A New Mechanistic Model To Predict Boosting Pressure of Electrical Submersible Pumps Under High-Viscosity Fluid Flow with Validations by Experimental Data
Bibliographic record
Abstract
Summary As the second most widely used artificial-lift method in petroleum production (and first in accumulative production), electrical submersible pumps (ESPs) increase flow rates by converting kinetic energy to hydraulic pressure. ESPs are routinely characterized with water flow, and water performance curves are provided by the manufacturers (catalog curves) for designing ESP-based artificial-lift systems. However, the properties of hydrocarbon fluids are very different from those of water, especially the dynamic viscosities, which can significantly alter the ESP performance. Most of the existing methods to estimate ESP boosting pressure under high-viscosity fluid flow involve a strong empirical nature, and are derived by correlating experimental/field data with correction factors (e.g., Hydraulic Institute Standards 1955). A universally valid mechanistic model to calculate the ESP boosting pressure under viscous fluid flow is not yet available. In this paper, a new mechanistic model accounting for the viscosity effect of working fluids on ESP hydraulic performance is proposed, and it is validated with a large database collected from different types of ESPs. The new model starts from the Euler equations for characterizing centrifugal pumps, and introduces a conceptual best-match flow rate QBM, at which the outlet flow direction of the impeller matches the designed flow direction. The mismatch of velocity triangles, resulting from the varying liquid-flow rates, is used to derive the recirculation losses. Other head losses caused by flow-direction change, friction, leakage flow, and other factors. are incorporated into the new model as well. QBM is obtained by matching the predicted H-Q performance curve of an ESP with the catalog curves. Once QBM is determined, the ESP hydraulic head under viscous-fluid-flow conditions can be calculated. The specific speed (NS) of the studied ESPs in this paper ranges from 1,600 to 3,448, including one radial-type ESP and two mixed-type designs. The model-predicted ESP boosting pressure with water flow is found to match the catalog curves well if QBM is properly tuned. With high-viscosity fluid presence, the model predictions of ESP boosting pressure also agree well with the corresponding experimental data. For most calculation results within medium to high flow rates, the model prediction error is less than 15%. Unlike the empirical correlations that take experimental data points as inputs, the mechanistic model in this study does not require entering any experimental data, but can predict ESP boosting pressure under viscous fluid flow with a reasonable accuracy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".