Solubility Of Hydrocarbons Research Articles

Thermodynamic modelling of systems involved in natural gas dehydration with triethylene glycol using a group contribution association model

Natural gas (NG) dehydration through absorption into Triethylene Glycol (TEG) is one of the most important applications in the NG industry. The optimal design of the TEG dehydration process requires a deep understanding of the thermodynamic behavior of mixtures containing TEG, water, hydrocarbons, and other compounds present in natural gas. In this work, the recently developed Universal Mixing Rule – Cubic Plus Association (UMR-CPA) group contribution equation of state (EoS) is extended to these systems. UMR-CPA combines the PR-CPA EoS with the UNIFAC group contribution activity coefficient model through the Universal Mixing Rules. Parameters for pure water, TEG and NG components were determined by accurately fitting vapor pressure, density and heat capacity data. For non-associating compounds, the model leads to overall deviations of 1.2 % in vapor pressures and 6.1 % in isobaric heat capacities. Water properties are also quite accurately described, with overall deviations of approximately 0.4 %, 1.2 % and 5.7 % in vapor pressures, liquid densities and isobaric heat capacities, respectively. The model was then applied to mixtures of water and TEG with gases and hydrocarbons by correlating the proper group interaction parameters. Very satisfactory results were obtained for both vapor-liquid and liquid-liquid phase equilibria in these systems, where also an adequate reproduction of the minimum of hydrocarbon solubility in water was noted. Finally, the UMR-CPA EoS was further validated through the prediction of the phase behavior of ternary systems including TEG and/or water and NG compounds. Very good predictions were achieved for the low TEG and water content in the vapor phase of the TEG-H2O-CH4 ternary system, with absolute deviations of around 0.05 and 23.26 ppm, respectively. Overall, the model yields accurate predictions, suggesting its suitability for designing the TEG dehydration process.

Surfactant-Based Chemical Washing to Remediate Oil-Contaminated Soil: The State of Knowledge.

As the energy demand increases, there is a significant expansion and utilization of oil resources, resulting in the inevitable occurrence of environmental pollution. Oil has been identified as a prevalent soil contaminant, posing substantial risks to the soil ecosystems. The remediation of soil contaminated with oil is a formidable undertaking. Increasing evidence shows that chemical washing, a remediation technique employing chemical reagents like surfactants to augment the solubilization, desorption, and separation of petroleum hydrocarbons in soil, proves to be an efficacious approach, but the latest advances on this topic have not been systematically reviewed. Here, we present the state of knowledge about the surfactant-based chemical washing to remediate oil-contaminated soil. Using the latest data, the present article systematically summarizes the advancements on ex situ chemical washing of oil pollution and provides a concise summary of the underlying principles. The use of various surfactants in chemical washing and the factors influencing remediation efficiency are highlighted. Based on the current research status and knowledge gaps, future perspectives are proposed to facilitate chemical washing of oil-polluted soil. This review can help recognize the application of chemical washing in the remediation of oil-polluted soil.

Micro-particle-clay aided microporous support for removal of hydrocarbon toxicity from petroleum refinery wastewater in single-slope solar distillation

The removal of toxic hydrocarbon pollutants is not sustainable via conventional treatment technology. This research investigates the feasibility of a solar distillation (SSD) system to eliminate soluble hydrocarbon salts, stubborn organics, and separation of oil emulsion from petroleum refinery wastewater (PRW). The PRW collected from the petroleum refinery facility and hydrophilic micro-particle clay (MP-clay) samples were characterized following the ASTM and SEM techniques. The treatment process for the removal of total dissolved salts (TDS), biological oxygen demand (BOD), and separation of oil emulsion was monitored in the SSD still using MP-clay aided microporous filter support, at varying MP-clay concentrations (0.5–3.5 mg/L), pH (6.6–7.3), temperature (27–80 0C), and exposure time (1–18hrs) respectively. The results showed MP-clay as self-assembled monolayers on microporous support with a 98 % reduction in BOD concentration under optimum conditions: pH of 7.3, contact time of 9 hours, and MP-clay concentration of 3.2 mg/L. The TDS concentration decreased by 94 % at the optimal exposure time (15hrs) and temperature (48 0C) in SSD still. The investigation also examined the recyclability of MP-clay for oil content accumulation and water separation. The findings demonstrated MP-clay on reuse retained 90.5 % of its activity in water separation and established MP-clay can be operated to the optimum of 3 cycles, each lasting 60 minutes. After this period, reactivity loss becomes severe owing to lipase leakage and substrate obstruction. The research findings will help harness solar energy for environmentally friendly and cost-effective PRW remediation.

Geochemical Characteristics of Mature to High-Maturity Shale Resources, Occurrence State of Shale Oil, and Sweet Spot Evaluation in the Qingshankou Formation, Gulong Sag, Songliao Basin

The exploration of continental shale oil in China has made a breakthrough in many basins, but the pure shale type has only been found in the Qingshankou Formation, Gulong Sag, Songliao Basin, and the evaluation of shale oil occurrence and sweet spot faces great challenges. Using information about the total organic carbon (TOC), Rock-Eval pyrolysis, vitrinite reflectance (Ro), kerogen elemental composition, carbon isotopes, gas chromatography (GC), bitumen extraction, and component separation, this paper systematically studies the organic geochemical characteristics and shale oil occurrence at the Qingshankou Formation. The G1 well, which was cored through the entire section of the Qingshankou Formation in the Gulong Sag, was the object of this study. On this basis, the favorable sweet spots for shale oil exploration are predicted. It is concluded that the shale of the Qingshankou Formation has high organic heterogeneity in terms of organic matter features. The TOC content of the source rocks in the Qingshankou Formation is enhanced with the increase in the burial depth, and the corresponding organic matter types gradually changed from Ⅱ2 and Ⅱ1 types to the Ⅰ type. The distribution of Ro ranges from 1.09% to 1.67%, and it is the mature to high-mature evolution stage that generates a large amount of normal crude oil and gas condensate. The high-quality source rocks of good to excellent grade are mainly distributed in the Qing 1 member and the lower part of the Qing 2 member. After the recovery of light hydrocarbons and the correction of pyrolytic heavy soluble hydrocarbons, it is concluded that the occurrence state of shale oil in the Qingshankou Formation is mainly the free-state form, with an average value of 6.9 mg/g, and there is four times as much free oil as adsorbed oil. The oil saturation index (OSI), mobile hydrocarbon content, Ro, and TOC were selected to establish the geochemical evaluation criteria for shale oil sweet spots in the Qingshankou Formation. The evaluation results show that interval 3 and interval 5 of the Qingshankou Formation in the G1 well are the most favorable sections for shale oil exploration.

Machine learning methods for predicting CO2 solubility in hydrocarbons

The application of carbon dioxide (CO2) in enhanced oil recovery (EOR) has increased significantly, in which CO2 solubility in oil is a key parameter in predicting CO2 flooding performance. Hydrocarbons are the major constituents of oil, thus the focus of this work lies in investigating the solubility of CO2 in hydrocarbons. However, current experimental measurements are time-consuming, and equations of state can be computationally complex. To address these challenges, we developed an artificial intelligence-based model to predict the solubility of CO2 in hydrocarbons under varying conditions of temperature, pressure, molecular weight, and density. Using experimental data from previous studies, we trained and predicted the solubility using four machine learning models: support vector regression (SVR), extreme gradient boosting (XGBoost), random forest (RF), and multilayer perceptron (MLP). Among four models, the XGBoost model has the best predictive performance, with an R2 of 0.9838. Additionally, sensitivity analysis and evaluation of the relative impacts of each input parameter indicate that the prediction of CO2 solubility in hydrocarbons is most sensitive to pressure. Furthermore, our trained model was compared with existing models, demonstrating higher accuracy and applicability of our model. The developed machine learning-based model provides a more efficient and accurate approach for predicting CO2 solubility in hydrocarbons, which may contribute to the advancement of CO2-related applications in the petroleum industry.

Machine learning prediction of methane, ethane, and propane solubility in pure water and electrolyte solutions: Implications for stray gas migration modeling

Hydraulic fracturing is an effective technology for hydrocarbon extraction from unconventional shale and tight gas reservoirs. A potential risk of hydraulic fracturing is the upward migration of stray gas from the deep subsurface to shallow aquifers. The stray gas can dissolve in groundwater leading to chemical and biological reactions, which could negatively affect groundwater quality and contribute to atmospheric emissions. The knowledge of light hydrocarbon solubility in the aqueous environment is essential for the numerical modelling of flow and transport in the subsurface. Herein, we compiled a database containing 2129 experimental data of methane, ethane, and propane solubility in pure water and various electrolyte solutions over wide ranges of operating temperature and pressure. Two machine learning algorithms, namely regression tree (RT) and boosted regression tree (BRT) tuned with a Bayesian optimization algorithm (BO) were employed to determine the solubility of gases. The predictions were compared with the experimental data as well as four well-established thermodynamic models. Our analysis shows that the BRT-BO is sufficiently accurate, and the predicted values agree well with those obtained from the thermodynamic models. The coefficient of determination (R2) between experimental and predicted values is 0.99 and the mean squared error (MSE) is 9.97 × 10−8. The leverage statistical approach further confirmed the validity of the model developed.

Enhanced Removal of Hydrocarbons from Crude Oil Sludge through Phytoremediation with Biosurfactant-producing Rhizobacteria

Discharge of crude oil (or its products) during the extracting, refining, and transporting into the environment have caused serious environmental distress due to their highly hydrophobic resistance, and persistence in nature and very difficult to be remediated from the environment. Therefore, an environmentally conscious approach to enhance the bioavailability (or solubility) of petroleum hydrocarbon pollutants in soil involves the utilization of biosurfactants. Biosurfactants play a crucial role in enhancing the desorption and solubilization of petroleum hydrocarbons, facilitating their assimilation by microorganisms. This research investigated the application of biosurfactant supplementation derived and purified from rhizobacteria of Scirpus grossus, which are capable of producing biosurfactants and degrading hydrocarbons, in the context of phytoremediation. The crude oil sludge used in this study was obtained from an industrial area containing 56,600±3;900 mg/kg of total petroleum hydrocarbon (TPH). The crude oil sludge was inoculated with biosurfactant, sodium dodecyl sulfate (SDS) as commercial surfactant and only with the presence of S. grossus in the vegetated tanks and monitored for 90 days in a greenhouse. The results indicated that the growth of S. grossus with the addition of biosurfactant was improved and new saplings were produced. After a 90-day exposure period, the removal efficiency of TPH from the soil demonstrated significant increases, reaching 90.3%, 84.1%, and 73.7% when treated with biosurfactant+S. grossus, SDS+S. grossus, and S. grossus only respectively. These percentages were notably higher compared to the non-planted control crates (CC) where the removal efficiency was only 17.9%. These results provide evidence that the introduction of biosurfactant through inoculation can elevate the bioavailability of organic pollutants, consequently augmenting their microbial degradation in the soil.

Unravelling Diatoms’ Potential for the Bioremediation of Oil Hydrocarbons in Marine Environments

The search for practical solutions to alleviate the destructive impact of petroleum hydrocarbons in marine environments is contributing to the implementation of prospecting strategies for indigenous microorganisms with biodegradative and bioremediation potential. The levels of petroleum contamination entering the marine environment each year have been estimated at around 1.3 million tonnes, a figure that is expected to increase by 1.9% annually over the next decade. The recent interest in decarbonizing our energy system and accelerating the clean energy transition has created a demand for greener technologies and strategies to find innovative, sustainable, and cost-effective treatments for the marine environment. Diatoms (Bacillariophyta) are one of the most diverse and successful taxa in coastal–marine environments and are a relatively untapped pool of biodiversity for biotechnological applications. Recent reports have revealed the significant presence of diatoms associated with oil spills and petroleum hydrocarbon degradation. Most diatoms can secrete substantial amounts of exopolysaccharides (EPSs) into their environment, which can act as biosurfactants that, in addition to oxygen and other enzymes produced by diatoms, create suitable conditions to enhance hydrocarbon solubility and degradation into less toxic compounds in seawater. Recent reports on the biodegradation of aliphatic and aromatic hydrocarbons by diatoms are indicative of the potential of these taxa to achieve success in the bioremediation of hydrocarbons in marine environments. This review highlights the main attributes and roles that diatoms could play in integrated strategies for biodegradation and bioremediation of petroleum hydrocarbon pollutants and as such represent a green, eco-friendly, and sustainable contribution to mitigate damage to biodiversity and value chains of marine ecosystems.

In vitro analysis of the three-component Rieske oxygenase cumene dioxygenase from Pseudomonas fluorescens IP01.

Rieske non-heme iron-dependent oxygenases (ROs) are a versatile group of enzymes traditionally associated with the degradation of aromatic xenobiotics. In addition, ROs have been found to play key roles in natural product biosynthesis, displaying a wide catalytic diversity with typically high regio- and stereo- selectivity. However, the detailed characterization of ROs presents formidable challenges due to their complex structural and functional properties, including their multi-component composition, cofactor dependence, and susceptibility to reactive oxygen species. In addition, the substrate availability of natural product biosynthetic intermediates, the limited solubility of aromatic hydrocarbons, and the radical-mediated reaction mechanism can further complicate functional assays. Despite these challenges, ROs hold immense potential as biocatalysts for pharmaceutical applications and bioremediation. Using cumene dioxygenase (CDO) from Pseudomonas fluorescens IP01 as a model enzyme, this chapter details techniques for characterizing ROs that oxyfunctionalize aromatic hydrocarbons. Moreover, potential pitfalls, anticipated complications, and proposed solutions for the characterization of novel ROs are described, providing a framework for future RO research and strategies for studying this enzyme class. In particular, we describe the methods used to obtain CDO, from construct design to expression conditions, followed by a purification procedure, and ultimately activity determination through various activity assays.

Plant biomass extracted eco-friendly natural surfactant enhanced bio-electrokinetic remediation of crude oil contaminated soil

The current work investigates bioremediation (BIO) and electrokinetic (EK) remediation of crude oil hydrocarbons utilizing the biomass-electrokinetic (BIO-EK) approaches. The use of natural surfactants derived from plant biomass may improve remediation capacity by enhancing the solubility of organic pollutants. Sapindus mukorossi, a natural surfactant producer, was extracted from plant biomass in this study. The crude oil biodegradation efficiency was reported to be 98 %. In nature, FTIR confirms that plant biomass is lipopeptide. GCMS revealed that the crude oil (C7 – C23) was efficiently bio-degraded from lower to higher molecular weight. The application of natural surfactants in electokinetic remediation increased the plant biomass degradation of crude oil polluted soil by 98% compared to electrokinetic 55% in 2 days. Natural surfactant improves hydrocarbon solubilization and accelerates hydrocarbon electro migration to the anodic compartment, as confirmed by the presence of greater total organic content than the electrokinetic. This study proves that BIO-EK compared with a natural surfactant derived from plant biomass may be utilized to improve in situ bioremediation of crude oil polluted soils.

Quantifying the Influence of Electrolytes on the Kinetics of Spontaneous Emulsification.

This study quantifies the influence of electrolytes on the kinetics of the spontaneous emulsification phenomenon (SEP) of heavy hydrocarbons in a nonionic surfactant solution. The rate of emulsifying hexadecane in Triton X-100, with the presence of sodium chloride and potassium chloride, has been measured using a technique of monitoring single oil droplet photography. The emulsion droplet size produced in the process was measured under the same conditions by using dynamic light scattering. The data obtained from the two experiments were employed to investigate the mass transfer coefficient of the surfactant molecules through the intermediate layer formed between hexadecane and the surfactant solution. It was found that the electrolytes in an aqueous solution increase the surfactant diffusion rate through the intermediate layer and reduce the emulsion droplet size. As a result, both electrolytes reduce the rate of spontaneous emulsification, with potassium chloride having a more substantial reduction. A model was developed to quantify the influence of electrolytes on the kinetics of the SEP. The data and modeling results verify the influence of ions on the kinetics of spontaneous emulsification. The results provide a significant foundation for predicting the solubilization of heavy hydrocarbons in an electrolyte solution.

Modeling Sorption of Hydrocarbons in Polyethylene with the SAFT-γ Mie Approach Combined with a Statistical-Mechanical Model to Describe Semicrystalline Polymers.

A recently developed statistical-mechanical model is applied systematically to estimate the fraction of tie-molecules (polymer chains linking different crystals directly or via entanglements) in semicrystalline polyethylene (PE) samples. The amorphous domains of the polymer are divided into constrained interlamellar domains and "free" outer-lamellar domains. A set of model parameters is assigned to each sample by correlating previous experimental measurements and minimizing the difference between the predicted solubility of pure hydrocarbons in the sample and the experimental values. We show that the sorption isotherms of multiple pure fluids in each sample can be described by a single parameter set, proving that the polymer-solute interactions (described accurately by the SAFT-γ Mie EoS) are decoupled from the sample-specific properties of the polymer. We find that ∼30% of the crystalline stems in the lamellae of PE are connected to tie-molecules, within the bounds suggested by previous theoretical and computational work. The transferability of the sample-specific parameters is assessed by predicting cosolubility effects and solubility at different temperatures, leading to good agreement with experimental data.

Production of Biosynthesized Silver Nanoparticles using Metarhizium anisopliae fungus for the Treatment of Petroleum Pollutants in Water

In this study, a silver nanoparticle (AgNPs) was created using a biological technique from an extract of the fungus Metarhizium anisopliae. The characteristics of the prepared AgNPs were identified by utilizing the optical, ultraviolet, and infrared absorbance spectroscopy. The shape, size, and charge distribution on the particles were determined by using scanning electron microscopy and zeta voltage analysis. The analysis of biological activity of the silver nanoparticles showed its effectiveness in treating pollutants, as confirmed by the reduction of higher than 93% weight of crude oil in contaminated water samples. The crude oil mass was effectively transformed into the gelatinous mass that lacks consistency and emulsification. The chemical analysis of NP-treated and untreated crude oil- contaminated water samples was performed using gas chromatography mass spectrometry (GC MASS). The results displayed the emergence of 55 graphic peaks, each of them indicating a chemical compound, in the control sample, while in the study sample, about 51 of these peaks disappeared and the area of the remaining 4 peaks was reduced. The silver nanoparticles' capability to maintain their effectiveness under cryogenic storage conditions for six months was tested and compared to that of the fungal isolation before the production of the silver nanoparticles. The results showed no significant changes in the shape, size, and efficiency of the silver nanoparticles in the treatment of oil pollutants in water. The results indicated the higher efficiency of the silver nanoparticles, as compared to chemicals, in treating petroleum pollutants as well as enhancing the solubility, emulsification, and degradation of hydrocarbons. In addition, the AgNPs are characterized by the availability of inexpensive, easy, fast to produce, and environmentally friendly production materials, as compared to the usage of chemical products that are highly toxic to aquatic organisms, expensive to produce, and highly accumulative in the ecosystem, i.e. environmentally unsafe.

Correlation of properties in hydrocarbons homologous series

Correlations between combustible substances properties in the homologous series of n-alkanes and n-alcohols with a length of nС=1–20 were studied in order to determine ways to increase the methods convergence for assessing fire hazard parameters. The cluster length was added to the substance modulating parameters set. It should be noted that substances properties are often predicted using a molecule coarse-grained model, which has discreteness, does not describe short molecules, and requires an individual approach. It is shown that there are substance "arithmetic" parameters that directly depend on the certain atoms number. Among them, "length" better reflects isomeric, conformal, cluster differences, which are associated with parameters anomalies of hydrocarbons. A vaporization heat linear description from nС separately for n-alkanes and n-alcohols gives R=0,999. Exponential approximation of the n-alkanes boiling point tbp and flash point tfp by nС change fractions has R=0,999. It is shown that there is a correlation between tfp and tbp, but with a systematic difference, which indicates that the cluster composition is not completely similar at these temperatures; between tfp and tmp there is a smaller correlation, but its presence indicates clusters partial similarity. A universal formula for predicting hydrocarbons vaporization heats of 10 homologous series has been created, which has R=0,996. The description change hydrocarbons pulsations of in tmp was carried out on the cluster schemes alternation basis in homologous series, as well as taking into account their length and molar mass, which gives R=0,9997. According to similar principles, a formula for the hydrocarbons solubility in the water has been developed, which has the satisfactory accuracy. The study showed that the cluster length is a determining factor by which substance properties are modulated.

Development of computational approach for calculation of hydrogen solubility in hydrocarbons for treatment of petroleum

For hydrogenation process, knowing the solubility of hydrogen (H2) in hydrocarbons is critical to improve the efficiency of process. We investigated the H2 solubility computation in four heavy crude oil feedstocks using machine learning techniques. Temperature, pressure, and feedstock type were considered as the inputs to the models, while the hydrogen solubility was the sole response. Specifically, we employed three different models: Support Vector Regression (SVR), Gaussian process regression (GPR), and Bayesian ridge regression (BRR). To achieve the best performance, the hyper-parameters of these models optimized using the whale optimization algorithm (WOA). We evaluated the models using a dataset of solubility measurements in various feedstocks, and we compared their performance based on several metrics. Our results show that the WOA-SVR model tuned with WOA achieves the best performance overall, with a RMSE of 1.38 × 10−2 and an R-squared of 0.991. These findings suggest that machine learning techniques can provide accurate predictions of hydrogen solubility in different feedstocks, which could be useful in the development of hydrogen-related technologies. Besides, the solubility of hydrogen in the four heavy oil fractions are estimated in different ranges of temperatures and pressures of 150 °C–350 °C and 1.2 MPa–10.8 MPa, respectively.

Biosurfactant: an emerging tool for the petroleum industries.

The petroleum sector is essential to supplying the world's energy demand, but it also involves numerous environmental problems, such as soil pollution and oil spills. The review explores biosurfactants' potential as a new tool for the petroleum sector. Comparing biosurfactants to their chemical equivalents reveals several advantages. They are ecologically sustainable solutions since they are renewable, nontoxic, and biodegradable. Biosurfactants are used in a variety of ways in the petroleum sector. They can improve the mobilization and extraction of trapped hydrocarbons during oil recovery procedures. By encouraging the dispersion and solubilization of hydrocarbons, biosurfactants also assist in the cleanup of oil spills and polluted locations by accelerating their breakdown by local microorganisms. The review gives insights into alternative methods for the petroleum industry that are more viable and cost-effective.

Intelligent solubility estimation of gaseous hydrocarbons in ionic liquids

The research focuses on evaluating how well new solvents attract light hydrocarbons, such as propane, methane, and ethane, in natural gas sweetening units. It is important to accurately determine the solubility of hydrocarbons in these solvents to effectively manage the sweetening process. To address this challenge, the study proposes using advanced empirical models based on artificial intelligence techniques like Multi-Layer Artificial Neural Network (ML-ANN), Support Vector Machines (SVM), and Least Square Support Vector Machine (LSSVM). The parameters for the SVM and LSSVM models are estimated using optimization methods like Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Shuffled Complex Evolution (SCE). Data on the solubility of propane, methane, and ethane in various ionic liquids are collected from reliable literature sources to create a comprehensive database. The proposed artificial intelligence models show great accuracy in predicting hydrocarbon solubility in ionic liquids. Among these, the hybrid SVM models perform exceptionally well, with the PSO-SVM hybrid model being particularly efficient computationally. To ensure a comprehensive analysis, different examples of hydrocarbons and their order are included. Additionally, a comparative analysis is conducted to compare the AI models with the thermodynamic COSMO-RS model for solubility analysis. The results demonstrate the superiority of the AI models, as they outperform traditional thermodynamic models across a wide range of data. In conclusion, this study introduces advanced artificial intelligence algorithms such as ML-ANN, SVM, and LSSVM in accurately estimating the solubility of hydrocarbons in ionic liquids. The incorporation of optimization techniques and variations in hydrocarbon examples improves the accuracy, precision, and reliability of these intelligent models. These findings highlight the significant potential of AI-based approaches in solubility analysis and emphasize their superiority over traditional thermodynamic models.

Intelligence-based simulation of solubility of hydrogen in bitumen at elevated pressure and temperature: Models optimization using fireworks algorithm

This research aimed to model the solubility of hydrogen in Athabasca bitumen as crude oil byproduct using temperature and pressure as input features. Correlative models based on machine learning were developed and validated via comparing with measured data of hydrogen solubility in bitumen at elevated pressure and temperature. Three ensemble machine learning algorithms based on Decision Trees were trained and evaluated: Extra Trees, Random Forest, and Adaboost DT, with hyperparameters optimized using the Fireworks Algorithm (FWA). The Extra Trees model (FWA-ET), with 26 trees achieved the highest performance, with an R2 score of 0.986, RMSE of 0.051, and AARD% of 6.981. The Random Forest model (FWA-RF) with 9 trees achieved an R2 score of 0.838, RMSE of 0.194, and AARD% of 27.830. The Adaboost DT model (FWA-ADT) with 153 trees achieved an R2 score of 0.846, RMSE of 0.142, and AARD% of 21.264. The results suggest that ensemble models based on Decision Trees can effectively predict the solubility of hydrogen in heavy hydrocarbons. Hyperparameter optimization using FWA improved model performance. The Extra Trees model achieved the highest R2 score, lowest RMSE, and AARD%. These findings may provide insights for the oil and gas industry to estimate hydrogen solubility in hydrocarbons.

Enrichment of free oil in alkaline lacustrine Fengcheng Formation in Mahu Sag

Estimation of free hydrocarbon quantities in tight formations where pore sizes are at the nano and micron size is very important to understand formation producibility. In the Mahu sag, the Fengcheng Formation is being considered as an important source rock. Although the importance, characteristics of the Fengcheng Formation in terms of controlling factors on the enrichment of free vs. adsorbed hydrocarbons in pore spaces is still not clear. In this study, 13 samples were collected from the Fengcheng Formation and a combination of analytical techniques: X-ray diffraction analysis (XRD), nitrogen gas adsorption, total organic content (TOC), and Rock Eval pyrolysis were employed to analyze the microstructures and geochemical properties of the samples. These analyses were performed on two groups: 1) intact samples and 2) when the soluble hydrocarbon were extracted from the samples. The pore surface area and pore volume were measured slightly higher in the latter group, which were around 2.12 and 1.23 times of the intact samples, respectively. Moreover, deconvolution of pore size distribution (PSD) curves demonstrated that soluble oil mainly exists in the pore phases: 1, 2, 3, and 4 with the mean pore diameter of 15, 35, 51, and 67 nm, respectively. Overall, the oil in the samples of the Fengcheng Formation can become enriched with mobile (vs. adsorbed) fluids if the quantities of the free generated oil and pore size, each exceeds 0.7 wt% and 10.94 nm, respectively. This study can help researchers deepen their understandings of oil mobility and the evaluation of formation producibility in reservoirs deposited in an alkaline lake sedimentary environment.

Investigation of the Solubility of Methane and Ethane in Hybrid Sweetening Solutions of 2-Aminoethanol and 1-Methylpyrrolidin-2-one/Water

Hydrocarbons of natural gas are captured by the solvent in the gas-sweetening absorption process and added to losses in production. Therefore, knowledge of hydrocarbon solubilities in solvents is required to assess and overcome these losses. In this study, the solubility of the main components of natural gas, methane and ethane, in n-methyl-2-pyrrolidone and monoethanolamine-based solvents was determined at the temperature of 313.15 K and pressures up to 2.366 MPa. Mass fractions of 0.10, 0.20, and 0.30 amine in the solutions were used in measurements that were performed using a static-synthetic method. The experimental data were modeled using the Soave–Redlich–Kwong Equation of State. Experimental results showed that the replacement of the aqueous part of conventional monoethanolamine solvents with n-methyl-2-pyrrolidone increased the solubility of desirable components of natural gas which is a negative side effect. The addition of monoethanolamine to n-methyl-2-pyrrolidone outperformed the Purisol solvent in terms of carbon dioxide absorption and decreased hydrocarbon solubility.