Reservoir Modelling

A Practical Guide

Steve Cannon

• Publisher: Wiley-Blackwell
• Year: 2018
• Print ISBN: 9781119313465
• eBook ISBN: 9781119313441
• Edition: 1
• Topic: Physical Sciences
• Subtopic: Geophysics

The essential resource to an integrated approach to reservoir modelling by highlighting both the input of data and the modelling results

Reservoir Modelling offers a comprehensive guide to the procedures and workflow for building a 3-D model. Designed to be practical, the principles outlined can be applied to any modelling project regardless of the software used. The author — a noted practitioner in the field — captures the heterogeneity due to structure, stratigraphy and sedimentology that has an impact on flow in the reservoir.

This essential guide follows a general workflow from data QC and project management, structural modelling, facies and property modelling to upscaling and the requirements for dynamic modelling. The author discusses structural elements of a model and reviews both seismic interpretation and depth conversion, which are known to contribute most to volumetric uncertainty and shows how large-scale stratigraphic relationships are integrated into the reservoir framework. The text puts the focus on geostatistical modelling of facies and heterogeneities that constrain the distribution of reservoir properties including porosity, permeability and water saturation. In addition, the author discusses the role of uncertainty analysis in the static model and its impact on volumetric estimation. The text also addresses some typical approaches to modelling specific reservoirs through a mix of case studies and illustrative examples and:

• Offers a practical guide to the use of data to build a successful reservoir model
• Draws on the latest advances in 3-D modelling software
• Reviews facies modelling, the different methods and the need for understanding the geological interpretation of cores and logs
• Presents information on upscaling both the structure and the properties of a fine-scale geological model for dynamic simulation
• Stresses the importance of an interdisciplinary team-based approach

Written for geophysicists, reservoir geologists and petroleum engineers, Reservoir Modelling offers the essential information needed to understand a reservoir for modelling and contains the multidisciplinary nature of a reservoir modelling project. 

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.

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 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.

1.2 Exploration to Production Uncertainty

Even before the first exploration well is drilled on a prospect, a geologist will h...

Table of contents

• Cover
• Title Page
• Copyright
• Dedication
• Table of Contents
• Preface
• Chapter 1: Introduction
• Chapter 2: Data Collection and Management
• Chapter 3: Structural Model
• Chapter 4: Stratigraphic Model
• Chapter 5: Facies Model
• Chapter 6: Property Model
• Chapter 7: Volumetrics and Uncertainty
• Chapter 8: Simulation and Upscaling
• Chapter 9: Case Studies and Examples
• Afterword
• References
• Appendix A: Introduction to Reservoir Geostatistics
• Index
• End User License Agreement