In the first part of this series, we explored the importance of establishing the reason (i.e., the why) behind an organization’s digital transformation to rally the support it needs. The next step of how, on the other hand, has often proven itself to be the most complicated part to manage.
A great first step is to boil down the task to its essence and adopt the very effective mindset of thinking big, starting small, and scaling fast. This is more than a catch phrase. Most people consider the concept in a linear fashion—think and strategize first, start with experiments, and then scale successful initiatives quickly. If you repeat this pattern enough times, you have a digital transformation. Sounds simple, but it’s far more complex to juggle all the initiatives in a cohesive whole.
The question remains: how can you enable all this big thinking, starting, and scaling?
At the macro level, you have a wider vision. At the micro level (e.g., for each domain), workflows must improve and evolve to fulfil that vision. A thorough “think big” blueprint identifies the gaps and obstacles that need to be addressed systematically to drive progress across both value chain and workflows. For example, you can employ artificial intelligence (AI) to significantly improve seismic interpretation, but it won’t have the expected effects if your on-premise computing capabilities are unable to process large amounts of data.
This is a paradigm shift in thinking that you can quickly adopt and then self-correct along the way. A lot of projects fall under the weight of their ambition to cover 100% of the requirements and satisfy all stakeholders in one go. If you simplify and break the problem down into smaller chunks, progress can be made quicker—but you still need to think ahead.
For example, a risk-based inspection (RBI) program for optimizing maintenance intervals typically starts with a large study of all assets (led by a big committee) before a solution is developed and subsequently launched globally with a big bang. But what if you were to focus on the equipment specific to one production platform first? That way, you could develop the solution and only then chase down the associated value levers (e.g., maintenance schedule, spare parts, and suppliers).
Some teams declare victory too quickly at the analytical stage when, in fact, value is only entirely captured once the maintenance schedule and call-offs are properly modified. Only then would the schedule provide insights into the next material equipment to focus on. These same teams go on to make “further enhancements” to accommodate other equipment and help scale the same solution or methodology (with some required modifications) to another production platform.
Traditional E&P companies are known for systematically identifying and funding projects and pilots through a heavy capital value process (with front-end loading) geared toward multimillion- or billion-dollar programs. Other companies employ critical decision points and season-based governance frameworks for their digital projects. Each domain (and cross-domain) champion strategizes and orchestrates the many agile projects through this governance process towards the macro vision. The roadmaps for exploration (extremely data-intensive and with less stakeholders) and production (less data-intensive, complex stakeholder set, and large instalment of legacy physical assets and systems) can look very different.
Note that not all solutions need to be built from scratch. There are opportunities to leverage commercial solutions readily available on the market, in which case the initiative’s focus is on evaluating, modifying, and embedding these solutions into the organization to accelerate value delivery. Here, the governance process ensures all initiatives work toward the larger vision (think big) while simultaneously channelling resources and investments appropriately.
Many companies find scaling successful "start small” experiments organization-wide to be the single largest challenge. Why? Because they are almost immediately faced with the global realities of:
The ability to scale fast is something you must enable. This includes the new ways of working (e.g., governance processes) mentioned earlier in combination with an improved data ecosystem that takes both enterprise architecture and IT/OT infrastructure into account.
Your data ecosystem needs to be an integral part of your digital transformation. Advancements can help you leverage the cloud to scale, provide simulation and optimization capabilities, and facilitate AI and machine learning workflows whilst also ensuring cyber security. Take the Open Subsurface Data Universe platform as an example. It’s a game changer in liberating and connecting data across the E&P industry, yet it still allows each company to differentiate the tools and solutions it uses for faster and better decision making.
The complex nature of digital transformation in the energy industry can be simplified if its individual elements and interdependencies are clearly understood. Rest assured that you are not alone. Many companies and partnerships have taken—or are in the process of taking—their own evolutionary steps dedicated to domain innovation, data ecosystem enhancements, and improved IT/OT infrastructure.
D&I Strategy Manager
Alvin See is known as the keeper of trivia and obscure stories to his friends and colleagues. An engineer and business consultant, his passion is in the digitalization of the energy sector and the house in which he lives.