Iranian Journal of Oil and Gas Science and Technology

Abstract Recently, several techniques have been suggested for measuring fluid density, as a highly applicable parameter in industries. Among these, gamma-ray densitometry is highly recommended due to its benefits such as non-destructive, operator dependence and online monitoring. This study evaluates the feasibility of using gamma-ray-based density measurement at the Shahid Ahmadi-Roshan oil storage facility in Kerman Province, Iran. The results of this work are valuable due to the specific condition of pipeline maps and their physical properties at this site, as unique case study. A Monte Carlo simulation was conducted with FLUKA code, modeling two pipelines used for gasoline and gas oil transport, based on the dimensions, geometries, and elemental compositions of each to replicate actual conditions. Cobalt-60 and Cesium-137 isotropic gamma-ray sources were defined to measure photon flux with a simulated detection system. The simulations explored various densities of gasoline and gas oil, recording flux values at each density level. According to the results, both gamma-ray sources could effectively serve as primary components of the proposed densitometer. However, Cesium-137 is preferable due to its significant photon flux variations corresponding to changes in pipeline wall thickness and fluid density. A gamma-ray-based densitometer is recommended for practical use at this site, as it offers a more efficient alternative to the current operator-dependent, offline hydrometric method. Given the proximity of the two pipelines, a single-source dynamic densitometer could be implemented to substantially lower costs.

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 Process pipelines are essential for operations, but their failures can have severe consequences. This study investigates and analyzes the consequence modeling of the pipeline corridor located in southern Iran. It also determines the restricted and impacted areas around the pipelines using PHAST software. The consequence modeling of the main scenarios, including flammable and toxic gas dispersion, fire, and explosion, is analyzed. According to the results, the restricted and impacted areas around the pipelines are 426.9 meters and 454.3 meters for section 1, and 648 meters and 802 meters for section 2, respectively.

Following the results, preventive and mitigative measures are recommended to lower risks. Multiple strategies should be employed to reduce the risks associated with the pipelines. Some strategies aim to decrease the frequency of incidents, while others focus on reducing the impacts and consequences of leakage, rupture, or any other type of containment failure.

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 This work is a primary achievement in studying the CO₂ and N₂–oil systems. To predict gas-liquid relative permeability curves, a Shan-Chen type multicomponent multiphase lattice Boltzmann model for two-phase flow through 2D porous media is developed. Periodic and bounce back boundary conditions are applied to the model with the Guo scheme for the external body force (i.e., the pressure gradient). The influence of relationship between cohesion and adsorption parameters and the interfacial tension values in Young's equation, pore structure (micro scan image derived porous media response is compared with corresponding porosity and permeability ideal sphere pack structure), and saturation distribution on relative permeability curves are studied with the aim to achieve the realistic stable condition for the simulation of gas-liquid systems with a low viscosity ratio.

Abstract In sour gas flares,  content like any other components in inlet gas influences adiabatic flame temperature, which, in turn, impacts on the pollutant emission. Wherever flame temperature increases, the endothermic reaction between  and  is accelerated, which means higher  emission to the atmosphere. In this work, we developed an in-house MATLAB code to provide an environment for combustion calculations. Then, this written code was used to perform sensitivity analyses on  content, air temperature, and excess air ratio in sour gas flares. We used Environmental Protection Agency (EPA) reports to assign weighting indexes to each air contaminant according to its harmfulness to environment; thereafter, sour gas flaring conditions were optimized for two real field case studies, namely Ahwaz (AMAK) and South Pars, to reach the minimum integrated pollutant concentrations. The results show that each 2% increase in the  content of the entrance feed may produce 0.3% additional  in the exhaust. The results also confirm that decreases of 20 °F and 50 °F in the oxidant temperature cause  emission to reduce by 0.5% to 1% respectively. Finally, to verify and validate our results acquired from the written MATLAB code, FRNC 2012 industrial software was used to duplicate the oxidation results for the two sour flare case studies.

Abstract Pore pressureis defined as the pressure of the fluid inside the pore space of the formation, which is also known as the formation pressure. When the pore pressure is higher than hydrostatic pressure, it is referred to as overpressure. Knowledge of this pressure is essential for cost-effective drilling, safe well planning, and efficient reservoir modeling. The main objective of this study is to estimate the formation pore pressure as a reliable mud weight pressure using well log data at one of oil fields in the south of Iran. To obtain this goal, the formation pore pressure is estimated from well logging data by applying Eaton’s prediction method with some modifications. In this way, sonic transient time trend line is separated by lithology changes and recalibrated by Weakley’s approach. The created sonic transient time is used to create an overlay pore pressure based on Eaton’s method and is led to pore pressure determination. The results are compared with the pore pressure estimated from commonly used methods such as Eaton’s and Bowers’s methods. The determined pore pressure from Weakley’s approach shows some improvements in comparison with Eaton’s method. However, the results of Bowers’s method, in comparison with the other two methods, show relatively better agreement with the mud weight pressure values.

Abstract In last decades, oil spill pollution has become an important issue of concern due to its serious environmental impacts; therefore, necessary actions should be taken to prevent or reduce these types of pollution and their environmental consequences. Natural organic sorbents are emerging as proper choices for oil spill cleanup due to their availability, eco-friendliness, and low cost. In this study, phragmites australis, sugarcane leaves straw, and sugarcane bagasse were used for crude oil sorption in dry (only oil) systems. The results indicated that sugarcane bagasse had a higher oil sorption capacity compared to the others. Therefore, sugarcane bagasse was selected as the preferred sorbent and the effects of sorbent contact time and its particle size on oil adsorption capacity were evaluated for the systems of dry and crude oil layer on water. The results showed that the maximum adsorption capacity of raw sugarcane bagasse for dry system and crude oil layer system was about 8 and 6.6 gram crude oil per gram sorbent respectively.

Abstract Reaction furnace is the most important part of the Claus sulfur recovery unit and its performance has a significant impact on the process efficiency. Too many reactions happen in the furnace and their kinetics and mechanisms are not completely understood; therefore, modeling reaction furnace is difficult and several works have been carried out on in this regard so far. Equilibrium models are commonly used to simulate the furnace, but the related literature states that the outlet of furnace is not in equilibrium and the furnace reactions are controlled by kinetic laws; therefore, in this study, the reaction furnace is simulated by a kinetic model. The predicted outlet temperature and concentrations by this model are compared with experimental data published in the literature and the data obtained by PROMAX V2.0 simulator. The results show that the accuracy of the proposed kinetic model and PROMAX simulator is almost similar, but the kinetic model used in this paper has two importance abilities. Firstly, it is a distributed model and can be used to obtain the temperature and concentration profiles along the furnace. Secondly, it is a dynamic model and can be used for analyzing the transient behavior and designing the control system.