Another View: The Innovation Path to Deployment

Many confuse “an” innovation model for “the” innovation model, and we have confounded innovators with entrepreneurs. They are not the same thing.


It is almost a cliché today that our industry requires much and more innovation—incremental, disruptive, exponential, superlinear—the jargon of intent, but what about execution? It is fair to say that we are not particularly innovative at being innovative. To become better at innovation, we need to examine the process and constraints of our innovative capabilities.

Our global society, and the energy systems on which it is based, is being asked today to transform to a system that delivers on demand, large amounts of Joules/BTUs while continuing to lower emissions of CO2 and use of water. The lack of innovative capability today, and changes required to our innovation systems, are evident to anyone working in this domain.

Up to the middle of the last century, innovation was conducted by large organizations in big research centers. Places like Bell Labs, or the Shell or Standard Oil Research centers, created much of our innovation infrastructure. In the last 30 years, however, the pharmaceutical and information industries became known as hubs of innovation. In this model, the major players acquire successful independent startups, avoid large research costs and risks, and grow by encapsulation. This model has driven the creation of whole new companies and successful innovation. The requirement to fund the failures and not just the successes also created the venture capital industry, to fund the entire innovation population by reaping great gains through the successes.

It also drove the pitch-based (Dragon’s Den/Shark Tank) and crowd-based models of innovation where it is asserted that large companies cannot be innovative—they target individuals or small groups in what is sometimes called “open innovation.” It is now popular wisdom that innovation is like the X-Files: it’s all out there. In pursuing this model, governments, industry, and popular culture have confused “an” innovation model for “the” innovation model, and we have confounded innovators with entrepreneurs. They are not the same thing.

The process from invention, through proof of concept and piloting, to commercial realization is called the Path to Deployment. Each innovation has a unique path, and paths have different constraints along the way. For example, if you lack ideas, a crowd-sourced innovation challenge or “X-Prize” is an excellent way to garner ideas. If, however, your constraint is customer reluctance, a better mitigating action may be customer subsidies or loss-leaders. If you require early-stage funding, a venture capitalist may be a good idea, if your Path to Deployment allows them a sale or off ramp within 3-5 years.

In industries with low barriers to entry and established platforms such as the IT/digital industry, it is possible to assemble “plug and play” innovations. New consumer goods (even artificial meat) can plug effortlessly into an existing supply chain. In oil and gas, innovations that fit into the existing value chain include technologies such as water treatment, chemicals, and drilling equipment, and hydrocarbon processing units. These are innovations that are referred to sometimes as “continuous improvement” and, over the years, these can add up to significant realizations.

Systemic innovation, or deep changes to existing systems and infrastructure, have different constraints. These constraints are usually outside of the capability, capacity, and patience of the venture capital model.

An example: At Suncor Energy, over the past 5 years we developed and are deploying autonomous haul systems (computer-driven trucks) at our oil sands mines. Trucks may seem like a plug-and-play innovation, yet during the effort we realized that to enable the trucks to deliver value we would have to examine every single procedure in the mine, because to enable the trucks to deliver value would require revision of the entire mining system. The Path to Deployment involved initial reviews of vendors and innovations, the creation of single-truck tests, then multi-vehicle pilots, and, finally, commercial scale testing and learning. That was paralleled by the development of internal supply chain schedules, viable commercial models, staffing and personnel strategies, regulatory development, and other systemic changes. The deployment of complex systems such as this are beyond the capability of the pitch-based model. Just as the current app-driven economy is based upon the deep industrial innovative capabilities of Apple, Google, Microsoft, and Bell Labs, the original development of autonomous systems requires deep institutional innovation from many organizations. The additive and multiplicative outcomes of multiple evolving innovations at multiple scales of execution was key to the final innovative realization of computer-driven trucks.

Each innovation’s Path to Deployment requires as much creativity, business acumen, and tenacity as the invention itself, yet we do not recognize or teach it. Again, for discrete products the constraint is usually money, hence the pitch model. For transformational change, it is multi-faceted, and the Path to Deployment must be recognized, designed, and managed.

If we recognize that the constraint to innovation is how we are leading, managing, and teaching innovation today, how can we improve our industry’s innovative capability?

In our paper (SPE-167020-MS), we postulated an innovation rate function, dependent on the generation and retention of ideas:

The left term in the equation is the inventory of ideas and innovations in the pipeline. We suggest that two major impediments exist today in our industry and universities. The well-recognized one is the departure of many great minds due to “aging out.” The “Big Crew Change” that the SPE identified 20 years ago is occurring. The other impediment is fear, which inhibits the creativity of individuals (and groups and divisions and corporations). To be clear, there are current norms in companies and academia that individuals will not risk breaking during extensive periods of their careers that impair the ability to integrate fresh perspectives and ideas, which in turn harms innovation.

The central term in the equation is straightforward: how many resources applied to an issue clearly affects the rate of solution. But it is not just money and people, it is also quality that matters. How much effort are we expending today on ideas that have no Path to Deployment, but are bright shiny objects?

The last term refers to whether or not you believe you need innovation. We suggest we do.  

To be clear, the world will still be using oil in 2100 for lubricants, petrochemicals, long-distance transport, and agriculture. We need to find a way to make production, processing, and use as responsible as possible. As an industry and academic community, we need to create options ourselves. Waiting for entrepreneurs (or even more unlikely, politicians and bureaucrats) to develop the innovations necessary carries the same risk as sitting in your basement, going through the 400 channels in a feeble hope that there is something to watch, only to find out there is nothing there. Our industry’s future depends on us all creating the future, and now is as good a time as any to start.

For Further Reading:

G. Bunio and I.D. Gates, (2013), “Innovation, Motivation and Fear,”SPE-167020-MS.

G. Bunio and I.D. Gates, (2017), “Path to Deployment”, International Engineering and Technology (IET) Forum.

V. Smil, (2010), “Energy Transitions,” Praeger.