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Soft computing for qualitative and quantitative seismic object and reservoir property prediction. Part 3: Evolutionary computing and other aspects of softGreen Open Access

Author: F. Aminzadeh
Journal name: First Break
Issue: Vol 22, No 6, June 2004
Language: English
Info: Article, PDF ( 723.15Kb )

This is the third part of the series of review papers on soft computing applications in the petroleum industry. In this paper we will focus on evolutionary computing, with related topics such GA (genetic algorithms), genetic engineering, genome, DNA, artificial life and emergence intelligence. We will begin with a brief overview of evolutionary computing technology. We will also give an overview of GA in exploration and production (E&P). We will then highlight some recent applications of genetic algorithms in various aspects of hydrocarbon E&P. These include applications in production optimization, reservoir characterization, and permeability prediction. We will also propose a framework for the more effective use of GA (Genome) as well as likely applications of ‘complexity theory’ in seismic exploration. Introduction Evolutionary computing techniques cover a large spectrum of related technologies. Among them are: GA, genetic engineering, genome, DNA and emergence intelligence. These technologies are already having a profound impact on many areas. Most notably, human genome has already found practical applications in life sciences (e.g. medicine and pharmaceutical industry). Figure 1, from the US Department of Energy’s human genome initiative shows the link between DNA and life. Some of these methods have been used on their own or in conjunction with other soft computing methods in many aspects of geosciences and hydrocarbon exploration and production problems. Most GA algorithms have been used as a means of efficient optimization. They also have been used to discover and extract knowledge or rules, especially when a large body of information has to be searched. Limited applications have used genome or DNA type concepts to categorize rock formations, recognize seismic patterns or describe the sedimentation process. These are the most promising application of evolutionary computing for hydrocarbon exploration. The rest of this introductory section gives an overview of evolutionary computing. It also gives an overview of some of these methods in the petroleum industry. The rest of the paper highlights some recent applications in E&P, and provides a brief description of complexity theory which is expected to have many applications in exploration.

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