Method and apparatus for predicting dual-porosity structure parameters of deep coalbed methane reservoir

An apparatus for predicting dual-porosity structure parameters of a deep coalbed methane reservoir driven by a rock physics model, the apparatus comprising:

• an acquisition module configured to acquire coal seam mineral data, regional sedimentary background data, and well logging data corresponding to the deep coalbed methane reservoir, and perform simplification processing on mineral components of the deep coalbed methane reservoir based on the coal seam mineral data and the regional sedimentary background data, to obtain organic matter data, clay data, and brittle mineral data;
• a determination module configured to determine an initial clay content using a resistivity extremum method based on the clay data, determine an organic matter content using a resistivity-porosity overlay method based on the organic matter data, and determine a remaining part except the initial clay content and the organic matter content in the mineral components of the deep coalbed methane reservoir as a brittle mineral content, wherein a resistivity corresponding to the resistivity extremum method and a porosity corresponding to the resistivity-porosity overlay method are determined through the well logging data;
• an establishment module configured to establish a deep coalbed methane reservoir dual-porosity coal rock physics model using an equivalent medium theory model based on the initial clay content, the organic matter content, and the brittle mineral content;
• a calculation module configured to calculate a matrix porosity, a crack porosity, and a crack aspect ratio of a near-well coalbed methane reservoir based on logging-observed P-wave velocity and S-wave velocity in the well logging data, and take the matrix porosity, the crack porosity, and the crack aspect ratio of the near-well coalbed methane reservoir as porosity parameter prior information of the deep coalbed methane reservoir dual-porosity coal rock physics model;
• and
• an inversion module configured to perform pre-stack seismic Bayesian inversion processing using a pre-stack Bayesian inversion method through the deep coalbed methane reservoir dual-porosity coal rock physics model based on designated three-dimensional seismic horizon data and the porosity parameter prior information, to obtain dual-porosity structure data of the deep coalbed methane reservoir, and take the dual-porosity structure data of the deep coalbed methane reservoir as a dual-porosity structure parameter prediction result of the deep coalbed methane reservoir.