The Health Index Prediction Model and Application of PCP in CBM Wells Based on Deep Learning

Chaodong Tan,Wenrong Song,Guoqing Han,Xiongying Zhang,Song Wang,Guanghao Du,Hanwen Deng,Guanglong Sheng

doi:10.1155/2021/6641395

Abstract

Aiming at the problems of the current production and operation status of the progressive cavity pump (PCP) in coalbed methane (CBM) wells which cannot be timely monitored, quantitatively evaluated, and accurately predicted, a five-step method for evaluating and predicting the health status of PCP wells is proposed: data preprocessing, principal parameter optimization, health index construction, health degree division, and health index prediction. Therein, a health index (HI) formulation was made based on deep learning, and a statistical method was used to define the health status of PCP wells as being healthy, subhealthy, or faulty. This allowed further research on the HI prediction model of PCP wells based on the long short-term memory (LSTM) network. As demonstrated in the study, they can reflect both the change trend and the contextual relevance of the health status of PCP wells with high accuracy to achieve real-time, quantitative, and accurate assessment and prediction. At the same time, the conclusion gives good guidance on the production performance analysis and failure warning of the PCP wells and suggests a new direction for the health status assessment and warning of other artificial lift equipment.

Highlights

Coalbed methane is a kind of clean energy; it is drained through depressor desorption; when the reservoir pressure is reduced to the desorption pressure of methane, the methane gas in the pores is desorbed, diffuses and percolates into the wellbore [1,2,3]
In view of the real-time evaluation and prediction of the health status of the progressive cavity pump (PCP) wells, this paper proposes a method based on deep learning to construct a health index calculation model and prediction model to reflect the before and after trends of the health status of the PCPs and realize the real-time, quantitative, and accurate evaluation and prediction of the health status of the wells
The original data was deleted and replaced, and the 10 parameters collected by the coalbed methane (CBM) well were processed into 8 items

Summary

Introduction

Coalbed methane is a kind of clean energy; it is drained through depressor desorption; when the reservoir pressure is reduced to the desorption pressure of methane, the methane gas in the pores is desorbed, diffuses and percolates into the wellbore [1,2,3]. Based on the above technical methods, the service life of PCP can be prolonged and the output of CBM wells can be increased, real-time evaluation and prediction of the health status of the lifting equipment PCP cannot be carried out. According to the different strategies of constructing the HI curve, it can be divided into two types: direct HI and indirect HI [12] The former refers to the direct construction of health values with a certain physical significance based on the original monitoring data, guided by experts or empirical knowledge, through simple statistical analysis or feature extraction. Indirect HI is usually obtained by using machine learning methods to fuse or reduce the time domain features or frequency domain features of the sensor It has no physical meaning and is often called virtual HI (VHI). In view of the real-time evaluation and prediction of the health status of the PCP wells, this paper proposes a method based on deep learning to construct a health index calculation model and prediction model to reflect the before and after trends of the health status of the PCPs and realize the real-time, quantitative, and accurate evaluation and prediction of the health status of the wells

Establishment of the HI Model

HI Prediction Model

Application Results

Conclusions