Application of Automated Machine Learning (AML) to Predict Fracture Intensity Using High Frequency Drilling Vibration Data
High-quality fracture network map is essential to investigate the integrity [...]
A 3D Data Assimilation Workflow for Reservoir Pressure Forecasting Using Surface Displacement from InSAR
SMART researchers proposed a 3D data assimilation workflow for reservoir-wide pressure [...]
SMART featured prominently in Forbes interview with Pamela Isom, director AI & Technology (AITO) at DOE
The US Department of Energy (DOE) has long stood out as [...]
Development of a data-driven reduced order model to estimate the stimulated reservoir volume (SRV) from microseismic data in near real time
The problem of predicting the geometry of the multi-stage multi-fracture horizontal [...]
Multi-level Fracture Analysis and Visualization Workflow Improves Unconventional Reservoir Management
Innovatively exploring knowledge from static data, dynamic data, and [...]
SMART researchers highlighted on the DOE Artificial Intelligence & Technology Office (AITO) Web page
The article provides a high-level summary for the SMART project and [...]
Deep Learning Assisted Framework Accelerates Inverse Modeling and Forecasting
Traditional physics based simulation approaches for inverse modeling and forecasting in [...]
Machine Learning Strategies for Segmentation of Rock Core Computed Tomography Images
Labeling—or segmentation—of rock core computed tomography (CT) allows for micro- to [...]
SMART Project Holds Technical Meeting October 5-7, 2021
On October 5-7, the SMART team convened for an internal technical [...]
Cross-Cutting Algorithms Support Real-Time Decision Making
Integration of Multiple Algorithms and Workflows The use of multiple [...]