Reservoir sensitivity intelligent prediction data processing method and device

Abstract

The embodiment of the invention provides a reservoir sensitivity intelligent prediction data processing method and device, and belongs to the technical field of reservoir sensitivity. The method comprises the following steps: collecting reservoir sensitivity data; comparing the new reservoir sensitivity factor item with a standard reservoir sensitivity factor item to obtain a comparison result; under the condition that the comparison result indicates that the new reservoir sensitivity factor item lacks one reservoir sensitivity influence factor, calculating the numerical value of the lacked reservoir sensitivity influence factor through inversion; judging whether the numerical value of the lacked reservoir sensitivity influence factor calculated by inversion is accurate or not; if yes, serving the reservoir sensitivity data calculated through inversion and the inversion numerical value of the reservoir sensitivity data as new reservoir sensitivity data to be stored in a corresponding database. According to the embodiment of the invention, the database capable of accurately reflecting the internal relation between the reservoir sensitivity influence factors and the sensitivity result can be established, so that the accuracy of intelligent prediction of the reservoir sensitivity is improved.

Description

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

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