Lithofacies Types, Characteristics And Well Logging Fine Identification Methods Of Lacustrine Mud Shale

With the research deepening of unconventional oil and gas exploration and development,the fine grained shale oil and gas gradually become the replacement of conventional fossil energy.The shale has variety lithology,complex structures,and stronger heterogeneity in aspects of mineral component,reservoir space characteristics,oil-gas possibility and abundance of organic matter.Because lithofacies is closely related to reservoir quality,lithofacies classification,characteristic analysis and logging precise recognition become the first question for reservoir assessment and "sweet heart" prediction in shale formation.Jiyang Depression palaeogene developed thick rich organic matter lacustrine shale which has major exploration potential for shale oil.Based on the study case of the lower third member of Shahejie formation of Zhanhua sag,using mass data which include core description,thin section authentication,pyrolysis,XRD,physical property,mercury penetration,nuclear magnetic resonance,electron microscope and logging to research lithofacies classification,characteristic,parametric inversion about mineral content,physical property and geochemistry,and lithofacies identification methods.(1)divide the lithofacies to 12 subclasses,6 classes by using thin section authentication and core description data from the point of view of structure and lithology.Furthermore,considering adding organic matter information to the classification and proposing "structure+lithology+organic matter information" triple information lithofacies scheme for lacustrine shale.(2)On the basis of all kinds of test data,comparing the characteristic of physical property,mineral content,geochemistry,pore types and pore structure,and furthennore using the elastic coefficient of evaluation method to calculate brittleness index to analyze brittleness difference of lithofacies.Ultimately,all the analyses determined that the mid-rich organic matter lamellar argillaceous limestone is favorable lithofacies.(3)Using core test data to scale logging data,and built calculation model for mineral content and geochemical parameters by multiple regression method.Then,the model of geochemical parameters improved according to different lithology,which enhanced the calculation accuracy.Combining the rock physical model of the shale and variable skeleton density the porosity model can be build,and the matrix permeability also can be calculated by mineral content to.Besides,using wavelet analysis algorithm to identify fracture development intensive stratum is preferably which lays a foundation for lithology recognition and favorable stratum division.(4)According 2D and 3D logging infonnation intersection analysis and the result of optimal filtering process of logging data,lithofacies sensitive logging infonnation can be screened qualitative and quantitative,which provide sensitive curves for lithofacies recognition.(5)Utilizing sensitive logging data 3D cross plot analysis to enhance accuracy of lithology distinguish,and because of fractal of curve has better indication for structure,taking fractal dimension of KTH curve as a variable to optimize structure identification method.(6)Finally,using optimal partition algorithm layering logging curves automatically,and combine clustering method,TOC curves and organic matter type to realize triple information lithofacies quantitative identification on the log section,then providing a reliable foundation for reservoir quality analysis of lacustrine shale.The series of methods in the article improve the veracity effectively of lithofacies identification by logging for lacustrine shale,and combine the lithofacies study and logging closely which take full logging advantages such as continuous information and high resolution.All of these are very beneficial to divide effective reservoirs and 'sweet spot'prediction by using logging data.