Over the last two decades, there has been significant activity in the soft computing arena with focus on various computer paradigms such as neural networks, genetic algorithms, and fuzzy logic to more efficiently solve complex engineering problems. Further work concentrated on integrating two or more of these paradigms and led to what is known as "hybrid systems". The power of the hybrid system relies on the fact that technologies are intended to complement each other and exploit their individual strengths to enhance solution generation. Case-based reasoning (CBR) is another soft computing technology developed to deal with uncertainty, approximate reasoning and exploit knowledge domain. Case-based reasoning, also known as computer reasoning by analogy, is a simple and practical technique that solves new problems by comparing them to ones that have already been solved in the past, thus saving time and money. This paper provides a general framework of case-based reasoning along with a review of the four-step cycle that characterizes the 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 job execution, utilizes a rule-based system to make adaptations, and then suggests the most appropriate solution for new well fracturing candidates. The technique was tested as a front-end tool using sample data from a tight gas field with significant hydraulic fracturing activity. This simple case demonstrates how case-based reasoning can be applied to improve hydraulic fracturing design, planning and execution of wells, thus significantly increasing the job execution success while avoiding known pitfalls. In addition, the work demonstrates the value of captured "on-site" experience and shows the advantages of using intelligent systems in decision-making. (C) 2011 Elsevier B.V. All rights reserved.
