Applied mathematicians from IBM Research are working with the
Norwegian University of Science and Technology (NTNU) to maximize oil
exploration in the North Sea.
Oil shapes the quality of daily life, the world over. And nearly
everything associated with it – especially finding it and getting it out
of the ground – poses an international challenge. To solve that
challenge, applied mathematicians at IBM Research are looking for ways
to help oil companies find oil faster and less expensively.
Research mathematician Andrew Conn (pictured) launched the Reservoir
Management and Production Optimization project last year to develop
algorithms to optimize petroleum production network simulator
parameters, using proxy models and structural constraints. The project
will also make its open code available to developers through IBM's Open Collaborative Research
Launching Reservoir Management and Production Optimization
Conn, an advanced analytics and optimization researcher, was asked to
join the technical committee of the Norwegian University of Science and
Technology's Center for Integrated Operations while working with
Norway's Statoil in 2006. He took advantage of the multiple in-person
meetings each year to strengthen IBM's relationship with NTNU. This
included working on the OCR project with IBM's summer interns from the
university. They focused on optimizing the extraction of oil and gas
from the subsea. The goal: create models and simulate numerous scenarios
to locate and manage petroleum in the subsea more rapidly and
efficiently than currently possible.
Now in its second year, Conn's OCR project continues to develop
optimization applied to simulations and models that will help energy
companies maximize the amount of oil they can get out of a reservoir
basin. And as with any underwater exploration, the search is complicated
by the difficulty of getting a clear picture of the tremendous
geological diversity in the reservoir basin.
Improving the search for underwater oil
The OCR team has relied on various optimization techniques in looking
for underwater oil supplies. Such techniques typically use either line
search or trust region methods.
Using the line search method, researchers determine where they will
begin and in what direction they will go and iterate. Using the trust
region method, researchers create a model which is compared with the
actual function to be optimized. If there is reasonable agreement
between the model and the behavior of the actual function, then the
researchers will expand the region of applicability of their model to
include a wider area of potential exploration. Conversely, if the actual
objective does not behave like the model, the researchers downsize the
region of exploration. Iterating on this paradigm, the goal is to home
in on the solution in a region for which one has created a sufficiently
|Diagram of undersea drilling platform|
The team is also working at simulating the entire field that they are
trying to optimize. The model for these simulations includes the various
components (wells, pipelines, manifolds, separators) that make up an
exploration application. Simulations might be done for pressure drops on
the pipelines, for how wells behave, and other real-world oil
extraction scenarios. Normally for such problems, some derivatives are unavailable – and both discrete and continuous variables are involved – which have significant consequences for the optimization methods that can be used.
Conn and his colleagues at the Center for Integrated Operations compared their optimization approach with NOMAD Black Box
optimization software – a generally well-considered package for
optimization without derivatives. Where Conn's algorithm required four
iterations to determine an appropriate approximate solution, NOMAD's
required 351. Where Conn's team needed to render 82 well simulations,
NOMAD needed 23,402. Likewise, Conn's team rendered 1,662 pipeline
simulations; whereas NOMAD needed 15,602. Furthermore, the resulting
IBM-NTNU result had a greater than 10 percent improvement over NOMAD’s
approximate optimal solution.
“You would be surprised at the number of people who don’t know that IBM
is engaged in this kind of work,” Conn said. “They can’t believe that
we are in the business of helping oil companies save money by optimizing
their exploration processes.
“Through the OCR, we are getting companies inside and outside the
petroleum industry to understand that if they can improve their models,
combined with the optimization, by even one percent, they are going to
save millions and millions of dollars.”
This is just one of IBM Research's several upstream petroleum projects. And these techniques can be broadly applied to
other industries where simulation and optimization with both discrete
and continuous variables are required.