Skip to main content

Well Degradation Assessment and Leakage Risk Prediction in a Carbon Sequestration Project Using Neural Networks

Buy Article:

$29.95 plus tax (Refund Policy)

One of the main challenges of carbon storage is the integrity of the confi ning system over long timescales. The role of CO2 injection well tubular corrosion and cement sheath carbonation or mechanical degradation have gained special attention. However, the complexity and variability of CO2 injection well attributes makes it hard to model the well degradation effects. This study used artifi cial neural networks to predict the well degradation ratio using actual data from one carbon sequestration field. A neural network model was developed to identify the likelihood of leakage for current CO2 injection wells. Thirty-two CO2 injection wells of the WH field were used to develop the neural network. Results indicate that neural networks are a powerful tool to predict the well degradation ratio and that the validity of the results is closely in accord with the current experience and knowledge for carbon sequestration.
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
No Data/Media
No Metrics

Keywords: ARTIFI CIAL NEURAL NETWORKS; CARBON SEQUESTRATION; DEGRADATION RATIO; NORMALIZATION; WELL

Document Type: Research Article

Publication date: 2015-04-01

More about this publication?
  • Welcome to the home page of the Journal of Sustainable Energy Engineering (JSEE), committed to publishing peer-reviewed original research seeking sustainable methods of worldwide energy production through engineering, scientific, and technological advances.
  • Editorial Board
  • Submit a Paper
  • Subscribe to this Title
  • Ingenta Connect is not responsible for the content or availability of external websites
  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
X
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more