An Interactive 5-Day Training Course
Predictive Maintenance for Drilling Equipment
Predicting Rig Equipment Failures
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Duration
5 Days
Price
$ 3,500
Starting From
07 Feb 2027
Predictive Maintenance for Drilling Equipment
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Predictive Maintenance for Drilling Equipment - DC-PMD-PEA27
| Code | Date | Time | Duration | Location | Currency | Early Bird Fee Per Person |
|---|---|---|---|---|---|---|
| DC-PMD-PEA27 | 07 - 11 Feb 2027 | 10 AM CST | 4 Hours / Day |
Online |
USD |
3500 |
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Predictive Maintenance for Drilling Equipment
This course covers predictive maintenance for drilling equipment. Participants study maintenance strategies and criticality, failure modes of rig equipment, condition monitoring techniques, vibration and oil analysis, sensor and data requirements, predictive models, alarm and work order integration, spares and planning, maintenance performance measurement and implementation.
Description
Rig equipment failures cause downtime at full day rate and sometimes safety incidents. Preventive maintenance by calendar or hours over-maintains some equipment and misses failures in others; predictive maintenance uses condition data to intervene when the equipment indicates it needs attention, which reduces both downtime and maintenance cost when it is implemented properly.
This course covers that implementation. It starts with maintenance strategies and criticality assessment to decide which equipment justifies a predictive approach, then covers the failure modes of major rig equipment: mud pumps, drawworks, top drives, rotating equipment, engines and hydraulic systems. It covers condition monitoring techniques, including vibration analysis, oil analysis, thermography and performance monitoring, and the sensors and data infrastructure each requires. It then covers predictive models from simple threshold methods to machine learning, the integration of predictions into alarms, work orders and planning, spares and logistics implications, maintenance performance measurement, and the practical steps of implementation in a drilling organisation.
Criticality decides where to start. Instrumenting everything is expensive and produces data nobody reviews; instrumenting the equipment whose failure stops the rig, with known failure modes that give warning, delivers value quickly. Mud pumps and top drives are usually where the business case is clearest.
Condition monitoring only works if it reaches the work order system. A vibration analysis that identifies a developing bearing fault and sits in a report changes nothing. The value comes from the prediction triggering planned work, with the part available and the work done during a window that was going to exist anyway.
Predictive maintenance is largely an organisational project. Sensors and models are the easy part; changing a maintenance organisation from schedule-driven to condition-driven, training technicians to interpret and trust the data, and adjusting spares policy and planning is where implementations succeed or stall.
By the end of this training, participants will be able to:
- Compare maintenance strategies and select by equipment criticality.
- Carry out criticality assessment for rig equipment.
- Identify failure modes of major drilling equipment.
- Select condition monitoring techniques for each failure mode.
- Apply vibration analysis and interpret results.
- Apply oil analysis, thermography and performance monitoring.
- Specify sensors and data infrastructure for condition monitoring.
- Build predictive models from threshold methods to machine learning.
- Integrate predictions into alarms, work orders and maintenance planning.
- Manage spares, measure maintenance performance and implement a predictive programme.
The course is instructor-led. Failure modes and monitoring techniques are covered with equipment data, vibration spectra and oil analysis reports, with participants interpreting real cases. Participants build a criticality assessment and an implementation plan for a rig. Cases cover programmes that reduced downtime and those that produced data without action. Participants are encouraged to bring their own equipment data for discussion.
Organisations sending participants to this training will:
- Reduce rig downtime from equipment failures.
- Target maintenance effort where it delivers value.
- Reduce maintenance cost through condition-based intervention.
- Improve spares planning and availability.
- Implement predictive maintenance as an operating practice rather than a pilot.
Participants will:
- Understand maintenance strategies and criticality assessment.
- Know the failure modes of major rig equipment.
- Be able to apply and interpret condition monitoring techniques.
- Be able to build and use predictive models.
- Be able to plan a predictive maintenance implementation.
- Rig maintenance engineers and supervisors
- Drilling contractor technical and maintenance staff
- Drilling engineers and superintendents
- Reliability and asset management staff
- Digital and analytics teams supporting maintenance
- Equipment and service company personnel
Module 1: Maintenance Strategies
- Reactive, preventive and predictive maintenance
- Reliability centred maintenance
- Strategy selection by equipment
- Cost and downtime trade-offs
- Current practice in drilling
Module 2: Criticality Assessment
- Criticality criteria for rig equipment
- Consequence of failure analysis
- Equipment ranking
- Deciding where to apply predictive methods
- Business case construction
Module 3: Rig Equipment Failure Modes
- Mud pump failure modes
- Drawworks and hoisting equipment
- Top drive and rotary systems
- Engines, generators and power systems
- Hydraulic and pneumatic systems
- Pressure control equipment
Module 4: Vibration Analysis
- Vibration measurement and sensors
- Spectrum interpretation
- Bearing, gear and imbalance signatures
- Trending and alarm limits
- Practical interpretation exercises
Module 5: Oil and Wear Analysis
- Oil sampling practice
- Wear particle analysis
- Contamination and degradation indicators
- Trending and interpretation
- Integration with other monitoring
Module 6: Other Monitoring Techniques
- Thermography
- Ultrasonic testing
- Performance and efficiency monitoring
- Electrical signature analysis
- Technique selection by failure mode
Module 7: Sensors and Data Infrastructure
- Sensor selection and placement
- Data acquisition on rigs
- Historians and data storage
- Data quality requirements
- Cost of instrumentation
Module 8: Predictive Models
- Threshold and rule-based methods
- Trending and remaining life estimation
- Machine learning approaches
- Model validation on equipment data
- Avoiding false alarms
Module 9: Integration and Implementation
- Alarm and work order integration
- Planning work around predictions
- Spares and logistics implications
- Technician training and trust
- Performance measurement
- Implementation roadmap and common obstacles
Upon successful completion of this training course, delegates will be awarded an official Certificate of Completion issued by the Petroleum Engineers Association (PEA), an ISO 9001:2015 certified training organization. The certificate carries 10 Credits and formally records the total learning hours completed.
Each certificate is signed by the Course Facilitator and the CEO of the Petroleum Engineers Association, and serves as verifiable proof of professional training that delegates can present to employers and professional bodies worldwide.
Frequently Asked Questions
All course bookings made through PEA are strictly non-refundable. By registering for a course, you acknowledge and accept that all fees are payable in full and are not subject to refund under any circumstances, including changes in personal or professional commitments or partial attendance.
PEA reserves the right to make reasonable adjustments to course content, trainers, or schedules where necessary, without entitling delegates to a refund. Comprehensive details of each course — including objectives, target audience, and content — are clearly outlined before enrolment, and it is the responsibility of the delegate to ensure the course's suitability prior to booking.
For any inquiries related to cancellations or bookings, please contact our support team, who will be happy to assist you.
