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Identification Method of Effective Reservoir for Glutenite Body Using Well Logging Based on Rock Texture

LI Zun-zhi

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Abstract

This paper deals with the research on Yanjia region glutenite body of the upper Es4 in the northern steep slope zone of Dongying Depression.Effective reservoir samples are taken by core drilling,formation testing and gas logging data,and observing the relationship among the core,thin section and image logging,it is found that the effective reservoir appears as well sorted pebbly sandstone and conglomeratic sandstone in macroscale and microscale.The simulating of digital core also indicates that the difference of pore configuration which is derived from different rock textures is the major impact factor for electric resistivity response,so various rock textures should be well considered when identify the validity of reservoir using well logging.Therefore,during the data processing,detailed identification of rock textures by image model of image logging,based on automatic layering of conventional logging curve and automatic identification of lithologic category by means of logging facies cluster method,is indispensable.With the newly double constrains of rock texture and lower limit of electrical property on effective reservoir,this semi-quantitative method is preliminarily proposed to be a preferable application for multi-information identification of effective reservoir.

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What this paper is about

This paper deals with the research on Yanjia region glutenite body of the upper Es4 in the northern steep slope zone of Dongying Depression.Effective reservoir samples are taken by core drilling,formation testing and gas logging data,and observing the relationship among the core,thin section and image logging,it is found that the effective reservoir appears as well sorted pebbly sandstone and conglomeratic sandstone in macroscale and microscale.The simulating of digital core also indicates that the difference of pore configuration which is derived from different rock textures is the major impact factor for electric resistivity response,so various rock textures should be well considered when identify the validity of reservoir using well logging.Therefore,during the data processing,detailed identification of rock textures by image model of image logging,based on automatic layering of conventional logging curve and automatic identification of lithologic category by means of logging facies cluster method,is indispensable.With the newly double constrains of rock texture and lower limit of electrical property on effective reservoir,this semi-quantitative method is preliminarily proposed to be a preferable application for multi-information identification of effective reservoir.

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Available abstract

This paper deals with the research on Yanjia region glutenite body of the upper Es4 in the northern steep slope zone of Dongying Depression.Effective reservoir samples are taken by core drilling,formation testing and gas logging data,and observing the relationship among the core,thin section and image logging,it is found that the effective reservoir appears as well sorted pebbly sandstone and conglomeratic sandstone in macroscale and microscale.The simulating of digital core also indicates that the difference of pore configuration which is derived from different rock textures is the major impact factor for electric resistivity response,so various rock textures should be well considered when identify the validity of reservoir using well logging.Therefore,during the data processing,detailed identification of rock textures by image model of image logging,based on automatic layering of conventional logging curve and automatic identification of lithologic category by means of logging facies cluster method,is indispensable.With the newly double constrains of rock texture and lower limit of electrical property on effective reservoir,this semi-quantitative method is preliminarily proposed to be a preferable application for multi-information identification of effective reservoir.

Key concepts: Geology, Facies, Lithology, Logging, Well logging, Texture (cosmology), Drilling, Petrology

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