Reservoir Micro Pore Structure Evaluation Method and Device Based on Neural Network

Abstract

Embodiments of the present invention provide a neural network-based evaluation method and device for microscopic pore structure of reservoirs, the method comprising: acquiring core pore structure data obtained based on high-pressure mercury injection experiments on cores, and obtaining core pore structure data corresponding to the core pore structure data category; according to the core density and the density curve in the logging data, the core depth is aligned with the logging depth to obtain the logging data corresponding to the core, and continue to obtain the pre-processed logging data; the pre-processed logging data is used as Input and the category of the core pore structure are used as output to train the initial neural network model to obtain a trained neural network model; when it is necessary to evaluate the reservoir microstructure of the target well, input the target well logging data into the trained The neural network model can obtain the pore category corresponding to the target logging data, and can accurately and quickly evaluate the microscopic pore structure of the reservoir.

Description

technical field [0001] The embodiments of the present invention relate to the technical field of oil and gas field development, and in particular to a neural network-based evaluation method and device for reservoir microscopic pore structure. Background technique [0002] Oil and gas are stored and flow in the pores of rocks, so the shape, size, development and connectivity of rock pores, that is, the pore structure of rocks, can directly affect the storage capacity and productivity of oil and gas, especially the microscopic Pore ​​structure is the basis for studying the physical properties and storage properties of rocks, and is of great significance to the exploration and development of oil and gas resources. [0003] At present, the methods for studying the microstructure of reservoirs are mainly divided into two categories. The first category is core analysis, which mainly includes capillary force curve method, cast thin section method and scanning electron microscope me...

AI Technical Summary

Problems solved by technology

[0004] However, the core analysis method is generally manual identification, and the analysis speed is slow; the logging data field evaluation method mainly reflects the macroscopic pore structure of the rock, and the identification accuracy is low

Images