Shahab D. Mohaghegh

Virtual-Intelligence Applications in Petroleum Engineering: Part 1—Artificial Neural Networks

This is the first article of a three-article series on virtual intelligence and its applications in petroleum and natural gas engineering. In addition to discussing artificial neural networks, the series covers evolutionary programming and fuzzy logic. Intelligent hybrid systems that incorporate an integration of two or more of these paradigms and their application in the oil and gas industry are also discussed in these articles. The intended audience is the petroleum professional who is not quite familiar with virtual intelligence but would like to know more about the technology and its potential. Those with a prior understanding of and experience with the technology should also find the articles useful and informative.

Design and development of an artificial neural network for estimation of formation permeability

Permeability is one of the most important characteristics of hydrocarbon-bearing formations and one of the most important pieces of information in the design and management of enhanced recovery operations. With accurate knowledge of permeability, petroleum engineers can manage the production process of a field efficiently. Although formation permeability is often measured in the laboratory from cores or evaluated from well-test data, core analysis and well-test data are only available from a few wells in a field, while the majority of wells are logged. In this study, the authors have designed an artificial neural network that can accurately predict the permeability of the formations by use of the data provided by geophysical well logs. Artificial neural network, a biologically inspired computing method which has an ability to learn, self-adjust, and be trained, provides a powerful tool in solving pattern recognition problems.

Petroleum reservoir characterization with the aid of artificial neural networks

Abstract We introduce a new application of artificial neural network technology in the characterization of reservoir heterogeneity. Different reservoir properties, such as porosity, permeability and fluid saturation, in highly heterogeneous formations can be predicted with good accuracy using information deduced from readily available geophysical well logs. The methodology by which this is carried out is based on the intelligent and adaptive pattern recognition capabilities of an artificial neural network (three-layer feed forward, back propagation). The need for expensive processes to acquire porosity, permeability and fluid saturation data (such as well testing and extensive coring of the formation) may therefore be greatly reduced. Examples of several neural networks developed during this study are presented.

State-Of-The-Art in Permeability Determination From Well Log Data: Part 1- A Comparative Study, Model Development

This study discusses and compares, from a practical point of view, three different approaches for permeability determination from logs. These are empirical, statistical, and the recently introduced virtual measurement methods. They respectively make use of empirically determined models, multiple variable regression, and artificial neural networks. All three methods are applied to well log data from a heterogeneous formation and the results are compared with core permeability, which is considered to be the standard. In this first part of the paper we present only the model development phase in which we are testing the capability of each method to match the presented data. Based on this, the best two methods are to be analyzed in terms of prediction performance in the second part of this paper.

Recent Developments in Application of Artificial Intelligence in Petroleum Engineering

With the recent interest and enthusiasm in the industry toward smart wells, intelligent fields, and real-time analysis and interpretation of large amounts of data for process optimization, our industry’s need for powerful, robust, and intelligent tools has significantly increased. Operations such as asset evaluation; 3D- and 4D-seismicdata interpretation; complex multilateral-drilling design and implementation; log interpretation; building of geologic models; well-test design, implementation, and interpretation; reservoir modeling; and simulation are being integrated to result in comprehensive reservoir management. In recent years, artificial intelligence (AI), in its many integrated flavors from neural networks to genetic optimization to fuzzy logic, has made solid steps toward becoming more accepted in the mainstream of the oil and gas industry. In a recent set of JPT articles,1‐3 fundamentals of these technologies were discussed. This article covers some of the most recent and advanced uses of intelligent systems in our industry and discusses their potential role in our industry’s future.

A Methodological Approach for Reservoir Heterogeneity Characterization Using Artificial Neural Networks

This paper was selected for presentation by an SPE Program Committee following review o 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 th e author(s). The material, as presented, does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Papers presented at SPE meetings are subject to publication review by Editorial Committees of the Society of Petroleum Engineers. Permission to copy is restricted to an abstract of not more than 300 words. Illustrations may not be copied. The abstract should contain conspicuous acknowledgment of where and by whom the paper is presented. Write Librarian, SPE, P.O. Box 833836, Richardson, TX 75083-3836, U.S.A. Telex, 163245 SPEUT. logging tools became available. These calculations assume a linear

Development of Surrogate Reservoir Model (SRM) for fast track analysis of a complex reservoir

Reservoir simulation is the industry standard for reservoir management that is used in all phases of field development. As the main source of information, prediction and decision-making, the Full Field Models (FFM) is regularly updated to include the latest measurements and interpretations. A typical FFM consists of large number of grid blocks and usually takes hours for each run. This makes comprehensive analysis of the solution space and incorporation of the FFM in smart fields impractical. Surrogate Reservoir Models (SRMs) are introduced as a bridge to make Real-Time Reservoir Management possible. SRMs are replicas of FFM that can run in fractions of a second. They accurately mimic the capabilities of FFM and are used for automatic history matching, real-time optimisation, real-time decision-making and quantification of uncertainties. This paper presents the development of SRM using the state of the art Artificial Intelligence and Data Mining (AID Accepted: November 4, 2008]

Virtual-Intelligence Applications in Petroleum Engineering: Part 2—Evolutionary Computing

The goal of this second article is to provide an overview of evolutionary computing, its potential combination with neural networks to produce powerful intelligent applications, and its applications in the oil and gas industry. The most succesful intelligent applications incorporate several virtual-intelligence tools in a hybrid manner. Virtual-intelligence tools complement each other and are able to amplify each others effectiveness. This article also presents the background of evolutionary computation as related to Darwinian evolution theory. This is followed by a more detailed look at genetic algorithms, the primary evolutionary-computing paradigm currently used. The article concludes by exploring application of a hybrid neural network/genetic algorithm system to a petroleum-engineering-related problem.

Virtual-Intelligence Applications in Petroleum Engineering: Part 3—Fuzzy Logic

The focus of this article is fuzzy logic. The article provides overview of the subject and its potential application in solving petroleum-engineering-related problems. As the previous articles, the most successful applications of intelligent systems, especially when solving engineering problems, have been achieved by use of different intelligent tools in concert and as a hybrid system. This article reviews the application of fuzzy logic for restimulation-candidate selection in a tight-gas formation in the Rocky Mountains. We chose this particular application because it uses fuzzy logic in a hybrid manner integrated with neural networks and genetic algorithms.

Quantifying Uncertainties Associated With Reservoir Simulation Studies Using a Surrogate Reservoir Model

Reservoir simulation is routinely used as a reservoir management tool. The static model that is used as the basis for simulation is the result of an integrated effort that usually includes the latest geological, geophysical and petro-physical measurements and interpretations. As such, it is inherently a model with some uncertainty. Analysis of these uncertainties and quantification of their effects on oil production and water cut using a new and efficient technique is the subject of this paper. Typical uncertainty analysis techniques require many realizations and runs of the reservoir model. In the day and age that reservoir models are getting larger and more complicated, making hundreds or sometimes thousands of simulation runs can put considerable strain on the resources of an asset team. This paper summarizes the results of uncertainty analysis on a giant oil field in the Middle East using a new technique that incorporates a Surrogate Reservoir Model (SRM). A Surrogate Reservoir Model that runs and provides results in real-time is developed to mimic the capabilities of a full field model that includes about one million grid blocks and takes 10 hours to run on a cluster of twelve 3.2 GHz CPUs. This Surrogate Reservoir Model is used as the objective function of a Monte Carlo Simulation to study the impact of the uncertainties associated with several parameters on the model outcome, i.e. oil production and water cut is analyzed. The analysis can be performed individually on each of the 165 horizontal wells. During the analyses of uncertainty, the Surrogate Reservoir Model will serve as an objective function for the Monte Carlo Simulation. In this study, uncertainties associated with several reservoir parameterts and their quantitative effect on cumulative oil production and stantaneous water cut are examined.