Oil and Gas Data Science for Predictive Maintenance Training Course

Course overview

Unplanned failure of rotating equipment, valves, compressors and process assets can interrupt production, increase maintenance cost and create safety exposure. Yet many oil and gas organisations hold years of historian tags, work orders, inspection records, alarm logs and condition-monitoring data without a reliable way to turn them into maintenance decisions. This course addresses the practical gap between operational data and predictive maintenance: identifying which failure modes can be predicted, assembling fit-for-purpose datasets, and producing evidence maintenance and operations teams can act on.

Participants learn a structured data-science workflow for oil and gas assets, from data extraction and quality assessment through feature engineering, model selection and deployment planning. They work with time-series sensor data, maintenance events and ISO 14224 equipment taxonomy; create health indicators; detect anomalies; and build classification and remaining-useful-life models in Python. The course also covers imbalanced failure data, false-alarm trade-offs, model explainability, validation across operating regimes, and the connection between model outputs, CMMS work orders and maintenance planning.

Teaching combines instructor-led technical sessions with guided Python notebooks and an integrated asset case study. Teams investigate a simulated centrifugal-pump degradation problem using historian and work-order data, then present a predictive-maintenance use case with data requirements, model metrics, alert logic and implementation controls. Each participant leaves with a reusable predictive-maintenance project template, documented analytical workflow and an application plan for one asset class or reliability problem in their operation.

The programme is suited to reliability, maintenance, integrity, operations and digital professionals who need to commission, build, assess or govern asset analytics rather than treat machine-learning outputs as a black box.

Course objectives

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

• Frame a predictive-maintenance use case around asset criticality, failure modes and measurable maintenance decisions
• Profile historian, condition-monitoring and work-order data for completeness, timestamp alignment and data-quality defects
• Apply ISO 14224 equipment and failure-taxonomy concepts to label maintenance events and structure analytical datasets
• Engineer time-series features including rolling statistics, lag variables, operating-state indicators and degradation trends
• Build and compare anomaly-detection and failure-classification models in Python for rotating-equipment data
• Evaluate predictive models using precision, recall, PR-AUC, lead time and false-alarm cost rather than accuracy alone
• Interpret model drivers with feature-importance and explainability methods to support reliability-engineer review
• Produce a predictive-maintenance use-case pack containing alert rules, workflow integration, controls and value assumptions

Benefits of attending

Target competencies

Who should attend

• Reliability Engineers — who need to convert failure history and condition data into earlier maintenance interventions
• Maintenance Engineers — who must prioritise inspections and work orders using evidence from asset data
• Asset Integrity Engineers — who assess degradation signals and need defensible monitoring thresholds
• Operations Engineers — who need to distinguish process-condition effects from developing equipment faults
• Data Analysts and Data Scientists — who require oil and gas failure context, historian data practices and maintenance metrics
• Digital Transformation and Asset Performance Managers — who must select, govern and scale predictive-maintenance use cases

Requirements and prerequisites

Participants should be comfortable working with spreadsheets or tabular datasets and understand basic descriptive statistics such as averages, ranges and correlations. Familiarity with oil and gas equipment, maintenance work orders, process tags or condition-monitoring data is helpful, particularly for reliability and operations roles. Some prior exposure to Python is beneficial, but participants do not need to be programmers: guided Jupyter notebooks and starter code are provided. No previous machine-learning project, advanced mathematics, database administration or production access to a historian or CMMS is required. Complete beginners should expect focused practice with Python data tables and model outputs.

Training methodology

The course uses short instructor-led demonstrations followed by guided analysis in Jupyter notebooks. Participants work with a realistic oil and gas dataset combining process-historian tags, vibration indicators, operating states and maintenance records for rotating equipment. Exercises progress from data-quality diagnosis to feature creation, model testing and alert design. Small groups review cases from the perspectives of operations, reliability and maintenance planning, challenging assumptions about failure labels and intervention windows. The final workshop converts model results into a governed pilot proposal for a selected asset class.

Course outline

Day 1: Predictive maintenance use cases and asset data foundations

• Predictive, preventive and condition-based maintenance decision models
• Asset criticality screening and consequence-of-failure assessment
• Failure modes for pumps, compressors, turbines and valves
• ISO 14224 equipment hierarchy and failure-data terminology
• Process historian tags, sampling rates and event timestamps
• CMMS work orders, notification codes and maintenance narratives
• Data lineage across operations, reliability and maintenance systems

Workshop: Participants define a centrifugal-pump predictive-maintenance use case, including failure mode, decision owner, intervention window and required data sources.

Day 2: Preparing oil and gas maintenance datasets

• Python and Jupyter notebook workflow for asset analytics
• Importing and inspecting historian and work-order extracts
• Timestamp alignment, resampling and operating-period segmentation
• Missing values, sensor drift, bad-quality flags and outliers
• Maintenance-event labelling from failure codes and text records
• Class imbalance and rare-failure sampling strategies
• Train, validation and test splits that prevent time leakage

Workshop: Participants build a cleaned, labelled pump dataset and document the data-quality issues that could invalidate a model.

Day 3: Feature engineering and anomaly detection

• Rolling means, standard deviations, slopes and rate-of-change features
• Lagged variables and multivariate process-condition features
• Operating-state classification for start-up, steady-state and shutdown periods
• Vibration, temperature, pressure and flow health indicators
• Baseline modelling with normal operating envelopes
• Isolation Forest and statistical anomaly-detection approaches
• Threshold selection using alert burden and maintenance lead time

Workshop: Participants engineer health features and create an anomaly-alert rule for a pump operating across variable process conditions.

Day 4: Failure prediction, validation and explainability

• Supervised classification for impending equipment failure
• Remaining useful life concepts and degradation-window definition
• Random Forest and gradient-boosting model comparison
• Precision, recall, PR-AUC and confusion-matrix interpretation
• Lead-time analysis and cost-sensitive alert threshold setting
• Feature importance and SHAP-based model explanations
• Validation by asset, operating regime and maintenance campaign

Workshop: Participants compare two failure-prediction models, select an alert threshold and prepare an explanation of the selected model for a reliability review.

Day 5: Deploying predictive insights into maintenance work

• Alert-to-work-order workflow design in a CMMS environment
• Human review gates and reliability-engineer decision rights
• Model monitoring for data drift, performance decay and sensor changes
• Cybersecurity, data access and operational-technology governance considerations
• Pilot scoping, baseline measurement and value-realisation metrics
• Dashboard requirements for operators, planners and maintenance leaders
• Predictive-maintenance operating model and scale-up roadmap

Workshop: Participants produce and present a predictive-maintenance use-case pack with pilot scope, data map, model measures, alert workflow and implementation actions.

Tools & standards covered

Python, Jupyter Notebook, AVEVA PI System, ISO 14224

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