The Tarim Basin has revealed numerous tight sandstone oil and gas reservoirs.The tidal fl at zone in the Shunbei area is currently in the detailed exploration stage,requiring a comprehensive description of the sand bo...The Tarim Basin has revealed numerous tight sandstone oil and gas reservoirs.The tidal fl at zone in the Shunbei area is currently in the detailed exploration stage,requiring a comprehensive description of the sand body distribution characteristics for rational exploration well deployment.However,using a single method for sand body prediction has yielded poor results.Seismic facies analysis can eff ectively predict the macro-development characteristics of sedimentary sand bodies but lacks the resolution to capture fine details.In contrast,single-well sedimentary facies analysis can describe detailed sand body development but struggles to reveal broader trends.Therefore,this study proposes a method that combines seismic facies analysis with single-well sedimentary microfacies analysis,using the lower section of the Kepingtage Formation in the Shunbei area as a case study.First,seismic facies were obtained through unsupervised vector quantization to control the macro-distribution characteristics of sand bodies,while principal component analysis(PCA)was applied to improve the depiction of fine sand body details from seismic attributes.Based on 3D seismic data,well-logging data,and geological interpretation results,a detailed structural interpretation was performed to establish a high-precision stratigraphic framework,thereby enhancing the accuracy of sand body prediction.Seismic facies analysis was then conducted to obtain the macro-distribution characteristics of the sand bodies.Subsequently,core data and logging curves from individual wells were used to clarify the vertical development characteristics of tidal channels and sandbars.Next,PCA was employed to select the seismic attributes most sensitive to sand bodies in diff erent sedimentary facies.Results indicate that RMS amplitude in the subtidal zone and instantaneous phase in the intertidal zone are the most sensitive to sand bodies.A comparative analysis of individual seismic attributes for sand body characterization revealed that facies-based delineation improved the accuracy of sand body identification,eff ectively capturing their contours and shapes.This method,which integrates seismic facies,single-well sedimentary microfacies,and machine learning techniques,enhances the precision of sand body characterization and off ers a novel approach to sand body prediction.展开更多
基金Collaborative Project Grant from the Exploration and Development Research Institute of SINOPEC Northwest Oilfi eld Company(Grant No.KY2021-S-104).
文摘The Tarim Basin has revealed numerous tight sandstone oil and gas reservoirs.The tidal fl at zone in the Shunbei area is currently in the detailed exploration stage,requiring a comprehensive description of the sand body distribution characteristics for rational exploration well deployment.However,using a single method for sand body prediction has yielded poor results.Seismic facies analysis can eff ectively predict the macro-development characteristics of sedimentary sand bodies but lacks the resolution to capture fine details.In contrast,single-well sedimentary facies analysis can describe detailed sand body development but struggles to reveal broader trends.Therefore,this study proposes a method that combines seismic facies analysis with single-well sedimentary microfacies analysis,using the lower section of the Kepingtage Formation in the Shunbei area as a case study.First,seismic facies were obtained through unsupervised vector quantization to control the macro-distribution characteristics of sand bodies,while principal component analysis(PCA)was applied to improve the depiction of fine sand body details from seismic attributes.Based on 3D seismic data,well-logging data,and geological interpretation results,a detailed structural interpretation was performed to establish a high-precision stratigraphic framework,thereby enhancing the accuracy of sand body prediction.Seismic facies analysis was then conducted to obtain the macro-distribution characteristics of the sand bodies.Subsequently,core data and logging curves from individual wells were used to clarify the vertical development characteristics of tidal channels and sandbars.Next,PCA was employed to select the seismic attributes most sensitive to sand bodies in diff erent sedimentary facies.Results indicate that RMS amplitude in the subtidal zone and instantaneous phase in the intertidal zone are the most sensitive to sand bodies.A comparative analysis of individual seismic attributes for sand body characterization revealed that facies-based delineation improved the accuracy of sand body identification,eff ectively capturing their contours and shapes.This method,which integrates seismic facies,single-well sedimentary microfacies,and machine learning techniques,enhances the precision of sand body characterization and off ers a novel approach to sand body prediction.
