Abstract
The selection of appropriate wells for hydraulic fracturing is one of the most important decisions faced by oilfield engineers. It has significant implications for the future development of an oilfield in terms of its productivity and economics. In this study, we developed a fuzzy model for well selection that combines the major objective criteria with the subjective judgments of decision makers. This was done by fusing the analytic hierarchy process (AHP) method, grey theory and an advanced version of fuzzy logic theory (FLT). The AHP component was used to identify the relevant criteria involved in selecting wells for hydraulic fracturing. Grey theory was used to determine the relative importance of those criteria. Then a fuzzy expert system was applied to fuzzily process the aggregated inputs using a Type-2 fuzzy logic system. This undertakes approximate reasoning and generates recommendations for candidate wells. These techniques and technologies were hybridized by using an intercommunication job-sharing method that integrates human judgment. The proposed method was tested on data from an oilfield in Western China and finally the most appropriate candidate wells for hydraulic fracturing were ranked in order of their projected output after fracturing.
Highlights
Fractured reservoirs represent a significant percentage of oil and gas reservoirs throughout the world [1]
In order to enable a clearer understanding of the input data, we first perform an analysis of the correlation coefficients between initial production (IP) and each of the input variables
Neural networks have the ability to infer general rules, extract patterns from a set of examples and recognize input output mappings from complex multi-dimensional field data. These properties give the neural networks the ability to interpolate between typical patterns or data and generalize their learning in order to extrapolate to a region beyond their training domains
Summary
Fractured reservoirs represent a significant percentage of oil and gas reservoirs throughout the world [1]. Over the last few decades, a number of studies have investigated the application of a range of decision support and artificial intelligence techniques and technologies in candidate well selection. These range from decision support systems using multivariate nonlinear regression [4,5,6] to neural networks [7,8,9,10], analytical hierarchy process (AHP) [11,12,13,14,15,16] and fuzzy logic [10,17,18,19,20].
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