Retaining knowledge through the Big Crew Change

Now that the Big Crew Change is upon us, and the easy oil is gone, oil and gas producers not only have to focus on rebuilding their base of knowledgeable employees. They also have to maintain existing projects—and develop new ones—while losing the senior people who possess the skills and knowledge to make effective operational decisions in challenging situations.

There’s no quick fix that addresses this situation. But on the positive side, while they are finding and hiring the right mix of people, producers also have the opportunity to rethink and redesign the way that they operate.

More technology isn't enough

An obvious way to mitigate the effects of the Big Crew Change is to use more technology, and to use it in new ways. The various Digital Oilfield initiatives that are in process across the industry are prime examples of this approach. But implementing technology alone isn’t enough: important operational decisions still have to be made on the basis of the flood of information that's generated by much of the technology currently in use.

Capturing knowledge is critical to success

Finding a way to capture the knowledge of experienced employees is critical, to prevent “terminal leakage” of insight into decisions about operational processes, best practices, and so on. Using of optimization technology is one way that producers can capture and apply this knowledge. Actenum's technology, for example, embeds years of cumulative knowledge and experience from multiple individuals into operational models that underlie our applications. So, if you look closely at Actenum MPS, you'll find that it employs such a model to bring together the two worlds of maintenance and production in a way that enables a scheduler with a cursory knowledge of Reliability Centered Maintenance to schedule operations for maximum uptime and throughput, while taking into account the need for preventive maintenance shutdowns.

In an earlier post I mentioned the trade-off between relevance and fidelity of the operational model underlying the optimization application. If the balance is correct, the model will provide powerful insight into decision making for individuals with adequate, but not necessarily expert, levels of knowledge. What's more, this insight can be shared, so that collaborative approaches to decision-making and problem-solving become possible. This has broader implications, since collaboration is often extremely difficult to do well when the experts have informal, unshared knowledge in their heads.

The ball is rolling …

We're starting to see more and more conferences, articles, and papers published on this topic. There's no doubt that producers need to develop strategies to use optimization technologies, and thereby harness the captured knowledge embedded in them, most effectively. This means reviewing operations, looking at where decisions are made, and where gaps in expertise are already appearing or are predicted. It's not hard to find opportunities with immediate (and substantial) payoffs.