Upstream operations produce enormous volumes of data from seismic surveys, wireline logs, drilling rigs, wellhead sensors and production facilities. Used well, that data reduces cost, extends asset life and improves safety. Used poorly, or not at all, it leaves engineers making decisions from spot checks and intuition while problems quietly develop. Digital oilfield programmes and national data repositories have raised expectations that every technical professional can work with data confidently.

This ten-day course takes participants from raw data to deployed insight. The first block builds foundations: the types and sources of oil and gas data, relational databases and SQL, Python with pandas for cleaning and analysis, and visualisation of wells, logs and production trends. The second block applies statistical and machine learning methods to real industry problems such as production forecasting, well test interpretation, drilling parameter optimisation, facies classification, artificial lift diagnostics and equipment health monitoring. The third block scales up with big data tools, cloud services, streaming data and dashboards, and addresses data governance, security and ways of getting analytics adopted by operations teams.

Most of the time is spent on laboratories using representative field datasets. In the closing project, teams define a business question, build a pipeline and model, and present findings to a panel as they would to asset managers.

By the end of the course, participants will be able to:

• Describe the main sources, formats and quality issues of upstream oil and gas data.
• Query and combine production, drilling and well data using SQL.
• Clean, analyse and visualise well and production data with Python and pandas.
• Forecast production and fit decline curves with quantified uncertainty.
• Build and evaluate machine learning models for classification, regression and anomaly detection.
• Apply predictive maintenance methods to rotating equipment and artificial lift systems.
• Explain big data architectures, streaming and cloud options for operations data.
• Create dashboards and communicate analytical results to technical and business audiences.

The course is aimed at technical and analytical staff who use or manage upstream data, including:

• Petroleum, production, drilling and reservoir engineers
• Geoscientists and petrophysicists
• Data analysts and data scientists in oil and gas companies
• Digital oilfield and automation specialists
• Production surveillance and operations support staff
• IT and database administrators supporting technical data
• Regulatory and national data repository officers
• Asset managers who commission analytics work

Participants leave with:

• Working Python and SQL notebooks for cleaning and analysing well and production data.
• A decline curve forecasting workflow with uncertainty ranges.
• A tested approach to anomaly detection and predictive maintenance.
• A dashboard prototype for production performance monitoring.
• A checklist for data quality and governance in operations data.
• A completed analytics project and a plan for applying it in their own asset.

Because this is a skills programme, most of the ten days is spent working with data. It combines:

• Daily laboratories in Python, SQL and Power BI using representative field datasets
• Instructor demonstrations of end-to-end workflows on production and drilling problems
• Mini case studies of analytics projects in operators and service companies
• Pair programming and troubleshooting clinics for participants new to coding
• A team capstone project with panel review

Participants who attend the ten days and complete the laboratories and capstone project receive a CPD-accredited Certificate of Completion issued by Vision Reach Global Consultancy.