https://jogpt.pgu.ac.ir/ Journal of Oil, Gas and Petrochemical Technology en daily 1 Wed, 01 Oct 2025 00:00:00 +0330 Wed, 01 Oct 2025 00:00:00 +0330 https://jogpt.pgu.ac.ir/article_239129.html Implementing and developing carbon capture and storage (CCS) technologies can significantly impact the economic and environmental performance of oil and gas companies, creating at the same time various opportunities and threats for them. Given that resources—particularly financial, human, and technical—are indeed limited, it is essential for companies to evaluate the opportunities and threats associated with each CCS technology in order to allocate these resources effectively to research and development (R&D) projects. This research specifically focuses on prioritisation and resource allocation, presenting a fuzzy multi-criteria group decision-making methodology that was successfully applied to assess the development opportunities for CCS technologies at the Pars Special Economic Energy Zone (PSEEZ). In fact, the proposed methodology serves as a systematic and effective decision support tool, thereby enabling decision-makers to prioritise and select the most attractive technologies, where the attractiveness of each technology is defined by the associated opportunities and threats inherent in its acquisition and development. https://jogpt.pgu.ac.ir/article_239128.html Shale volumes are essential for lithology identification, reservoir evaluation, and stratigraphic correlation in subsurface formation analysis. This study evaluates the performance of three machine learning algorithms—Random Forest (RF), Extra Trees (ET), and Artificial Neural Network (ANN)—for shale volume prediction using conventional well log data. The models were trained and tested on a comprehensive dataset from one of the oil fields in southern Iran, incorporating parameters such as sonic travel time (DTC), bulk density (RHOZ), resistivity (RT), neutron porosity (HTNP), and caliper (HCAL). Results demonstrated that ANN achieved superior accuracy with an R² of 0.9779 and Root Mean Squared Error (RMSE) of 0.0130 API, outperforming both RF (R² = 0.9640, RMSE = 0.0166 API) and ET (R² = 0.9007, RMSE = 0.0275 API). While ANN excelled in capturing complex nonlinear relationships, tree-based methods offered faster training times and greater interpretability through feature importance metrics. The findings highlight ANN as the preferred choice for high-fidelity shale volume prediction, whereas RF provides a balanced solution for scenarios requiring both speed and competitive accuracy. This study underscores the transformative potential of machine learning in petrophysical analysis, offering practical recommendations for model selection based on project-specific needs. https://jogpt.pgu.ac.ir/article_239127.html Increasing the concentration of H2S in the acid gas entering the sulfur recovery unit is one of the key parameters for enhancing the sulfur recovery efficiency. This research aims to determine the optimal solvent for a gas treatment unit with a focus on enriching hydrogen sulfide gas in the feed of the sulfur recovery unit. The data from the design of an industrial-scale gas-treating unit was utilized to achieve the research objectives. For simulation, analysis of results, and plotting graphs, ASPEN-HYSYS V11 and DESIGN EXPERT V10 software were employed, respectively. The solvents available in the GPSA handbook were utilized for the solvent and additives selection. In the formulation of a composite solvent, DEA and MDEA solvents were considered as the base solvents, considering the weight percentage range used in the gas processing industry, and other solvents were considered as additives to the composite solvent. Simulation results were analyzed by comparing them with the standards of the Iranian National Gas Company. The results indicated that the combination of MDEA with 42.5wt% as the base solvent and Sulfolane with 7.5wt% as an additive will be the optimum solvent. The reason for this selection is that there will be the maximum H2S concentration in the acid gas while adhering to the mentioned standards. The optimum solvent can increase the concentration of H2S from 33.6 mol% to 41.84 mol% in the acidic gas to the Sulphur recovery unit. https://jogpt.pgu.ac.ir/article_239126.html The development of innovative and sustainable processes for biodiesel production, as one of the most important renewable biofuels, plays a crucial role in reducing dependence on fossil fuels and mitigating environmental pollution. Considering the high costs of conventional processes and challenges in designing stable catalysts, the utilization of waste-derived materials for synthesizing heterogeneous catalysts represents a valuable and innovative approach. In this study, an iron-silica catalyst was synthesized using waste iron, and its performance was evaluated in the transesterification reaction for biodiesel production. The results demonstrated that the synthesized catalyst exhibited high activity, stability, and reusability, facilitating biodiesel production with significant yield. Furthermore, FTIR and XRD analyses confirmed the presence of methyl ester groups in the biodiesel and the structural features of the catalyst, highlighting the effectiveness of the proposed synthesis route. These findings suggest that using waste iron for catalyst synthesis not only reduces production costs but also contributes to advancing green and sustainable technologies. Ultimately, this approach may provide a foundation for future research focused on industrial-scale applications and further optimization of operating parameters in sustainable biodiesel production.