Data Analytics and AI for Oil and Gas

Qué suele abarcar

1. 1

   Data in Oil and Gas

   • Why Data Matters in Oil and Gas
   • Where Oilfield Data Comes From
   • The Data Landscape: Five Kinds of Data
   • Four Types of Analytics
2. 2

   Data Quality and Preparation

   • What Good Data Looks Like
   • Common Problems in Sensor Data
   • Units, Timestamps and Tag Names
   • Cleaning Data and Adding Context
3. 3

   Statistics and Visualisation for Engineers

   • Mean, Median and Mode
   • Spread and Outliers
   • Correlation Is Not Causation
   • Simple Linear Regression
   • Charts and Dashboards That Help Decisions
4. 4

   Machine Learning in Plain Terms

   • How Machines Learn from Examples
   • Supervised and Unsupervised Learning
   • Training Data, Test Data and Overfitting
   • Measuring a Model: Accuracy, Precision and Recall
   • Physics-Based, Data-Driven and Hybrid Models
5. 5

   Analytics and AI Use Cases in Oil and Gas

   • Predictive Maintenance of Rotating Equipment
   • Production Forecasting, Anomaly Detection and Virtual Flow Metering
   • Drilling Optimisation and Seismic Interpretation
   • Corrosion Prediction and HSE Text Analytics
   • Computer Vision for PPE and Leak Detection
6. 6

   Generative AI and Large Language Models

   • What Generative AI and Large Language Models Are
   • Useful Work for Generative AI
   • Limits: Hallucination and Verification
   • Using Company Data Safely with AI Tools
7. 7

   Running Analytics Projects and Governing AI

   • Framing the Business Question
   • Data Access, Pilots and Domain Experts
   • Moving to Production and Measuring Value
   • Data and AI Governance
   • Model Drift and Human Oversight
   • Skills, Roles and the Citizen Data Scientist