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.

An Integrated Mass Balance Approach for Assessing Hydrocarbon Resources in a Liquid-Rich Shale Resource Play:An Example from Upper Devonian Duvernay Formation,Western Canada Sedimentary Basin

Petroleum resource assessment using reservoir volumetric approach relies on porosity and oil/gas saturation characterization by laboratory tests.In liquid-rich resource plays,the pore fluids are subject to phase changes and mass loss when a drilled core is brought to the surface due to volume expansion and evaporation.Further,these two closely related volumetric parameters are usually estimated separately with gas saturation inferred by compositional complementary law,resulting in a distorted gas to oil ratio under the circumstances of liquid hydrocarbon loss from sample.When applied to liquid-rich shale resource play,this can lead to overall under-estimation of resource volume,distorted gas and oil ratio(GOR),and understated resource heterogeneity in the shale reservoir.This article proposes an integrated mass balance approach for resource calculation in liquid-rich shale plays.The proposed method integrates bulk rock geochemical data with production and reservoir parameters to overcome the problems associated with laboratory characterization of the volumetric parameters by restoring the gaseous and light hydrocarbon loss due to volume expansion and evaporation in the sample.The method is applied to a Duvernay production well(14-16-62-21 W5)in the Western Canada Sedimentary Basin(WCSB)to demonstrate its use in resource evaluation for a liquid-rich play.The results show that(a)by considering the phase behavior of reservoir fluids,the proposed method can be used to infer the quantity of the lost gaseous and light hydrocarbons;(b)by taking into account the lost gaseous and light hydrocarbons,the method generates an unbiased and representative resource potential;and(c)using the corrected oil and gas mass for the analyzed samples,the method produces a GOR estimate close to compositional characteristics of the produced hydrocarbons from initial production in 14-16-62-21 W5 well.