AI Applications In Oil & Gas
Physics-Informed Machine Learning for Reservoir Engineering
Combine conservation laws and reservoir physics with machine learning to build credible surrogates, forecasts and inverse solutions.
Overview
Practical learning for workplace transfer.
Combine conservation laws and reservoir physics with machine learning to build credible surrogates, forecasts and inverse solutions. 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 reservoir physics and scientific ml using traceable evidence and an explicit decision criterion.
- Calculate physics-informed neural-network formulation using traceable evidence and an explicit decision criterion.
- Diagnose surrogates, emulators and reduced-order models using traceable evidence and an explicit decision criterion.
- Evaluate inverse problems, uncertainty and validation using traceable evidence and an explicit decision criterion.
- Deliver physics-informed reservoir case 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: Reservoir physics and scientific ML
Interpret the source measurements, engineering assumptions and acceptance criteria governing Reservoir physics and scientific ML
Calculate or model the key performance quantities for Reservoir physics and scientific ML and reconcile the result against field evidence
Complete a Physics-Informed Machine Learning for Reservoir Engineering exercise that converts Reservoir physics and scientific ML findings into a documented technical decision
Module 2: Physics-informed neural-network formulation
Calculate or model the key performance quantities for Physics-informed neural-network formulation and reconcile the result against field evidence
Diagnose failure modes, uncertainty and operating limits associated with Physics-informed neural-network formulation
Build an assurance checklist, action owner and review trigger for Physics-informed neural-network formulation
Module 3: Surrogates, emulators and reduced-order models
Diagnose failure modes, uncertainty and operating limits associated with Surrogates, emulators and reduced-order models
Complete a Physics-Informed Machine Learning for Reservoir Engineering exercise that converts Surrogates, emulators and reduced-order models findings into a documented technical decision
Interpret the source measurements, engineering assumptions and acceptance criteria governing Surrogates, emulators and reduced-order models
Module 4: Inverse problems, uncertainty and validation
Complete a Physics-Informed Machine Learning for Reservoir Engineering exercise that converts Inverse problems, uncertainty and validation findings into a documented technical decision
Build an assurance checklist, action owner and review trigger for Inverse problems, uncertainty and validation
Calculate or model the key performance quantities for Inverse problems, uncertainty and validation and reconcile the result against field evidence
Module 5: Physics-informed reservoir case workshop
Build an assurance checklist, action owner and review trigger for Physics-informed reservoir case workshop
Interpret the source measurements, engineering assumptions and acceptance criteria governing Physics-informed reservoir case workshop
Diagnose failure modes, uncertainty and operating limits associated with Physics-informed reservoir case 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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