Chapter 1

Introduction

1.1 Summary

It is well known that reservoir simulation studies are very subjective and vary from simulator to simulator. While SPE benchmarking has helped accept differences in predicting petroleum reservoir performance, there has been no scientific explanation behind the variability that has frustrated many policy makers and operations managers and puzzled scientists and engineers. In this book, a new approach is taken to add the Knowledge dimension to the problem. Some attempted to 'correct' this shortcoming by introducing 'history matching', often automatizing the process. This has the embedded assumption that 'outcome justifies the process' - the ultimate of the obsession with externals. In this book, reservoir simulation equations are shown to have embedded variability and multiple solutions that are in line with physics rather than spurious mathematical solutions. With this clear description, a fresh perspective in reservoir simulation is presented. Unlike the majority of reservoir simulation approaches available today, the 'knowledge-based' approach does not stop at questioning the fundamentals of reservoir simulation but offers solutions and demonstrates that proper reservoir simulation should be transparent and empower decision makers rather than creating a black box. For the first time, the fluid memory factor is introduced with a functional form. The resulting governing equations become truly non-linear. A series of clearly superior mathematical and numerical techniques are presented that allow one to solve these equations without linearization. These mathematical solutions that provide a basis for systematic tracking of multiple solutions are emulation instead of simulation. The resulting solutions are cast in cloud points that form the basis for further analysis with advanced fuzzy logic, maximizing the accuracy of unique solution that is derived. The models are applied to difficult scenarios, such as in the presence of viscous fingering, and results compared with experimental data. It is demonstrated that the currently available simulators only address very limited range of solutions for a particular reservoir engineering problem. Examples are provided to show how the Knowledge-based approach extends the currently known solutions and provide one with an extremely useful predictive tool for risk assessment.

1.2 Opening Remarks

Petroleum is still the world's most important source of energy, and, with all of the global concerns over climate change, environmental standards, cheap gasoline, and other factors, petroleum itself has become a hotly debated topic. This book does not seek to cast aspersions, debate politics, or take any political stance. Rather, the purpose of this volume is to provide the working engineer or graduate student with a new, more accurate, and more efficient model for a very important aspect of petroleum engineering: reservoir simulations. The term, "knowledge-based," is used throughout as a term for our unique approach, which is different from past approaches and which we hope will be a very useful and eye-opening tool for engineers in the field. We do not intend to denigrate other methods, nor do we suggest by our term that other methods do not involve "knowledge." Rather, this is simply the term we use for our approach, and we hope that we have proven that it is more accurate and more efficient than approaches used in the past.

1.3 The Need for a Knowledge-Based Approach

In reservoir simulation, the principle of GIGO (Garbage in and garbage out) is well known (latest citation by Rose, 2000). This principle implies that the input data have to be accurate for the simulation results to be acceptable. Petroleum industry has established itself as the pioneer of subsurface data collection (Islam et al., 2010). Historically, no other discipline has taken so much care in making sure input data are as accurate as the latest technology would allow. The recent superflux of technologies dealing with subsurface mapping, real time monitoring, and high speed data transfer is an evidence of the fact that input data in reservoir simulation are not the weak link of reservoir modeling.

However, for a modeling process to be knowledge-based, it must fulfill two criteria, namely, the source has to be true (or real) and the subsequent processing has to be true (Islam et al., 2012; 2015). The source is not a problem in the petroleum industry, as great deal of advances have been made on data collection techniques. The potential problem lies within the processing of data. For the process to be knowledge-based, the following logical steps have to be taken:

• Collection of data with constant improvement of the data acquisition technique. The data set to be collected is dictated by the objective function, which is an integral part of the decision making process. Decision making, however, should not take place without the abstraction process. The connection between objective function and data needs constant refinement. This area of research is one of the biggest strength of the petroleum industry, particularly in the information age.
• The gathered data should be transformed into Information so that they become useful. With today's technology, the amount of raw data is so huge, the need for a filter is more important than ever before. However, it is important to select a filter that doesn't skew data set toward a certain decision. Mathematically, these filters have to be non-linearized (Abou-Kassem et al., 2006). While the concept of non-linear filtering is not new, the existence of non-linearized models is only beginning to be recognized (Islam, 2014).
• Information should be further processed into 'knowledge' that is free from preconceived ideas or a 'preferred decision'. Scientifically, this process must be free from information lobbying, environmental activism, and other forms of bias. Most current models include these factors as an integral part of the decision making process (Eisenack et al., 2007), whereas a scientific knowledge model must be free from those interferences as they distort the abstraction process and inherently prejudice the decision making. Knowledge gathering essentially puts information into the big picture. For this picture to be distortion-free, it must be free from non-scientific maneuvering.
• Final decision making is knowledge-based, only if the abstraction from the above three steps has been followed without interference. Final decision is a matter of Yes or No (or True or False or 1 or 0) and this decision will be either knowledge-based or prejudice-based. Figure 1.1 shows the essence of the knowledge based decision making.

Figure 1.1 The knowledge model and the direction of abstraction.

The process of aphenomenal or prejudice-based decision-making is illustrated by the inverted triangle, proceeding from the top down (Figure 1.2). The inverted representation stresses the inherent instability and unsustainability of the model. The source data from which a decision eventually emerges already incorporates their own justifications, which are then massaged by layers of opacity and disinformation.

Figure 1.2 Aphenomenal decision-making.

The disinformation referred to here is what results when information is presented or recapitulated in the service of unstated or unacknowledged ulterior intentions (Zatzman and Islam, 2007a). The methods of this disinformation achieve their effect by presenting evidence or raw data selectively, without disclosing either the fact of such selection or the criteria guiding the selection. This process of selection obscures any distinctions between the data coming from nature or from any all-natural pathway, on the one hand, and data from unverified or untested observations on the other. In social science, such maneuvering has been well known, but the recognition of this aphenomenal (unreal) model is new in science and engineering (Shapiro et al., 2007).

1.4 Summary of Chapters

Chapter 1 summarizes the main concept of the book. It introduces the knowledge-based approach as decision making tool that triggers the correct decision. This trigger, also called the criterion, is the most important outcome of the reservoir simulation. At the end, every decision hinges upon what criterion was used. If the criterion is not correct, the entire decision making process becomes aphenomenal, leading to prejudice. The entire tenet of the knowledge-based approach is to make sure the process is soundly based on truth and not perception with logic that is correct (phenomenal) throughout the cognition process.

Chapter 2 presents the background of reservoir simulation, as has been developed in last five decades. This chapter also presents the shortcomings and assumptions that do not have knowledge-base. It then outlines the need for new mathematical approach that eliminates most of the short-comings and spurious assumptions of the conventional approach.

Chapter 3 presents the requirements in data input in reservoir simulation. It highlights various sources of errors in handling such data. It also presents guideline for preserving data integrity with recommendations for data processing that does not turnish the knowledge-based approach.

Chapter 4 presents the solutions to some of the most difficult problems in reservoir simulation. It gives examples of solutions without linearization and elucidates how the knowledge-based approach eliminates the possibility of coming across spurious solutions that are common in...