Oil and Gas Data Analytics for Production Performance Training Course

Course overview

Production teams often hold years of historian, well test, allocation, maintenance, drilling and laboratory data, yet struggle to turn it into decisions that improve uptime, defer water breakthrough or explain declining production. Analysts must reconcile inconsistent well identifiers, time bases and units before they can distinguish a true reservoir, artificial-lift or facility constraint from bad data. This course addresses the practical gap between operational data availability and defensible production-performance analysis.

Participants learn to build production datasets from AVEVA PI System extracts, SQL tables and operational files; validate data quality; and analyse oil, gas, water, pressure, choke, run-time and downtime records. They apply production surveillance methods, decline curve analysis, rate-normalisation, production-loss accounting, nodal constraint logic and anomaly detection. Using Python and Power BI, participants create repeatable analytical workflows, assess well and asset performance, prioritise intervention candidates and communicate findings with appropriate engineering context and uncertainty.

The five-day programme combines instructor-led technical sessions with worked oilfield datasets representing wells, gathering systems and processing facilities. Participants clean and join production data, develop analytical notebooks, create interactive management dashboards and test conclusions against a field-performance case. They leave with a documented production-performance analytics pack: a data-quality assessment, KPI definitions, Python analysis workflow, Power BI dashboard design and an action register for applying the method to their own asset.

The course is best suited to production, petroleum, reservoir, operations and data professionals who already work with field data and need a structured way to translate it into credible performance decisions.

Course objectives

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

• Construct an analysis-ready production dataset by joining historian, allocation, well-test and downtime records
• Validate production data using completeness, range, unit-consistency and time-alignment checks
• Calculate well and facility KPIs including uptime, deferment, water cut, gas-oil ratio and production efficiency
• Apply decline curve analysis and rate-normalisation to separate expected decline from actionable underperformance
• Diagnose production constraints using pressure, choke, artificial-lift, downtime and facility-capacity evidence
• Build Python workflows with pandas to automate data cleaning, KPI calculation and anomaly screening
• Design a Power BI production-surveillance dashboard with drill-through views for wells, pads and assets
• Produce a ranked intervention opportunity register supported by assumptions, evidence and estimated production impact

Benefits of attending

Target competencies

Who should attend

• Production Engineers — who must identify well underperformance and justify intervention priorities
• Petroleum Engineers — who need to integrate surveillance data with well and reservoir performance interpretation
• Reservoir Engineers — who require production evidence to distinguish reservoir behaviour from operating constraints
• Operations and Asset Integrity Engineers — who investigate downtime, equipment reliability and production losses
• Oil and Gas Data Analysts — who must transform operational datasets into trusted analytical products
• Production Supervisors and Asset Managers — who need consistent KPIs and evidence for daily production decisions

Requirements and prerequisites

Participants should have practical exposure to oil and gas production operations, including common measures such as oil, gas and water rates, pressure, choke setting, uptime, downtime and water cut. They should be comfortable working with spreadsheets and interpreting charts, and should understand basic data concepts such as tables, fields, timestamps and joins. Prior use of Python, SQL, Power BI or AVEVA PI System is helpful but not mandatory; guided templates are provided. This is not a coding-from-zero course: participants without any spreadsheet confidence or production-domain experience should first build those foundations.

Training methodology

Delivery alternates concise instructor-led explanations with guided analysis of a realistic production dataset covering wells, artificial lift, gathering constraints and downtime events. Participants use Python notebooks to clean, join and analyse time-series data, then build Power BI views for surveillance and management review. Case discussions require teams to defend whether observed losses are data artefacts, reservoir effects, well constraints or facility limitations. Each day closes with a practical output, culminating in an asset-specific application plan and intervention-prioritisation presentation.

Course outline

Day 1: Production data foundations and data quality

• Production surveillance decisions and the production-data value chain
• Well, completion, facility and allocation data models
• Time-series sampling, aggregation and production-day alignment
• Well identifier, unit and datum standardisation
• Completeness, range, duplicate and missing-value data-quality tests
• Reconciling allocated production, test rates and historian measurements
• SQL joins and Python pandas data-ingestion patterns

Workshop: Participants build an analysis-ready well production table from simulated historian, well-test, allocation and downtime files and document its data-quality issues.

Day 2: Production surveillance and performance diagnostics

• Daily, monthly and rolling production KPI design
• Production efficiency, uptime and deferment calculation
• Water cut, gas-oil ratio and fluid-rate surveillance
• Choke, flowing pressure and artificial-lift operating envelopes
• Well test interpretation for rate validation
• Rate normalisation by choke, run time and operating conditions
• Production-loss categorisation and event-based accounting

Workshop: Participants create a well-surveillance scorecard that identifies rate changes, downtime losses and suspicious allocation or test-data conflicts.

Day 3: Decline analysis, constraints and anomaly detection

• Exponential, harmonic and hyperbolic decline curve models
• Selecting decline windows and handling shut-ins
• Forecast uncertainty and forecast-versus-actual variance
• Separating reservoir decline from operating downtime
• Nodal-analysis concepts for well and surface constraints
• Statistical anomaly detection using rolling baselines and residuals
• Root-cause hypotheses from production, pressure and maintenance signals

Workshop: Participants analyse declining and unstable wells, fit decline models, flag anomalies and prepare evidence for the most likely constraint mechanism.

Day 4: Automation and production-performance visualisation

• Python pandas transformations for production time series
• Reusable functions for KPI and deferment calculations
• Data-quality exception logging in analytical workflows
• Power BI data modelling for wells, events and facilities
• DAX measures for rolling rates, uptime and production efficiency
• Drill-through and decomposition views for well-performance investigation
• Dashboard design for morning surveillance and asset reviews

Workshop: Participants build a Python-driven KPI dataset and a Power BI dashboard with asset, pad and well drill-down views.

Day 5: Intervention prioritisation and operational application

• Opportunity framing for wells, artificial lift and facility bottlenecks
• Estimating production impact and value ranges
• Ranking opportunities by impact, confidence, cost and operability
• Assumption registers and analytical audit trails
• Communicating uncertainty and avoiding false precision
• Production review packs for engineering and operations stakeholders
• Scaling analytics workflows through governance and ownership

Workshop: Participants present a ranked intervention opportunity register and a 90-day implementation plan for applying the analytics workflow to their asset.

Tools & standards covered

Python (pandas and scikit-learn), Microsoft Power BI, AVEVA PI System, SQL

A typical training day

Live online deliveries follow the same structure in the East Africa Time zone, with shorter screen blocks and longer breaks.

What the fee includes

How you can take this course

Certification

Participants who complete the full five days receive the Skillset Development Certificate of Completion, stating the course title, course code, dates and delivery format — suitable for professional-development records and employer reimbursement.

Frequently asked questions