Building Trust in History Matching: The Role of Multidimensional Projection
Bibliographic record
Abstract
Abstract 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".