Introduction
The programme “Using Artificial Intelligence in Equipment Maintenance” develops professional practice in Oil and Gas. It connects the subject with workplace challenges by examining existing evidence and practices and choosing methods appropriate to the organization and its available resources.
The learning progresses from maintenance planning, work orders, and execution measurement to project planning and implementation control, with practical work on artificial intelligence applications and controls, the oil and gas value chain and operations, process safety, barriers, and management of change. Participants prepare outputs to support workplace application and review the quality and results of implementation.
Learning objectives
- Build a maintenance plan and work order connecting asset criticality, resources, and documentation in an application related to “Using Artificial Intelligence in Equipment Maintenance”.
- Design an AI use case with verification criteria and human review.
- Analyze an oil and gas value chain process and connect performance to reliability and safety.
- Analyze an operational hazard scenario and review barriers and change control responsibilities.
- Prepare a project plan connecting scope, time, resources, and risks.
Learning areas
1. Maintenance Planning, Work Orders, and Execution Measurement
- Inventory assets and build a hierarchy, coding system, technical data, and maintenance history, classifying criticality by safety, service, and cost.
- Select a mix of preventive, condition-based, and corrective maintenance, connecting the decision to failure modes, data, and manufacturer instructions.
- Plan the work order's scope, resources, materials, time, isolation, and permits, and define readiness conditions before scheduling.
- Organize schedules, shutdowns, and equipment handovers between operations and maintenance, recording actual work, causes, parts, and time.
- Analyze backlog, recurrence, plan compliance, and reliability, and propose documented adjustments to maintenance tasks and their frequency. The application examines a case related to “Using Artificial Intelligence in Equipment Maintenance”.
2. Artificial Intelligence Applications and Controls
- Identify a task that AI can support, and separate inputs and human decisions from outputs produced by the tool.
- Prepare suitable test data and examples while respecting confidentiality, privacy, and permissions, and use de-identified training exercise data.
- Write clear instructions covering the task, context, constraints, and output format, and compare multiple formulations using a fixed set of examples.
- Check outputs for errors, bias, and unsupported information, and identify cases that require specialist review.
- Assess the feasibility of use in terms of quality, time, cost, and risk, and document controls for approval, monitoring, and stopping when failures occur.
3. The Oil and Gas Value Chain and Operations
- Understand the links between exploration, development, production, processing, and transportation, and define process boundaries, participants, and intended outputs.
- Read a training process diagram, identifying material and energy flows, measurement points, critical equipment, and their relationship to product quality.
- Analyze production, downtime, losses, and resource consumption data, distinguishing measurement problems from process or equipment problems.
- Define coordination requirements among operations, maintenance, safety, and contractors, and document handovers and changes under facility procedures.
- Prepare a process improvement proposal covering hypotheses, controls, resources, and review criteria, using a training case without unauthorized field operations.
4. Process Safety, Barriers, and Management of Change
- Identify sources of hazardous materials and energy and process boundaries, distinguishing process safety from individual occupational safety in the case presented.
- Analyze the sequence, causes, and consequences of a hazardous event, and identify preventive and mitigating barriers and evidence for verifying their readiness.
- Review work permits, isolation, and coordination among parties under facility procedures, and identify hold points requiring specialist approval.
- Prepare a change request identifying its rationale, risks, and effects on documents, training, and emergencies, with technical review before implementation.
- Conduct a tabletop exercise involving a hypothetical barrier failure or leak, and document escalation and response actions, lessons, and the subsequent verification plan.
5. Project Planning and Implementation Control
- Define the project rationale, expected results, stakeholders, and success criteria, and document the scope of work and exclusions.
- Break deliverables into work packages and related activities, and estimate durations, resources, and activity dependencies.
- Prepare a schedule and cost baseline, identify delivery and review milestones, and clarify approval and escalation responsibilities.
- Analyze implementation risks, define responses and warning indicators, and control change requests and their effects on time and cost.
- Build a progress report covering variances, decisions, and lessons learned, and define procedures for accepting deliverables and closing the project.
Practical outputs
- A maintenance plan, work schedule, work order card, and monitoring indicators.
- A use case brief and a test template for the quality of AI outputs.
- An oil operations process map, requirements matrix, and operating indicators.
- A register of risk scenarios and barriers, and an operational change review plan.
- A project charter, implementation schedule, and change and risk tracking register.
Who should attend
- Engineers, technicians, supervisors and operations staff whose work relates to the programme subject.
- Managers and teams working in oil and gas, according to their practical responsibilities.
- Professionals responsible for applying the outputs of “Using Artificial Intelligence in Equipment Maintenance” within their organizations.
Prerequisites
- Working familiarity with the programme field and the ability to discuss a professional case or challenge.
- Confirm the suitability of your experience and any specialist prerequisites with the Union before participation.
Conclusion
The programme “Using Artificial Intelligence in Equipment Maintenance” brings together concepts, case review and a practical application. Participants prepare steps that can be adapted to their responsibilities and organizational resources, with an indicator to follow application and review results after returning to work.
Participation arrangements and any special requests are specified in the confirmation issued by the Union.