Adduco CASE STUDY: From Nameplate Capacity to Sustainable Performance

Using RAM modelling and operational insight to build an evidence-based reliability roadmap

The Challenge

An oil sands operator was experiencing a persistent gap between its facility’s nameplate capacity and sustainable production performance.

Operations, Maintenance and Engineering understood that limited sparing of critical equipment made it difficult to sustain nameplate production while completing the maintenance required for long-term reliability. However, a formal Reliability, Availability and Maintainability (RAM) study had not been completed during the original facility design, leaving the organization without an analytical basis to validate sustainable capacity.

Following a significant unplanned equipment outage, Adduco was engaged to provide an evidence-based understanding of facility reliability and identify practical opportunities to improve performance.

The Approach

Adduco combined RAM modelling, operational experience and business analysis.

Because maintenance and reliability history was incomplete, AI-assisted analysis was used to review available operational and maintenance records and develop a more complete picture of historical reliability issues and recurring “bad actors.”

This analysis became the starting point for an iterative series of workshops with Operations, Maintenance and Engineering. Equipment performance, failure and repair assumptions, actual facility configurations and operational constraints were progressively reviewed and validated.

The process provided another benefit: bringing different functions together created an opportunity to discuss chronic reliability issues collectively and identify actions that had not previously been fully addressed.

What the Analysis Revealed

The RAM analysis estimated sustainable production at approximately 8,300 BPD—well below the facility’s 10,000 BPD nameplate capacity.

More importantly, the model demonstrated why.

Limited critical-equipment sparing meant that necessary maintenance, inspections and equipment failures could directly reduce production. Maintaining near-term production had also sometimes required deferring maintenance activities that required equipment or facility outages. Modelling demonstrated the potential short-term production benefit of this approach while also illustrating its longer-term reliability implications.

The analysis validated what Operations and Maintenance had experienced and gave leadership an engineering basis for understanding sustainable performance.

Adduco then modelled alternative equipment configurations. Adding redundancy to selected critical systems indicated the potential to improve production availability by approximately 4–5%, depending on the investment scenario. These represented modelled opportunities rather than realized production gains.

From Analysis to Reliability Roadmap

Adduco translated the findings into a prioritized roadmap rather than stopping with the technical model.

Immediate opportunities included improved production-loss monitoring, preventive maintenance development, bad-actor root cause analysis and stronger turnaround planning. Medium-term recommendations addressed asset management, condition monitoring, critical spares and reliability engineering. Longer-term recommendations focused on strategic investments in critical-equipment redundancy.

Several actions began following the study, including improved production-loss monitoring, stronger root cause analysis processes, increased operational and engineering support for turnaround planning, and attention to outstanding reliability issues. RAM results were also incorporated into economic evaluation of potential capital investments.

The five-month engagement was completed on time and on budget.

Why It Matters

The project transformed reliability from an operational concern into information leadership could use for production planning, maintenance strategy and investment decisions.

“RAM modelling turns reliability from an assumption into a decision-making tool. It helps leaders understand how a facility can realistically perform and evaluate the operational, maintenance and capital decisions that can improve that performance.”