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In-depth understanding of interactions between crude oil and CO2 provides insight into the CO2-based enhanced oil recovery (EOR) process design and simulation. When CO2 contacts crude oil, the dissolution process takes place. This phenomenon results in the oil swelling, which depends on the temperature, pressure, and composition of the oil. The residual oil saturation in a CO2-based EOR process is inversely proportional to the oil swelling factor. Hence, it is important to estimate this influential parameter with high precision. The current study suggests the predictive model based on the least-squares support vector machine (LS-SVM) to calculate the CO2-oil swelling factor. A genetic algorithm is used to optimize hyperparameters (γ and σ2) of the LS-SVM model. This model showed a high coefficient of determination (R2 = 0.9953) and a low value for the mean-squared error (MSE = 0.0003) based on the available experimental data while estimating the CO2-oil swelling factor. It was found that LS-SVM is a straightforward and accurate method to determine the CO2-oil swelling factor with negligible uncertainty. This method can be incorporated in commercial reservoir simulators to include the effect of the CO2-oil swelling factor when adequate experimental data are not available. [ABSTRACT FROM AUTHOR]

Comprehensive knowledge and analysis of in situ rock strength and geo-mechanical characteristics of rocks are crucial in hydrocarbon and mineral exploration stage to maximize wellbore performance, maintain wellbore stability, and optimize hydraulic fracturing process. Due to the high cost of laboratory-based measurements of rock mechanics properties, the log-based prediction is a viable option. Nowadays, the machine learning tools are being used for estimation of the in situ rock properties using wireline log data. This paper proposes a machine learning approach for rock strength (uniaxial compressive strength) prediction. The main objectives are to investigate the performance of data-driven predictive model in determining this vital parameter and to select features of predictor log variables in the model. The backpropagation multilayer perception (MLP) artificial neural network (ANN) with Levenberg–Marquardt training algorithm as well as the least squares support vector machine (LS-SVM) with coupled simulated annealing (CSA) optimization technique is employed to develop the dynamic data-driven models. Capturing nonlinear, high dimensional, and complex nature of real field log data, the rock strength models' performances are evaluated using statistical criteria to ensure concerning the model reliability and accuracy. The model predictions are compared and validated against the measured values as well as the results obtained from existing log-based correlations. Both the MLP-ANN and the CSA-based LS-SVM connectionist strategies are able to predict the rock strength so that there is a very good match between the model results and corresponding measured values. The input log parameters are ranked based on their contributions in prediction performance. The acoustic travel time and gamma ray are found to have the highest relative significance in estimating rock strength. New correlations are also developed to obtain the in situ rock strength of the siliciclastic sedimentary rocks using the most important log parameters such as dynamic sonic slowness, formation electron density, and shalyness effect. The developed correlations can be used to obtain quick estimation of dynamic uniaxial compressive strength profile using wireline logging data, instead of static data from the surface measurements or laboratory data. It is expected that the proposed models and tools will enable oil and gas engineers to better predict rock strength and thus enhance wellbore performance. [ABSTRACT FROM AUTHOR]

This study focused on developing a new cocktail extraction agent (CEA) composed of solvent and a new surfactant material (SM) for enhancing the efficiency of fuel recovery from real waste oil sludge (WSO). The effects of different solvents (e.g. methyl ethyl ketone (MEK), naphtha, petrol and kerosene), SMs (Dowfax and sodium thiosulfate), extraction time (10–20 min), extraction temperatures (20–60 °C) and CEA/sludge ratios (1–4) on the extraction performance were investigated. SMs and DBBE design enhanced the extraction efficiency by increasing the dispersion of solvent in WSO and enhancing the mixing and mass transfer rates. Results proved that Dowfax was the best SM for oil recovery under various conditions. The best CEA (e.g. MEK and Dowfax) provides the maximum fuel recovery rate of 97% at a period of 20 min, temperature of 60 °C and 4:1 CEA/sludge ratio. The produced fuel was analysed and fed to the distillation process to produce diesel oil. The characteristics of diesel oil were measured, and findings showed that it needs treatment processes prior its use as a finished fuel. [ABSTRACT FROM AUTHOR]

In this work, the MCM-48 mesoporous material was prepared and characterized to apply it as an active adsorbent for the adsorption of 4-nitroaniline (4-Nitrobenzenamine) from wastewater. The MCM-48 characterizations were specified by implementing various techniques such as; scanning electron microscopy (SEM), Energy dispersive X-ray analysis (EDAX), X-ray diffraction (XRD), Brunauer–Emmett–Teller (BET) surface area, pore size distribution (PSD), and Fourier transform infrared (FTIR). The batch adsorption results showed that the MCM-48 was very active for the 4-nitroaniline adsorption from wastewater. The adsorption equilibrium results were analyzed by applying isotherms like Langmuir, Freundlich, and Temkin. The maximum experimental uptake according to type I Langmuir adsorption was found to be 90 mg g−1 approximately. The Langmuir model with determination coefficient R2 = 0.9965 is superior than the Freundlich model R2 = 0.99628 and Temkin model R2 = 0.9834. The kinetic adsorption was investigated according to pseudo 1st order, pseudo 2nd order, and Intraparticle diffusion model. The kinetic results demonstrated that the regression coefficients are so high R2 = 0.9949, that mean the pseudo 2nd order hypothesis for the adsorption mechanism process appears to be well-supported. The findings of adsorption isotherms and kinetics studies indicate the adsorption mechanism is a chemisorption and physical adsorption process. [ABSTRACT FROM AUTHOR]

Incorporating PVT properties and compositional evolution of oil inclusions into reservoir engineering simulator protocols can enhance understanding of oil accumulation, reservoir charge history, and migration events. Microthermometry and volumetric analysis have proven to be useful tools in compositional reconstitution and PT studies of oil inclusions and were used to determine composition, thermodynamic conditions, physical properties, and gas-to-oil ratios of heavy oil samples from Asmari carbonate reservoir in Kuh-e-Mond heavy oil field in Iran. PVT properties were predicted using a PVT black-oil model, and an acceptable agreement was observed between the experiments and the simulations. Homogenization temperatures were determined using microthermometry techniques in dolomite and calcite cements of the Asmari Formation, as well. Based on the homogenization temperature data, the undersaturated hydrocarbon mixture prior to formation of the gas cap migrated with a higher gas-to-oil ratio from a source rock. According to the oil inclusion data, the onset of carbonate cementation occurred at temperatures above 45 °C and that cementation was progressive through burial diagenesis. PVT black-oil simulator results showed that the reservoir pressure and temperature were set at 100 bar and 54 °C during the initial stages of oil migration. Compositional modeling implies that primary and secondary cracking in source rocks were responsible for retention of heavy components and migration of miscible three-phase flow during hydrocarbon evolution. The PT evolution of the petroleum inclusions indicates changes in thermodynamic properties and mobility due to phenomena such as cracking, mixing, or/and transport at various stages of oil migration. [ABSTRACT FROM AUTHOR]