Modeling of Three-Phase Flow in the Annuli During Directional UBD Operations
Bibliographic record
Abstract
Abstract Underbalanced drilling (UBD) has several advantages compared to conventional drilling. These advantages include the elimination of formation damage, higher penetration rate, reducing circulation loss and the possibility of actually producing hydrocarbons during the drilling process. UBD technology and applications have recently been applied while drilling challenging wells in Iranian fields. It is generally accepted that the success of underbalanced drilling is dependant on maintaining the wellbore pressure in an operational window determined by the formation pressure, wellbore stability, and the capacity of the surface equipment. There are several models which can predict the wellbore pressure. Traditional models are mostly empirical and lead to acceptable results for specific conditions but fail for other conditions. In the last decade of the last century, some mechanistic models have been developed which result in an acceptable range of outcomes for a wide variety of reservoirs. On the other hand due to the dynamic nature of the process, some researchers have recently focused on development of dynamic models. This paper presents an improved, comprehensive, mechanistic model for pressure prediction through a well during UBD operations. The comprehensive model consists of a set of correlations for predicting flow pattern and estimating the pressure in addition to two-phase flow parameters in bubble, dispersed bubble, and slug flow. On the other hand the most recently developed empirical correlations have been applied to determine PVT properties. The accuracy of these correlations has been tested in more than 20 oil wells in Iran. Naseri et al. and Almarhoun correlations was applied to determine the live and dead oil viscosities. Naseri et al. model which was originally developed for Iranian reservoirs was used in our model and the results are promising.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".