Potential Applications for Artificial Intelligence in the Petroleum Industry
- Lideniro Alegre (Petrobras)
- Document ID
- Society of Petroleum Engineers
- Journal of Petroleum Technology
- Publication Date
- November 1991
- Document Type
- Journal Paper
- 1,306 - 1,309
- 1991. Society of Petroleum Engineers
- 4.2.3 Materials and Corrosion, 7.6.6 Artificial Intelligence, 5.1.7 Seismic Processing and Interpretation, 5.1.2 Faults and Fracture Characterisation, 5.6.4 Drillstem/Well Testing, 1.8 Formation Damage, 2 Well Completion, 4.1.2 Separation and Treating, 5.6.1 Open hole/cased hole log analysis, 4.1.5 Processing Equipment, 6.5.5 Oil and Chemical Spills, 4.2 Pipelines, Flowlines and Risers, 1.6 Drilling Operations, 5.1.5 Geologic Modeling, 7.6.1 Knowledge Management, 6.1.5 Human Resources, Competence and Training, 3.1.1 Beam and related pumping techniques
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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 ofAI 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.
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.
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