Deep learning coalbed methane screw pump well health index prediction method and system

A technology of health index and deep learning, applied in electrical digital data processing, instruments, design optimization/simulation, etc., can solve problems such as lack of testing equipment, high failure rate, untimely management, etc., and achieve real-time quantitative evaluation and high prediction The effect of precision

Active Publication Date: 2021-05-28
CHINA UNIV OF PETROLEUM (BEIJING) +2
View PDF5 Cites 2 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0008] Aiming at the problem of high failure rate caused by untimely management and lack of effective testing equipment for coalbed methane well screw pumps, a coalbed methane screw pump well monitoring system is proposed in the prior art such as the patent document with publication number CN102169337B , which uses the received test data to form a database, conducts a comprehensive analysis and diagnosis of each test parameter in the database, and combines historical data parameters to automatically describe the changes in parameters of coalbed methane wells

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Deep learning coalbed methane screw pump well health index prediction method and system
  • Deep learning coalbed methane screw pump well health index prediction method and system
  • Deep learning coalbed methane screw pump well health index prediction method and system

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0111] In this example, the real-time dynamic production data of 30 PCP fault wells and 6 normal operating wells in a coalbed methane block in the SURAT Basin of Australia were collected from June 2017 to January 2020, including bottomhole flow pressure, dynamic fluid Surface, gas production, water production, current, voltage, torque, oil pressure, casing pressure, speed and voltage, etc. 10 parameters, the data collection density is 1min. The fault types of the collected faulty wells include six types of faults: pump dry grinding, oil pipe blockage, stator blockage, oil pipe breakage, joint breakage, and pump efficiency reduction. The following mainly takes Well E001 as an example to analyze the production characteristics, calculate the health index, and diagnose and warn the faults.

[0112] 1> Analyze the production characteristics of CBM screw pump wells, and select at least one main control parameter suitable for the wells in the entire block.

[0113] In the data prepr...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention relates to a deep learning coalbed methane screw pump well health index prediction method. The method comprises one or more of the following steps: selecting at least one of a plurality of original parameters collected from a coalbed methane screw pump well as a main control parameter; performing merging processing on the at least one master control parameter to construct a health index; dividing the health state of the coal bed gas screw pump well into at least two different stages according to the health index; extracting health index data of the coal bed gas screw pump well as sample data, and constructing a health index prediction model by adopting a long-short term memory neural network; and predicting the health state change of the coal bed gas screw pump well by using the health index prediction model.

Description

technical field [0001] The invention relates to the technical field of gas recovery in oil production engineering, in particular to a deep learning method and system for predicting the health index of a coalbed methane screw pump well. Background technique [0002] The output of coalbed methane is the organic unity of desorption-diffusion-seepage process. By continuously discharging the water in the coal seam (or intruding into the coal seam), the pressure of the reservoir is reduced, and after the pressure of the reservoir is reduced to the desorption pressure of methane, it is adsorbed on the coal matrix. The methane gas in the pores desorbs, and then enters the wellbore through diffusion and seepage. The screw pump is one of the drainage and lifting methods in coalbed methane wells. Screw pumps often fail in operation in coalbed methane wells, resulting in large output loss and short equipment life. Therefore, the health status monitoring, diagnosis and early warning of...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Applications(China)
IPC IPC(8): G06F30/27G06F119/02
CPCG06F30/27G06F2119/02
Inventor 檀朝东王松宋健冯钢宋文容马丹
Owner CHINA UNIV OF PETROLEUM (BEIJING)
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap