时移地震多属性智能分析在CO2驱油波及范围识别与圈定中的应用—以胜利油田G89区块为例OA

Under the"dual-carbon"goals,CO2 flooding,as a development method that combines enhanced oil recovery with emission reduction and efficiency improvement,is an important technological approach to promote efficient oilfield development and low-carbon transformation.Accurate identification of CO2 migration pathways and effective sweep extent are crucial for evaluating displacement effects and adjusting injection-production schemes.However,conventional production performance analysis and single monitoring methods have limited capability in characterizing the subsurface migration process and spatial distribution of CO2,making it difficult to meet the demand for fine-scale characterization.To address the problems associated with existing identification methods based on time-lapse seismic difference attributes,which are susceptible to noise,non-re-peatability errors,and reservoir heterogeneity and thus often lead to scattered anomalous responses and blurred boundaries,this study proposes a method for identifying the sweep extent of CO2 flooding based on intelligent integration of time-lapse seismic multi-attributes.Difference volumes were constructed from time-lapse seismic data,and sensitive difference attributes,including amplitude,phase,and attenuation,were selected.Then,a fuzzy neural network(FNN)was introduced to perform nonlinear fusion of multiple attributes based on fuzzy rules,thereby constructing a continuous response indicator characterizing the intensity of CO2 sweep.The results show that the proposed method can effectively suppress scattered false anomalies and improve the boundary clarity and spatial connectivity of the predicted results.Time-series comparison indicates that the predicted sweep extent expanded outward from the vicinity of injection wells as injection proceeded,and migrated upward along the up-dip direction toward structurally higher positions.The delineated sweep area increased from approximately 1.7 km² in 2010 to approximately 2.6 km² in 2022.Validation against production performance data further shows that strong predicted responses generally correspond to the vicinity of high gas-injection wells and high gas-production wells,indicating that the proposed method can more accurately reflect reservoir response differences during the CO2 flooding process and provide an effective geophysical approach for identifying CO2 migration pathways,quantitatively characterizing sweep extent,and evaluating displacement performance.