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Permeability Prediction Of Low Permeability Sandstone Reservoir Based On Multi-parameter Inversion Of Pre-stack Seismic Data

Posted on:2022-08-01Degree:MasterType:Thesis
Country:ChinaCandidate:Y XieFull Text:PDF
GTID:2530307109962299Subject:Geological Resources and Geological Engineering
Abstract/Summary:
Permeability prediction has always been a hot and difficult point in petroleum geophysical exploration and development.The prediction result of permeability obtained by the regression of a single pore-permeability relationship is very similar to the inversion result of porosity,which can not accurately reflect the heterogeneity of reservoir permeability.Low permeability sandstone reservoir is characterized by low porosity,complex pore structure and rapid lateral change of permeability.The permeability with different lithology,pore structure and porosity have great differences.Therefore,it is of great significance for reservoir permeability prediction to build a reasonable relationship between porosity and permeability for different types of reservoirs.Porosity,pore structure and shale content are coupled together,and jointly affect reservoir permeability.Porosity reflects the space size of rock fluid,pore structure affects the connectivity of pores and the distribution characteristics of fluid,and shale content affects the properties of rock matrix and the characteristic of the whole pore system.In view of the fact that the reservoir characteristics of low permeability sandstone in the actual work area are greatly affected by pore structure,shale content and calcite,the differences between primary deposition and epigenetic deposition of calcite,various pore shapes and disconnected pores are fully considered in the process of rock-physics modeling,so as to more reasonably characterize the relationship between physical properties and elastic parameters of low permeability sandstone.The equivalent pore structure is further introduced γ,which provides a basis for characterizing different pore types with equivalent parameters.On this basis,the effects of parameters such as porosity,equivalent pore structure γ and shale content on various elastic characteristics of rocks are quantitatively analyzed,which lays a theoretical foundation for the calculation of porosity,equivalent pore structure γ and shale content using multi-parameter inversion data of prestack seismic data.In order to use the inversion result of porosity,equivalent pore structure γ and shale content to gain classification prediction of reservoir permeability,based on the weighted combination of various pore shapes and different aspect ratios in the theoretical model of rock physics,the calculation formulas of porosity and equivalent pore structure γ with bulk modulus and shear modulus of dry rock are derived,and the mapping relationship between these two parameters and P-wave impedance is established.Furthermore,by using the mapping relationship between the coupling of shale content and porosity and P-wave and S-wave velocity,the quantitative calculation models of porosity,equivalent pore structure γ,shale content and various elastic parameters are obtained.Based on a variety of elastic parameters of prestack seismic inversion,the above three physical parameters affecting reservoir permeability can be calculated.According to the porosity permeability relationship under different lithology and different equivalent pore structure conditions,a set of method and technical process for reservoir permeability classification prediction is constructed.The model validation and actual data test results show that the multi-parameter inversion based on prestack seismic data can better obtain the porosity,equivalent pore structure γ and shale content,and can carry out permeability classification prediction more reasonably for different types of reservoirs,and effectively improve the prediction accuracy of reservoir permeability.
Keywords/Search Tags:permeability prediction, low permeability sandstone, porosity, equivalent pore structure, shale content, rock-physics modeling, multi-parameter inversion
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