Article
Computer Science, Interdisciplinary Applications
Yat Tin Chow, Wing Tat Leung, Ali Pakzad
Summary: We propose, analyze, and test a novel continuous data assimilation two-phase flow algorithm for reservoir simulation. We show that the solutions of the algorithm, constructed using coarse mesh observations, converge at an exponential rate in time to the corresponding exact reference solution of the two-phase model. Numerical computations demonstrate the effectiveness of this approach, including variants with data on sub-domains, and synchronization achieved for data collected from a small fraction of the domain.
JOURNAL OF COMPUTATIONAL PHYSICS
(2022)
Article
Engineering, Multidisciplinary
Meng Tang, Yimin Liu, Louis J. Durlofsky
Summary: A deep-learning-based surrogate model for two-phase flow in 3D subsurface formations is presented, along with a CNN-PCA procedure for parameterizing complex geomodels. By training the surrogate model and combining it with the CNN-PCA procedure, a challenging data assimilation problem involving a channelized system is successfully addressed.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2021)
Article
Computer Science, Interdisciplinary Applications
Yufei Wang, Daniel Fernandez-Garcia, Maarten W. Saaltink
Summary: This paper presents a reactive multi-component multi-phase flow program, MRST_CO2, implemented in Matlab Reservoir Simulation Toolbox (MRST), for simulating Geological Carbon Sequestration (GCS). The program can simulate multi-phase flow and transport of species undergoing chemical reactions and mass exchanges among gas, liquid, and solid phases. It has been tested with 1D benchmark and applied to heterogeneous 2D and 3D cases with structured or unstructured grid.
COMPUTERS & GEOSCIENCES
(2023)
Article
Engineering, Environmental
Shih-Jung Wang, Quoc Cuong Nguyen, Yu-Chen Lu, Yonatan Garkebo Doyoro, Duc-Huy Tran
Summary: This study developed a synthetic geological model and assessed simulated geological models with different levels of data sufficiency. The results showed that incorporating geological knowledge in the model significantly improved its accuracy. Additionally, using geophysical data with correction based on borehole data provided better results. The study highlights the importance of additional data in reducing uncertainty in geological and numerical models.
BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT
(2022)
Article
Mathematics, Interdisciplinary Applications
Dongrui Shao, Junyu Chu, Luonan Chen, Huanfei Ma
Summary: Data assimilation is crucial for both data driven and model driven research. The Kalman filter, a widely used data assimilation framework, has traditionally relied on theoretical models. However, recent efforts have aimed to develop model-free Kalman filters that solely rely on data. In this study, we propose a hybrid model framework that combines delay embedding theory and machine learning to bridge the gap between exact model-based and totally model-free methods. This hybrid approach is more flexible in application and has been validated using benchmark systems and real-world problems.
CHAOS SOLITONS & FRACTALS
(2023)
Article
Environmental Sciences
Youssef Toubri, Isabelle Demers, Nicholas Beier
Summary: This study integrates geological modeling and kinetic modeling to inform upstream mine waste classification based on the pH generated from the main acid-generating and acid-neutralizing reactions once the mine solid waste is stored in oxidizing conditions. The outcome is a spatio-temporal visualization of the pH defining ante-mining geo-environmental domains, thereby providing the opportunity for formulating proactive management measures.
ENVIRONMENTAL POLLUTION
(2022)
Article
Geochemistry & Geophysics
Guy Schumann, Laura Giustarini, Angelica Tarpanelli, Ben Jarihani, Sandro Martinis
Summary: Satellite flood mapping technology has been widely used for over 40 years. In recent years, with the availability of a large amount of open-access remote sensing data, there have been significant advancements in flood mapping, monitoring, and integrating with flood models.
SURVEYS IN GEOPHYSICS
(2023)
Article
Nuclear Science & Technology
Seungjin Kim, Mamoru Ishii, Ran Kong, Guanyi Wang
Summary: This article discusses the development and application of the interfacial area transport equation (IATE), focusing on studies within the past decade. Through extensive experimental databases and the application of constitutive models, IATE has been successfully used to predict interfacial area concentration in two-phase flows, with validation in practical system analysis codes and computational fluid dynamics.
NUCLEAR ENGINEERING AND DESIGN
(2021)
Article
Geosciences, Multidisciplinary
Imen Mezni, Hayet Chihi, Mohamed Aymen Bounasri, Abdelhamid Ben Salem, Simge Ayfer
Summary: This study presents a comprehensive approach, combining geophysical and geological data, to improve the characterization of multilayered reservoir systems in complex geological settings. The use of 3D geological modeling and data integration provides a better understanding of the reservoirs' structure, connectivity, and fluid flow paths.
NATURAL RESOURCES RESEARCH
(2022)
Article
Geosciences, Multidisciplinary
Amir Ouyed, Nadia Smith, Xubin Zeng, Thomas Galarneau Jr, Hui Su, Ross D. D. Dixon
Summary: The lack of measurements of 3D distribution of horizontal wind vectors is addressed by developing an algorithm that retrieves winds from two polar satellites. Testing the algorithm shows that it qualitatively agrees with ERA-5 and produces a large number of wind profiles per day, with errors within the range of reported cloud-tracking winds.
GEOPHYSICAL RESEARCH LETTERS
(2023)
Article
Engineering, Multidisciplinary
Asawer A. Alwasiti, Zainb Y. Shneen, Raheeq I. Ibrahim, Abbas K. Al Shalal
Summary: This study focuses on reducing pump energy requirements by using different types of additives for two-phase flow, with CTAB showing the highest reduction in power consumption (84%).
AIN SHAMS ENGINEERING JOURNAL
(2021)
Article
Engineering, Marine
Wenhua Li, Qing Zhou, Guang Yin, Muk Chen Ong, Gen Li, Fenghui Han
Summary: This study investigates the gas-liquid flow and induced vibration in a subsea jumper using experimental and numerical techniques. The experimental results show that the flow patterns and vibration amplitudes are highly related to the gas content rate, mixing velocity, and gas and liquid superficial velocity. The numerical simulations agree well with the experimental data in terms of flow patterns and induced vibrations.
JOURNAL OF MARINE SCIENCE AND ENGINEERING
(2022)
Review
Geosciences, Multidisciplinary
Zhengjing Ma, Gang Mei
Summary: The paper provides an overview of the advances in utilizing deep learning for geological hazard analysis, covering data sources, deep learning models, and application paradigms. It also highlights the challenges and opportunities in applying deep learning models for geological hazard analysis.
EARTH-SCIENCE REVIEWS
(2021)
Article
Engineering, Civil
M. Y. Ben Ali, G. Tissot, S. Aguinaga, D. Heitz, E. Memin
Summary: This paper investigates the applicability of the variational data assimilation approach in reconstructing three-dimensional wind flows around a high-rise building model, and provides guidelines for an efficient reconstruction. The results show that wall-pressure data is meaningful for accurately recovering the wake flow extension, and using a hybrid control parameter and Sobolev gradient descent method can achieve precise results and fast convergence.
JOURNAL OF WIND ENGINEERING AND INDUSTRIAL AERODYNAMICS
(2022)
Article
Geochemistry & Geophysics
A. Plunder, L. Le Pourhiet, L. Rass, E. Gloaguen, M. Pichavant, C. Gumiaux
Summary: Among the crystalline rocks in the continental crust, pegmatites stand out for their large crystals and have both aesthetic and economic value. This study presents numerical experiments to simulate the movement of pegmatite-forming melts and proposes porosity waves as a possible mechanism for rapidly extracting and transporting these melts. The results are consistent with observations of pegmatite distribution in different crustal levels, suggesting that porosity waves play a significant role in pegmatite formation.
Article
Energy & Fuels
Tao Bai, Pejman Tahmasebi
Summary: Underground hydrogen storage is a feasible solution to address the imbalance between supply and demand caused by fluctuating renewable energy production. By converting surplus wind power to hydrogen and storing it in geological systems, it can meet a portion of household electricity consumption during summer.
JOURNAL OF ENERGY STORAGE
(2022)
Article
Computer Science, Interdisciplinary Applications
Tsimur Davydzenka, Pejman Tahmasebi, Mark Carroll
Summary: In many industries and applications, obtaining and classifying remote sensing imagery is crucial. This study explores a solution of using a stochastic method to generate variations of training images, thereby improving the accuracy of machine learning classification. By increasing the training set with additional realizations, consistent improvements in classification accuracy can be achieved, providing an opportunity to enhance prediction accuracy when sufficient data is not available.
COMPUTERS & GEOSCIENCES
(2022)
Article
Computer Science, Interdisciplinary Applications
Yanrui Ning, Hossein Kazemi, Pejman Tahmasebi
Summary: This paper proposes a machine learning-based time series forecasting method for predicting the production performance of unconventional reservoirs. Through the study and comparison of three algorithms, ARIMA and LSTM models are found to perform well and exhibit robustness in predicting oil and gas production in wells across the DJ Basin.
COMPUTERS & GEOSCIENCES
(2022)
Article
Materials Science, Multidisciplinary
Tsimur Davydzenka, Daniel Sinclair, Nikhilesh Chawla, Pejman Tahmasebi
Summary: This study discusses the application of X-ray micro-computed tomography imagery in materials science, as well as the challenge of time-intensive segmentation of large data sets. Researchers propose a machine learning method to improve segmentation accuracy by increasing variations in training images, significantly enhancing accuracy in X-ray microscopy imaging.
MATERIALS CHARACTERIZATION
(2022)
Article
Engineering, Chemical
Xiaoming Zhang, Pejman Tahmasebi
Summary: Particle motion in granular systems is common in nature and industrial processes, with particle size, density, and shape playing crucial roles in dynamic behavior. Research shows that the repose angle of sandpiles increases in irregular models but decreases when ambient water is present. Irregular particle contact force chains are stronger than those of spherical sandpiles, but are weakened by ambient water.
Article
Thermodynamics
Yuqi Wu, Pejman Tahmasebi, Keyu Liu, Samuel Fagbemi, Chengyan Lin, Senyou An, Lihua Ren
Summary: Pore-scale fluid flow simulation on digital rocks is crucial for environmental remediation and geo-materials. This study presents a unified modeling approach for constructing multiscale digital images and simulating multiphase flow in complex and heterogeneous pore systems. By using a hybrid modeling method and non-uniform meshes, the computational efficiency of the simulations is improved.
INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER
(2022)
Article
Geochemistry & Geophysics
Xiaojun Chen, Luong Duy Thanh, Chengfei Luo, Pejman Tahmasebi, Jianchao Cai
Summary: The relationship between electrical conduction and pore structure in reservoir rocks was analyzed through theoretical development, petrophysical experiments, error analysis, core-scale displacement experiments, and pore-scale numerical simulations. The electric formation factor was found to be a function of porosity, tortuosity fractal dimension, and pore fractal dimension. The model provided satisfactory predictions for reservoir rocks when the ratio of minimum to maximum pore radius was suitable. Porosity-based formation factor models had high errors at high formation factors, but our model improved predictions with an error factor of +/- 10. Hydraulic and electrical conductions showed different dependencies on pore structure, with hydraulic conductance being influenced by pore size, dominant flow channels, and threshold pressure, while electrical conduction had no dominant channel and did not reflect pore size information at the same porosity.
Article
Engineering, Civil
Xiaojun Chen, Xiaobo Zhao, Pejman Tahmasebi, Chengfei Luo, Jianchao Cai
Summary: A data-driven model based on nuclear magnetic resonance (NMR) was developed to predict matrix permeability using nine machine learning models. The type of input data had a strong influence on the machine learning modeling. By using cumulative T2 relaxation data instead of the original T2 data, the gradient boosting decision tree model tuned by GridsearchCV showed a stronger agreement between experimental results and NMR estimates of matrix permeability. The correlation coefficient reached 0.92 with the lowest MSE of 0.12. Evaluation: 9 out of 10.
JOURNAL OF HYDROLOGY
(2023)
Article
Engineering, Civil
Tao Bai, Pejman Tahmasebi
Summary: In this study, a graph neural network (GNN) is used for accurate prediction of groundwater dynamics. The model incorporates spatial relationships between wells using graph convolution layers and temporal features using gated temporal convolutional networks. The proposed model outperforms two baseline models in terms of evaluation metrics, even when the spatial dependencies are unknown.
JOURNAL OF HYDROLOGY
(2023)
Article
Engineering, Chemical
Mehryar Amir Hosseini, Serveh Kamrava, Muhammad Sahimi, Pejman Tahmasebi
Summary: The wettability of porous media significantly impacts the spatial distribution of fluid phases. Computer simulations show that contact angle affects particle dynamics, fluid velocity, and rupture in the pore space. Additionally, increasing contact angle reduces inter-particle interactions and increases drag force, leading to larger particle displacement.
CHEMICAL ENGINEERING SCIENCE
(2023)
Article
Mechanics
Xiaoming Zhang, Pejman Tahmasebi
Summary: In this study, the DKT process of irregular particles was investigated numerically, and it was found that the particle shape plays an important role. Particles with low sphericity are more sensitive to orientation, and lower roundness accelerates the separation of particle pairs. The vertical velocity decreases when roundness or sphericity is smaller.
INTERNATIONAL JOURNAL OF MULTIPHASE FLOW
(2023)
Article
Computer Science, Interdisciplinary Applications
Mehryar Amir Hosseini, Pejman Tahmasebi
Summary: This paper investigates the influence of particle morphology on granular collapse behavior and wave generation in multiphase fluid systems. The study establishes a clear relationship between particle morphology and important characteristics such as displacement, velocity, inter-particle forces, and kinetic energy. The findings demonstrate that as irregularity increases, interlocking between particles becomes more prominent, leading to reduced particle travel distances. Additionally, interlocking also influences particle-fluid interactions, resulting in significant alterations in the formation of generated waves.
COMPUTERS AND GEOTECHNICS
(2023)
Article
Thermodynamics
Yuqi Wu, Pejman Tahmasebi, Keyu Liu, Chengyan Lin, Serveh Kamrava, Shengbiao Liu, Samuel Fagbemi, Chang Liu, Rukuai Chai, Senyou An
Summary: This study proposes a novel hybrid modeling approach integrating X-ray CT imaging technology, morphological operation algorithm, and quartet structure generation set method to investigate the dependence of the physical properties of hydrate-bearing sediments (HBS) on hydrate occurrence patterns and saturation levels. The findings suggest that different hydrate types have varying heterogeneity in the distribution of pore and throat radii.
Review
Energy & Fuels
Hossein Mirzaee, Serveh Kamrava, Pejman Tahmasebi
Summary: This article reviews the most promising studies in machine learning-assisted reconstruction of porous media, categorizing the approaches and discussing their characteristics, advantages, and disadvantages. It also provides information on various methods for evaluating algorithm performance. Furthermore, the article explores the current research status and challenges in ML-assisted porous media reconstruction in energy-related applications and suggests potential areas for future studies.
Review
Materials Science, Multidisciplinary
Pejman Tahmasebi
Summary: Modeling of heterogeneous materials and media plays a crucial role in various phenomena and systems, including condensed matter physics, soft materials, composite media, porous media, biological systems, geosystems, ceramic engineering, pharmaceutical science, and space discoveries. This review paper examines recent developments in experimental and computational methods, such as neutron and nanometer-scale tomography, magnetic resonance imaging, digital image correlation, and 4D techniques. It also explores the shift towards micro-scale and the development of multiscale approaches in modeling, as well as the exploration of coupled or multiphysics systems.
PROGRESS IN MATERIALS SCIENCE
(2023)