Recent increase in global demand for energy and the consequent high prices have prompted a need for improving the recovery from mature reservoirs. Identifying sweet spots in these fields for in-fill drilling and ranking the infill locations based on their potential productivity as well as underperformer wells as candidates for remedial operations are important for improving the economics of mature fields.

One of the most important issues that make analysis of mature fields quite challenging is lack of data. Production rate data is about the only data that can be easily accessed for most of the mature fields. The most accessible production data usually does not include flowing bottom-hole or well head pressure data. Lack of pressure data seriously challenges the use of conventional production data analysis techniques for most of the mature fields. The motivation behind development of the techniques that are presented in this study is to demonstrate that much can be done with only monthly production rate data in order to help the revitalization of mature fields.

Methods currently used for production data analysis are decline curve analysis, type curve matching, and history matching using numerical reservoir simulators. Each one of these methods has its strengths and weaknesses. They include significant amount of subjectivity when they are used individually in the context of production data analysis.

In this paper, intelligent systems are used in order to iteratively integrate the abovementioned techniques into one comprehensive methodology for identification of infill drilling locations as well as underperformer wells that would be the prime candidates for restimulation and/or workovers. Application of this technique to a large number of wells in the Carthage field, Cotton Valley formation is presented.

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