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ABSTRACT: 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.
1. INTRODUCTION Well cementing is an important operation in the oilfield well development; it consists of pumping the cement slurry between the casing steel and the formation represented by the annulus (Nelson and Guillot, 2006). This process is considered a solution to prevent fluid communication between different geological formations (for instance, salt formation and aquifers) and the leakage pathways created toward the surface (Costa et al., 2021). A good cementing operation will ensure adequate zonal isolation and well integrity related to the ability to control the upward undesirable fluid migration for a solid well life cycle (Iyer et al., 2022). Serval factors impact the cement sheath contact with the formation of rock from one side and the casing steel from the other side, increasing the probability of cement failure (Krakowiak et al., 2015). These factors include the fluid injection due to underground storage or water disposal, pressure testing, drilling vibrations, high pressure and temperature (HPHT), formation shifting due to the reservoir depletion or fault reactivation, salt creeping, casing shrinkage, chemical reactions with the formation fluids and minerals, hydraulic fracturing and perforating. The presence of each of the previously mentioned factors will cause the fissures creation within the cement sheath due to the compression, traction, or microannulus, which reduces the cement integrity performances (Phyoe et al., 2015). In fact, laboratory experiment testing is fulfilled before any oilfield cementing operation to study the cement slurry proprieties in different conditions. The cement concrete quality control is mandatory, which can be adjusted by proposing some additives and materials to enhance the cement proprieties and efficiency. These experiments are costly and time-consuming, for instance, the stability test, density test, thickening time, compressive strength free water test, fluid loss, and rheology test (Lenin Diaz, 2016). The compressive strength is the most critical mechanical propriety to secure a strong cement bond. It is a destructive test in which the cement simple is under axial loading conditions until it reaches the failure point. The direct measurement tool is called the uniaxial compressive test (Alkinani et al., 2021).
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