Entropy Generation Minimization of Two-Phase Flow Irreversibilities in Hydrocarbon Reservoirs
Bibliographic record
Abstract
The efficient use of available energy in hydrocarbon extraction processes is essential to reducing overall emissions in the petroleum industry. The inefficient design of an extraction process leads to higher emissions per unit mass of hydrocarbon recovery. Fluid friction and heat transfer are irreversible processes that are vital in decreasing the overall system’s operational efficiency. To reduce these irreversible energy losses in the petroleum reservoir production’s life, contributing factors such as the characteristic features of a reservoir formation, reservoir fluids, and production rate are investigated in this paper. This study examines irreversible energy loss in porous media and wellbore formations using entropy generation minimization at various stages of production and thermodynamic conditions, eventually achieving higher hydrocarbon recovery factors. Entropy production is used to develop predictive models that calculate reservoir and wellbore energy losses for multiphase flow. The proposed models consider oil and water as the working fluids in a porous medium and a wellbore. This paper also investigates the thermophysical effects around the wellbore by incorporating Hawkin’s model. A sensitivity analysis assessed the impact of rock and fluid properties and thermodynamic conditions such as temperature, wettability, and capillary pressure on the total entropy generation. The findings reveal that the capillary pressure significantly impacts the oil and water recovery factor and total entropy production. Additionally, the capillary pressure strongly influences the reservoir production life. The two-phase models show that as the recovery factor increases, the total entropy production decreases at lower production rates. This article helps to address the impact of irreversible processes on multiphase hydrocarbon reservoir operational efficiency. Furthermore, the results obtained from the numerical-simulation model open up a new research area for scholars to maximize the recovery factor using entropy generation minimization in heterogeneous reservoirs.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".