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Antibiotics are administered to livestock animals as medications and, in some jurisdictions, as growth promotors. This review examines the impact of veterinary antibiotics on methane production from manure anaerobic digestion (AD). The animals excrete about 17–90% of the administered antibiotics in manure unchanged or as metabolites, which adversely affect microorganisms catalyzing the manure AD, thereby reducing methane yields. Different antibiotics influence methane production to different extents (0–80%). The results from studies on manure artificially spiked with antibiotics differ from those on manure from antibiotic-fed animals, likely due to the effect of other bioactive substances in the manure. Over time, the microbial culture might adapt to the antibiotics, altering its composition, and further affecting the methane yield. Such adaptation indicates that short-term studies might not fully capture the antibiotic's long-term effects on AD. Effects of oxytetracycline and chlortetracycline on methane production are debatable, with chlortetracycline generally believed to have a slightly stronger inhibition. Correlation, nonlinear modeling/simulation, and principal component analysis (PCA) reveal that the antibiotic effects on methane yield are complex and depend on various parameters such as antibiotic type, concentration, application mode, duration, specific microbial communities, and digester conditions. The PCA showed that the temperature and concentration rather than the manure origin (pigs vs cows) dictate the magnitude of methane production inhibition. Data on the kinetics of antibiotics' impact, isomerization, and effects of operation strategies are missing. This review summarizes the main knowledge gaps concerning AD of antibiotics-containing manure and suggestions for operational strategies and future research. [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]

The solar flux distribution on the walls of the receivers in solar parabolic and circular collectors (PTCs and CTCs) is highly non-uniform. To prevent major problems caused by flux non-uniformity in such devices, a partially open aperture evacuated receiver (OAER) with an internal twisted insert is introduced, and its performance is compared to that of conventional evacuated receivers (CERs). A comprehensive computational fluid dynamic (CFD) model is developed for the receivers. The simulation results show that the proposed receiver has a more uniform solar flux distribution, and consequently greater energy and exergy efficiencies compared to CERs. The proposed OAER acts as a fluid turbulator, absorbs solar energy more efficiently, distributes the temperature gradient inside the absorber more uniformly, shifts the maximum temperature on the exterior surface of the CERs to the interior surface, and reduces radiative losses. The enhancement in total heat absorbed by OAER with 10 blades (OAER_10) compared to the CER_0 is 7.69%. The simulation results reveal that the average daily exergy efficiency of an OAER_10 is about 27.8% higher than a CER_0. The average daily exergy efficiency of a CER_0 is obtained to be about 7.55%, while it decreases to 7.42% for a CER_6 due to larger exergy destruction caused by air pressure drop. The introduced design offers a higher potential for efficient absorption of solar energy (proper sensible heat storage device) and provides a more useful contact area. [ABSTRACT FROM AUTHOR]

Determination of the essential properties of petroleum reservoir rocks and parameters of fluid flow in the porous media is a challenging part of reservoir characterization. Among other reservoir rock and two-phase flow properties, the relative permeabilities of gas–oil and water–oil systems are required as critical inputs for reservoir simulation models. Generally, laboratory experiments are considered the best way to obtain accurate information on reservoir rock and flow properties. However, due to the high costs and lengthy procedures of laboratory analyses of reservoir rocks and due to other reasons such as unavailability of the required rock samples, particularly during the early stages of reservoir life, the use of other techniques to predict these properties is inevitable. In this study, three advanced machine learning methods were used to predict relative permeability of oil in the presence of water (krow), relative permeability of water (krw), relative permeability of oil in the presence of gas (krog), and relative permeability of gas (krg). These methods are adaptive network-based fuzzy inference system (ANFIS), the hybrid of least square support vector machine (LSSVM) with coupled simulated annealing (CSA), and extremely randomized trees, also known as extra trees (ET). We used two extensive sets of experimental data on relative permeabilities in the water–oil and gas–oil systems to develop models with good generalization and universality. The prediction accuracy and reliability of the developed models were evaluated using two standard statistical quality measures, i.e., root mean square deviation (RMSD) and the regression coefficient (R2). The results show that while all the developed models were capable of matching the experimental results with reasonable accuracies, the ET and hybrid LSSVM–CSA models provided better prediction performance and superior correlations between the measured and predicted values. Moreover, this study shed light on to what degree each rock-flow property is important in the estimation of water–oil and gas–oil relative permeabilities. In this regard, we applied feature reduction based on the ET feature importance rankings for developing new ET models to estimate water–oil relative permeabilities using only two input features, i.e., Sw and Swc, for prediction of krow, and Sw and wettability type for prediction of krw, instead of the seven input parameters used in development of the main models. [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]

Offshore oil and gas reservoirs comprise a significant portion of the world's reserve base, and their development is expected to help close a potential gap in the supply of hydrocarbons in the near future. Continuous advances in technology have helped the oil and gas industry to extend the exploration and production activities to deep and ultra-deep waters in harsher environments. Field development in the offshore environment is associated with numerous significant challenges in different phases including exploration, reservoir description and characterization, development planning, drilling, production, improved oil recovery (IOR) and enhanced oil recovery (EOR), transportation, and decommissioning. These challenges are further complicated by economic restrictions, especially in periods when low or unstable oil prices weaken the incentives of investments due to the high risks and uncertainties involved. Environmental concerns including the adverse effects of seismic surveys, drilling activities, discharge of waste material and produced water, and accidental spills and blowouts add another level of complexity to the design and implementation of offshore projects. Safe, reliable, and efficient development of offshore hydrocarbon reservoirs can be achieved through the proper identification of the challenges and use of modern technologies and innovative methods. This work is an effort to provide a comprehensive review of the history, global distribution, production share, classification, characteristics, and characterization methods of offshore reservoirs, and some of the main aspects and challenges of the experimental and modeling works as well as IOR/EOR planning and implementations from the reservoir engineering and production standpoint. These challenges are categorized based on their area of impact, i.e., characterization and recovery (production) phases. Furthermore, economic and environmental challenges are reviewed. After the recent downturn that caused a decline in offshore investments, current investment trends and the forecasts show that the offshore industry is booming again with more focus on cost saving and operational efficiency. The key technologies that can expedite the growth of the offshore petroleum industry have also been discussed. [ABSTRACT FROM AUTHOR]

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]

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]