Prediction of Downhole Flow Regimes in Deviated Horizontal Wells for Production Log Interpretation
Bibliographic record
Abstract
Abstract Production logging is used to evaluate wells production performance. Interpretation of production log data provides velocity profile and contribution of each zone on total production. In multi-phase flow conditions, production log interpretation can be challenging since producing fluids do not have similar densities and travel with different speed depending on fluids properties and wellbore deviation. Production log interpretation in multi-phase producing wells requires identifying downhole flow regimes and determining velocity profile for each phase. There are different flow regimes and velocity models available, which are being used in production log interpretation to determine wells flow profile in multi phase flow. However in the case of deviated horizontal intervals, the flow model might fail to provide reliable results for gas and liquids velocities. This paper represents the use of Computational Fluid Dynamics (CFD) simulation in order to model multi-phase flow in the wellbore and evaluate velocity profile across deviated horizontal intervals. The simulation results when integrated with production log data can help estimating velocity for each phase in different flow rates and for different wellbore trajectories. The approach can help interpretation of production logging data in horizontal multi-phase producing wells, as it can predict downhole flow regimes and identify the intervals having chance of downhole liquids re-circulation. Production logging data in a gas-oil producing deviated horizontal well with water loading in wellbore was used to verify reliability of the numerical simulation. The approach could help to evaluate the well production performance with less uncertainty.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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 teacher head, 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".