Iranian Journal of Oil and Gas Science and Technology

Abstract Accurate pore pressure estimation is a critical component of geomechanical modeling, essential for maintaining wellbore stability and optimizing drilling fluid density. This study proposes a Grey Wolf Optimizer-Supported Vector Machine (GWO-SVM) workflow to predict pore pressure in a complex carbonate reservoir in southwestern Iran. While traditional empirical correlations like Eaton and Bowers are widely used, their reliance on continuous calibration can be challenging. To address this, discrete Repeating Formation Test (RFT) pressure points were utilized to calibrate baseline empirical trends, creating continuous reference profiles for the entire depth. The GWO-SVM model was then deployed to automate the replication of these calibrated baselines from standard petrophysical logs (sonic, density, resistivity, porosity, and shale volume). Using a dataset from five wells (four for training, one for blind validation), the GWO optimally tuned the Support Vector Regression (SVR) hyperparameters. The model demonstrated exceptional fidelity in replicating the calibrated baseline on the blind test well, achieving an RMSE=37.96psi and an R^2=0.998. Finally, the 1D well-based predictions were integrated into a 3D geostatistical model using co-kriging to visualize pressure compartmentalization influenced by the local tectonic stress regime. This workflow offers a replicable, high-precision alternative for pre-drill pore pressure modeling in data-limited reservoir settings.

Abstract Gas compression systems in petrochemical facilities are subjected to extreme pressures, cyclic loading, and variable humidity levels, which collectively challenge the integrity of lubricating films. This study investigates the performance of ester-based lubricants, which benefit from polar molecular structures and enhanced hydrolytic stability, in forming robust boundary lubrication layers under such demanding conditions. Experiments were conducted using a pin-on-disk tribometer to simulate compressor stresses, employing polyol esters and diesters across a relative humidity (RH) range of 20% to 80%. Adhering to ASTM G99 standards, friction and wear metrics were quantified, followed by detailed characterization of the resultant films via scanning electron microscopy (SEM), transmission electron microscopy (TEM), Fourier-transform infrared spectroscopy (FTIR), and X-ray photoelectron spectroscopy (XPS). The results indicate that these esters generate denser and more ordered films in humid environments, achieving up to 30% wear reduction relative to conventional mineral oils. Complementary molecular dynamics (MD) simulations corroborate these observations, illustrating enhanced adhesion of ester functional groups to metallic substrates in the presence of moisture. These insights underscore the potential of humidity as a beneficial factor in lubrication efficacy, informing the development of optimized formulations for severe industrial applications. Such advancements could extend equipment service life, minimize downtime, and promote environmentally sustainable alternatives. Future efforts should prioritize field validations and formulation refinements for operational deployment.