Computational Transport Phenomena Lab

Permanent URI for this collection

For more information visit: https://ctpl.kaust.edu.sa/Pages/Home.aspx

Browse

Recent Submissions

Now showing 1 - 5 of 412
  • Preprint

    Enhancing the Accuracy of Physics-Informed Neural Network Surrogates in Flash Calculations Using Sparse Grid Guidance

    (Elsevier BV, 2023-09-30) Wu, Yuanqing; Sun, Shuyu; Computational Transport Phenomena Laboratory (CTPL), Division of Physical Sciences and Engineering (PSE), King Abdullah University of Science and Technology (KAUST), Thuwal 23955- 6900, KSA; Earth Science and Engineering Program; Physical Science and Engineering (PSE) Division; School of Computer Science and Technology, Dongguan University of Technology, Dongguan 523808, Guangdong, China

    Flash calculations pose a significant performance bottleneck in compositional-flow simulations. While sparse grids have helped mitigate this bottleneck by shifting it to the offline stage, the accuracy of the surrogate model based on physics-informed neural networks (PINN) is still inferior to that of the sparse grid surrogate in many cases. To address this issue, we propose the sparse-grid guided PINN training algorithm. This approach involves rearranging the collocation points using sparse grids at each epoch to capture changes in the residual space. By doing so, the PINN surrogate achieves the required accuracy using the fewest collocation points possible, thereby avoiding potential performance bottlenecks. Moreover, the training time complexity of the sparse-grid guided PINN training is significantly lower compared to the normal training while maintaining the same level of accuracy. Consequently, the sparse-grid guided PINN training method enhances the accuracy of the PINN surrogate with minimal computational overhead.

  • Article

    An energy-stable and conservative numerical method for multicomponent Maxwell–Stefan model with rock compressibility

    (AIP Publishing, 2023-09-26) Kou, Jisheng; Wang, Xiuhua; Chen, Huangxin; Sun, Shuyu; Earth Science and Engineering Program; Physical Science and Engineering (PSE) Division; Key Laboratory of Rock Mechanics and Geohazards of Zhejiang Province, Shaoxing University, Shaoxing, Zhejiang 312000, China; School of Mathematics and Statistics, Hubei Engineering University, Xiaogan, Hubei 432000, China; School of Mathematical Sciences and Fujian Provincial Key Laboratory on Mathematical Modeling and High Performance Scientific Computing, Xiamen University, Fujian 361005, China

    Numerical simulation of gas flow in porous media is becoming increasingly attractive due to its importance in shale and natural gas production and carbon dioxide sequestration. In this paper, taking molar densities as the primary unknowns rather than the pressure and molar fractions, we propose an alternative formulation of multicomponent Maxwell–Stefan (MS) model with rock compressibility. Benefiting from the definitions of gas and solid free energies, this MS formulation has a distinct feature that it follows an energy dissipation law, and namely, it is consistent with the second law of thermodynamics. Additionally, the formulation obeys the famous Onsager's reciprocal principle. An efficient energy-stable numerical scheme is constructed using the stabilized energy factorization approach for the Helmholtz free energy density and certain carefully designed formulations involving explicit and implicit mixed treatments for the coupling between molar densities, pressure, and porosity. We rigorously prove that the scheme inherits the energy dissipation law at the discrete level. The fully discrete scheme has the ability to ensure the mass conservation law for each component as well as preserve the Onsager's reciprocal principle. Numerical tests are conducted to verify our theories, and in particular, to demonstrate the good performance of the proposed scheme in energy stability and mass conservation as expected from our theories.

  • Preprint

    Transport Properties of Oil-Co2 Mixtures in Nanopores: Physics and Machine Learning Models

    (Elsevier BV, 2023-09-22) Zhang, Hongwei; Wang, Xin; Kang, Qinjun; Yan, Bicheng; Sun, Shuyu; Qiao, Rui; Energy Resources and Petroleum Engineering Program; Ali I. Al-Naimi Petroleum Engineering Research Center (ANPERC); Physical Science and Engineering (PSE) Division; Earth Science and Engineering Program; Department of Mechanical Engineering, Virginia Tech, Blacksburg, VA 24061, United States; Earth and Environmental Sciences Division, Los Alamos National Laboratory, Los Alamos, NM 87545, United States

    Fundamental understanding and quantitative models of the transport properties of oil-CO2 mixtures in nanopores are indispensable for physics-based models of CO2-enhanced oil recovery in unconventional oil reservoirs. This study determines the Maxwell-Stefan (M-S) diffusivities of CO2-decane (1: CO2; 2: decane /C10) mixtures in calcite nanopore with compositions relevant to CO2 Huff-n-Puff by molecular dynamics (MD) simulations. In the compositional space explored, D12 characterizing CO2-C10 interactions is relatively insensitive to composition, in contrast to that of bulk mixtures with similar compositions. D1,s characterizing CO2-wall interactions increases sharply with CO2 loading in the nanopore. In contrast, D2,s characterizing C10-wall interactions shows a nonmonotonic dependence on C10 loading. In addition, surprisingly, D2,s is negative, opposite to the expectations for dense fluid mixtures or pure decane confined in nanopores. These features of the M-S diffusivities can ultimately be traced to the fact that CO2 molecules adsorb far more strongly on pore walls than the C10 molecules, which leads to significantly heterogeneous distribution of CO2 and C10 in the nanopore and a low mobility of the adsorbed CO2 molecules. As MD simulations are computationally expensive, a non-parametric machine learning technique called the multitask Gaussian process regression method, is used to build a surrogate model to predict M-S diffusivities based on limited MD data. The surrogate model performs well in the compositional space it was trained with a relative root mean square error less than 10%.

  • Preprint

    Doublet Huff and Puff (Dhp): A New Technology Towards Optimum Scco2 Sequestration with Stable Geothermal Recovery

    (Elsevier BV, 2023-09-11) Gudala, Manojkumar; Yan, Bicheng; Tariq, Zeeshan; Sun, Shuyu; Physical Science and Engineering (PSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia; Ali I. Al-Naimi Petroleum Engineering Research Center (ANPERC); Physical Science and Engineering (PSE) Division; Energy Resources and Petroleum Engineering Program; Earth Science and Engineering Program

    The heat energy extracted from the geothermal reservoirs is clean and plays an important role in decarbonizing the energy sector. Carbon emissions increase in day-to-day operations due to the utilization of hydrocarbons, which contributes to global warming. Therefore, it is crucial to capture and storeSCCO2 while developing clean energy technologies. In this study, we develop a new technology that stores SCCO2 while extracting clean energy from geothermal reservoirs. Our goal is to achieve sustainable thermal recovery and maximize SCCO2 storage capabilities in geothermal reservoirs. To investigate the thermal recovery and SCCO2 storage behavior of geothermal reservoirs, we use a thermo-hydro (two-phase) mechanical (THM) model. This technology is adapted from conventional Huff-Puff technology used in the hydrocarbon industry and applied using well pairs with different injection and perforation operating cycles. We also compared the numerical results with the CO2 plume geothermal (CPG) models. The levelized cost of energy (LCOE) analysis is conducted and compared with the CPG models. The numerical results show that the reduction in production temperature is less than 10 % of the original temperature (base case), the injected SCCO2 accumulates at the top of the reservoir, and the cold front progresses in the vicinity of wells over time. We also investigate the sensitivity of the rock and operating parameters on the heat power and the amount of SCCO2 stored. The implementation of DHP technology is more economical than CPG in geothermal reservoirs (LCOEDHP

  • Article

    WETTABILITY ANALYSIS IN SHALE ORGANIC PORES AT THE NANOSCALE 页岩纳米有机质孔隙中的润湿性研究

    (Chinese Society of Theoretical and Applied Mechanics, 2023-08-01) Liu, Jie; Chen, Yin; Zhang, Tao; Sun, Shuyu; Earth Science and Engineering Program; Physical Science and Engineering (PSE) Division; Institute of Geophysics and Geomatics, China University of Geosciences (Wuhan), Wuhan 430074, China; School of Mathematics, Sichuan University, Chengdu 610065, China

    In view of the problems that wettability is difficult to distinguish in shale nanoscale organic matter pores and the organic matter model cannot truly characterize the pore properties of reservoirs, molecular dynamics research on wettability in shale gas nanoscale pores based on real kerogen organic matter model is proposed. The simulation of smooth and rough graphene ideal models as well as the actual organic matter model of kerogen are built, and wetting behavior characteristics in kerogen pores are analyzed by using the model visualization, the distribution of the density of space, and the analysis of potential energy. There is also an investigation of the effects of temperature, the size of the pores, and the size of the liquid bridge on the wetting condition. Since the traditional organic matter model is based on ideal assumptions, it is difficult to accurately describe the wetting behavior of water in graphene models. As a result of the complex molecular structure and various element types, the kerogen model is more realistic in characterization of wettability of water phase in the organic pore. The water phase in the organic nanopores presents two types of regions: high density region and low density region. Water molecules in the low density region concentrates on the gas-liquid phase interface, where the hydrogen bond interaction is weaker compared with that in the water bulk phase, indicating that this part of molecules are able to diffuse into the gas phase. In addition, the diffused water molecules can also be easily trapped by the kerogen matrix due to its strong attractive interaction. Afterwards, the water molecules will adsorb on the kerogen matrix, presenting a fake wetting condition from the visualization. However, the water phase in the high density region shows the presence of non-wetting condition, which is more realistic for water in the organic pores.