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COURSE
Modern Machine Learning and Data Analytics for Nigeria’s Oil and Gas Sector
A Practical, Compliance-Aligned, Industry-Focused Professional Training Program
Kaynix Global Link LLC,
Loumos Group LLC, and
Texas Southern University, Houston, Texas
Course Overview
Nigeria’s oil and gas industry is under increasing pressure to operate more efficiently, transparently, and sustainably. Regulatory scrutiny, aging infrastructure, emissions obligations, and data integrity challenges are redefining how assets are managed across the upstream, midstream, and downstream value chain.
Machine learning and data analytics have moved beyond experimentation. They are now practical operational tools for surveillance, forecasting, integrity monitoring, compliance reporting, and decision support.
This training provides professionals with a grounded, industry-relevant understanding of machine learning and analytics within Nigeria’s petroleum environment. It connects core analytical concepts directly to operational realities such as SCADA gaps, manual dataprocesses, legacy systems, regulatory reporting obligations, and brownfield
optimization.
The program blends theory, demonstrations, hands-on exercises, and Nigerian-relevant case studies, ensuring that participants can apply what they learn immediately in their
organizations.
Target Audience
This program is designed for professionals across the petroleum value chain:
- Upstream Operators
- Geoscience, drilling, reservoir, production, and operations teams.
- Regulators (NUPRC and NMDPRA)
- Personnel involved in compliance reviews, data validation, asset oversight, safety monitoring, and policy implementation.
- Midstream and Downstream Professionals
- Pipeline operations, depot management, transportation and logistics, product quality, and market analytics teams.
- Cross-Functional Roles Team
- HSE, planning, commercial, metering, IT, digital transformation, and performance management teams.
Learning Outcomes
By the end of the course, participants will be able to:
- Understand essential machine learning and analytics concepts in petroleum contexts
- Identify and classify key petroleum data types across the value chain
- Assess common Nigerian data quality challenges and mitigation strategies
- Apply exploratory data analysis to operational and regulatory datasets
- Use supervised and unsupervised learning for industry problems
- Validate models and understand their limitations in real environments
- Interpret ML outputs for operational, regulatory, and management decisions
- Appreciate real-time deployment considerations and sensor data integration
- Apply analytics to safety, reliability, emissions, and compliance reporting
- Communicate analytical results effectively across technical and regulatory audiences
COURSE MODULES
MODULE 1: Foundations of Machine Learning for Oil and Gas
- ML concepts, terminology, and workflows
- Petroleum data structures: seismic, well logs, SCADA, sensors, pipelines, depots, emissions
- Data governance and regulatory data protocols
- Scaling and normalization for noisy field data
- Regression and classification performance metrics
- Practical model selection based on business context
MODULE 2: Data Quality and Validation in the Nigerian Context
- Manual logging and legacy system challenges
- Cleaning and preprocessing operational datasets
- NUPRC and NMDPRA validation expectations
- Accuracy in reserves, production, and emissions reporting
- Metadata management and audit trails
- Compliance-ready data structures
MODULE 3: Machine Learning for Regulatory Compliance and Asset Oversight
- Reserves verification and audit support
- Well integrity pattern recognition
- Production allocation and reconciliation
- Flaring compliance anomaly detection
- Seismic QC for regulatory review
- Using PRDR, NEITI, and legacy regulatory datasets
MODULE 4: Midstream and Downstream Analytics
- Pipeline integrity monitoring
- Leak detection with DAS/DTS and SCADA
- Transportation and supply forecasting
- Depot automation and loss control
- Product quality and contamination detection
- Market and pricing analytics
MODULE 5: Industry Case Studies and Practical Application
Participants work with curated datasets covering:
- Production and flaring compliance
- Pipeline surveillance
- Drilling sensor streams
- ESP performance
- Depot logistics
- Simple reserves and integrity datasets
Activities include:
- Exploratory data analysis
- Feature engineering
- Model development and validation
- Error analysis
- Result interpretation
- Communication of findings
Course Duration and Delivery
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