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Streamline-Based History Matching
Streamsim Technologies is creating 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 page. |
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History matching of permeability, porosity, and facies which is
- Fast and efficient
- Geologically consistent
- Proven technology, tested on several full field reservoir models
Our history matching solution is tailored to
- Well-by-well history matching
- Waterflood applications
- Large fields
- High-resolution grids
- Hundreds of producing wells
- Long production histories
- Uncertainty in permeability, porosity, NTG, and facies locations
Additional features
- Plug-in to studioSL
- Easy selection of individual wells for history matching
- History match at user-specified timesteps
- Compare well vs field level matches among multiple runs
- Smart Painting mode skips the geostatistical step, allowing for rapid history matching
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Click here to visit our tutorials page. This page is continually evolving - watch that space and contact us if there is a tutorial missing that you would like to see. |
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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. |
Streamline-based history matching has been a subject of intensive research over the last several years (see references below). History matching using streamlines has been shown to be an attractive alternative to traditional history matching when streamline simulation is applicable. All streamline-based history matching methods take advantage of the simple relationship between the time-of-flight (TOF) of the streamline, and the effective porosity and effective permeability along the streamline. The TOF relationship was first derived by Pollock (1988) as,
The TOF is the time required for a neutral particle to travel from source to sink along streamline path. The path that the particle traverses traces the path of the streamline. Since the total interstitial velocity is directly related to the effective permeability and porosity along the streamline, it follows that
Equation 2 implies that if the effective porosity or permeability of the streamline is modified, the change in the TOF can be inferred directly. For example, if the permeability were doubled, the time-of-flight (and by analogy the arrival time of fluids traveling down the streamline) would be halved. We describe the ratio of TOFs as the correction factor c*.
The old TOF is the time required to travel along a streamline derived using an old static model, while the new TOF is the time required to travel along the same streamline but with a new static model. The correction factor is determined by analysis of the water cut mismatch between historical and simulated data at each well. An example of how c* can be calculated is given in Fenwick et al. (SPE 95940).
The Streamsim/Stanford HM JIP has developed history matching tools that perform geologically consistent history matching. A method has been developed to modify the input trend of the grid properties using the correction factors calculated from Equation 3. The trend is described as a locally varying mean (LVM) which is defined for each grid block over the entire reservoir. Bundles of streamlines are considered in order to capture large scale trends. The grid properties are reconstructed using geostatistics. The locally varying mean is adjusted iteratively to match water cut data using the streamline-based corrections. Since we reconstruct our grid properties using geostatistics, we can account for well data, spatial correlation, and correlations between grid properties automatically, thus maintaining geological consistency in the reservoir model.
Another tool that has been developed uses the Probability Perturbation Method (PPM). The PPM provides a powerful methodology for modifying grid properties to match historical water production. With the PPM, the overall histogram, LVM trends, and facies proportions are maintainted, but the grid scale heterogeneity within the model is redistributed in order to match production data. The LVM updates and PPM can be combined in a useful manner. Through the iterative LVM updates, the approach accounts for variability in the large scale permeability trends in the reservoir. After the trend is determined, the fine scale variability of the given trend can be modified using the PPM. After PPM iterations, the LVM procedure can be repeated.
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Wang, Y. and Kovscek, A.R. (2000), "Streamline Approach for History Matching Production Data," SPE Journal (December 2000) 353.
Caers, J., Krishnan, S., Wang, Y., and Kovscek, A.R. (2002), "A Geostatistical Approach to Streamline-Based History Matching," SPE Journal (September 2002) 250.
Agarwal, B. and Blunt, M.J. (2003), "Streamline-Based Method with Full-Physics Forward Simulation for History-Matching Performance Data of a North Sea Field," SPE Journal (June 2003) 171.
Agarwal, B. and Blunt, M.J. (2003), "A Streamline-Based Method for Assisted History Matching Applied to an Arabian Gulf Field," SPE Journal (December 2004) 437.
Gross, H., Thiele, M.R., Alexa, M.J., Caers, J., and Kovscek, A.R. (2004), "Streamline-Based History Matching Using Geostatistical Constraints: Application to a Giant, Mature Carbonate Reservoir," proceedings of the 2004 SPE ATCE, September 26-29, Houston, Texas.
Hoffman, B. T., and Caers, J.: "Geostatistical History Matching Using a Regional Probability Perturbation Method," proceedings of the 2003 SPE ATCE, October 5-8, Denver, Colorado.
Hoffman, B.T., and Caers, J.: "Regional probability perturbations for history matching," Journal of Petroleum Science and Engineering, 2005 46: 53-71.
Fenwick, D.H., Thiele, M.R., Agil, M., Hussain, A., Humam, F., and Caers, J.K., "Reconciling Prior Geologic Information with Production Data Using Streamlines: Application to a Giant Middle-Eastern Oil Field". proceedings of the 2005 SPE ATCE, October 9-12 2005, Dallas, Texas.
Batycky, R.P., Seto, A., and Fenwick, D.H., "Assisted History Matching of a 1.4-Million-Cell Simulation Model for Judy Creek 'A' Pool Waterflood/HCMF Using a Streamline-Based Workflow". proceedings of the 2007 SPE ATCE, November 11-14 2007, Anaheim, CA. |
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