Research Institute of Petroleum Industry (RIPI)Journal of Petroleum Science and Technology2251-659X14420241001A Comprehensive Study on Bakelite Valves in Diaphragm Gas Meters and Improvement of their Physical and Mechanical Properties by Electron Beam Irradiation210151910.22078/jpst.2025.5697.1976ENMustafaMohammadiDepartment of Polymer Engineering, Shi.C., Islamic Azad University, Shiraz, Iran.Gholam-RezaNejabatDepartment of Polymer Engineering, Shi.C., Islamic Azad University, Shiraz, Iran.\Department of Applied Researches, Chemical, Petroleum & Polymer Engineering Research Center, Shi.C., Islamic Azad University, Shiraz, Iran.0000-0001-6721-1011M.SadeghTavallaliDepartment of Applied Researches, Chemical, Petroleum & Polymer Engineering Research Center, Shi.C., Islamic Azad University, Shiraz, Iran.\Department of Chemical Engineering, Shi.C., Islamic Azad University, Shiraz, Iran.0000-0002-9918-0173M.MehdiRastiFars Province Gas Company (FPGC), Shiraz, Fars, IranJournal Article20250528The present study investigates the effects of electron beam irradiation on the physical and mechanical properties of Bakelite valves used in diaphragm gas meters. The irradiation is performed under vacuum at ambient temperature. Various analytical techniques, including FTIR, SEM, EDX, XRD, AFM, and TGA, are employed to monitor the physical characteristics of the samples before and after the treatment. In addition, mechanical properties, specifically impact and tensile behaviors, are assessed using an Izod impact tester and a universal testing machine (UTM). A 12 MeV electron beam is utilized at two doses: 60 kGy and 80 kGy. The results demonstrate that irradiation at 60 kGy results in significant enhancements in both physical and mechanical properties. In contrast, at 80 kGy, while some properties exhibit slight improvements, others show deterioration. Moreover, FTIR analysis reveals the elimination of hydroxyl groups at both irradiation doses. In addition, SEM and AFM analyses confirm that the surface properties of samples irradiated at 60 kGy are improved. Furthermore, XRD shows a decrease in crystallinity at this dose. Also, TGA results indicate that samples irradiated at 60 kGy possess higher thermal stability. Specifically, samples irradiated at 60 kGy show a 180% increase in impact strength, whereas those at 80 kGy show a 55% increase. Moreover, the Tensile strength increases by 2% for samples treated at 60 kGy, whereas it decreases by 34% for those treated at 80 kGy. Ultimately, the results confirm that upon electron beam irradiation of Bakelite valves, crosslinking dominates over degradation at 60 kGy, while degradation becomes dominant at 80 kGy.https://jpst.ripi.ir/article_1519_5607fe8879e4fd269e88387e8cb30b7e.pdfResearch Institute of Petroleum Industry (RIPI)Journal of Petroleum Science and Technology2251-659X14420241001Comprehensive Assessment of Supervised Machine Learning Models for Prediction of Oil Recovery Factor and NPV in Surfactant-Polymer Flooding: Bayesian Optimization and Stacking Ensembles1121151810.22078/jpst.2025.5709.1979ENKasraEkhtiyaran HaghighiDepartment of Petroleum and Geo-Energy Engineering, Amirkabir University of Technology (AUT), Tehran, IranNovinNekueeDepartment of Petroleum and Geo-Energy Engineering, Amirkabir University of Technology (AUT), Tehran, IranMaryamGhorbani-BavarianiDepartment of Petroleum and Geo-Energy Engineering, Amirkabir University of Technology (AUT), Tehran, IranErfanZareiDepartment of Petroleum and Geo-Energy Engineering, Amirkabir University of Technology (AUT), Tehran, IranJournal Article20250610Surfactant-polymer (SP) flooding is recognized as an effective chemical enhanced oil recovery (EOR) method, where accurate prediction of oil recovery factor (RF) and net present value (NPV) is vital for field development planning and economic analysis. This study systematically evaluates a range of supervised machine learning algorithms—including CatBoost, artificial neural networks (ANN), XGBoost, LightGBM, and gradient boosting regressor (GBR)—for forecasting RF and NPV based on experimental SP flooding data. Baseline model results were established using default hyperparameters, followed by comprehensive two-stage hyperparameter tuning using grid search and Bayesian optimization with Optuna, along with five-fold cross-validation to ensure robustness. CatBoost and ANN consistently achieved the highest predictive accuracy. In addition, ensemble stacking was then performed by combining top-performing models, further enhancing prediction reliability and generalization. Additional post-processing using quantile adjustment (linear residual correction) addressed residual bias and improved calibration between predicted and observed values. Furthermore, model performance was benchmarked using standard statistical metrics and comparative graphical analysis. Also, the results demonstrate that integrating well-established supervised learning methods with systematic optimization, stacking, and output calibration offers a robust and practical framework for accurate prediction of SP flooding outcomes. Moreover, this approach provides valuable support for data-driven decision-making in EOR project design and evaluation. Furthermore, the proposed framework achieved strong predictive accuracy in the all-stacking ensemble with cross-validation, yielding an R² of 0.978 and AAPRE of 2.71 for recovery factor, and an R² of 0.944 and AAPRE of 6.18 for net present value. Ultimately, then applying quantile adjustment to the all-stacking ensemble, the performance remained competitive, with an R² of 0.964 and AAPRE of 3.61 for recovery factor, and an R² of 0.924 and AAPRE of 7.94 for net present value, further demonstrating the robustness of the approach.https://jpst.ripi.ir/article_1518_42d6c7d61481d1c21bd1635f59edae05.pdfResearch Institute of Petroleum Industry (RIPI)Journal of Petroleum Science and Technology2251-659X14420241001Design and Development of an Optical-Based Analyzer for Real-Time Moisture Detection in High-Pressure Natural Gas2227150910.22078/jpst.2025.5577.1961ENVahidehFaghihiPhotonics and Quantum Technologies Research School, Nuclear Science and Technology Research InstituteRasoulNasiriPars Smart Sensors Engineering (MAHPA) ltd., University of Tehran Science and Technology Park, Tehran, IranJournal Article20241125This study presents the design and development of an optical-based moisture dewpoint analyzer employing the chilled mirror technology for real-time dewpoint measurement at high-pressure gas streams. Moreover, the increasing demand for natural gas, along with the negative impact of moisture on energy efficiency and pipeline integrity, highlights the need for accurate moisture detection. Furthermore, the analyzer’s innovative design enables direct measurement of dewpoint temperature through the condensation of water vapor as nano-droplets on a cooled mirror surface, overcoming the limitations of traditional methods that operate most of the time at atmospheric pressures. We successfully reproduced our experimental results from the South Pars Gas Complex (SPGC) with HYSYS Process Simulation Software based on the actual gas composition model. This approach confirmed the accuracy of the experimental findings. Ultimately, the results indicate that this analyzer offers a robust, low-maintenance solution for monitoring moisture levels, which is crucial for preventing pipeline corrosion and gas hydrate formation. By addressing the limitations of existing Oxide-Aluminum (Ceramic-based) sensors, this technology enhances measurement accuracy and reliability, contributing significantly to advancements in natural gas quality monitoring.https://jpst.ripi.ir/article_1509_1cd3882394520876dc88d1472aa2a93f.pdfResearch Institute of Petroleum Industry (RIPI)Journal of Petroleum Science and Technology2251-659X14420241001Improving geosteering performance using rate of penetration and gas ratio: case studies in a limestone reservoir2836150810.22078/jpst.2025.5534.1951ENSinaFathi HafshejaniDepartment of Chemical & Petroleum Engineering, Sharif University of Technology, Tehran, IranMohamadmehdyAhiReppco Company, Tehran, IranSaeidJamshidiDepartment of Chemical & Petroleum Engineering, Sharif University of Technology, Tehran, IranJournal Article20240904Geosteering is an essential method employed in oil and gas drilling, particularly for horizontal wells, to precisely locate the wellbore within hydrocarbon-rich formations. To carry out this process, the gamma-ray logs from the laterals are matched with logs from a reference vertical well to position the lateral in the desired path accurately. Numerous studies have been carried out in the field of geosteering, focusing on the application of machine learning and the creation of automated geosteering methods. Due to the high cost of repeated use of steering, it can be helpful to establish a logical mathematical correlation between two or more parameters for movement within the reservoir. This study investigates the relationship between Rate of Penetration (ROP) and gas ratio data in three laterals drilled in a heterogeneous limestone reservoir in Iran by plotting normalized ROP vs. normalized gas ratio. Geomaster software is used to direct the geosteering process in order to ascertain the reservoir’s depth. Once the ROP and gas ratio data have been normalized and outliers removed, different models such as linear, polynomial, power, and exponential are utilized in MATLAB. As a result, we can observe that for the majority of laterals, the second-degree polynomial model offers the best correlation. Also, the presence of heterogeneity affects some results of laterals. These results can be applied to reduce the expenses associated with recurrent geosteering operations, enable the drilling of new or extended laterals, and optimize drilling operations in the field.https://jpst.ripi.ir/article_1508_ebd6d2f5d60ff9afaeda1a81fc53e2d0.pdfResearch Institute of Petroleum Industry (RIPI)Journal of Petroleum Science and Technology2251-659X14420241001Evaluation of the Potential of Natural Gas Sweetening by Using Imidazolium Ionic Liquid [bmim][NTf2] based on PC-SAFT Equation of State3747150610.22078/jpst.2025.5632.1971ENMehranFeyzianDepartment of Chemical Engineering, Isfahan University of Technology, IranAliT. ZoghiGas Refining Technology Group, Gas Research Division and Gas Processing Institute (GPI), Research Institute of Petroleum Industry (RIPI), 1485733111 Tehran, IranMohammad RezaEhsaniDepartment of Chemical Engineering, Isfahan University of Technology, IranAmir HosseinJaliliGas Refining Technology Group, Gas Research Division and Gas Processing Institute (GPI), Research Institute of Petroleum Industry (RIPI), 1485733111 Tehran, IranJournal Article20250220In the present study, for the first time, the potential of gas sweetening of Iranian gas composition by using an imidazolium-based ionic liquid solvent called 1-butyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide [bmim][NTf2] has been evaluated. The results are compared with natural gas sweetening using an amine-based solvent. The PC-SAFT equation of state was used as a thermodynamic model in Aspen Plus software version 10. Since the thermophysical properties of the ionic liquid are not available in the software, and these properties for pure and multicomponent systems are in vital demand, all the necessary properties are correlated to PC-SAFT EoS as well as related thermophysical properties. The gas sweetening process is simulated for both ionic liquid and amine-based solvents, and the results are compared. The results show that to meet the sweet gas pipeline specification (4 ppm H2S and 2% CO2), the energy consumption of the ionic liquid-based process is much higher than that of the MDEA-based solvent. This result indicates that ionic-based solvents ([bmim][NTf2]) are not suitable for gas sweetening due to their lack of desirable properties, including low vapor pressure, high thermal stability, high solubility, and tunability.https://jpst.ripi.ir/article_1506_b5488aeff42889188d03c9895255cecc.pdfResearch Institute of Petroleum Industry (RIPI)Journal of Petroleum Science and Technology2251-659X14420241001Mathematical Modelling of Thermal Profile of Well Drilling Thermal Profile in One of the Iran Southern Vertical Wells to Optimize the Rheological Characteristics of the Drilling Fluid4856146010.22078/jpst.2024.5056.1862ENMahdiNazari SaramDepartment of Petroleum, Mining and Materials Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran0000-0002-6444-2890MehrangizGhaziDepartment of Petroleum, Mining and Materials Engineering, Central Tehran Branch, Islamic Azad University, Tehran, IranJournal Article20230317The present work deals with finding the thermal profile of fluid in a vertical well to determine the temperature distribution in different depths and parts of a well during digging. Since drilling fluid plays an important role in a well drilling process, it is important to study the effect of several parameters on it. Moreover, one of the main purposes of using fluid is to cool down the drill and the process. This study aims to investigate the role of fluid as a heat exchanger from the bottom to the surface of the well and the surrounding area. In addition, on this base, the heat can affect the fluid characteristics such as rheology, density, and pressure variation. Moreover, knowledge about the thermal distribution of drilling fluid plays an important role in designing the fluid, estimating drop pressure, cementing and fencing, and drop of thermal energy in the well. In addition, this thermal profile is made by using mathematical modeling based on energy conservation and heat transfer auxiliary equations in the form of displacement, delivery, and fluid movement in the well. Furthermore, the equations in the model are solved by coding in MATLAB, and thermal profiles show the results obtained for every part of the well. Moreover, the temperature distribution of fluid in the digging pipe, lining pipe, lining, and surface lining is specified. Furthermore, the thermal profile is obtained for both the water and oil base fluids. Ultimately, in this research, it is proved that the thermal gradient of the earth, well depth, rate of fluid flow in the well, density, and thermal capacity of the fluid affect the thermal profile of the fluid, and also, there is a large difference between the thermal profile of the fluid in a digging well.https://jpst.ripi.ir/article_1460_07042ac7d03d3b9911a00da43ce0079a.pdf