Breakthrough Pressure Prediction Based on Neural Network Model

Shuren Hao,Yulian Liu,Mingdong Li,Hua Zhang,Jixiang Cao,Haian Liang,Zhongqiong Zhang

doi:10.1155/2021/6154468

Abstract

The increasing carbon dioxide content is identified as the main cause of global warming. Capturing carbon dioxide in the atmosphere and transporting it to deep salt layer for storage have been proven and practiced in many aspects, which considered to be an effective way to reduce the content of carbon dioxide in the atmosphere. The sealing property of cap rocks is one of the key factors to determine whether CO2 can be effectively stored for a long time. In view of the disadvantages of tedious and time-consuming laboratory test methods for breakthrough pressure of cap rock, this paper explores the relationship between breakthrough pressure and other parameters such as porosity, permeability, density, specific surface area, maximum throat radius, and total organic carbon. The results show that the rock breakthrough pressure is closely related to the maximum throat radius and permeability determined by the mercury injection method, followed by the porosity and specific surface area, and less related to the density, depth, and TOC content of the rock itself. Then, with the selected parameters, a neural network model is established to predict the breakthrough pressure of cap rock, which can achieve good prediction results.

Highlights

For more than a decade, the problem of greenhouse gas effects has been one of the focuses of research
The results showed that the breakthrough pressure of a rock is most closely related to its permeability and maximum throat radius, followed by the relationship between porosity and specific surface area
The analysis showed that the breakthrough pressure of a rock is most closely related to its permeability and the maximum throat radius, followed by the relationship between porosity and specific surface area, and the weakest relationship is between the density and sampling depth, whilst total organic carbon (TOC) content has no correlation

Summary

Introduction

For more than a decade, the problem of greenhouse gas effects has been one of the focuses of research. Scholars have conducted numerous studies on the continuous increase of CO2 content in the atmosphere and the problem of greenhouse effect [1, 2]. Various methods could be used for testing the breakthrough pressure of cap rock, including indirect and direct methods. Some scholars have attempted to predict the breakthrough pressure of mudstone by using the parameters of porosity and permeability, but the testing accuracy and applicable range could not meet the needs for evaluation. Various rock parameters were combined to establish a neural network model to predict the CO2 breakthrough pressure of rocks and further improve the accuracy and universal trial range of the prediction results. The entire dataset contains data from nuclear waste storage, hydrocarbon sealing, and CO2 storage research, including rock permeability, porosity, maximum throat radius, specific surface area, and total organic carbon (TOC). A neural network model was used to comprehensively analyse the relationship between these factors and the breakthrough pressure of rocks, and a prediction model of breakthrough pressure of mudstone was established

Data Acquisition and Processing

Correlation Analysis of Factors Affecting Breakthrough Pressure

Prediction and Analysis of BP Neural Network Model

Findings

Analysis and Discussion

Conclusion