocentre was located in the area

offshore the northern Netherlands and Germany [van Ojik et al., 2011].

A schematic overview of the facies distributions sedimentary environment is given in Figure 2.5.

Figure 2.5 - Conceptual facies distribution for the fluvially dominated Slochteren Formation in the Netherlands. The study area is indicated by the red circle, the most important structural elements delineated in black (modified after [McKie, 2011])

The sedimentary environment is progressively more distal towards the north: the playa lake is in the

central part of the SPB, as can be seen in Fig. 2.5. Feeders of the fluvial systems predominantly run from

south to north, towards the basin centre. The main sediment source was situated at the southern edge

of the basin, in the London-Brabant Massif. Prevailing wind direction is from the east, and dune

sedimentation occurs in the dry area caused by the rain shadow of the existing topography in the south.

The average sedimentation rate along the southern fringes of the SPB was low, on average

approximately 0.1 mm/yr [McKie, 2011].

chapter 2-10 page


The tectonic setting and the geographic location of the SPB are the main controls for the

palaeogeographic depositional environment which resulted in the present-day stratigraphy in the Dutch

subsurface [McKie, 2011].

During the deposition of the Rotliegend, climatic forcing

influenced the progressive basin infill. Five large-scale

stratigraphic cycles have been identified, with internal

drying-upward and drying-wetting-upward sequences

sketch the sequence stratigraphic setting [Minervini et al.,

2011]. The lowermost boundary of the sedimentary

succession corresponds to the BPU, and is overlain by a

retrogradational sequence which covers two cycles (U1 &

U2) in which the Lower Slochteren formation is deposited.

During this period, the increase of accommodation space

was larger than the sediment supply. The first

stratigraphic cycle (U1) represents a relatively humid

climate which is ended by the first aridity peak belonging

to the second large-scale cycle (U2).

The first Upper Rotliegend-sediments were deposited

during the third cycle (U3), after a maximum-flooding

surface recording the most important lake expansion.

From this time on, the generation of accommodation space

was balanced by aeolian and fluvial sediment supply,

hence invoking the change from a backstepping to a forestepping sequence. The fourth stratigraphic

cycle (U4) is characterized by another aridity peak resulting in significant lake contraction, whereas the

sequence is ended by a last progradational, again humid period (U5).

A schematic diagram of lake level and the humidity of the sedimentary environment throughout the

deposition of the Rotliegend Group can be seen in Figure 2.6.

The exact initiation of Permian sedimentation across the Netherlands has not been dated in an absolute

manner; the first Rotliegend sediments are assumed to be approximately 263 Ma old. The end of

Rotliegend sedimentation occurred quite abruptly, when the SPB was flooded and drowned at 257.3 ±

1.6 Ma, invoking the deposition of the Zechstein marine evaporitic- and carbonate sequence [van Ojik et

al., 2011].

The northern part of the Netherlands lay at the fringes of the low-angle sloped SPB, where dominantly

aeolian, fluvial and lacustrine sedimentation occurred. Cyclic variations in lake level and sediment supply

caused the sedimentary succession which is depicted in Figure 2.7.

Figure 2.6 – Schematic diagram of lake level variation and ‘wetness’of the sedimentary environment during the deposition of various Rotliegend Formations (modified after [Minervini et al., 2011])

chapter 2-11 page


Figure 2.7 – Schematic overview of the sedimentary succession in the Dutch part of the Southern Permian Basin [van Adrichem Boogaert and Kouwe, 1993-1997b]

d) Petroleum system The main constituents for the hydrocarbon play of the Greater Ameland Area were formed during the

Carboniferous (source rocks) and the Permian (reservoir rocks and seals) [de Jager and Geluk, 2007].

Coal measures from the Carboniferous (Westphalian and Namurian) provide a type III-source rock

responsible for gas generation after maturation. In the Permian, the Upper Rotliegend Group and in

particular the Upper Slochteren Member accounted for the deposition of clastic reservoir rocks whereas

different members, but mainly halite, of the Zechstein Group provide an excellent seal. Locally, the

halite, originally deposited up to 800m thickness in some areas, has been severely affected by

halokinesis forming large diapirs and other salt-related structure in the subsurface [Grötsch and Gaupp,

2011].

Maturation of the source rock and charging of the reservoirs took place from the Jurassic until present-

day, interrupted only during Late Jurassic uplift. Most of the traps in the Rotliegend consist of simple

horst blocks formed by the extensive tectonic activity in the region, although dip closures can locally

form the trapping mechanism [Grötsch et al., 2011].

chapter 3-12 page


3. Available data To investigate the various study aims, different approaches were combined in this project. A database

was utilized, containing a diverse range of restricted internal Shell-reports and proprietary data as well

as publically accessible data sources. In this chapter an overview of the data that was used for this study

will be given.

The Greater Ameland Area is a highly mature area in which extensive exploration and development

activities have been undertaken. This results in a lot of data available for study. This is useful, as the

study goals described in the introduction are of a multidisciplinary nature and hence a large variety of

data is necessary.

Various data sources were addressed, and given the large (corporate) data base for the area of interest

most of the data requirements could be satisfied.

The GAA has been studied extensively in terms of geology, and is situated in perhaps one of the best-

known hydrocarbon basins in NW Europe. This results in a large knowledge base regarding the Permian

Rotliegend reservoir rocks.

All available data that has been used for this study is displayed in Figure 3.1. Data is ‘grouped’ per

category, based on its related discipline or study focus area.

An extensive literature study was carried out, to investigate existing knowledge and hypotheses

regarding the study topics.

Well data formed a dominant part of the data that was used. Well design and completions were of

importance, but also data extracted through the various wells played an important role for delivering

data. Wireline logs, core plug data and core slabs were available in various formats and extensively used

for various analyses.

For seismic interpretation, a newly reprocessed 3D seismic cube was available, accompanied by a range

of existing horizon- and fault interpretations. The new 3D cube provided improved imaging and time to

depth control.

Lastly, information about the present surface facilities and well test/production data were used for

validating simulation results.

The well density in the southern North Sea is very high due to the large number of fields which have

been the focus of the large scale of hydrocarbon production activities since the eighties. This provides a

lot of data on account of geology and petrophysics.

For all wells a variety of wireline log data is available. Logs which are present for all wells include gamma

ray, shale volume, net reservoir, porosity, permeability and hydrocarbon saturation.

chapter 3-13 page


Figure 3.1 – Overview of the available data, coloured by ‘discipline’ and/or data type

chapter 3-14 page


Apart from wireline logs, well tops representing the basin-wide stratigraphic subdivision are present.

These well tops have been interpreted based on log data according to the standard nomenclature for

the North Sea lithostratigraphy [NAM, 2002].

Data from in total 62 wells (including sidetracks) was used for this study. The location of these wells is

indicated in Figure 3.2.

A comprehensive database of cored material is present for the Greater Ameland Area. The larger part of

these cores has been described in detail by either Shell/NAM-staff or by external parties such as Core

Laboratories (U.K.), and is stored in the Core Shed on the NAM premises in Assen.

Apart from the core slabs a large number of core plug samples are available, for which routine- and

special core analysis has provided data regarding porosity, (relative) permeability and capillary pressure

curves for the reservoir rocks.

Wells with cored material that has been studied have an orange label in Figure 3.2. The core material of

16 wells was studied for this thesis.

For the geophysical aspect of this thesis use was made of a newly processed (Q4, 2013) 3D seismic cube,

which was available both in time and depth. This cube consists of 3086 N-S oriented inlines and 1701

E-W trending crosslines with a trace spacing of 25 meters. Length of respectively the inlines and

Figure 3.2 – Well locations in the study area with the outline of the Netherlands in grey, existing fields and reservoirs in green and discoveries in orange. Orange well labels indicate that core material is present and it was used for this thesis

chapter 3-15 page


crosslines is 42.5 and 77.125 kilometers. Data was pre-stack depth migrated, and last reprocessed in

December 2013 to provide optimal resolution at (Rotliegend) reservoir depths. The location of the 3D

seismic cube is indicated by the black square in Figure 3.3. The red square in the same figure indicates

the areal extent of the 3D seismic data from 2000 that was used for constructing existing reservoir

models.

Furthermore an interpreted surface of the Upper Rotliegend Ten Boer Member was available, which

served as main reference horizon for well top correlation, seismic interpretation and model building.

Figure 3.3 – Location overview of the seismic data used (red = cube from 2000 used for model building; black = 2013 reprocessed data used for seismic interpretation)

chapter 4-16 page


4. Methods This chapter will contain a description of all the methods used in this thesis. First an overview of the

overall workflow will be given. Subsequently the various sub-topic methods will be explained in some

more detail.

a) Workflow To optimize the use of available data and optimally fulfil the study goals, an integrated workflow was

followed. A schematic overview of the various components of the workflow is depicted in Figure 4.1. In

this figure, the box colour indicates data type, or summarizes the goal of the activity undertaken (when

the box is coloured or grey-scale, respectively).

A variety of literature sources was addressed to get familiarized with the study area and the different

study topics, including an assessment of the existing information around these topics. After this

literature research, the project started by doing an extensive sedimentological study. Based on present-

day knowledge a new approach for core study was composed and following that approach log

evaluation and Markov-chain transition analysis was carried out. Next, to investigate the reservoir

quality distribution and quantify levels of heterogeneity, wireline log and plug data were carefully

evaluated and Lorenz plots and Dykstra-Parsons coefficients were calculated. This was sided by seismic

interpretation of a horizon which had not received much specific seismic attention to date.

In the next sections, certain of the applied methods will be described in more detail.

b) Literature research A large amount of literature has been written concerning the studied Permian Rotliegend deposits and

its petroleum system in which large reserves have been discovered. For example, a report dating from

1998, describes a large regional study that was undertaken within NAM, correlating the Upper

Slochteren Sandstone Member (ROSLU) throughout the Greater Waddenzee Area and capturing all the

significant results from earlier studies into one report. This study incorporated 42 cored wells, and log-

and seismic data and focused on sedimentology and diagenesis, seismic interpretation, basin- and

reservoir modelling and prospect evaluation.

In spite of all previous research there are still lacunas in existing knowledge and understanding in view

of predicting reservoir quality distribution. The causes for the exact distribution of reservoir properties

and the controls on this are not yet fully understood. Some of these gaps are identified in this thesis and

an alternative hypothesis is proposed, based on the research undertaken and reviewed opposed to

analogue studies found in literature.

chapter 4-17 page


Figure 4.1 – Schematic overview of how available data was incorporated in various study topics

chapter 4-18 page


c) Core study Several different facies classifications have hitherto been applied to the available cores of the Greater

Ameland Area. These classifications were tested and their suitability will be discussed in the chapter

“Project results”. Also, during the core study a new facies classification was derived which is based on

the intensity of soft sediment deformation and other early post-depositional alteration of original

sedimentary structures. A detailed description of this classification scheme will also be given in the

Project results-chapter.

To obtain these results, the following procedure was followed (pursuing a similar workflow to the one

described in Reijers et al. [1993]:

Wells in which applicable intervals (ROSLU) have been cored were selected;

These intervals were laid out for sedimentological observation and interpretation;

(The necessity of) a depth correction was determined based on GR-log response and visual

comparison to clay content at specific depths in both the logs and the cores;

Depths at and lithologies in which core plug measurements were done were verified;

The adequacy of existing lithofacies- and depositional environment descriptions was checked;

The newly devised facies classification scheme was applied to the selected wells and associated

core intervals by systematically determining the facies per depth interval, and accompanying

this interpretation by observations regarding sedimentary structures, diagenetic effects and

other characteristic features.

d) Core analysis A large amount of core plugs have been taken from the core material of Rotliegend rocks in the study

area. These were used for routine- and special core analyses (RCAL and SCAL, respectively) which result

in various types of reservoir property measurements.

(i) Routine- and special core analysis

Routine core analysis is necessary to obtain, amongst others, calibration data for calculated logs for

stress correction and absolute porosity values. Measured variables include matrix density, porosity and

air permeability. Routine core analysis involves core plug cleaning in order to dissolve possibly present

salt and solid hydrocarbons, and subsequently drying the sample until the weight change is negligible.

Grain volume measurement then is carried out according to Boyle’s law and subsequently the grain

(matrix) density is calculated. Stressed porosity and permeability are determined at net confining stress

conditions, where porosity is measured by quantitative helium injection and the Klinkenberg corrected

air permeability is assessed by applying a constant air pressure and measuring flow rate at different

pressure steps [Appel et al., 2013].

Special core analysis provides properties such as capillary pressure curves and relative permeability

data. In order to determine these parameters for imbibition, the sample has to be aged and cleaned in

an appropriate manner. Stressed porosity and permeability are again measured, albeit in a pressure cell

this time. Porosity is calculated by measuring the volume of injected brine, whereas permeability values

are determined by obtaining steady state flow at three flow rates [Dudley et al., 2013].

chapter 4-19 page


(ii) Mini-permeameter data

Apart from core plug analysis, measurements can be carried out on (parts of) core slabs. Mini-

permeameter measurements provide an ‘alternative’ permeability measure which gives quantitative

insight into the amount of permeability heterogeneity in (successive) core intervals [Halvorsen and

Hurst, 1990]. Mini-permeameter apparatus uses pressure sensors and -controllers to apply a static air

pressure on a piece of core slab. Subsequently the pressure decay is measured and converted to a

permeability value in millidarcy. It should be noted that permeability measurements obtained through

this technique are not valid in an absolute sense for permeability values, but they can very well be used

relative to each other.

e) Statistical methods Basic statistical entities were determined and used for analyzing simple trends and characteristics.

Average and standard deviation values were determined in order to get a feel for overall properties of

both sedimentological observations and log data.

Interpolation of data was done in Petrel to visualize lateral property distributions. Convergent

interpolation uses control points (e.g. well data) and iteratively provides a proper spatial resolution

[Schlumberger Information Solutions, 2012]. In areas where data is present this is honoured, whereas

general trends are retained when data is scarce.

(i) Heterogeneity quantification

An important aspect of reservoir quality distribution is the scale at which heterogeneity is significant.

Especially for static and dynamic reservoir model-building it is crucial to understand what level of detail

(and hence grid block dimensions) is necessary to construct an appropriate model. In order to get a feel

for the heterogeneity present in the study area, (Stratigraphic) Modified Lorenz Plots (SMLPs) were

generated in Excel based on core plug data. Apart from analysis in Excel, a Petrel plug-in

developed within Shell was also used to create Lorenz plots.

Lorenz plots present a powerful graphical tool for identifying and quantifying

reservoir flow units based on petrophysical parameters. A Lorenz plot displays

percent flow capacity versus percent storage capacity [Gunter et al., 1997].

Flow and storage capacity are the product of thickness and porosity or

permeability, respectively. Inflection points in the graph represent

significant changes in reservoir properties, and hence give an

indication of the scale of heterogeneity.

If a Lorenz plot honours the stratigraphic order in which

subsequent flow units occur, it is a ‘Stratigraphy Modified

Lorenz Plot’ (SMLP). When flow units are depicted in order of

decreasing storage- and flow capacity, regardless of their

stratigraphic position, the plot is called a ‘Modified Lorenz Plot’

(MLP).

Gini coefficients are calculated based on Lorenz plots and reflect the amount of inequality of reservoir

quality (represented by flow vs. storage capacity) between the flow units (see Figure 4.2, [2013; 2014]).

Figure 4.2 – Schematic drawing of a Lorenz curve and a line of equality, and the areas used for calculating Gini coefficients

chapter 4-20 page


A Gini coefficient of 0 means that there is no statistical dispersion within the various flow units, i.e. their

reservoir quality trend is the same throughout the whole succession that is considered. When the Gini

coefficient increases towards 1, this signifies maximum inequality between the flow units.

The Gini coefficient is defined as

where A and B are the areas depicted in Figure 4.2 (i.e. the

area below the line of equality –which has a gradient of 1 – and the area below the Lorenz curve,

respectively).

Apart from Lorenz plots and Gini coefficients, Dykstra-Parson coefficients were calculated for the

reservoir rocks, using the Shell Petrel plug-in.

The Dykstra-Parsons coefficient measures reservoir heterogeneity by quantifying the spread of

permeability data, assuming that these are log-normally distributed.

It is calculated by

, where VDP is the Dykstra-Parsons coefficient, k50 is the mean

permeability and k84.1 is the permeability mean plus one standard deviation [Charles, 2008].

A Dykstra-Parsons coefficient of 0 occurs when rocks are perfectly homogeneous, whereas an extremely

heterogeneous deposit is characterized by a DP-coefficient that approaches 1.

(ii) Sequence analysis

Markov chains are sequences in which the studied property is intermediate between deterministic and

completely random [Davis, 2002]. This investigation tests the (presence of a) dependency relation

between subsequent facies and is of use in detecting possible cycles or repetitions and assessing the

predictability of existing trends and patterns.

Two types of sequences were examined. The first sequence-type consists of data between which the

distance varies and hence needs specification for every point. The raw facies log fulfils this

characteristic. In the second case, data points are equally and regularly spaced and the ‘distance data’

can be characterized by a single constant. Logs were sampled at a fixed interval, creating a so-called lag-

facies log. A transition frequency matrix expresses the occurrence of one state succeeding another. It is

asymmetric, and in general ai,j ≠ aj,i. In a way, each matrix element provides an estimate of the

conditional probability P(j|i): the probability that, given the present state is i, the following state will be j

[Davis, 2002].

The actual dependency test requires a ‘marginal (or fixed) probability vector’, which is the vector

containing the row totals divided by the total number of transitions. Using this vector, the number of

expected facies transitions that would occur if the transition was completely independent can be

calculated. A Χ2-test is then applied to compare the actual and expected transition frequency matrices

[StatTrek, 2013]. Whenever the critical value for Χ2 for a 5% level of significance is smaller than the

calculated Χ2 it can be concluded that the hypothesis of independence of successive states is not correct

[Davis, 2002]: there is a statistically significant relationship between various successive facies.

The dependency relationship between facies that are two ‘intervals’ apart can also be investigated. This

is done by first squaring the transition probability matrix (multiplying by itself) and then following the

steps described above, and resulting values demonstrate ‘second order transition probability’, if such a

relationship is present.

chapter 4-21 page


(iii) Log data analysis

An extensive collection of various wireline log types is available for a large number of wells in the GAA,

including all the wells of which core material was studied. Although no extensive petrophysical analysis

of the logs was carried out for this thesis, gamma ray and net-to-gross logs were used for well log

correlation and investigation of the reservoir quality distribution in the study area. Furthermore,

porosity and permeability logs were used to investigate reservoir properties and their areal spread.

(iv) Well log correlation

Well correlation and log evaluation was undertaken synchronously with most of the core study. This

allowed matching core observations to measured log data, and clarified the distribution of sediment

types.

(v) Property distribution maps

For each well location the true vertical thickness of the ROSLU-sequence was calculated based on

available well tops and the known well inclination.

Using this true vertical thickness (TVT) and the wireline log data the average property value was

calculated per reservoir interval, and for the complete Upper Rotliegend stratigraphy.

Finally, all the values at well locations were interpolated and hence data was distributed throughout the

whole study area.

f) Seismic interpretation Seismic interpretation was carried out to obtain an interpreted surface of the top Carboniferous.

Wells that contain well tops indicating the log interpretation of the top Carboniferous were identified.

These well tops were displayed on the depth-converted seismic cube. In a seismic cross section through

multiple wells, with a NW-SE strike, the seismic loop best matching the well tops was then interpreted.

This specific orientation of seismic sections was chosen because of the general trend of the underlying

structural features (in particular the Hantum Fault zone) which is assumed to influence the sediment

distribution. Consequently the main onlapping direction that is expected is perpendicular to the strike of

the seismic sections chosen.

To account for the structural features in the subsurface, the seismic data was flattened using an existing

interpretation of the top Rotliegend surface. The top Carboniferous interpretation was then extended

every 16th seismic line until the whole grid was covered. Followingly, seismic sections perpendicular to

this interpretation were interpreted, using the earlier interpretations as a guideline to also cover the

grid with interpretations in the NE-SW direction.

To achieve a smoother surface, the first-pass interpretation was then converted to a reference surface.

This reference surface was displayed and used as guidance for interpretation directly on the seismic

inlines and crosslines, which results in a N-S and E-W grid of interpretation lines.

After obtaining a satisfactory top Carboniferous surface, this was interpolated and smoothed in Petrel

and checked for inconsistencies (for example extreme thickness variation within small regions).

chapter 5-22 page


5. Project results The geological part of this thesis focuses on two main subjects. Firstly, the sedimentology of the study

area and an associated refined conceptual model which was developed based on undertaken analysis

will be discussed. Secondly, observations regarding reservoir quality and by what this is affected most

severely will be shared and reviewed. Lastly, the part concerning seismic interpretation focuses on the

geometry of the basal Rotliegend deposits on the top Carboniferous and its relation to earlier topics.

a) Sedimentology In this section a sedimentological framework is provided. First of all, the standard lithofacies

classification used to describe the sedimentary rocks present in GAA core material, together with the

existing reservoir zonation is explained. This is then linked to a GAA core study undertaken, followed by a

description of soft sediment deformation mechanisms and features of interest for this study. A summary

of the regional sedimentological correlation and the sequences inferred from observed soft sediment

deformation structures is given, and finally all these topics are merged into a short summary of the

conceptual sedimentological history of the study area.

(i) Lithofacies classification

The term lithofacies refers to an individual rock unit which is characterized by distinct sedimentary

features that define a particular depositional process. One lithofacies unit is bounded by top and bottom

stratigraphic surfaces that define the time at which the depositional process has changed [Campbell,

1967; Collinson and Thompson, 1982; De Reuver, 1994].

Lithofacies are a practical way of describing a depositional setting when sedimentological data is

available. This is the case in the GAA, and lithofacies have therefore been widely used for registering the

characteristics of the siliciclastic rocks resulting from the reigning depositional environment in the

Permian basin.

The facies distribution map in Figure 5.1

illustrates the four most important

sedimentary environments proposed by

George and Berry [1994] during

deposition of the Permian aged Lower

and Upper Slochteren units in the GAA .

They comprise sand seas, fluvial

fairways, sand flats and (saline)

mudflats.

This lithofacies classification scheme

has been largely maintained throughout

the entire period of exploration and production of the Rotliegend to date, with only minor variations in

the degree of detail.

Table 5.1 provides a lithofacies classification scheme described by Reijers and Kosters [1993], which has

been used by the NAM and contractors for sedimentological descriptions.

Depositional Complex Lithofacies Association

Lake/Wet Sabkha Desert Lake (Bb)

Aeolian Mudflat (Pma)

Aeolian Wet Sandflat (Psaw)

Damp Sabkha Aeolian Damp Sandflat (Psah)

Dunes/Aeolian Sandsheets Aeolian Dry Sandflat (Psay)

Aeolian Dune (Ads, Adb)

Wadi Fluvial Pond (Bp)

Sheetflood (Fh)

Channelized stream (Cfb, Cfm)

Table 5.1 – Lithofacies classification scheme used within NAM and accompanying abbreviations [Reijers et al., 1993]

chapter 5-23 page


Figure 5.1 - Schematic overview of dominant depositional environments in the Southern Permian Basin around the Greater Ameland Area [Crouch et al., 1996]

All of the existing

facies classification

schemes are

characterized by

similar general

properties, and are

therefore in essence

comparable to the

combination of

sedimentary

environments listed

above. Some studies

have not included

wadi-deposits as a

discrete lithofacies,

as there is no significant reservoir potential in these sediments and have thus received little attention.

The geology of Rotliegend sandstones in the GAA has

been studied extensively by NAM geologists, starting in

the late 80’s when the field was first deployed for gas

production. One of the first sedimentological reports,

dating from 1989, states that “the sediments were

mainly wind-blown and deposited onto damp to wet

mudflat and sandflat surfaces by adhesion processes.

Some mud units represent shallow lacustrine

environments. Shallow streams deposited a relatively

minor proportion of the sands within the cored sections

and tend to be associated with the most sand-rich

aeolian intervals” [Cohen et al., 1989].

On a regional scale, three major east-west striking

facies belts can be identified. The southernmost of

these represents the alluvial plain, consisting of sandy

and pebbly braided-stream deposits and alluvial-fan

conglomerates. The middle facies belt is characterized

by a mix of fluvial and aeolian deposits, the proximal

area being represented by fluvial-dominated sandflats

with some aeolian input and the more distal parts

comprising damp and dry sandflats with common fluvial

Lithostratigraphic

subdivision

Reservoir

unit Age (Ma)

Ten Boer Claystone Member (ROCLT)

258

Upper Slochteren

Sandstone Member

(ROSLU)

ROSLU1

ROSLU2

ROSLU3

ROSLU4 260

ROSLU5

ROSLU6 Ameland Claystone

Member (ROCLA)

Lower Slochteren Sandstone Member (ROSLL)

262

264

Figure 5.2 – Reservoir unit subdivision scheme as used in the Greater Ameland Area (modified after Ladipo [1995])

chapter 5-24 page


reworking. The northernmost facies belt contains both transgressive and regressive playa and wet

sandflat deposits which demonstrate climatically controlled phases of playa lake expansion and

contraction [Cohen et al., 1989].

This hypothesis is widely accepted and has been confirmed by multiple consecutive core and regional

geological studies.

(ii) Reservoir zonation

Within NAM, the Upper Slochteren Sandstone has been divided into six reservoir units in the Ameland

area, named ROSLU1-6 from top to bottom. This subdivision, shown in Figure 5.2, is based essentially

upon the identification of intervals which demonstrate major variations in gamma ray response

corresponding with the clay/sand content [Ladipo, 1995].

ROSLU1 is dominated by damp to wet sandflat deposits. Damp sandflat deposits are mainly located in

the Ameland East-main field, and towards the northwest of the study area.

ROSLU2 is dominated by wet sandflat and mudflat deposits, except for the Ameland-East main area that

contains significant damp sandflat deposits. Overall, this is the most clay-rich unit of the Upper

Slochteren Sandstone.

ROSLU3 is dominated by damp to wet sandflat deposits, grading to lower energy deposits in the

Ameland-North area. However, this is not laterally continuous as other distal wells (M09-3, for example)

contain a fair proportion of interbedded dry sandflat and dune deposits.

ROSLU4 is dominated by damp sandflat deposits with interbedded drier sandstones in the southern

part. This is the thickest unit with the highest sand content and the best core coverage. Also, this

reservoir interval is the main target for many gas production wells.

ROSLU5 is dominated by wet sandflat deposits with significant damp lithofacies in the southeast.

ROSLU6 is clearly dominated by damp sandflat deposits, apparently representing an even higher

proportion than ROSLU4. However, core coverage is limited so hard conclusions cannot be drawn.

Correlation panels show that ROSLU6 is sand-rich in the Ameland-East area. Its thickness is limited and

rapidly decreases further to the north (after Hoetz et al. [2007]).

(iii) Core study

The lithofacies classification indicated in Table 5.1 is adopted in almost all sedimentological analyses and

reports compiled throughout the field life of the production blocks in the GAA.

For this thesis, a total length of approximately 840m of core was studied. The wells to which this core

material belongs are listed in Table 5.2, and their location is depicted in

Figure 5.3 The core interpretation undertaken has mostly confirmed existing sedimentological

observations and interpretations. For this thesis, only three of the four sedimentary environments

indicated in Table 5.1 have been used, as the wadi- and wet sabkha-deposits were merged into one

depositional complex. These deposits are limited in thickness and contained similar ‘water content’

during the respective depositional processes responsible for these facies.

Core interpretation indicates that a considerable proportion of the depositional facies can be attributed

to either a damp sandflat (Psah) or wet sandflat (Psaw) setting. Regarding the depositional environment,

the terminology employed does not provide clear insight into the true nature of the sedimentary regime

chapter 5-25 page


and thus leads to a number of questions. How ‘wet’ is a damp sandflat (Psah), compared to a wet

sandflat? What is the main depositional agent in both the damp and wet sandflat: is it wind or water? Is

the wetness of the depositional setting linked to a certain location in the sedimentary environment, or

more specifically a result of long(er)-term climatic conditions or episodic climatic events?

Table 5.2 – Table listing core intervals studied during the core study

Approximately 80% of the core analyzed is characterized

by wavy lamination. This sedimentary feature is not

unequivocally linked to any one of the specific

depositional environments. Wavy lamination is observed

to be ubiquitous in both the ‘wet’ and ‘damp’ aeolian

sandflat (Psaw and Psah), and does not imply wave action

as a sedimentary process but indicates that the laminae

observed are undulating irregularly. The depositional

cause of these laminae cannot unambiguously be inferred

from their sedimentological description.

Only localized, isolated intervals of preserved aeolian

sediments have been identified and in general the

Rotliegend rocks in the area of interest are observed to be

relatively clay-rich as opposed to what is expected for

deposits originating from an aeolian setting.

well top depth –AHD [m]

length studied [m]

AME- 2A 3861.00 18.00

AME-101 3593.30 17.70

AME-107 4450.00 15.08

AME-201 3560.00 17.30

AME-203 3706.00 27.54

AMN- 1 3351.50 90.98

AMN- 3A 3551.00 17.80

AWG-1 3358.00 15.70

AWG-102 4203.54 35.46

AWG-106 4131.00 94.50

AWG-107 3757.00 18.35

M09-3 3448.00 116.95

MGT- 2 4453.00 89.50

N07A103 4145.00 81.01

NSN- 1 3330.00 73.80

TRN- 1 4079.00 108.00

Figure 5.3 – Overview of well locations of which core material was studied

chapter 5-26 page


During the core study it was recognized that the ubiquitous wavy lamination described can be related to

soft sediment deformation. A facies classification was devised in order to investigate the various

governing mechanisms causing soft sediment deformation structures and their significance for reservoir

quality enhancement. This classification is based on various intensities of soft sediment deformation,

distinguishing to what extent original deposits are preserved or sedimentary structures have been

severely disturbed.

For approximately 770m of the total length of studied core a sedimentary log description of core slabs

was carried out using this soft sediment deformation facies classification, which will be described later in

this chapter. All the core material listed in Table 5.2 was investigated in terms of soft sediment

deformation intensity, apart from the wells AME-107, AWG-102.

(iv) Soft sediment deformation

Soft sediment deformation is an expression of remobilization processes that have occurred before

lithification of the sediment. Remobilization is common in a variety of settings, for example in

halokinesis, the formation of injectites and soft sediment deformation.

In this study, the focus lies on soft sediment deformation that is specific to the depositional setting of

the GAA where grain size is in general small although contrasting grain sizes still occur.

Background

Soft sediment deformation structures are sedimentary features that originate during or soon after

deposition. These structures are caused by either biogenic disturbance or are related to processes of a

hydrodynamic origin. The reigning sedimentary environment in Permian times was not suitable for

widespread biogenic activity, due to the extreme climate and proximity to a highly saline playa lake.

Therefore, when referring to soft sediment deformation (SSD) in this thesis, reference is made to

features created by changes in the hydrodynamic setting during deposition.

Soft sediment deformation structures occur most frequently in siliciclastic rocks with grain sizes ranging

from silt to fine sand [Mills, 1983; Collinson, 2005]. The controlling parameters for these structures are

grain cohesion, permeability and deposition rate. Contrasts in grain cohesion, permeability differences

and changes in deposition rate or a combination of these factors increase the chance of soft sediment

deformation occurring in sedimentary deposits [Mills, 1983].

A number of mechanisms have been postulated to explain the occurrence of soft sediment deformation.

These are liquefaction and fluidization; reverse density gradients; shear stress and slumping/sliding

(slope failure) [Blatt et al., 1980].

Liquefaction and fluidization are similar processes which both involve differential fluid flow. Liquefaction

occurs through compaction of loosely packed grains or by an increase in pore-fluid pressure, temporarily

transferring the grain weight to the pore fluid. Fluidization takes place when upward-flowing fluid in a

porous medium causes a shear stress which counteracts the grain weight and reduces the material

strength [Owen and Moretti, 2011]. However, this distinction between liquefaction and fluidization is

not conclusive: sometimes the difference between the two processes is assumed to be a result of

differences in velocity and turbulence of the system. Following [Lowe, 1975; Allen, 1982; Duranti et al.,

2002] the definition of liquefaction is slow percolation of pore-fluid movement (laminar flow) with a

passive role in grain displacement. This is in contrast to fluidization which occurs in an open system and

chapter 5-27 page


involves vertical upward turbulent particle movement from pore fluids that causes grain displacement

[Chan et al., 2007].

A reverse density gradient means that deposited material has a higher density than the underlying

substrate [Harrison and Maltman, 2003]. In the sedimentary environment of interest for this study,

reverse density gradients develop when silt and sand are deposited on top of clay due to differential

porosities of the sediment types, or due the early loss of pore water. Apart from the disequilibrium

caused by this situation, shear stress can additionally be provoked by water flowing over the bed

surface. This subjects the newly deposited, less shear-resistant sediments to an additional stress. Slope

failure occurs when the angle of the surface of deposition cannot be retained and the newly deposited

sediments subsequently collapse [Loope et al., 1999; Glennie and Hurst, 2007].

In order to determine the origin of soft sediment deformation, two questions need to be answered:

“what is the reason for the strength loss of the sediment?” and “which forces act upon the deposits,

causing deformation?” [Collinson, 2005].

Soft sediment deformation structures can provide insight into these questions. However, they are

generally not distinctive as various combinations of deformation mechanisms can lead to the same

sedimentary structures [Maltman and Bolton, 2003]. Common soft sediment deformation structures

include (a) load casts and pseudonodules; (b) convolute lamination; (c) dish and pillar structures, (d)

sand injectites and (e) flame structures.

Load casts and pseudonodules usually occur at the interface between clay and overlying sands. Gravity

acts on unconsolidated, density-stratified sediments in which the upper layer (sand) is denser than the

lower layer (clay) which causes distortion of internal lamination of both layers. In extreme cases, sand

may be detached from its source bed and occur as isolated load balls or pseudonodules [Collinson,

2005].

Convolute lamination consists of overturned bedding lamination, in various intensities. Convolute

lamination is bounded at bottom and top by undeformed laminae, and it is generally associated with

fluid escape caused by liquefaction [Moretti and Sabato, 2007].

Dish and pillar structures also reflect fluid escape but then in a more explosive way, where dish

structures are oriented parallel to horizontal bedding features and pillars are vertical permeable paths.

Sand injectites can, just as dish and pillar structures, appear both parallel to and cross-cutting existing

bedding structures. They are attributed to rapid burial of fine-grained sands by low-permeability

sediments (clay) [Mills, 1983].

Flame structures are closely related to load structures and are interpreted to be the result of a

combination of water escape and shearing force. Mud intrudes a fluid-saturated overlying sediment

burden of a higher density, and is subsequently diverted due to surface forces [Ross et al., 2011].

chapter 5-28 page


Summarizing, soft sediment deformation structures most commonly occur in non-consolidated,

heterogeneous and (partly) water-saturated sediments; they are triggered by a disturbance which can

be due to a variety of mechanisms, e.g. a seismic shock or sudden sediment loading and consequently

are the result of disequilibrium in material strength vs. stress state. 1

Observed soft sediment deformation structures

Cores from 13 wells have been reviewed for the occurrence and type of SSD described above. Despite

the nature of soft sediment deformation-structures, a number of interesting structures have been

observed in various frequencies.

Sedimentary features related to soft sediment deformation-processes present in the studied core

material include wavy lamination (WL), load casts (LC), dish-and-pillar structures (DP), sedimentary sills

and dikes (SS), flame structures (FS) and fluid escape pipes (EP). Examples of these observed structures

are shown in Figure 5.4. Furthermore, halokinetic disturbances have been observed, which have

1 Image sources:

(a) http://www.ualberta.ca/~jwaldron/images/sedCD1024/33.jpg (b) http://www.geol.umd.edu/~jmerck/geol342/images/05convoluted.jpg (c) http://www.ualberta.ca/~jwaldron/images/sedCD1024/26.jpg (d) http://upload.wikimedia.org/wikipedia/commons/4/4a/Clastic_dike_at_cecil_swc.jpg (e) http://www.geol.umd.edu/~jmerck/geol342/images/05flamestructure.jpg

Figure 5.4 – From top left clockwise (a) Load casts (LC) and pseudonodules (PN); (b) convolute lamination; (c) dish and pillar structures; (d) sand injectites at various scales; (e) flame structures. Note the large scale variations between the various structures. [1]

http://www.ualberta.ca/~jwaldron/images/sedCD1024/33.jpg

http://www.geol.umd.edu/~jmerck/geol342/images/05convoluted.jpg

http://www.ualberta.ca/~jwaldron/images/sedCD1024/26.jpg

http://upload.wikimedia.org/wikipedia/commons/4/4a/Clastic_dike_at_cecil_swc.jpg

http://www.geol.umd.edu/~jmerck/geol342/images/05flamestructure.jpg

chapter 5-29 page


reworked the sediment by means of (repeated) syn- and early post-depositional dissolution and

precipitation of evaporitic material.

Fluid escape and loading are the processes lying at the basis of the most commonly observed features.

Therefore liquefaction and fluidization, and reverse density gradations are the most plausible

mechanisms for observable structures. However, components of shear stress are also present (i.e. by

the flame structures).

Figure 5.5 – Observed soft sediment deformation features. Core photographs from the internal NAM-database, belonging to core material from well (a) AWG-106; (b) AME-203 and (c) N07-FA-103

Analogues

To date, limited attention has been devoted to soft sediment deformation structures observed in Dutch

Rotliegend sediments. Notion is made in several publications that some degree of post-depositional

reworking and soft sediment deformation has been observed and mentioned, but proper explanations

or regional hypotheses for the origin of soft sediment-deformation structures are absent.

However, detailed research on the foundations of soft sediment deformation-governing processes has

been carried out on several other areas. Permian Rotliegend rocks from the UK and Germany as well as

chapter 5-30 page


Jurassic, Early Cretaceous and Pleistocene deposits have been studied in view of SSD and provide

analogues for the Rotliegend Formation in the GAA.

Soft sediment deformation structures were observed in Early Cretaceous gravity flow deposits in the

Outer Moray Firth, Scotland [Downie and Stedman, 1993]. The main deformation-driving factor is slope

failure and slumping, due to gravity flow deposits being laid down on gravitationally instable slopes.

Observed features include discontinuous, wavy lamination; fluidization pipes and sheets (dewatering

structures); contorted sediments; sheared structures and clastic intrusions.

Moretti & Sabato have investigated the trigger mechanisms for soft sediment deformation in

Pleistocene lacustrine deposits in the Sant’Arcangelo Basin (South Italy) [Moretti and Sabato, 2007]. The

main driving forces for soft sediment deformation are stated to be gravitational instability, extension,

(differential) vertical stress and a combination of these factors.

In both examples of soft sediment deformation mentioned above, the structures observed are very

similar to those observed in the GAA. However, both the turbiditic environment studied by Downie &

Stedman and the lacustrine setting in Moretti & Sabato’s article are not reasonably comparable to the

area of interest in this thesis. The main goal of mentioning these examples is to show that structures

resulting from soft sediment deformation are not conclusive in terms of depositional environment by

any means.

In 2007, the AAPG published a memoir on the relevance of sand injectites for hydrocarbon exploration

and production [Hurst and Cartwright, 2007]. This volume contains two articles in which relevant

analogue clastic rocks for the clastic rocks in the GAA are described.

Chan et al. [2007] describe the abundance of SSD-structures in Jurassic aeolian deposits from the

Colorado Plateau, U.S.A. The deformation features found are again comparable to those observed in the

GAA, although the range of scales at which structures were investigated is significantly larger than in this

study. A number of processes are assumed to govern the distribution and occurring frequency of the

soft sediment deformation structures: (1) deposition and dissolution of evaporites associated with inter-

dunes, sabkhas and adjacent shallow seas; (2) high water table levels, especially in response to adjacent

marine transgressions; (3) dune progradations and sediment loading on top of saturated, poorly

consolidated, marine and sabkha substrates; (4) alternation (interbedding) of mobile sabkha deposits

and laminated aeolian sands which records the overpressurization and deformation of sabkha and

aeolian deposits.

The authors state that there is no general agreement on the formation mechanisms of existing

deformation structures, although the strong influence of a prevalent water table and proximity to

coastal environments is mentioned. In terms of triggering mechanisms, sediment loading is discarded as

stand-alone cause as this would be a slow process unable to provoke the scale and type of deformation

features observed. Seismic activity related to large fault systems or explosive volcanism is said to be a

more compatible explanation for the recurrent deformation. Recurring themes in the development of

the abundant soft sediment deformation structures comprehend long-lived erg conditions; a fluctuating

water table; rapid deposition and compaction and basinal conditions favourable for good preservation

potential.

chapter 5-31 page


Two publications by Glennie & Hurst [Glennie and Hurst, 2007; Hurst and Glennie, 2008] describe large-

scale soft sediment deformation structures in the Permian Hopeman Sandstone (Outer Moray Firth,

Scotland), to which the UK Rotliegend aeolian deposits are compared in one of the articles. The driving

force for the wide variety of deformation structures is believed to be widespread heavy rain, related to a

climatic change near the end of the Permian related to the widespread Zechstein marine transgression.

The authors state that two main mechanisms are responsible for the deformation. First of all,

fluidization of air-filled dune sands takes place as a result of a rising water table and a change in wetness

of the depositional surface due to rain. This fluidization consequently invokes overpressures, causing the

partially water-saturated dunes to degrade and collapse. Secondly, sand injection features are most

likely formed due to the loading of adjacent strata.

The observed sedimentary features are probably coeval with deformation structures present in the

central and southern North Sea. However, the mechanisms proposed for the deformation are less likely

in these regions. Flood-induced air-escape structures and minor slumping could be responsible for

deformation, but down slope sliding is not plausible in this setting.

In the context of soft sediment deformation structures the transition from aeolian conditions during

deposition of the Rotliegend to a marine and evaporitic setting is also acknowledged by Peryt et al.

[2010]. The top part of the Upper Rotliegend section is significantly reworked, partly due to the marine

transgression but evidence that increased fluvial activity caused at least part of this reworking is also

present (K.W. Glennie, pers. comm., in Geluk [2005]).

Finally, in the SEPM special publication devoted to the Permian Rotliegend of the Netherlands [Grötsch

and Gaupp, 2011] van den Belt & van Hulten discuss the sedimentology of the Lower Slochteren

Formation in the Markham field in the southern North Sea [van den Belt and van Hulten, 2011]. Also in

this part of the SPB soft sediment deformation structures have been observed in sand- and mudflat

facies. Structures encountered include wavy lamination, mud cracks, adhesion ripples and local bedding

disruption due to either sand injection and desiccation features or salt precipitation-dissolution

processes [Crouch et al., 1996].

It is apparent from all available analogues that, so far, there is not an unequivocal explanation for the

origin of similar soft sediment deformation-structures. Neither in terms of governing processes nor

initial or boundary conditions during sedimentation a suitable hypothesis exists to explain the large

extent (both areally and thickness-wise) of the soft sediment deformation structures present in the GAA.

However, sediment loading and water table fluctuations are very significant in causing the SSD-features

that are similar to those observed during this study. The large areal and vertical extent of very similar,

assumedly small-scale SSD-structures makes it hard to accept seismicity as principal triggering agent.

Climatic variation magnifies effects of the most frequent deformation mechanisms, although it remains

debatable what climate-related aspects (sedimentation rate, water table fluctuations, sediment supply

vs. increase in accommodation space etc.) have the highest impact.

chapter 5-32 page


SSD facies classification

The rocks present in a significant part of the cores studied have been subjected to soft sediment

deformation. Original sedimentary structures have been masked or even completely erased by

reworking, which hampers the interpretation of associated depositional environments.

To account for the variability of rocks significantly affected by the soft sediment deformation, and to

describe them in an appropriate way five ‘soft sediment deformation-facies’ were defined. These facies

indicate the intensity of post-depositional, pre-lithification deformation. Intensity in this case is defined

as the degree of preservation of original structures: more intense SSD means less preserved primary

sedimentary structures. The SSD-facies classification is appointed a number between 1 and 5, ranging

from undefined (1) to most severely deformed (5). Table 5.2 provides a list of the individual facies and

their characteristics, and a typical example of each facies is shown in Figure 5.6. Note that the term

fluidization in the facies descriptions is used to indicate that fluid has had an effect on the deposits

during the soft sediment deformation stage. Appendix A gives the complete overview of defined SSD-

facies and their characteristics.

SSD Facies

Characteristics Comments

1 Primary sedimentary structures have been preserved: there is no significant post-depositional homogenization or reworking. In general sediments are fairly sorted; there is no predominant grain size.

No soft sediment deformation.

2

Existing lamination has been slightly deformed although laminae and other present structures are still clearly recognizable as authentic. Sorting (which is in general moderate) and original grain size has been preserved. No significant fluidization. Original sedimentary

layering is still recognizable.

3

Bedding features and sedimentary structures are significantly deformed, although (sub-) parallel laminae are locally still visible. Some degree of fluidization has occurred, although sorting and original grain size has been more or less preserved so mixing has only played a minor role.

4

Original sedimentary structures have been segmented but are relatively well-preserved in for example dish-and-pillar ‘chips’. Fluidization was significant, remaining sediments are very chaotic. Dish-and-pillar structures, fluid escape pipes and sedimentary sills and dikes are common.

Primary sedimentary layering has been completely distorted (is hard to recognize/ trace).

5

Sediments are very chaotic, original sorting and grain size has been poorly preserved. Fluidization was very significant, primary sedimentary structures have been reworked and/or deformed significantly. Dish-and-pillar structures regularly occur.

Table 5.3 – Definition of the soft sediment deformation-facies classification and their characteristics

chapter 5-33 page


Figure 5.6 – Core photographs with typical examples of the facies defined by the soft sediment deformation-facies classification

A number of things should be taken into account when adopting this facies classification scheme in core

descriptions.

Firstly, grain size does not play a role in the various SSD-facies defined. This is because internal grain size

variation is fairly limited due to the nature of the predominantly aeolian depositional setting assumed.

This assumption is valid, as was confirmed by the core study and various existing documents reporting

on the sedimentology of distal Rotliegend deposits.

Secondly, a property related to the grain size is the clay content. It is challenging to estimate clay

content values in core material with the naked eye. Unfortunately, microscopic point count data is not

available in a sufficient quantity to use this property for facies classification. Consequently the SSD-facies

classification is made without taking clay content into account.

Another feature that was not incorporated into the definition of the five facies is the type of initially

present sedimentary structures initially present prior to occurrence of soft -sediment deformation.

These structures have been masked and reworked partially or completely in all but some deposits, and

hence only provide significant information in very localized sections. Attributing significant weight to

pre-SSD sedimentary structures would therefore limit the applicability of the new facies classification.

Two characteristics of the observed SSD-structures were also identified as potential pitfalls of the

proposed SSD-facies classification.

The size of the available core data limits the scale of the soft sediment deformation-structures that can

chapter 5-34 page


be observed. The core diameter is at most 10 cm so only very fine-scale structures can be fully captured,

and even these small structures are rarely fully sampled in the core slabs. Given the fact that soft

sediment-deformation structures occur at a wide variety of scales (ranging from a few millimeters- to

tens of meters), the limited availability of rock might significantly bias the types of structures that have

been observed and their relative frequency.

Partly due to the discrepancy in scale variation between the data and the actual soft sediment-

deformation structures described, it is hard to distinguish the exact type of soft sediment deformation.

Furthermore, even after correct identification of a certain SSD-structure, this does not provide

conclusive information on its origin as “structures are not conclusive in terms of deformation

mechanisms that are responsible for their formation” [Mills, 1983].

The facies are distinguished based on the soft sediment deformation-intensity, which is assumed to

somehow relate to the amount of water in the depositional setting. This is in the same line of thought as

the existing subdivision of sedimentary environments, in which the wetness of the system is the main

determining factor. Figure 5.7 displays the initially proposed conceptual sedimentological model

responsible for the various types of sandflat environments that were used in the existing core

descriptions and associated facies interpretations. The transition from level 1 to level 2 indicates a drop

in groundwater table, which might be due to climatic changes, structural events (a change in the rate of

subsidence vs. the rate of sediment supply), changes in basin configuration or more likely an interplay of

the foregoing. The result of this transition, as shown, is a lateral shift in ‘facies belts’ that is related to

the effect of water on the sedimentary processes taking place (i.e. the wetness of the depositional

surface or the amount of adhesion of windblown mud to the surface).

Figure 5.7 – Conceptual sedimentological model responsible for the deposition of Rotliegend sediments in the GAA (modified after Cohen et al. [1989])

chapter 5-35 page


Regional correlation

The SSD-facies classification was used to log a total length of 934.5 meters of core material belonging to

13 different wells. These wells were picked strategically to adequately cover an area suitable for

identifying trends. This correlation exercise was made more complicated by the fact that in various wells

different intervals were cored. Most core material was acquired from ROSLU4, as this is the main target

interval for gas production due to its best reservoir quality properties.

Well correlation for the SSD-facies log was carried out in three different sections. These cross sections

are displayed in Figure 5.8. To ensure that as many wells as possible could be incorporated in these

profiles, the correlation was made only for ROSLU4.

Figure 5.8 – Overview of the study area and the cross sections in which SSD-well log correlation was carried out

From the well correlation panels it is clear that the deformation intensity increases towards the north. In

west-east direction a strong trend is harder to identify. This is mostly because differences in terms of

facies occurrence are significantly less pronounced: the overall variability of SSD-facies present is much

higher which obscures clear trends.

In Figure 5.9 the north-south well correlation is depicted. As mentioned above, it is apparent that

towards the north the occurrence of SSD-facies 4 and 5 increases significantly relative to the other

facies.

chapter 5-36 page


Figure 5.10 and Figure 5.11 show the two west-east well correlations. It is clearly visible that the

deformation intensity in the west is substantially higher than in central part of the study area. Also, the

intensity deformation seems to decrease when going from the central field towards the east (cross-

section B-B’), although this observation is only clear in the lower part of ROSLU4.

The SSD-facies classification is irrespective of clay content. In the displayed well correlations, it is clear

that deformation intensity is not related to the amount of clay. Facies occurrence is irrespective of clay

content. Furthermore, the scale of the gamma ray log is significantly coarser than the detail of

interpretation which was applied during the SSD-facies logging which confirms that data from this well

log would not have been helpful in determining the SSD or defining the facies classification.

The areal distribution of facies occurrences was more thoroughly investigated by making ‘average

property’-maps per reservoir interval (see Figure 5.12). The maps show the interpolation of facies

distribution based on the SSD-facies which results when averaging all data points at a specific well

location. Due to data scarcity in some of the ROSLU-flow units some of these maps are deemed to be

not very representative. Nonetheless trends that have been identified in the log correlation can be

cross-checked and confirmed on these maps.

The SSD-facies maps indicate an evident increase in deformation intensity towards the north, similar to

the interpretation based on well correlation. However, it is also inferable from the property maps that

there is a ‘ribbon’ of lower SSD-facies in the central part of the study area which is sided by areas of

higher deformation intensity both on its east and west.

The SSD-facies maps of individual reservoir flow units show that the average level of deformation

intensity in the GAA increases towards the upper ROSLU-flow units. In particular between ROSLU4 and

ROSLU3 there is quite a distinctive contrast where the lower reservoir intervals are on average less

deformed than the upper flow units.

The interpolation methods used to create the property maps invokes artefacts. Both convergent

interpolation and kriging-algorithms were used and resulting maps were compared to each other. In the

kriging-process different variogram ranges and orientations were tested, which affect the lateral

continuity and orientation of the interpolated facies.

All in all, these findings lead to the conclusion that the distribution of soft sediment deformation and its

intensity is governed by the gradual N-S transgression of the Silverpit Lake. The fluctuating water level,

induced by the Silverpit transgression, provides the necessary physical boundary conditions for the

formation of soft sediment deformation structures.

Local variations in soft sediment deformation-occurrence and intensity have been observed in the E-W

direction, more or less parallel to the playa lake edge. These variations occur most probably due to local

differences in pre-existing topography that influenced the sediment distribution and water table

fluctuations affecting the newly deposited material.

chapter 5-37 page

Figure 5.9 – North-south well correlation displaying the gamma ray log, cored intervals, net reservoir log, the interpreted SSD-facies log and the porosity log. Wells are, from north to south (A-A’; left to right) AMN- 1; AWG-107; AWG- 1; AME- 2A; AME-106 and TRN- 1

chapter 5-38 page

Figure 5.10 – West-east well correlation displaying the gamma ray log, cored intervals, net reservoir log, the interpreted SSD-facies log and the porosity log. Wells are, from west to east (B-B’; left to right) NSN- 1; AWG-202A; AWG- 2A; AWG-102 and AWG-106

chapter 5-39 page

Figure 5.11 – West-east well correlation displaying the gamma ray log, cored intervals, net reservoir log, the interpreted SSD-facies log and the porosity log. Wells are, from west to east (C-C’; left to right) AMN- 3A; AMN- 1; N07-FA-103 and N07-FA-102

chapter 5-40 page


Figure 5.12 shows the interpolated average SSD-facies maps for ROSLU4. These maps are based on the

value that results from averaging all SSD-facies values in each well per reservoir flow unit, and

interpolating these. Larger maps including those of other reservoir units can be found in Appendix B.

(a)

(b)

Figure 5.12 – Interpolated ‘average SSD-facies’ maps for (a) all logged intervals and (b) reservoir interval ROSLU4

chapter 5-41 page


0,00

0,05

0,10

0,15

0,20

0,25

1 2 3 4 5 no facies

Occ

urr

en

ce [-

]

Facies

Facies occurrence in all wells

Sequence analysis

In order to gain further insight into the governing processes and mechanisms responsible for the

ubiquitous soft sediment deformation in the studied cores, the transitions between the defined SSD-

facies were investigated by

calculating Markov (lag-)

transition probabilities.

This statistical analysis was used in

an attempt to clarify the origin of

SSD-structures. Facies transitions

are assumed to result from a shift

in depositional setting, and

therefore trends in transition

probability could be associated to

potential climatic cyclicity or

palaeotopographic variation.

In terms of facies occurrence, it is

clear that a very significant part of

the logged core material has been

subject to soft sediment

deformation. A mere 18% of all the studied rock is undeformed, whereas the remaining 82% is roughly

evenly distributed over SSD-facies 2 to 5.

Facies interpretations were carried out based purely on apparent transitions: no fixed interval of

interpretation was used. To ensure that the percentages were not severely affected by a length-bias, the

facies occurrence ratio was also calculated for the ‘lag-facies’. For this property the SSD-facies log was

sampled at a regular interval, in this case 10 cm. In the lag transition, intervals without SSD-facies value

are present: this is due to local missing core sections. Figure 5.13 and Table 5.4 display the percentages

of facies occurrence in all logged wells, as well as the ‘lag-facies’ percentage.

1 2 3 4 5 no facies

Facies occurrence [-] 0.17 0.23 0.21 0.15 0.23 -

Lag-facies occurrence [-] 0.18 0.21 0.20 0.14 0.23 0.04 Table 5.4 – Facies occurrence for the SSD-facies log and the lag-distance SSD-facies

Taking into account the discrepancy in the lag-facies caused by the non-logged intervals, the

percentages show a very good match. This signifies that the facies occurrence is not affected by

incorporating a lag-distance.

Per well the average difference between the facies- and lag facies-occurrence was calculated (see Table

5.5). This was done to validate the conclusion in terms of similarity between the two properties drawn

above is valid. It proved that there are only two wells for which there is a statistically significant

discrepancy between the SSD facies occurrence and its lag-facies counterpart. The average difference in

facies occurrence is 0.0432, and the standard deviation is 0.0534. In the wells AME-201 and AMN- 3A

Figure 5.13 – Bar chart of facies occurrence (in red) and lag-facies occurrence (in blue) in all 13 studied wells

chapter 5-42 page


the difference in average facies is (noticeably) higher than the standard deviation of the difference

values for all the wells together, but in all other wells this is not the case.

Table 5.5 – Average difference between facies occurrence and lag facies occurrence for all wells, compared to the standard deviation of these differences for all the wells together

Figure 5.14 displays the transition probability matrix (Table

5.6) in a bar chart. The colours of the bars indicate the

initial SSD-facies, and the number on the x-axis depicts the

facies after (above) the transition. At first sight there is no

obvious conclusion that can be drawn from these results:

the transition probability is not determinative in terms of

certain facies always grading into a specific successor.

For creating the facies maps, the facies were simply

averaged based on their occurrence frequency in the logged

interval (split out per reservoir interval). Given the similarity

between percentage and lag-percentage per well, this was

deemed fit for purpose. Only in two wells there are

significant differences, whereas in the remaining eleven wells the average difference percentage falls

well below the standard deviation of all differences. Furthermore, the most discrepant wells are only

cored over a relatively short depth (~ 17m each) so their influence on the regional average is limited.

The transition from facies 4 to facies 5 is

the most frequent. At an almost

equivalent level are the transitions from

facies 3 to 5, facies 1 to 2 and facies 2 to 1.

Self-transitions are relatively rare which is

inherent to the definition of the SSD-facies

classification. However, some ‘internal’

boundaries were defined in facies 1 and 5

because they are the facies that show the largest variation in terms of general characteristics.

Facies 3, 4 and 5 often succeed each other in various sequences, whereas facies 1 and 2 show a similar

correlation. This is due to the nature of the SSD-facies defined and supports the assumption that the

deformation intensity is a measure that could be related to a high-level parameter affecting the

sedimentary environment (i.e. climatic variations, changes in accommodation space etc.) rather than

small-scale, local variations.

In Figure 5.15 the lag-transition probability matrix (see Table 5.7) is shown. In this bar chart it is clear

that there are much more self-transitions than any other transitions. This is because the lag distance

which was used for calculation considerably smaller than the average facies-interval. When disregarding

the self-transition probability, in terms of relative abundance the overall behaviour again is similar to

the general transition probability discussed above.

well Δ avg [%] Δ < stdev?

AME- 2A 0.0210 1

NSN- 1 0.0284 1

AWG- 1 0.0238 1

AME-201 0.0874 0

AMN- 3A 0.2075 0

AME-203 0.0147 1

TRN- 1 0.0286 1

AWG-107 0.0492 1

M09- 3 0.0277 1

AWG-106 0.0298 1

N07-FA-103 0.0202 1

MGT- 2 0.0223 1

AMN- 1 0.0223 1

Table 5.6 – Transition probability matrix (row-to-column indicates transition from under- to overlying facies observed)

--> 1 --> 2 --> 3 --> 4 --> 5

1 --> 0.0704 0.3869 0.2915 0.1206 0.1307

2 --> 0.3333 0.0299 0.2179 0.1538 0.2650

3 --> 0.1840 0.1960 0.0160 0.2120 0.3920

4 --> 0.1359 0.1902 0.2663 0.0054 0.4022

5 --> 0.1172 0.2381 0.3223 0.2637 0.0586

chapter 5-43 page


The intervals without a facies interpretation were not taken into account during calculation of the lag-

transition probability values. Missing core sections were assumed to be homogeneous and similar to

underlying facies, without any facies transitions within the interval.

Figure 5.14 – Bar chart of transition probability matrix in which bar colour indicates the resulting facies (after the transition) and the initial facies is depicted on the horizontal axis

0,00

0,05

0,10

0,15

0,20

0,25

0,30

0,35

0,40

0,45

1 2 3 4 5

Tra

nsi

tio

n p

rob

abili

ty [-

]

Underlying facies

Transition probability

--> facies 1

--> facies 2

--> facies 3

--> facies 4

--> facies 5

chapter 5-44 page


Figure 5.15 – Bar chart of lag-transition probability matrix in which bar colour indicates the resulting facies (after the transition) and the initial facies is depicted on the horizontal axis

The overall conclusion from the sequence

analysis described above is that there is no

statistically significant dependency

relationship for certain (lag-) facies

transitions. This is confirmed by the Χ2-

tests that were carried out.

For the “regular” facies transition

frequency, the critical value for a 5% level of significance is 26.3 whereas the actual Χ2-test value is

342.1. This implies no meaningful dependency between any of the facies. The critical value for the lag-

transition frequency is the same as for the regular facies transitions (26.3), but the resulting Χ2-test

value is 16527.1. This means that there is definitely no statistically significant dependency in the lag-

transition probability.

0,7799 0,8099 0,8078

0,7747

0,8206

0,00

0,01

0,02

0,03

0,04

0,05

0,06

0,07

0,08

0,09

0,10

1 2 3 4 5

Lag-

tra

nsi

tio

n p

rob

abili

ty [-

]

Underlying facies

Lag-transition probability

--> facies 1

--> facies 2

--> facies 3

--> facies 4

--> facies 5

--> 1 --> 2 --> 3 --> 4 --> 5

1 --> 0.7799 0.0439 0.0762 0.0517 0.0483

2 --> 0.0420 0.8099 0.0564 0.0318 0.0599

3 --> 0.0336 0.0504 0.8078 0.0362 0.0720

4 --> 0.0189 0.0468 0.0933 0.7747 0.0663

5 --> 0.0251 0.0367 0.0675 0.0500 0.8206

Table 5.7 – Lag-transition probability matrix (row-to-column indicates transition from under- to overlying facies observed)

chapter 5-45 page


Figure 5.16 – Bar chart of the second order facies transition probability matrix in which bar colour indicates the resulting facies (after the transition) and the initial facies is depicted on the horizontal axis

To test if there is a relationship between facies that are two ‘intervals’ apart, second-order (lag-)

transition probabilities were calculated.

Figure 5.16 displays the transition probability matrix shown in Table 5.8. The slight trend observed in the

first-order transition probabilities is not valid anymore. Facies that are two intervals apart do not

necessarily represent a SSD intensity difference of 2 ‘degrees’.

This same observation holds for the

lag-facies transition probability matrix

(see Figure 5.17 and Table 5.9). As to

be expected, self-transitions are

dominant. However, no other clear

trends can be observed from the

probability matrix.

--> 1 --> 2 --> 3 --> 4 --> 5

1 --> 0.6148 0.0779 0.1316 0.0869 0.0888

2 --> 0.0707 0.6644 0.1015 0.0575 0.1059

3 --> 0.0580 0.0874 0.6661 0.0642 0.1243

4 --> 0.0362 0.0821 0.1562 0.6092 0.1162

5 --> 0.0450 0.0667 0.1186 0.0847 0.6849

0,0000

0,0500

0,1000

0,1500

0,2000

0,2500

0,3000

0,3500

1 2 3 4 5

2nd

ord

er

tra

nsi

tio

n p

rob

abili

ty [-

]

Underlying facies

2nd order transition probability

--> facies 1

--> facies 2

-->facies 3

--> facies 4

--> facies 5

--> 1 --> 2 --> 3 --> 4 --> 5

1 --> 0.2193 0.1500 0.1837 0.1649 0.2821

2 --> 0.1255 0.2649 0.2335 0.1617 0.2143

3 --> 0.1560 0.2139 0.2794 0.1603 0.1905

4 --> 0.1698 0.2072 0.2164 0.2082 0.1983

5 --> 0.1896 0.1798 0.1803 0.1360 0.3143 Table 5.8 – Second order facies transition probability matrix (row-to-column indicates transition from under- to overlying facies observed)

Table 5.9 – Second order lag- facies transition probability matrix (row-to-column indicates transition from under- to overlying facies observed)

chapter 5-46 page


Figure 5.17 – Bar chart of the second order lag-facies transition probability matrix in which bar colour indicates the resulting facies (after the transition) and the initial facies is depicted on the horizontal axis

In an earlier study of Rotliegend sedimentology Markov chain analysis was carried out on sequences of

lithofacies [Reijers and Kosters, 1993]. The most common series of transitions in the ROSLU-units was

found to be an alternation of damp sandflats and dry sandflats or aeolian dune sediments. However,

clear correlations between wells were not identified based on the lithofacies transitions and the

dominant transitions found do not provide a robust hypothesis regarding the detailed evolution of the

depositional environment.

(v) Results

The lithofacies classification which is commonly used for description of the Ameland reservoir rocks was

tested in the core study carried out. There appears to be no clear physical significance of the subdivision

into three sub-environments: the ‘wet’, ‘damp’ and ‘dry’ sandflat (Psaw, Psah, Psay) do not seem

conclusive, although the rationale behind the proposed conceptual depositional model was found to be

quite correct. A new facies classification scheme was defined which is based on the intensity of soft

sediment deformation.

Facies were discriminated based on the amount of reworking and preservation of original sedimentary

structures, because it has proven to be hard to pinpoint specific sedimentary structures to their original

depositional environment in distal Rotliegend deposits. This does not lead to a completely unambiguous

facies classification, as sedimentary characteristics such as grain size and sorting were not taken into

0,6148

0,6644 0,6661 0,6092 0,6849

0,00

0,02

0,04

0,06

0,08

0,10

0,12

0,14

0,16

0,18

0,20

1 2 3 4 5

2nd

ord

er la

g-tr

an

siti

on

pro

bab

ility

[-]

Underlying facies

2nd order lag-transition probability

--> facies 1

--> facies 2

--> facies 3

--> facies 4

--> facies 5

chapter 5-47 page


account. Furthermore, the fundamental principles responsible for the soft sediment deformation

structures are too complex to directly relate to sedimentary processes and resulting sedimentary

structures.

Climatic variation plays an important role in the occurrence of the SSD-structures. The existing

conceptual depositional model for the distal Rotliegend deposits and the resulting lithofacies

distribution is largely governed by water table fluctuation. The wetness of the system is also an

important factor in the prevalence of SSD-structures, and therefore this model can also be used to

explain the occurrence of regions with different intensities of soft sediment deformation.

There is no material in the sediments that lends itself for dating methods and a significant amount of

reworking has taken place, and hence it is hard to determine the time captured by the sediments. The

observed soft sediment deformation features only record the last-occurring state before lithification,

but it could well be possible that there various antecedent periods of reworking had already taken place.

This complicates the research related to the governing processes of the soft sediment deformation.

Water table variations do occur and can be traced at a regional scale, especially in the uppermost

reservoir zones of the Upper Slochteren Member as defined within NAM (ROSLU1 & ROSLU2), as stated

in various literature sources and core study reports.

Based on the results of the core study carried out, no hard conclusions can be drawn on the governing

processes or possible trigger mechanisms of soft sediment deformation. Due to the large areal and

vertical extent of the deformation structures, however, it is unlikely that seismicity is the principal

triggering mechanism.

Facies transition analysis has pointed out that although there are marked differences in facies and their

occurrence, there are no extremely sharp transitions in general. Facies generally grade into their sequel

or predecessor in terms of deformation intensity (i.e. facies 4 grades into facies 5 or 3) and large jumps

in soft sediment deformation facies are relatively uncommon.

There is no statistically significant dependency of certain (lag-) facies transitions. There is not a clear

tendency of specific transitions taking place for certain initial/underlying facies.

The same observations hold for the second order (lag-) transition probabilities: there are no

‘preferential’ sequences which occur more frequently than others.

Earlier studies show that also in terms of lithofacies sequences Markov chain analysis does not improve

the understanding of specific depositional series.

There is a link between location and deformation intensity. Local differences in water level and clay

content are present, which play an important role in the resulting facies distribution.

More detailed investigation, in particular on the nature of SSD-governing processes, could lead to a

better explanation of the mechanisms governing soft sediment deformation in the GAA, and to support

the applicability of the SSD-facies classification.

The reservoir subdivision of the Upper Slochteren Formation used within NAM is predominantly based

on clay content. Although specific interval properties vary per ROSLU-flow unit, in terms of the new

facies classification there is a visible transition within the lower and upper half of the Upper Slochteren

Formation. SSD-facies of lower deformation intensity are more frequent in these lower reservoir

intervals whereas deformation intensity increases towards the younger, overlying flow units.

chapter 5-48 page


Earlier publications suggest that there is a large-scale transgression during deposition of the bottom

three ROSLU-units whereas the later reservoir intervals have been deposited in a retrograding

environment eventually resulting in the marine Zechstein transgression. This matches the observations

based on soft sediment deformation structures. This supports the hypothesis that the defined facies are

climate-related.

For the GAA, the existing lithofacies classification shows some lacunas and inconsistencies. As

mentioned before, the three sub-environments ‘wet’, ‘damp’ and ‘dry’ sandflat are not conclusive in the

distal aeolian depositional setting.

The term ‘damp’ sandflat should be omitted, as there is no clear universal indicator for the ‘wetness’ of

the sedimentary environment. Using only the term ‘wet sandflat’ covers the fact that fair amounts of

water are necessary for most of the soft sediment deformation structures observed. Moreover, it does

not mean that water is present all the time but indicates that water is not scarce in general. The term is

therefore representative of both the ‘wet’ and ‘damp’ sandflat employed in earlier lithofacies

classifications.

Figure 5.18 displays the proposed refined sedimentological model. It represents the ideal steady state

sandflat-mudflat conditions, where there is a constant difference between ground water table and

sediment surface. In a wet sandflat, water is the main carrier agent and a larger variation in grain size

exists. In a dry sandflat, the main depositional mechanism is adhesion of wind-blown sediments and

sorting is fairly good. Soft sediment deformation occurs by (recurrent) variations in ground water table

level.

It is argued in this thesis that the N-S transgression of the Silverpit Lake can be held responsible for the

widespread occurrence of soft sediment deformation features in the Rotliegend deposits found in the

GAA. Local E-W differences in soft sediment deformation intensity and occurrence are governed by pre-

depositional topographic variations.

Figure 5.18 – Proposed refined conceptual sedimentological model (modified after Cohen et al. [1989])

chapter 5-49 page


b) Reservoir quality distribution This part of the results-section will describe the distribution of properties influencing the reservoir quality

in the study area. Porosity and permeability are the two petrophysical parameters that most prominently

affect how well a reservoir is suited for hydrocarbon storage and production, and hence primarily

determine the reservoir quality. Furthermore detrital clay content and diagenetic impairment impact the

reservoir quality properties.

In this section, firstly an overview of general reservoir quality distribution will be given. Secondly the

diagenetic history of interest for the study area is described followed by a summary of existing

knowledge and regarding the occurrence and importance of high permeability streaks. The reservoir

quality distribution reviewed by areal property maps and subsequently the results of (statistical) analyses

carried out to quantify reservoir heterogeneity will be summarized. As a conclusion, a link is made to the

proposed SSD-facies classification of the previous subchapter and the accompanying conceptual

sedimentological model.

There is a large scatter in reservoir quality throughout Upper Rotliegend reservoir rocks. Porosity of the

red-bed sandstones ranges between 2 and 25% and permeability varies with 2 to 4 orders of magnitude

from < 0.01 mD to several hundreds mD.

Reservoir quality is primarily linked to lithofacies distribution because pore size distribution and

morphology are fundamentally related to porosity and permeability. Depositional trends therefore have

a direct impact on the vertical and lateral distribution of reservoir quality. Clean sandstones show the

best reservoir properties, and mudstones the poorest.

However, as mentioned in the previous chapter(s), the most frequently occurring lithofacies described

as ‘wavy lamination’ shows a very large spread in measured porosity and permeability values. This

implies that the predictability of reservoir quality distribution is limited, which is also stated in various

other reports: “Attempts were made to derive relationships between lithofacies and reservoir

properties, but with limited success.” [Cohen et al., 1989; Hoetz et al., 2007].

Within each reservoir unit reservoir quality deteriorates from South to North. This trend correlates with

the tendency of northward fining and pinching out of sand layers, which is related to the more

prominent presence of playa lake conditions in that direction.

Individual reservoir units demonstrate varying reservoir quality properties. ROSLU1 and ROSLU6 are of

very varying quality in a lateral sense. ROSLU2 contains relatively much non-reservoir lithology and may

therefore act as a baffle or barrier to flow. ROSLU3 and ROSLU5 have been found to not contribute to

gas production significantly, whereas ROSLU4 has the best reservoir properties of all units and is the

main producing reservoir section. Even in the north of the Ameland area, which is dominated by poor

quality rocks, ROSLU4 still contains very thin streaks of better quality [Hoetz et al., 2007].

chapter 5-50 page


(i) Diagenesis

In addition to the inherited uncertainty related to the sedimentology, there is also a diagenetic overprint

which has a large impact on the reservoir quality in the study area.

Diagenesis has played a significant role in the evolution of the hydrocarbon prospectivity in the whole of

the Southern Permian Basin.

In principle, diagenetic processes and products are comparable within the entire Southern Permian

Basin [Gaupp and Okkerman, 2011].

Various researchers have extensively examined the diagenetic history of Rotliegend rocks, but so far

there remains significant ambiguity in explanations for several aspects of diagenesis including for

example the causes for major differences in reservoir quality impairment in different fault

compartments.

Only a broad overview of the general main earlier findings from existing literature will be given in this

report as it is far beyond the scope of this study to accurately analyze the diagenetic history of the

studied Rotliegend rocks.

Three phases of diagenesis are widely recognized: diagenetic effects are related to early (eodiagenetic),

intermediate (meso- and locally telodiagenetic) or late deep-burial altering processes. However,

between authors significant differences occur regarding the interpretation, importance and timing of

individual diagenetic processes [Gaupp and Okkerman, 2011].

Depositional setting and sediment texture are guiding factors for the type of diagenesis that occurs.

Mature, well-sorted sandstone facies suffered least from diagenesis whereas immature lithologies have

lost much of their porosity and permeability by pervasive diagenetic cementation and clay-mineral

growth [Hoetz et al., 2007]. A similar correlation holds for the water content of facies during their

deposition: sediments derived from wetter depositional environments are more prone to reservoir

quality deteriorating processes compared to facies from dry environments. However, in the dry

lithofacies a very large variation in reservoir quality occurs [Burfoot et al., 1998; Greenwood et al., 1999].

In a regional study done within NAM, the diagenetic clay mineral distribution was investigated and

found to be controlled by primary depositional facies. Five different diagenetic provinces were

identified:

- kaolinite dominated (south)

- mixed kaolinite and chlorite (north of the Groningen Field into northern Friesland)

- chlorite-prone (northern part of Lauwerszee Trough, central part of Ameland)

- severe platy kaolinite cementation (upthrown side of Hantum Fault Zone)

- abundant flaky kaolinite with minor illite

The areal distribution of these provinces can be seen in Figure 5.19 [Greenwood et al., 1999].

chapter 5-51 page


Figure 5.19 – Mineralogical trend map of diagenetic clays throughout the GAA (Mee [2010], modified after Greenwood et al. [1999])

The diagenetic impact on Rotliegend reservoir sandstones within the Greater Ameland Area has been

investigated in several other projects. From these studies it can be concluded that the main pore-

occluding cements present are [Hoetz et al., 2007]:

carbonates and sulphates (dolomite & anhydrite)

grain-coating chlorite

flaky kaolinite

quartz

minor illite

Carbonate cement is predominantly present as (Fe-) dolomite and occurs as nodular in ‘wetter’

sediments, whereas pore occlusion is more pronounced in damp- and dry sandflat-sediments. Anhydrite

is present as a patchy and grain-replacing, locally pore-filling cement. Grain-coating chlorite reduces

compaction and prevents the nucleation of other cement, which preserves reservoir quality. Kaolinite

significantly deteriorates reservoir quality by occlusion of pore space, although this applies primarily to

permeability; porosity is affected to a lesser extent. The presence of illite is most probably related to

pulses of significant pore water migration hence changing temperature, pressure and formation water

chemistry conditions invoked by structural reconfiguration of the area [Greenwood et al., 1999; Hoetz et

al., 2007; Gaupp and Okkerman, 2011].

Extensive statistical analysis of data available from the north-eastern Netherlands and the Netherlands

offshore indicates that the most important control on reservoir quality even after significant diagenetic

alteration remains depositional facies. The main factors controlling current reservoir permeability are

chapter 5-52 page


porosity, depth, grain size, and clay content (both detrital and authigenic) [Gaupp and Okkerman, 2011].

This validates the approach which was used for this thesis in which the impact of diagenetic effects on

reservoir quality distribution was largely neglected.

(ii) High permeability streaks

High permeability streaks (HPS) are intervals that are characterized by a high permeability (in the 10-100

mD range) within a dominantly lower permeability (0.1 to <10 mD) background [Visser, 2003; Romero,

2005]. Various magnitudes of HPS can be observed, from mm- to m-scale depending on which

measurement tools are used and what total dimensions are considered.

The definition and prediction of HPS occurrence is particularly subject to discussion when for example

geologists and reservoir engineers communicate about these features as the geological explanation and

‘rules of thumb’ for HPS distribution might not coincide with the engineering definition based on

petrophysical cut-offs [Visser, 2003]. Care should therefore be taken when discussing HPS, because

scales at which they are considered can vary significantly within various disciplines.

Sedimentary deposits inherently possess heterogeneity, independent of scale. In specific depositional

environments, differences in permeability related to this heterogeneity variation can be quite extreme.

For example, in aeolian sediments grain fall deposits there may be a significant permeability contrast

between the base and top of the bed [Prosser and Maskall, 1993]. This is related to the grain deposition

mechanism (grain fall vs. grain flow) occurring in different parts of the deposited bed.

For reservoir modelling the permeability of a rock plays an important role in determining ultimate flow

behaviour. Therefore, the concept of ‘high permeability streaks’ is essential to take into account when

upscaling data to construct a reservoir simulation model.

Especially in relatively poor quality reservoir rocks, HPS are important in predicting hydrocarbon

drainage [Hoetz et al., 2007]. HPS influence drainage efficiency by possibly facilitating fingering and early

water breakthrough, and consequently could have a significant effect on ultimate recovery. Various,

though predominantly geological, studies on permeability variation within the GAA have been carried

out in the past [Visser, 2003; Romero, 2005].

In the Greater Ameland Area, HPS are interpreted to result from dry aeolian sedimentation followed by

a rapid transgression of wet sediments. Rapid transgression leads to a short period of passage to the

capillary fringe, only leaving a short period of time for early cementation [Visser, 2003]. For this study,

description and ‘prediction’ of HPS was not a primary goal.

Although the small-scale permeability variation might significantly impact production behaviour, HPS are

generally hard to incorporate in any kind of reservoir model. Even within a detailed static reservoir

model generally the wireline log data provide the finest level of detail based on which upscaling is

carried. Logs do not capture HPS, and even without considering these small-scale structures the

upscaling procedure can already be challenging.

chapter 5-53 page


(iii) Reservoir quality distribution

It is vital to establish a minimum reservoir quality and locate the areas in which this criterion is met for

successful hydrocarbon exploration and production. Therefore the reservoir quality distribution was

investigated in this thesis.

Reservoir quality decreases towards the north, as is described by various authors [Cohen et al., 1989;

Romero, 2005; Hoetz et al., 2007; Fryberger et al., 2011]. This is attributed to distal thinning and

pinching out of sand-rich layers near the edge of the playa lake, whereas in this depositional setting

shale-rich sediments increase volumetrically.

Figure 5.20 – Facies distribution in and around the GAA (the study area is indicated by the red circle), after Gaupp and Okkerman [2011]

Maps were created to get a better understanding of the reservoir quality distribution in the GAA. An

areal distribution map is presented for the whole ROSLU-sequence and for each flow unit for all the flow

and storage properties that are discussed here. However, some of the maps are not shown within this

chapter but are placed in Appendix C. The well locations of only those wells that were used for creating

the property map are displayed, others are omitted.

Figure 5.21 shows the shale content distribution throughout all ROSLU flow units. It is obvious that the

sand content decreases towards the north, which supports the statement made in earlier reports. The

direction of sand content change is more or less straight towards the north, although undulations in the

iso-sand content-lines are clearly visible in the map.

The maps for individual flow units confirm the overall trend, although there are variations in overall

sand content per flow unit (available in Appendix C).

chapter 5-54 page


Figure 5.21 – Shale content distribution map for all ROSLU flow units together

Property maps are also useful for identifying areas with good reservoir quality. In contrast to the sand

content and net-to-gross, it is less valuable to look at the map in which all ROSLU-units have been

grouped. This is because there is a large scatter in porosity values within the flow units: each flow unit

has a relatively large standard deviation and a significantly different average porosity. These

characteristics are wiped out when all flow units are considered together. The same holds for the

permeability maps.

In ROSLU1, ROSLU3 and ROSLU5 the porosity clearly decreases towards the north (see a), c) and e) of

Figure 5.22). For ROSLU2, ROSLU4 and ROSLU6 this conclusion is less straightforward. In ROSLU6 (see f)

in Figure 5.22 the porosity is highest in the southeast but this rapidly declines towards the west, similar

to the porosity decrease towards the north. It seems as though the porosity is relatively homogeneous

throughout this ROSLU4 (map d) in Figure 5.22), with a slight decrease in absolute values towards the

north. However, the map depicts that in the south the porosity values diminish significantly. This is

contradictory to existing knowledge, but could be due to averaging effects. The map of porosity

distribution in the GAA for ROSLU2 shows a fairway-like shape in the central part of the study area in

which the porosity is lower than to both the west- and eastern side. This is probably due to local slightly

higher values in an overall low-porosity interval.

chapter 5-55 page


Both within and between the various flow units porosity differences are not extremely high: average

porosity never exceeds 22% and is on average significantly lower (between 10 and 15%).

Unfortunately permeability distribution maps do not add information throughout the whole GAA

because of data limitations. In total, 36 wells contain permeability logs but there are many values that

are very close to zero (well locations are displayed on the maps). This results in large areas with a zero-

permeability on the map due to the interpolation method used. Figure 5.23 shows the permeability

distribution for all ROSLU-units.

Even disregarding the areas with zero permeability the average permeability is low, ranging from 20 to

50 mD with peaks towards 100 mD in some flow units. The overall permeability is relatively low in all the

flow units, but the highest permeability values are present in ROSLU4 and ROSLU6. ROSLU2 and ROSLU5

exhibit the poorest permeability characteristics. This corresponds to findings in earlier studies and

reports. Due to the limited availability of non-zero data the permeability maps show a relatively high

amount of non-permeable area. However, this is not a true representation of reality but reflects the

absence f permeability logs in a large number of wells. No conclusive remarks can be made regarding

lateral permeability trends. However, the overall tendency of average permeability in each flow unit

corresponds with earlier postulated reservoir quality of individual flow units.

All in all, the property maps are in good agreement with general accepted regional trends for all

properties and flow units but display a larger amount of detail.

(iv) Heterogeneity quantification

For the identification of flow units and associated reservoir characterization it is important to

understand the amount and range of heterogeneity affecting the reservoir rocks as a function of scale.

As already referred to in the discussion of HPS, the scale at which heterogeneities are present does

influence the reservoir quality.

Table 5.10 provides an overview of available data to infer permeability heterogeneity and the scale at

which they are generally considered (PP – petrophysics, PG – production geology, GP – geophysics and

RE – reservoir engineering).

Data type Discipline Scale

mm cm dm m dam hm km > km

Mini-permeameter data PP

Sedimentological observations PG

Core plug measurements PP

Wireline log data PP

Seismic surfaces GP

Model input Discipline Scale

mm cm dm m dam hm km > km

Lateral grid block size (static) PG

Vertical layering (static model)

Lateral grid block size (dynamic) RE

Vertical layering (dynamic model)

Table 5.10 – Scales of available data and necessary model input

chapter 5-56 page


Figure 5.24 provides an example of differences in data scale for wireline logs, core plug measurements

and mini-permeameter data. In the figure, permeability responses for two different sedimentary

features are depicted (dune and interdune facies). Note that various measurement techniques result in

significantly different permeability profiles due to sample spacing differences. The sample spacing is

often related to the maximum scale at which the data is available and therefore different measurements

are taken to be guiding on different scales. This is often moreover a result of unavailability of desired

data, and clearly illustrates the error sensitivity of permeability data and the importance of

appropriately considering scales of measurements.

Figure 5.22 – Porosity distribution map for a) ROSLU1; b) ROSLU2; c) ROSLU3; d) ROSLU4; e) ROSLU5 and f) ROSLU6

chapter 5-57 page


Figure 5.23 – Permeability distribution map for a) ROSLU1; b) ROSLU2; c) ROSLU3; d) ROSLU4; e) ROSLU5 and f) ROSLU6

chapter 5-58 page


The variety of data and their marginal overlap with

the input for both static and dynamic models

elucidates the challenges in data analyses and

required communication between various

disciplines.

Several techniques can be identified and adopted

to relate available permeability data.

One of these techniques involves the overlap but

disagreement of scale between wireline logs and

core plug measurements when it comes to

permeability data. Effectively, wireline logs and

plug measurements both offer permeability data,

but discrepancies often occur while comparing

them due to differences in the scale for which they

are representative. Log data is valid over an

interval of (at least) ~ 30 cm, whereas plug

measurements provide detailed permeability data

for an inch-sized sample.

Wireline log data is the most convenient for building

any type of reservoir model that can be used for volume calculations and subsequent production

forecasting, as it overlaps with the desired scale for input data. However, wireline logs only offer a

calculated average value for horizontal permeability. Core plug data on the contrary provide measured

values for both horizontal and vertical permeability based on more local rock properties. Especially in

view of the presence of HPS it is crucial to incorporate as much (local) heterogeneity into the log data as

possible.

To account for the lack of resolution in the wireline logs opposed to plug measurements, a

‘heterogeneity correction factor’ can be determined by calculating the ratio between permeability in the

core plug measurements and the log permeability data, averaged over the complete cored interval. This

correction factor is then multiplied with the well log data and the resulting values for permeability are

assumed to better represent reality. It is assumed that the plug permeability data do not suffer from

significant averaging effects [Hoetz et al., 2007], but it should be noted that this method inherits the

plug spacing sampling bias (plugs are generally not taken in shale intervals but in reservoir lithologies).

Lorenz plots and Gini coefficients based on data from both core plugs and wireline logs were used for

acquiring a better understanding of the reservoir heterogeneity.

Lorenz plots do not provide unequivocal results in terms of linking reservoir quality behaviour to the

existing reservoir zonation. There are three wells in the study area in which core material has been

recovered from the entire reservoir section (ROSLU1 through ROSLU6), and there are two major

observations that pop up when looking at the Lorenz plots for these wells (AME-107, AMN- 1, MGT- 1B

and TRN- 1: see Figure 5.25 for an overview indicating their location).

Figure 5.24 – Permeability characteristics of aeolian dune and interdune deposits (after Fryberger et al. [2011])

chapter 5-59 page


Firstly, there is a notable discrepancy between the Lorenz plots based on wireline log data versus those

constructed using core plug measurements: the lines for both data types do not overlap at all (for an

example, see the SMLP of AME-107 in Figure 5.26 where the blue line depicts the wireline log data and

the red line is based on core plug data).

Secondly, the shape of the plug-based Lorenz plots is comparable for all three wells. However, the

inflection points of the curves do not coincide with (major) stratigraphically correlatable surfaces nor do

they coincide in terms of depth for either of the data points. A hypothesis for this is that significant knee

points concur with flow unit boundaries but that is not clearly the case for either of the wells. When

examining the log-based Lorenz plots a similar observation results: there is no obvious correlation

between flow unit boundaries and changes in reservoir quality ‘trend’.

For wells in which only an incomplete reservoir section was cored, Lorenz plots were constructed for the

interval for which core plug data was available (in total 35 wells were evaluated). Typically the depth

intervals range from around 10 to a couple of tens of meters. There is no clear agreement between the

Lorenz plots based on respectively core- and log data. An example of this discrepancy is shown in Figure

5.26, which depicts the Lorenz plots belonging to the AME-107 well (of which the location is also

depicted in Figure 5.25).

There are various causes to the deviation between the two. The most prominent of these are related to

discrepancies in depth measurements, either during drilling and coring activities or during wireline

Figure 5.25 – Location map of wells AME-107, AMN- 1, MGT- 1B and TRN- 1

chapter 5-60 page


logging campaigns. Various data depth ranges were used to obtain results in order to achieve a better

match between the log- and core-based Lorenz plots.

Depth measurements during drilling activities are carried out based on the number of drill pipe sections

inserted into the hole. Mechanical stretch of the total drill string may cause deviations in depth

measurements and total depth reached. This is related to the total weight of drill string hanging from

the wellhead (a deeper well naturally is prone to a larger depth error to the significantly higher weight

of drill string when compared to a shallow well). Furthermore core material can get damaged or even

disappear during core raising and -handling, and this may cause significant errors in depth allocation to

certain parts of core.

During wireline logging depth is measured by determining the cable length which has been run into the

hole. In this procedure, the major source of error is related to wireline stretch. This is a well-known

factor, and petrophysical editing of wireline log data involves a stretch correction to account for this.

In Figure 5.26 two stratigraphic modified Lorenz plots (SMLP) are displayed. The plot based on original

plug data retrieved from the internal NAM-database is depicted in red whereas the original log data as

available in the regional Petrel reference-project was used to construct the blue curve. It should be

noted that these logs were not corrected for stretch during the logging campaign.

The locations at which inflection points occur, i.e. the depths at which there is a clear change in ratio

between storage- and flow capacity, do not agree for the logging- and drillers’ depth. On average depths

of the inflection points lie approximately 5.2m apart (see Table 5.11). There is no correspondence

between the depth of inflection points and major reservoir interval boundaries.

Table 5.11– (a) Table showing the depth of inflection points in the SMLP in Figure 5.26 calculated based on plug data; (b) Table showing the depth of inflection points in the SMLP in Figure 5.26 calculated based on wireline log data

chapter 5-61 page


Figure 5.26 – Stratigraphic Modified Lorenz Plot for well AME-107

Table 5.12– (a) Table showing the depth of inflection points in the SMLP in Figure 5.27 calculated based on plug data; (b) Table showing the depth of inflection points in the SMLP in Figure 5.27 calculated based on wireline log data

chapter 5-62 page


Figure 5.27 – Stratigraphic modified Lorenz plot for well AME-107 with shifted depth interval for the plug data points

After manual adjustment of the interval depths as to remove the plug data points causing the upper,

straight part of the red plot there is a significant difference in depth of the inflection points although the

absolute value has decreased. On average the depth deviation is now around 2.5m (see Figure 5.27 and

Table 5.12).

Manual shifts (picking the starting- and end-points for both of the curves and adjusting the

corresponding input values) of the SMLP leads to a significantly better match. The minimum average

depth difference between the plug- and log data obtained by trying various depth ranges, is 1.2m (see

Figure 5.28 and Table 5.13). The closest match was obtained by plotting the plug data from 4450.1m to

4495.05m MD (drillers’ depth) against the log data between 4457m to 4499m MD (logging depth).

chapter 5-63 page


Figure 5.28 – Stratigraphic modified Lorenz plot for well AME-107 with shifted depth interval for the plug data points and a manually adapted depth range for the stretch-corrected log data

As mentioned above, the logs used to create the SMLPs described were not stretch-corrected. Edited

porosity- and permeability logs for the AME-107 well were obtained and used for yet another Lorenz

plot to resolve the issue of non-overlapping curves. Plotting the Lorenz plot for the same depth interval

with both plug and corrected log data results in Figure 5.29 (the values are also shown in Table 5.13).

There is still a difference in the depth of the inflection points, but the deviation depth has decreased to

Table 5.13 – (a) Table showing the depth of inflection points in the SMLP in Figure 5.28 calculated based on plug data; (b) Table showing the depth of inflection points in the SMLP in Figure 5.28 calculated based on wireline log data

chapter 5-64 page


0.3m. The average depth difference is now approximately the wireline logging interval distance, which is

a very reasonable result.

The curves for well AME-107 do not show the most extreme mismatch between the log- and plug data-

Lorenz plots, but these plots were used to illustrate the effects of different depth ranges and -

measurements.

Stretch-corrected logs were not easily accessible in the corporate database, and more in particular not

used in the Petrel reference project. It may be concluded that these edited petrophysical well logs

provide data which is more reliable than the uncorrected logs, and that in this case the measured depth

labelled on the core data matches the wireline depth quite accurately.

Table 5.14 (a) Table showing the depth of inflection points in the SMLP in

Figure 5.29 calculated based on plug data; (b) Table showing the depth of inflection points in the SMLP in

Figure 5.29 based on stretch-corrected wireline log data

chapter 5-65 page


Figure 5.29 – Stratigraphic modified Lorenz plot for well AME-107 with shifted depth interval for the plug data points and the stretch-corrected log data

Gini coefficients were calculated for the complete reservoir section in the study area (ROSLU1-6) in all

log-based Lorenz plots. The map showing these values is shown in Figure 5.30. Also, Gini coefficients

were investigated for individual intervals representing a single (complete) flow unit. A high Gini

coefficient (close to 1) indicates a low level of heterogeneity within the flow unit.

Figure 5.31 displays a map of the study area on which the Gini-coefficients for ROSLU4 are shown at the

well location they were measured in. The Gini coefficient-values for all wells and flow units can also be

found in Appendix D, together with maps of the other flow units.

Table 5.15 – Gini coefficients for every ROSLU-flow unit, averaged for all 36 investigated wells

Table 5.15 displays the average Gini coefficient per reservoir interval. ROSLU4 shows the least internal

heterogeneity, whereas ROSLU2 is relatively heterogeneous. However, its Gini coefficient is still

significantly larger than 0, so that means that there is still a fair degree of heterogeneity in ROSLU4.

ROSLU1 ROSLU2 ROSLU3 ROSLU4 ROSLU5 ROSLU6

0.4503 0.5797 0.5455 0.4274 0.5138 0.4503

chapter 5-66 page


The analysis of the Gini coefficients results in the conclusion that the rocks studied exhibit a significant

level of heterogeneity. Locally, heterogeneity is limited but overall the sediments are more hetero- than

homogeneous.

Figure 5.30 – Map view of Gini coefficients averaged for the complete ROSLU-interval in the study area

There is no clear areal trend in Gini coefficients, for neither the complete ROSLU-section nor in ROSLU4

or any of the other individual flow units.

Another measure of heterogeneity quantification, the Dykstra-Parsons coefficient, was also used to

investigate the heterogeneity within the Rotliegend in the GAA. Dykstra-Parsons coefficients reflect the

variation in permeability data. A Dykstra-Parsons coefficient of 0 occurs when rocks are perfectly

homogeneous, whereas an extremely heterogeneous sedimentary succession is characterized by a DP-

coefficient that approaches 1. Table 5.16 lists the average DP-coefficient for each reservoir interval.

ROSLU1 ROSLU2 ROSLU3 ROSLU4 ROSLU5 ROSLU6

0.9247 0.2903 0.7576 0.9197 0.7576 0.8221 Table 5.16 – Dykstra Parsons-coefficients for every ROSLU-flow unit, averaged for all 36 investigated wells

chapter 5-67 page


Figure 5.31 – Map view of Gini coefficients for ROSLU4

All ROSLU-units except for ROSLU2 show a relatively high level of homogeneity, ROSLU1 and ROSLU4 are

the most homogeneous and ROSLU6, ROSLU5 and ROSLU3 displaying an ‘intermediate’ degree of

heterogeneity. ROSLU2 is very heterogeneous according to the DP-coefficient.

There is a pronounced difference in average heterogeneity indicated by the Gini coefficient versus that

shown by the Dykstra-Parsons coefficient. When ordering the flow units in terms of increasing

heterogeneity according to the Gini coefficient, this would result in the following sequence: ROSLU4 –

ROSLU 1 & ROSLU 6 – ROSLU 5 – ROLSU 3 – ROSLU2. Almost exactly the opposite sequence would occur

when grouping the flow units by their DP-coefficients: ROSLU 2 – ROSLU3 & ROSLU5 – ROSLU6 – ROSLU4

– ROSLU1. This shows that, when quantifying heterogeneity in the GAA sediments, there is a marked

effect of incorporating only permeability data (which is done in the DP-analysis) as to when porosity is

also taken into account (with Gini coefficients).

It remains puzzling why the two measures of heterogeneity show an almost exact inverse trend.

All in all, it can be inferred from the Lorenz plots and Gini- and Dykstra-Parsons coefficients that internal

heterogeneity is very significant within the whole section of Upper Rotliegend reservoir rocks. However,

to what extent heterogeneity determines the flow behaviour of the various units is unclear.

(v) SSD vs. reservoir quality

The porosity-permeability relationship per facies was investigated to determine whether the facies

classification based on SSD-intensity is a useful tool for assigning reservoir quality. Per interval with the

chapter 5-68 page


same SSD-facies, the distance-weighted average of plug data measurements falling within that same

interval was calculated.

Values of zero within the plug data, for both porosity and

permeability measurements, were neglected as they do not

represent useful information. For the purpose of general reservoir

quality trends/behaviour throughout the whole study area, data

from all the studied wells was included so data was not limited to

the wells that were studied for SSD-facies.

Analysis of individual wells did not result in valuable observations.

Table 5.17 – Number of data points per facies for porosity- permeability cross plot and the coefficient of determination (R2)

Figure 5.33 shows the cross plot of porosity versus permeability for all SSD-facies. The coloured lines on

the diagram indicate the trend line for each individual facies, and the thin black line shows the porosity-

permeability relationship when grouping all facies together.

From the trend lines it shows that facies 1 displays the best reservoir properties. Facies 2 follows but

facies 3, 4 and 5 do not exactly adhere to the expected trend of decaying reservoir quality properties

with increasing deformation intensity. However, in general the differences are very small. Especially in

the poorer reservoir quality domain (below 10% porosity) there is not a significant difference in

porosity-permeability relationships for all facies.

For each facies the number of data points that has been used to determine the porosity-permeability

relationship is listed in Table 5.17. Apart from the number of data points for each facies, the

corresponding coefficient of determination (R2) is given. An R2-value of 1 indicates perfect correlation

between all data points, whereas a lower number signifies a less solid correlation.

There is a large scatter around the trend line for facies 3, facies 4 and facies 5 whereas facies 1 and

facies 2 display a slightly better correlation as indicated by the R2-values.

An aspect that should be taken into account when evaluating these results is that the defined SSD-facies

do not discriminate between sedimentary structures and lithofacies. Therefore, facies 1 might include

both clay-rich lacustrine-like sediments and coarser-grained siliciclastic deposits. This limits the

applicability of any relationship between facies and average permeability.

Within the individual facies there is

not a predominant porosity and/or

permeability, coherent porosity- and

permeability values lack. Large ranges

occur in all SSD-facies. This confirms

the statement made above, and is

related to the fact that initial lithology is not accounted for by the SSD-facies classification.

data points R2

all 756 0.4604

facies 1 113 0.5050

facies 2 137 0.5546

facies 3 168 0.3452

facies 4 132 0.2023

facies 5 206 0.3574

Table 5.18 – Average mini-permeameter values per facies and associated deviations

average [mD] stdev variance data points

facies 1 7.00 17.30 2.99E+02 297

facies 2 5.17 14.92 2.22E+02 350

facies 3 199.23 3495.86 1.22E+07 796

facies 4 140.28 3115.78 9.71E+06 881

facies 5 25.06 513.44 2.64E+05 1289

chapter 5-69 page


For the wells in which mini-permeameter-data was available, an assessment was made of the when

internal heterogeneity of the variation of permeability data.

During and after the core study it was concluded that, initially, there does not seem to be a clear

relationship between internal heterogeneity of a facies and its deformation intensity (see Figure 5.32 for

an example log section). Also, the reservoir quality characteristics do not show trends that are in any

way directly relatable to the intensity of soft sediment deformation.

This is confirmed by the quantitative analysis of average heterogeneity per facies.

Table 5.18 shows the average mini-permeameter-permeability value of all facies. In the second and third

column, the standard deviation and variance of these averages is depicted. The mini-permeameter data

is contradictory to what was expected in terms of absolute permeability values: facies 1 and facies 2

clearly show the lowest permeability values, whereas facies 3 and facies 4 have a significantly higher

permeability. Facies 5 has a low average permeability value.

The standard deviation and variance of the permeability

variation within each facies are clearly related to average

permeability value. However, sample size also plays a role:

for facies 3, facies 4 and facies 5 the sample number is

approximately double that of facies 1 and facies 2, and both

the standard deviation and variance are significantly higher.

The high value of variation from the mean within facies 3 is

remarkable, but this is probably related to the high average

permeability value.

These results show that the heterogeneity within the more

intensely deformed facies is significantly higher than in the

non- and lightly-deformed facies.

Figure 5.32 – Section of ROSLU4 in well AME-203 which illustrates the lack of consistent heterogeneity measures per SSD-facies

chapter 5-70 page


Figure 5.33 – Cross plot displaying the porosity-permeability relationship per facies

(vi) Results

A lot of research has been devoted to the reservoir quality and its distribution throughout the GAA, both

in this study and in earlier work. However, no straightforward conclusions in terms of predictability have

been drawn so far.

The study area has undergone a complex diagenetic history, which can be grouped into three major

phases of diagenesis. The initial depositional facies is the dominant factor influencing the type of

diagenesis that occurs, and the main types of diagenetic minerals include kaolinite, chlorite and illite in

various conditions, carbonate cement (principally dolomite and anhydrite) and some quartz.

Reservoir quality tends to decrease towards the north. On a large scale (SPB-wide) diagenetic belts run

more or less east-west, but on the scale of the study area more undulating iso-quality-lines are

demonstrated by various property maps.

Heterogeneity is a very important feature affecting the reservoir quality distribution in the study area.

The presence of high permeability streaks, which have a sedimentological origin, illustrates this.

chapter 5-71 page


A major challenge arises when assessing reservoir quality by the various scales of data, especially in

terms of integrating and validating observations and conclusions on multiple scales. Small-scale data is

readily available but this needs to be upscaled for modelling exercises.

Especially in terms of heterogeneity this scale issue might significantly influence results. A heterogeneity

correction factor is therefore applied to permeability log data to mitigate the discrepancy between plug

and log data, but many more techniques to account for heterogeneity could be incorporated.

Lorenz plots were calculated to define intervals with similar reservoir quality-behaviour, both based on

wireline log- and plug data. There is a significant discrepancy in depth of interval boundaries between

the two data types, and no clear correlation was observed between the Lorenz-defined intervals and the

ROSLU-flow units. Using stretch-corrected logs and adjustments based on the visual match in interval-

boundary depths was increased. The best correspondence between the well- and plug data Lorenz plots

was obtained by using stretch-corrected log data.

Gini coefficients provide a measure of heterogeneity based on Lorenz plots and take into account both

porosity and permeability. For all reservoir intervals the value of this coefficient lay between 0.45 and

0.6, which is within the “general class in which most reservoirs fall in terms of heterogeneity” [Willhite,

1986]. ROSLU1 and ROSLU4 are the most homogeneous, whereas ROSLU2 is the most heterogeneous

reservoir interval.

Another heterogeneity quantifier is the Dykstra-Parsons coefficient, which purely assesses the spread

scatter in permeability. The Dykstra-Parsons coefficient provides almost exactly the opposite trend: it

shows that ROSLU1 and ROSLU4 are the most heterogeneous and ROSLU2 is the most homogeneous.

A cross-plot of reservoir properties per SSD-facies classification shows mildly different porosity-

permeability relationships for different deformation intensities.

chapter 5-72 page


c) Seismic interpretation The aim of this part of the project was to assess if there is pre-existing topography at the base of the

Rotliegend Group, at the contact with the Carboniferous. An earlier internship study focusing on the

basal Rotliegend geometry in the Groningen area (onshore Netherlands) demonstrated that there is clear

onlap of ROSLL and ROCLA. It was investigated for this thesis if a similar geometry is also present in the

GAA. This section provides the result of the seismic interpretation that was carried out to answer this

question. Also, the relationship of this basal geometry including possible palaeotopography with soft

sediment deformation and reservoir quality distribution was investigated.

(i) Onlap

Carboniferous sediments are unconformably overlain by the Permian Rotliegend Group. This

unconformity was caused by (postulated) significant topographic variation related to tectonic activity

prior to deposition in the Permian in the Southern Permian Basin [Ziegler, 1990; Lokhorst, 1998]. The

contact between the base of the Rotliegend and the uppermost Carboniferous units is therefore often

also called the Base Permian Unconformity (BPU) and has an angular nature. It should be taken into

account that this hiatus inhibits an amalgamation of several unconformities into a single mega-

unconformity [Glennie, 1998].

In earlier reports and studies it is stated that the geometry of the basal Rotliegend Group deposits fits a

so-called ‘wedge model’ [Hoetz et al., 2007]. This implies that layers are present throughout the whole

of the sedimentary basin, and that their thickness gradually decreases towards the more distal end of

the depositional setting. However, a recent study indicates that in the Groningen area this wedge model

is not applicable for the large-scale stratigraphy: the Lower Slochteren -and oldest members of the

Silverpit Formation show a clear onlap geometry [Berghuijs, 2013].

(ii) Synsedimentary tectonics

In the chapter ‘Geological Setting’ the cyclicity of lake level variation during the deposition of the

Rotliegend is mentioned, as described by Minervini et al. [2011]. A similar cyclicity has been established

by various other authors [Gast, 1991; Yang and Nio, 1994; George and Berry, 1997; Glennie, 1998;

Moscariello, 2011]. It is argued that these cycles are climatically controlled, although various authors

state that “intra-basinal synsedimentary tectonics have overprinted the depositional sequences” (e.g.

George and Berry [1997].

The pull-apart basins of which the SPB is composed were formed in Early Permian times as a result of

dextral strike-slip faulting. These faults clearly follow a pre-existing structural grain originating from the

Variscan orogeny. Faulting continued well into Late Permian times, and reactivation of faults occurred

during the Late Jurassic to Early Tertiary [George and Berry, 1997].

Locally, synsedimentary tectonics have increased sediment accumulation rates or affected the number

of sequences that have been deposited. Also, the facies distribution is severely affected by the tectonic

activity during deposition.

Based on an extensive study of the palaeogeographic setting it was observed that alluvial fans and fan

deltas laterally displace, due to backstepping of splay points along major fault zones. In the Dutch part of

chapter 5-73 page


the SPB, lobes are progressively younging in a south east direction [George and Berry, 1997].

This highlights the influence of tectonic activity on deposition of the Upper Rotliegend.

(iii) Palaeotopography

Various authors imply significant palaeotopography at the end of the Carboniferous. Mijnlieff and Geluk

[2011] state that careful examination of the sediment fill in the SPB reveals that there are prominent

steps in thickness and facies. This implies the presence of jumps in palaeotopography of the basin prior

to deposition. Similarly, Minervini et al. [2011] conclude that the facies distribution would not have

showed its current characteristics without the presence of palaeotopography during deposition: “Facies

distribution is significantly influenced by palaeotopography.” It is postulated that “the palaeotopography

was formed at the end of the Carboniferous as a result of the uplift and inversion of underlying

Carboniferous strata” [Minervini et al., 2011]. A comparable statement is made by [Mijnlieff and Geluk,

2011], who establish that the “topographic variation is related to pre-Variscan and Variscan structural

elements”.

Apart from large-scale topographic steps, local erosion and inversion caused smaller-scale topographic

elements. Especially these smaller-scale features play an important role in the sediment- and facies

distribution.

Although no specific details about palaeotopography are given in the publications focussing no

synsedimentary tectonics mentioned in the previous section, the postulated tectonic activity does

support the presence of large- and smaller-scale topographic variation in the study area.

Seismic interpretation was carried out to investigate the presence and dimensions of possible

palaeotopography within the GAA. Only the large-scale topographic elements could be identified

through this approach. The seismic resolution lies in the order of several tens of meters and this is too

large for the observation of small-scale palaeotopographic features.

(iv) Seismic interpretation

Given the nature of seismic imaging, it can be challenging to identify angular unconformities on seismic

data. A seismic reflector is most pronounced when there is a clear vertical change in acoustic impedance

. That is, when a clear change in density times sonic velocity occurs when

encountering a significant change in lithology. When an angular unconformity is present, however, there

might not be such a large variation in acoustic impedance between the overlying reference horizon and

various underlying (angular disconformable) sediments.

Despite possible challenges in recognizing angular unconformities in seismic data, a previous internship

study in the Groningen area [Berghuijs, 2013] showed that an onlapping geometry is present at the BPU

close to the study area for this thesis.

chapter 5-74 page


Figure 5.34 – Map indicating the location of wells that have penetrated Carboniferous strata, including the well correlation shown in Figure 5.36 in red and the location of the seismic section in Figure 5.37 in blue

Figure 5.35 – Overview of interpretation grid after first-pass interpretation

chapter 5-75 page


Figure 5.34 displays a map in which the wells that have penetrated Carboniferous rocks are indicated.

These wells were identified in Petrel and the corresponding well tops were displayed on a depth-

converted seismic cube. Using the well tops as guideline a grid of interpretation lines created (see Figure

5.35) and was converted to a surface in Petrel. This first-pass interpretation was then used for a more

meticulous interpretation and to double-check the consistency of the interpreted horizon.

The polarity of the seismic marker that belongs to the top Carboniferous could be ambiguous. This is

related to the fact that it represents an unconformity which, as mentioned above, is characterized by

varying acoustic impedances along its boundary. This is demonstrated by the seismic loop in which the

different well tops fall. Although all the wells that were used to identify the correct seismic have been

used to calibrate the velocity model in the recent data reprocessing [Buczynski, 2013], the resulting

corresponding reflector is not consistent in terms of representing a soft or hard kick. Table 5.19 lists the

polarity of the seismic marker closest to the well top location, and an example seismic line crossing

multiple Carboniferous well locations is shown in Figure 5.35. The location of this correlation panel is

shown in red in Figure 5.34.

The velocity model used for depth-conversion of the seismic data is calibrated to optimize the seismic

imaging at depths where Upper Slochteren is encountered. This is because the Lower Slochteren and

Carboniferous Formations are (currently) not the primary target for hydrocarbon production. The depth

difference between the Carboniferous-Lower Slochteren contact and the Upper Slochteren Formation

can be up to hundreds of meters, which decreases the degree of focus around the BPU drastically.

Also, care was taken at a number of locations because due to the novelty of the reprocessed data

certain artefacts (pull-up related to a major salt dome and local velocity model limitations, for example)

have not been removed yet.

Figure 5.36 – Seismic W-E trending cross-section through multiple well locations indicating the ambiguity of the polarity of the seismic reflector corresponding to the various well tops (indicated in white)

chapter 5-76 page


Artefacts influence the seismic image significantly,

and in some areas the data quality is fairly poor.

Figure 5.37 displays a seismic section that

illustrates the challenges of picking the seismic horizon corresponding to the

Base Permian Unconformity (BPU). The location of this seismic section is

shown in blue in Figure 5.34.

To increase the visibility and continuity of the seismic reflectors, a filtering

algorithm was applied to the seismic cube. A so-called ‘Van Gogh-filter’ was

applied, which is a structure-oriented, edge-preserving noise reduction filter

which uses structural tensor to compute the structural field. While fault

imaging was preserved, this filter significantly enhanced the image and

enabled easier interpretation. Also, the colour scheme of the seismic data was

changed to increase the contrast between reflectors. The seismic section in

Figure 5.37 displays the colour scheme that was used for interpretation.

The ghost-tool in Petrel proved very useful to consistently pick the nth

reflector and hence improve the robustness of the interpretation. This tool

makes a ‘photograph’ of a certain part of the seismic section which can then

be dragged along the rest of the section, which allows for picking the same reflector throughout the

whole seismic slice.

It was impossible to take the existing interpretation

as strict starting point for the new interpretation.

Berghuijs [2013] mapped the horizon in time, using

Shell proprietary nDI-software, and only depth-

converted surfaces were available in Petrel. The

difference in used software impedes loading the

reference seismic data into Petrel. Furthermore the

extent of overlapping area was (very) limited with

the regional Ameland-seismic cube and newly

reprocessed seismic data (see Figure 5.37).

This limited areal extent is a significant limitation of

using the recently processed dataset, but it was

nevertheless used for interpretation of the top

Carboniferous because the increased quality of the

seismic imaging was deemed favourable against the

smaller area coverage.

The depth horizon mapped in the GAA is not very

close to the depth of the depth-converted

interpretation in the Groningen-area. Average depth

differences are in the order of 100 meters. However, there is already an average deviation of 20 meters

between the top Rotliegend reference surfaces that were used for both interpretation exercises.

well polarity

AME- 1 soft

AME-203A hard

AME-205B soft

AML- 1 soft

AMN- 1 soft

AMN- 3A soft

AWG-102 hard

BLF-107 hard

BUR- 1 soft

M09- 3 hard

MGT- 1B hard

MGT- 2 hard

N07A101 soft

N07A102 soft

N07A103 soft

NSN- 1 soft

Table 5.19 – Polarity of seismic reflectors indicated by the top Carboniferous well tops

Figure 5.37 – N-S seismic section (inline 6244) displaying the colour scheme used for interpretation and showing the (severe) influence of artefacts on the data quality

chapter 5-77 page


Furthermore limitations in the velocity model at depths of more than 4000m might easily have a very

large effect. Therefore the depth discrepancy was not considered crucial or devaluating the results.

Figure 5.38 – Overview of lateral extent of seismic cubes (red = regional Ameland seismic data, black = newly processed Ameland seismic data used for interpretation, blue = seismic cube used in earlier internship project [Berghuijs, 2013])

(v) Well correlation

Well correlation panels were made to confirm the postulated onlap geometry of the oldest Permian

stratigraphic entities. However, due to a limited number of wells in the area and their very scattered

location throughout the GAA it was hard to identify trends in the correlation panels.

Figure 5.40 displays a N-S and W-E correlation panel of the whole Rotliegend section flattened on the

top of the Ten Boer Claystone Member, respectively. Figure 5.41 shows the same correlated wells but

then focuses on the lower part of the Permian stratigraphy only. In these last correlation panels, a

number of intra-formational horizons were interpreted. It is clear that there are discrepancies in GR-

response which indicates varying sequences, but the variations do not immediately show onlapping

patterns.

A thickness map of the Rotliegend Group is displayed in Figure 5.39. On the thickness map the fault

pattern of preceding (and concurrent?) tectonic activity is clearly recognizable.

chapter 5-78 page


From the well correlations it shows that most of the ROSLU-reservoir intervals are characterized by a

fairly constant thickness except for ROSLU6. The clay intervals (ROCLT, ROCLA and ROCLH) display a

significantly larger thickness variation, of up to more than 100 meters.

Figure 5.39– Thickness map of the Rotliegend Group in the Greater Ameland Area. In grey is the outline of the Netherlands, in green the existing fields are depicted and the orange outlines show the hydrocarbon discoveries

chapter 5-79 page


Figure 5.40 – (a) N-S well correlation and (b) E-W well correlation showing thickness variation within the complete Rotliegend interval

chapter 5-80 page


Figure 5.41 – (a) N-S well correlation and (b) E-W well correlation showing thickness variation within the lower part of the Rotliegend Group

chapter 5-81 page


(vi) Results

In a number of regional studies regarding the geometry of the SPB, significant palaeotopography is

postulated. Significant thickness variation occurs in various Rotliegend formations as a result of this, and

this is shown in well correlation panels that were made. Especially in the lower stratigraphic units large

thickness variations are present. These thickness variations confirm the implication of major topographic

elements that are most probably caused by tectonic activity prior to the Permian. The fault pattern is

clearly recognizable in the thickness maps of Rotliegend deposits, so tectonic activity has played a

significant role in the sediment distribution.

Differences in formation thickness occur in the Ameland Member of the Silverpit Formation and in all

underlying formations, which implies that either tectonic activity continued during deposition of these

formations or that the pre-existing palaeotopography was too large to be filled up before the deposition

of the Ameland Claystone.

Seismic interpretation proved to be very challenging due to data quality limitations and the angular

unconformable nature of the surface to be mapped. The polarity of the reflector indicated as top

Carboniferous by well top data was not unambiguous and many artefacts impeded reliable

interpretation of the top Carboniferous horizon.

Various approaches and interpretation tricks were applied to check the consistency of the interpreted

surface. The interpretation was cross-checked with an existing top Carboniferous surface in a nearby

area to the extent possible.

All in all, the attempt to map the top Carboniferous on seismic data was relatively successful. Onlap was

encountered and the presence palaeotopography within the GAA can be postulated. Synsedimentary

tectonics have influenced the sediment- and lithofacies distribution significantly.

However, a more extensive regional study should be carried out to further confirm these findings and

provide more rigid conclusions on the relationship between palaeotopography, tectonic activity and

sediment distribution.

chapter 6-82 page


6. Discussion

a) Sedimentology The lithofacies classification that was used earlier is not conclusive in terms of predicting reservoir

quality distribution. Even though the lithofacies are clearly defined (by their varying clay content) they

are not conclusive in terms of depositional environment and governing geological processes. The

existing subdivision raises a number of (unanswered) questions related to fundamental depositional

processes. How wet is a damp sandflat? What is the main depositional agent in a wet or damp sandflat,

is it water or wind or a combination of those? What is the timescale at which the ‘wetness’ is of

influence?

The conceptual sedimentological model supporting the lithofacies classification was simplified.

Exclusively deploying ‘wet’ and ‘dry’ sandflat as facies belts increases the difference between the various

facies, and more clearly defines in what sedimentary sub-environment facies have been deposited by

what processes. In a wet sandflat, water is not necessarily present at all times but does represent the

main carrier agent. Grain size variation is large, and fluctuations in water table might invoke significant

soft sediment deformation. A dry sandflat is characterized by a fairly high degree of sorting and the

absence of surface water, where adhesion of wind-blown sediments is the main depositional

mechanism.

The existing core description classifies a considerable amount of sedimentary structures as “wavy

lamination”. This structure is related to the questions regarding water content of depositional

environment posed above.

The definition of facies based on soft sediment deformation intensity provides a classification scheme

that allows dealing with the wavy lamination. This SSD-facies classification was applied in the core study

and investigated extensively.

This new SSD-facies classification is especially useful in depositional environments similar to those of the

GAA. Numerous earlier studies could not provide an unambiguous explanation for the facies- and

reservoir quality distribution. However, the SSD-facies classification still does not automatically relate

sedimentary structures to reservoir quality. This is mainly due to a limited understanding of the detailed

governing processes and the unknown areal extent of individual structures.

A number of processes are inferred as responsible for the ubiquitous soft sediment deformation within

the GAA. The main driving mechanism is most probably fluctuation in the water table. The distribution

of SSD improves the understanding of fundamental geological processes that reigned during and shortly

after deposition of the Slochteren Formation. However, a direct link to climatic variations could not be

made.

Proper identification of SSD-structures is significantly hampered by the discrepancy in scale at which

observations could be done (in core material, so only vertical sections of a very limited lateral extent

were available) versus the large gamma of possible SSD-structures.

Existing analogue studies show that debate exists on the processes governing and triggering soft

sediment deformation for a variety of depositional environments in which SSD-structures occur. A

chapter 6-83 page


relationship to climatic variation is inferred by multiple authors [Mijnlieff and Geluk, 2011; Minervini et

al., 2011] but it remains questionable what has the largest impact on SSD in terms of sedimentation

rate, water table fluctuations, sediment supply vs. increase in accommodation space.

An outcrop study of SSD-structures resulting from a distal aeolian depositional setting, perhaps

combined with experimental work, might prove useful for further investigation of the nature and origin

of large-scale soft sediment deformation structures.

No clear trends are indicated by the analysis of facies sequences. This is contradictory to what was

expected. Climatic effects are considered the driving mechanism for soft sediment deformation, and

climatic variations throughout the period of Rotliegend deposition are generally considered as cyclical.

The absence of clear sequence trends might be due to the scale issue mentioned above. The SSD-facies

observations are of a very limited lateral extent their validity might be limited given the possible large

lateral variation. This lateral variation is not captured by the SSD-facies, and therefore sequence trends

might not be captured by the available data.

Lag-facies transitions and second-order facies transitions also don’t provide additional meaningful

insight, but follow the observations and conclusions drawn based on the general Markov-chain

sequence analysis.

The average SSD-intensity increases from the oldest to youngest Slochteren reservoir intervals. This

supports the postulated effect of climatic variation on SSD-occurrence. The deposition of the Zechstein

Group marks the onset of a major phase of transgression, invoking an increase in overall water content,

related to a climate change.

Changes in water table are inferred as the main triggering mechanism for the widespread SSD-structures

observed, both laterally and in a vertical extent.

Apart from the climatic variation that plays an important role in the distribution of SSD,

palaeotopography might enhance the type and frequency of occurring soft sediment deformation

structures.

Water table fluctuations can be magnified or annihilated by local palaeotopographic variation, and a

combination of these factors presumably has a larger effect on the presence of soft sediment

deformation than either of them on a stand-alone basis.

Synsedimentary seismic activity is discarded as sole driving mechanism for the SSD-structures observed.

It seems unlikely that for such a continuous period of time (throughout the whole period of Rotliegend

deposition) so much seismicity was present all the time.

b) Reservoir quality distribution The GAA is characterized by tight reservoir rocks, with a porosity ranging between 2-25% and

permeability values of 0.01 to several hundreds mD varying throughout the area.

A complex diagenetic history has severely affected the reservoir quality, and a variety of diagenetic

minerals is present including kaolinite, chlorite, Fe-dolomite and anhydrite.

HPS are present in the study area and have a sedimentological origin. They influence the heterogeneity

chapter 6-84 page


at a scale that is intermediate between the small-scale SSD-facies observations and the large-scale

reservoir modelling grid blocks.

It is challenging to adequately capture data related to reservoir quality in a model, related to the

heterogeneity scale/sample spacing, and many other scale discrepancies in data. In general available

data is at a much smaller scale than what is used or necessary for reservoir modelling.

There is no obvious relationship between reservoir quality and lithofacies. Dry sandflat deposits show

slightly better characteristics but not always, and damp/wet sandflat facies are characterized by a very

large spread in reservoir quality properties. Overall reservoir quality deteriorates towards the north.

In terms of the SSD-facies classification that was defined in this thesis, facies 1 and 2 show better

porosity and permeability values but facies 3-5 still show a large spread in reservoir quality. No

significant relationship between porosity and permeability is present per facies.

In facies 4 and 5, that is in which the intensity of SSD is very high, SSD might have enhanced reservoir

properties instead of decreasing them. The break-up of flow baffles and barriers (e.g. small shale

layers/lenses) might be invoked by the deformation and hence the vertical heterogeneity is

‘homogenized’.

There is no statistically significant relationship in reservoir heterogeneity and the defined ROSLU-flow

units.

Identification of flow units based on intervals of similar storage vs. flow capacity (Lorenz plots) did not

correspond to the existing unit boundaries. This could be due to the lack of corrections incorporated in

the data used (i.e. wireline stretch- and stress/depth corrections).

Various wireline corrections and a number of corrections for the core plug data have proven to

significantly increase the match between plug- and log-based Lorenz plots.

Gini- and Dykstra-Parsons coefficients show that heterogeneity plays an important role in the reservoir

rocks within the GAA. Arranging the ROSLU-flow units in order of increasing heterogeneity for both

measures, however, leads to an almost exact opposite order.

Mini-permeameter data confirms the presence of small-scale heterogeneity. It also shows that the

internal heterogeneity of the defined SSD-facies is lower in facies 1 and 2 and significantly higher in

facies 3, 4 and 5.

c) Seismic interpretation Due to limitations in data quality and an unclear correlation between wells tops and seismic markers it

proved hard to identify the top Carboniferous surface in seismic data.

A range of interpretation tricks improved the feasibility of this exercise and allowed for a successful

interpretation.

Similar to earlier findings [Berghuijs, 2013], an onlap geometry has been observed. However, the limited

areal extent of the data set combined with the severe influence of artefacts impedes drawing very

robust conclusions based on these observations only.

chapter 6-85 page


Synsedimentary tectonics have influenced Upper Rotliegend sediment distribution, and tectonic activity

generally occurred in pre-existing Variscan fault zones. Topographic variation is postulated by various

authors, and this is confirmed by the observation of (significant) sediment thickness variations.

These thickness variations occur in all parts of the Rotliegend Group, and follow a pre-existing structural

grain. The thickest deposits generally coincide with the northern half of fault blocks, and overall

thickness increases northwards. N-S trending faults are less influential for thickness variations compared

to NW—SE-trending faults.

Extending the interpretation of the top Carboniferous surface throughout a larger area could prove

beneficial for supporting some of the hypotheses in this thesis regarding palaeotopography,

synsedimentary tectonic activities, sediment distribution and related SSD-occurrence. More extensive

well correlation, especially in combination with a larger areal seismic interpretation, would also add

value to the conclusions drawn based on this study.

Summary The existing conceptual sedimentological model was confined to better distinguish individual facies. A

new facies classification based on soft sediment deformation was devised, and the mechanisms causing

SSD are closely linked to the sedimentological model.

Climatic variations are the most dominant cause for the ubiquity of SSD-structures, and are in term

governed by the large-scale transgression throughout deposition of the Upper Rotliegend Group.

Palaeotopography is the second factor influencing the frequency and occurrence of SSD-structures, by

causing local base level fluctuations.

Reservoir quality could not evidently be linked to the SSD-facies classification. However, there is an

intricate relationship between soft sediment deformation and reservoir quality distribution. It would be

interesting to further investigate the connection between the two.

High levels of heterogeneity are present in the study area. This heterogeneity is related to the soft

sediment deformation, but complicates the unraveling of the relationship between reservoir quality and

facies distribution. Scale issues and challenges in quantification of internal heterogeneity point out that

there is still a lot to be gained in terms of understanding the causes and effects of reservoir

heterogeneity.

The structural setting and tectonic history played a significant role in the sediment distribution within

the SPB. Onlap of Upper Rotliegend sediments on top Carboniferous deposits was identified, and

synsedimentary tectonic activity that is postulated by many authors relates to observed thickness

variations.

chapter 7-86 page


7. Conclusions & recommendations Based on all observations and points of discussion mentioned in this thesis a number of conclusions and

recommendations can be drawn.

The GAA is situated in the transition zone of an aeolian, fluvial and playa lake setting. Sediments

are fine-grained, well-sorted siliciclastics deposited on aeolian sandflats.

Sedimentary structures classified as ‘wavy lamination’ are ubiquitous in many of the available

core material, and detailed sedimentological investigation links these structures to soft

sediment deformation processes.

There is no unequivocal explanation for the origin and triggering mechanisms of the soft

sediment deformation structures that have been observed.

No clear trends could be identified in SSD-sequences, contrary to what was expected. Perhaps

the largest issue for unravelling trends is related to the limited scale of possible observations on

which the SSD-facies classification is based.

The existing conceptual sedimentological model was simplified and now better allows

appointing geological processes to the individual lithofacies.

The new SSD-facies classification was tested rigorously but unfortunately does not prove a

better ‘solution’ for predicting reservoir quality distribution in the GAA than existing

classification schemes.

The contrast in heterogeneity measures is puzzling: apparently there is a relationship between

porosity and permeability that causes an opposite heterogeneity behaviour when merely

analyzing heterogeneity based on permeability data: Gini coefficients and Dykstra-Parsons

coefficients provide contradictory results in terms of flow unit heterogeneity.

Water table fluctuations and palaeotopography are proposed as the governing factors for

varying degrees of SSD-intensity and consequently reservoir heterogeneity.

Climatic variation is responsible for the water table variations, and hence influences the large-

scale distribution of soft sediment deformation. Both temporally and laterally, climate cyclicity

and the overall transgression of the Silverpit Lake is the main cause for an increase in SSD

towards the younger members and reservoir intervals of the Upper Rotliegend Group.

Palaeotopography affects sediment distribution on a smaller scale. Lateral differences in facies

and small-scale depositional features are governed by the presence of local palaeo-highs and

-lows. Synsedimentary tectonics also influences the distribution of (litho- and SSD-)facies.

There might be a link between thickness variation in the Rotliegend and the intensity of soft

sediment deformation. However, this link was not firmly established by the results of this study.

Onlap of the lowermost Upper Slochteren deposits onto the BPU was observed by seismic

interpretation of the Top Carboniferous. This confirms recent findings regarding the Rotliegend

basal geometry in the Groningen area, but contradicts the widespread assumption that a so-

called wedge model is applicable on sediments of the Upper Rotliegend Group.

chapter 7-87 page


More research on the governing processes of soft sediment deformation, especially in distal

(fine-grained, well-sorted) sediments might improve the understanding of the occurrence and

areal extent of early post-depositional sediment reworking. This could refine the understanding

of the relationship between SSD and reservoir quality distribution, and the influence of SSD on

reservoir heterogeneity.

An outcrop study of SSD- structures in a similar depositional setting as that of the GAA, on all

scales, might be helpful in achieving this goal.

Heterogeneity quantification and reservoir quality distribution might be further investigated by

fractal modelling. Fractal modelling perhaps could resolve the issue of scale effects that

complicate the construction of valid reservoir models.

A better degree of detail and a larger areal extent of the top Carboniferous surface possibly

improve the understanding of the tectonic evolution of the SPB at the time of Permian

deposition. A better palaeotectonic reconstruction might elucidate respective phases of

movement and their timing, which could then be related to deposition.

In terms of data management a better correspondence between petrophysical, geophysical and

geological data storage and –management would be valuable. Two examples of mishaps in data

occurred during the work for this study: the elusiveness of mini-permeameter data that was

only found back after an elaborate quest, and the difficulty of obtaining corrected wireline log

data. After resolving these issues it proved that the ‘new’ data was significantly better and more

useful compared to the existing data, and hence a better correspondence in data could lead to

significant improvement.


Acknowledgements

I would like to take the opportunity here to thank the many people who have contributed to or

facilitated work on my thesis.

First and foremost I would like to thank Rick Donselaar for allowing me to write my thesis with Shell, on

this very interesting topic. Many thanks for the fruitful discussions related to the studied core material

in Assen, the useful feedback and the inspirational photographs during my visits back in Delft.

This thesis topic would not have been possible without the support of Frank Pardoel, Douwe van

Leverink and Martin Ecclestone, who were always available for feedback and technical guidance during

my internship. Many thanks for having me write my thesis in your team, it was great!

Pacelli Zitha and Hans Veldkamp are thanked for completing my graduation committee and for investing

their time to read this thesis and share their feedback and have discussions afterwards.

Thanks go out to Kees van Ojik for the endless patience for yet another question related to

heterogeneity quantification or (modelling) best practices. Behrooz Bashokooh and Nicki Gardner are

thanked for their Petrel-support, and many thanks also go out to Gerrit Brouwer and Dirk Jan Kuik for

their petrophysical insight and data provision. Thanks to Yogesh Gupta and Ismail Usman who provided

necessary reservoir engineering handles for starting up the dynamic model in ECLIPSE. Also, thanks go

out to Michiel Bijker for setting me up in the office with almost no logistic or IT-issues at all! Jan

Penninga en Jan Tillema always laid out cores at my request which made the time I spent in the core

shed very pleasant, many thanks to them for that.

I would furthermore like to thank Dineke Wiersma, Peter-Paul de Graaf and in particular Menno

Brouwer for their love, friendship and support. Whenever I needed a break from my thesis or was in

need of a critical but friendly review of my activities they were always there– thanks guys!

Lastly, I would like to thank the crowd of fellow production geologists and other Shell/NAM staff in

Assen for acting like a sounding board or providing me with necessary and useful input (data). Also,

many thanks go out to all the people who made my internship (and hence writing my thesis) at NAM so

much fun. I had a great time, and look forward to re-join NAM/Shell as an employee!


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References marked with an asterisk (*) are “grey” references, meaning that they are internal, confidential Shell-reports not available in the public domain.

http://www.nlog.nl/nl/pubs/atlas/doAtlas/resources/Fig211.pdf

http://stattrek.com/online-calculator/chi-square.aspx

http://www.princeton.edu/~achaney/tmve/wiki100k/docs/Gini_coefficient.html

http://en.wikipedia.org/wiki/Gini_coefficient

http://rubencharles.com/geoscience/dykstra-parson-coefficient-spreadsheet

http://rubencharles.com/geoscience/dykstra-parson-coefficient-spreadsheet

AES/TG/14-02 An integrated study of Permian Rotliegend ...

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