Investigation of a new multiphase flow choke correlation by linear and non-linear optimization methods and Monte Carlo sampling
Bibliographic record
Abstract
In the production engineering point of view, the multiphase flow predictions from producing wells becomes important for an adept production design and management. As the associated costs of direct flow metering are considerable, the empirical and semi-empirical correlations are roughly efficient for this aim. Lack of sufficient data and their inherent epistemic and aleatory uncertainty due to human and equipment errors are, however, major limitations for these correlations. Following our previous article, in this paper, two optimization methods one linear and other non-linear are proposed. As it is shown, the linear regression is not dimensionally suitable to predict flow rate. It seems that due to the complexity of the objective function and also uncertain parameters, the linear regression is not the best algorithm for optimization. However, the non-linear method of Nelder–Mead (by means of MATLAB function Fminsearch) perfectly optimized the fitness function with a negligible average error. Due to the uncertain nature of main parameters in the correlation (such as Pwh, BS&W, T, etc.), a Monte Carlo sampling is used replacing these parameters with their PDFs (probability density function) to see if the proposed correlation works well or not. On this base, wellhead pressure (Pwh), choke size (S), basic sediment and water term ( ), temperature ( ) and gas/liquid ratio (GLR) are considered as random variables. The best probability distribution function (PDF) for each variable is then obtained which most closely reproduce flow through the choke. Monte Carlo sampling which deals with uncertain variables is used to predict the flow rates based on the proposed method and to show the level of uncertainty within the developed correlation results.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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) | 0.001 | 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".