Stories Identifying Value: Justifying Technology Projects in Energy

Abstract Digital transformation in the energy sector is no longer optional but essential. However, for every successful digital project, many stall or fail because their value is unclear, unproven, or poorly communicated. Value is often invisible until revealed through the right lens. Whether making a business case for predictive maintenance, alarm management, or any digital initiative, managers must go beyo nd assumptions. They must create clarity, bring evidence, and champion transparency. This paper presents practical, field-tested methods for identifying and justifying the value of technology projects in energy, empowering managers and executives to champion the right initiatives and ensure good practice is both rewarded and repeated. Drawing from real North Sea case studies and decades of hands-on experience, we explore three robust approaches to value justification: first principles engineering analysis, empirical/statistical analysis, and anecdotal/case-based justification. We introduce these as three complementary methods—Method 1, applied before a solution is deployed to estimate potential value, and Methods 2 and 3, applied after deployment to evaluate actual value realised. While Method 2 and 3 are applied retrospectively in this paper, they are designed to be used proactively to justify similar future projects as such being before the next deployment. Importantly, Method 2 makes use of openly available production data (e.g., from the NSTA), enabling transparent benchmarking and cross-operator comparison without exposing commercially sensitive information. Rather than focusing solely on results, the emphasis is on the repeatable process of value identification and justification. Each approach is unpacked with practical, repeatable steps and illustrated with real examples, so practitioners can adopt, adapt, and apply these methods to their own projects. The creation of this paper came from real experience, an operator was looking to justify a technology project (detailed in Method 1) and felt the value was obvious – they went to their head office to get the budget but failed as the ROI did not meet with that of other things that could be done. This led to a discussion on what the ROI would be – and initially the operator said that they could not share this calculation as it was commercially sensitive. Utilising public data from the NSTA we shared their own data with them to identify the value that would potentially be delivered. On presenting this to the operator, they pointed out that many of the numbers were under estimates, and brought other figures to the fore that helped make the case. This discussion was of incredible value to us and to the operator, and had this been done before the visit to head office, the outcome would likely have been that the investment would have gone ahead. Having a draft business case from public data removed the barriers around sharing of production figures which are often seen to be market sensitive even though they are published. It also came from the real experience where an operator mentioned that they noticed a significant increase in production efficiency after applying our technology. We asked them what the change was but we were never able to get a concrete answer or evidence to share – years later after doing the ROI case above, we considered applying the public figures with our knowledge of when the installs were done to find the change in production efficiency which was spoken about but we didn't have evidence for – in looking we found the evidence and captured the value that was delivered. This paper is put forward to the industry to help others to get to this place quicker – to have a value story and conversation with the operator such that these can be shared and others can gain value from the learnings. Beyond individual business cases, these experiences underscore the critical importance of fostering an open, data-driven culture within the energy sector. When practitioners leverage public benchmarks and collaborate transparently—sharing both successes and setbacks—the entire industry moves forward. Too often, value is siloed within organisations, obscured by commercial sensitivities or the simple lack of process to make benefits explicit. By demonstrating that robust, repeatable justification can be built on public data and structured discussion, we aim to lower the barriers to digital innovation and accelerate the spread of best practice. The approaches described herein are intentionally designed to be practical and adaptable, regardless of company size, asset type, or digital maturity. Each method not only delivers a framework for justifying new investments, but also encourages a more rigorous approach to learning from past projects. When Method 1 is applied up front, teams gain a structured hypothesis to guide both technical deployment and future evaluation. Methods 2 and 3, used after implementation, provide a reality check and help close the loop—enabling continuous improvement and more effective communication with decision-makers. Importantly, this cycle of hypothesis, measurement, and learning builds credibility and trust within organisations, creating a virtuous circle for ongoing digital progress. By systematically capturing and sharing the real-world outcomes and lessons from completed projects, these methods also equip practitioners with concrete, transferable evidence that can be used to justify new initiatives—both within their own organisations and across the wider industry. Each retrospective evaluation becomes a valuable reference case, lowering barriers to approval for similar projects in the future and supporting a culture of evidence-based decision-making. In this way, the impact of a single project can extend far beyond its immediate scope, providing a blueprint for others and accelerating digital transformation sector-wide. As the energy industry faces intensifying pressures—economic, environmental, and social—the ability to justify technology investments robustly and rapidly will only grow in importance. It is our hope that the methods, stories, and real-world lessons shared in this paper will empower practitioners across the sector to bridge the gap between technical promise and commercial value, creating a more innovative, resilient, and sustainable future.