Wellbore salt-deposition risk prediction of underground gas storage combining numerical modeling and machine learning methodology

Strengthening the construction of underground gas storage (UGS) is significant for securing national energy and achieving carbon neutrality. However, salt deposition occurs in the wellbore affecting the safety of UGS seriously. How to predict the salt-deposition risk in UGS wells accurately and quickly is still challenging. This work aims at establishing a fast and convenient framework of wellbore salt-deposition risk prediction in UGS wells. Firstly, the influence of the production parameters on wellbore salt-deposition is analyzed by an in-house simulator. Further, an intelligent diagnostic model of salt-deposition risk is proposed based on the machine learning algorithm. The effect of salt deposition on reservoir is analyzed by reservoir salt-deposition simulation model. Finally, downhole video survey is used to validate the modeling accuracy of diagnostic model. Results indicate that the contribution ratio of daily gas production and water-gas-ratio to wellbore salt deposition is 3:2. After the salt deposition, the reduction in water saturation and permeability of the near wellbore areas are 77.14 % and 98.91 %. The modeling accuracy of diagnostic model is 97 %, which is applied in the Y UGS successfully. The proposed framework can predict the salt-deposition risk of UGS wells in real time to help engineers control salt clogging issues.