Contribution of Fluid Substitution and Cheetah Optimizer Algorithm in Predicting Rock-Physics Parameters of Gas-Bearing Reservoirs in the Eastern Mediterranean Sea, Egypt

In this study, the elastic characteristics of reservoir rocks and their relationship to porosity and pore fluid were predicted using the fluid substitution method in combination with machine learning techniques. We first discarded the data at gas points to remove the erroneous effect of gas on the prediction process of Poisson’s ratio using the three proposed machine learning models. Then, the prediction was carried out after substituting the gas zones by oil and by water. As a result, the prediction was enhanced and showed stronger correlation coefficient values. The integration of fluid substitution and machine learning methods was applied in the reservoir of Scarab field as a case study from the Eastern Mediterranean to detect the effect of different pore fluids (gas, oil, and water) on Poisson's ratio estimation. The main objective of the study was to analyze the seismic and well log data to estimate and predict the Poisson’s ratio in four fluid-content cases; these are gas-bearing reservoir, reservoir after removal of log data of gas-bearing zones, and reservoirs after gas-substitution with oil and with water. These four cases were dealt with directly and by using the machine learning algorithms based on the proposed model of random vector functional link (RVFL), which was enhanced by the Cheetah optimizer (CO). This study shows how the performance of RVFL is affected by the presence or absence of gas zones. It is shown that the Poisson’s ratio value increases when gas is substituted with water more than when gas is substituted with oil. For validation of these results, regression analysis technique was used and the correlation coefficient of the CO–RVFL model increased after removing well log data of gas zones and was more enhanced after fluid substitution from gas to oil or to water.