Proposed Methodology to Predict Electric Power Requirements for ESP Wells in a Heavy Oil Field - A Case Study
Bibliographic record
Abstract
Abstract This paper describes the methodology used to determine the electric power requirements of all the ESP systems to be installed over a period of study (2007-2017) in the Icotea-Misoa heavy oil reservoir, Urdaneta West field, Venezuela, in order to ensure a reliable and safe electric supply to cover the field development plan. The methodology used consisted in: selection of a representative sample of the ESP wells population; running several sensitivities on the ESP and well performance using an industry proved ESP software for the most critical cases of reservoir, equipment and production conditions; tabulate and analyze the data obtained from the simulations with statistical techniques to determine the most probable electric power requirement range as well as its tendency over time; finally, generate relationships between the electric power consumption and different production parameters (total production rate, oil and water production rates, reservoir pressure, productivity index and sand production) to predict the power requirements changes with time. The results will be used by the operating company to decide whether the existing surface electrical facilities are sufficient for the field development plan, or if a KVA capacity expansion is required. One of the main conclusions from the study was that the required electric power capacity was being overestimated, based on simple predictions using only historical consumptions or non-statistical methods. The methodology applied led to a more reliable prediction of the power consumption per well. This will allow the company to better estimate the required facilities and hence, reduce the expected costs of investment required.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".