Table of Contents

Cover

Title Page

Dedication

Preface

Chapter 1: Introduction

1.1 Reservoir Modelling Challenges
1.2 Exploration to Production Uncertainty
1.3 Content and Structure
1.4 What is a Reservoir Model?
1.5 The Modelling Workflow
1.6 An Integrated Team Structure for Modelling
1.7 Geostatistics
1.8 Data Sources and Scales
1.9 Structural and Stratigraphic Modelling
1.10 Facies Modelling
1.11 Property Modelling
1.12 Model Analysis and Uncertainty
1.13 Upscaling
1.14 Summary

Chapter 2: Data Collection and Management

2.1 Seismic Data
2.2 Well Data
2.3 Dynamic Data
2.4 Important Specialist Data
2.5 Conceptual Models
2.6 Summary

Chapter 3: Structural Model

3.1 Seismic Interpretation
3.2 Fault Modelling
3.3 Horizon Modelling
3.4 Quality Control
3.5 Structural Uncertainty
3.6 Summary

Chapter 4: Stratigraphic Model

4.1 How Many Zones?
4.2 Multi-Zone Grid or Single-Zone Grids?
4.3 Well-to-Well Correlation
4.4 Geocellular Model
4.5 Geological Grid Design
4.6 Layering
4.7 Grid Building Workflow
4.8 Quality Control
4.9 Uncertainty
4.10 Summary

Chapter 5: Facies Model

5.1 Facies Modelling Basics
5.2 Facies Modelling Methods
5.3 Facies Modelling Workflows
5.4 Flow Zones
5.5 Uncertainty
5.6 Summary

Chapter 6: Property Model

6.1 Rock and Fluid Properties
6.2 Property Modelling
6.3 Property Modelling Methods
6.4 Rock Typing
6.5 Carbonate Reservoir Evaluation
6.6 Uncertainty
6.7 Summary

Chapter 7: Volumetrics and Uncertainty

7.1 Work Flow Specification
7.2 Volumetric Model Work Flow
7.3 Resource and Reserves Estimation
7.4 Uncertainty Modelling
7.5 Summary

Chapter 8: Simulation and Upscaling

8.1 Simulation Grid Design
8.2 Upscaling Property Models
8.3 Work Flow Specification
8.4 Summary

Chapter 9: Case Studies and Examples

9.1 Aeolian Environments (Figure 9.1)
9.2 Alluvial Environments (Figure 9.3)
9.3 Deltaic Environments (Figure 9.4)
9.4 Shallow Marine Environment (Figure 9.6)
9.5 Deepwater Environments (Figure 9.8)
9.6 Carbonate Reservoirs (Figure 9.10)
9.7 Fractured Reservoirs (Figure 9.12)
9.8 Uncertainty Modelling
9.9 Summary

Afterword

References

Appendix A: Introduction to Reservoir Geostatistics

A.1 Basic Descriptive Statistics
A.2 Conditional Distributions
A.3 Spatial Continuity
A.4 Transforms
A.5 Lag Definition
A.6 Variogram Interpretation
A.7 Kriging
A.8 Simulation
A.9 Object Modelling
A.10 Summary

Index

End User License Agreement

List of Illustrations

Chapter 1: Introduction

Figure 1.1 Reservoir modelling workflow elements presented as a traditional linear process showing the links and stages of the steps as outlined in the following chapters.
Figure 1.2 Idealized evolution of resources with time over the oilfield life cycle showing the reduction in uncertainty after each stage.
Figure 1.3 Actual example of resource change during appraisal and development of an oil or gas field; appraisal often continues after project sanction as too many wells may erode project economics.
Figure 1.4 Histogram of the relative change in proven + probable estimates in ultimate recovery of oil over the period 1989–1996 for UK North Sea fields with reserves greater than 10 MMSTBOE.
Figure 1.5 The many components of a 3D reservoir model from seismic interpretation to well planning.
Figure 1.6 An example of a parallel workflow adopted by many companies for an efficient, integrated approach to reservoir modelling. The separate disciplines work together to design the model, analyse the available data and understand the uncertainties of the field under study.
Figure 1.7 The statistical analysis of 100 beach pebbles displayed as a histogram and used to describe simple statistical nomenclature; includes a normal distribution curves and tabulated results.
Figure 1.8 The scales of investigation of different types of data found in a reservoir study compared with the overall typical field size.
Figure 1.9 A matrix of horizontal and vertical heterogeneity classified by depositional environment.

Chapter 2: Data Collection and Management

Figure 2.1 Depth measurement and well path trajectory terminology.

Chapter 3: Structural Model

Figure 3.1 Examples of simple structural and stratigraphic trapping mechanisms that can be reproduced in 3D geocellular models.
Figure 3.2 Representation of the seismic response at lithological interfaces producing a reflection coefficient that may be used to generate a synthetic seismic seismogram used in depth conversion.
Figure 3.3 Example of sequence stratigraphy used in the interpretation of large seismic features typical of a lowstand to highstand cycle.
Figure 3.4 Examples of different velocity functions used for depth conversion showing the impact of additional data on the type of function applied; calibrated checkshot and VSP data are required for the detailed analysis of the overburden.
Figure 3.5 Common descriptive terminology for normal and reverse faults: the extrapolation distance is the interval the software interpolates in building a robust fault.
Figure 3.6 Modelling terminology used for the creation of horizon lines and surfaces.
Figure 3.7 Examples of horizon lines, fault surfaces and fault pillars used to edit simple and complex faulted structures.
Figure 3.8 The impact of only using development wells in depth conversion; the structure collapses because the depth conversion is based on a limited overburden distribution because offset wells are not included. In this case, the hydrocarbons in place were reduced by 30%.

Chapter 4: Stratigraphic Model

Figure 4.1 Classification and impact of different types of horizons used in modelling.
Figure 4.2 Comparison between lithostratigraphic correlation and sequence stratigraphy of the Brent Group, fluvio-deltaic system, North Sea.
Figure 4.3 Horizon, zone and sub-grid nomenclature used in geocellular modelling.
Figure 4.4 Depth reference terminology for use in well correlation.
Figure 4.5 Grid orientation and axes nomenclature used in geocellular grid construction.
Figure 4.6 Types of heterogeneity at different scales from the microscopic to basin.
Figure 4.7 Examples of different types of genetic units that can be modelled using facies object methods.
Figure 4.8 Different types of layers used in geocellular modelling.
Figure 4.9 Different layering and surface attachment methods used to represent medium-scale geological features.
Figure 4.10 Grid cell quality control examples of imperfect cells that reduce the efficiency of dynamic simulation.

Chapter 5: Facies Model

Figure 5.1 Walther's law in action; facies found adjacent laterally will be seen in the same order vertically. This example is of a progradational shoreface marine sequence.
Figure 5.2 Conceptual model of a desert setting including sand dunes, dry channels, alluvial fans and playa lake environments.
Figure 5.3 (a) Simplified lithology determination from wireline logs; natural gamma, bulk density, photoelectric and neutron porosity logs.
Figure 5.4 Upscaling or blocking raw discrete (facies) and continuous (porosity) well data.
Figure 5.5 Shifting and scaling blocked well data to correct the mismatch between raw data and model sub-grids.
Figure 5.6 Experimental variograms: The nomenclature, orientation and application of different variogram types.
Figure 5.7 Example of an indicator model of an alluvial floodplain comprising shales (green), overbank deposits (brown) and channel sands (yellow). Note that the channel sands are not continuous across the model.
Figure 5.8 Examples of truncated Gaussian simulation using trends model progradation and retrogradation of shallow marine environments.
Figure 5.9 Examples of different object shapes that can be modelled to represent facies.
Figure 5.10 Sand body width-to-thickness measurements classified by depositional environment: data compiled from numerous outcrop sources.
Figure 5.11 An indicator simulation model of a floodplain environment with three facies: shale, overbank and extensive coal deposits.
Figure 5.12 The same floodplain model used as a background in which low sinuosity channels are modelled as objects.
Figure 5.13 Flow zones and rock types, an alternative way to describe the reservoir architecture before property modelling.
Figure 5.14 The representative elementary volume (REV) concept and scales of investigation and measurement in heterogeneous and homogenous media.

Chapter 6: Property Model

Figure 6.1 Grain size and sorting of sediments in different depositional environments.
Figure 6.2 (a) Porosity: the relationship between volume of pore space and total volume of rock is a function of grain size, sorting and packing at the time of deposition. Post-depositional processes such as compaction and diagenesis can alter the original relationship. (b) Water saturation: the proportion of the total reservoir pore volume filled with water: the remaining pore volume is filled with oil or gas, not necessarily hydrocarbon gas. (c) Permeability: the ability of a reservoir to conduct fluids through an interconnected pore network.
Figure 6.3 (a): Description of capillary pressure based on a simple experiment of water rising in a tube; (b) description of wettability as the interaction between a surface and an adsorbed fluid.
Figure 6.3 (a): Description of capillary pressure based on a simple experiment of water rising in a tube; (b) description of wettability as the interaction between a surface and an adsorbed fluid.
Figure 6.4 Identify the point of inflection for a suite of wireline logs to determine bed boundaries and eliminate the ‘shoulder effects’; part of the blocking of continuous raw log data.
Figure 6.5 Porosity distribution: mapped, interpolated and stochastically distributed showing the increasing degree of heterogeneity in the property.
Figure 6.6 Schematic of a variogram showing the nugget, sill and range.
Figure 6.7 Examples of porosity distribution by kriging and simulation showing the greater variability in distribution away from well control in the latter realizations.
Figure 6.8 Facies-constrained porosity distribution: (a) the interpolated porosity model honours the well data but results in a smooth distribution between the wells; (b) and (c) a simple threefold facies scheme of channel, overbank and floodplain allows the porosity seen in the well to be distributed more meaningfully, capturing the rapid changes laterally in the model.
Figure 6.9 Facies-constrained porosity model showing channel, overbank and floodplain distribution.
Figure 6.10 Total versus effective porosity systems; log analysis gives total porosity including clay-bound immoveable water. Core analysis may also give total porosity depending on how the plugs have been cleaned and dried. For volumetric estimates, we should use effective properties, so we should model overburden corrected effective porosity.
Figure 6.11 An example of a typically skewed permeability distribution with approximately 50% of the observations being <20 mD.
Figure 6.12 Physics of the reservoir; representation of fluid distribution with an oil reservoir based on the relationship between water saturation, capillary pressure and the free water level datum.
Figure 6.13 Example of a water saturation model using a saturation height relationship; the free water level is identified where S_w = 1.
Figure 6.14 The relationship between capillary pressure, height and permeability demonstrating the impact of rock quality on water saturation.
Figure 6.15 Net-to-gross (NTG) terminology, whatever approach you take be consistent.
Figure 6.16 Total property modelling (TPM) avoids the need to apply NTG until upscaling properties for simulation.
Figure 6.17 Carbonate rock-type classification based on Lucia (1999). The example shown is from a non-vuggy dolomitic limestone and significant displacement boundaries are established at 20 and 100 µm pore throat sizes, defining the separate permeability fields.

Chapter 7: Volumetrics and Uncertainty

Figure 7.1 Scales of measurement: from core data through log, seismic and well test to demonstrate the several orders of magnitude difference between the various sources.
Figure 7.2 Standard oilfield terminology for resources and reserves.
Figure 7.3 Deterministic and stochastic reserves terminology.
Figure 7.4 Petroleum Reserves Management System (PRMS, 2011) terminology for resources and reserves.
Figure 7.5 Uncertainty in oilfield reserves classified by depositional environment.
Figure 7.6 A deterministic method for determining the low–mid–high range of hydrocarbon volumes. This might be the starting point for any volumetric exercise involving 3D modelling.

Chapter 8: Simulation and Upscaling

Figure 8.1 Upscaling of reservoir properties is dependent on sampling method, scale and region.
Figure 8.2 Grid resolution aligned to the major structural features creates a more robust and realistic faulted grid.
Figure 8.3 When a grid is aligned to the primary flow direction, a better grid for dynamic simulation is created.
Figure 8.4 The SmartModel concept promotes building the geocellular and simulation grids with the same orientation and complementary dimensions so that upscaling and down-gridding methodologies can be improved.
Figure 8.5 Numerical dispersion is created when the simulation grid requires rotation such that the flow paths between wells become distorted.
Figure 8.6 The results of upscaling porosity from the fine scale to the coarse scale retain all the primary property distribution and the same overall pore volume.
Figure 8.7 Permeability upscaling is a more challenging task and may require different averaging methods or dynamic pressure solver techniques.
Figure 8.8 Examples of two common averaging methods: (a) arithmetic–harmonic; (b) harmonic–arithmetic.
Figure 8.9 Different boundary condition that can be applied to pressure solver upscaling of permeability.
Figure 8.10 Examples of different upscaling regions: local, regional and global.
Figure 8.11 A comparison of resampling and direct sampling methods for cell centre-based and corner-based methods.

Chapter 9: Case Studies and Examples

Figure 9.1 (a) A schematic aeolian dune form showing the different elements of deposition each with potential different reservoir properties that might require rock typing and separate property distributions.
Figure 9.2 (a) SIS facies model of the Hyde Field based on the description in Sweet et al. (1996). (See Figure 9.1b for facies description and colours.)
Figure 9.3 (a) An annotated map of the River Indus picking out the major channel forming features of a high-energy, seasonal fluvial system. (b) A low-sinuosity, low-NTG fluvial system (sand volume ∼ 25%) with attached levee deposits. (c) Individual channel bodies are picked out as isolated bodies; only the pink-coloured bodies are well connected.
Figure 9.4 (a) The Brent Field conceptual model, based on the South Cormorant Field, UK North Sea.
Figure 9.5 These three images show how a complex deposition system like the Brent Group can be constructed in stages; (a) the use of TGSim to model facies belts representing the Rannoch–Etive sequence. The basal Broom package is in blue; (b) the use of channel objects modelled in a background of floodplain deposits created by indicator simulation; (c) the Tarbert interval is built using an SIS algorithm with a strongly N–S major direction.
Figure 9.6 (a) Shallow marine deposition takes place below wave base and results in a gradual progradation from upper shoreface to marine transition with a predictable change in grain size, sorting and clay content; (b) this is reflected in the upward coarsening profile and in the case of the Fulmar Formation, UK North Sea, a classical bow-shaped log profile; (c) these predictable characteristic are reflected in the poro–perm cross-plot, (d) where the higher energy shoreface deposit forms a separate cluster of data.
Figure 9.7 Examples of shallow marine models; (a) progradation sequence of fine sands, very fine sands and argillaceous sands modelled with TGSim to create belts; (b) additional high-energy clean shoal sands models as objects with strong linear orientation; (c) porosity model using SGS to distribute facies-specific ranges in porosity between 0.01 and 0.30 p.u.
Figure 9.8 (a) Schematic deepwater depositional model showing the variety of different potential environments from the shoreline/slope to the abyssal plain; (b) classical example of the Bouma depositional model for turbidite facies. The vertical profile controls reservoir quality, but it is seldom that all elements of the cycle are preserved.
Figure 9.9 (a) A two-zone model representing deepwater deposition. In the upper zone, the yellow sands are distributed using elliptical objects between 1000 and 5000 m long and 100 and 1000 m wide up to a sand volume of 50%. In the lower zone, a similar volume of sand is distributed using an indicator simulation method in which the major variogram direction is 5–10 times greater than the minor orientation; (b) the lower image is of a sand-rich, channelized turbidite system where the attached levees are up to 10 times the width of the channels. The background shales potentially act as vertical barriers to flow.
Figure 9.10 (a) A carbonate ramp conceptual model using sequence stratigraphy to build a series of prograding packages with episodic transgressions leading to non-carbonate sedimentation; (b) a model comprising three sequences of reservoir limestones (blue/green) and non-reservoir evaporites (purple/grey). The reservoir zones have been constructed using SIS and without trends to capture the different types of distribution, randomly mosaic (lower) and aggradational build-ups (upper). The three colours in the reservoir zones represent good-, moderate- and poor-quality rock types, each could subsequently be modelled with appropriate ranges of porosity.
Figure 9.11 (a) An example of the Lucia rock-type classification for non-vuggy dolomites based on core porosity and permeability data; (b) rock types distributed according to a well-defined facies scheme of low-energy peri-tidal to open offshore environments.
Figure 9.12 (a) Fracture density as seen in core and image data from three wells in a granite basement field. These are used to characterize fracture types for subsequent modelling; (b) the required geometry of each fracture type and how they may be modelled as vertical objects within a small prototype model.
Figure 9.13 A simple approach to structural modelling based on depth conversion uncertainty resulting in 3 deterministic models of top reservoir.
Figure 9.14 Results of a model driven workflow approach (Leahy and Skorstad, 2013); (a) shows the areas of seismic uncertainty when picking surfaces due to poor resolution; (b) shows the multi-realization surfaces having run the stochastic routines for both surface and fault uncertainty; (c) is a histogram of the range in volumetric results based on the workflow.
Figure 9.15 Uncertainty in channel geometry and orientation makes a significant difference in volumetric estimation and reservoir connectivity.
Figure 9.16 Examples of different distributions used in facies modelling to introduce a stochastic element into object geometry and orientation.
Figure 9.17 Conceptual model, uncertainty workflow and permeability distributions for three shallow marine depositional scenarios.
Figure 9.18 Time-of-flight results displayed as a function of the drained volume for each of the realizations as well as frequency and cumulative probability plots.

List of Tables

Chapter 1: Introduction

Table 1.1 List of key information required before starting a reservoir modelling project
Table 1.2 Data sources for modelling
Table 1.3 Typical list of products from a reservoir modelling study that may be requested by a peer reviewer

Chapter 2: Data Collection and Management

Table 2.1 Listing of curves and specific log names that should be defined in the modelling database
Table 2.2 List of major logs used for a CPI and available for the modelling database
Table 2.3 A list of the standard mnemonics used for core analysis data

Chapter 3: Structural Model

Table 3.1 Example of fault classification and naming procedure

Chapter 4: Stratigraphic Model

Table 4.1 The requirement for building a 3D reservoir model increases with permeability heterogeneity (1–3 orders of magnitude), fluid type and production mechanism

Chapter 6: Property Model

Table 6.1 Permeability ranges for different qualitative descriptions of permeability
Table 6.2 Conversion of laboratory capillary pressure data to reservoir conditions.
Table 6.3 Bulk volume water at irreducible water saturation as a function of grain size and type of carbonate porosity

Chapter 7: Volumetrics and Uncertainty

Table 7.1 Reserves definitions as recommended in PRMS
Table 7.3 Prospective resources definition as recommended in PRMS

Chapter 9: Case Studies and Examples

Table 9.1 Distribution of facies by reservoir zones from core and log data; these form targets for modelling
Table 9.2 Input variables for uncertainty modelling of a shallow marine deposition system

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Title: Reservoir modelling : a practical guide / Steve Cannon, principal consultant (Steve Cannon; Geoscience).

Description: First edition. | Hoboken, NJ : Wiley, 2018. | Includes bibliographical references. |

Identifiers: LCCN 2017056087 (print) | LCCN 2017056889 (ebook) | ISBN 9781119313434 (pdf) | ISBN 9781119313441 (epub) | ISBN 9781119313465 (cloth)

Subjects: LCSH: Reservoirs-Mathematical models. | Hydraulic structures-Mathematical models.

Classification: LCC TC167 (ebook) | LCC TC167 .C36 2018 (print) | DDC 627/.86015118-dc23

To all the Cannons, Nichols, Whitleys, Reeves and Watsons who\break have supported my geological studies, especially on the beach at Porthmadog and many other outcrops around the world!

Preface

This book has matured over 40 years of practical oilfield experience in mud logging and well site operations, from core analysis to sedimentology and reservoir modelling to field development: I have been fortunate to have had the opportunity to be employed in a variety of different roles for a wide range of companies and organizations. All of this has culminated in the opportunity to teach a successful course on integrated reservoir modelling, which forms the foundation of this book.

By profession, I am a geologist, by inclination a petrophysicist and I am a reservoir modeller by design. In reality, I promote the building of fit-for-purpose reservoir models to address specific uncertainties related to hydrocarbon distribution and geological heterogeneity that impacts fluid flow in the reservoir. A simple mantra for reservoir modelling, as in life, is ‘keep it simple’: we never have enough knowledge or data to rebuild the subsurface only to try and make a meaningful representation of the reservoir.

My background in reservoir evaluation gives me the experience to promote 3D modelling as a solution to most field development and production challenges as long as the question being asked is properly defined. Reservoir simulation projects are clearly designed to address specific issues, so should geological models, be it volumetric estimation, well planning or production optimization. This book is focused on the development of structurally complex, clastic, offshore fields rather than large onshore producing fields. This is largely because of the difference in well numbers and spacing; geostatistical software modelling products were developed specifically for these challenges. That the same tools have been expanded for use in giant onshore fields with a large well count has made 3D geo-modelling the tool of choice for reservoir characterization and dynamic simulation.

The person building a reservoir model can be part of a multidisciplinary team, the ideal situation in my view: or a geologist who knows how to use the software and is part of a linear workflow that starts with the geophysicist and ends with a reservoir engineer; in this case, each discipline often uses a different software product and there is minimal discussion at each stage of the process. Increasingly, the seismic interpreter can build the structural model as the first step and the geologist builds and populates the grid. Whichever situation you find yourself in, it is essential to take the rest of the stakeholders with you at each stage of the model.

The book does not promote one type of method over another or specify one commercial product above another; I am grateful to a number of organisations that have provided me with the tools of my trade, especially Schlumberger and Emerson-Roxar. My background as a consultant with Roxar Software Solutions from 2000 to 2008 defines my preference for object modelling of geological facies, rather than pixel-based methods, but in reality, the software tools available to the modeller allow a wealth of options. I would like to thank Aonghus O'Carrol, Dave Hardy, Neil Price, Doug Ross, Tina Szucs and all the people who have told me to ‘RTBM’ and play with the software. My thanks also to Steve Pickering and Loz Darmon from Schlumberger-NExT who encouraged me to develop the course and supported me during the delivery of the material to over 200 students worldwide and to Rimas Gaizutis who may recognize some of these ideas from working together in the past.

Finally, I am not an academic and this is not an academic treatise but a practical handbook. Many people will disagree with my philosophy when it comes to reservoir modelling, but when you are limited by: time, data or resources, pragmatism and compromise are the order of the day. A wise man once wrote, ‘all models are wrong, though some can be useful’ (Box, 1979).

Steve Cannon

2018