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

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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.
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Document Type: Research Article

Publication date: 01 April 2015

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