Iranian Journal of Oil and Gas Science and Technology

Abstract In this study, several robust and computationally efficient machine learning methods are developed to estimate the cementation exponent in carbonate formations characterized by heterogeneous pore systems, thereby addressing a critical need for accurate petrophysical property prediction in complex geological settings. Unlike conventional empirical correlations, support vector regression (SVR), multilayer perceptron neural network (MLPNN), probabilistic neural network (PNN), random forest (RF), decision tree (DT), and eXtreme gradient boosting (XGBoost) are implemented as predictive algorithms to simulate the cementation exponent based on detailed pore structure descriptions and total porosity. To optimize the parameters of the MLPNN model, the Levenberg–Marquardt (LM) algorithm is integrated with MLPNN, resulting in a hybrid framework referred to as MLPNN-LM. This hybrid approach enhances parameter optimization, leading to improved predictive accuracy and faster convergence. The database required for model development, validation, and testing is compiled from Ragland’s dataset and is systematically divided into training, validation, and testing subsets. The results indicate high predictive performance for the hybrid MLPNN-LM and RF models. Key statistical indicators include Average Absolute Percentage Relative Deviation AAPRD values of 4.3781% for MLPNN-LM and 4.8690% for RF, along with coefficients of determination R² of 0.8654 and 0.8731, respectively. These findings demonstrate that MLPNN-LM and RF provide more accurate cementation exponent estimates than widely used literature correlations and other machine learning approaches. Sensitivity analysis reveals that interparticle, moldic, and connected vuggy pore types exert the most significant influence on model predictions. The reliability of the dataset and the robustness of the MLPNN-LM and RF models are further evaluated using Williams plots. Approximately 93.75% and 91.96% of the data points fall within the applicability domain of the MLPNN-LM and RF models, respectively, confirming their generalizability. Trend analysis further demonstrates strong agreement between predicted and measured cementation exponent values, particularly for deep formations with depths greater than 1200 m and tight carbonate reservoirs with total porosity below 10%, where traditional correlations often fail due to the complex behavior of the cementation exponent. In conclusion, reliable and accurate computational frameworks are introduced for estimating the cementation exponent as a function of pore structure characteristics. These models are well suited for fluid flow simulation and reservoir characterization in carbonate and heterogeneous formations, especially in deep and tight carbonate reservoirs.

Abstract Optimizing transportation in organizational projects is critical, as identifying and evaluating transportation barriers represent key strategic decisions for managers and decision-makers. This study pursues three primary objectives: identifying the factors influencing lean logistics and transportation barriers, clarifying the interrelationships among the research criteria, and determining their relative importance and ranking in petroleum product distribution.
To achieve these objectives, a hybrid model was employed. First, the Delphi method was used to examine the interrelationships among the research criteria. In addition, fuzzy multi-criteria decision-making techniques, including the fuzzy Analytic Hierarchy Process, were applied to assign weights to the criteria. Subsequently, the VIKOR method was employed to prioritize the criteria.
The results confirm the existence of significant relationships among lean logistics, lean criteria, and transportation barriers. The findings further indicate that managerial barriers and lean managerial logistics represent the most critical obstacle and the most influential factor, respectively, in petroleum product distribution. These results provide practical guidance for managers seeking to enhance distribution performance and mitigate key transportation barriers.

Abstract Cryogenic nitrogen rejection from methane-rich natural gas is a critical operation for meeting LNG and pipeline gas specifications. However, it is highly susceptible to operational disturbances caused by solid formation in heat exchangers and distillation columns. This study proposes an integrated simulation framework that couples steady-state process modeling in Aspen HYSYS with rigorous solid–liquid–vapor equilibrium analysis in ThermoFAST to systematically evaluate freezing risks in CH4–N2 mixtures under nitrogen-rejection unit conditions. The nitrogen-rejection unit process, including multi-stream heat exchange, Joule–Thomson expansion, and cryogenic distillation, is modeled using the Peng–Robinson equation of state. ThermoFAST employs a Helmholtz-energy-based PC-SAFT equation of state, together with Lennard-Jones Weeks–Chandler–Andersen pure-solid references, to generate freezing envelopes over a wide pressure range of 0.1–10 MPa and across methane-rich to nitrogen-rich compositions relevant to industrial operations.
The framework is validated against available experimental solid–liquid equilibrium data, yielding mean absolute and relative deviations of 3.45 K and 5%, respectively, thereby demonstrating its suitability for hazard screening applications. Simulation results indicate that increasing pressure elevates the eutectic temperature and expands the stability region of the solid phase. In contrast, increasing nitrogen concentration depresses the eutectic temperature and narrows the solid stability range. Risk maps show that solid formation is most probable in methane-rich streams with CH4 greater than 0.75 at pressures of 10 MPa or higher, as well as in nitrogen-rich streams with CH4 less than 0.55 at extremely low temperatures, particularly after expansion and on the upper trays of the distillation column. The proposed integrated approach provides a predictive tool for identifying vulnerable operating zones, defining safe temperature–pressure margins, and enhancing the safety, operability, and efficiency of cryogenic nitrogen rejection processes.

Abstract Electric submersible pumps (ESPs) are widely used in oil and gas production; however, their reliability is challenged by diverse failure modes and inconsistent monitoring practices across manufacturers. This study develops a vendor-agnostic key performance indicator (KPI) framework to enable objective comparison of ESP performance across different brands. A representative dataset comprising 330 ESP runs across three major ESP brands was analyzed using standardized failure classification and statistical techniques, including Weibull reliability modeling. Failure modes were categorized into mechanical, electrical, hydraulic/gas-related, material, and operational classes and correlated with vendor-independent KPIs derived from thermal, electrical, hydraulic, and operational measurements. The results reveal distinct brand-specific failure patterns. Brand A is dominated by electrical failures associated with reduced thermal margin, Brand B exhibits a longer characteristic life with wear-out-dominated behavior, and Brand C shows greater susceptibility to hydraulic instability driven by gas interference. Several KPIs, including motor temperature margin (MTM), current imbalance (CI), and gas interference index (GII), consistently demonstrate potential as early-warning indicators of failure. The proposed framework enables cross-brand benchmarking, improves the interpretability of ESP health monitoring, and supports the development of predictive maintenance strategies independent of proprietary vendor systems.

Abstract The mechanisms underlying low-salinity water (LSW) flooding in carbonate reservoirs remain less well understood than those in sandstone reservoirs. This study investigates the role of rock composition through a comparative experimental analysis of natural limestone from the Shadegan oil field and pure synthetic calcite. Core-flooding experiments, contact angle measurements, zeta potential analysis, and pH monitoring were conducted under reservoir conditions of 70 °C and 100 bar. The results showed that tertiary LSW injection recovered 14.5% of the initial oil in place from the limestone, compared with only 3.4% from pure calcite. This substantial difference was attributed to a more pronounced wettability shift toward water-wet conditions in the limestone, as evidenced by a 42° reduction in contact angle compared with a 16° reduction in calcite. This wettability alteration was associated with a stronger negative shift in zeta potential in the natural limestone (−7.7 mV) than in the pure calcite (−4.1 mV). X-ray fluorescence (XRF) analysis revealed the presence of silica, sulfate, and phosphate impurities in the limestone, which enhanced the alteration of surface charge. These findings indicate that trace non-clay minerals can significantly enhance the effectiveness of LSW flooding in carbonate reservoirs, providing new insights into the optimization of enhanced oil recovery (EOR) in complex carbonate formations.

Abstract Matrix acidizing is an effective stimulation method for carbonate reservoirs that aims to reduce formation damage, enhance well productivity, and improve hydrocarbon recovery. Although numerous studies have investigated acid fluid types and their interactions with reservoir rocks, few have integrated the technical evaluation of matrix acidizing with an economic assessment for Iranian reservoirs. This study addresses this gap by providing a simulation-based technical and economic comparison of two acid systems—15% hydrochloric acid (HCl) and 15% emulsified acid (SXE)—applied to a carbonate reservoir well in Khuzestan Province, Iran. The performance of the two scenarios in terms of production rate, acid penetration, and wormhole development was modeled using WellBook software under identical conditions. The technical results indicated that emulsified acid achieved deeper wormhole penetration (approximately 5 ft compared with 2.5 ft for HCl), a greater reduction in skin factor (from +7 to −2.85 compared with +0.31 for HCl), and a larger increase in production rate (from 0.44 to 1.13 bbl/min compared with 0.75 bbl/min for HCl). Subsequently, the two scenarios were economically evaluated based on two procurement methods: domestic supply in Iranian rials and imported supply in U.S. dollars. The economic analysis was conducted using key financial indicators, including net present value (NPV), internal rate of return (IRR), modified internal rate of return (MIRR), and profitability index (PI). The technical and economic analyses demonstrated that emulsified acid outperformed 15% HCl in terms of production rate, wormhole penetration, and overall economic performance.