New progress and future prospects of oil and gas reservoir modeling technology for exploration

Abstract

Oil and gas reservoir modeling integrates multidisciplinary information from logging， geology， and seismic data through geostatistics and other methods， and it is a powerful tool for oil and gas field development. The geological model of reservoirs can quantitatively characterize the variation and distribution of various geological features of the reservoir in three-dimensional space and is a high generalization of the type， geometry， scale， internal structure of the oil and gas reservoir， reservoir parameters， and fluid distribution. The geological model of reservoirs is the core of the geological model of oil and gas reservoirs， which can comprehensively characterize the sedimentary characteristics， heterogeneity， physical properties， and fluid characteristics. However， it is difficult to characterize reservoir distribution under the condition of a large-scale sedimentary system and sparse well pattern in the exploration stage， covering ① quantitative representation of geological knowledge， including how to represent the experience of geological experts digitally； ② it is impossible to accurately and quantitatively describe the development scale， distribution direction， and structural characteristics of geological bodies directly with logging data and construct geological models under the condition of sparse well pattern， and complex sedimentary systems in large-scale space cannot be characterized by simple mathematical functions； ③ traditional geostatistics and other methods can not realize the fusion of seismic， logging， geological， reservoir， and other multi-dimensional data in the construction of exploration model. Therefore， the theory and technology of reservoir modeling based on deterministic modeling and stochastic modeling， such as traditional geostatistics， have met great challenges. On the basis of systematic analysis of traditional reservoir modeling technology processes and methods， the authors constructed a multidisciplinary big data knowledge base of geoscience covering geology， logging， seismic， analytical， and laboratory information and carried out the characterization of the sedimentary facies pattern library by hierarchical clustering through multi-dimensional data condensation and the intelligent modeling based on the generation network. In addition， a multidisciplinary collaborative modeling technology strategy and system are proposed for oil and gas reservoir exploration， which quantitatively characterized the matching relationship among structure， sediment， and reservoir. This technology system has been systematically applied in the exploration and deployment of steep slope and subsag belts in the north of Dongying Sag， and the geological models of lithofacies， physical properties， and oil and gas migration and accumulation has been constructed， which integrates multiple information such as paleogeomorphology， paleoprovenance， transport channels， logging， and seismic attributes. Based on the new paradigm of the model， the deployment of well locations has been guided， and the exploration practice of complex glutenites and shale oil in continental faulted basins has been supported. The authors further discuss the development trend and application prospect of oil and gas reservoir exploration and modeling technology in the future by deeply analyzing the practical difficulties and accuracy of exploration and modeling in the north belt of Dongying Sag.

Keywords

• reservoir exploration and modeling
• big data knowledge base of geoscience
• facies pattern library
• generative adversarial network
• intelligent modeling