Rapid Prediction of Unconventional Well Performance Made Possible with Deep Learning and Reduced Physics Models
Problem Addressed: Current industry practice mostly uses simplified/empirical models unable to [...]
SMART Technical Lead Honored by SPE
Battelle LinkedIn Announcement Dr. Srikanta Mishra Receives SPE [...]
SMART team develops reservoir models for three depositional environments
Numerical reservoir simulation of active reservoir management: 30 years of injection/extraction [...]
Rapid Forecasting Machine Learning Models Implemented on Large Field-Scale CO2 Storage Models
Accomplishment: The SMART Virtual Learning team has successfully tested a [...]
Real-Time Monitoring of CO2 Storage and Uncertainty Quantification
Fast and reliable imaging and monitoring geological carbon storage (GCS) is [...]
DOE Names Phase 1 Winners – SMART Visualization Platform Prize Challenge
Today, the U.S. Department of Energy’s (DOE) Office of Fossil [...]
PIML Tool Predicts Reservoir Behavior Even at Unmonitored Locations
Predictive Analysis of Pressure and Temperature in Carbonate Reservoirs The [...]
Rapid and accurate seismic data processing through AI
SMART researchers are adapting and developing machine learning models to [...]
Accurate stresses far from the borehole
Modeling the 3D Full Stress Tensor Accurate measurements of stress [...]
Multiple ML approaches enables rapid error identification
Enhanced Oil Recovery (EOR) history matching is achieved using three [...]