OFM Production Data Analysis and Forecasting Training Course

Overview

OFM Production Data Analysis and Forecasting Training Course is a one-week software-focused course for production engineers, reservoir engineers, petroleum engineers, and technical professionals who work with well and field production data. Participants build an OFM Project Analysis Pack that connects project structure, data validation, calculated variables, filters, plots, maps, decline analysis, forecasts, and governed reports. Agile Leaders Training Center develops practical OFM production data analysis through traceable engineering workflows.

Who Should Attend

• Production engineers responsible for well surveillance, performance diagnosis, and production forecasts
• Reservoir engineers who compare well, completion, pattern, reservoir, and field performance
• Petroleum engineers who prepare repeatable production-analysis workflows and decision records
• Technical data specialists who import, validate, organize, and maintain OFM project data
• Asset teams that use OFM dashboards, maps, plots, and reports for routine performance reviews

Participants should understand basic well and production terminology and be comfortable working with tabular data. The course does not cover software installation, license administration, corporate database engineering, reserves certification, or AI model development.

Departments and Industries

The course supports organizations that manage oil and gas production information and engineering surveillance.

• Onshore, offshore, conventional, unconventional, and mature-field production teams
• Production technology, reservoir management, petroleum engineering, and asset surveillance departments
• Field development, waterflood, artificial-lift, and well-performance groups
• Technical data management, digital oilfield, and engineering information functions
• Operating companies, joint ventures, engineering consultancies, and technical service providers

Learning Objectives

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

• Design an OFM project database structure for wells, completions, groups, and time-series production data.
• Import and validate production data using controlled mapping, units, dates, identifiers, and exception checks.
• Create calculated variables, filters, and reusable datasets for production surveillance.
• Build plots, reports, dashboards, and basemaps that reveal well and field performance.
• Perform decline curve analysis and prepare traceable production forecasts with stated assumptions.
• Package an engineering analysis with quality evidence, interpretation notes, and reporting controls.

Course Agenda

Day 1: Build a Controlled OFM Project

• OFM Project Scope and Engineering Question Map
• Well, Completion, Group, and Field Hierarchy Design
• Production, Injection, Test, Event, and Static Data Inventory
• Source-to-OFM Table and Variable Mapping Sheet
• Project Naming, Units, Dates, Identifiers, and Version Controls

Day 2: Import and Validate Production Data

• Production Data Import and Update Workflow
• Well and Completion Identifier Reconciliation
• Missing, Duplicate, Negative, and Outlier Value Checks
• Unit Conversion and Reporting-Period Validation
• Data Quality Exception Register and Correction Trail

Day 3: Configure Surveillance and Visualization

• Calculated Variables and Engineering Expression Register
• Project, Completion, Date, and Performance Filters
• Production Trend, Scatter, Stacked, and Diagnostic Plots
• Basemap, Bubble Map, Grid, and Spatial Comparison Views
• Dashboard and Exception Report Configuration

Day 4: Analyze Performance and Forecast Production

• Well, Completion, Group, and Field Comparison Workflow
• Rate, Cumulative, Ratio, Injection, and Downtime Diagnostics
• Decline Curve Analysis Data Window and Model Selection
• Forecast Assumptions, Constraints, Scenarios, and Review Checks
• Candidate Recognition and Follow-Up Evidence Record

Day 5: Govern and Present OFM Analysis

• Suggested Exercise: Build and Validate an OFM Project Dataset
• Suggested Exercise: Configure a Surveillance Dashboard and Basemap
• Suggested Exercise: Diagnose a Well Performance Change
• Suggested Exercise: Prepare and Challenge a Production Forecast
• Capstone Exercise: Present an OFM Project Analysis Pack

Practical Exercises

The course uses suggested software exercises based on realistic well, completion, and field datasets.

• Suggested activity: map source tables into an OFM project and document naming, units, dates, identifiers, and update rules.
• Suggested activity: detect data-quality exceptions, reconcile inconsistent well records, and record corrections without losing traceability.
• Suggested activity: configure calculated variables, filters, plots, reports, and maps for a defined surveillance question.
• Suggested activity: select a decline-analysis window, document assumptions, compare forecast scenarios, and prepare an engineering review note.

FAQs

Who suits OFM production data analysis training?

OFM production data analysis training suits production, reservoir, and petroleum engineers, technical data specialists, and asset personnel who organize and interpret well and field production information.

Does the course cover building an OFM project database?

Yes. Participants structure wells, completions, groups, time-series data, variables, units, dates, identifiers, and controlled update rules before creating analyses.

How does this course differ from general production engineering?

The course concentrates on repeatable workflows inside OFM: project data, validation, filters, calculations, visualization, decline analysis, forecasting, and governed reporting. It does not teach the full production-engineering discipline.

What makes an OFM production forecast traceable?

A traceable forecast records the source data, selected analysis window, decline method, assumptions, constraints, scenario, reviewer checks, and links between the plotted result and the engineering decision.

Conclusion

Participants leave with an OFM Project Analysis Pack for their organization. It combines a project data map, quality register, calculated-variable log, surveillance views, decline analysis, forecast assumptions, candidate record, and reporting checklist. The pack turns production data into repeatable, reviewable engineering evidence.