University of Calgary spinoff Gushor Inc. seeks technology overhaul in oil sands

The oil patch depends on new technology but is slow to implement it. Gusher Inc. CEO Steve Larter is pining for change

January 05, 2010

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IBM brands its current product line for the fossil fuels industry Smart Oil. It’s all about management of information and data and builds upon older systems already used by clients. There is little need to convince energy companies to invest in costly pilot projects.

Instead, the service focuses on the vast amounts of data generated by a process or system such as SAGD. There is nothing new about collecting information. Smart Oil is an advanced way of analyzing the data. IBM has derived pattern recognition algorithms which are processes or sets of mathematical rules used for problem-solving.

“You can look at it in a cross-disciplinary fashion. For example, reservoir management in a SAGD environment – you’ve got your geologists, your geophysicists, your reservoir engineers, your production engineers, and they all look at it with their own view. How do you bring all those disciplines together and actually map the reservoir performance? And then have some predictive capability that asks, What happens if I change my steam injection ratio or I drill more wells to try and produce more?”

The sheer number of data points is boggling. “If you look at a standard oilfield nowadays, you’re looking at two gigabytes of data. Think about it – you’ve got a thermocouple, for instance, that’s sampling every second or every 10 seconds. You collect that over a day every day and it’s a massive amount of data. You can’t do much with that unless you’ve got some algorithm that actually says here’s a trend. Now I can look at a graph over a period of time. And then go to the next level where it can send a flag off to the operator where something needs to be investigated. That’s the process in its simplest sense. But if you do that over a whole field, and you’ve got a hundred of those monitors and then you look at the overlay of all the pressure readings as well, and then all the volume readings, and then all the reservoir information – it all gets way beyond the human factor.” In the end, says MacRae, the smart computer system allows, for instance, a bitumen upgrader plant to plan for the varieties of materials arriving from the production site.

“The last piece is to overlay some intelligent tools over top of that automation where you can actually look at some more predictive capacity related to your operation,” says MacRae. “You could put some simple logic like, ‘I need my daily reports first’ or you could say, ‘I have a monthly meeting where we need to do some predictive flow analysis on the reservoir’ or put a different view on ‘I’m having a problem with my energy efficiency’ or ‘How do I look at this from an environmental footprint?’” And there’s no end to the number of sensitivity analyses that can then be run – for example, how will changing some given parameter like steam input affect reservoir outputs over time?

“It allows you to run much more complex scenarios in much shorter periods of time,” says MacRae. “Some scenarios that used to take weeks, we can now do in four hours.” He provides an example. “With natural gas prices really low now, over my field I can look at my expensive wells to operate and my low-cost wells to operate. If I’m running at prices of $3 per thousand cubic feet, I should be shutting in my extremely expensive wells and producing out of my low-cost wells so I can maximize my margin,” he says. “It’s that sort of structure that changes the overall business behavior – not just one discipline that owns the info, runs the schedules and plans the work. It now has an integrated view to it.”

Run nine multiple scenarios is particularly helpful in the combination of a bitumen extraction site and an upgrader, says MacRae. In that case, a company can ask, “How do I actually look at the combination of 10 or 15 variables to optimize my extraction process? Variability in the ore body – like sand content, bitumen quality, and alkaline or acidic water – has a massive impact on extracting the oil. There are other factors like heavy metals or contaminants in the water.”

Like Larter, MacRae makes comparisons to other economic sectors. “If you go back 10 to 15 years, the aerospace industry was in the exact same position that the oil industry is going through now,” the IBM partner recalls. “Very stressed and under huge economic pressures. It was taking three to five years to produce a new plane and once it was produced their ability to replicate it was a challenge. And now they and the auto industry are the most advanced.”

IBM’s energy specialists took cues from computer service relationships with aerospace firms Boeing and Bombardier as well as automotive companies like Honda and Toyota. “We’ve taken that intelligence and applied it to the oil and gas industry,” says MacRae. “Building an airplane is of course different than running an asset with a 50-year lifespan so we’ve had to evolve those tools for intelligence. They are comparable from a project perspective, but when you integrate the capital project with operations, I would say we’re still behind the auto and aerospace industry.”

How long will it take the energy sector to catch up with its customers? For the experts trying to nurse progress along, the only certainty is that change is coming, albeit more slowly than they’d like.

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