Over the last two decades, there has been significant activity in the soft computing arena with focus on various computerparadigms such as neural networks, genetic algorithms, and fuzzy logic to more efficiently solve complex engineeringproblems. Further work concentrated on integrating two or more of these paradigms has led to what is known as "hybridsystems." The power of the hybrid system relies on the fact that technologies are intended to complement each other andexploit their individual strengths to enhance solution generation.Case-based-reasoning (CBR) is another soft computing technology developed to deal with uncertainty, approximatereasoning and exploit knowledge domain. Case-based reasoning, also known as computer reasoning by analogy, is a simpleand practical technique that solves new problems by comparing them to ones that have already been solved in the past, thussaving time and money.This paper provides a general framework of case-base reasoning along with a review of the four-step cycle that characterizesthe technology (retrieve, reuse, revise and retrain), followed by a specific application to well fracture treatment design,planning and execution. The proposed methodology extracts the relevant historical information recorded during field jobexecution, utilizes a rule-based system to make adaptations, and then suggests the most appropriate solution for new wellfracturing candidates. The technique was tested as a front-end tool using sample data from a tight gas field with significanthydraulic fracturing activity. This simple case demonstrates how case-base reasoning can be applied to improve hydraulicfracturing design, planning and execution of wells, thus significantly increasing the job execution success while avoidingknown pitfalls. In addition, the work demonstrates the value of captured "on-site" experience and shows the advantages ofusing intelligent systems in decision making.
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