Conditioning reservoir models to dynamic data - A forward modeling perspective
Bibliographic record
Abstract
Abstract In order to predict accurately future production performance, a reservoir model should reflect the actual patterns of permeability connectivity (flow paths and barriers). Information about such patterns of connectivity is carried by flow response data recorded at wells. However, the flow response data are influenced by many factors other than permeability connectivity such as boundary conditions and fluid property variations. This paper presents a neural network-based procedure for filtering the information related to the permeability field in flow response data. The flow response data is modeled as specific multiple point averages of permeability values in the neighborhood of the well. Such multiple point averages allow accounting for the spatial connectivity of the permeability field. The superiority of such multiple point averages over single point permeability averages for representing flow response data is demonstrated over several reservoir examples. The ultimate quest is to integrate the permeability connectivity information contained in the flow response data into the numerical reservoir models. This amounts to ascertain that the permeability numerical models identify the previous multiple point averages. A Markov chain Monte Carlo simulation algorithm is implemented to perform this identification. Alternative equiprobable permeability fields are generated which, in addition to reproducing the production data, conform to a prior model for the spatial variability of the permeability field. The results demonstrate that flow simulation on the simulated permeability fields do indeed match historic well test data accurately. More importantly, future reservoir performance predictions are rendered more accurate.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".