Morgan Rodwell, P.Eng. (AB,BC) – Executive Director, Process Technology, Fluor Canada Ltd.
We have all heard the term “Energy Transition” and recognize that it encompasses a wide range of potential capital projects across many industries. This can include carbon capture and sequestration (CCS), carbon utilization (turning CO₂ into a useful product), low-carbon emission hydrogen production, biofuels, biomass utilization, electrification, energy storage, power generation from nuclear or a renewable source, or simply energy efficiency improvements. All with the same goal: produce the products and energy our civilization needs, while reducing the environmental impact, particularly to the atmosphere.
Since 2018, I have been involved in several early-stage projects that fall under the energy transition umbrella. I have worked with various partners from startups and technology developers to major international energy and chemical companies and financiers to providing input to the government. Based on my experience through these engagements, I believe there are two key risk areas that need to be considered when developing energy transition projects: novel hype and project cost risks. In this piece, I’ll cover project costs risks. For novel hype risks, see part one.
Project Cost Risks include the challenge of determining how much a project will cost to build and deciding whether it makes economic sense. When dealing with novel technology, the number one risk is whether or not it actually works. Beyond that, the next question is likely “how much will it cost?”
The literature on project capital cost estimating states that when you are in the early stages of a project, the most accurate method of cost estimating is to look at similar projects that were recently completed and adjust the actual costs for the differences in your project, such as scale and location. Referred to as “Reference Class Forecasting” (see the papers published by Bent Flyvberg et al on this subject), this method poses two possible challenges for energy transition projects – what if there is not a similar project to use as a reference and if there is, is there a clear understanding of the differences or nuances?
A dangerous predicament that some project teams find themselves in is the desire to use a more detailed cost estimating methodology. For example, forcing quantities even though insufficient engineering has been completed to generate a nearly complete set of quantities. It is recommended to look to the estimating class definitions as published by the AACEI, but instead of looking at the accuracy of the estimate, consider the extent of engineering completion necessary to support the estimate methodology. A class 3 estimate, which is often completed at the end of FEED or FEL-3, requires engineering to be 10-40% complete before attempting a “semi-detailed estimate with assembly level line details”. A class 4 estimate, which is often completed at the end of a DBM or FEL-2, requires engineering to be 1-15% complete, allowing for an equipment factored or parametric cost estimate. Before that, a class 5 capacity factored, or reference class estimate, is possible.
If you do not have a reference project because you are doing something “new”, how do you get there? The correct answer is to move to class 4 methodology, with careful consideration of the factors or parametric equation parameters. However, many people seem uncomfortable with the opacity of these factored estimates and feel that they need quantities to get a realistic assessment of the cost that they can defend.
Unfortunately, this rarely works. When attempting to execute a semi-detailed estimate without sufficient engineering, the quantities generated are often too low. The focus on details can distract from viewing the project as a whole. The evaluation of semi-detailed estimates should always compare quantities to historical values (length of pipe, tonnes of steel, or yards of concrete per equipment tag), and compare the overall ratios of direct field costs to mechanical equipment costs, and total installed costs to mechanical equipment costs to see if they look to be in a reasonable range. Often it becomes necessary to add “allowances” to the quantities to get the ratios into the right ballpark, and if you attempt the semi-detailed estimate too soon, these allowances start becoming a significant portion of the cost. In which case, how is the method any more accurate than simply using a factored equipment methodology? Parametric or automated quantity generation can provide more detail on how the estimate is building up, but again, does not provide a better cost estimate.
Further, in order to generate engineering quantities, it becomes necessary to expend resources developing deliverables that are needed to do so, such as P&IDs or 3D models. This may take resources away from looking at higher level questions like, “Do we understand all the operating modes of the plan?” or “Are we sure we have all the scope we need to startup, operate and maintain the facility?” In the rush to produce the details to feed the estimate, bigger issues are often bypassed. This leads to surprises later in the design that result in scope growth that no amount of detail can cover for.
The literature on why projects suffer cost overruns identifies these kinds of estimating errors, along with the optimism bias of not considering what might go wrong, as key issues. It is not necessarily that the project went badly – it is that the original estimate likely was not realistic in the first place, which ultimately leads to dangerous risks for energy transition projects.
The aspect of hype that impacts cost estimating and economic analysis is the confirmation bias problem of believing that a type of project should be economic because of the coverage it is getting in the media or online, or announcements on government support. There are often many other aspects that have not been worked out yet, but hope can overtake reality. Just because there is a lot of hype around a technology, product, or route to decarbonization does not mean it is economically viable, yet.
Because of the risks associated with novel technologies and the hype, media, and government pressure to move the projects forward, it runs a significantly higher risk of mal investment than we have witnessed historically. Already, we have seen projects funded and built around the world that are economically challenged and likely would not have met the economic criteria in the first place. To avoid more of these, project proponents should carefully consider both the technical and costing risks discussed here before rushing to construct.