|
|
Neil Edmunds is a serial
innovator. Now the vice-president of enhanced oil recovery for Laricina Energy, in the past he’s worked in a
variety of technical and executive positions with other companies. For
example, at EnCana he provided reservoir and operations direction for Foster
Creek’s Vapex and SAGD pilots. At CS
Resources he was responsible for the Senlac thermal project in Saskatchewan.
Later, as the CS vice president responsible for recovery technologies, he
focused on enhanced recovery research.
In the 1980s he was lead engineer on AOSTRA’s underground test facility
(UTF), which provided the definitive demonstration of the viability of SAGD.
The UTF proved the process beyond question in 1992, when it briefly achieved
positive cash flow at a production rate of about 2,000 barrels per day from
three horizontal pairs. Edmunds stresses that the use of horizontal well
pairs was not his idea, but was suggested by his predecessors at AOSTRA.
However, SAGD pioneer “Roger Butler wanted to try a vertical fracture,
but none of us wanted that, so we designed the horizontal well idea and
tested it at the UTF.” The rest is history.
Just as SAGD was constructed upon the physics of Roger Butler, the original
idea for what Edmunds calls his “favourite
claim to fame” is partly an adaptation of work begun by nuclear
engineer Terry Stone of the Alberta Research Council. Stone’s PhD
thesis included a mathematical model to calculate fluid flows at reactor
accidents.
At AOSTRA, Edmunds began applying this idea to heavy oil production, and sold
the idea to CS Resources when he joined that company in 1995. Eventually
acquired by Cenovus through a merger, the simulator
“gives a detailed model of what happens in the wellbore in terms of
heat transfer and fluid flow,” according to Edmunds. “This gives Cenovus quite an advantage in terms of engineering the
wellbores themselves. Think about it: you’ve got a 7-inch pipe and
it’s maybe 800 metres long; to make it
efficient you have to figure out how to circulate the fluids to heat the
reservoir uniformly.” He deadpans, “that involves some real
plumbing challenges.”
Replacing engineers with computers
So what is Edmunds up to today? “I like to say we’ve replaced
reservoir engineers with computers, but what we’ve really done is up
the level at which engineers can operate. Instead of being drones who try to
optimize stuff every day, we can now do rapid searches through classes of
variables to find the best approach to any given reservoir.”
The first company to attempt to develop commercial production from
Alberta’s bitumen carbonates, Laricina’s
bitumen carbonates project is now steaming up, and the company will follow
this with a program in the Grand Rapids formation, which is essentially the
same as the McMurray. Both projects will use thermal solvent programs.
To illustrate the nature of the reservoir engineering problems he faces,
Edmunds describes the chore as like finding the highest peak in a mountain
range – in the fog. The surface to be optimized can’t be seen,
only sampled at specific points. The problem exists in many dimensions, and
it’s non-linear – especially when you consider the economics
involved. Most frustrating of all, the same action can generate different,
even opposite, effects when applied in different situations. Given those
realities, he set out to develop an algorithm that could help the company
select the most economically efficient way to produce from these difficult
and largely unexplored strata.
The project – he says it began as a hobby before he helped create Laricina – now involves an algorithm of about 20
lines, and it could conceivably transform in situ oilsands
production. “We use a lot of machinery to run the input files, but the
basic algorithm is simple.” He adds, “This is pretty new in the
oil business.”
“What we have done is to program a genetic algorithm. We encode the
possible processes so the algorithm generates digital chromosomes out of 0s
and 1s. Once you’ve run each file you need a fitness score. Ours is
dollars per barrel. We create a class of possible processes with a fair
number of variables. Our computer may take a couple of weeks, but it can run
a huge number of possibilities. The computer takes the winners from each
trial, recombines their strings of 0s and 1s – the same thing biology does
with DNA. We use some from the mother and some from the father,”
Edmunds explains, “and we presumably end up with a better organism. You
never know if you have the best possible answer, but in the 5,000 trials the
computer ran for us it does seem to have ended up with a very good
answer.”
Dumb code
Your reporter’s skill in mathematics is limited, so to pursue
Edmunds’ ideas I referred to a paper he and co-authors Behdad Moini and Jeff Peterson
prepared for the 2009 Canadian Petroleum Conference. Titled “Advanced
Solvent-Additive Processes via Genetic Optimization,” the paper is a
partly whimsical, somewhat over-written but unquestionably accessible
description of the project. It seems to deliberately raise more questions
than it answers.
As the authors explain, the industry has long known that adding light
hydrocarbon solvents to steam can improve well performance, but the optimum
choice of additives involves evaluating vast numbers of alternatives. The
genetic approach may allow computers to quickly come up with solutions
tailor-made for each production system.
The authors describe the application of advanced mathematics to complex
factors in reservoir engineering and find that the results tally with
findings from trial and error. The convergence verifies the usefulness of
applying mathematics in this way to real-world problems. The computer run
takes a couple of weeks, while trial and error can take many years, so the
authors argue that employing mathematics in this way can save time and money,
big-time. While they admit that the model is greatly simplified, their
general conclusion is that an industry that devoted major effort to similar
projects could find itself spending months in the lab instead of decades in
the field. The argument is compelling.
After running the algorithm, Edmunds’ team used engineering models to
crack the code of the computer output and then applied an economics package
to the whole. “In this, we are trying to do an economics calculation.
The key thing for me was that working on these solvent processes involves too
many variables. When you think you’ve solved a problem, it can be hard
to look at the raw output and decide whether what you have come up with is
good or not. So we have an automatic economics package which looks at each of
the simulations. This permits computers to identify solvents and timetables
that will maximize profit.”
With a sense of pride that only the mathematically gifted can appreciate,
Edmunds observes that “our algorithm reproduced some of the best mathematical
ideas that people have written up in the last 15 years or so.” Not bad
for what he calls “a dumb piece of code.”
Peter McKenzie-Brown
Language Instinct
Although the writer is a director and officer of Stratabound, the thoughts and views herein are mine only
and not those of Stratabound. I am not registered
in any jurisdiction as a broker or investment adviser, so nothing herein
should be construed as advice on whether to buy, sell or hold shares of Stratabound or any other company mentioned herein.
|
|
