AI in Reservoir Dynamics: Optimizing Hydrocarbon Recovery

Explore the application of artificial intelligence to enhance reservoir management and optimize hydrocarbon recovery. Learn how AI can analyze complex geological data, including seismic surveys and well logs, to create highly accurate reservoir models. Participants will gain insights into how AI can be used to monitor reservoir pressure, fluid flow, and other critical parameters, enabling real-time adjustments to production operations. 

Target Audience: Reservoir engineers, geologists, geophysicists, production engineers, and reservoir simulation specialists.

Module 1: Introduction to AI in Reservoir Engineering

• The evolving role of AI in oilfield development and reservoir management
• Traditional vs AI-enhanced reservoir modeling
• Benefits of AI for hydrocarbon recovery: cost, accuracy, speed
• Industry case studies on AI-driven reservoir optimization

Module 2: Reservoir Data Sources and Preprocessing

• Types of data used in reservoir analysis:
  • Seismic surveys
  • Well logs (gamma ray, resistivity, porosity)
  • Production history and pressure data
  • Core samples and fluid properties
• Data integration across sources (structured and unstructured)
• Data cleaning, normalization, and handling missing values
• Understanding spatial and temporal data resolution issues

Module 3: AI Algorithms for Subsurface Modeling

• Introduction to supervised and unsupervised learning for reservoir characterization
• Neural networks for well-log prediction and facies classification
• Clustering techniques to identify geological patterns
• Feature selection and dimensionality reduction (e.g., PCA) for geophysical datasets
• Combining geostatistics with ML for enhanced reservoir property modeling

Module 4: AI-Driven Reservoir Simulation and Prediction

• Machine learning models to predict reservoir performance
• Time-series forecasting for pressure, flow rate, and recovery factor
• Hybrid reservoir simulation models: physics-based + data-driven
• Predicting production under different development scenarios
• Using AI to shorten the history matching process

Module 5: Real-Time Monitoring of Reservoir Dynamics

• AI-based interpretation of pressure transient analysis (PTA) and rate transient analysis (RTA)
• Automated analysis of fluid movement and sweep efficiency
• Monitoring water breakthrough, gas coning, and formation damage
• Smart sensor integration for real-time reservoir data feeds
• Live dashboards and alert systems for field operations

Module 6: AI for Enhanced Oil Recovery (EOR) Optimization

• Predicting the impact of EOR techniques using AI (gas injection, water flooding, chemical EOR)
• Real-time adjustment of injection/production strategies based on AI insights
• Machine learning for screening EOR candidates
• Case studies: AI applied in mature field rejuvenation and tertiary recovery

Module 7: Identifying Bypassed Oil and Untapped Potential

• Pattern recognition and clustering to detect missed pay zones
• AI for reservoir segmentation and heterogeneity mapping
• Use of unsupervised learning to highlight underperforming zones
• Predictive models for infill drilling and well placement
• Maximizing Net Present Value (NPV) with data-driven development strategies

Module 8: Building and Training AI Reservoir Models

• Data pipeline design: from raw logs to predictive models
• Building neural networks, decision trees, and ensemble models
• Model training, hyperparameter tuning, and validation
• Performance evaluation: R², RMSE, cross-validation
• Python-based implementation using TensorFlow, Keras, and Scikit-learn

Module 9: Deployment, Integration & Field Implementation

• Integrating AI models with reservoir simulation software (e.g., Eclipse, CMG, Petrel)
• Building automated workflows for continuous learning and model updating
• Edge/cloud deployment for fieldwide optimization
• Collaborating with multidisciplinary teams (geology, drilling, production)
• Bridging the gap between AI insights and operational decision-making

Module 10: Ethics, Uncertainty, and Decision Support

• Managing uncertainty in AI predictions
• Interpreting black-box models: SHAP values and explainable AI
• Avoiding overfitting and bias in geological interpretations
• AI as a decision support tool — not a replacement for engineering judgment
• Ensuring alignment with reservoir management objectives

Module 11: Practical Workshop and Capstone Project

• Hands-on use of real or synthetic seismic and well data
• Building a reservoir property prediction model
• Identifying untapped zones and simulating production scenarios
• Group-based project: AI-assisted reservoir optimization plan
• Presentation and critique of predictive workflows