Prediction of Oil Production for Heavy Oil Filed Based on Optimized Verhulst Model
Bibliographic record
Abstract
The development of heavy oil field is a complex process,it can be regarded as gray systems under the circumstances of less information.The oil production model of the research area in Liaohe oilfield has been established based on the Verhulst model theoretical,the result of Verhulst model for oil production from 2005 to 2014 shows an increasing trend of error.The residual modification model has been established by combining the Verhulst model with G(l,l)grey model,and the model accuracy increases from 96.0994%to 97.6763%.Oil production prediction results from 2012 to 2014 shows that prediction accuracy of residual modification model reached 99.2281%,and the correlation increases significantly.Comparing predicted results with the numerical simulation results shows that residual modification Verhulst model is a quick,concise and accurate forecasting method that can be used on heavy oil fields development,under certain conditions,the residual modification Verhulst model not only can be used to predict the oil production,but also can provide basis for decision making.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".