Congrats on Dr. Zeeshan Tariq and Prof. Yanfrom DGYM for their publication, Predicting wettability of mineral/CO2/brine systems via data-driven machine learning modeling: Implications for carbon geo-sequestration,published on Chemosphere(2023/12). This work is a collaboration with Dr. Aliakbar Hassanpouryouzband and Professor Hussein Hoteit's ARMS group. https://doi.org/10.1016/j.chemosphere.2023.140469
Congrats on Dr. Chen Li from Chengdu University and Prof. Yan from DGYM for their recent publication on innovative method for rapid permeability inference : Rapid Inference of Reservoir Permeability from Inversion of Traveltime Data Under a Fast Marching Method-Based Deep Learning Framework, published on SPE Journal (2023/9). https://doi.org/10.2118/214385-PA
Congrats on Dr. Zeeshan Tariq and Prof. Yanfrom DGYM for their publication, Enhancing Fracturing Fluid Viscosity in High Salinity Water: A Data-Driven Approach for Prediction and Optimization, published on ACS Energy & Fuels(2023/8). https://doi.org/10.1021/acs.energyfuels.3c02272
Congrats on Dr. Zeeshan Tariq from DGYM for leading the wettability modeling work using machine learning. This work is a collaboration with Professor Hussein Hoteit's ARMS group. See the publication in Enhancing wettability prediction in the presence of organics for hydrogen geo-storage through data-driven machine learning modeling of rock/H2/brine systems, published on Fuel (2023/8).https://doi.org/10.1016/j.fuel.2023.129354
Congrats on Billal Aslam and Prof. Yan from DGYM for their work on data-driven modeling to predict chemical retention on porous media. This work is a collaboration with EOR Lab from ITB, Indonesia. See the publication in Data driven approach using capacitance resistance model to determine polymer in-situ retention level, published on Geoenergy Science and Engineering (2023/7). https://doi.org/10.1016/j.geoen.2023.212043
Congrats on Dr. Manojkumar Gudala from DGYM leading the geothermal work, Numerical investigations and evaluation of a puga geothermal reservoir with horizontal wells using a fully coupled thermo-hydro-geomechanical model (THM) and EDAS associated with AHP, published on Geoenergy Science and Engineering (2023/6). https://doi.org/10.1016/j.geoen.2023.212035
Congrats on Prof. Yanfrom DGYM leading the reservoir heterogeneity estimation work, Estimation of heterogeneous permeability using pressure derivative data through an inversion neural network inspired by the Fast Marching Method, published on Geoenergy Science and Engineering (2023/6). https://doi.org/10.1016/j.geoen.2023.211982
Congrats on Prof. Yan from DGYM in collaborating with Prof. George Moridis group from LBNL & TAMU and Prof. Afifi's APG Group at KAUST for the recently collaborated publication, Compositional Reservoir Simulation of Underground Hydrogen Storage in Depleted Gas Reservoirs, published on International Journal of Hydrogen Energy (2023/6). https://doi.org/10.1016/j.ijhydene.2023.05.355
Congrats on Prof. Yan from DGYM in collaborating with Prof. Hoteit's ARMS Group at KAUST for the recently collaborated publication, Uncertainty quantification and optimization method applied to time-continuous geothermal energy extraction, published on Geothermics. (2023/5). https://doi.org/10.1016/j.geothermics.2023.102675
Congrats on Prof. Yan & Dr. Gudala from DGYM for their recently led publication, Robust optimization of geothermal recovery based on a generalized thermal decline model and deep learning, published on Energy Conversion and Management. (2023/4). https://doi.org/10.1016/j.enconman.2023.117033
Congrats on Prof. Yan from DGYM in collaborating with Prof. Hoteit's ARMS Group at KAUST for the recently collaborated publication, Uncertainty Analysis of CO2 Storage in Deep Saline Aquifers Using Machine Learning and Bayesian Optimization, published on Energies. (2023/2). https://doi.org/10.3390/en16041684
Congrats on Dr. Tariq from DGYM for his recently led publication, Spatial-Temporal Prediction of Minerals Dissolution and Precipitation using Deep Learning Techniques: An Implication to Geological Carbon Sequestration, accepted by Fuel. (2023/2). https://doi.org/10.1016/j.fuel.2023.127677
