Practicing Digital Transformation Through Incremental Change
Note from the Author
In a past article, “What digital transformation can learn from the automotive industry?” I advocated for digital transformation leaders to embrace what the auto industry taught us: meaningful change can come through a series of incremental improvements. We, agents of change, often pursue large, disruptive changes in the interest of ushering in a completely remodeled workflow regardless of the success of the status quo, thoughts of those performing the work, or considerations for the timing of financial returns. And while that article was brilliantly written (have I mentioned I am known for my humility) it failed to provide examples of digital change leadership in action. Therefore, I ask you, the reader, to indulge me in recounting part of our digital transformation journey at Precision Drilling. - Russell Whitney
Introduction
Precision Drilling is a leader in land drilling technology, placing an emphasis on high performance rigs to deliver exceptional value to our customers. Technology on a drilling rig covers a wide gamut, often with a focus on acquiring, integrating and supporting hardware to meet the demands of our clients. However, we recognized there exists an opportunity to leverage best practices from other industries in monitoring the health of our assets, enabling a more proactive approach to maintenance; this recognition forms the basis of our digital twin solution. Precision had tried one of the major names in asset health before and failed; while not ideal, we learned from this failure. Broad, sweeping changes are tough to implement in a company with centralized facilities and even harder with remote assets, like 120 drilling rigs spread across the globe. To reach these remote teams, we needed to address an issue that affects all of them and we needed something that showed quick wins or we risk losing the field (our end user).
This was a considerable task, and one accomplished through many iterations. While I won’t go into the details of a drilling rig, I will share with the unfamiliar that a pump is a critical asset; often called “mud pump(s)”, these operate across a wide range of fluid types, pressures, and speeds. Rigs work tirelessly to maintain pumps, but pumps are but one of many critical assets on location. Therefore, a rig manager could prioritize repeated maintenance of pumps over other assets or fix the pump when it breaks. Due to no shortage of demands on the rig team, the latter was often the modus operandi. Many of our competitors focused on predicting when a pump would fail. Though our domain expertise recognized that predicting when the pump would fail was not valuable to the field team; determining where and when the pump would fail was more critical. Furthermore, analysis showed the majority of failures that affected the rig team were in a subsystem called the fluid end.
Our project scope focused on helping the field monitor and assess the health of this subsystem to make more informed maintenance decisions. However, building a solution for a subsystem is a large task; therefore, we further refined our scope to target the most frequent failure modes which would give the field users a series of quick wins and generate immediate feedback due to the frequent need to replace sacrificial components. This feedback from the field snowballed into voluntary feedback that helped us refine the entire solution into a usable tool which drove down failure rates 50% and cost of failures by more than 90% through informed maintenance off the operational critical path.
While the above is an extremely brief recap of our approach, I want to summarize the steps to our digital transformation project:
We realized starting off with a digital twin of an entire rig, a system of independent components, led to failure due to the complexity of the task.
We narrowed our scope to the asset with the greatest financial return.
We narrowed our scope to the most troublesome subsystem of that asset.
We narrowed our scope to address the most frequent failure modes of this sub system.
We stacked small wins to drive engagement with the remote users.
End user feedback, emboldened by successive wins, drove our iterative approach in refining the solution.
Our end goal is a drilling rig digital twin, an admittedly long-term goal with many challenges. However, we are extremely confident our approach to build it from the ground up, one asset at a time, will yield more material financial results, faster.
A testament to this approach is our initial solution reaching more than 120 rigs in a year and a half’s time; our solution helping every rig with one common pain point travelled well juxtaposed to our initial approach attempting to help one rig with every pain point.
Furthermore, deploying at scale allows us to affect lower probability failure modes. An analogy I might use to explain this comes back to the automotive industry: I can catch a transmission failure, a low probability failure mode, if I monitor many cars in various operating states compared to one car operating in many states. This approach is core to our belief that everything we do must be done at scale to maximize the business value.
For the article on what digital transformation in oil and gas can learn from the automotive industry, please read this article.
