Cambridge
Academy
In London

Introduction

AI is revolutionizing exploration by analyzing seismic data, identifying drilling prospects, and improving reservoir modeling. This course provides engineers with tools to optimize exploration efficiency and reservoir performance.

Objectives

- Learn AI applications in exploration and reservoir engineering.

- Apply machine learning for seismic interpretation.

- Use AI for reservoir simulation and performance prediction.

- Enhance oil recovery strategies using AI insights.

Audience

- Reservoir engineers

- Exploration geologists

- Petroleum engineers

- Data scientists in oil & gas

5-Day Outline

Day 1: AI in Exploration

- Overview of AI in upstream oil & gas

- Machine learning in seismic data interpretation

- Tools: Schlumberger DELFI, IBM Watson for Exploration

- Case study: AI in prospect identification

- Workshop: AI seismic analysis simulation

Day 2: Reservoir Modeling with AI

- AI in dynamic reservoir modeling

- Predicting reservoir behavior with ML algorithms

- AI-enhanced reservoir simulation workflows

- Case study: AI for unconventional reservoirs

- Group discussion: Comparing AI vs. traditional modeling

Day 3: AI in Drilling Data for Reservoir Characterization

- Using AI in well logging and petrophysical analysis

- AI-assisted pore pressure prediction

- Real-time reservoir updates during drilling

- Practical demo: AI-driven reservoir data dashboard

- Workshop: AI well log interpretation

Day 4: Enhanced Oil Recovery (EOR) with AI

- AI optimization of injection strategies

- Predicting recovery factors with AI

- AI in chemical and thermal EOR processes

- Case study: AI in CO₂ EOR projects

- Simulation: Build AI-driven EOR strategy

Day 5: Future of AI in Reservoir Management

- AI in reservoir surveillance

- Big data & IoT integration with AI for reservoirs

- Group project: AI reservoir management plan

- Presentations and feedback

- Wrap-up & certification