Machine Learning
Machine learning (ML) is increasingly being adopted in the oil and gas industry, particularly for production forecasting and Enhanced Oil Recovery (EOR) optimization. ML techniques leverage vast amounts of historical data to identify patterns, predict future outcomes, and optimize processes that would be complex, time-consuming, or impractical to model using traditional methods.
A ML Workflow for Oil and Gas Forecasting involves the following steps:
- Data Preprocessing: Clean, transform, and engineer features from the data. Split into training, validation, and test sets.
- Model Development: Choose and train the appropriate ML model (e.g., regression, neural networks). Optimize hyperparameters.
- Evaluation: Validate and test the model using appropriate metrics (e.g., RMSE). Ensure the model is interpretable.
- Deployment: Implement the model in a production environment, enabling real-time or batch forecasting.
- Monitoring: Continuously monitor and retrain the model to maintain accuracy. Incorporate user feedback for improvements.
- Documentation: Record the workflow and share insights with the team for transparency and ongoing optimization.
How we employ machine learning in our products?
EOR Pilot Simulation
ePROJECT leverages reservoir simulation to evaluate and optimize EOR techniques on a smaller section of a reservoir before full-scale implementation. By creating a detailed model of the pilot area and simulating various EOR methods, such as thermal, chemical, or gas injection, engineers can predict the potential success, optimize key parameters, and assess risks
Production Forecasting
tOPTIMA streamlines geomodeling and history matching workflow to accurately reflect past production data, ensuring its reliability. Various production scenarios are then simulated to predict future production rates, helping in planning field development, optimizing recovery strategies, and making informed decisions about resource management.
CO2 Storage
cNAV leverages numerical simulation for optimizing CO₂ storage development by providing a detailed model of the subsurface, which helps predict how CO₂ will behave when injected into a geological formation. By integrating data on the reservoir's geological structure, rock properties, and fluid dynamics, simulations can forecast CO₂ plume movement, pressure changes, and potential interactions with existing fluids. This allows engineers to optimize injection strategies, monitor storage security, and assess the long-term stability of the CO₂ storage site, ensuring safe and efficient storage while minimizing risks like leakage or environmental impact.