Investigating the efficiency of gas re‐injection process of an oil field using combined integrated field simulation and intelligent proxy model application
Bibliographic record
Abstract
Abstract Integrated asset modelling is a novel method to overcome the limitations associated with using individual models. This method integrates all the individual models of a field into a single model that relates all the sub‐models using proper boundary conditions. Reservoir, wells, surface, and economic models of an oil reservoir, under gas re‐injection, are integrated. The main goal of this study is to propose a novel approach in integrated asset modelling. An integrated model of a field is used to study how gas must be distributed among injection wells. Another aim of this study is to understand the effects of 4 input parameters on the Net Present Value (NPV) of the field. The input variables are: oil production rate, gas injection rate, and the distribution of gas between injection wells. A comprehensive model of a field was built. Using the experimental design results, a neuro‐fuzzy logic network was developed. The proxy model predicted the simulation outputs with a reasonable accuracy. The effects of input variables were studied. Oil production has an optimum value of 6050 STBD per well. The optimum fractions of injected gas for injection wells 1 and 2 are 0.4 and 0.6 of total injected gas, respectively. This means that 40 % of the total injection gas must be injected to well 1 to have the maximum NPV. The greater the gas injection rate, the higher NPV is.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".