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Evaluation Method of Low Permeability Reservoirs Based on Logging Petrophysical Facies Identification: A Case Study of the Upper Member of Mengyin Formation in Gaoqing Area, Dongying Depression

Abstract

The mechanism of the low-permeability reservoirs is complex in the upper member of Mengyin formation in Jurassic of Gaoqing area, Dongying depression. The strong heterogeneity of rock physical properties within the same micro-facies makes it difficult to evaluate and predict effectively using conventional methods. From the perspective of the mechanism of low-permeability reservoirs, comprehensive analysis of sedimentary rock facies and diagenetic reservoir facies was conducted using drilling, well logging, and thin section analysis. Taking the physical properties and pore structure parameters in the coring segment as constraints, the reservoirs were divided into five types of petrophysical facies. On this basis, the method of using the self-organizing neural network for neuron competition learning and mutual supervision in a supervised mode to fully tap the log response information and identify petrophysical facies was proposed. The results show that by taking the results of petrophysical facies division in the coring segment as learning samples, and using the LDA algorithm to optimize the logging curve input samples, the average accuracy of logging petrophysical facies identification is 84%. Based on single-well identification results, a comprehensive evaluation of the upper member of the Mengyin formation was carried out. The petrophysical facies of PF1, PF2 and PF3 are favorable types of pore-penetration development with well-developed secondary pores, good pore connectivity and percolation ability. The displacement pressure ranges from 0.02 to 0.20 MPa and the permeability is 1—200 mD. PF4 and PF5 are poor in pore connectivity and percolation ability, with a relative high displacement pressure greater than 0.20 MPa and low permeability less than 1 mD, which are non-favorable pore-penetration development types. The prediction of favorable pore-penetration development zones can be achieved by favorable planar distribution of petrophysical facies, which would provide basis for future exploration and development.