Improving history matching performance in a 3D field-scale case study by combining tracer and production data
Bibliographic record
Abstract
Reservoir models are often subject to uncertainties, which, if not properly taken into account, may introduce biases to the subsequent reservoir management process. To improve reliability and reduce uncertainties, it is crucial to condition reservoir models on available field datasets through history matching. There are different types of field data. Among others, production data are the most common choice, but they are subject to a major limitation of carrying relatively low value of information. On the other hand, inter-well tracer data have been shown to provide additional information about well-to-well connectivity and reservoir dynamics. However, jointly history matching production and inter-well tracer data still remains challenging due to the lack of a coherent quantitative workflow to fully integrate them. This work can be considered a step towards tackling this noticed problem. To this end, we propose a non-intrusive and derivative-free ensemble history matching workflow, in which reservoir models are more coherently conditioned on both production and inter-well tracer data with the help of a recently developed technique (correlation-based adaptive localization). The workflow is successfully implemented in the Brugge benchmark case. Our study indicates that the history matching algorithm matches the production data well, regardless of the presence or absence of the tracer data. Nevertheless, by including tracer data as an additional source of information, we are able to improve the quality of the estimated reservoir models, in terms of both improved data match and reduced model discrepancies. As such, the finding of this study can help to achieve a better understanding of the impacts of tracer data on history matching performance, and the proposed workflow could serve as a useful too for more proper uncertainty quantification, and more coherent utilization of different types of field data in real case studies in general.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".