Dynamic Uncertain Causality Graph Applied to the Intelligent Evaluation of a Shale-Gas Sweet Spot

Shale-gas sweet-spot evaluation as a critical part of shale-gas exploration and development has always been the focus experts scholars in unconventional oil gas field. After comprehensively considering geological, engineering, economic factors affecting sweet spots, dynamic uncertainty causality graph (DUCG) is applied for first time to evaluation. A graphical modeling scheme presented reduce difficulty model construction. The based on expert knowledge does not depend data. Through rigorous efficient reasoning, it guarantees exact diagnostic reasoning case incomplete information. Multiple conditional events weighted graphs are proposed specific problems evaluation, which an extension DUCG that defines only one event different function relies experience single expert. These solutions make process results more objective, credible, interpretable. verified with both complete data show compared other methods, this methodology achieves encouraging accuracy effectiveness. This study provides promising auxiliary tool spot