Spatial–temporal prediction of minerals dissolution and precipitation using deep learning techniques: An implication to Geological Carbon Sequestration

[1]  M. Reagan,et al.  Development of lean, efficient, and fast physics-framed deep-learning-based proxy models for subsurface carbon storage , 2022, International Journal of Greenhouse Gas Control.

[2]  Juan Gómez-Romero,et al.  Hydropower production prediction using artificial neural networks: an Ecuadorian application case , 2021, Neural Computing and Applications.

[3]  Zeeshan Tariq,et al.  An Experimental and Simulation Study of CO2 Sequestration in an Underground Formations; Impact on Geomechanical and Petrophysical Properties , 2021, Day 4 Wed, December 01, 2021.

[4]  Zeeshan Tariq,et al.  Machine Learning-Based Improved Pressure–Volume–Temperature Correlations for Black Oil Reservoirs , 2021 .

[5]  Abdul Azeez Abdul Raheem,et al.  Evolving strategies for shear wave velocity estimation: smart and ensemble modeling approach , 2021, Neural Computing and Applications.

[6]  Rajesh J. Pawar,et al.  A Gradient-based Deep Neural Network Model for Simulating Multiphase Flow in Porous Media , 2021, J. Comput. Phys..

[7]  Bailian Chen,et al.  A Physics-Constrained Deep Learning Model for Simulating Multiphase Flow in 3D Heterogeneous Porous Media , 2021, Fuel.

[8]  Arun Kumar Mishra,et al.  Prediction of thermal conductivity and damage in Indian Jalore granite for design of underground research laboratory , 2021, Neural Computing and Applications.

[9]  Rajesh J. Pawar,et al.  On the feasibility of using physics-informed machine learning for underground reservoir pressure management , 2021, Expert Syst. Appl..

[10]  Sally M. Benson,et al.  Towards a predictor for CO2 plume migration using deep neural networks , 2021 .

[11]  K. Sepehrnoori,et al.  Modeling and Analysis of Carbonate Matrix Acidizing Using a New Two-Scale Continuum Model , 2021, SPE Journal.

[12]  Sherif M. Hanafy,et al.  Deep Learning Driven Noise Reduction for Reduced Flux Computed Tomography , 2021, Sensors.

[13]  Lei Geng,et al.  Segmentation of Lung Nodules Using Improved 3D-UNet Neural Network , 2020, Symmetry.

[14]  Syed M Amir,et al.  Integration of Petrophysical Log Data with Computational Intelligence for the Development of a Lithology Predictor , 2020, Day 2 Tue, October 27, 2020.

[15]  K. Sepehrnoori,et al.  Modification of two-scale continuum model and numerical studies for carbonate matrix acidizing , 2020 .

[16]  Zeeshan Tariq,et al.  Development of New Rheological Models for Class G Cement with Nanoclay as an Additive Using Machine Learning Techniques , 2020, ACS omega.

[17]  V. Demyanov,et al.  Machine Learning Clustering of Reservoir Heterogeneity with Petrophysical and Production Data , 2020 .

[18]  Zeeshan Tariq,et al.  Effects of Nanoclay and Silica Flour on the Mechanical Properties of Class G Cement , 2020, ACS omega.

[19]  He Tian,et al.  Applications of Artificial Intelligence in Oil and Gas Development , 2020, Archives of Computational Methods in Engineering.

[20]  R. Gholami,et al.  Feasibility of limestone reservoirs as a carbon dioxide storage site: An experimental study , 2020 .

[21]  Janis Keuper,et al.  Detection of point scatterers using diffraction imaging and deep learning , 2019, Geophysical Prospecting.

[22]  Zeeshan Tariq,et al.  Method for Estimating Permeability in Carbonate Reservoirs From Typical Logging Parameters Using Functional Network , 2019 .

[23]  Zhi Zhong,et al.  Predicting CO2 Plume Migration in Heterogeneous Formations Using Conditional Deep Convolutional Generative Adversarial Network , 2019, Water Resources Research.

[24]  C. Sondergeld,et al.  Laboratory Study of Ultrasonic Velocity Variations During CO2 Flooding in Tuscaloosa Sandstone , 2019, SPE Reservoir Evaluation & Engineering.

[25]  Mohamed Mahmoud,et al.  Core log integration: a hybrid intelligent data-driven solution to improve elastic parameter prediction , 2019, Neural Computing and Applications.

[26]  Zeeshan Tariq.  An Automated Flowing Bottom-Hole Pressure Prediction for a Vertical Well Having Multiphase Flow Using Computational Intelligence Techniques , 2018 .

[27]  Abdulazeez Abdulraheem,et al.  Utilizing State of the Art Computational Intelligence to Estimate Oil Flow Rate in Artificial Lift Wells , 2018 .

[28]  B. Lowney,et al.  An Outlook on Seismic Diffraction Imaging Using Pattern Recognition , 2018, 80th EAGE Conference and Exhibition 2018.

[29]  Babak Shabani,et al.  A fast and robust TOUGH2 module to simulate geological CO2 storage in saline aquifers , 2018, Computational Geosciences.

[30]  Nicholas Zabaras,et al.  Bayesian Deep Convolutional Encoder-Decoder Networks for Surrogate Modeling and Uncertainty Quantification , 2018, J. Comput. Phys..

[31]  E. Artun.  Characterizing interwell connectivity in waterflooded reservoirs using data-driven and reduced-physics models: a comparative study , 2017, Neural Computing and Applications.

[32]  W. Seyfried,et al.  Whole rock basalt alteration from CO2-rich brine during flow-through experiments at 150 °C and 150 bar , 2017 .

[33]  Yunwei Sun,et al.  Applicability of aquifer impact models to support decisions at CO2 sequestration sites , 2016 .

[34]  Amir H. Mohammadi,et al.  Toward prediction of petroleum reservoir fluids properties: A rigorous model for estimation of solution gas-oil ratio , 2016 .

[35]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[36]  C. Pentland,et al.  CO2-brine displacement in heterogeneous carbonates , 2015 .

[37]  Mohammad Ebadi,et al.  Connectionist model predicts the porosity and permeability of petroleum reservoirs by means of petro-physical logs: Application of artificial intelligence , 2014 .

[38]  Zheming Zhang,et al.  Numerical Simulation and Optimization of CO2 Sequestration in Saline Aquifers , 2012 .

[39]  Axel Liebscher,et al.  Petrophysical analysis to investigate the effects of carbon dioxide storage in a subsurface saline aquifer at Ketzin, Germany (CO2SINK) , 2010 .

[40]  B. Laenen,et al.  Experimental identification of CO2–water–rock interactions caused by sequestration of CO2 in Westphalian and Buntsandstein sandstones of the Campine Basin (NE-Belgium) , 2006 .

[41]  Henrique Ventura da Silva,et al.  Leak detection in petroleum pipelines using a fuzzy system , 2005 .

[42]  Kamy Sepehrnoori,et al.  Reservoir Simulation of CO 2 Storage in Aquifers , 2005 .

[43]  Carl I. Steefel,et al.  Evaluation of the impact of CO2, co-contaminant gas, aqueous fluid and reservoir rock interactions on the geologic sequestration of CO2 , 2005 .

[44]  Richard J. Beckman,et al.  A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output From a Computer Code , 2000, Technometrics.

[45]  W. Gunter,et al.  Aquifer disposal of CO2-rich greenhouse gases: Extension of the time scale of experiment for CO2-sequestering reactions by geochemical modelling , 1997 .

[46]  Charles L. Karr,et al.  Determination of lithology from well logs using a neural network , 1992 .

[47]  L. Nghiem,et al.  Phase equilibria of oil, gas and water/brine mixtures from a cubic equation of state and henry's law , 1986 .

[48]  George Thodos,et al.  The viscosity of pure substances in the dense gaseous and liquid phases , 1962 .

[49]  S. Arrhenius.  Über die Dissociationswärme und den Einfluss der Temperatur auf den Dissociationsgrad der Elektrolyte , 1889 .

[50]  P. Zitha,et al.  Numerical modeling of CO2 mineralisation during storage in deep saline aquifers , 2011 .

[51]  S. Sharma.  Determinants of carbon dioxide emissions: Empirical evidence from 69 countries , 2011 .

[52]  R. Grigg,et al.  CO2 Transport Mechanisms in CO2/Brine Coreflooding , 2006 .

[53]  B. Kohse,et al.  Simulation of CO 2 EOR and Sequestration Processes With a Geochemical EOS Compositional Simulator , 2004 .

[54]  Duy Nghia Nguyen,et al.  Carbon Dioxide Geological Sequestration: Technical and Economic Reviews , 2003 .

[55]  Allen M. Rowe,et al.  Pressure-volume-temperature-concentration relation of aqueous sodium chloride solutions , 1970 .