Case Study: Supermajor Uses QRO to Predict 94.22% Availability and Associated HSE Risk, Using Model to Drive Benefit-to-Cost Design and Maintenance Optimization

Learn how we helped a large energy producer drive a more proactive maintenance approach and quantify future risk and costs by simulating thinning and vibration scenarios through a QRO pilot.

Introduction

A global super-major with an integrated portfolio of upstream and downstream assets wanted to better quantify its decision-making processes to maximize reliability across its fixed and non-fixed assets at one of its gas-oil separation facilities. The facility, which went into service about ten years ago, was an early adopter of technology and looked to remain at the forefront of the industry in reliability and maintenance digital transformation.

The Challenge

While the facility had implemented a risk-based inspection (RBI) program, the facility’s reliability and integrity programs operated in data silos. This siloed approach made it difficult to quantify maintenance priorities and left the leadership team with a qualitative approach to managing integrated downtime events and annual maintenance plans. This static, siloed program, coupled with the facility’s varying feed composition and rates, caused the maintenance teams to be in a constant reactive state and spend a significant amount of its budget on unplanned costs.

While management trusted their data, they were unable to quantify the future failure dates of the facility’s assets or predict how future equipment corrosion and damage across fixed equipment, rotating machinery, and instrumentation would affect the availability of the facility. Facility leadership recognized that to be at the forefront of the industry, they would need to adopt a more proactive and quantitative approach to managing assets and needed a solution that would provide three primary elements:

Pinnacle’s Solution

The facility decided to pilot Quantitative Reliability Optimization (QRO) for its capability to visualize the impact of data over time. QRO is an evolution in reliability modeling that combines the best traditional reliability methods, data science, and subject matter expertise into a hybrid model. The QRO approach provided a framework for this facility to achieve its goal of bringing the best of digital transformation to its reliability and maintenance work processes, satisfying the three solution elements above.

The QRO pilot included a select group of 56 assets within an oil train unit. This group of selected assets was critical to the success of crude oil production and included compressors, pressure vessels, heat exchangers, and pumps. The QRO pilot was completed over three months and had the following scope of work:

With QRO, the facility was able to model two “What If” scenarios based on current data and visualize the impact that specific measurements have on the long-term risk of the asset. Two of these “What If” scenarios have been detailed below, including a severe thinning scenario for a dehydrator and an excessive vibration scenario for a pump. With both these examples, management was able to quantitatively predict equipment damage and associated risk to enable better maintenance and inspection planning.

Scenario 1: Thinning

The first “What If” scenario that QRO modeled was a severe thinning scenario. Figure 1 is a thickness LVC for a dehydrator developed using the facility’s thickness measurements. Because the facility had low measured corrosion rates, the initial predicted failure date of the dehydrator was May 2100, and the PoF for this asset was 0%.

However, the facility wanted to visualize what would happen if the dehydrator were to experience severe corrosion. To model this scenario, the Pinnacle team replaced the most recent thickness measurement with a lower value. Figure 2 shows the updated LVC with a new predicted failure date of March 2025 and an updated band of uncertainty, displayed in blue.

To demonstrate the decision-making and planning functionality of Newton™, a repair task was planned for the dehydrator. The results of this plan can be seen in Figure 3, which displays the current and mitigated risk curves.  With the addition of a planned repair task, the “risk with plan” curve (dashed grey line) drops to zero.

Scenario 2: Vibration

The second “What If” scenario that QRO modeled was a severe vibration scenario. Figure 4 is a vibration LVC for a pump bearing within the crude train created with the facility’s vibration data. The analysis did not show any immediate onset of damage with a predicted failure date of December 24, 2030.  However, due to the nature of the failure mode, there is a high degree of uncertainty displayed on the LVC by the blue band.

The Pinnacle team added exponentially increasing vibration measurements to the model to visualize the impact vibration will have on the pump bearing. Figure 5 shows the updated LVC with a new predicted failure date of early 2022.

The Pinnacle team added exponentially increasing vibration measurements to the model to visualize the impact vibration will have on the pump bearing. Figure 5 shows the updated LVC with a new predicted failure date of early 2022.

Results

As a result of piloting QRO, the facility was able to visualize the impact of its data on failure and risk over time. The deliverables of the pilot included:

Conclusion

After completing the pilot for the oil train, the facility is moving forward with implementing QRO on the remaining assets within the oil train and other gas trains at the facility, which includes 2,000 fixed and non-fixed assets. The expansion of the QRO implementation is expected to result in a 14% total inspection spend reduction, 6% total maintenance spend reduction, and 2-3% total utilization improvement through reduction of loss of containment events and downtime.