Advanced Analytics in Oil & Gas
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Program Professional Framework
Advanced Analytics in Oil & Gas
Program Overview
Oil and gas operations generate vast volumes of sensor, production, drilling and maintenance data, but value comes only when that data is turned into reliable decisions. This course covers the analytics workflow for upstream and downstream applications: data integration from historians and engineering databases, data quality, exploratory analysis, regression and machine learning models, time-series forecasting, anomaly detection for predictive maintenance, production forecasting and decline analysis, and drilling performance analytics. Participants learn to validate models with domain knowledge, avoid misleading results and deploy insights through dashboards, supporting the digital transformation agendas of national oil companies in the region.
Learning Outcomes
- ✓Integrate and clean time-series and tabular data from historians, production databases and maintenance systems.
- ✓Apply exploratory data analysis to identify trends, outliers and relationships in operational data.
- ✓Build and validate regression and machine learning models for a production or equipment problem.
- ✓Develop an anomaly detection approach that gives early warning of rotating equipment failure.
- ✓Forecast well production using decline curve analysis and data-driven methods and compare their uncertainty.
- ✓Communicate model results and limitations to engineering and management stakeholders.
Target Audience
- Petroleum, reservoir and production engineers
- Reliability and maintenance engineers
- Data analysts and data scientists in energy companies
- Digital transformation and operations excellence teams
Detailed Program Modules
01Analytics Across the Energy Value Chain
- Use cases from reservoir to refinery
- Descriptive, predictive and prescriptive analytics
- Building a business case for an analytics project
02Data Foundations
- Historians, SCADA and engineering data sources
- Data cleaning, resampling and feature engineering
- Data governance and industry data platforms
03Statistical and Machine Learning Methods
- Regression, classification and clustering
- Model training, cross-validation and overfitting
- Interpreting models with physical understanding
04Production and Reservoir Analytics
- Decline curve analysis and data-driven forecasting
- Well performance clustering and opportunity screening
- Waterflood and artificial lift optimisation insights
05Equipment and Drilling Analytics
- Anomaly detection for predictive maintenance
- Failure prediction for pumps and compressors
- Drilling performance benchmarking and ROP models
06Deployment and Value Realisation
- Dashboards and decision support tools
- Model monitoring and retraining
- Change management and measuring value
Training Methodology
Applied Project & Assessment
Applied Project
Using a provided field or plant dataset, participants define a business question, clean and explore the data, build and validate a predictive model, and present a short decision brief with expected value, limitations and a deployment plan.
Assessment
Short diagnostic at the start, practical tasks linked to each learning outcome, and evaluation of the applied project against published criteria.