An Industrial Affiliates Program for
Subsurface Data Analytics and Machine Learning - DiReCT
The DIRECT industrial affiliates program, based in the Hildebrand Department of Petroleum and Geosystems Engineering, at the University of Texas at Austin, is working to develop these integrated modeling and decision support data analytics and machine learning methods and workflows to solve the following outstanding problems:
Integration
Maximizing the integration of deterministic engineering, geological description, target-oriented drilling, geophysical measurements, borehole formation evaluation, and core data to construct high-resolution reservoir models for improved production forecast accuracy.
Characterization
Improving the spatial resolution of reservoir description and modeling based on enhanced data integration for improved development decision-making.
Grey Box Modeling
Development of big data analytics and machine learning methods that fully account for geospatial and engineering knowledge.
Robust Decision Making
Automated, expert systems to support consistent evaluation of subsurface and production data.
System Interpretability
Advanced system summarization and spatial visualization for model interrogation and learning from models for credible decision support.
Optimum Drilling
Development of modern, production-oriented drilling strategies by designing trajectories for optimum well placement to maximize reserves intersection and recovery factors by primary or secondary production means.
In-fill Drilling
Development of modern, efficient, and cost-effective strategies to evaluate in-fill drilling, primary or secondary production, and intelligent feedback control systems for reactive production under variable geological, fluid and financial constraints.
Uncertainty Quantification
Development of modern methods to ascertain the value of measurements and the uncertainty of descriptions and quantification.
Modern Software Solutions for Reservoir Characterization
Development of modern computer and software solutions for rapid and efficient 3D collocated multi-physics description, visualization, modeling, well geosteering, and production forecasting.
The consortium will develop new methods and workflows in spatial, big data analytics for petrophysical, geophysical, reservoir engineering and geomechanical integration into subsurface models for optimum well trajectories and reservoir recovery, including:
- Novel big data analytics methods and workflows for data debiasing, imputation of missing data, feature and anomaly detection.
- Novel reservoir-oriented methods for geophysical data processing and interpretation for high-resolution reservoir description and updating.
- Multiscale, generalizable flow forecasting surrogate models
- Integration of engineering physics, geophysics, petrophysics and geoscience interpretation to augment data-driven methods
- Well-documented examples, best practice workflows and case studies, training and mentoring for development of member company operational capability.
The consortium will develop novel machine learning-based geomodeling and forecasting methods and workflows.
- Novel integrated machine learning methods and workflows for spatiotemporal, multivariate modeling that account for data bias, spatial correlation and trends, multivariate physics-based constraints that are robust in the presence of sparse data and big data.
The consortium will develop real-time updateable expert systems for optimum field development.
- Novel integrated systems for optimum production-oriented well geo-steering and completion.
- Port algorithms and key findings into a modern computer and software architecture and protocols for user-friendly interactions, diagnostics, learning and decision support.
Membership:
The DIRECT consortium membership cost is $60k/year. Through member company support, the DIRECT consortium supports graduate students conducting planned research supervised by leading faculty while integrating input from the consortium member companies. We partner closely with our member companies. Interested companies are welcome.
For more information feel free to contact us:
| Prof. Michael J. Pyrcz, Ph.D., P.Eng. mpyrcz@austin.utexas.edu |
| Prof. John Foster, Ph.D. jfoster@austin.utexas.edu |
| Prof. Carlos Torres-Verdin, Ph.D. cverdin@austin.utexas.edu |
| Prof. Eric van Oort, Ph.D. vanoort@austin.utexas.edu |
We are happy to discuss...
