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Streamline-Based History Matching
Streamline-Based History Matching Solutions
Streamsim Technologies offers a software package for reservoir engineers and geoscientists which addresses the difficult problem of well-level history matching. We propose a novel method which uses unique information coming from streamlines. Streamlines can tell you *where* and by *how much* to make corrections. This information is combined with geostatistics to provide a technology ideally suited to modify grid block properties for well-level history matching of large, heterogeneous models with many wells and many timesteps.
Designed to replace tedious, time-consuming manual history matching, our method can history match your field faster, and equally important, maintain geological-consistency. No more need for using BOX keyword multipliers, which has become standard practice for history matching, but is universally recognized as having no relation to reservoir geology.
The software is being developed as a plug-in to studioSL through the Streamsim/Stanford HM Joint Industry Project (JIP). For more information about the JIP, please refer to our JIP group.
History matching of permeability, porosity, and facies which is
Our history matching solution is tailored to
If you wish to purchase the HM software, please contact us at firstname.lastname@example.org.
Effective history matching of real fields requires the resolution of two outstanding problems. First, a conflict may exist between the production data and the existing geological model built solely from static information. Second, during model updates, geological consistency must be maintained by honoring the prior geologic information. In resolving the first problem one must relate the inherent multi-scale nature of production data to petrophysical properties of the reservoir at the proper scale. For the second problem, the geoscientist has to choose what prior information of the static model is known (hence fixed), and what is uncertain (hence modifiable). In many instances, the geostatistical algorithms are fixed, while key prior geostatistical parameters are perturbed (e.g. facies proportions, petrophysical properties trends, variogram parameters, and random seed).
We propose a new methodology that addresses these problems. First, streamlines are used to relate the production data to petrophysical properties. Because streamlines follow the flow paths of the reservoir fluids, they tell you *where* and by *how much* to make corrections. A combination of geostatistical tools (locally varying mean and probability perturbation method) are then used to jointly map corrections back to the geological model through changes to the prior geostatistical parameters. The mapping reconciles the fixed prior geologic information with the production data. The geological model is then explicitly recreated by re-running the geostatistical simulation using the new parameter values.
This approach differs from other history matching techniques where the petrophysical properties of each grid block are modified directly, often manually and without respect to the underlying geological information. While a successful history match may be obtained, the resulting model may be inconsistent with important prior geological information, hence retaining little predictive power.