Building Trust in History Matching: The Role of Multidimensional Projection
- Yasin Hajizadeh (U. of Calgary) | Elisa Amorim (Universidade Federal de Juiz de Fora) | Mario Costa Sousa (U. of Calgary)
- Document ID
- Society of Petroleum Engineers
- SPE Europec/EAGE Annual Conference, 4-7 June, Copenhagen, Denmark
- Publication Date
- Document Type
- Conference Paper
- 2012. Society of Petroleum Engineers
- 5.5 Reservoir Simulation, 5.5.3 Scaling Methods, 5.6.9 Production Forecasting, 5.5.8 History Matching
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- 293 since 2007
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Assisted history matching frameworks powered by stochastic population-based sampling algorithms have been a popular choice for real-life reservoir management problems for the past decade. These methods provide an ensemble of history-matched models which can be used to quantify the uncertainty of future field performance. As a critique, population-based algorithms are generally considered black-boxes with little knowledge of their performance during history matching. In most cases, the misfit value is used as the only criteria to monitor the sampling algorithms and assess their quality.
This paper applies three recently developed multidimensional projection schemes as a novel interactive, exploratory visualization tool for gaining insights to the sampling performance of population-based algorithms and comparing multiple runs in history matching. We use Least Square Projection (LSP), Projection by Clustering (ProjClus) and Principle Component Analysis (PCA) to examine the relationship between exploration of search space and the uncertainty in predictions of reservoir production. These projection techniques provide a mapping of the high dimensional search space into a 2D space by trying to maintain the distance relationships between sampled points. The application of multidimensional projection is illustrated for history matching of the benchmark PUNQ-S3 model using ant colony, differential evolution, particle swarm and the neighbourhood algorithms.
We conclude that multi-dimensional projection algorithms are valuable diagnostic tools that should accompany assisted history matching workflows in order to evaluate their performance and compare ensembles of history-matched models. Using the projection tools, we show that misfit value - as an indicator of match quality - is not the only important factor in making reliable predictions. We demonstrate that exploration of the search space is also a critical element in the uncertainty quantification workflow which can be monitored with multidimensional projection schemes.
History matching is a process where the reservoir simulation model is conditioned to the available field data. It aims to tune the model in order to be consistent with the field performance. A simulation model which can capture the past life of a reservoir is more likely to make accurate predictions. History matching is an ill-posed inverse problem with non-unique solutions. Multiple realizations of the reservoir may give equally good matches to available data. Over the years our industry has moved from "in data we trust?? to "in uncertainty we trust??. One of the main concerns in reservoir engineering studies is to get reliable production forecasts to make optimal management decisions both from technical and economical viewpoints.The ultimate goal of a history matching study is to have calibrated reservoir models with high prediction capability.
|File Size||2 MB||Number of Pages||15|