Iranian Journal of Oil and Gas Science and Technology

Abstract Unlike traditional approaches, 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 utilized as predictive algorithms to simulate the cementation exponent based on various pore descriptions and total porosity. To optimize the parameters of the MLPNN approach, Levenberg-Marquardt (LM) is coupled with MLPNN, leading to the development of a hybrid approach named MLPNN-LM. This hybrid model efficiently optimizes neural network parameters, significantly improving accuracy and convergence speed. The necessary databank for constructing, validating, and predicting with the proposed models is derived from Ragland's work and classified into test, validation, and train subsets. The results reveal high precision for the hybrid MLPNN-LM and RF techniques, with key statistical measures such as the Average Absolute Percentage Relative Deviations (AAPRD%) (i.e., the percentage of relative error) of 4.3781% for MLPNN-LM and 4.8690% for RF, and determination coefficients (R²) (i.e., the fitness magnitude of estimated and measured values around Y=X line) of 0.8654 for MLPNN-LM and 0.8731 for RF. The highly accurate estimates of the cementation exponent provided by MLPNN-LM and RF surpass those from commonly applied literature correlations. Sensitivity analysis shows the significant impact of interparticle, moldic, and connected vuggy pore types on the modeling output. The trustworthiness of the databank and the accuracy of the proposed MLPNN-LM and RF approaches are verified by Williams’ plot, with approximately 93.75% and 91.96% of the databank within the applicability domain, respectively. Trend analysis demonstrates a good match of predicted cementation exponent with actual data, especially for deep formations (i.e., depth greater than 1200 m) and tight carbonate reservoirs (i.e., total porosities less than 10%), where traditional correlations face challenges due to the noticeable complexity in the behavior of cementation exponent.

Abstract Optimization of transportation for organizational projects is paramount, as identifying and evaluating transportation barriers constitutes key strategic decisions for managers and decision-makers. This study focuses on three main 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. A hybrid model was employed to achieve these objectives. First, the Delphi method was utilized to facilitate the examination of interrelationships among the research criteria. Additionally, fuzzy multi-criteria decision-making techniques, including the fuzzy Analytic Hierarchy Process, were applied to assign specific weights to the criteria. Subsequently, the VIKOR method enabled the prioritization of these criteria. The findings of this study confirm the existence of significant relationships between lean logistics, lean criteria, and transportation barriers, highlighting that managerial barriers and lean managerial logistics are the most critical obstacles and factors, respectively, in petroleum product distribution. These findings can assist managers in improving distribution processes and mitigating key transportation barriers.

Abstract Cryogenic nitrogen rejection from methane-rich natural gas is a critical operation for meeting LNG and pipeline gas specifications. Yet, 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 (SLVE) analysis in ThermoFAST to evaluate freezing risks in CH4 systematically–N2 mixtures under nitrogen-rejection unit (NRU) conditions. The NRU 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 (LJ-WCA) pure-solid references, to generate freezing envelopes over a wide pressure range (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, demonstrating its suitability for hazard screening applications. Simulation results reveal that increasing pressure elevates the eutectic temperature and expands the stability region of the solid phase. In contrast, increasing nitrogen concentration depresses the eutectic point and narrows the solid stability range. Risk maps indicate that solid formation is most probable in methane-rich streams (CH4 > 0.75) at pressures of 10 MPa or higher, as well as in nitrogen-rich streams (CH4 < 0.55) at extremely low temperatures, particularly after expansion and in 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, yet 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 longer characteristic life with wear-out-dominated behavior, and Brand C shows higher susceptibility to hydraulic instability driven by gas interference. Several KPIs, including Motor Temperature Margin (MTM), Current Imbalance (CI), and Gas Interference Index (GII), consistently provide early-warning indicators of failure.

The proposed framework enables cross-brand benchmarking, improves interpretability of ESP health monitoring, and supports the development of predictive maintenance strategies independent of proprietary vendor systems.

Abstract The mechanisms of Low-Salinity Water (LSW) flooding in carbonate reservoirs remain less well understood than in sandstones. This study investigates the role of rock composition through a comparative experimental analysis of genuine limestone from the Shadegan oil field and pure synthetic calcite. Core flooding tests, contact angle measurements, zeta potential analysis, and pH monitoring were conducted under reservoir conditions (70°C, 100 bar). Results show that tertiary LSW injection recovered 14.5% of the initial oil in place in limestone, compared to only 3.4% in pure calcite. This significant difference is attributed to a more pronounced wettability shift towards water-wet conditions (42° contact angle reduction vs. 16° in calcite), driven by a stronger negative zeta potential shift (-7.7 mV vs. -4.1 mV) in the genuine rock. XRF analysis revealed the presence of silica, sulfate, and phosphate impurities in the limestone, which amplified the surface charge alteration. The study concludes that trace non-clay minerals significantly enhance LSW efficacy in carbonates, providing new insights for optimizing EOR in complex carbonate formations.

Abstract Matrix acidizing is one of the effective methods for carbonate reservoirs, aiming to reduce formation damage, enhance well production, and improve hydrocarbon recovery. Although numerous studies have examined acid fluid types and their interaction with reservoir rock, few have combined technical matrix acidizing with economic evaluation for Iranian reservoirs. This study addresses that gap by integrating a simulation-based technical and economic comparison of two acid types — 15% hydrochloric acid (HCL) and emulsified acid (SXE 15%) — in a carbonate reservoir well in Khuzestan province, Iran. The outcomes of these two scenarios, in terms of production rate, acid penetration, and wormhole creation, were modeled using WellBook software under identical conditions. Technical results indicated that emulsified acid achieved deeper wormhole penetration (approximately 5 ft versus 2.5 ft for HCL), greater skin factor reduction (from +7 to –2.85 versus +0.31 for HCL), and higher production increase (from 0.44 to 1.13 bbl/min versus 0.75 bbl/min). Subsequently, the scenarios were evaluated economically based on two procurement methods, i.e., domestic supply in Iranian Rials and import supply in US Dollars. The economic analysis was conducted using key financial indicators such as NPV, IRR, MIRR, and PI. The technical and economic analyses demonstrated that emulsified acid outperforms HCL 15% acid in terms of production rate, wormhole penetration, and overall economic performance.