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Wei-yu C, Sun L, Zhou J, Li X, Huang L, Xia G, Meng X, Wang K. Toward Predicting Interfacial Tension of Impure and Pure CO 2-Brine Systems Using Robust Correlative Approaches. ACS OMEGA 2024; 9:7937-7957. [PMID: 38405476 PMCID: PMC10882694 DOI: 10.1021/acsomega.3c07956] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 01/18/2024] [Accepted: 01/23/2024] [Indexed: 02/27/2024]
Abstract
In the context of global climate change, significant attention is being directed toward renewable energy and the pivotal role of carbon capture and storage (CCS) technologies. These innovations involve secure CO2 storage in deep saline aquifers through structural and capillary processes, with the interfacial tension (IFT) of the CO2-brine system influencing the storage capacity of formations. In this study, an extensive data set of 2811 experimental data points was compiled to model the IFT of impure and pure CO2-brine systems. Three white-box machine learning (ML) methods, namely, genetic programming (GP), gene expression programming (GEP), and group method of data handling (GMDH) were employed to establish accurate mathematical correlations. Notably, the study utilized two distinct modeling approaches: one focused on impurity compositions and the other incorporating a pseudocritical temperature variable (Tcm) offering a versatile predictive tool suitable for various gas mixtures. Among the correlation methods explored, GMDH, employing five inputs, exhibited exceptional accuracy and reliability across all metrics. Its mean absolute percentage error (MAPE) values for testing, training, and complete data sets stood at 7.63, 7.31, and 7.38%, respectively. In the case of six-input models, the GEP correlation displayed the highest precision, with MAPE values of 9.30, 8.06, and 8.31% for the testing, training, and total data sets, respectively. The sensitivity and trend analyses revealed that pressure exerted the most significant impact on the IFT of CO2-brine, showcasing an adverse effect. Moreover, an impurity possessing a critical temperature below that of CO2 resulted in an elevated IFT. Consequently, this relationship leads to higher impurity concentrations aligning with lower Tcm values and subsequently elevated IFT. Also, monovalent and divalent cation molalities exhibited a growing influence on the IFT, with divalent cations exerting approximately double the influence of monovalent cations. Finally, the Leverage approach confirmed both the reliability of the experimental data and the robust statistical validity of the best correlations established in this study.
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Affiliation(s)
- Chen Wei-yu
- CNOOC
EnerTech-Drilling & Production Co., Ltd., Tianjin 300452, China
| | - Lin Sun
- CNOOC
EnerTech-Drilling & Production Co., Ltd., Tianjin 300452, China
| | - Jiyong Zhou
- CNOOC
EnerTech-Drilling & Production Co., Ltd., Tianjin 300452, China
| | - Xuguang Li
- CNOOC
EnerTech-Drilling & Production Co., Ltd., Tianjin 300452, China
| | - Liping Huang
- CNOOC
EnerTech-Drilling & Production Co., Ltd., Tianjin 300452, China
| | - Guang Xia
- CNOOC
EnerTech-Drilling & Production Co., Ltd., Tianjin 300452, China
| | - Xiangli Meng
- CNOOC
EnerTech-Drilling & Production Co., Ltd., Tianjin 300452, China
| | - Kui Wang
- State
Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum (Beijing), Beijing 102249, China
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Dalal Isfehani Z, Sheidaie A, Hosseini M, Fahimpour J, Iglauer S, Keshavarz A. Interfacial tensions of (brine + H2 + CO2) systems at gas geo-storage conditions. J Mol Liq 2023. [DOI: 10.1016/j.molliq.2023.121279] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
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Rostami A, Kordavani A, Parchekhari S, Hemmati-Sarapardeh A, Helalizadeh A. New insights into permeability determination by coupling Stoneley wave propagation and conventional petrophysical logs in carbonate oil reservoirs. Sci Rep 2022; 12:11618. [PMID: 35804036 PMCID: PMC9270338 DOI: 10.1038/s41598-022-15869-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 06/30/2022] [Indexed: 11/09/2022] Open
Abstract
The need to determine permeability at different stages of evaluation, completion, optimization of Enhanced Oil Recovery (EOR) operations, and reservoir modeling and management is reflected. Therefore, various methods with distinct efficiency for the evaluation of permeability have been proposed by engineers and petroleum geologists. The oil industry uses acoustic and Nuclear Magnetic Resonance (NMR) loggings extensively to determine permeability quantitatively. However, because the number of available NMR logs is not enough and there is a significant difficulty in their interpreting and evaluation, the use of acoustic logs to determine the permeability has become very important. Direct, continuous, and in-reservoir condition estimation of permeability is a unique feature of the Stoneley waves analysis as an acoustic technique. In this study, five intelligent mathematical methods, including Adaptive Network-Based Fuzzy Inference System (ANFIS), Least-Square Support Vector Machine (LSSVM), Radial Basis Function Neural Network (RBFNN), Multi-Layer Perceptron Neural Network (MLPNN), and Committee Machine Intelligent System (CMIS), have been performed for calculating permeability in terms of Stoneley and shear waves travel-time, effective porosity, bulk density and lithological data in one of the naturally-fractured and low-porosity carbonate reservoirs located in the Southwest of Iran. Intelligent models have been improved with three popular optimization algorithms, including Coupled Simulated Annealing (CSA), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA). Among the developed models, the CMIS is the most accurate intelligent model for permeability forecast as compared to the core permeability data with a determination coefficient (R2) of 0.87 and an average absolute deviation (AAD) of 3.7. Comparing the CMIS method with the NMR techniques (i.e., Timur-Coates and Schlumberger-Doll-Research (SDR)), the superiority of the Stoneley method is demonstrated. With this model, diverse types of fractures in carbonate formations can be easily identified. As a result, it can be claimed that the models presented in this study are of great value to petrophysicists and petroleum engineers working on reservoir simulation and well completion.
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Affiliation(s)
- Alireza Rostami
- Department of Petroleum Engineering, Petroleum University of Technology (PUT), Ahwaz, Iran.
| | - Ali Kordavani
- Department of Petrophysics Engineering, National Iranian South Oil Company (NISOC), Ahwaz, Iran
| | - Shahin Parchekhari
- Department of Petroleum Engineering, Kish International Campus, University of Tehran, Kish, Iran
| | - Abdolhossein Hemmati-Sarapardeh
- Department of Petroleum Engineering, Shahid Bahonar University of Kerman, Kerman, Iran. .,Key Laboratory of Continental Shale Hydrocarbon Accumulation and Efficient Development, Ministry of Education, Northeast Petroleum University, Daqing, 163318, China.
| | - Abbas Helalizadeh
- Department of Petroleum Engineering, Petroleum University of Technology (PUT), Ahwaz, Iran
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Ensemble Tree-Based Approach towards Flexural Strength Prediction of FRP Reinforced Concrete Beams. Polymers (Basel) 2022; 14:polym14071303. [PMID: 35406177 PMCID: PMC9003558 DOI: 10.3390/polym14071303] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 03/07/2022] [Accepted: 03/18/2022] [Indexed: 11/18/2022] Open
Abstract
Due to rise in infrastructure development and demand for seawater and sea sand concrete, fiber-reinforced polymer (FRP) rebars are widely used in the construction industry. Flexural strength is an important component of reinforced concrete structural design. Therefore, this research focuses on estimating the flexural capacity of FRP-reinforced concrete beams using novel artificial intelligence (AI) decision tree (DT) and gradient boosting tree (GBT) approaches. For this purpose, six input parameters, namely the area of bottom flexural reinforcement, depth of the beam, width of the beam, concrete compressive strength, the elastic modulus of FRP rebar, and the tensile strength of rebar at failure, are considered to predict the moment bearing capacity of the beam under bending loads. The models were trained using 60% of the database and were validated first-hand on the remaining 40% database employing the correlation coefficient (R), error indices namely, mean absolute error, root mean square error (MAE, RMSE) and slope of the regression line between observed and predicted results. The developed models were further validated using sensitivity and parametric analysis. Both models revealed comparable performance; however, based on the comparison of the slope of the validation data (0.83 for GBT model against 0.75 for the DT model) and higher R for the validation phase in case of the GBT model in comparison to the DT, the GBT model can be considered more accurate and robust. The sensitivity analysis yielded depth of the beam as the most influential parameter in contributing flexural strength of the beam, followed by the area of flexural reinforcement. The developed GBT model surpasses the existing gene expression programming (GEP) model in terms of accuracy; however, the current American Concrete Institute (ACI) model equations are more reliable than AI models in predicting the flexural strength of FRP-reinforced concrete beams.
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Modeling Interfacial Tension of N2/CO2 Mixture + n-Alkanes with Machine Learning Methods: Application to EOR in Conventional and Unconventional Reservoirs by Flue Gas Injection. MINERALS 2022. [DOI: 10.3390/min12020252] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
The combustion of fossil fuels from the input of oil refineries, power plants, and the venting or flaring of produced gases in oil fields leads to greenhouse gas emissions. Economic usage of greenhouse and flue gases in conventional and unconventional reservoirs would not only enhance the oil and gas recovery but also offers CO2 sequestration. In this regard, the accurate estimation of the interfacial tension (IFT) between the injected gases and the crude oils is crucial for the successful execution of injection scenarios in enhanced oil recovery (EOR) operations. In this paper, the IFT between a CO2/N2 mixture and n-alkanes at different pressures and temperatures is investigated by utilizing machine learning (ML) methods. To this end, a data set containing 268 IFT data was gathered from the literature. Pressure, temperature, the carbon number of n-alkanes, and the mole fraction of N2 were selected as the input parameters. Then, six well-known ML methods (radial basis function (RBF), the adaptive neuro-fuzzy inference system (ANFIS), the least square support vector machine (LSSVM), random forest (RF), multilayer perceptron (MLP), and extremely randomized tree (extra-tree)) were used along with four optimization methods (colliding bodies optimization (CBO), particle swarm optimization (PSO), the Levenberg–Marquardt (LM) algorithm, and coupled simulated annealing (CSA)) to model the IFT of the CO2/N2 mixture and n-alkanes. The RBF model predicted all the IFT values with exceptional precision with an average absolute relative error of 0.77%, and also outperformed all other models in this paper and available in the literature. Furthermore, it was found that the pressure and the carbon number of n-alkanes would show the highest influence on the IFT of the CO2/N2 and n-alkanes, based on sensitivity analysis. Finally, the utilized IFT database and the area of the RBF model applicability were investigated via the leverage method.
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Effect of Calcination Temperature on the Structural and Optical Properties of (ZnO)0.8 (ZrO2)0.2 Nanoparticles. J Inorg Organomet Polym Mater 2022. [DOI: 10.1007/s10904-022-02238-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Bi W, Zhang P, Du X, Lü W, Wang S, Yang T, Ma L, Liu X, Zhao H, Ren S. Stabilization of natural gas foams using different surfactants at high pressure and high temperature conditions. J SURFACTANTS DETERG 2021. [DOI: 10.1002/jsde.12564] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Affiliation(s)
- Weiyu Bi
- Oil and Gas Technology Research Institute Changqing Oilfield Company (PetroChina) Xi'an China
- National Engineering Laboratory for Exploration and Development of Low Permeability Oil and Gas Fields Xi'an China
| | | | - Xiangrui Du
- School of Petroleum Engineering China University of Petroleum (East China) Qingdao China
| | - Wei Lü
- Oil and Gas Technology Research Institute Changqing Oilfield Company (PetroChina) Xi'an China
| | - Shitou Wang
- Oil and Gas Technology Research Institute Changqing Oilfield Company (PetroChina) Xi'an China
| | - Tangying Yang
- Oil and Gas Technology Research Institute Changqing Oilfield Company (PetroChina) Xi'an China
| | - Liping Ma
- Oil and Gas Technology Research Institute Changqing Oilfield Company (PetroChina) Xi'an China
| | - Xiaochun Liu
- Oil and Gas Technology Research Institute Changqing Oilfield Company (PetroChina) Xi'an China
| | - Haifeng Zhao
- Oil and Gas Technology Research Institute Changqing Oilfield Company (PetroChina) Xi'an China
| | - Shaoran Ren
- School of Petroleum Engineering China University of Petroleum (East China) Qingdao China
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Nait Amar M. Towards improved genetic programming based-correlations for predicting the interfacial tension of the systems pure/impure CO2-brine. J Taiwan Inst Chem Eng 2021. [DOI: 10.1016/j.jtice.2021.08.010] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
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Alsabaa A, Elkatatny S. Improved Tracking of the Rheological Properties of Max-Bridge Oil-Based Mud Using Artificial Neural Networks. ACS OMEGA 2021; 6:15816-15826. [PMID: 34179625 PMCID: PMC8223408 DOI: 10.1021/acsomega.1c01230] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/07/2021] [Accepted: 06/02/2021] [Indexed: 05/17/2023]
Abstract
Lab measurements for the rheological properties of mud are critical for monitoring the drilling fluid functions during the drilling operations. However, these measurements take a long time and might need more than one person to be completed. The main objectives of this research are to implement artificial intelligence for predicting the mud rheology from only Marsh funnel (μf) and measuring mud density (ρm) easily and quickly on the rig site. For the first time, an artificial neural network (ANN) was used to build different models for predicting the rheological properties of Max-bridge oil-based mud. The properties included the plastic viscosity (μp), yield point (γ), flow behavior index (η), and apparent viscosity (μa). Field measurements of 383 samples were used to build and optimize the ANN models. The obtained results showed that 32 neurons in the hidden layer and tan sigmoid function transfer function were the best parameters for all ANN models. The training and testing processes of models showed a strong prediction performance with a correlation coefficient (R) greater than 0.91 and an average absolute percentage error (AAPE) less than 5.31%. New empirical correlations were developed based on the optimized weights and biases of the ANN models. The developed empirical correlations were compared with the published correlations, and the comparison results confirmed that the ANN-developed correlations outperformed all previous work.
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Dehaghani AHS, Soleimani R. Estimation of Interfacial Tension for Geological CO 2Storage. Chem Eng Technol 2019. [DOI: 10.1002/ceat.201700700] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Amir Hossein Saeedi Dehaghani
- Tarbiat Modares UniversityFaculty of Chemical EngineeringDepartment of Petroleum Engineering Jalal AleAhmad Street 14115-143 Tehran Iran
| | - Reza Soleimani
- Tarbiat Modares UniversityDepartment of Chemical Engineering P.O. Box 14115-111 14117-13116 Tehran Iran
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Rostami A, Kamari A, Panacharoensawad E, Hashemi A. New empirical correlations for determination of Minimum Miscibility Pressure (MMP) during N2-contaminated lean gas flooding. J Taiwan Inst Chem Eng 2018. [DOI: 10.1016/j.jtice.2018.05.048] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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13
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Rostami A, Arabloo M, Esmaeilzadeh S, Mohammadi AH. On modeling of bitumen/n-tetradecane mixture viscosity: Application in solvent-assisted recovery method. ASIA-PAC J CHEM ENG 2017. [DOI: 10.1002/apj.2152] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Alireza Rostami
- Department of Petroleum Engineering; Petroleum University of Technology (PUT); Ahwaz Iran
| | - Milad Arabloo
- Young Researchers and Elites Club, North Tehran Branch; Islamic Azad University; Tehran Iran
| | | | - Amir H. Mohammadi
- Institut de Recherche en Génie Chimique et Pétrolier (IRGCP); Paris Cedex France
- Discipline of Chemical Engineering, School of Engineering; University of KwaZulu-Natal; Howard College Campus, King George V, Avenue Durban 4041 South Africa
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