Hybrid knowledge-driven and data-driven intelligent reservoir characterization and its research progress
Reservoir characterization is a scientific and technical problem full of uncertainty,since oil and gas reservoirs are extremely complex underground systems.The traditional theories and methods of reservoir characterization,mainly driven by knowledge,have inherent limitations and encounter numerous development bottlenecks.In recent years,artificial intelligence,especially deep learning,has been widely applied in reservoir characterization research,and breakthroughs have been made in specific problems.However,research in various problems is relatively dispersed,lacking systematic theory,and the integration of artificial intelligence and domain knowledge is insufficient.This paper proposes the concept and method of intelligent reservoir characterization driven by hybrid knowledge and data.Based on the characteristics of reservoir data,advanced artificial intelligence technology is used to integrate domain knowledge,fully mining the useful information hidden in big data,in order to achieve more reliable,high-precision,and efficient reservoir characterization.This paper summarizes the latest research progress in the key research content of reservoir intelligent characterization,mainly including deep learning-based reservoir logging evaluation,seismic reservoir prediction,and stochastic reservoir modeling.Finally,the development prospects of intelligent reservoir characterization are proposed.
Reservoir characterizationReservoir big dataKnowledge-drivenData-drivenArtificial intelligenceDeep learning
万方数据
维普
