Technology Update: New Workflows Reduce Forecast Cycle Time, Refine Uncertainty
- _ JPT staff (_)
- Document ID
- Society of Petroleum Engineers
- Journal of Petroleum Technology
- Publication Date
- July 2006
- Document Type
- Journal Paper
- 18 - 22
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The application of global optimization methods to reservoir simulation through assisted history matching (AHM) and dynamic uncertainty quantification workflows has been heralded as a “game changer” and is significantly reducing the cycle time for history matching and production forecasting of reservoir models, as well as helping to quantify dynamic uncertainty.
Reliable reservoir-simulation models are the foundation of the decision-making process when it comes to field development planning. Subsurface-asset teams constantly strive to improve their understanding of the reservoir on the assumption that the more accurately their model matches past reservoir behavior, the more value it will have in making future development decisions. But with dozens of parameters influencing flow in the reservoir, and most of them changing in three dimensions throughout the field, there are many possible solutions that can match past production profiles. In short, the concept of a single perfect match is not realistic, nor does the best match mean the best prediction—a shortcoming of traditional workflows for history matching that can find only one match.
Faster History Matches
Fortunately for the reservoir engineer, a new class of AHM tools that use global optimization methods is coming to his aid. These tools not only provide an enabling technology for the new workflows needed to find the multiple solutions that exist to the typical history-matching problem, but usually can do this more quickly than a single history match can be found manually.
One tool now available is the Multipurpose Environment for Parallel Optimization (MEPO) from Scandpower Petroleum Technology. The method offers both a framework for assisted history matching and a choice of optimization methods to suit different development scenarios. Among the most versatile are evolutionary algorithms, such as genetic algorithms, which (as their name suggests) mimic evolution by spawning successive generations of simulation models and then extract the attributes of those models that produce results closest to the actual history in order to create the next generation. Progressive cycles of runs converge on groups of good solutions.
Another “smart” feature is its ability to take advantage of parallel computers such as Linux clusters or personal computer networks. Whereas the human engineer generally makes his runs in serial fashion, looking at the results and making decisions about the next run, it is possible using the new method to make tens of runs at the same time and learn from all of them, dramatically shortening the whole cycle.
Of the dozens of AHM and uncertainty studies performed, almost all have demonstrated reductions in cycle time for the history-matching phase, usually around 50–80% time reduction, and sometimes as much as 95%. Fig. 1 shows improving history-match quality (reducing overall error) of a typical simulation-model study in which several good matches were found in roughly 30 iterations, each iteration consisting of six simultaneous runs (i.e., a total of 180 simulations). This particular model had been history matched on three separate occasions previously, and each occasion took close to a month to complete manually. Using MEPO, the model was matched in 2 days.
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