Closing_the_loop_R.pdf

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Closing the Loop between geo&engineering in building

calibration and history matching carbonate reservoir models?

Patrick Corbett

BG Group Professor

Carbonate Petroleum Geoengineering

AAPG Distinguished Lecture May 2014

3

Fractured or not?

Reservoir Engineering YES Well test response Negative skin Cross-flow

Geology NO No fractured core No open fractures on image logs No significant losses

4

Fractured or not?

Reservoir Engineering YES >>> NO Well test response Negative skin >> not fractures >> double matrix Cross-flow >> not fractures >> double matrix

Geology NO No fractured core No open fractures on image logs No significant losses

5

Fractured or not?

Reservoir Engineering YES Well test response Negative skin >>>> double matrix + fractures Cross-flow >>>> double matrix + fractures

Geology YES Fractured core Open fractures on image logs Significant losses

6

Closing the loop

Highly heterogeneous carbonate reservoirs Fractured vs non-fractured well tests? Build a model without fractures Care to distribute RTs appropriately Check History Match without fractures Not conclusive but potentially useful Role for PLTs

7

Dual porosity Horizontal well Closely bounded reservoir Negative skin

Horizontal well Dual porosity Rectangular bounded Negative skin

Horizontal well Dual porosity Infinite boundary Small positive skin

Dual porosity Vertical well Negative skin

A2

0.01 1.0 100Time, hr

1E+6

1E+7

Gas

pot

enti

al, p

sia/

cp

A1

A3 A4

0.1 10 1000

Time, hr

1E+6

1E+7

Gas

pot

enti

al, p

sia/

cp

0.1 10 1000Time, hr

1E+6

1E+7

Gas

pot

enti

al, p

sia/

cp

0.1 10 1000Time, hr

100

1000

Pres

sure

, psi

a

Fracture performance of well test??

Kazemi et al, 20118

Composite log

Layer 2

Layer 3

Layer 4

Prograding ramp facies with higher frequency cycling

Layer 1

Kazemi et al, 20119

11020 200 290 370 460 550 640

Wel

l bot

tom

hol

e pr

essu

re, p

sia

A4

A3

A2

A1

Time, day

Figure 10b

Kazemi et al, 201110

11020 200 290 370 460 550 640

11020 200 290 370 460 550 640

A1

A2

A3

A4

Wel

l gas

pro

duct

ion

rate

, MM

sm3/

day

Wel

l oil

prod

ucti

on r

ate,

sm

3/da

y

Time, day

Time, day

Figure 10a


Facies Model

Simple depositonal model with dolomite modification>>>PODS


Well Location in Facies Model

Interdigitation of Mid- to Outer- Ramp facies

From Simpson, 2010

Low PorosityIntragranular Porosity

Foraminiferal Packstone

Higher Inter XL Porosity

Higher Permeability

Un-dolomitised

Strong primary control on property distribution

Dolomitised


Review of H Field Rock Types

0.001

0.010

0.100

1.000

10.000

100.000

0.00 0.05 0.10 0.15 0.20 0.25 0.30

Perm

eabi

lity

mD

Porosity

L1L2L3L4

0.001

0.010

0.100

1.000

10.000

100.000

0.00 0.05 0.10 0.15 0.20 0.25 0.30

Perm

eabi

lity

mD

Porosity %

H1H2H3H4

0.001

0.010

0.100

1.000

10.000

100.000

0.00 0.05 0.10 0.15 0.20 0.25 0.30

Perm

eabi

lity

Porosity

GB

GN

GPB

GPN

MWB

MWT

By facies?0.01

0.1

1

10

100

0 0.05 0.1 0.15 0.2 0.25

By well?

By layer?

By RRT?By GHE?

Simpson, 2010

How do we distribute properties?

From full field model?


Composite log

Layer 2

Layer 3

Layer 4

Based on given Log Porosity

H Field Well H2

NB: super-k >16%F >40mD?

GHE Proportion Curve

GHE Grouping

Layer 2U

Layer 2L

Layer 1

Use GHE grouping approach


PLT Performance

H2

H A1

H2

16

H Field Model

17

Distribution of Rock Types


Cell dimension (m): 100X100X1Total number of cells: 95760Local grid refinement: 5X5X3

Porosity

Permeability

Simulation Model


Horizontal correlation length, m

Ver

tica

l cor

rela

tion

leng

th, m

1000500100

13

6

PODS Distribution lengths


SHORT CORRELATION

LONG CORRELATION

Example Models


Storage capacity Flow capacity

H100V1

H100V3

H100V6

H500V1

H500V3

H500V6

H1000V1

H1000V3

H1000V6

REALISATIONS


Kv/Kh0

Kv/Kh= 0

Short correlation length Long correlation length

Bars

Vertical Permeability


Short correlation lengthLong correlation lengthHomogenous model

Gas

pot

enti

al

Time, hr

Numerical Well Tests


H100V1 H1000V6

Short correlation length vs. long correlation length

Numerical PLT


PorosityPermeabilityShort correlation length

Long correlation length

Full Field Model


PorosityPermeability

Short correlation length

Long correlation length

Full Field Model


Full fieldSector modelShort correlation

Full Field vs Sector Model


Sector model

Full field model

Short correlation length Long correlation length

Bars



Short correlation lengthLong correlation lengthShort correlation length, field

Gas

pot

enti

al

Time, hr

Long correlation length , field



Short correlation lengthLong correlation length

Gas

pot

enti

al

Time, hr

sector Full field GHElong

GHEShort

Next stage:PLT and WT history matching


Poroperm data and effective RTs

32

PC and Saturation Height

0

5

10

15

20

25

30

0.0 0.2 0.4 0.6 0.8 1.0

Hei

ght

(m) a

bove

FW

L

Water Saturation

Capillary Pressure Data

Plug 1 (Por:=18.7%)

Plug 2 (Por: =14.4%)

Plug 3 (Por: 12.4%)

0

5

10

15

20

25

30

0.0 0.2 0.4 0.6 0.8 1.0

Hei

ght

(m) a

bove

FW

L

Water Saturation

Saturation Height Modelling

GHE2

GHE4

GHE5

33

Well H2

Petrotype Model Calibration

Layer 2

Layer 4

Layer 3

2U

2L

34

Porosity Permeability

Full Field RT based poroperm scenarios


Days

Gas

pro

duct

ion

rate

Oil

prod

ucti

on r

ate

History

History Match Gross Production


300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600

A1

A2

A4A3

Time, hr

Pres

sure

History

Rate

Pres

sure

Rate

6000 8000 10000 12000 14000 3000 4000 6000 8000 10000

400 600 8000 1000 12001000 3000 5000 7000 9000 1400 1600

12000 14000 16000

History

History

History

History Match - Pressure

Kazemi et al, 2011

38

1E-4 1E-3 0.01 0.1 1 10 100 10001E-4 1E-3 0.01 0.1 1 10 100 1000

A1

A2

1E+6

1E+7

Gas

pot

enti

al, p

sia/

cp 1E+8

1E+6

1E+7

Gas

pot

enti

al, p

sia/

cp 1E+8

1E-3 0.1 10 1000

1E-3 0.1 10 1000

Time, hr

History Match well rate


1E-5 1E-4 1E-3 0.01 0.1 1 10 100 10000.1

1

10

1E-5 1E-4 1E-3 0.01 0.1 1 10 100 10000.1

1

10

A3

A4

1E+7

Gas

pot

enti

al, p

sia/

cp1E

+81

10

Pres

sure

, psi

a

1E-3 0.1 10 1000

1E-3 0.1 10 1000

1E-3 0.1 10 1000

Time, hr

History Match Well rate


1E-5 1E-4 1E-3 0.01 0.1 1 10 100 1000

A2

A11E

+71E

+8

Gas

pot

enti

al, p

sia/

cp1E

+61E

+7

Gas

pot

enti

al, p

sia/

cp

1E-3 0.1 10 1000

1E-3 0.1 10 1000

Time, hr

Model WT match


A3

A4

1E+7

Gas

pot

enti

al, p

sia/

cp1E

+81

10

Pres

sure

, psi

a

1E-3 0.1 10 1000

1E-3 0.1 10 1000

1E-3 0.1 10 1000Time, hr

Model WT Match


PLT Predictions


Aternative match option


Applied a new modelling strategy based on PODS Porosity Defined System for a carbonate reservoir.

The effect of horizontal and vertical correlation length of PODS observed on WT response.

Matching WT and PLT data in sector before going to full field modelling

Sector model and full field model compare well

WT and PLT Calibration of full field model

Reasonable match achieved without incorporating any fractures

H Field study conclusions

45

SPE 166033: Using Near Wellbore Upscaling to Improve Reservoir Characterization and

Simulation in Highly Heterogeneous Carbonate Reservoirs

V. Chandra1,2, S. Geiger1,2 , P.W.M. Corbett1,2,4,R. Steele3, P. Milroy3 , A. Barnett3 , P. Wright3 , P. Jain3

1Institute of Petroleum Engineering, Heriot-Watt University2International Centre for Carbonate Reservoirs

3BG Group, Reading, U.K.4 Universidade Federal do Rio de Janeiro

Acknowledgements:46

Key points of this research

47

Overall aim Using novel near wellbore upscaling (NWU) workflow to obtain

improved permeability model of Field G

Main conclusions Improved characterisation of key small-scale geological

heterogeneities

Revised permeability model eliminated the K-multipliersScientific impact Improved reservoir characterisation and simulation of

carbonates using NWU workflow

Chandra et al, 2012

Low relief anticline trap Thin oil rim, gas cap, deep seated aquifer Main HC bearing layers: Zone A, Zone B

Field G Background

E-W section: see gas over oil over water. See the two main reservoir layers (Image courtesy: Zoe Watt)

48

A/B Unconformity

Chandra et al, 2012

Field G Simulation Model

49Chandra et al, 2012

Field G Production Profiles: Oil, Gas and Water


Re-evaluating Field G Permeability

- DST K-transform >> core K-transform

- Average K in geomodel ~ 20 mD and Ke in simulation model ~ 200 mD

What was undersampled?How should it be modelled?

Kh-multiplier required for history match : x20 in Zone A, x10 in Zone BPlus local well K and well PI multipliers

Around 90% of the permeability missing !?

Correct the K = Better Simulation Model = Better Production Forecast51

Removing the K-multiplier

Can

be r

esol

ved

usin

g re

vise

d K-

mod

el?

All K-multipliers removed

History matched case with K-multipliers


Evaluating the Role of Meteoric Karst vs Burial Corrosion in an Offshore Indian Carbonate Field

Michael OatesViswa Santhi Chandra

Patrick Corbett

Outline

Field G overview Evidence of late burial corrosion Impact on poro-perm Key conclusions

Oates et al, 201254

Ramp foraminifera facies

Depositional Facies

Coskinolina

1000 m

Miliolids

1000 mCoskinolinids and

Alveolinids

1000 m

Platy corals

1000 m

Fine bioclastic Hash with Rotalid forams

1000 m

Fine bioclastic Hash with Echinoderm debris

1000 m

Nummulitids

1000 m

Discocyclinids

1000 m

Oates et al, 201255

Cold Karst ?Meteoric karstic porosity development caused the

conduits?

Diagenesis vs Permeability concepts

Indication of dissolution porosity

Solution enhanced stylolitesand associated fractures in well cores

Evident high perm network, pervasive and stratiform

Long producing data and tracer data indicating good lateral and vertical communication in reservoir

The dissolution porosity +stylolites+associated fractures are the pervasive permeability network ?

Hot Karst ?Late stage (hydro)thermal karstification could have

formed the conduits?

Oates et al, 201256

Proposed Paragenetic Sequence

Transpressional tectonics at the end of Miocene

Early stage extensive microporosityLate stage macroporosity along fractures, unconformities, vertical pipes

Corrosive fluids penetrated the unloaded dissolution seams and stylolites predating the HC charge

Unloading event

Depositional setting:Ramp settingTransgressive stacking patterns

Cementation+Compaction+Pressure dissolution=

Very tight carbonate units

Very common Associated with late

carbonate cementscalcitedolomiteankeritesiderite

Dissolution seamsStylolitesTension gashes

Oates et al, 201257

Burial Corrosion- Field Scale

(Modified from Esteban)Oates et al, 2012

58

Burial Corrosion- Field Scale

(Modified from Barnett et al . 2010)

Oates et al, 2012

59

Back to Basics: Key Observations from Core

Stylolites and associated tension gashesCorrosion enhanced micro- and macro-porosity

Corroded zone

Matrix adjacent to corroded zone

60

Post Stylolite Dissolution

Fine vuggy porosity

1 cm

0.5 mm0.5 mm

Extensive porosity along stylolites and microstylolites

61

Corrosion along Stylolites and SAF

Corrosion vs Stylolite CorrelationDensity of distribution of corroded zones is proportional to that of stylolites.

2 cm

Oates et al, 201262

Corrosion fluid front

Corrosion fluid front

Invasion of Corrosive Fluids

Corrosion enhanced porosity



Burial Corrosion Mechanism at Core Scale

Oates et al, 201263

Post-Saddle Dolomite Dissolution

Fractures with leached bladed calcite cement, saddle dolomite and dickite

Saddle dolomite

Bladed calcite cement

Dickite

Saddle dolomite in a fracture has undergone corrosion followed by dickite precipitation

Dickite

Corroded saddle dolomite

0.5 mm 0.5 mm

Oates et al, 201264

Dissolution of Tectonic Vein-filling Calcite

Corroded calcite cement in a fracture

0.5 mm

Oates et al, 201265

Dickite and Pyrite

Dickite is not common in carbonate reservoirs in general

BUT it is a very common mineral phase in Field G

Deeply etched stylolites and associated fractures

pyrite nodules this size (up to 10mm across) are rare

Dickite is a kaolin mineral thought to indicate the former activity of organic-rich acidic fluids

Oates et al, 201266

Highlights: Diagenetic Features

2 cm

(Courtesy Paul Wright )

Oates et al, 201267

Key Observations from Core

Key Characteristics of Corroded Zones:

- Higher porosity

- Higher mini-perm

- Dark patches of highly conductive zones on image logs

- High Uranium signature

R1= unmodified limestone matrixR2= corroded matrix

Oates et al, 201268

Corrosion Enhanced Porosity

Collapse breccia porosityVuggy/Moldic porosityCorrosion along Stylolites and SAF

BSEM images of typical corroded matrix with microporosity

2 cm

Oates et al, 201269

Reservoir Permeability Issues

- DST K- transform >> core K- transform

- Average K in geomodel ~ 20 mD and Ke in simulation model ~ 200 mD

What was undersampled?How should it be modelled?

Core and miniperm data

Sample insufficiencySample bias towards tighter zones

K-multiplier required for History match :

x20 in A Zone x10 in B Zone Plus local well K and well PI multipliers

Around 90% of the permeability missing !?

Correct K = Better Simulation Model = Better Production Forecast

Oates et al, 201270

Permeability Model Before and After Considering Corrosion Enhanced Porosity

Geomodel using core K-transform After incorporating solution enhanced porosity features in the geomodel

711000

AFTER K-multiplier

Geomodel permeability Modified permeability scenario

100050

Simulation results

72

Cumulative oil production curves

Key Conclusions

Distribution of high permeable corroded zones correlated with stylolites+fractures

Evidence supports the occurrence of thermal karstification causing stratiform pervasive high permeable network

The reservoir permeability model should be improved with considerations to late burial corrosion

Oates et al, 201273

Near wellbore rock-typing and upscaling

Chandra et al, 201474

m-mm cm-dm m-km

75

Core vs upscaled permeability

Corroded matrix porosity

Leached stylolites and tension gashes in highly Corroded matrix

Chandra et al, 2014

Poro-perm trends used for GeoPoDS


GeoPoDS saturation-height curves

77

Near-wellbore upscaled permeability

GeoPoDS J functions

Improved RQI

GeoPoDS water-oil relative permeability

GeoPoDS gas-oil relative permeability

GeoPoDS summary

GeoPoDS NWRT PHIE K-Transform Kv/Kh Sw-H function

Kr curve

Shale Shale 0.15 K = 663749*(PHIE)5.5071 y = 8E-07*(Kh)2 + 0.0016*(Kh)+ 0.878

G2 G2

Now History match


Triple Porosity Systems

Indian FieldN African Field

No Fractures Needed in the Models for Reasonable History Matching

Chandra et al, 2014Kazemi et al, 2011

83

Field G Conclusions

Mismatch between geological model and reservoir simulation modelled resolved

Finer detail petrophysics Very high resolution NWB model Upscaled Rock Types ( GeoPODS) Improved History Match (without tuning) No significant fractures

apart from the stylolite-related fractures that are incorporated in stylolite GeoPOD.

84

Triple Matrix Porosity Systems

Indian Field GNorth African Field H

Three RTs only needed in the Models for Reasonable History Matching without need for fracture modelling 85

Conclusions

Missing permeability in carbonates due to biased sampling and missing scale modelling

Simulation model can show large corrections to geomodels to get history match

Identifying key (3?) RT variations and upscalingthese can lead to improved history matching

Use of upscaled RTs enabled through NWB modelling

Consider Triple Matrix Porosity (GeoPODS) for heterogeneous reservoir models

86

Acknowledgements Total Professorship (1994-2011) BG Group Professorship (2012-2017) Colleagues

Sebastian Geiger, Alireza Kazemi Students

Viswasanthi Chandra International Centre for Carbonate Reservoirs

DynaCARB Project Schlumberger (Ecplise), Geomodelling (SBED),

CMG (CMOST, IMEX )

87

Fractured Reservoir Myths

Majority of fractures have a high angle origin but only to bedding (Lewis, HWU) Is use of curvature good OK for thinly bedded systems (Couples, HWU) Is fracture porosity always low (1-2%) yes generally but not always (Quenes,

Sigma3) is the fractal model a good one there are length scales, layering and the

mechanical stratigraphy is important (Couples, HWU; Riva GE Plan), Mechanical fractures follow existing fracture patterns (Alverellos, Repsol) Thermal Fracturing in low permeability rocks also high permeability sandstones

(Tovar, IES) Continuum fracture models vs Discrete Fracture models upscaling DFN is very

challenging (Geiger, HWU) There is no REV in fractured reservoirs except possibly at the seismic bin scale

(Quenes, Sigma3) and at the bed scale (Couples, HWU; Riva GEPlan) Basement provide seals and migration barriers but not if fractured (Hartz, Det

Norske Oljeselskap) Ruger equation can give fracture orientation and density simple laboratory

models show this equation sometimes holds (Chapman, Edinburgh University)

Source: EAGE-SBGf Fracture workshop Rio Nov 201388

Fracture Reservoir Agreement

Fractures are difficult to locate but easy to predict with the correct structural model (Lewis, HWU)

Fracture Models should be driven by data and concepts (Riva, GE Plan)

Fractures develop though complex history of burial and many stress episodes(Bezerra, UFRN; Betotti (TUDelft)

Lithology and facies have an impact on fracture distributions (Cazarin, Petrobras)

Need to model fractures in 3D (Hartz, Det Norske Oljeselskap; Moos, Baker-Hugues)

A multidisciplinary approach to tackle fractures is necessary

Source: EAGE-SBGf Fracture workshop Rio Nov 201389

All reservoirs are fractured!

What Gary Couples and I can agree on: We think all carbonates are fractured, but the

fractures MAY not be playing a major role in flow

So All reservoirs are fractured and some fractures are useful for flow

And Sometimes reservoirs that appear fractured may actually have very high matrix contrasts

90

ReferencesChandra, Corbett, Hamdi and Geiger, 2011, Improving Reservoir Characterisation and Simulation with Near Well boremodelling, SPE 148104, SPE Reservoir Characterisation and Simulation Conference, 9-11 October, Abu Dhabi.

Kazemi, Corbett, and Wood, 2012, New approach for geomodeling and dynamic calibration of carbonate reservoirs usingporosity determined system (PODS). Presented at 74th EAGE conference and Exhibition, Copenhagen, Denmark, 4-7 June2012.

Chandra, Geiger, Corbett, Steele, Milroy, Barnett, Wright, Jain, 2012, Using Near Well Bore Upscaling to improve reservoircharacterisation and simulation in highly heterogeneous carbonate reservoirs, SPE 166033, SPE Reservoir SimulationConference

Oates, Chandra, and Corbett, 2013, Evaluating the role of meteoric karst vs burial corrosion in an offshore IndianCarbonate Field, AAPG

Chandra, Corbett, Hamdi, and Geiger. 2013. Improving Reservoir Characterisation and Simulation with Near-WellboreModeling. SPE Res Eval & Eng 16 (2): 183-193. SPE-148104-PA. May.

Chandra, V., Steele, R., Milroy, P., Corbett, P.W.M. and Geiger, S. 2013b. Using near-wellbore modelling and dynamiccalibration to improve permeability modelling in a giant carbonate field. Oral presentation at 75th EAGE Conference &Exhibition incorporating SPE EUROPEC 2013, TU 14 15

Chandra, Wright, Barnett, Steele, Milroy, Corbett, Geiger and Mangione, 2014, Evaluating the Impact of a Late BurialCorrosion Model on Reservoir Permeability and Performance in a Mature Carbonate Field Using Near Wellbore Upscaling,Geol Soc Spec Publication Fundamental Controls on Fluid Flow in Carbonates: Current Workflows to EmergingTechnologies (Paper accepted for publication)

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