Swift Drilling BV: Knowing when best to maintain and replace parts

Client Challenge

Dutch-based Swift Drilling BV is an innovative drilling contractor, operating in the southern part of the North Sea. The company concentrates on producing the best performance in safety and efficiency.

Our client operates a high-tech mobile drilling unit across a variety of projects, including drilling, completions, work overs and abandonments. The rig was designed to give drilling cost stability over the longer term, keeping marginal field developments profitable. In a continuing effort to improve rig performance, Swift Drilling wanted to run a pilot project. The objective was to optimise the preventive maintenance of a mud pump, using statistical analysis of failure and replacement data.

There are several systems on a drilling rig whose failure during drilling results in significant financial loss. Failure of a mud pump is one such component, when a backup is unavailable. Many of the necessary preventive maintenance tasks for such systems are time or condition-driven, without any consideration for when the part will actually wear out. Swift wanted a better solution to predict more precisely the best time to conduct preventive maintenance and replacement of parts.

How we helped

We conducted statistical analysis of failure and replacement data to optimise preventive maintenance and the management of rig spare parts. This study enabled us to determine the likelihood of failure and, consequently, the expected cost.

With the known costs of failure, Swift could optimise investment in preventive maintenance and the required number of spare parts. Results here were not general nor arbitrary, as with other methods. They gave a specific recommendation for the system on Swift’s rig.

Similar services like an expert opinion or reliability centred maintenance provide a qualitative recommendation, where reliability engineering provides the actual optimum strategy for the equipment in use.

Insight

Statistical analysis of the failure and replacement data resulted in the Mean Time To Failure (MTTF). Here, we used the censored usage until failure data. This gave the number of working hours at failure of the component. Subsequently, the MTTF was determined as shown in figure 1. With the MTTF, the future likelihood of failure of a system was determined. Once the likelihood of failure was known, the expected cost of failure was determined.

Results and benefits

• This pilot gave the optimal usage time to preventatively replace components on the mud pumps, avoiding any downtime when both mud pumps were required.
• Our client also knew the optimum number of spare parts to have to hand, providing cost-effective inventory levels, while minimising risk of downtime because of missing spare parts.