Comparative Analysis Between Different Artificial Intelligence Based Models Optimized with Genetic Algorithms for the Prediction of Oilfield Cement Compressive Strength

A proper cementing job is an essential application in any successful drilling operation since it is heavily related to the well integrity. The cement quality performance used in this process is quantified by the cement compressive strength measured in three standard periods: 2, 7, and 28 days. The chemical-mineralogical synthesis of the cement and fineness factor significantly affects the cement strength for the well's life cycle to avoid unwanted fluid leakage. This study aims to develop two Artificial Intelligence algorithms: Artificial Neural Networks (ANN) and Support Vector Regression optimized by Genetic Algorithm (SVR-GA) to estimate the oilfield cement compressive strength in three periods based on the particle distribution, the size fraction and the chemical-mineralogical composition of the cement mixtures. The intelligent models are validated with 98 laboratory samples to investigate their prediction performances. The ANN displays a strong relationship with the experimental data with a 98.7%, 87.9% and 97.5% coefficient of correlation for 2, 7 and 28 days respectively. The SVR-GA exhibit a higher accuracy with 98%, 98% and 97.5% coefficient of correlation for 2, 7 and 28 days respectively. Our study demonstrates the accuracy of algorithm performance of the cement compressive strength prediction for better well integrity problems elimination.