AI Applications In Oil & Gas
Reinforcement Learning for Process Control and Operations
Evaluate reinforcement-learning controllers for constrained process decisions using safe simulation, offline data and rigorous assurance gates.
Overview
Practical learning for workplace transfer.
Evaluate reinforcement-learning controllers for constrained process decisions using safe simulation, offline data and rigorous assurance gates. 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 sequential decisions and process-control use cases using traceable evidence and an explicit decision criterion.
- Calculate states, actions, rewards and constraints using traceable evidence and an explicit decision criterion.
- Diagnose offline reinforcement learning and simulation using traceable evidence and an explicit decision criterion.
- Evaluate safe exploration, robustness and operator oversight using traceable evidence and an explicit decision criterion.
- Deliver controller evaluation and assurance 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: Sequential decisions and process-control use cases
Interpret the source measurements, engineering assumptions and acceptance criteria governing Sequential decisions and process-control use cases
Calculate or model the key performance quantities for Sequential decisions and process-control use cases and reconcile the result against field evidence
Complete a Reinforcement Learning for Process Control and Operations exercise that converts Sequential decisions and process-control use cases findings into a documented technical decision
Module 2: States, actions, rewards and constraints
Calculate or model the key performance quantities for States, actions, rewards and constraints and reconcile the result against field evidence
Diagnose failure modes, uncertainty and operating limits associated with States, actions, rewards and constraints
Build an assurance checklist, action owner and review trigger for States, actions, rewards and constraints
Module 3: Offline reinforcement learning and simulation
Diagnose failure modes, uncertainty and operating limits associated with Offline reinforcement learning and simulation
Complete a Reinforcement Learning for Process Control and Operations exercise that converts Offline reinforcement learning and simulation findings into a documented technical decision
Interpret the source measurements, engineering assumptions and acceptance criteria governing Offline reinforcement learning and simulation
Module 4: Safe exploration, robustness and operator oversight
Complete a Reinforcement Learning for Process Control and Operations exercise that converts Safe exploration, robustness and operator oversight findings into a documented technical decision
Build an assurance checklist, action owner and review trigger for Safe exploration, robustness and operator oversight
Calculate or model the key performance quantities for Safe exploration, robustness and operator oversight and reconcile the result against field evidence
Module 5: Controller evaluation and assurance workshop
Build an assurance checklist, action owner and review trigger for Controller evaluation and assurance workshop
Interpret the source measurements, engineering assumptions and acceptance criteria governing Controller evaluation and assurance workshop
Diagnose failure modes, uncertainty and operating limits associated with Controller evaluation and assurance 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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