Reservoir sensitivity intelligent prediction data processing method and device

Abstract

Embodiments of the present invention provide a reservoir sensitivity intelligent prediction data processing method and device, which belong to the technical field of reservoir sensitivity. The method includes: collecting reservoir sensitivity data; comparing the new reservoir sensitivity factor item with a standard reservoir sensitivity factor item to obtain a comparison result; when the comparison result indicates that the new reservoir sensitivity factor item In the case of a lack of a reservoir sensitivity influencing factor in the property factor item, the value of the missing reservoir sensitivity influencing factor is calculated by inversion; whether the value of the missing reservoir sensitivity influencing factor calculated by the inversion is Accurate; if accurate, store the reservoir sensitivity data calculated by inversion and its inversion value as a new piece of reservoir sensitivity data into the corresponding database. The embodiments of the present invention can establish a database that accurately reflects the internal relationship between reservoir sensitivity influencing factors and sensitivity results, thereby improving the accuracy of intelligent prediction of reservoir sensitivity.

Description

technical field [0001] The invention relates to the technical field of reservoir sensitivity prediction, in particular to a method and device for processing reservoir sensitivity intelligent prediction data. Background technique [0002] Reservoir sensitivity refers to the property that the original pore structure and permeability of the reservoir are changed due to various physical or chemical interactions between oil and gas reservoirs and external fluids. When the reservoir interacts with foreign fluids, the reservoir permeability tends to deteriorate and damage the oil and gas reservoir to varying degrees, resulting in a loss of productivity or a decline in production. Therefore, reservoir sensitivity can be understood as the degree of sensitivity of the reservoir to various types of reservoir damage. Specifically, reservoir sensitivity can be divided into five types: water sensitivity, speed sensitivity, alkali sensitivity, acid sensitivity, and stress sensitivity. On...

AI Technical Summary

Problems solved by technology

With the help of numerical analysis models, fuzzy statistical theory, or expert practical experience, these methods have greatly improved the speed of obtaining reservoir sensitivity results through machine calculations. The processing methods are different, and it is difficult for the established original data database or expert experience knowledge base to accurately reflect the internal relationship between reservoir sensitivity influencing factors and sensitivity results, resulting in low prediction accuracy and limited scope of application, making it difficult to effectively Promote application

Images