Predictive maintenance is now widely recognised as a key lever for improving the reliability, availability and safety of drilling operations. Yet many projects struggle to move beyond the pilot phase or fail to deliver tangible operational impact.
The root cause is rarely technological. More often, it lies in an initial scoping error: trying to predict everything, everywhere, from day one.
On a drilling unit, equipment is numerous, heterogeneous, highly interconnected and exposed to constantly changing operating conditions. Not all assets are suitable candidates for a first predictive maintenance initiative. The real question is therefore not whether predictive maintenance is relevant, but where to start.
Why prioritisation is critical in drilling operations
In the oil & gas sector, every predictive maintenance project operates under significant constraints: operational pressure, strict HSE requirements, high downtime costs and limited resources. In this context, projects that are too broad or overly ambitious from the outset are likely to lose clarity, credibility and user adoption.
A successful first project must primarily demonstrate clear and understandable value, both for field teams and management. It should build trust, not complexity. Equipment prioritisation is therefore a strategic decision, not merely a technical one.
What makes a good candidate for predictive maintenance?
Not all critical assets are good candidates for predictive maintenance, at least not initially. Several criteria must be assessed together.
A good candidate is first and foremost an asset whose failure has a significant impact, whether in terms of safety, availability or operational costs. However, this criterion alone is not sufficient. The asset must also exhibit behavioural variability, enabling deviations from normal operation to be identified.
Actionability is equally essential. Detecting an anomaly only creates value if it opens a realistic window for action: operational adjustments, maintenance planning or early replacement. Without a practical response option, even the most accurate prediction remains theoretical.
Finally, data availability plays a central role. Not in terms of sheer volume, but in terms of consistency, continuity and contextualisation. Imperfect data can be sufficient, provided that the analytical approach is adapted to field realities.
Why is drilling a specific case for predictive maintenance?
As discussed in our article From sensor to decision: what really happens to data on a drilling unit?, drilling units generate massive volumes of sensor data, but under conditions that make their exploitation particularly challenging. Highly variable operating regimes, offshore constraints and limited failure history all reduce the effectiveness of generic predictive approaches.
This is precisely why the choice of the first assets to target is so critical. A good entry point should leverage existing data while fully accounting for the inherent constraints of the drilling environment.
Drilling equipment with strong potential for a first project
Certain assets naturally offer strong predictive potential, provided the approach is properly framed.
Rotating and hoisting systems, such as top drives and drawworks, are often strong candidates. They are subjected to significant mechanical stresses, display rich dynamic behaviour and are generally well instrumented. Fatigue phenomena, vibration patterns and load drifts provide real opportunities for early detection.
Drilling pumps are another common use case. Their operation relies on sensitive mechanical and hydraulic balances, and gradual deviations often appear before a more abrupt failure. Here again, the key challenge lies in distinguishing normal variations linked to operating conditions from genuinely abnormal behaviour.
Hoisting and tensioning systems, particularly on offshore units, are of particular interest due to their criticality and the cyclic loads they experience. Predictive maintenance can help better characterise fatigue mechanisms and anticipate certain forms of degradation.
Finally, embedded electronics and data acquisition systems are often underestimated. Yet, as illustrated by our use case on anticipating electronic board failures in offshore drilling, modelling the normal behaviour of these systems can, in some cases, reveal significant drifts well before failure. These assets are especially relevant when physical access is impossible and the consequences of failure are severe.
Critical assets… but not always the right priority
Conversely, some assets, while critical, are not necessarily the best candidates for an initial predictive maintenance project.
Poorly instrumented equipment offers limited analytical leverage without significant investment in additional sensors or integration. Similarly, systems with very stable behaviour and little variability leave little room for meaningful drift detection.
There are also situations where anomaly detection does not lead to any realistic action. In such cases, the risk is to generate frustration and undermine the credibility of the predictive approach from its earliest stages.
The trap of “predict everything”
One of the most common mistakes is attempting to deploy predictive maintenance across an entire drilling unit from the outset. This approach dilutes efforts, multiplies use cases and unnecessarily complicates interactions between teams.
A first predictive project should instead be seen as a targeted value demonstration. The objective is not to cover all assets, but to prove that data can genuinely support operational decision-making in a specific context.
This logic is all the more important in drilling, where not all failures exhibit detectable early warning signs. As shown in the embedded electronics use case, some failures occur without clear precursors. Acknowledging this reality strengthens the credibility of the approach and avoids unrealistic promises.
How to frame a successful first predictive maintenance project
An effective first predictive maintenance project starts with rigorous scoping.
It should be driven by a clear operational objective, expressed in terms that are meaningful for field teams. The key question is not “what can we predict?”, but “which decision do we want to anticipate more effectively?”.
The scope should deliberately remain limited, both in terms of the number of assets and analytical complexity. This focus enables rapid iteration, model refinement and progressive trust building.
Early involvement of maintenance, operations and reliability teams is also critical. They are the ones who give meaning to signals, validate alert relevance and transform information into action.
Finally, a predictive project should be designed as an evolving process. Models improve over time, use cases mature and the scope can gradually expand once value has been clearly demonstrated.
Conclusion: start small to think big
Predictive maintenance has the potential to fundamentally transform how drilling operations are managed. But adoption necessarily starts with well-scoped initial projects aligned with field realities.
Prioritising the right equipment maximises the chances of success, builds trust and lays the foundations for a sustainable predictive strategy. It also means accepting that not all assets are equally predictable, nor at the same stage of maturity.
Going further
Are you considering a first predictive maintenance project for your drilling equipment but unsure where to start?
We support industrial teams in framing and deploying predictive maintenance projects tailored to the constraints of oil & gas drilling operations.
Request a personalised demonstration of DiagFit to identify the most relevant equipment to prioritise across your asset base.
