Potential Applications for Artificial Intelligence in the Petroleum Industry

Abstract

Summary This article clarifies some concepts of artificial intelligence (AI), discusses some of its applications, and demonstrates its potential applicationin the petroleum potential application in the petroleum industry. AI is dividedinto two levels: the psychological, where it attempts to represent knowledgeexplicitly, and the intuitive, where explication of knowledge is not importantand the emphasis is on brain architecture. Expert systems, which implementexplicit knowledge, are discussed in more detail. A brief discussion of use of AI in Brazil, particularly at Petrobras, is presented. Petrobras, is presented. Introduction AI has been, and will be, a controversial subject because it involves suchtopics as human intelligence, knowledge, brain architecture, and things that wehumans do not yet comprehend. Furthermore, the knowledge of this science, itstechniques, and its applications is dominated by a few people. Therefore, AIgenerally appears like a science fiction story to outsiders. AI has beendefined in several ways, but each definition aims at the same thing: thecomputer has to be more intelligent. This means that the computer has to beable to imitate the human being performing what we consider 'intelligent tasks. This attempt to duplicate human intelligence in a machine requires anenrichment of the actual ability of the computer to perform fast calculationswith the reasoning and learning mechanisms common in our natural intelligence. These mechanisms are very complex, and we know very little about them. Althoughthere have been great advances in AI techniques, the intended reproduction ofnatural intelligence in its abundance is very difficult, if not impossible. Because of this complexity, AI research is proceeding in two directions. One isthe pure science concerned with the pure science concerned with theunderstanding and the perfect reproduction of the true mechanisms of naturalintelligence. The other is the engineering side that seeks to devise datastructures and algorithms that, although they do not copy the naturalmechanism, are good enough to reproduce some aspects of natural intelligence. These data structures and algorithms are used to perform some "learningability" and to perform some "learning ability" and to representreasoning through a limited and specific knowledge base. The latter approachhas shown more practical and profitable results. With this in mind and withoutbeing pessimistic, I believe that the reproduction of pessimistic, I believethat the reproduction of human intelligence is far from being achieved. Onlytime will reveal how and when this could happen, but there are those among uswho visualize the unknown more quickly and will accelerate achievement of thegoal of AI. Duda gives a more academic definition of AI: "AI is the subfield ofcomputer science concerned with the use of computers in tasks that are normallyconsidered to require knowledge, perception, reasoning, learning, understandingand similar cognitive abilities." Thus, the goal of AI is a qualitativeexpansion of computer capabilities. If the parameters Duda uses to define AIare accepted as adequate (humans have them all at a high level), then thesefactors, when incorporated into computer programs, reveal intelligence. Amongthe most-cited applications of AI are computer vision, intelligent robots, natural language interpretation, game theory, automatic theorem proving, andexpert systems. Among the problems to be solved by AI are diagnosis, planning, design, prediction, interpretation, monitoring, debugging, prediction, interpretation, monitoring, debugging, repair, control, and instruction. AI History In 1956, 10 scientists in a conference at Dartmouth C. delineated what todayis called artificial intelligence (AI). Those scientists assumed thatintelligence was based primarily on reasoning techniques and that human beings, because of their intelligence, would easily reproduce it in a computer. Theypredicted that in 25 years we would be involved only in recreationalactivities, while computers would be doing all the hard work. AI has proved tobe more complex than originally expected. However, the AI efforts of thatperiod were not without merit. Many new things were learned that havecontributed to AI's success today. It was learned that knowledge is veryimportant to the intelligence. Perception, both visual and in language, isbased on knowledge that is found to be cumulative, voluminous, and hard tocharacterize. An example is "common sense, found to be simple reasoningbased on a great amount of experimental knowledge. During the first 15 years, AI had few successes. Automatic machine translation was attempted, but withlittle success. JPT P. 1306

A Knowledge-Based System for Optimal Economic Design of Improved Recovery Processes

“…39 Readers interested in the rules used for EOR method selection and simulator's input preparation should consult the paper. Once steps (1) and (2) are executed, the process of optimizing the selected EOR process starts. Unlike the previous paper, the optimization process by sensitivity analysis is combined with an economic model in order to obtain a design that will yield optimum project profitability.…”

“…The economic analysis is based on the estimated recovery, residual oil inplace, oil price, and operating costs. The expert system is able to perform three consultations: (1) EOR selection process on the basis of the reservoir characteristics, (2) input data sets preparation for designing the EOR process, and (3) performing sensitivity analysis and application of discounted-cash-flow method to optimize the net present value of the selected EOR process. Two three-dimensional, finite difference reservoir simulators are connected to the expert system.…”

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