Compressive strength prediction of ultra lightweight cement using data-driven modeling techniques

Abstract The advancement of predictive modeling techniques using machine learning (ML) has emerged as a pivotal component in optimizing the performance of materials in the oil and gas industry. This paper presents an in-depth analysis of employing four distinct ML algorithms—Artificial Neural Networks (ANN), Gradient Boosting Regressor (GBR), Random Forest (RF), and Support Vector Regression (SVR)—to accurately estimate the Unconfined Compressive Strength (UCS) of lightweight cement formulations. This study tested 516 different cement samples, and the models were trained and validated using a portion of the dataset. The ANN and the GBR ensemble approach produced highly accurate and consistent results when tested on unseen data, supported by Root Mean Squared Error (RMSE) scores of 140 psi and 130 psi and Mean Absolute Percentage Error (MAPE) of approximately 10% and 9%, respectively. All models had computed coefficient of determination (R 2 ) values exceeding 0.9 for all dataset splits, indicating good model fits. By leveraging a dataset comprising various cement samples enhanced with strength-improving additives, the study explores the effectiveness of each algorithm in predicting UCS under different wellbore conditions. Through meticulous hyperparameter tuning and validation processes, the research assesses the performance metrics of each algorithm, thereby providing valuable insights into their applicability for real-time cementing operations in the oil and gas sector. The findings illustrate the potential of these advanced modeling techniques to enhance decision-making processes, reduce costs, and improve the reliability of cementing practices in challenging environments.