Use of Geomodelling and Visualization for Earth, Energy, Economy and Environmental (EEEE)...

7/28/2019 Use of Geomodelling and Visualization for Earth, Energy, Economy and Environmental (EEEE) Management.pdf

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Technical Proposal for Research Work onUse of Geomodelling and Visualization for Earth, Energy,

Economy and Environmental (EEEE) Management

Prepared bySYED ADNAN HAIDER ZAIDI

Submitted To

University of the Punjab, Lahore, Pakistan


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Introduction

Preparing for the energy transition

We are entering a transition period between situations - the current situation - in which

oil is preponderant and a situation in which new energy sources will have taken its place.It will be a period during which we will still use oil, while at the same time gradually

developing alternatives that are kinder to the environment.

Reducing emissions of CO2

Because we are going to continue using oil, gas, and coal during the transition, andbecause their combustion produces large quantities of CO2, we must do everything we

can to control these emissions and combat the predicted climate change. Geoscientists

should be strongly committed to the search for new technologies for the capture,

transport, and storage of CO2.

Developing other energy sources

There is no miracle solution: bioresources, fuel / electricity hybridization, synfuels,hydrogen, etc. These different approaches must all be explored. Some are ready today,

while others will take longer because of the technological obstacles that remain to be

overcome. As a pioneer, Geoscientists should contribute their research work in thedevelopment of alternative energy resources.

Optimizing petroleumThe goal is not to produce petroleum down to the last drop, but to give society time to

develop energies likely to replace it. In this regard the key issues are, of course:

1) How to find more hydrocarbons (oil and gas), and2) How to recover more hydrocarbons from the reservoirs

Continuous depletion of hydrocarbon reserves all over the world and environmental

consciousness even about alternative energy resources, demand development of new

technologies and access to the opportunities for the disposal of waste materials into an

environment friendly manner. To encounter the coming energy crisis, Energy Resources

Geoscientists should prepare themselves to develop innovative technologies in this

regard. To understand the interaction of Physical and Chemical Processes continued in

Earth, Energy and Environmental (EEE) systems rigorously, Use of Geomodelling and

Visualization for Earth, Energy and Environmental (EEE) Management will be more

prominent in near future especially into following categories:

Petroleum Systems Evaluation and Prospectivity Analysis

Use of Reservoir Characterization and Modelling in the Development of Oil andGas Fields

Better Utilization of Nuclear, Hydrodynamic and Alternative (NHA) EnergyResources

Modelling of the interaction of Physical and Chemical Processes continued inEarth, Energy and Environmental Systems


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Data Synthesis Programme with Sedimentological, Chronostratigraphical,

Geochemical, Isotopic and Seismic Analysis and Interpretations

Targets

1) Definition of Sequence Stratigraphic Framework and a series of Depositionalmodels

2) Establishment of a Chronostratigraphic Framework with Biostratigraphic andIsotopic analysis to provide essential age and environmental data

3) Correlations will make use of any available 2D / 3D Seismic data to preparei. Depositional cross sections

ii. Fence Diagramsiii. 3D Block Diagramsiv. Chronostratigraphically resolved block diagrams or

Geophantasmograms e.g. at Barremian or Aptian levels

4) 1D, 2D, and 3D Basin Modelling and Prospect Evaluation5) Definition and Prediction of Reservoir Distribution and the major controls onreservoir quality variation

6) Production of Reservoir Distribution, Reservoir Quality, Reservoir Carriers &Barriers and Seal Effectiveness maps for different Reservoir Intervals

Sedimentology and Reservoir Geology

1) Facies analysis based on core descriptions2) Augmentation of Core descriptions by Sedimentological interpretations of

FMI logs

3) Additional Lithological and Facies analysis through reservoir sectionsbased on conventional wireline log suites and integration with datasets

derived from core and FMI studies


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4) Reservoir Quality Investigationa) Primarily on core analysis datab) Log porosity evaluation beyond core control

5) Augmentation of Petrographic analysis to review the main controls onreservoir quality investigation

6) Development of Sedimentological and Petrographic inputs to theSequence Stratigraphic framework and generation of Depositional models7) Regional mapping of variation in depositional environments and reservoir

and seal quality

a) Facies Analysis:

1) Generation of depositional models and interpretation of candidate stratalsurfaces

a) Core descriptions as primary databasei) Sedimentological description

ii) Petrographic, Isotopic and biostratigraphic analysisb) Calibration between the cores and the wireline logs

b) Conventional Wireline Log and FMI Analysis

8) Understanding of the lithological, facies and mineralogical characteristics9) Interpolation of the facies analysis and reservoir quality interpretations

beyond the cored intervals10)Core to log correlation and a facies breakdown and lithological

interpretation of the uncored intervals based on wireline log signatures and

using the constructed depositional models

11)Interpretation will be constrained by any relevant isotopic &biostratigraphic data

12)Generation of high resolution facies data by FMI logs interpretation13)Calibration of FMI reviews / interpretation to the available core data will

result into:

i)Generation of Borehole Structures Log and typing of allrelevant dip information

The final interpreted information will have the potential:

1) To greatly improve the depositional modeling by extending the high-resolution information derived from detailed core sedimentology to a

significantly thicker interval

2) To aid the Sequence Stratigraphic interpretation of reservoir and sealintervals, providing a basis for accurately picking flooding surfaces,sequence boundaries and parasequences


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b) Petrographic and Diagenetic Analysis

1) Reservoir quality estimation by Petrographic and Diagenetic analysis2) Optical petrographic analysis of selected samples typically including 300

point counts per thin section, to generate a representative digital dataset

3) Augmentation of Thin section petrography of specially chosen samples, tohelp quantify and characterize the mineralogy, study the quality andprovenance of sediments, and to understand the distribution and

paragenesis of clays and cements

4) Development of a high quality descriptive dataset and a diagenetic modelto account for the observed pore system characteristics

5) Observation of the impact of chlorite authigenesis and its relationship toquartz cementation on Clastic lithostratagraphic units

6) Understanding of the Radiogenic isotope systematics by analysingRadiogenic isotope data (mainly Rb-Sr, Sm-Nd, Lu-Hf, U-Th-Pb and Re-

Os) used as indicators of rock-forming processes, including petrogenesis

of rocks, provenance and mass balance of sediments, evolution of thecontinental crust, ore-forming processes and fluid-rock interaction.

c) Reservoir Qualtiy Investigation

1) Integration of core description, petrographic and core analysis data toinvestigate reservoir quality and the primary controls on reservoir quality

variation2) Derivation of porosity and permeability characteristics from routine core

analysis data and Investigation of the relative role of primary (depositional

or facies related) and secondary (diagenetic) processes on these

3) Log porosity calculations calibrated to core analysis

Chronostratigraphy using Biostratigraphic and Isotopic analysis

1) Biostratigraphic emphasis will be placed on quantitative palynology, withvolumetric micropaleontology and semi-quantitative nanno paleontology

in which quantitative palynology will be undertaken as the primediscipline

2) Some chronostratigraphic sections of clastic sediments are less faciescontrolled therefore volumetric micropaleontology and semi-quantitative

nanno paleontology will be run over intervals that are suggested from thepalynological results

3) 30 m palynological sampling interval will be adopted, with a provision forinfill over boundaries/events or potential intra- formation hiatuses

4) Identification of argillaceous horizons that have yielded palynoflorasindicative of deposition under fully or near fully marine conditions

5) Recovery of age-diagnostic taxa to assist in calibration of bioevents e.g.dinocyst which is considered as the best age dating criteria for someCretaceous Clastic Formations


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6) Interpretation of events and relate them to the sequence stratigraphicframework of Haq et al., 1987 or Hardenbol et al., 1998

7) In combination with the sedimentological and isotopic analysis provisionof a biostratigraphically constrained sequence stratigraphic framework

8) Recording of miospore component of palynofloras as the quantitative and

qualitative events in different epochs which are known to be of age-datingand correlative value9) Identification of large scale vegetational changes on the hinterland from

gross changes in the miospore composition and their relation to

isochronous climatic change or changes in the water table10)Quantitative documentation of the marine microplankton and terrigenous

miospores to determine lateral trends in the non-marine/marine ratios and

their maps in relation to the biostratigrpahic framework to define onshore-

offshore trends11)Documentation of reworked palynomorphs to provide valuable

information on the stratal composition of the sediment source area. As the

composition changes through time and certain types and volumes ofrecycled material can be diagnostic of specific depositional packages

12)Micropaleontology analysis using semi-quantitative (five-fold abundance)logging of all taxa in conjunction with volumetric techniques to identify

maximum flooding surfaces in deepwater areas where variation in wirelinelog character may be limited

13)Individual calcareous nannofossil taxa present in the samples will becounted over 200 fields of view and their totals expressed semi-quantitatively using a five-fold abundance scheme

14)Integration of the information resulting from above mentionedBiostratigraphic techniques with the information derived from

geochronologically significant isotope systems e.g. U-Pb, K-Ar, and Ar-Ar for developing a more accurate and quantitative chronostaratigraphic

units.Moreover use isotope geology to prepare Relative Sea Level curvesfor each basin adressing history of its indvidual tectonic evolution

Integrated High Resolution Sequence Stratigraphic Framework

1) Integration of all available biostratigraphic, isotopic and sedimentologicaldata to generate an integrated high resolution sequence stratigraphic

framework

2) Use of that integrated high resolution sequence stratigraphic framework tounderstand and predict reservoir geometries in the subsurface

3) Provision of chronostratigraphic framework and definition of depositionalenvironments with the help of results derived from biostratigraphic and

isotopic analysis4) Filtering of the facies influence on biostratigraphic and isotopic recovery

to increase the value of quantitative biostratigraphic and isotopic events

which will consequently increase the number of potential datums forcorrelation


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5) This framework will be constrained by knowledge of the most relevantdepositional models and the character of candidate stratal surfaces from

sedimentological analysis6) This data driven (not model driven) sequence stratigraphic scheme would

be used to better understand reservoir and seal distribution, predict

reservoir connectivity and flow characteristics, and act as a powerful toolfor future exploration7) Recognition of Type -1 and Type -2 sequence boundaries, normal or

forced regression, potential by-pass zones or hiatuses to suggest the

potential for development of detached Low Stand System Tracts in anoffshore / distal direction (if sedimentation rates were high) and

identification of possible Shelf Margin System Tracts . It requires

combination of sedimentological data, biostratigraphic data, isotopic data

and seismic data.8) Mapping exercise with combination of sedimentological data,

biostratigraphic data, isotopic data and seismic data as the final element of

the reservoir geological phase of the study.

Petroleum Geochemistry

1) Determination of the quality of source rocks which are present with in differentchronostratigraphic sections. This will include but not be limited to:

a) Depositional environmentb) Thickness, organic richness and Kerogen typec) Pyrolysis datad) Kerogen maturity datae) Biomarker dataf) Oil datag) Oil to source correlation datah) Isotope data

2) Specific tasks will include the following:

a) Review of current knowledge of the hydrocarbon shows and discoveriesfound to date in the basin and assess the likely character of the source

rocks for those shows and tests.

b) Review the available temperature data for wells drilled in the basin withthe objective of creating a geothermal database.

c) Review existing ID and any other modeling work that has been done todate, and report on the findings.

d) Review the existing understanding of the distribution of hydrocarbonkitchens, timing of hydrocarbon generation and migration.

e) Recognition of source rock intervals of potential interest from wirelinelogs (SRFL).


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f) Confirmation of the maturity of the section and derive from that the heatflow history.

g) Making some broad conclusions on the likely effective source rockscontributing to the known hydrocarbons in the basin.

3) Basic data synthesis including the following tasks:

a) Geothermal data collation and temperature correction; creation of maps ofpresent day geothermal gradients and temperatures at key horizons;

b) Source rock data synthesis by formation to create maps of posted sourcerichness and quality (TOC and HI);

c) Source rock richness and quality prediction from logs for all relevant wellsusing (SRFL). Resultant data in yield (Kgs/ton) to be added to the source

rock richness maps as above;d) Determining maturity gradients (VR and SCI) for all possible wells and

posting to maps with maturity at key source rock horizons;

e) Reviewing all known hydrocarbon occurrences and posting to maps byreservoir with an analysis of any recognized variability.

4) Some requirement for the geochemical analysis will encompass the followingissues:

a) Kerogen maturity data to determine palaeoheat flow (vitrinite reflectivityand spore colouration). A provision of samples will be made for analysisfrom a selection of wells across the study area.

b) Source rock characterization with selected samples of proven sourcepotential from existing screening analysis and possible additional

screening work where good source potential is suspected from otherinformation is likely to be required.

c) Characterization of fluids and shows where there are no existing data orexisting data are inadequate.

5) Static modeling will involve the following major tasks:

a) Creation of maps of erosion at key horizons most particularly of thesignificant tectono stratigraphic events. This work will include the

identification and estimation of amounts of uplift and erosion (inversion)

from geological, geochemical and geophysical data sourcesb) 1D modeling of selected wells to include the establishment of a uniform

breakdown of events for use in modeling in the study area, calibration and

determination of present day and palaeo heat flows, selection of

appropriate source rock kinetic parameters, calculation of productivity ofselected source rocks and calibration to porosity and pressure data where

available;

c) Creation and 1D modeling of further pseudo wells and iteration todetermine hydrocarbon productivity.


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d) The work programme described above will ensure that all geochemicaldata is effectively integrated to produce the source quality, distribution,

maturity, kitchen and timing maps. These will then be available for inputto migration modeling.

Seismic Interpretation

1) Seismic interpretation of key surfaces required for maturity mapping andmigration modeling

2) After loading of all the available seismic data to a workstation, all availableZVSP, VSI, Check shot and or any other velocity data will be used to tie the study

wells to the seismic data

3) The chronostratigraphic and sequence stratigraphic framework established frombiostratigraphic, isotopic and sedimentological analysis and interpretation will beused to pick key horizons. Principally these will comprise the source, reservoir,

and seal surfaces which will be the main input for migration modeling. They will

also assist with maturity mapping of the source facies.4) TectonoStratigraphic surfaces will also be mapped regionally to understanduplifting, erosion, and compaction to assist restoration of paleo-topography. This

will then be used to establish geometry of the reservoir horizons and seal intervals

at the time of hydrocarbon generation.5) Fault distribution maps will also be prepared together with an understanding of

fault movement through time and fault seal analysis.

Participation in relevant research projects continued at various international

research centers and institutes

After data synthesis and its integration into a user friendly GIS database we can getbenefit from 3D Basin modeling softwares based on complex real case studies of

Geometrical, Analytical, Hydraulic and Diffusion Oriented Normal & Inverse Process to

Response Simulation of Sedimentation for 3D multi-lithological stratigraphic modelingto understand basin architecture and for subsequent reservoir characterization and

modeling. We will then focus our research work into Petroleum Systems Evaluation and

Prospectivity Analysis and Use of Reservoir Characterization and Modelling in theDevelopment of Oil and Gas Fields.


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Technical Proposal for Petroleum Systems Evaluation and Prospectivity Analysis

1) Migration Modelling

Migration modeling will be conducted using a migration modeling software e.g. IES

PetroCharge. Given the location of source kitchens and depth structure maps of carrier /reservoir horizons at the time of hydrocarbon charge, migration modeling will beconducted to determine drainage areas with in the carrier / reservoir horizons and the

likely flow paths or migration routes of hydrocarbons from the hydrocarbon kitchen

through the carrier / reservoir system to individual closures.

Variation in carrier / reservoir facies, their geomechanical properties and porosities, seal

effectiveness and fault distribution together with an understanding of fault movementthrough time and fault seal analysis will be modeled to determine their influence on

migration paths and establish the presence of potential migration shadows.

1D modeling results can help to establish the timing and the amount of generated

hydrocarbons that have to be injected into the carrier systems. It will be possible to


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analyze potential fill and potential spill histories for given accumulations. Furthermore

closure volumes can be calculated and figures for possibly trapped hydrocarbon

accumulations can be given depending on reservoir thickness and porosity.

Modelling parameters can be modified in order to run sensitivity analysis. Analysis can

be calibrated to best fit known accumulations and can establish the reason for dry holes.

2) Prospectivity Review

The results of migration modeling will be used to define hydrocarbon accumulations.These accumulations will then be subjected to a rigorous and consistent process of

prospect risking and ranking.

3) Prospect Risking

Each prospect will be risked on the following criteria:

Source quality Source maturity

Reservoir presence

Reservoir quality

Trap geometry

Trap seal

Timing of charge in relation to timing of closure

Presence of migration routes at time of charge including the influence of faultsand fractures

Preservation of hydrocarbon in trap

Degradation of hydrocarbon in trap

4) Prospect Ranking

Monte Carlo analysis will then be used to determine upside and downside hydrocarbon

volumes (P10:P90). This will then be used to determine a final ranking of the prospects.


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TECHNICAL PROPOSAL FOR USE OF RESERVOIRCHARACTERIZATION AND MODELING IN THE DEVELOPMENT

OF OIL AND GAS FIELDS

By

SYED ADNAN HAIDER ZAIDI

SUBMITTED TO

University of the Punjab, Lahore, Pakistan


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Abstract

Each approach of reservoir characterization and modeling gives specific constraints for

the geometry of sedimentary bodies and their internal structures but reservoir

characterization and modeling with 3D reservoir restoration and de-compaction for paleo-

topographic reconstruction, establishment of sequence stratigraphic framework and theirassociated diagenetic overprints with their impact on variations in porosity, permeability,

and hydrocarbon saturations and forward and inverse modeling of geological processes

(both depositional and post depositional), oil and gas reserve determination anddevelopment methods can be improved, specially in case of complex reservoir types.

We aim to fill the scale gap between modeling on hydraulic scale as practiced byhydrologists, and at basin scale, as practiced by geodynamicists. With increasing wire

line logging expertise, seismic technology and computing power this technique is rapidly

expanding now, and in our view a very promising approach for the near future.

Ultimately, we plan to test the applicability of our forward models generated by these

techniques with inverse modeling from existing data. Nevertheless, in order to be realisticthese surrogated 3D static reservoir models need to be validated with dynamic flow

simulations by production history matching.

The expected findings of our planned case studies of oil and gas fields will be as follows:

1) Generation of best-fit stratigraphic layering and compartmentalization of reservoirsusing 3D Seismic Multi-Attribute Geo-Volume Visualization Interpretation (GVI)

techniques.

2) Fine tuning of the initial interpretation by integrating results of numerical models,which examine the relationships between fault geometries, slip distributions, and

horizon displacements. The combination of these tools allows us to accuratelyconstrain fault geometries in 3-D and to develop a hypothesis for fault evolution in oil

and gas fields that augments previous reservoir characterizations.3) Preparation of sub-seismic faults and the fracture intensity maps which will form

input forDiscrete Fracture Networkmodeling to derive various flow properties like

fracture permeability tensors, matrix-fracture interaction parameters, which can beused as input for flow simulators.

4) Establishment of 3D Seismic cube with the combination of inversion, lithologic

crossplot cut-offs and coherency to visualize the relationship between variouspetrophysical and seismic parameters.

5) Development of a reservoir scale model (in the form of Depositional cross sections,

Fence Diagrams, 3D Block Diagrams and Geophantasmograms) that will predict andquantify facies variations and stratal architecture for the inter-well space, beyond theseismic resolution on the basis of correlation panels of conventional wireline log data

and image log data, core data, net to gross ratio maps and recent and ancient outcrop

analogues.

6) As part of the project, sensitivity studies and variable-resolution simulations will berun for different data sets, to show that our expected model can be used to identify


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optimum lateral and vertical scale characters of the reservoir, as well as to capture the

salient lateral and vertical variations and facies continuity.

7) Diagenesis and subsequent compaction will be estimated for the computer generatedsedimentary geo-bodies. Then synthetic lithologies will be translated into the

distributions of rock / flow unit properties, thus surrogated 3D static reservoir models

will be generated in a format, which will be suitable for a multiphase flow reservoirsimulator.

8) Calculation and display of variables (such as water saturation and neutron / sonic

minus density porosity) as color attributes to show interparticle, intraparticle andtotal porosity, bulk volume of water (Sw x porosity), bulk volume of hydrocarbons

{(1-Sw) x porosity} and also visual displays of empirical estimates of separately

developed porosities and permeabilities.

9) Application of filters to target areas of the fields with specific reservoir propertiesbased on selected clay volume, porosity and water saturation cutoffs.

10) Systematical examination of variables in the modeled reservoir volumes to assess

controls and perhaps enhanced views of areas to be targeted for additional

hydrocarbon recovery.11) Comparison of these visual, empirically derived models of reservoirs to the

simulations of temporally equivalent reservoirs.

12) 4D seismic time lapse monitoring and production history matching to remove theconstraints of static reservoir models and the dynamic flow simulations.


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1) Introduction

Integrated reservoir characterization and modeling is central to all aspects of hydrocarbon

field development and when performed effectively can help to reduce development risks

and to maximize field returns. It is a continuous process, from field discovery toabandonment, and embraces a number of complementary geological, geophysical and

engineering techniques. The simulation of reservoirs needs a quantified and realistic

description of formations, together with an assessment of their uncertainties.

Reservoir characterization and modeling requires a multidisciplinary team effort. It

involves a systematic integration of geological, geophysical and engineering data and

improves description of reservoir properties in and between wells. This research workaims to utilize a comprehensive application of modern concepts in reservoir modeling for

field simulation.

A vast array of new reservoir characterization techniques has been developed in the lastdecade by use of 3D Seismic Multi-Attribute Geo-Volume Visualization Interpretation

(GVI) techniques, Establishment of 3D Seismic cube with the combination of inversion,

lithologic crossplot cut-offs and coherency, multivariate statistics, geostatistics, artificialintelligence, borehole imaging, nuclear magnetic resonance and spectroscopy methods,

dewnhole seismic imaging in real time, and sophisticated directional drilling methods.

These new techniques greatly aid the petroleum geoscientists in understanding anddeveloping reservoirs, and we will actively investigate how these methods are improving

geological models. Despite this progress, subsurface models still rely heavily on

interpolation and extrapolation techniques. Our approach will consist in combining these

techniques with a priori knowledge of geohistory and geological processes. That

knowledge can be obtained from recent and ancient subcrop and outcrop analogues andfrom forward and inverse modeling of geological processes.

2) Literature Review

Once an accumulation of petroleum has been discovered it is essential to characterize thereservoir as accurately as possible in order to calculate the reserves and to determine the

most effective way of recovering as much of the petroleum as economically possible

(Lucia and Fogg, 1990; Lake et al., 1991; Worthington, 1991; Haldersen and Damsleth,1993). Reservoir characterization first involves the integrations of a vast amount of data

from seismic surveys, from geophysical well logs, and from geological samples (fig. 1).

Note that the data come in a hierarchy of scales, from the megascopic and mesoscopic tothe microscopic. It is important to appreciate both the scale and the reliability of thedifferent data sets, e.g. the problems of reconciling porosity and permeability data from

logs and rock samples.


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The first aim of reservoir characterization is to produce a geological model that honours

the available data and can be used to predict the distribution of porosity, permeability,and fluids throughout the field (Geehan and Pearce, 1994). Reservoirs possess a wide

range of degrees of geometric complexity (fig. 2). The rare, but ideal, layer-cake

reservoir is the easiest to model and to predict from, but reservoirs range from layer-cake via jigsaw puzzle to labyrinthine types. Geologists apply their knowledge toproduce a predictive model for the layer-cake model with ease, and the jigsaw variety

with some difficulty. But the labyrinthine reservoir can only be effectively modeled

statistically.

Pressure build up and draw down tests provide an opportunity to obtain estimates of the

following well and reservoir properties:

Permeability to the produced phase (oil, gas, or water), which is an average valuewith in the radius of investigation achieved in the test

Skin factor, which is a quantitative measure of damage or stimulation in the well Current average pressure in the drainage area of the tested well

Verification of flow barriers (such as fault) and estimates of distance to thesebarriers

Well bore effects dominate early test data. The end of the well bore effects is found usinglog-log plots of test data, which are compared to pre-plotted type curves, as illustrated infigure 3. The shapes of test data plots are also used to identify the reservoir type, such as

homogeneous acting, naturally fractured, layered, or hydraulically fractured. Derivative

type curves (basically the slope of a plot of pressure versus the logarithm of time) are

particularly helpful for identifying reservoir type and well bore effects (W. John Lee,

1992), as shown in figures 4 (a) and (b).

The location of a particular reservoir on the layer-cake-jigsaw-labyrinthine spectrumdecides whether it can be modeled deterministically using geology, or probabilistically

using statistics, such as the stochastic and fractal methods (Weber and van Guens, 1991).

Whichever approach is used, the objective is to produce a threedimensional grid of thefield, and to place a value for the porosity, permeability, and petroleum saturation with in

each cell of the grid (fig. 5).

Once this has been done the reserves may be calculated and the most effective method ofproducing them may be simulated on a computer. Computer simulation enables the

production characteristics of the field to be tested for different well spacings, productionrates, enhances recovery schemes and so forth. As the field is drilled up and goes on

stream an iterative process constantly updates and revises the reservoir grid, and enablesprogressively more accurate production scenarios to be tested.


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Oil and gas fields in paralic successions commonly comprise a stacked series of

reservoirs (e.g. Verdier et al., 1980; Jev et al., 1993). Each reservoir may reflect a distinct

depositional environment or a range of sub environments. Sequence Stratigraphy can helpto unravel this complexity by: a) elucidating the geometry of stratigraphic traps;

b) Outlining seal architecture; c) refining the choice of analogue data for input into

stochastic reservoir models; and d) delineating flow units.

Sand body dimensions vary with sequence sratigraphic settings. Therefore sequence

stratigraphy is helpful in choosing the correct analogue sand body dimensions with whichto populate stochastic models (Emery and Myers, 1996).


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For modeling purposes reservoirs are commonly divided into flow units, i.e. unitswhich have a distinct and internally consistent effect on fluid flow (Ebanks, 1987). In

many cases flow units are bounded by sequence stratigraphic surfaces. For example,

intra-formational seals above flooding surfaces mark flow unit boundaries. In addition,the grain-size and facies changes that occur across sequence boundaries typically result inpermeability contrasts and therefore, in flow unit boundaries (fig.6).

Sequence stratigraphy is now also widely applied to carbonate sequences as a mean ofidentifying unconformities, and thus predicting horizons of solution porosity and

dolomitization (Loucks and Sarg, 1993; Saller et al., 1994). Petroleum reservoirs in

secondary dolomite are very complex, and it may be difficult to assess their reserves and

to characterize the reservoir. In the Lisburne field at Prudhoe Bay, Alaska, for example, alimestone bears the dolomitization overprints of two superimposed unconformities

(Jameson, 1994).

Improved characterization of petroleum reservoirs must include better geologic models in

order to predict quantitative attributes of reservoir units. By necessity, petroleum

reservoir prediction and modeling must make both interpolation and extrapolations from

limited data. Several approaches to modeling of sedimentary rocks include descriptive orconceptual (qualitative) geologic models and geostatistical simulation (process), and

visualization (quantitative) models. Each type of model has its advantages and

limitations, including the appropriate scale of application, data requirements, andknowledge as to how the reservoir was formed. All of the models compliment one

another, providing views of complex reservoirs from different perspectives. Quantitative

modeling potentially can create a more coherent, integrated view of the reservoir than

qualitative conceptual models. An optimum model probably includes a combination ofapproaches based on the extent and type of knowledge about the reservoir. Sequence

Stratigraphic modeling is a powerful tool that can improve our ability to understand the

complexities of petroleum reservoirs, and thereby assist in the recovery of oil and gasfrom those reservoirs. Types of models can be classified into a ) conceptual models a

geologic interpretation or construct; b) correlation modelsa manual interpretation of the

spatial association of geologic units; c) interpolation models a machine generatedassociation of spatial data, that is a visualization; d) forward simulations machine

generation of geology based on input of processes; and e) inverse models machine

derivation of process parameters from geologic data (W.L. Watney, J.A. French, and W.J

Guy, 1996).

Conventional approach for interpreting 3D is in 2D or 2.5 D environment. Balanced

cross-sections are currently used in petroleum industry to help construct and validate

structural traps, to understand facies relationships, and to examine the relative timing ofhydrocarbon generation, migration, and trap formation. New visualization technology

allows 3-D seismic interpreters to rapidly analyze enormous data volumes. Along with

this technology come new techniques that take advantage of the visualizationbreakthroughs. Workstation and paper-section techniques used before 3-D visualization


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required laborious, time-consuming line-by line interpretation. This is no longer cost

effective. True volume interpretation, which does not rely on the creation of maps and /

or cross sections, is the next logical step to find more reserves with fewer wells.


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The four main techniques of 3D Multi-Attribute Geo-Volume Visualization Interpretation(GVI) are recognition, color, motion, and isolation (Sheffield, et al., 2000). Recognition

refers to determining the distinguishing characteristics of an event to be mapped, then

processing the data to enhance those characteristics for the purpose of visualization andgeo-body mapping. Color refers to the selection of optimum color scheme for visualizingthe property of interest. Motion is one of the most critical aspects of GVI; it is motion

that taps the human subconscious and allows interpreter to see relationships between data

in space and time. Isolation is the ability to separate the events of interest from other data,and is another key feature of GVI (Harvey et. al., 2000).

These Geo-volume visualization and interpretation in 3D environment with multi

attribute analysis enhance the structural boundaries as well as internal reservoirinformation for 3D seismic reflection data. The following attributes are used for

structural and stratigraphic enhancement of data.

Instantaneous Phase enhances the continuity of events by ignoring the amplitude

information in time samples. It is always a value between -180 and +180 and is primarily

used to visualize stratigraphic relationships (prograding reflections, onlaps, pinchouts

etc.) on a regional and local basis. In some cases, fluid contacts can be seen and phasereversals can indicate water contacts. Instantaneous phase and phase pi can be used to

pick discontinuous low amplitude events and to extend the interpretation of regional

events into discontinuous areas (Harvey and Sheffield., 2000).

Instantaneous amplitude measures reflection strength in time. It is primarily used to

visualize regional characteristics such as structure, sequence boundaries, thickness, and

lithological variations. In some cases, bright and dim spots can be hydrocarbonindicators, and tuning characteristics can sometimes be used to identify reservoirs on a

local basis (Harvey and Sheffield., 2000).

Instantaneous Frequency is rate of change of the instantaneous phase from one time

sample to the next. It is primarily used to visualize regional depositional patterns and

sequence boundaries. In some cases high frequency absorption can cause shadow zonesbeneath condensate and gas reservoirs, also frequency tuning can indicate changes in the

thickness (possibly pinchouts). Spikes indicate noise and /or fractures. Instantaneous

frequency can be useful in recognizing regional events and may be helpful to evaluate

areas of interest for thickness variations (Harvey and Sheffield., 2000).

Semblance is a measure of coherency for lateral changes in the seismic response caused

by variation in structural events (faults, uplifts, subsidence and erosion), stratigraphic and

sedimentary events (channels, onlaps, offlaps, and other lithological variations), porosityand the presence of hydrocarbons. Reflection discontinuities can also be caused by

seismic acquisition / processing and poor imaging. Various similar techniques exist to

bring out the discontinuity in the seismic data volume like: semblance, continuity, andcovariance. The cross-correlation techniques are also used to determine the local dip and


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azimuth attributes. Coherency processing requires certain parameters to be specified

(e.g. time window, amount of dip, etc.) and this should be carefully checked to control

the quality of the output (cf Marsh et al.2005).

Semblance is calculated over a vertical Window Length (in samples). Smaller window

lengths generate sharper discontinuities, but also create more noise. Window lengths thatare too long smear the discontinuities. It is recommended to start with a window lengthof 12 samples on 4 ms data, and modifying the window length, if necessary, in small

increments from the starting points. Using 3 traces is faster, and is often what we use for

quick first look, but the 9-trace calculation is more accurate and is preferred forinterpretation purposes (Harvey and Sheffield., 2000).

Links are possible between the inversion results and lithologic crossplots. These

crossplots summarise the relationship between various petrophysical and seismicparameters. Subsequent 3D clustering provides a means to do prediction of reservoir

characteristics in the studied data set (Guilbot et al. 1996). Seismic attributes may be

utilized to classify the seismic response in a certain interval.

Instantaneous amplitude/square root (Frequency) is the attribute which is defined by

dividing instantaneous amplitude to the square root of instantaneous frequency. It is sand

to shale indicator for siliciclastic environments highlighting the low frequency /highinstantaneous amplitude events (Harvey and Sheffield., 2000).

Conventional multi-attribute mapping, like density, acoustic and elastic impedance,Poissons ratio, is certainly useful for lithology and fluid content identification (Walker et

al. 2005). Complex multi-attributes or meta-attributes customized per study can be

computed to discriminate better certain reservoir characteristics in a N-dimensional

space. The PCA technique is hereby of great help. The automated detection ofrelationships hidden in the seismics is a non biased prediction tool, even more so because

the computer is a very robust observer of details (Veeken, 2008). Multi-attribute

autotracking will gradually become more mature (cf Sternbach 2002). The amplitudecoherency display shows the resolution power of such displays (Figure no. 7). The

delineation of voxsets is then done on a routine basis. These voxsets are assemblies of

voxels within a 3D volume, having specific multi-attribute characteristics. Their carefulselection will ensure a better description and reveal more details in individual or

composite geobodies i.e. flow units (Veeken, 2008).

Lithology influence on amplitudes can often be recognized by the pattern of amplitudesas observed on horizon slices and by understanding how different lithologies occur within

a depositional system. By relating lithologies to depositional systems we often refer to

these as lithofacies or facies. The link between amplitude characteristics and depositional

patterns makes it easier to distinguish lithofacies variations and fluid changes inamplitude maps.


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Figure No. 7. Multi-attribute display with the coherency and amplitude combined. It

shows a meandering channel system in a time slice mode.

Traditional seismic facies interpretation has been predominantly qualitative, based onseismic travel times. The traditional methodology consisted of purely visual inspection of

geometric patterns in the seismic reflections (e.g., Mitchum et al., 1977; Weimer and

Link, 1991). Brown et al. (1981), by recognizing buried river channels from amplitudeinformation, were among the first to interpret depositional facies from 3D seismic

amplitudes. More recent and increasingly quantitative work includes that of Ryseth et al.

(1998) who used acoustic impedance inversions to guide the interpretation of sandchannels, and Zeng et al. (1996) who used forward modelling to improve the

understanding of shallow marine facies from seismic amplitudes. Neri (1997) used neural

networks to map facies from seismic pulse shape. Reliable quantitative lithofacies

prediction from seimic amplitudes depends on establishing a link between rock physicsproperties and sedimentary facies.

The subsurface is by nature a layered medium, where different lithologies or facies have

been superimposed during geological deposition. Seismic stratigraphic interpretationseeks to map geologic stratigraphy from geometric expression of seismic reflections in

traveltime and space. Stratigraphic boundaries can be defined by different lithologies

(facies boundaries) or by time (time boundaries). These often coincide, but not always.Examples where facies boundaries and time boundaries do not coincide are erosional

surfaces cutting across lithostratigraphy, or the prograding front of a delta almost

perpendicular to the lithologic surfaces with in the delta.

There are several pitfalls when interpreting stratigraphy from traveltime information.

First, the interpretation is based on layer boundaries or interfaces, that is, the contrasts

between different strata or layers, and not the properties of the layers themselves. Twolayers with different lithology can have the same seismic properties; hence, a

lithostratigraphic boundary may not be observed. Second, a seismic reflection may occur

without a lithology change (e.g., Hardage, 1985). For instance, a hiatus with no

deposition within a shale interval can give a strong seismic signature because the shales


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above and below the hiatus have different characteristics. Similarly, amalgamated sands

can yield internal stratigraphy within sandy intervals, reflecting different texture of sands

from different depositional events. Third, seismic resolution can be a pitfall in seismicinterpretation, especially when interpreting stratigraphic onlaps or downlaps. These are

essential characteristics in seismic interpretation, as they can give information about the

coastal development related to relative sea level changes (e.g., Vail et al., 1977).However, pseudo-onlaps can occur if the thickness of individual layers reduces beneaththe seismic resolution. The layer can still exist, even if the seismic expression yields an

onlap.

Quantitative interpretation of amplitudes can add information about stratigraphic patterns,

and help us to avoid some pitfalls. First, relating lithology to seismic properties can help

us to understand the nature of reflections, and improve the geologic understanding of the

seismic stratigraphy. Gutierrez (2001) showed how stratigraphy guided rock physicsanalysis of well log data improved the sequence stratigraphic interpretation of a fluvial

system in Colombia using impedance inversion of 3D seismic data. Conducting

impedance inversion of the seismic data will give us layer properties from interfaceproperties, and an impedance cross-section can reveal stratigraphic features not observed

on the original seismic section. Impedance inversion has the poetential to guide the

stratigraphic interpretation, because it is less oscillatory than the original seismic data, it

is more readily correlated to well log data, and it tends to average out random noise, thereby improving the detectability of laterally weak reflections (Gluck et al., 1997).

Moreover, frequency-band-limited impedance inversion can improve on the stratigraphic

resolution, and the seismic interpretation can be significantly modified if the inversionresults are included in the interpretation procedure. Forward seismic modeling is also an

excellent tool to study the seismic signatures of geologic stratigraphy.

Griffiths and Hadler-Jacobsen (1993) and Nordlund and Griffiths (1993) discuss thederivation of input parameters for forward models from seismic observations. A range of

input parameters for simulation and points to consider prior to simulation are as follows:

Intial Topography/ Bathymetry

This needs to be determined via backstripping and must be carried out in threedimensions. Even if the modeling is to be carried out in two dimensions, we need to

know the three dimensional surface in order to estimate sediment transport directions.

Nature of Depositional Surface

What is the nature of depositional surface loose sand, clay or crystalline rock? The

answer determines the degree to which clastic input is supplemented by erosion products.

Climate: rainfall

The runoff volume and sediment concentration can be input either directly or predictedfrom a climate model.


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Climate: wave and storm magnitude and frequency

River discharge is extremely irregular, and most sediments are transported and deposited/ re-deposited during brief extreme events, hence the statistics of such behaviour are of

interest to modelers. It will not be possible to predict exactly when a flood or storm event

will happen, but much can be said about the probability of its happening in a givenclimatic regime. Such information can be built either directly or indirectly into forwardmodels (Koltermann and Gorelick, 1992)

Currents

Is the model in an area where marine currents might have redistributed sediment? If so

what were their characteristics?

Subsidence rates

Is or was, subsidence being driven by sediment loading, or was it a function of platestress or thermal recovery happening independently of sediment load. The sediment

geometries produced are very different in the two cases as subsidence rates may be

different in different tectonic settings.

Sea-Level

Sea level is one of the most controversial variables, ever discussed. The simulation mayuse the Haq et al. (1987) sea level curves, or a local relative sea-level (RSL) curve. If

many such curves are available around a basin then a regional curve may be extracted.

One practical decision that has to be made in computer modeling is how to treat the RSL

curve. One option is to treat it as the sum of all changes to accommodation space,including global sea level, tectonic subsidence, compaction, thermal subsidence, etc. This

may be acceptable in a one dimensional simulation but not in two or three dimensions.

The three dimensional shape of accommodation space creation varies with the differentcauses. An attempt should be made to break out the component parts.

Sediment Supply

Sediment supply can be treated either as a function of climatic process, or as an

observation. Measurement of the volume of sediment input to the basin during a given

time period can be made. Integrating the sediment supply rate over this time period mustgive the observed sediment volume after decompaction. The sediment supply curve with

in the simulated period can take a number of forms, such as constant, cyclic, or matched

to chromosome areas.

Post-depositional processes

In order to match the modeled geometries to observations there have to be some post-depositional processes, such as compaction, burial, faulting, etc. The timing of these


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processes, in relation to the depositional event is often critical. Present-day steady state

compaction concepts are not adequate when used in conjunction with forward modeling.

The rate of compaction largely determines the successive location of delta lobes andshale drapes, for example.

Some workers are now also integrating structural and sedimentological attributes withinbasin formation models. Which has given rise to quantitative dynamic sequencestratigraphy (QDS) in which quantitative techniques are being used to analyze the

geodynamic, stratigraphic, lithostatic, diagenetic and hydraulic attributes of sedimentary

basins, treating them as features produced by the interaction of dynamic processesoperating at specific time and places.

One of the characteristic features of fluvial systems is the presence of sinuous abandoned

sand-filled channels embedded in the background of floodplain shale, which ischaracterized by very low permeability / porosity material. In fluvial reservoirs,

hydrocarbon reserves are mostly contained in a number of distributed sand bodies

isolated by faults or partially connected to one another via good permeability / porositymaterial but tiny size pathway. This poses special challenges for geological modeling

because the existence of such connected pathway and barriers between isolated fluvial

bodies has great influence on fluid transport and thus production profiles. Detailed

knowledge of sand channel geometry, spatial distribution, and connectedness areessential to develop a model that accurately describes fluid flow, reliably predict the

future performance, and help in decision making management in fluvial system (V.Q.

Phan, 2000).

The response of a fluvial reservoir depends on spatial distribution of rock property

(permeability / porosity), which, in turn, can be determined by the set of parameters

describing channels. The procedure of computing such set of parameters from productionand seismic data usually begins with an inverse technique because the data are non-

linearly related with reservoir rock property, and thus channel parameters, through a

system of mass balance equations. Due to the complexity of the problem, the reservoirresponse must be computed numerically making use of a numerical simulator (V.Q.

Phan, 2000).

The recognition of the importance of sand/shale heterogeneity in the fluvial reservoirs

dates back to at least 1978 by Allen, L. Jr. One characteristic feature of fluvial reservoirs

is the existence of sand-filled channel complexes embedded in the background of low

permeability/porosity shale. Several researchers have addressed the problem ofcharacterization of fluvial reservoir using geological intuition (two decades ago) to

simple interpretation of well log data and just recently to integration of data from various

sources (geological, geophysical, petrophysical, and even production history). The

traditional methods for describing complex reservoir geology such as contouringparameters and hand-made models are not able to adequately represent the

heterogeneities and, therefore, to capture most of the aspects that impact on fluid

displacement. The Hand-Made models proposed by Johnson and Krol in 1984 rely on thegeological interpretation of well-log data. The well data provided locations where the


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channels certainly passing through, but the channels are positioned arbitrarily in the inter-

well areas as sandstones are insufficiently laterally extensive to be correlated between

wells (V. Q. Phan, 2000).

Alternative methods are random-object distribution of the sand bodies or

sedimentological process related models. These methods made use of stochasticsimulations to describe the complex geological architecture honored to the well data andconditioned to the probability distributions obtained from outcrop or other studies of

analog depositional environments. This stochastic technique was first credited to

Haldorsen and Lake in 1984 for development. Henriquez et al. applied the method to firstbuild a stochastic model conditioning to sand-body thickness and position in well bores

and then the geological model was transferred to a reservoir simulation grid by use of

transmissibility multipliers and an NGR value for each block. The authors reported that

the transfer of data smoothes out much of the detailed geological information, and thecalculated recovery factors are insensitive to the continuity measured in the geological

model and therefore proposed an improvement to the interface between geological model

and reservoir simulation model (V.Q Phan, 2000).

Smith and Morgan developed conceptual and stochastic models for both faulted reservoir

sands and fluvial sands. These models enabled the calculations of the permeability

reduction across fault sections and the recovery factors in fluvial reservoirs that can beused in a conventional numerical simulator. Stochastic simulation is an excellent tool to

integrate static data from various sources (well log, seismic, etc.). It can be used to

describe/generate the complex geological architecture and the spatial distributions offacies, sand-bodies, and the rock properties within each compartmented unit (V. Q. Phan,

2000).

Although these simulated scenarios are conditioned to hard data at the well locations andsubject to secondary constraints, the simulated models still incur large uncertainty in the

inter-well areas and predictions of reservoir performance. There are a number of recently

developed techniques for characterizing inter-well heterogeneities that were reported tobe efficient and useful to study the sensitivity of production performance to the

uncertainty in geological modeling. Begget et al. developed a new, quantitative, object-

based model of the distribution of litho types within each major facies and convertedthese models to spatial distributions of porosity/permeability and these are then up scaled

for use in flow simulation. The authors reported that the quantitative litho type models

contain a number of variable parameters which enable sensitivities to uncertainties in the

geological description to be studied (V.Q. Phan, 2000).

Yet, some other researchers have lately proposed a systematical technique that allows

describing the geological features in fluvial system even more efficient and realistic.

Among these are Deutsch and Wang. Their technique is based on a hierarchical set ofcoordinate transformations involving relative stratigraphic coordinates, translations,

rotations and straightening functions. All methods mentioned so far made use of static

information (time independent data). For reservoirs characterized by simpleconfigurations or in some purposes, the models obtained by these methods provide a


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good reservoir description and are adequately reliable for use in predicting future

reservoir performance. But other cases, particularly in most fluvial systems, often require

a more reliable model the model that honors most available information (V.Q. Phan,2000).

The introduction of dynamic data (time dependent data) in the problem ofcharacterization of fluvial reservoirs is useful in terms of obtaining detailed modelconfigurations and significantly reducing the uncertainty in modeling and in reservoir

predictions. Branagan et al. performed a three-well interference testing of a naturally

fractured, tight fluvial reservoir located near Rifle, Colorado in incorporation withgeologic and geophysical data in a series of simulations using a naturally fractured

reservoir model. The model parameters were then systematically altered in order to

provide the best history match of the well test data (V.Q. Phan, 2000).

The work concluded that the reservoir simulation history matching of well test and

interference data from the three wells produced a very detailed model of the fluvial EI

reservoir. The model provided significant insight into the complex productioncharacteristics of this tight, anisotropic naturally fractured reservoir. Lord et al. have

demonstrated that the history matching of well performance with a simple

multicompartment material balance model provides excellent estimates for directly

drained pore volumes and inter-compartment transmissibilities in fluvial reservoirs. Theyalso reported that a successful history match for the inter-channel model was achieved

using only rate history and single pressure observation at the end of production.

However, the limitations of the compartment model showed in case of dual permeabilityreservoir where the real pressure is not uniformly distributed as in a tank, and therefore,

is not accurately described by compartmental models (V.Q. Phan, 2000).

Stewart and Whaballa have shown that a material balance simulator can be used withpressure histories from well tests in compartmented oil reservoirs to identify geological

configurations. Hower and Collins compartmentalized a fluvial gas reservoir into two

compartments coupled by a low permeability barrier and also introduced a twocompartment material balance model. A further study by Lord and Collins extended this

approach to any number of permeable compartments with communication between

compartment pairs through low permeability barriers. This numerical method provided atwo-way of history matching allowing the users to match pressure history specifying rate

data or to match rate history specifying pressure data (V.Q. Phan, 2000).

In 1996, Zheng et al. studied the impact of variable formation thickness on pressuretransient behavior and well test permeability in fluvial meander loop reservoirs using a

commercial simulator. The modeling of the meander loop reservoir was based on a

simple linear-channel model with parabolic cross-sectional profile. A numerical and

analytical representation of channel complexes in fluvial reservoirs has been studied forseveral decades but the use of numerical simulators to dynamically incorporate fluvial

channels into the interpretation of seismic and production data has not yet become

common in a complete or systematical process (V.Q. Phan, 2000).


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Early papers were concerned more with numerical than geological considerations.

Missing scale between geological and simulation models results in a loss of information

which could enhance the difficulty in intergrating well test and/or production data.Sometimes in practice, the same problem could also arise due to another missing scale

between a simulation model used for long term forecast and an inversion model used in

the history matching process. Massonnat and Bandiziol review the interdependencebetween geology and well test interpretation. The authors investigated the effects thegeological model has upon the well test interpretation. The authors investigated the

effects the geological model has upon the well test response and how the well tests are

used to confirm a geological model through several field examples (V.Q. Phan, 2000).

Time-lapse seismic data monitoring is useful to document the change in seismic

response due to production and injection in the wells (cf Oldenziel 2003). The production

of hydrocarbons changes the acoustic impedance contrast. It results in a time andamplitude difference in and below the reservoir sequence. The differences are due to

change in pressure and water saturation for the reservoir rocks. Swept areas are

conveniently visualized and bypassed zones are easily recognized. The flow pattern in thereservoir is better resolved. The prediction of water breakthrough is feasible and a better

estimation of the well production figures is obtained (Veeken, 2008).

The fracture density can be assessed by anisotropic behaviour of the seismic velocities(Thomsen parameters). For this purpose the degree of non-hyperbolic Move Out (residual

move out) is determined. Geostatistical decomposition can be useful to quantify the

anisotropy effect in the data gathers with separation of a common part in all azimuthgathers, the anisotropic signal and the noise (Coleou et al. 2002). The fracture intensity

map forms input for Discrete Fracture Network modeling to derive various flow

properties like fracture permeability tensors, matrix-fracture interaction parameters,

which can be used as input for flow simulators (Wong and Boerner 2004).

Recent work on fault systems has emphasized the importance of the segmented nature of

fault geometries in matters of fault evolution (Peacock and Sanderson, 1994; Trudgill andCartwright, 1994; Cartwright et al. 1995; Dawers and Anders, 1995; Childs et al., 1996;

Marchal et al., 1998), sedimentary basin development (Anders and Schlische, 1994;

Dawers and Underhill, 2000), geothermal fluid migration (Coussement et al., 1994;Martinez, 1998), and seismological behavior (Crone and Haller, 1991; de Polo et al.,

1991; Machette et al., 1991; Wells and Coppersmith, 1994). A widely recognized impact

of fault systems is with regard to fault controlled hydrocarbon traps in oil and gas fields.

Well placement and recovery efforts in many oil and gas fields have benefitedsignificantly from highly detailed characterization of segmented fault systems (Bouvier et

al., 1989; Morley et al., 1990; Pegrum and Spencer, 1999; Knipe et al., 1998; Ottesen

Ellevest et al., 1998; Maerten, 1999). Breaks in fault continuity provide potential flow

zones through which hydrocarbon can migrate across a faulted region, therefore, athorough methodology for the analysis of segmented fault systems is needed to enhance

fault interpretations and thus recognize potential water breakthroughs and hydrocarbon

escape points with in fault compartmentalized reservoirs (Kattenhorn, S.A., and Pollard,D.D, 2001).


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Although distinct fault segments may be clearly visible at the surface of the Earth, some

large fault systems have evolved to a point where evidence of initial segmentation hasbeen eradicated through fault segments linking together, allowing the accumulation of

large amounts of slip over a resultant composite fault surface (Wesnousky, 1988;

Peacock and Sanderson, 1991). Slip profiles along fault traces at the Earths surfacecommonly exhibit heterogeneities in slip distributions (Cartwright and Mansfield, 1998;Morley, 1999) that may imply a relict segmented nature. This process of initial

segmentation and subsequent linkage is characteristic of fault system evolution

(Cartwright et al., 1995; Dawers and Anders, 1995) and may be associated withgeometric irregularities along fault strike at the points of linkage (Peacock and

Sanderson, 1994).

Fault traces at the Earths surface provided limited information on the 2-D evolution offaults where they intersect a horizontal plane but cannot be used to elucidate the three

dimensional (3-D) evolution of the fault system. The 3-D characteristics of fault systems

that can be determined from 3-D seismic reflection data (Childs et al., 1995; Mansfieldand Cartwright, 1996; Ottesen Ellevset et al., 1998; Yielding et al., 1999; Dawers and

Underhill, 2000) are crucial for accurate reservoir characterization where faults act as

barriers to hydrocarbon migration and may thus potentially compartmentalize the

reservoir. Insights into 3-D fault geometries are also important for formulatingmechanical models that examine fault geometries, tip-line shapes, slip distributions, fault

scaling laws, and mechanical interaction effects within segmented fault systems

(Kattenhorn, S. A., and Pollard, D.D, 2001).

Within any faulted reservoir there are large numbers of faults that are below the

resolution of seismic surveys. Some of these faults are encountered in wells, but vast

majority of them remain undetected. Such sub-seismic faults can significantly influencethe flow of hydrocarbons during production. The effect of faulting on transmissibility

with in the reservoir is evidenced by high-pressure differentials across faults with in the

oil and gas fields (Smith and Hogg, 1997). Such differences in pressure sometimesindicate fault sealing effects, which may be related to clay smearing along the faults (R.

Knipe, 1994).

Fault interpretations from only 3-D seismic data, however, are limited by interpretation

subjectivity, structural complexity, processing artifacts, seismic resolution, and

insufficient use of principle of fracture mechanics that can aid the interpretation. This

may result in erroneous fault interpretations that incorporate unrealistic fault geometriesand overlook important geometric features such as segmentation or fault linkage zones.

The size-distribution of sub-seismic faults can be predicted by extrapolating the size

distribution measured at the seismic scale down to the sub-seismic scale. However, the

positions and orientation of the sub-seismic faults are more difficult to determine. Forsub-seismic fault modeling at reservoir scale a new method is developed by Stanford

University Team for Rock Fracture Studies, which will be discussed in our research

methodology section.


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Naturally fractured and faulted systems can have a dramatic impact on reservoir

performancethey may act both as highly permeable flow conduits or baffles and seals.

The complexity of a fracture network typically leads to an extremely heterogeneous andanisotropic permeability distribution with in the reservoir. Successful management of

these reservoirs is impossible without substantial knowledge of the natural tensile and

shear fracture systems. It is essential to know their spatial distribution and hydraulicproperties on an inter-well scale to properly simulate the field-wide recovery processes(BOURNE, S. J., RIJKELS, L., STEPHENSON, B.J., AND WILLEMSE, E.J.M., 2001).

It is neither possible nor generally necessary to accurately predict individual fractureswithin a reservoir. Rather we should restrict our attention to predicting just the properties

of those tensile and shear fracture networks that are hydraulically conductive. We should

calculate the stress field responsible for reservoir fracturing using geomechanics. Brittle

fractures form where this stress field exceeds the local material strength as characterizedby the brittle failure envelope for both tensile and shear fractures (BOURNE, S. J.,

RIJKELS L., STEPHENSON, B.J., AND WILLEMSE, E.J.M., 2001).

A workflow of a new method by Bourne in 2001 for predicting natural fracture

distributions and their effect on reservoir simulations is shown in the figure no.8.

Figure no.8. Integrated model for naturally fractured reservoirs based on: (i)geomechanical models of rock deformation, (ii) fracture mechanics, and (iii) multi-phaseflow simulation. This work flow incorporates all the available static and dynamic data in

order to constrain the model and minimize uncertainty in fracture prediction and flow

forecasting.

In this method the first step uses geo-mechanical models of rock deformation to calculate

the field scale distribution of stress responsible for fracturing from observed structural

Geometry Stress Fractures Flow

Data Sources

Seismic

Well logs

Outcrop

Constraints

Material Properties

Regional Stress

Constraints

Matrix Properties

Fluid Properties

Model Validation

Core & BHI

Outcrop

Model Validation

Well test

Productionhistory

MaficOil

MoReS

Poly 3D

DIANA


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geometry of the field. Fracture network geometries are then obtained by simulating the

initiation, growth, and termination of fracture within the calculated stress field. These

predicted network geometries are partially constrained and validated by core, boreholeimage, mud loss, and outcrop data. Thereafter, multi-phase, well scale or field scale flow

simulations of the fracture model are validated and calibrated against well test and

production data (BOURNE, S. J., RIJKELS, L., STEPHENSON, B. J., ANDWILLEMSE, E.J.M., 2001).

Close integration of fracture prediction and flow simulation enables significant reductions

in uncertainty by using all the available static and flow data to constrain a single model.In this way, for instance, standard ambiguities in borehole fracture data due to sampling

bias can be overcome by the use of well inflow data. Moreover, as the fracture model is

field-scale, the greater the number of wells available the smaller the uncertainty in

fracture prediction becomes across the whole field and not just around the wells. Suchreduction in uncertainty allows improved field development through: (i) better

assessment of the recovery mechanism, (ii) more reliable production forecasts, (iii) well

placement for optimal drainage, (iv) minimal water-cut and (v) recognition of drillinghazards associated with fractures (BOURNE, S.J., RIJKELS, L., STEPHENSON, B.J.,

AND WILLEMSE, E.J.M., 2001).

To characterize reservoirs at pore levels, we should have enough information about CO2reactions in siliciclastic reservoirs, effect of provenance on reservoir quality, porosity

depth trends, timing of illite growth and use of image analysis to define reservoir

heterogeneity (Kaldi and Tingate, 2003).

Reservoir sandstone exhibit porosity enhancement by dissolution of calcite and quartz

overgrowth cement, moreover, presence of chlorite inhibit quartz overgrowth

cementation, while, ductile clays not only reduce the pore and pore throat sizes, but alsocan cause severe damage during production and development by retaining water or

collecting fines. Oxidation through invasion of meteoric water modifies the clays and

cements micro-pores with goethite. Glaucony cemented sandstones; show the effect ofswelling clays on the distribution of pores and pore throats, these types of expandable

clays can cause severe damage to reservoir porosity and greatly reduce permeability.

Illite replaces grains but illite and kaolinite together fill porosity, fibrous and bladed illitegreatly increases surface area and irreducible water, and it can also act as a filter to

collect mud and fines, therefore clay distribution patterns greatly influence permeability

(Kaldi and Tingate, 2003).

Table 1 summarizes potential rock fluid reactions based on knowledge of clays present,

damage prevention, and corrective procedures (Kersey, 1986). Prevention is preferred

and, when possible, is likely to cost less than correction.


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3) Objectives of the study

This research work aims to achieve better quantitative dynamic 3-D geological models of

the sedimentary architecture of the subsurface on reservoir scale beyond seismicresolution. The geological modeling techniques developed in consequence of this

research work will enable us to distinguish flow units and to quantify the geometry and

connectivity of the subsurface as precisely as possible.

The goal of reservoir-scale research is (1) to assess and reduce uncertainties in reservoir

modeling, (2) to predict production behavior of hydrocarbon reservoirs, and (3) to locate

by-passed hydrocarbon and increase the efficiency of hydrocarbon production. Inharmony with the last objective, specific production-geological research approaches will

be included in the programme. The purpose of this study is to establish the key concepts

and methodologies required to develop and build complex reservoir models. Specific

emphasis will be given to the integration of data arising from different origins and at avariety of scales (geology, seismics, well logging, cores, PVT, production, etc.). The use

of data analysis, geostatistics and up scaling techniques will be made with reference to

case studies. The study aims to develop a common language and a shared understandingof concepts between the various disciplines, thereby promoting collaboration and team

working with well known researchers and professionals from both academic institutions

and the oil industry.

4) Significance of the study

A multi-disciplinary approach is important, enabling an accurate evaluation to be made ofreservoir geometry, internal structures and heterogeneities, as well as their impact on

fluid flow. The process is critical during all phases in the life of a field, as it determinesthe size of reserves, reservoir producing mechanisms, field development strategy and

costs. Therefore, reservoir characterization and modeling is vital for optimum

exploitation of reserves and utilization of company financial resources.

The need for an accurate characterization of the reservoirs (e.g. by better understanding

of reservoir connectivity or accurate modeling of fracture networks) is a key to optimizethe reservoir management plans and then to reduce the development costs by:

Better control on the well planning (number, location and trajectory) Better decisions on the development strategy (reserves estimation, recovery

process, drilling schedule, etc.)

Helping in relating fundamental understanding of reservoir properties toimprovements in business practice and decision making processes throughout the

exploration and production process of oil and gas fields.

Being aware of the range of uncertainties in the process of exploration andproduction and their impact on business decisions.


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5) Research Planning & Schedule

The search for new hydrocarbon and water reservoirs and the need to enhance recoveryfrom existing reservoirs require concerted action of geologists, geophysicists and

engineers. The role of the geologists in this is to improve the predictive power of their

models. The objective of this research programme is to provide a spatial description ofpetrophysical properties in heterogeneous reservoirs. It is done by integrating geology

(geological rules and experience), geophysics, petrophysics, reservoir and production

engineering. The research programme aims to derive static properties (porosity andpermeability) in wells and inter-well regions at log scale, or at grid-block scale. When

coupled with dynamic properties at grid-block scale, the result is a reliable simulation

model which can be used to improve performance prediction in relatively new fields and

to rejuvenate old fields by locating by-passed and undrained hydrocarbons for improvedoil & gas recovery. This study will focus on following major research phases each of

which may take 3 to 4 months in its completion:

a) Introductory field session to familiarize with a range of reservoir facies;structural set-up; relationship between reservoir facies and their respectivesedimentary environments; and the impact of such environments on petrophysical

properties.

b) Filed work on outcrops, which have been the subject of extensive reservoiranalogue studies. Examination of the relationship between reservoir facies,internal structures and heterogeneities at different scales.

c) Description and mapping of reservoir structures and heterogeneities and

assessment of their impact on fluid flow and reservoir development strategy under

various reservoir configurations, fluid types and distribution patterns.

d) Seismic interpretation of key surfaces required for reservoir characterization andmodeling. After loading of all the available seismic data to a workstation, all

available ZVSP, VSI, Check shot and or any other velocity data will be used to tie

the study wells to the seismic data. The chronostratigraphic and sequence

stratigraphic framework established from biostratigraphic, isotopic andsedimentological analysis and interpretation will be used to pick key horizons.

After data synthesis and its integration into a user friendly GIS database we can

get benefit from 3D Basin modeling based on complex real case studies andDiffusion Oriented Normal and Inverse Simulation of Sedimentation for 3D

multi-lithological stratigraphic modeling to understand Basin architecture and forsubsequent reservoir characterization and modeling. Tectono Stratigraphic

surfaces will also be mapped regionally to understand uplifting, erosion, andcompaction to assist restoration of paleo-topography. This will then be used to

establish geometry of the reservoir horizons and seal intervals by forward and

inverse modeling. Fault distribution maps will also be prepared together with anunderstanding of fault movement through time and fault seal analysis will also be

carried out.


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determine the prediction confidence. This will particularly useful for quantifying

reservoirs with rapid facies changes and thin beddings.

Four example images are shown below:

2D Lithofacies Model 2D Porosity Model

3D Porosity Model 3D Porosity Model

h) Formalizing Geological Knowledge: The objective of this phase is to developscenario-based geological models in uncertain depositional environment. We will

consider multiple interpretations derived from geological knowledge(perceptions), rather than the more commonly used equally-probable realizations.

The latter effectively result from analyzing the sensitivity of a single

interpretation. In contrast, we will use granular computing to formalize geologicalperceptions and model imprecise, qualitative and linguistic geological events.These can be used to simulate scenario-based reservoir architecture. This allows

us to assess the true uncertainty of reservoir modeling beyond the observed data.

i) Development of Team-work in the reservoir-geological and petrophyscicalmodeling using deterministic and stochastic methods with integration of seismic

and dynamic constraints by identification of key heterogeneities, quantification of


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uncertainties and up-scaling of petrophysical models to appropriate fluid flow

models for specific reservoir simulations.

j) 4D seismic time lapse monitoring and production history matching to removethe constraints of static reservoir models and the dynamic flow simulations.

6) Research Methodology

We will integrate information from four complementary data sources:

1. Subsurface data: deep 3D seismic reflection data, cores, well logs, data fromproduction history, data from borehole imaging, nuclear magnetic resonance andspectroscopic methods, real time down hole seismic images, and subsurface

information come from sophisticated directional drilling methods in combination

with apriori knowledge of geo-history and geological processes obtained from

recent & ancient subcrop and outcrop analogues and from forward and inversemodeling of geological processes.

2. Recent & ancient subcrop and outcrop analogues: with detailed verticalinformation by ground penetrating radar and shallow seismics.

3. Complete reservoir analogues: provided by including field petrophysics, deep andshallow seismics, ground penetrating radar, and borehole data in the outcrop and

subcrop analogues with excellent information on the vertical and lateral extent of

sediment bodies, which ideally suit to match core data with sequence stratigraphy

of rock sequences.

4. Process-response modeling of sedimentary systems aims to simulate reservoirarchitecture by developing suitable mathematical formulations

Use of Geomodelling and Visualization for Earth, Energy, Economy and Environmental (EEEE)...

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