AI Applications In Oil & Gas
Edge AI for Remote Oil and Gas Assets
Design resilient edge inference for wells, pipelines and remote facilities with constrained connectivity, power and safety requirements.
Overview
Practical learning for workplace transfer.
Design resilient edge inference for wells, pipelines and remote facilities with constrained connectivity, power and safety requirements. 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 remote-asset use cases and edge architecture using traceable evidence and an explicit decision criterion.
- Calculate sensors, gateways and data conditioning using traceable evidence and an explicit decision criterion.
- Diagnose model compression and constrained inference using traceable evidence and an explicit decision criterion.
- Evaluate offline resilience, security and safe fallback using traceable evidence and an explicit decision criterion.
- Deliver edge ai pilot and fleet-operations 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.
Outline points are grouped in one designed block instead of being treated as separate module cards.
01Remote-asset use cases and edge architecture3 topics
- Interpret the source measurements, engineering assumptions and acceptance criteria governing Remote-asset use cases and edge architecture
- Calculate or model the key performance quantities for Remote-asset use cases and edge architecture and reconcile the result against field evidence
- Complete a Edge AI for Remote Oil and Gas Assets exercise that converts Remote-asset use cases and edge architecture findings into a documented technical decision
02Sensors, gateways and data conditioning3 topics
- Calculate or model the key performance quantities for Sensors, gateways and data conditioning and reconcile the result against field evidence
- Diagnose failure modes, uncertainty and operating limits associated with Sensors, gateways and data conditioning
- Build an assurance checklist, action owner and review trigger for Sensors, gateways and data conditioning
03Model compression and constrained inference3 topics
- Diagnose failure modes, uncertainty and operating limits associated with Model compression and constrained inference
- Complete a Edge AI for Remote Oil and Gas Assets exercise that converts Model compression and constrained inference findings into a documented technical decision
- Interpret the source measurements, engineering assumptions and acceptance criteria governing Model compression and constrained inference
04Offline resilience, security and safe fallback3 topics
- Complete a Edge AI for Remote Oil and Gas Assets exercise that converts Offline resilience, security and safe fallback findings into a documented technical decision
- Build an assurance checklist, action owner and review trigger for Offline resilience, security and safe fallback
- Calculate or model the key performance quantities for Offline resilience, security and safe fallback and reconcile the result against field evidence
05Edge AI pilot and fleet-operations workshop3 topics
- Build an assurance checklist, action owner and review trigger for Edge AI pilot and fleet-operations workshop
- Interpret the source measurements, engineering assumptions and acceptance criteria governing Edge AI pilot and fleet-operations workshop
- Diagnose failure modes, uncertainty and operating limits associated with Edge AI pilot and fleet-operations 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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