Reliability, Availability, and Maintainability (RAM) analysis is a critical tool for offshore oil and gas operators facing increasing project complexity, which threatens production output and financial performance. This complexity stems from extending the life of ageing hubs, integrating new developments with existing infrastructure, and developing facilities in harsher, more remote environments. The common results are equipment failure, production losses, and downtime, impacting economic sustainability at a time of tight capital spending control.

The Value and Application of RAM Analysis

RAM analysis predicts an asset's productive ability and identifies improvement avenues. Offshore operators lose an average of 8% of production to unplanned downtime, with 60% of that related to equipment. Mitigating these losses delivers critical value, especially as projects become more marginal, heavier, remote, and processing-intensive. KBR applies RAM analysis across diverse facilities, including fixed platforms, production gathering systems, FLNG, and FPSOs, to answer the fundamental question of how much product a specific facility configuration and operating scenario will actually deliver.

Addressing Modern Project Challenges

Operators must grapple with production reliability and efficiency amid rising project complexity and economic pressure. New developments tied to existing hubs face limitations from legacy equipment and fixed maintenance schedules, while ageing facilities are pushed beyond their original design life. Simultaneously, greenfield projects in harsher environments deal with heavier, more corrosive fluids. Projects are no longer simple, standalone facilities but involve multiple interacting assets, where a disruption in one area has a knock-on impact elsewhere. Economic pressures demand high availability and strong cash flow without over-engineering, yet early-stage availability targets, such as an assumed 95%, are often not grounded in reality.

A Proactive Approach to Design

KBR advocates for a shift from using RAM analysis as a late-stage verification exercise to employing it proactively from the beginning to steer project design. The greatest leverage comes during pre-concept, concept, and FEED stages, when changes are easier to make. RAM models test how different design and operational choices play out, precisely spelling out production delivery based on configuration and setup. This approach helps determine the optimal level of redundancy and operational setup to maximize performance.

Quantifying Hidden Production Impacts

RAM analysis reveals routine maintenance activities with outsized production impacts. In one offshore project, pigging operations between an offshore facility and an onshore plant, treated as standard maintenance, were found to account for over a 10% annual production loss—equivalent to losing more than a month of production per year. In an FPSO project with onboard compression, assessing different compressor train configurations showed availability differences of 2–5%, equating to one-to-two weeks of lost output annually. These tangible figures make decision-making concrete.

Detailed Design and Lifecycle Management

As projects move into detailed design, RAM models incorporate vendor data, spare parts strategies, turnaround schedules, and restart times to understand facility behavior during operation. This is crucial for interconnected developments, where RAM can explore whether turnarounds should be aligned or staggered and what equipment is needed to maintain production continuity. Later in the asset lifecycle, RAM identifies bottlenecks, chronic failures, and operational practices that reduce availability. For example, updating RAM models for a group of FPSOs identified that synchronizing filter changes for maintenance convenience caused predictable production hits, allowing operators to quantify losses and justify corrective actions.

Integration and Human Expertise

RAM analysis is increasingly intersecting with historical performance data and digital analytics tools to optimize areas like spares holding and maintenance strategies. However, the process remains centered on human experience and operator engagement. Understanding response times, constraints, and operational intent requires knowledgeable personnel with firsthand experience in running the facilities; this expertise cannot be automated and is essential to avoid flawed