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 low, medium and high 3 categories. 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.
Translated title of the contribution | Selection of candidate wells for re-fracturing in tight gas sand reservoirs using fuzzy inference |
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Original language | Chinese (Traditional) |
Pages (from-to) | 383-389 |
Number of pages | 7 |
Journal | Shiyou Kantan Yu Kaifa/Petroleum Exploration and Development |
Volume | 47 |
Issue number | 2 |
DOIs | |
Publication status | Published - Apr 23 2020 |
Externally published | Yes |
Keywords
- Artificial intelligence
- Fuzzy logic
- Fuzzy rule
- Horizontal wells
- Hydraulic fracture quality
- Re-fracturing
- Refracturing potential
- Tight gas sands
ASJC Scopus subject areas
- Geotechnical Engineering and Engineering Geology
- Energy Engineering and Power Technology
- Geology