A Human-Computer Interaction Reservoir Configuration Characterization Method Based on Big Data Analysis and Its Application in Development

For old oil fields in the middle and later stages of development, the heterogeneity within the reservoir (such as the separation of reservoirs, interlayers, thin reservoirs, microstructures, etc.), especially the distribution of remaining oil controlled by reservoir configuration, is a focus of research concern. The internal structural differences of thick sand layers are “homogenized”, and simple division based on “time units” is not conducive to overall research and cannot accurately characterize reservoir heterogeneity, greatly restricting the development effect of the oilfield. The article elaborates on the configuration characterization methods using the braided river delta in the ZaoNan of Dagang Fenghuadian Oilfield and the alluvial fan in the ZaoBei as examples. Based on big data analysis methods, cluster analysis single well configuration human-machine interaction division method, well seismic combined well configuration identification method, and multiphase coupling configuration spatial characterization method are created. The control effect of configuration on physical structure and heterogeneity is analyzed using core data, in order to guide the establishment of residual oil distribution patterns under configuration control and provide reliable basis for further tapping remaining oil potential.