Hybridising Human Judgment, AHP, Grey Theory, and Fuzzy Expert Systems for Candidate Well Selection in Fractured Reservoirs

Abstract

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.

Efficiency Evaluation and Policy Analysis of Industrial Wastewater Control in China

“…It has great advantages in avoiding subjective factors, simplifying algorithms and reducing errors. Hence we chose it instead of other alternative assessment methods, such as Analytic Hierarchy Process (AHP) [55][56][57][58], Principal Components Analysis (PCA) [59,60], and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) [61][62][63], etc. In recent years, the DEA model has been widely used in the study of industrial wastewater control efficiency.…”

Dissolved Gas Analysis Principle-Based Intelligent Approaches to Fault Diagnosis and Decision Making for Large Oil-Immersed Power Transformers: A Survey

“…Among them, the most important theory in the grey decision model is the weighted grey target theory. Recently, the weighted grey target theory and GRA model have been extensively adopted in fault diagnosis of the transformer [59][60][61][62][63][64][65][66][185][186][187]. The two are briefly introduced as follows:…”

“…Procedures of GRA Primary Means needs less data for fault diagnosis high precision without prior information good at dealing with the small samples with some information known and some information unknown, as well as the uncertain systems with poor information first construct a comparative sequence then use GRA to calculate grey correlation between comparative sequence and reference sequence lastly determine the actual fault mode according to the calculated grey correlation weighted grey target theory [59][60][61][62][63][64][65][66][185][186][187]190] GRA model [154,188] combined with hot spot temperature [59] combined with fuzzy SVM [154] grey relation entropy [61] grey prediction-clustering analysis [63] improved grey correlation analysis model [191] combined with feedback cloud entropy model [192] combined with improved SVM, cloud entropy model, BN, ML and data mining techniques…”

“…In other words, it is essential to explore the principles, methods and means from various disciplines that are helpful to the fault diagnosis of transformers, so as to make the fault diagnosis technology interdisciplinary. Aiming at the limitations of traditional methods above, with the rapid development of computer technology and artificial intelligence (AI) theory, multiple intelligence techniques, including artificial neural network (ANN) [37][38][39][40][41][42][43][44][45][46], expert system (EPS) [47][48][49][50][51], fuzzy theory [52][53][54][55][56][57][58], rough sets theory (RST) [36], grey system theory (GST) [59][60][61][62][63][64][65][66], and other intelligent diagnosis tools [5, such as swarm intelligence (SI) algorithm, data mining technology, machine learning (ML), mathematical statistics method, wavelet analysis (WA), optimized neural network, Bayesian network (BN), and evidential reasoning approach, have been introduced to the research field of transformer fault diagnosis based on the DGA approach. These intelligent methods make up for the deficiencies of the mentioned traditional DGA methods, and directly or indirectly improve the accuracy of transformer fault diagnosis, and provide a new train of thought for high-precision transformer fault diagnosis.…”

A Novel Method for Idle-Stop-Start Control of Micro Hybrid Construction Equipment—Part B: A Real-Time Comparative Study

“…Grey models are powerful estimation tools to deal with partially unknown systems [17][18][19][20][21]. In this research, the grey prediction model has been built to estimate the ICE coming modes once the machine is repeatedly operated with the same cycle.…”