Cement Bond Quality Prediction Based on Wide and Deep Neural Networks

Abstract Cementing quality is critical for the safety, efficiency, and cost-effectiveness of oil and gas wells. However, predicting cementing quality remains highly challenging due to the numerous influencing factors and their interdependencies. Current cementing quality prediction methods are still in the developmental stage, making it difficult to build robust models that meet the stringent requirements of cementing design. This study collected cementing data from an oilfield in western China. Various data-cleaning techniques were applied to the raw data, effectively addressing issues with missing values and anomalies. Subsequently, correlation analysis was conducted to eliminate irrelevant variables, and the dataset for model construction was finalized. Based on the correlation analysis and the characteristics of cementing data, a cement bond quality prediction model was developed using a Wide & Deep Neural Network. The model simultaneously predicts the cement bond quality of both the casing-cement sheath and the cement sheath-formation interfaces. Given the significant impact of thickening time on cementing outcomes, this study incorporated the Arrhenius equation to correct thickening time under varying temperature and pressure conditions, embedding this correction into the neural network. This further enhanced the model’s accuracy. The final model achieved a prediction accuracy of 87.5% for the casing-cement sheath interface and 89.5% for the cement sheath-formation interface, enabling the prediction of wellbore quality prior to cementing operations. Finally, the interpretability of the intelligent model was analyzed using the SHAP (SHapley Additive exPlanations) method, which identified the factors with the most significant impacts on cement bond quality after model training. The proposed approach provides valuable guidance towards the optimization of cementing operations.