https://jpst.ripi.ir/ Journal of Petroleum Science and Technology en daily 1 Tue, 01 Oct 2024 00:00:00 +0330 Tue, 01 Oct 2024 00:00:00 +0330 https://jpst.ripi.ir/article_1519.html The 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_1518.html Surfactant-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_1509.html This 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_1508.html Geosteering 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_1506.html In 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_1460.html The 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_1539.html The dispersed-phase velocity measurement module in two-phase flow represents one of the most critical components of a two-phase flowmeter, with particular importance in monitoring oil, gas, and pipeline-transported products. Compared with conventional velocimetry methods, digital X-ray imaging offers a novel solution due to its ability to penetrate opaque materials, its high sensitivity to density variations, and its capability for rapid imaging of dynamic processes. By employing advanced image-processing algorithms, the dispersed phase can be identified and tracked effectively. This study investigates the feasibility of tracking and computing the trajectory equation of the dispersed phase through the integration of digital X-ray imaging and the CSRT (Discriminative Correlation Filter with Channel and Spatial Reliability) tracking algorithm. The innovation of this research lies in applying image-processing algorithms to compute particle trajectories in multiphase flow metering systems. Implemented in Python, the proposed method achieved particle detection and tracking accuracy of 94.7% with an error below 3% under laboratory conditions. Results demonstrated that combining digital X-ray imaging with the CSRT tracking algorithm enables trajectory recognition of dispersed particles with a maximum error of ±5%. https://jpst.ripi.ir/article_1552.html The increasing population growth and industrial development have led to increasing electricity demand, greenhouse gas emissions, and water consumption. On the other hand, climate change can influence the amount of available renewable water. The production of electricity, water, and carbon is highly interdependent. One of the most prevalent fluids for energy conversion, transfer, and consumption is water. For this reason, in all industries, the topic of energy consumption is somehow integrated with the topic of water, so that achieving the goals of energy consumption reduction is not possible without paying attention to water management. In the present article, using the engineering equation solver (EES) software, the mass balance of the intended steam cycle was first carried out and then the required parameters, such as efficiency, exergy, power, etc., of different equipment were calculated. Finally, by providing solutions and scenarios, the assessment of the optimization of water and energy in the steam cycle of Petrochemical Unit A has been dealt with. By implementing scenarios for a condensing steam turbine named the STC-3001 turbine, the amount of steam consumption was reduced by 1.5 to 10.7 tons/hour, equivalent to an annual savings of $210,000 to $1,433,507 according to the intended utilities. Furthermore, by improving the steam system of the STC-1001 gland turbine, the amount of its steam consumption was reduced by about 6 tons/hour, equivalent to an annual savings of $778,400. https://jpst.ripi.ir/article_1553.html Carbonate reservoirs account for nearly 50% of global proven oil reserves, with 70% in the Middle East occurring in fractured formations. Their heterogeneous pore structures and oil-wet surfaces limit recovery efficiency. Matrix acidizing is a well-established stimulation technique to improve productivity in carbonate reservoirs; however, its success is strongly influenced by acid composition, ionic strength of the carrier fluid, and surface wettability conditions. This study experimentally evaluates the effects of acid concentration (15% and 28% HCl), brine composition (distilled water vs. seawater), and surfactant pre-flush (CTAB) on the dissolution kinetics and porosity evolution of carbonate cores under both oil-free and oil-saturated conditions. Laboratory experiments were conducted on core samples at 26 °C using controlled acid injection tests. The initial contact angle (~140°) confirmed the strongly oil-wet nature of the untreated rock surface. Results revealed that seawater-based acid systems produced more stable wormholes, 20–35% higher porosity enhancement, and approximately 15% lower sludge formation compared with distilled-water-based acids. The CTAB pre-flush significantly improved acid accessibility, reduced CO₂ bubble blockage, and enhanced dissolution uniformity, particularly under oil-saturated conditions. These findings demonstrate that combining seawater-based acids with surfactant-assisted pre-treatment provides a more sustainable, efficient, and field-applicable strategy for carbonate reservoir stimulation. https://jpst.ripi.ir/article_1564.html The non-orthogonal fractures in conventional Warren and Root (WR) induce directional permeabilities and anisotropic flow patterns that cannot be captured using simple fracture properties. In this paper, we use the proposed equivalent fracture aperture through a correction coefficient (η), to account for this effect Previous studies used an estimation of this coefficient by calibration to the static field data. The main contribution is to treat this coefficient as a physics-dependent parameter rather than a calibration value to bridge fracture-scale flow with continuum model. A data-driven approach is developed to quantify η as a function of fracture geometry and reservoir-scale properties. A dataset of 2,478 samples of simulations was generated using COMSOL Multiphysics, covering a range of fracture and reservoir properties. An artificial neural network (ANN) was trained and optimized on this dataset to learn the nonlinear relation with the correction coefficient, achieving a predictive accuracy of R² = 0.9946. By using the ANN-predicted correction coefficient into the WR approach, a multiscale bridge between fracture-scale physics and dual-porosity models can be achieved. The cubic relationship between permeability and fracture aperture (k_θ=η^3 k) highlights the importance of accurate η estimation, as any error in η propagate results into large permeability uncertainties. The numerical results showed the dependency of the correction coefficient η on fracture orientation is much greater than its dependency on either fracture size and matrix shape or system size. Also using this simple correction factor extends the applicability of conventional dual-porosity models to anisotropic fractured reservoirs with non-orthogonal fractures. https://jpst.ripi.ir/article_1565.html Refining and natural gas processing are among the most carbon-intensive industrial sectors, with CO2 emissions mainly arising from combustion systems, hydrogen production, fluid catalytic cracking, and acid gas treatment. In refineries, combustion-related sources can account for approximately 30–60% of total CO2 emissions, while hydrogen production and fluid catalytic cracking typically contribute 10–20% and 15–25%, respectively. This review evaluates the integration of carbon capture, utilization, and storage (CCUS) with blue hydrogen production as a practical pathway for reducing emissions in refineries and gas processing plants. Major capture routes, including post-combustion, pre-combustion, and oxy-fuel systems, are compared in terms of technical performance, energy demand, and deployment suitability. Typical capture efficiencies range from 85% to 95%, with solvent regeneration energy requirements of 2.5–4.0 GJ/tCO2 and CO2 compression demand of 0.08–0.12 MWh/tCO2. Blue hydrogen produced from steam methane reforming or autothermal reforming with CO2 capture can reduce hydrogen carbon intensity from 9–12 kg CO2/kg H2 for grey hydrogen to about 1–4 kg CO2/kg H2, provided that upstream methane leakage is minimized. Economic benchmarks indicate that capture costs vary from approximately $15–40/tCO2 for high-purity natural gas processing streams to $50–80/tCO2 for hydrogen production and $70–120/tCO2 for dilute refinery flue gases. Overall, integrated CCUS and blue hydrogen systems provide a scalable near- to medium-term decarbonization pathway, especially when prioritized for high-concentration CO2 streams and supported by shared transport and storage infrastructure.