This document provides an introduction to reservoir modelling. It defines reservoir modelling as the construction of 3D computer models of oil and gas reservoirs to estimate reserves, plan field development, predict production, and evaluate reservoir management strategies. Building accurate reservoir models is challenging due to the variety of data types involved and complex steps required. The key is to keep models practical and focused on addressing specific questions about the reservoir. Modern software aims to integrate seismic, geological, and simulation data into a single workflow. While technology has advanced modelling capabilities, reservoirs remain complex, so models need to capture important heterogeneity at appropriate scales without unnecessary complexity. The overall goal is to represent reservoirs usefully rather than replicate every detail.

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Reservoir Modelling: A
Practical Guide
Steve Cannon
Principal Consultant
Steve Cannon Geoscience
UK

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This edition first published 2018
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Library of Congress Cataloging-in-Publication Data
Names: Cannon, Steve, 1955- author.

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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
LC record available at https://lccn.loc.gov/2017056087
Cover Design: Wiley
Cover Image: (Reproduced) Courtesy of Emerson-Roxar
To all the Cannons, Nichols, Whitleys, Reeves and Watsons whobreak have supported my geological
studies, especially on the beach at Porthmadog and many other outcrops around the world!

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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.

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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

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Chapter 1
Introduction
The purpose of this practical guide is to summarize the procedures and workflow towards
building a 3D model: the principles are applicable to any modelling project regardless of the
software; in other words, this is an attempt at a practical approach to a complex and varied
workflow (Figure 1.1). What we are not trying to do in this book is to build detailed geological
models of depositional environments but to capture the heterogeneity due to structure,
stratigraphy and sedimentology that has an impact on flow in the reservoir.

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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.
The key to building a reservoir model is not the software; it is the thought process that the
reservoir modeller has to go through to represent the hydrocarbon reservoir they are working
on. This starts with a conceptual model of the geology and a diagram of the ‘plumbing’ model
to represent how fluids might flow in the reservoir. Modern integrated modelling software
starts with seismic input in terms of both interpreted horizons and faults and seismic attribute
data that characterizes reservoir from non-reservoir and ends by linking to dynamic
simulation; the so-called seismic-to-simulation solution. I have always been concerned that
geophysicists and reservoir engineers might forget the geology that actually creates their oil
or gas accumulation.
Wikipedia defines reservoir modelling as ‘the construction of a computer model of a
petroleum reservoir, for the purposes of reserves estimation, field development planning,
predicting future production, well placement and evaluating alternative reservoir
management.’ The model comprises an array of discrete cells arranged as a 3D grid populated
with various attributes such as porosity, permeability and water saturation. Geological models
are static representations of the reservoir or field, whereas dynamic models use finite
difference methods to simulate the flow of fluids during production. You could of course
construct a reservoir model using paper and coloured pencils, but analysis of that model is
challenging!
Geo-modelling is ‘the applied science of creating computerized representations of the Earth’s
crust based on geophysical and geological observations.' Another definition is ‘the spatial
representation of reservoir properties in an inter-well volume that captures key heterogeneities
affecting fluid flow and performance.’ However you define it, geo-modelling requires a
balance between hard data, conceptual models and statistical representation. Whether you are
working on a clastic or carbonate reservoir, the workflow is the same, although the challenges
are different: in carbonate reservoirs, characterizing the petrophysical properties properly is
paramount because diagenesis will usually destroy any primary deposition controls on
reservoir quality. We will look at carbonate reservoir characterization separately.
A few key statements should be made at the outset:
 • Every field is unique and therefore has different challenges
 • Every challenge will have a unique solution
 • Every solution is only valid for the given situation and therefore …
 • KEEP IT SIMPLE …… at least to begin with.
1.1 Reservoir Modelling Challenges
Building a model of an oil and gas reservoir is complex and challenging as much because of
the variety of data types involved as the many different steps required. The process is made
easier if you can establish why you are building the model; what is the objective of the model?
Today, we generally build 3D geocellular models for volumetric estimation, dynamic
simulation, well planning and production optimization or to understand the uncertainty
inherent in any hydrocarbon reservoir. Above all, a successful 3D model aids in the

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communication of concepts and the interpretation of data used to characterize a potential or
producing oil or gas field.
We model reservoirs in 3D because nature is three dimensional and because the reservoir is
heterogeneous and we have restricted opportunities for sampling. Additionally, to understand
flow in the reservoir, we need to consider connectivity in three dimensions, rather than simple
well-to-well correlation. Having built a 3D representation of the reservoir, it can be used to
store, edit, retrieve and display all the information used to build the model; in effect, a model
is a means to integrate data from all the subsurface disciplines, so the data are not just stored
in the minds of geologists.
Reservoir modelling is also a challenge because we are dealing with a mix of geological and
spatial properties and also the complex fluids present in the reservoir. The data available to
build a representative model are generally either sparse, well data or poorly resolved, seismic
data. The resulting model is dependent on the structural complexity, the depositional model,
the available data and the objectives of the project. Building a usable reservoir model is always
a compromise: we are trying to represent the reservoir not replicate it.
The advances in computer processing power and graphics over the past 20 years has meant
that geoscientists can build representative models of a reservoir to capture the variability
present at all the appropriate scales from the microscopic to the field scale. However, as
reservoirs are complex, we need to be highly subjective about the scale at which we model
and the level of detail we incorporate: a gas reservoir may well be a tank of sand but faults
may compartmentalize that tank into a number of separate accumulations.