Adaptive Option in Geological Modeling of Petroleum Reservoirs
Bibliographic record
Abstract
Abstract The adaptive geological model differs from the deterministic one in that it takes into account the uncertainty of the initial data, has a degree of detail corresponding to the level of accuracy of the geological information, and reproduces the main regularities of the structure of the oil and gas object under consideration, primarily for the purpose of predicting its geology in undeveloped zones. At the first stage, adaptive geological models of separate layers identified by the results of detailed correlation of the considered object are constructed independently from each other, and then the single-layer models are summed up into a multilayer adaptive geological model of the object as a whole. With the adaptive approach, traditional methods of interpolation of geological and production data are not applied. The basis of adaptive geological modeling is the seismic data. The essence is that the seismic data vector is available both at the wells and at the points of the inter-well spacing, so any geological parameter can be calculated from this vector using a special fuzzy-logic function. At the same time, such function cannot be the same for the whole model polygon, so an additional so-called fuzzy-grid is developed. A grid with large cells, which contain local fuzzy-logic functions. Since fuzzy-logical functions are formed on the basis of geological and production information of the considered object, they can differ substantially for other objects. Thus, the mathematical apparatus of a geological model automatically modifies to the specific initial data, and therefore it is called adaptive. The calculated parameters of the adaptive geological model cells through which the wells pass are not necessarily the same as the actual well data. The reason is that these parameters are calculated using functions in which the initial data of neighboring wells participate. Deviations of calculated results from actual data characterize the degree of "defectiveness" of geological and production information. Thus, the adaptive geological model in comparison with the deterministic one gives a more meaningful result both about the geological structure of the considered object and the degree of reliability of its representation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".