AI Applications In Oil & Gas
Causal AI for Production Optimization and Intervention Decisions
Move beyond correlation by estimating intervention effects, controlling confounding and testing production decisions with causal models.
Overview
Practical learning for workplace transfer.
Move beyond correlation by estimating intervention effects, controlling confounding and testing production decisions with causal models. Participants work through five connected modules using discipline-specific evidence, calculations and an applied decision case.
Prerequisites
Prior exposure to petroleum data, statistics and machine-learning workflows is recommended.
Objectives
- Interpret causal questions in production operations using traceable evidence and an explicit decision criterion.
- Calculate causal graphs, assumptions and confounding using traceable evidence and an explicit decision criterion.
- Diagnose treatment-effect and uplift estimation using traceable evidence and an explicit decision criterion.
- Evaluate causal discovery and operational experiments using traceable evidence and an explicit decision criterion.
- Deliver intervention-ranking decision workshop using traceable evidence and an explicit decision criterion.
Target audience
- Petroleum engineers, data scientists and industrial AI product owners
- OT, model-risk and technical-assurance specialists
- Technical assurance, data and reliability practitioners
- Supervisors and discipline leads responsible for operational decisions
Program outline
A clear structure for the learning journey.
Program outline
Outline points are grouped in one designed block instead of being treated as separate module cards.
Module 1: Causal questions in production operations
Interpret the source measurements, engineering assumptions and acceptance criteria governing Causal questions in production operations
Calculate or model the key performance quantities for Causal questions in production operations and reconcile the result against field evidence
Complete a Causal AI for Production Optimization and Intervention Decisions exercise that converts Causal questions in production operations findings into a documented technical decision
Module 2: Causal graphs, assumptions and confounding
Calculate or model the key performance quantities for Causal graphs, assumptions and confounding and reconcile the result against field evidence
Diagnose failure modes, uncertainty and operating limits associated with Causal graphs, assumptions and confounding
Build an assurance checklist, action owner and review trigger for Causal graphs, assumptions and confounding
Module 3: Treatment-effect and uplift estimation
Diagnose failure modes, uncertainty and operating limits associated with Treatment-effect and uplift estimation
Complete a Causal AI for Production Optimization and Intervention Decisions exercise that converts Treatment-effect and uplift estimation findings into a documented technical decision
Interpret the source measurements, engineering assumptions and acceptance criteria governing Treatment-effect and uplift estimation
Module 4: Causal discovery and operational experiments
Complete a Causal AI for Production Optimization and Intervention Decisions exercise that converts Causal discovery and operational experiments findings into a documented technical decision
Build an assurance checklist, action owner and review trigger for Causal discovery and operational experiments
Calculate or model the key performance quantities for Causal discovery and operational experiments and reconcile the result against field evidence
Module 5: Intervention-ranking decision workshop
Build an assurance checklist, action owner and review trigger for Intervention-ranking decision workshop
Interpret the source measurements, engineering assumptions and acceptance criteria governing Intervention-ranking decision workshop
Diagnose failure modes, uncertainty and operating limits associated with Intervention-ranking decision workshop
Materials provided
- Course-specific technical workbook
- Engineering datasets and diagnostic exercises
- Decision templates and quality checklists
- Applied capstone case
- 4D Certificate of Completion
Training Options
Programs can be delivered in-house, online, or in a blended format depending on your team's schedule, location, and learning objectives. When an external certificate or exam is included, certification rules and fees remain under the relevant awarding body's policies, while 4D provides the training and preparation support.
Why choose 4D
4D adapts the technical depth, evidence, calculations and capstone decisions to the client's assets, operating context and participant responsibilities.
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