Selection of candidate wells for re-fracturing in tight gas sand reservoirs using fuzzy inference
Emre Artun, B. Kulga
Abstract
An artificial-intelligence based decision-making protocol is developed for tight gas sands to identify re-fracturing wells and used in case studies. The methodology is based on fuzzy logic to deal with imprecision and subjectivity through mathematical representations of linguistic vagueness, and is a computing system based on the concepts of fuzzy set theory, fuzzy if-then rules, and fuzzy reasoning. Five indexes are used to characterize hydraulic fracture quality, reservoir characteristics, operational parameters, initial conditions, and production related to the selection of re-fracturing well, and each index includes 3 related parameters. The value of each index/parameter is grouped into three categories that are low, medium, and high. For each category, a trapezoidal membership function all related rules are defined. The related parameters of an index are input into the rule-based fuzzy-inference system to output value of the index. Another fuzzy-inference system is built with the reservoir index, operational index, initial condition index and production index as input parameters and re-fracturing potential index as output parameter to screen out re-fracturing wells. This approach was successfully validated using published data.