Centram Centre for Applied Artificial Intelligence

AI & MACHINE LEARING FOR OIL & GAS REGULATION COURSE

Course overview

The AI & ML for Oil & Gas Regulation Course designed for regulators, industry compliance staff, data scientists working with regulators, and policy-makers who need to apply AI/ML in regulatory workflows. The structure balances domain context, ML techniques, practical labs, governance/ethics, and a capstone project.


Target audience: Regulators, compliance officers, O&G data scientists, consultants, policy makers.

Course duration: 4 weeks – intense boot camp OR 2 weeks physical training and 3 weeks online training

 Learning outcomes:

  1. Understand realistic ML use cases in regulatory work (emissions, leaks, production anomalies, document review).
  2. Build, validate, and deploy ML models suitable for regulatory evidence and compliance monitoring.
  3. Implement data pipelines, MLOps, explainability, and auditability that meet regulatory needs.
  4. Evaluate legal, ethical, and governance implications of AI/ML in regulation.

Course Content

Introduction & Regulatory Context

  1. Topics: Why AI/ML for regulation; key regulatory goals (safety, environment, compliance, transparency); typical data sources in O&G (SCADA, sensors, seismic, satellite, permits, inspection reports).
  2. Learning objectives: Map use cases to regulatory outcomes; identify stakeholder requirements.
  3. Mini-activity: Case-studies review (methane monitoring, pipeline leak detection, production reporting).

Data Foundations & Ingestion

  1. Topics: Data types (time-series, spatial/GIS, unstructured text, imagery), data quality, metadata, sensor calibration, time alignment, labelling strategies for regulators.
  2. Lab: Build an ingestion pipeline for SCADA + sensor metadata (Python, pandas), basic cleaning and exploratory analysis.
  3. Regulatory notes: Chain-of-custody, tamper-evidence, data retention policies.

Time-Series & Anomaly Detection for Operations

  1. Topics: Time-series ML & stats (ARIMA basics), unsupervised anomaly detection (autoencoders, isolation forest, EWMA), supervised approaches for labeled incidents, evaluation metrics for rare events.
  2. Lab: Detect abnormal flow/pressure patterns in SCADA data; evaluate false positives/negatives with regulatory risk tolerance.
  3. Regulatory notes: Threshold setting, alerting workflows, documented validation.

Predictive Maintenance & Integrity Management

  1. Topics: Predictive models for equipment failure, survival analysis, classification vs regression framing, cost-sensitive learning for inspection scheduling.
  2.  Lab: Build a predictive maintenance model using historical failure records; generate inspection prioritization list.
  3.  Regulatory notes: Using ML outputs to justify inspection intervals in audits.

Remote Sensing, Satellite & Aerial Data

  1. Topics: Satellite and airborne sensors for flaring and methane (spectral basics), image pre-processing, change detection, object detection (YOLO/Mask R-CNN), geospatial analysis.
  2. Lab: Detect flaring or thermal anomalies from satellite imagery (Google Earth Engine or Sentinel data) and map to facilities.
  3. Regulatory notes: Cross-validating satellite detections with ground measurements, attribution challenges.

Leak & Emissions Detection

  1. Topics: Multimodal detection approaches (spectroscopy, infrared imaging, acoustic sensors), source localization, plume dispersion basics, probabilistic estimation and uncertainty.
  2. Lab: Simulate/score methane leak detection using sensor arrays; generate confidence intervals for source estimates.
  3.  Regulatory notes: Evidence standards, linking detection to operator responsibility.

NLP for Regulatory Documents & Permitting

  1. Topics: Text pre-processing, classification, information extraction, named entity recognition (permits, conditions, deadlines), semantic search for non-compliant language.
  2. Lab: Build a document classifier to flag permits missing key conditions and extract compliance dates.
  3.  Regulatory notes: Automating review while keeping human-in-the-loop and audit logs.

Model Explainability, Validation & Governance

  1.  Topics: Explainable AI methods (LIME, SHAP), fairness, model risk management, validation protocols, performance documentation for audits.
  2.  Activity: Produce an “explainability report” and validation checklist for a leak-detection model.
  3.  Regulatory notes: Requirements for model explainability in enforcement decisions; documentation templates.

MLOps, Deployment & Continuous Monitoring

  1. Topics: CI/CD for ML, model versioning, monitoring drift, alerting, secure deployment considerations on-prem vs cloud, logging for audit trails.
  2. Lab: Containerize a model (Docker), instrument metrics, create a simple dashboard for regulators.
  3.  Regulatory notes: Immutable logs, access control, reproducibility in enforcement cases.

 Legal, Ethics, Standards & Capstone Kick-off

  1. Topics: Relevant legal frameworks (data privacy, data sharing agreements), international regulations (intro: EPA, EU rules, IOGP guidance), standards (ISO, API), ethical considerations, public transparency.
  2. Capstone: Project briefing — teams propose a regulatory ML solution (e.g., methane monitoring for a region; automated permit compliance review; predictive inspection scheduler).
  3. Deliverables: Prototype, data provenance documentation, explainability report, validation tests, deployment plan.

Labs and project work

1.Hands-on labs using realistic public or synthetic datasets.

    2.Labs: SCADA time-series anomaly detection; satellite flaring detection; methane plume localization simulation; NLP permit classifier; predictive maintenance model.

    Final capstone (team or individual): End-to-end prototype addressing a regulatory use case, plus governance documentation, test results, and deployment plan

      Assessment Grading

      1. Weekly labs / exercises: 40%
      2. Mid-course mini project or exam: 20%
      3. Final capstone project & presentation: 35%
      4. Participation / peer review: 5%

      Datasets & tools

      1. Languages & libraries: Python, pandas, numpy, scikit-learn, TensorFlow/PyTorch, statsmodels, geopandas, rasterio.
      2. ML ops: Docker, MLflow, Git, Airflow or Prefect, Prometheus/Grafana for monitoring.
      3. Geospatial & satellite: Google Earth Engine, Sentinel-2/Landsat data, Planet (if available), QGIS.
      4. Public datasets sources: NASA, USGS, NOAA, Sentinel Hub, Open Data portals.
      5. (For methane/flare specific data, use public satellite products from TROPOMI/Sentinel-5P, or validated research datasets.)
      6. Text/NLP: spaCy, transformers (Hugging Face).
      7. Visualization: Kepler.gl, Folium, Plotly.

      Regulatory & Governance Specifics to emphasize

      1. Evidence standards: reproducibility, provenance, tamper-evident logs.
      2. Validation: holdout sets, backtesting, stress testing under different scenarios, human review thresholds.
      3. Explainability: required for enforcement actions—produce summaries understandable to non-technical stakeholders.
      4. Accountability: roles/responsibilities, escalation for model-driven decisions.
      5. Data sharing and privacy: anonymization, legal agreements, jurisdictional restrictions.

      Recommended readings & resources

      1.  “Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow” — Aurélien Géron (practical ML).
      2.  “An Introduction to Statistical Learning” — James et al. (statistics & fundamentals).
      3. “Interpretable Machine Learning” — Christoph Molnar (explainability).
      4. Relevant standards/organizations: IOGP reports, API standards, ISO 9001/14001 fundamentals, OECD Principles on AI.
      5. Regulatory sources: EPA guidance (U.S.), EU environmental compliance frameworks — adapt to local jurisdiction.

      Sample capstone ideas

      1. Region-wide methane monitoring dashboard: ingest satellite detections, cross-reference facility registry, prioritize high-confidence events for inspection.
      2.  Automated permit compliance reviewer: NLP to extract permit conditions, cross-check operator reporting, generate exception reports.
      3. Predictive inspection prioritization: integrate predictive maintenance and anomaly scores to optimize field inspections under budget constraints.
      4. Pipeline integrity ML system: combine sensor time-series and third-party inspection reports to predict risk of failure.