Refracturing candidate selection relates to the quantitative evaluation of factors for individual members as well as to their comprehensive influence. As a result, it becomes extremely difficult and knowing the key factors that control the refracturing effect is particularly important for tight oil reservoirs development. A new hybrid method combining analytic hierarchy process (AHP), grey relation analysis (GRA) and reservoirs numerical simulation (RNS) was proposed in this study to solve this problem. First, the multi-level evaluation system was established by using AHP to cope with complex relationships between the uncertain parameters. Then, GRA was used to calculate comprehensive evaluation factor (CEF) which considers both the reservoir quality and initial completion efficiency. Based on this, quadrant analysis was applied to determine the priority of the refracturing wells. Finally, RNS was used to further evaluate reservoirs energy of the optimum refracturing wells. The hybrid method has been successfully applied to 26 wells of the tight oil fields in Northeast China. Average daily oil production was increased from 2.5 tons/day to 16.5 tons/day after refracturing treatment. The research results can guide the development of tight oil and gas reservoirs effectively.

Keywords:
Artificial Intelligence, hydraulic fracturing, Engineering, shale gas, Upstream Oil & Gas, complex reservoir, reservoir quality, initial completion efficiency, candidate selection, oil production
Subjects:
Hydraulic Fracturing, Unconventional and Complex Reservoirs, Shale gas, Re-fracturing
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2019, Unconventional Resources Technology Conference (URTeC)
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