About
Recent advances in machine learning and the broader availability of computational power have made it possible to interpret rich, heterogeneous, and even real-time data. The oil and gas industry is leveraging this data-driven revolution to generate actionable insights from real-time production, drilling, and completions data, SCADA data streams, 3D and 4D seismic data, and well data such as cores, well-logs, thin-sections, and SEM images. Additionally, newer data types like DTS/DAS measurements are being utilized.
Target Audience
Geoscientists, petrophysicists, engineers, or anyone interested in subsurface engineering and geoscience applications of machine learning and data analytics
You Will Learn
- Essential terminology specific to data analytics and machine learning
- Data type and reporting protocols in the oil and gas industry
- Practical approaches to ensuring and verifying data quality
- Exploratory data analyses to visualize and quantify relationships as well as identifying outliers
- The basic principles of common machine learning tools in the petroleum industry
- Unsupervised learning
- Supervised learning
- Reinforcement learning
- Use cases of subsurface geoscience and engineering data-driven applications
- Recognize and address pitfalls of data-driven methods in the oil and gas industry