The other school argues that AI techniques are "black boxes" with vague ... In the technical sense, a black box has been defined as "a device, system or object ...
SPE 155413 Artificial Intelligence Application in Reservoir Characterization and Modeling: Whitening the Black Box Fatai Adesina Anifowose, SPE, King Fahd University of Petroleum and Minerals, Saudi Arabia
Copyright 2011, Society of Petroleum Engineers This paper was prepared for presentation at the SPE Saudi Arabia section Young Professionals Technical Symposium held in Dhahran, Saudi Arabia, 14–16 March 2011. This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright.
Abstract There are two schools of thought on the application of Artificial Intelligence (AI) techniques in reservoir characterization and modeling. The first school considers AI as a step forward in the future of reservoir characterization and modeling in line with the increased advancement in technology. The other school argues that AI techniques are "black boxes" with vague architectures, whose concepts do not follow fundamental petroleum engineering principles. This paper presents AI as a "white box", highlighting its basic concepts, revealing the architectural composition of some of its techniques, and showcasing examples of its successful applications in various reservoir characterization and modeling tasks. AI techniques are used in the prediction of oil and gas reservoir properties such as porosity, permeability, water saturation, well-bore stability and identification of lithofacies. The recent developments in the hybridization and "ensemblage" of some AI techniques are also discussed. The outcome of this paper will provide a better understanding of the basic concepts of AI and offer a strong background for further study of AI techniques. Overall, it will provide an appraisal of the successful applications of AI in petroleum engineering and increase the necessary synergy required for a multi-disciplinary collaboration among petroleum engineers, computer scientists and mathematicians; and to ensure the delivery of the future AI-driven/AI-assisted reservoir models for better exploration, production and management of petroleum resources. Introduction The application of Artificial Intelligence (AI) techniques in science and engineering was met with mixed reactions. It meant different things to different people. To the pessimists and antagonists, it is perhaps another fairy tale or science fiction in which computers are presented to be able to "develop the ability to think and feel and take over the world in some dystopian future" [Valentino-DeVries 2010]. It is simply treated with undeserved disbelief, distrust and total despondency. In the technical sense, a black box has been defined as "a device, system or object which can be viewed solely in terms of its input, output and transfer characteristics without any knowledge of its internal workings, that is, its implementation is "opaque" (black)" [Wikipedia 2011]. Some of the things that can be described as a black box are an algorithm, a technique or a tool. The opposite is a system where the inner components or logic are available for study, inspection, examination and validation. This is known as a "white box". To the proponents however, it is possibly the next generation and a direct consequence of the rapid development of the Information Technology age. Since data is no longer a scarce resource as it is now abundant and exists, in most of the cases, in databases that are geographically distributed, AI is seen as a new and more intelligent set of methods, tools, and theories for the discovery, modeling and extraction of patterns and relationships embedded in huge amounts of these available datasets. This was
the brain behind the concept of Data Mining. The present interest in the application of the advances in AI has, in fact, proven the pessimists wrong. At the moment, in the OnePetro database, there are 709 occurrences of the term "Artificial Intelligence", 169 for "Machine Learning" and 370 for "Data Mining". This shows both the relative level of interest in the application of AI in oil and gas, and the low level of awareness of the application. It is hoped that with the high level of interest in the subject, more awareness and appreciation will be created. As a means of creating the needed awareness, this paper is aimed at "whitening" the "black box" phenomenon attached to AI. The basic concepts of AI are highlighted, the architectural composition of some of its techniques are discussed and few cases of its successful applications in various reservoir characterization and modeling tasks, such as in the prediction of oil and gas reservoir properties like porosity, permeability, water saturation, well-bore stability and identification of lithofacies, are presented. Also, the recent advances in AI in the areas of hybrids and ensembles of AI techniques are discussed. The outcome of this paper will improve the level of understanding and better appreciation of the application of AI in oil and gas as well as serving as an impetus to enthusiastic researchers to delve into AI and reap the benefits of better accuracy and robustness that it promises. The Two Sides Artificial Intelligence Techniques are Black Boxes! It is interesting to know that one of the experts in the application of AI in oil and gas research once alluded to the claim that Artificial Neural Networks (ANN), the earliest of the AI techniques to be used in oil and gas, can be viewed as a "black box". In their paper, [Mohaghegh and Ameri 1995] wrote "Our experience with neural networks on the estimation of formation permeability from well log data and prediction of gas storage well performance after hydraulic fracturing, has been that, one will get some sort of results by treating neural network as a black box, where one inputs the data, trains the network and gets some output." While describing the concept of "hidden layers" in ANN, the authors wrote "Sometimes they are likened to a "black box" within the network system." A similar statement was made by [Mohaghegh 2000]. In yet another work, [Mohaghegh 2005] reported his experience with engineers and scientists who believe that ANN and, by extension, all AI techniques are "black boxes". Another indication that AI techniques were viewed as a set of "black box" was also seen in the work of [Kravis and Irrgang 2005], while presenting a case-based AI-driven system for a complex problem like oil field design. They claimed that "An important aspect of the design [of oil fields] was that engineers want to feel in control. They are not prepared to allow a black box system [referring to ANN] to design their new wells with complete autonomy." A research report [aCentre for Water Systems, University of Exeter 2005] mentioned ANN in connection with some of the methods used for creating "black box" mathematical models in the engineering literature. In another research report, [bCentre for Water Systems, University of Exeter 2005] described a project "to investigate the application of ANN models as 'black-box' models of rainfall-runoff processes, on real catchments." Another report described ANN as a technique that "... usually provide models that are capable of good predictions, but they don't give any insight into the structure of the process. They are commonly called black boxes, one that puts the data in and gets results from the model, but does not know anything about the underlying relationships between input and output data" [cCentre for Water Systems, University of Exeter 2005]. [McMahon 2008] described AI as a set of algorithms that are "hidden". Other reports that allude to AI as a black-box include [Potter and Moonis 1997, Suykens and Vandewalle 1998, Moonis 2006 and Russell and Norvig 2010]. Despite all these negative depictions of AI techniques as "black-box", some other reports have presented its good sides as well. No, They are White! [Mohaghegh and Ameri 1995, Mohaghegh 2000], in their attempt to describe the architecture of ANN, argued that "... just because they are not immediately visible does not mean one cannot examine what goes on in those layers (referring to the hidden layers)". In line with this, [Mohaghegh 2005] suggested to professionals "to take serious steps toward changing the 'black box' image that has been associated with several AI-related techniques and bring it closer and closer to a 'transparent box'." A lot of work has been done to show that AI techniques are white boxes with few of them in the oil and gas application area and very many of them in mathematics and computer science fields. The former case is understandable since focus is usually on application and less emphasis is put on details of the algorithms. Nevertheless, the increasing number of publications on AI
techniques especially in the oil and gas domain as found in the OnePetro online database is an indication of its wide acceptance as "white box". Some of these will be discussed in subsequent sections.
Whitening the Black Box Artificial Intelligence. Many definitions have been given for AI in the literature. Some defined it as “the science and engineering of making intelligent machines” [Ridha and Mansoori 2005]. Others have defined it as “the study of ideas that enable computers to do the things that make people seem intelligent” [Schlumberger's Oilfield Glossary 2011]. Still, another author described it as “a collection of several analytical tools that attempts to mimic life” [Mohaghegh 2007]. Simply put, AI is the intelligence of machines and the branch of computer science which aims to create it. It has come to be widely understood as the study, analysis, design and development of intelligent systems. AI has caught the interest of most researchers and has today become an essential part of the technology industry, providing a good ground for solving many of the most difficult problems in various areas of applications, including oil and gas. Data Mining. It would be incomplete to discuss AI without mentioning the concept of its predecessor, Data Mining (DM). DM is the process of finding previously unknown, profitable and useful patterns embedded in data, with no prior hypothesis [Symeonidis and Mitkas 2005]. It is the process of analyzing data from different perspectives, summarizing it into useful information and finding correlations or patterns among datasets in large relational databases. The objective of DM is to use the discovered patterns to help explain current behavior or to predict future outcome. DM borrows some concepts and techniques from several longestablished disciplines viz. Artificial Intelligence, Database Technology, Machine Learning and Statistics. The field of DM has, over the past couple of decades, produced a rich variety of algorithms that enable computers to learn new relationships/knowledge from large datasets. Although DM is a relatively new term, the technology is not. It has witnessed a considerable growth of interest over the last couple of years, which is a direct consequence of the rapid development of the information industry. Historically, Data Mining has evolved into a mainstream technology because of two complementary, yet antagonistic phenomena: the data deluge, fueled by the matured database technology and the development of advanced automated data collection tools such as the Logging While Drilling (LWD) and the Measurement While Drilling (MWD); and the starvation for knowledge, defined as the need to filter and interpret all these massive data volumes stored in huge databases, data warehouses and other information repositories. DM can be thought of as the logical succession to Information Technology (IT). Figure 1 summarizes the evolution of DM over the past 50 years. The DM process entails the application of various techniques to a dataset, in order to extract specific patterns and to evaluate them on the data. The process is iterative and interactive as shown in figure 2. Machine Learning. Machine learning is a scientific aspect of AI that is concerned with the design and development of algorithms that allow computers to learn based on data, such as from log or core datasets. A major focus of machine learning research is to automatically learn to recognize complex patterns and make intelligent decisions based on data. Hence, machine learning is closely related to fields such as statistics, probability theory, data mining, pattern recognition, artificial intelligence, adaptive control, and theoretical computer science. The relationship between Machine Learning and other technologies, including Data Mining, is shown in figure 3. The three common algorithms of machine learning are supervised, unsupervised and hybrid. Supervised learning is the type of machine learning technique in which the algorithm generates a function that maps inputs to the desired outputs with the least error. Examples are the prediction of permeability; and the identification of lithofacies and rock types. Unsupervised learning is the machine learning technique in which a set of inputs are analyzed without the target output. This is called clustering. The hybrid learning is the machine learning technique that combines the supervised and unsupervised techniques to generate an appropriate function. The computational analysis of machine learning algorithms and their performance is a branch of theoretical computer science known as computational learning theory.
Data Collection (60’s)
Data Warehousing (90’s)
Data Management (70’s)
Data Access (80’s)
Data Mining (00’s)
Figure 1: Summary of the Evolution of Data Mining [Symeonidis and Mitkas 2005].
Figure 2: The Data Mining Process [Symeonidis and Mitkas 2005].
Figure 3: Data Mining and other technologies [Symeonidis and Mitkas 2005].
Hybrids of Artificial Intelligence Techniques. An approach resulting from the combination of two or more approaches is called a Hybrid. The combination of two or more AI techniques is called Hybridization or, more technically, Hybrid Computational Intelligence (HCI). It has also been defined as an approach that combines different theoretical backgrounds and algorithms such as data mining and soft computing methodologies. This process of combining the results of multiple computational intelligence techniques to produce a single technique is becoming popular. The increased popularity of hybrid intelligent systems in recent times lies in their extensive success in many real-world complex problems, especially in oil and gas. A key prerequisite for the merging of technologies, techniques and methodologies is the existence of a "common denominator" to build upon such as the inference procedures and excellent predictive capabilities exhibited by the techniques. There are various published reports that have shown that hybrid techniques offer more powerful tools than the existing techniques for further improvement of the prediction of permeability and other reservoir properties. Such publications include [Anifowose F. and AbdulRaheem A. 2010]. Ensembles of Artificial Intelligence Techniques. Ensemble learning is a machine learning paradigm where multiple learners are trained to solve the same problem. In contrast to ordinary machine learning approaches that try to learn one hypothesis from the training data, ensemble methods attempt to construct a set of hypotheses and combine them. Techniques emanating from this concept are called Ensembles. Learners composed of an ensemble of the technique are usually called base learners. Ensembles, though more recent than hybrids, have gained almost equal interest in various application areas. However, very little studies have been done on it in the field of reservoir characterization. Some of the few references available are those of [Yu et al. 2007 and Wang et al. 2011]. Some Artificial Intelligence Techniques. There are many AI techniques. For the benefit of oil and gas professionals, this discussion will be limited to only those that have been widely applied in the petroleum field. This includes Artificial Neural Networks (ANN), Fuzzy Logic (FL) and Support Vector Machine (SVM). Artificial Neural Networks. The development of ANN has been inspired by the workings of the human brain. As such, there have been many attempts to artificially simulate the biological processes that lead to intelligent behavior. ANN is a close emulation of the biological nervous system. Figure 4 shows a simplified diagram of a neuron. Just as a biological neuron acts as an integrator of the multiple excitatory and inhibitory inputs it receives at the cell body and dendrites, combining all these sum of inputs and excitations; and sending the result down the axon to other neurons and muscles, in the form of a pulse, the Artificial Neural Network's neuron multiplies the inputs by weights, calculates the sum, applies a
threshold, and transmits the result of the computation to subsequent neurons. Basically, the ANN has been generalized to:
(1)
where xk are inputs to the neuron i, wik are weights attached to the inputs, μi is a threshold, offset or bias, f (·) is a transfer function and yi is the output of the neuron. The transfer function f (·) can be any of linear, non-linear, piece-wise linear, sigmoidal, tangent hyberbolic, and polynomial functions. A simple ANN framework is shown in figure 5.
Figure 4: The Structure of a Biological Neuron [Petrus, Thuijsman and Weijters 1995]
There are different versions of ANN, depending on which algorithm is used at the summation stage. Some of these are: Probabilistic Neural Networks, Generalized Regression Neural Networks, Multi-Layer Perceptron Neural Networks and the popular Feed-Forward Back-Propagation Neural Networks. A very good reference on the architecture and mathematical foundations of ANN is [Demuth, Beale and Hagan 2009].
Figure 5: The Structure of an Artificial Neural Network [Petrus, Thuijsman and Weijters 1995]
Fuzzy Logic. The basic objective of Fuzzy Logic is to map the input space to an output space, and the primary mechanism for doing this is a list of "if-then" statements called rules. All rules are evaluated in parallel, and the order of the rules is unimportant. The rules themselves are useful because they refer to variables and the adjectives that describe those variables. Before a system that interprets rules can be built, all the variable terms to be used and the adjectives that describe them must be defined. With the traditional fuzzy logic system, now known as Type-1 Fuzzy Logic System (T1FLS), it has become very difficult and insufficient to solve certain very complex problems. This gave rise to the introduction of Type-2 Fuzzy Logic Systems (T2FLS). T2FLS was introduced by Zadeh [Karnik and Mendel 1999] as an extension of the concept of T1FLS. T2FLS have grades of membership that are themselves fuzzy. For each value of the primary variable (e.g. gamma ray and water saturation), the membership is a function (not just a point value). This is the secondary Membership Function (MF), whose domain, the primary membership, is in the interval [0,1], and whose range, secondary grades, may also be in [0,1]. Hence, the MF of a T2FLS is threedimensional, and the new third dimension provides new design degrees of freedom for handling uncertainties. Such sets are useful in circumstances where it is difficult to determine the exact MF for a FLS, as in modeling a word. Fig. 6 shows the structure of a T2FLS. It is similar to T1FLS except that the out-processing block contains the defuzzifier. The fuzzifier maps the crisp input into a fuzzy set for the purpose of inferencing. The inference process in T2FLS combines rules and gives a mapping from input to output T2FLS. To do this, one needs to find unions and intersections of Type-2 sets, as well as compositions of Type-2 relations. Extended versions, called Zadeh’s extensions, can be used to give a T1FLS. Since the operation takes one from Type-2 output sets of the FLS to Type-1 sets, this operation can be called “Type Reduction” which is used to produce a “type-reduced set”. To obtain a crisp output from Type-2 FLS, the type-reduced set needs to be defuzzified. The most natural way of doing this is to find the centroid of the type-reduced set. The distinction between Type-1 and Type-2 is associated with the nature of membership functions, which is not important while forming rules. Hence, the structure of rules remains the same in Type-2. A very good reference on the architecture and mathematical foundations of T2FLS can be found in [Karnik and Mendel 1999; and Mendel 2003].
Figure 6: Structure of a Type-2 Fuzzy Logic System [Mendel 2003].
Support Vector Machine. According to the succinct work of [Anifowose 2009], Support Vector Machines (SVMs) are a set of related supervised learning methods used for classification and regression. They belong to a family of Generalized Linear Classifiers. They can also be considered as a special case of Tikhonov Regularization (known as ridge regression in statistics) which is the most commonly used method of regularization of ill-posed and non-linear least-squares problems. SVMs map input vectors to a higher dimensional space where a maximal separating hyperplane is constructed [El-Sebakhy et al. 2007]. This is shown in figure 7. The generalization ability of SVMs is ensured by the special properties of the optimal hyperplane that maximizes the distance to training examples in a high dimensional feature space. SVMs were initially introduced for the purpose of classification until 1995 when Vapnik et al., as reported by [El-Sebakhy et al. 2007], developed a new ε-sensitive loss function technique that is based on statistical learning theory, and which adheres to the principle of structural risk minimization, seeking to minimize an upper bound of the generalization error. This new technique is called Support Vector Regression (SVR). It has been shown to exhibit excellent performance. A very good reference on the architecture and mathematical foundations of SVM can be found in [Cristianini and Shawe-Taylor 2000].
Regression in Primal Space
Y
Training Dataset
X Figure 7: Mapping input vectors to a higher dimensional space in SVM.
General Framework. Irrespective of the AI technique used, the basic framework of AI application in any field, especially in oil
and gas, involves preparing the data with identified variables and target (such as gamma ray, porosity, density, neutron and water saturation logs); dividing the input data into training and testing/evaluation sets; designing and building the AI model; training the model with the training dataset and passing the validation dataset through the trained model to get the predicted output. Figure 8 summarizes the framework.
Learning
Gamma Ray
Training Set
Porosity Log AI
Density Log
Input Dataset
Trained AI
Testing Set Water Saturation
Predicted Permeability
Model Validation
Figure 8: Framework for Artificial Intelligence Application
Few Cases of Artificial Intelligence Applications A good number of studies have been carried out on the use of various AI techniques to predict the characteristics of oil and gas reservoirs such as depth, temperature, pressure, volume, drive mechanism, structure and seal, diagenesis, well spacing, well-bore integrity, porosity and permeability. Few of these cases will be discussed.
Prediction of Permeability. [Anifowose and AbdulRaheem 2010] applied two hybrid models of Functional Networks, Fuzzy Logic and Support Vector Machines to predict the porosity and permeability of some pacific and middle eastern oil and gas reservoirs. The results showed the hybrid models performed better than using the individual techniques. [Al-Fattah 2007] presented a modeling technology to predict accurately the water-oil relative permeability of giant Saudi Arabian carbonate oil fields reservoirs using ANN. The results showed excellent agreement with the experimental data. A fuzzy model was applied by [Shokir 2006] for permeability estimation in heterogeneous sandstone oil reservoirs using core porosity and gamma ray logs. The results showed that the fuzzy model's prediction was accurate and in perfect agreement with the measured core permeability. Estimation of PVT Properties. An implementation of ANN, Support Vector Regression and Functional Networks was used to predict the Pressure-Volume-Temperature (PVT) properties of crude oils. Instead of the usual single or multi-data point prediction that is described by a curve, the approach predicted PVT over a specified range of required reservoir pressures. The shapes of the predicted curves were smooth and consistent with the experimental curves [Oloso et al. 2010]. [Goda et al. 2003] applied ANN technique in the determination of PVT parameters in the estimation of the bubble point pressure of a reservoir. A comparative study between the performance of ANN and other published correlations showed an excellent response with smaller absolute relative average errors, and higher correlation coefficients for the designed networks among all
correlations. In their study, [Gharbi and Elsharkawy 1999] presented ANN-based models for the prediction of PVT properties of crude oils from a middle east reservoir. The models were able to predict the bubble point pressure and the oil formation volume factor as a function of the solution gas-oil ratio, gas specific gravity, oil specific gravity and temperature. The results produced by the models were comparable with those predicted by other correlations. Detection of Drilling Problems. [Marana et al. 2010] presented an ANN model with Multi-Layer Perceptron (ANN-MLP) and Support Vector Machines (SVM) for the classification of drill cuttings using image processing technology in order to detect stuck pipe problems. The results identified ANN-MLP and SVM as candidate promising techniques for further study. In another study, [Miri et al. 2007] presented two different types of ANN model: Multi-Layer Perceptron (MLP) and Radial Basis Functions (RBF), that provided solutions for problems associated with differential pipe sticking using a stuck pipe database of 64 side-tracked and horizontal wells drilled in a reservoir section. The power of ANN to model the dynamic behavior of the non-linear, multi-input drilling system was demonstrated by [Dashevskiy et al. 1999]. They utilized drilling parameters such as hook load, revolution per minute (RPM), flow rate, mud density, viscosity, etc. to predict the occurrence of a stuck pipe. Estimation of Hydraulic Flow Units. [Kadkhodaie-Ilkhchi and Amini 2009] used a hybrid neuro-fuzzy approach to estimate the hydraulic flow units and well log responses using Porosity-Permeability relationships to characterize heterogeneous reservoir rocks in the mixed carbonate-clastic Asmari Formation of the Ahwaz oilfield in Southern Iran. The results of the study showed that the Fuzzy logic model was successful in modeling flow units from well logs at well locations for which no core data was available. Presently, there is a work in progress by the author on the estimation of hydraulic flow units of some carbonate reservoirs in the Middle East through the prediction of permeability. It was observed that the application of AI is very new in the estimation of Hydraulic Flow Units. This explains the scarcity of much published work in this area. Conclusion This paper has succinctly presented the application of Artificial Intelligence techniques in the modeling and characterization of oil and gas reservoir properties as a white box. The two sides of the argument were presented and the "whitening process" was carried out by giving an overview of Artificial Intelligence and the concepts associated with it such as machine learning and data mining. A review of some of the popular techniques was given and few examples of their successful implementations in oil and gas were briefly presented. The recent developments of hybrids and ensembles of AI techniques were also discussed. This paper has provided a better understanding of the basic concepts of AI and has offered a strong background to develop the keen interest of oil and gas professionals who may wish to enter the exciting world of AI and its applications, especially in the oil and gas industry, and has sought to increase the necessary synergy required for a multi-disciplinary collaboration among computer scientists, mathematicians and petroleum engineers to ensure the delivery of the future AI-driven/AI-assisted reservoir models for better exploration, production and management of petroleum resources. Acknowledgement The author would like to thank the SPE Young Professional and Student Outreach committee for the encouragement to prepare this paper. The support provided by King Abdulaziz City for Science and Technology (KACST) through the Science & Technology Unit at King Fahd University of Petroleum & Minerals (KFUPM) for funding this work under Project No. 08-OIL824 as part of the National Science, Technology and Innovation Plan, is also appreciated
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