A decision support engine for infill drilling attractiveness evaluation using rule-based cognitive computing under expert uncertainties

Abstract Optimally drilling new wells in a developed reservoir is an essential strategy to potentially tap remaining oil for a complete life circle of oilfield development. Further, the determination of optimal infill drilling targets is a challenging issue which involves the integration of data, experts' knowledge and human decisions. The decision process can be essentially regarded as a systematic evaluation of drilling attractiveness. To automate drilling attractiveness evaluation, we develop a decision support engine using rule-based cognitive computing to rank and recommend drilling candidates. Such drilling candidates are chosen by the quantification of regional drilling attractiveness. Then we use two cases with different settings to show its general applicability and human-like reasoning abilities. The reasoning process considers expertise and human-involved uncertainties. The expertise is characterized by certain representation of fuzzy rules sets. Our results highlight the potential of our recommendation engine in pinpointing the most productive drilling location. And our method avoids the expensive reservoir simulation runs. Moreover, fuzzy drilling attractiveness evaluation can serve as an alternative initialization method of model-based infill well optimization, which avoids local optimum problem and greatly saves iteration time. Our approach extends human's reasoning capability and accelerates human's decision-making process with very low computational cost.