Petroleum Industry

Applications of AI and Expert Systems to Oil and Gas Exploration, Production, Refining, ...

AITopics > Applications / Expert Systems > AI Applications Areas > Petroleum Industry

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"[T]he possible use of computing tools for automatic recognition of risk patterns in oil installation is of high value. Risk is always present in submarine, marine, land or underground installations, either at short, medium or long terms. Risks can come from natural forces like earthquakes, hurricanes or volcano eruptions; or due to illegal human settlements. The prevention of such risks could be made at low cost and high precision using intelligent systems able to process the large volume of data with the required accuracy and efficiency. The economic savings and the decrease of risks to humans is possible using such information management tools."
- from the Introduction to the 2nd Workshop on Intelligent Computing in the Petroleum Industry

Robotics for oil & gas platforms. SINTEF Robotic Lab. "The Norwegian oil and gas company StatoilHydro has developed a new concept for a remotely operated oil & gas platform located offshore. The goal is to develop this platform within 2015. In order to support research on robotic and instrumentation systems for this platform concept, StatoilHydro has cooperated with SINTEF and financed a robotic lab facility in Trondheim. ... A remotely operated platform must be equipped with intelligent and reliable robotic and instrumentation systems that enable operators located onshore to monitor and control all processes taking place on the platform."

• The SINTEF Robotic Lab is a project of the Applied Cybernetics, which is a department within the Information and Communication Technology (ICT) Unit. "The term cybernetics refers to the study of control systems and communication systems in living organisms and machines. We apply methods and theories to control and monitor a system such as a robot, a human muscle or a distillation column."

Workshop on Intelligent Computing in the Petroleum Industry:

Artifical Intelligence Promises Major Advance in E&P Technology. By John A. Sullivan. Natural Gas Week (November 28, 2005; subscription req'd.). "In just a few years, an operator sitting at a console in Houston will be able to monitor oil and gas wells around the world, whether they are off the coast of Nigeria, in the middle of a jungle -- or even in the Arctic. It's not the stuff of science fiction, but a concept called the intelligent oilfield, now being developed by IBM through a partnership with the energy industry."

Artificial Intelligence entry in Schlumberger's Oilfield Glossary: "The study of ideas that enable computers to do the things that make people seem intelligent. The term is commonly abbreviated as A.I.Many computer programs written for use in the oil field utilize 'rule based' approaches to provide expert systems. The rules are taken from an expert working in the field and are written in a way that attempts to reproduce the knowledge and approaches used by that expert to solve a range of real problems. ..."

• Also see:
  • Reid G. Smith's Keynote Speech: Artificial Intelligence in the Oilfield: A Schlumberger Perspective. Artificial Intelligence in Petroleum Exploration and Production Conference, Texas A&M University, College Station, TX, USA, May, 1990.
  • The Dipmeter Advisor System - A Case Study In Commercial Expert System Development. By Reid G. Smith and James D. Baker, Schlumberger-Doll Research. In Proceedings of the Eighth International Joint Conference on Artificial Intelligence, Volume 1 (IJCAI-83). [Available from the Digital Library of India].
    • The Design of the Dipmeter Advisor System. By R.G. Smith and R. L. Young. In Proceedings of the ACM Annual Conference, pp. 15-23, New York, October 1984. Abstract: "The Dipmeter Advisor system attempts to emulate human expert performance in an important and specialized oil well-log interpretation task. The system is currently being used in a small number of Schlumberger Field Log Interpretation Centers as an aid to human dipmeter interpreters. In this paper, we describe the problem just enough to establish the vocabulary for discussing the program and the characteristics of the domain. We then present the internal structure of the program and attempt to put it in perspective with respect to first-generation expert systems. We discuss ways in which characteristics of the task domain impose constraints on the design or signal interpretation programs, and attempt to extract knowledge useful to future expert system developers."
    • On the Development of Commercial Expert Systems. By Reid G. Smith. AI Magazine 5(3): Fall 1984, 61-73. "We use our experience with the Dipmeter Advisor system for well-log interpretation as a case study to examine the development of commercial expert system."
• Oil-Weil Data Interpretation Using Expert System and Pattern Recognition Technique. By Alain Bonnet (Ecole Nationale Superieure des Telecommunications) and Claude Dahan (Schlumberger). In Proceedings of the Eighth International Joint Conference on Artificial Intelligence, Volume 1 (IJCAI-83). [Available from the Digital Library of India].

Actenum Corporation: "Actenum develops advanced Asset Scheduling Management (ASM) software solutions that empower enterprises to improve operational performance. Our ASM solutions provide reliable asset scheduling capabilities, simplify complex scheduling processes, and enable effective management of operational disruptions caused by unplanned events.In the real world, schedules have to be flexible, and must be reworked as operational conditions or requirements change. As well, end users want solutions that amplify their abilities, rather than replacing them. Actenum ASM solutions are designed and developed with these needs in mind, using techniques from the Artifical Intelligence and Operations Research fields."

• Read about their Rig Activity Scheduler (RAS).

Intelligent Solutions, Inc. "Recently a three part article has been published in the Society of Petroleum Engineer's Journal of Petroleum Technology - JPT (Distinguished Author Series; September, October and November issues of year 2000) - that explains the theory and application of virtual intelligence techniques to petroleum engineering related problems:"

• Virtual Intelligence and Its Applications in Petroleum Engineering. By Shahab Mohaghegh. Journal of Petroleum Technology, Distinguished Author Series.
  • Part 1: Artificial Neural Networks (September 2000)
  • Part 2: Evolutionary Computing (October 2000)
  • Part 3: Fuzzy Logic (November 2000)

"CiSoft is a USC-ChevronTexaco Center of Excellence for Research and Academic Training on Interactive Smart Oilfield Technologies. The Center was established in December 2003."

MannTall: A Rescue Operations Assistant. Institute for Information Technology National Research Council Canada & Center for Industrial Research (SINTEF). "MannTall is a decision support system for deployment of rescue craft during an emergency on an offshore oil platform. MannTall uses heuristic counting and estimation techniques to compute best- and worst-case scenarios for the locations of the platform crew. MannTall is fully operational as a part of Saga Petroleum's safety and emergency preparedness procedures."

Artificial Intelligence and the Internet: An Integral Part of the Factory. By Marla P. Rogers. Journal of Industrial Technology (Volume 18, Number 2; February 2002 to April 2002). "Texaco has four 3-D Visualization Centers and Giselle Smith (1999) reported on the Houston center. Geologists and geophysicists use the facility to analyze seismic data and predict oil well placement more accurately. Facilities like Texaco's have the potential to dramatically reduce the number of low or non-producing wells drilled."

Applications of Machine Learning. Provided by the Alberta Ingenuity Centre for Machine Learning. See: Oil Industry.

Some Articles from AI in the news:

• Slick technology. The Engineer Online (October 2, 2006).
• What's new in artificial lift. Part 1 - Fifteen new systems for beam, progressing-cavity, plunger-lift pumping and gas lift. By James F. Lea, Herald W. Winkler, and Robert E. Snyder. WorldOil.com (April 2004; Vol. 225 No. 4).
• Intelligent fuzzy system helps offshore platform start-up. "Intelligent systems with a fuzzy controller can play an important role in controlling and managing start-ups of offshore facilities...." By Mario Cesar M. Campos. Oil&Gas Journal March 26, 2001).
• Virtual Prospecting. From oil exploration to neurosurgery, new tools are revealing the secrets hidden in mountains of data. By Otis Port. BusinessWeek Online (March 23, 2001).

The Road Ahead To Real-Time Oil & Gas Reservoir Management. By R.G. Smith and G. C. Maitland. Transactions of the Institution of Chemical Engineers: Chemical Engineering Research and Design, Vol 76A, pp. 539-552, 1998. "This paper presents a vision and road map for how the processes and materials used to extract hydrocarbons from underground reservoirs might evolve over the next decade or so. The goal is to stimulate and provide signposts for the research and development which is needed to meet the industry’s future needs. The target is to double the recovery of hydrocarbon in place from today’s typical values of 30-40%. This will require real-time reservoir management, for which the ability to simulate, monitor and control all the key processes that take place within the reservoir and production system is a fundamental requirement."