A Novel Approach to Production Logging in Multiphase Horizontal Wells
Bibliographic record
Abstract
Abstract Production logging sensors based on center-point measurements are not always successful in obtaining accurate flow profiles in triphase horizontal wells because of the effects of factors particular to these wells: fluid segregation, small changes in the well inclination, and the flow regime. This paper presents a new tool designed specifically for highly deviated and near to completely horizontal wells. The tool provides a recording of holdup and velocity profiles along the vertical diameter of the borehole cross-section. Three sensor arrays consisting of six optical probes, six electrical probes, and five spinners are spread across the well bore on retractable arms that can be opened and closed with a hydraulic sub to better locate holdup interfaces. The optical probes use the fluid's reflectance to derive the gas holdup, and the electrical probes measure the fluid's impedance to derive the water holdup. The spatial location of the different sensors is accurately known through the use of an integrated relativebearing sensor and a caliper measurement. The direct measurement of the velocity and fluid holdup profile enhances the capability of the analyst to determine the downhole phase split and reduces the uncertainties associated with triphase flow interpretation. This paper will discuss the problems mentioned and will illustrate through worldwide field examples the success of the new tool.
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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".