Carbon Capture and Storage (CCS) is a key climate mitigation technology. Leakage of the injected CO₂ is one of the major environmental concerns. The potential for CO₂ leakage from wells is one of the critical risks identified in geological CO₂ sequestration. The objective of this study is to develop a computerized statistical model with the neural network algorithm for predicting the probability of long-term leak of wells in CO₂ sequestration operations. Well design and operation data for over 500 CO₂ exposed wells were generated from the West Hastings oil field and Oyster Bayou oil field in southern Texas, USA. The well integrity conditions were assessed by analyzing the well attribute data (well type, well age, CO₂ exposed period, well construction details and materials), well operation histories and regulatory changes. Leakage-safe Probability Index (LPI) was assigned to individual wells. A computerized statistical model with network algorithm was developed based on data processing and grouping. Comprehensive training and testing of the model were carried out to ensure that the model was accurate and efficient enough for predicting the probability of long-leak of wells in CO₂ sequestration operations. The accuracy of the trained neural network for well leakage prediction was also verified by the field operation in the Cranfield Field, Mississippi, USA. The developed neural network model can improve the efficiency of the storage operations by predicting the risk of CO₂ leakage in the current exposed wells. In addition, it can also contribute in developing best practices standards by proposing recommendations for well construction in future wells.

碳捕集与封存（CCS）是一项关键的缓解气候变化技术。注入的CO ₂泄漏是主要的环境问题之一。从井中泄漏CO ₂的潜力是地质隔离CO _2中确定的关键风险之一。这项研究的目的是使用神经网络算法开发一个计算机统计模型，以预测CO ₂封存作业中井长期泄漏的可能性。超过500个CO _2的油井设计和运行数据裸露的油井来自美国德克萨斯州南部的西黑斯廷斯（West Hastings）油田和牡蛎巴约（Oyster Bayou）油田。通过分析油井属性数据（油井类型，油井年龄，CO ₂暴露时间，油井建设细节和材料），油井运营历史和监管变化来评估油井完整性条件。将泄漏安全概率指数（LPI）分配给各个井。在数据处理和分组的基础上，开发了一种基于网络算法的计算机统计模型。对该模型进行了全面的培训和测试，以确保该模型足够准确和有效，可以预测CO ₂井长期漏水的可能性。隔离行动。美国密西西比州克兰菲尔德油田的野外作业也验证了训练有素的神经网络进行井漏预测的准确性。通过预测当前裸露井中CO ₂泄漏的风险，开发的神经网络模型可以提高存储操作的效率。此外，它还可以通过为未来的油井提出建造建议来为制定最佳实践标准做出贡献。