01 / CONTEXT
Keeping complex equipment ready for work.
Industrial field operations rely on multiple pieces of equipment, each with its own maintenance schedule. When those assets are working at well sites far from the maintenance facility, every service decision has consequences for travel time, maintenance capacity and the equipment available in the field.
Our principals’ experience includes optimization work for Calfrac addressing this challenge: understanding how different maintenance schedules, equipment movements and geographically distributed operations interact across a fleet.
02 / THE CHALLENGE
See the system, not just the next service.
A maintenance decision affects more than one asset. Equipment may need to leave a geographically distant well site, travel to a maintenance facility, wait for capacity, undergo service and return to work—while other pieces of equipment follow their own schedules. The timing of those events influences what remains available at the site and what arrives at the maintenance facility.
Representing those relationships makes it easier to reason about the trade-offs between maintenance requirements, operational continuity and the resources needed to support both.
03 / THE WORK
Model the operation as a sequence of events.
The work used discrete-event simulation to explore equipment planning, maintenance and routing between well sites and a district facility. Rather than treating each asset in isolation, the model brings together competing maintenance schedules, travel, available equipment and limited service capacity over time.
Routine maintenance can happen at the well site, while more extensive service sends equipment to the district facility. The model accounts for minimum equipment needs at a well site, equipment in transit, service queues, a limited number of maintenance bays and the availability of replacement equipment. Each event changes what can happen next: a unit sent for service is unavailable at the well until it or a spare can be brought back.
A visual interface brings together equipment status, maintenance queues and utilization, making a complex operational scenario easier to investigate. The simulation supports the exploration of planning and routing trade-offs; it is not presented here as a claim of a particular production deployment or quantified outcome.
04 / WHY IT MATTERS
Make complex trade-offs tangible.
This work reflects the value of combining domain understanding, modelling and interface design. A useful optimization tool must capture the realities of the operation and communicate them in a way that supports decisions by the people responsible for the work.