Shengnan Chen

Associate Professor

University of Calgary

Canada

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Shengnan Chen is an Associate Professor at the University of Calgary in Canada. His research focuses on mineralogical evolution, mechanical degradation of oil well cement, and advanced predictive modeling in shale gas production. Recent works include studies on hydrogen seasonal storage, fracture propagation, and innovative neural network approaches for reservoir simulation. Dr. Chen's contributions to the field aim to enhance understanding of geomechanical processes and production forecasting in energy resources.

Shengnan Chen

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Articles (24)

Mineralogical evolution and mechanical degradation of oil well cement under high-temperature and high-pressure CO2 carbonation

Feasibility and economic analysis of hydrogen seasonal storage in depleted gas reservoirs: A case study in Alberta

Integrating geological model via A multimodal machine learning approach in shale gas production forecast

A Hybrid Tabular-Spatial-Temporal Model with 3D Geomodel for Production Prediction in Shale Gas Formations

Strike–Slip Fault Reactivation Triggered by Hydraulic-Natural Fracture Propagation during Fracturing Stimulations near Clark Lake, Alberta

Coupled Simulation of Fracture Propagation and Lagrangian Proppant Transport

A novel prediction model of oil-water relative permeability based on fractal theory in porous media

A novel governing equation for shale gas production prediction via physics-informed neural networks

An intelligent lithology recognition system for continental shale by using digital coring images and convolutional neural networks

A Physics-Informed Spatial-Temporal Neural Network for Reservoir Simulation and Uncertainty Quantification

Modeling of methane and carbon dioxide sorption capacity in tight reservoirs using Machine learning techniques

Robust prediction for CH4/CO2 competitive adsorption by genetic algorithm pruned neural network

Investigation and Application of Perforation Optimization Method on Shale Gas Horizontal Well with Numerical Simulation of Multicluster Fracturing under Dense-Segment Pattern

Long-term production forecast in tight and shale reservoirs: Adapting probability density functions for decline curve analysis

Optimization of machine learning approaches for shale gas production forecast

Nanoparticles for Enhancing Heavy Oil Recovery: Recent Progress, Challenges, and Future Perspectives

Predicting long-term production dynamics in tight/shale gas reservoirs with dual-stage attention-based TEN-Seq2Seq model: A case study in Duvernay formation

Insights into the Application of Machine Learning in Reservoir Engineering: Current Developments and Future Trends

Effect of control domains of fractures and caves on reactive transport in porous media

Mini Review on Wettability in the Methane–Liquid–Rock System at Reservoir Conditions: Implications for Gas Recovery and Geo-Storage

Predicting Adsorption of Methane and Carbon Dioxide Mixture in Shale Using Simplified Local-Density Model: Implications for Enhanced Gas Recovery and Carbon Dioxide Sequestration

Evaluation of Gas Adsorption in Nanoporous Shale by Simplified Local Density Model Integrated with Pore Structure and Pore Size Distribution

Pore-fracture network alteration during forced and spontaneous imbibition processes in shale formation

Pressure transient analysis of a fractured well in multi-region linear composite reservoirs

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