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Reservoir Modelling - a Practical Guide : A Practical Guide - Steve Cannon

Reservoir Modelling - a Practical Guide

A Practical Guide

Hardcover

Published: 23rd April 2018
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Published: 1st February 2018
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Over the last 20 years, since the building of three-dimensional models of oil and gas reservoirs became almost de rigeur, many books have been published on the geostatistics of model building and the design of reservoir models, but little has been written about the evaluation of those same reservoirs that require modelling.  Geoscientists working in the hydrocarbon industry have focussed largely on the specialist tools that have been developed for interpreting modern data, while post graduate courses in Petroleum Geoscience and Engineering try to teach their students the necessary skills to transfer to the job market.  This book combines the practical experience of nearly 40 years of reservoir evaluation including twenty years of geological modelling to provide an integrated approach to both the input data and the modelling results.  The book draws on a public course developed for the Schlumberger NExT organisation in 2013 by the author, but is not an advert for one particular modelling philosophy or software solution.

The successful characterization of an oil or gas field comprises data evaluation and often the construction of a static model prior to conducting dynamic simulation of how that reservoir might perform under different development scenarios and production regimes.  The evaluation of a variety of different static and dynamic parameters is required before a meaningful model may be constructed: geophysical, geological and petrophysical interpretations are the elements required to build the static model, while fluid properties under different pressure and temperature condition are needed to define the dynamic response of the reservoir.  The ultimate objective of a reservoir model is to establish the volumes of hydrocarbons that may be trapped within a geological structure and then what proportion might be recovered under different production scenarios.  Key to this evaluation is an understanding of the uncertainty associated with the acquisition and interpretation of all of the data upon which a financial investment may be undertaken. 

The reservoir framework is a function of seismic interpretation and the conversion from time to depth of key horizons and faults; the internal architecture of the reservoir depends on the depositional environment of the reservoir and the connectivity at a variety of scales from the pore-scale to the reservoir unit; and the reservoir properties, such as porosity and permeability, need to be evaluated from core, wireline log and seismic responses to both the rocks and the fluids present in the reservoir.  An understanding of the available data, the limitations inherent in the data and how far the data may be pushed during evaluation is of primary importance to understanding the finished model.  The use of geostatistics to distribute reservoir properties and dynamic simulation to test different development options are both ways to evaluate the subsurface uncertainties that abound in reservoir modelling.  The key to successful reservoir characterization is the integration of all of the disparate data types by a team of professionals all motivated by the same objective and with a clear understanding of why the study or project is required.
Contents

Contents
Chapter 1 - Introduction to reservoir modelling
What is a reservoir model?
Why do we build reservoir models?
Why is reservoir modelling so challenging?
What data do you need for reservoir modelling?
What is the objective of the modelling project?
Reservoir modelling workflows
Project management

Chapter 2 - Conceptual model design
When planning a reservoir-modelling project, the ultimate purpose of the model must be defined.  The process of building a 3D reservoir model will always follow the same general workflow, regardless of the tools available to the modelling team.  Each of the steps will be outlined in the course with an appreciation of the required input data, associated uncertainties and likely deliverable and its use. The database used for the exercises is from a real field that includes two different clastic reservoir types with different modelling challenges.
• Reservoir envelope: top and base structure
• Internal framework: correlation scheme
• Reservoir compartments: fault geometry
• Reservoir architecture: facies model
• Petrophysical property distribution
• Volumetric Assessment
• Retention of relevant fine-scale detail through upscaling

Chapter 3 - Reservoir Framework
In volumetric models the greatest uncertainty is usually the gross rock volume: the top structure map and the hydrocarbon contact.  Depth conversion, where well data are sparse, leads to much of this uncertainty.   Structural models that result in over complexity, much of which cannot be modelled, may drive seismic interpretation.  Deciding what structural elements to include can be a source of much debate.  Likewise too much well-to-well correlation can over science a model especially when still in development and drilling surprises are common.
• Depth conversion uncertainty
• Model surface selection and quality control
• Fault modelling and compartments
• Stratigraphy and correlation
• Grid construction

Chapter 4 - Reservoir Architecture
This section will address different clastic and carbonate depositional environments and how to best characterise them for subsequent modelling.  The dependence on statistics is introduced as a way to demonstrate the different methods of facies modelling available.
• Depositional models and facies analysis
• Core-log integration
• Basic statistics
• Objects and indicators
• Seismic conditioning
• Facies modelling

Chapter 5 - Property modelling
Running through the book is the theme of petrophysics, always calibrated to the geology, as a way to distribute reservoir properties.  This section will present simple rock typing methods that are readily applicable to 3D models.  Particular attention will be paid to saturation height methods that accurately distribute fluids through the model.
• Basic petrophysics
• Rock typing
• More basic statistics
• Porosity models
• Saturation models
• Permeability models

Chapter 6 - Uncertainty and Upscaling
What makes a good static model will be discussed and methods of interrogating and analysing the results described before addressing the thorny question of upscaling for dynamic simulation.  Finally, what makes a model fit for purpose?  Do the ranges of possible outcomes cover the expectations of the stakeholders at each stage of the process of exploration through to development and beyond, into production?
• Geological model analysis
• Hydrocarbon volumes initially in place
• Drainable volumes
• Simulation grid construction
• Property upscaling
• Multiple scenarios, realisations and ranking

Chapter 7 – Outcomes and consequences
A few case studies that highlight some of the methods adopted and results of published reservoir modelling projects around the world; how they have been used for field development and the impact on reserves recovery.

Preface xiii 1 Introduction 1 1.1 ReservoirModelling Challenges 3 1.2 Exploration to Production Uncertainty 4 1.3 Content and Structure 6 1.4 What is a Reservoir Model? 9 1.4.1 ReservoirModel Design 12 1.5 The ModellingWorkflow 13 1.5.1 Project Planning 15 1.5.2 What Type of Model Are You Planning to Build? 16 1.6 An Integrated Team Structure for Modelling 17 1.7 Geostatistics 19 1.8 Data Sources and Scales 22 1.9 Structural and Stratigraphic Modelling 25 1.10 FaciesModelling 25 1.11 Property Modelling 26 1.12 Model Analysis and Uncertainty 27 1.13 Upscaling 29 1.14 Summary 29 2 Data Collection and Management 31 2.1 Seismic Data 33 2.1.1 Horizons 33 2.1.2 Fault Sticks and Polygons 33 2.1.3 Surface Intersection Lines 34 2.1.4 Seismic Data Volume 34 2.1.5 Velocity Model 34 2.2 Well Data 34 2.2.1 Wellbore Path 34 2.2.2 Computer-Processed Interpretation (CPI) Logs 36 2.2.3 Core Descriptions 39 2.2.4 Core Photographs 39 2.2.5 Core Plug Data 39 2.2.6 Reservoir Zonation 41 2.2.7 Pressure Data 41 2.3 Dynamic Data 41 2.3.1 Fluid Data 41 2.3.2 Well Test Data 42 2.4 Important Specialist Data 42 2.4.1 Special Seismic Cubes and Seismic Test Lines 42 2.4.2 SCAL Data 43 2.4.3 Borehole Image Logs and Interpretations 43 2.5 Conceptual Models 43 2.6 Summary 45 3 Structural Model 47 3.1 Seismic Interpretation 47 3.1.1 Depth Conversion 52 3.1.2 Interpretation in Time Versus Depth 55 3.2 Fault Modelling 55 3.2.1 Fault Interpretation Process 59 3.2.2 Fault Naming 59 3.3 Horizon Modelling 62 3.4 Quality Control 62 3.5 Structural Uncertainty 63 3.6 Summary 64 4 StratigraphicModel 65 4.1 How Many Zones? 67 4.2 Multi-Zone Grid or Single-Zone Grids? 67 4.3 Well-to-Well Correlation 69 4.4 Geocellular Model 70 4.4.1 Capturing Heterogeneity 71 4.5 Geological Grid Design 75 4.5.1 Goals of Geological Grid Design 76 4.5.2 Orientation of the Geological Grid 77 4.5.3 The SmartModel Concept 79 4.6 Layering 79 4.6.1 Potential Dangers Using Conformable Grids 81 4.6.2 Erosion 83 4.7 Grid BuildingWorkflow 83 4.8 Quality Control 84 4.9 Uncertainty 85 4.10 Summary 85 5 Facies Model 87 5.1 FaciesModelling Basics 88 5.1.1 Defining the Facies Scheme 90 5.1.2 Upscaling of Log Data (BlockingWells) 95 5.1.3 Simplified Facies Description 98 5.1.4 Verification of the Zonation and the Facies Classes 98 5.1.5 Facies Proportions fromWell Data 99 5.2 FaciesModelling Methods 99 5.2.1 Pixel-Based Methods: Indicator and Gaussian Simulation 100 5.2.2 Object-Based Methods 104 5.2.3 Multi-Point StatisticalMethods 106 5.2.4 Conditioning to a Seismic Parameter 107 5.2.5 Conditioning to Dynamic Data 107 5.3 FaciesModellingWorkflows 107 5.4 Flow Zones 112 5.5 Uncertainty 112 5.6 Summary 114 6 Property Model 115 6.1 Rock and Fluid Properties 117 6.1.1 Porosity 117 6.1.2 Water Saturation 119 6.1.3 Permeability 119 6.1.4 Poro Perm Relationship 120 6.1.5 Capillary Pressure 121 6.1.6 Wettability 122 6.2 Property Modelling 122 6.2.1 Property ModellingWorkflow 123 6.2.2 Data Preparation 124 6.2.3 Blocking or UpscalingWell Data 126 6.3 PropertyModellingMethods 127 6.3.1 DeterministicMethods 127 6.3.2 StatisticalMethods 129 6.3.3 Modelling Porosity 132 6.3.4 Modelling Permeability 134 6.3.5 ModellingWater Saturation 136 6.3.6 Modelling Net-to-Gross (NTG) 142 6.3.7 Incorporating Seismic Attributes 143 6.3.8 How Many Realizations? 145 6.3.9 Quality Control 146 6.4 Rock Typing 146 6.5 Carbonate Reservoir Evaluation 149 6.5.1 Rock Fabric Classification 150 6.5.2 Petrophysical Interpretation 152 6.6 Uncertainty 156 6.7 Summary 156 7 Volumetrics and Uncertainty 157 7.1 Work Flow Specification 161 7.1.1 Volumetrics Terminology 161 7.1.2 Products and Results 162 7.1.3 Necessary Data 162 7.2 Volumetric ModelWork Flow 163 7.2.1 Volumetrics with Stochastic Models 163 7.2.2 Volumetrics and Grid Resolution 164 7.2.3 Geo-model/Simulation Model Comparison 164 7.2.4 Reporting Volumetric Results 165 7.3 Resource and Reserves Estimation 165 7.3.1 Petroleum Resources Management System (PRMS) 166 7.4 UncertaintyModelling 171 7.4.1 Work Flow Specification 172 7.4.2 UncertaintyModelWorkflow 175 7.4.3 Ranking Realizations 177 7.4.4 Other UncertaintyMethods 178 7.4.5 Summary 179 8 Simulation and Upscaling 181 8.1 Simulation Grid Design 182 8.1.1 Grid DesignWork Flow 182 8.1.2 What is a Corner Point Grid? 183 8.1.3 Grid Design Goals 184 8.1.4 Grid Orientation Effects 186 8.1.5 Areal Grid Construction 187 8.1.6 Areal Representation of Faults 187 8.1.7 Aquifer Modelling 188 8.1.8 Local Grid Construction 188 8.1.9 Quality Control of Grids 189 8.2 Upscaling Property Models 190 8.2.1 Statistical Averages 191 8.2.2 Renormalization 193 8.2.3 Dynamic Upscaling 193 8.2.4 Comparison of UpscalingMethods 195 8.2.5 Local, Regional and Global Upscaling 196 8.2.6 Sampling for Upscaling 197 8.2.7 SamplingMethods Overview 197 8.2.8 Upscaling Porosity 199 8.2.9 Upscaling Permeability 199 8.2.10 Upscaling Net/Gross 200 8.2.11 Water SaturationModelling 201 8.2.12 Quality Control 202 8.3 Work Flow Specification 203 8.3.1 UpscalingWorkflow 203 8.4 Summary 204 9 Case Studies and Examples 205 9.1 Aeolian Environments 205 9.1.1 Building the Model 208 9.1.2 Remodelling 209 9.2 Alluvial Environments 210 9.2.1 Building the Model 218 9.3 Deltaic Environments 219 9.3.1 Building the Model 222 9.4 Shallow Marine Environment 226 9.4.1 Building the Model 226 9.5 Deepwater Environments 229 9.5.1 Building the Model 234 9.6 Carbonate Reservoirs 235 9.7 Fractured Reservoirs 244 9.8 UncertaintyModelling 248 9.8.1 Structural Model Uncertainty 249 9.8.2 FaciesModel Uncertainty 251 9.8.3 Petrophysical Uncertainty 254 9.9 Summary 255 Afterword 259 References 267 A Introduction to Reservoir Geostatistics 273 A.1 Basic Descriptive Statistics 275 A.2 Conditional Distributions 279 A.3 Spatial Continuity 280 A.3.1 Variogram Description 282 A.3.2 Zonal and Geometric Anisotropy 282 A.3.3 Variogram Estimation 284 A.4 Transforms 285 A.5 Lag Definition 286 A.6 Variogram Interpretation 287 A.6.1 Indicator Variograms 289 A.7 Kriging 290 A.7.1 Simple and Ordinary Kriging 290 A.7.2 Kriging with a Drift 291 A.7.3 Co-kriging 291 A.7.4 Indicator Kriging 293 A.8 Simulation 293 A.8.1 Sequential Gaussian Simulation (SGS) 295 A.8.2 Sequential Gaussian Simulation with External Drift 296 A.8.3 Sequential Indicator Simulation (SIS) 297 A.8.4 Sequential Co-located Co-simulation (SGCoSim) 297 A.8.5 Sequential Indicator Co-located Co-simulation 299 A.8.6 Truncated Gaussian Simulation (TGSim) 299 A.9 Object Modelling 302 A.10 Summary 305 Index 307

ISBN: 9781119313465
ISBN-10: 1119313465
Audience: Professional
Format: Hardcover
Language: English
Number Of Pages: 328
Published: 23rd April 2018
Publisher: John Wiley and Sons Ltd
Country of Publication: US
Dimensions (cm): 21.6 x 13.8  x 3.2
Weight (kg): 0.59