Using Production Logging Technology for Reservoir Management in the Persian Gulf
Bibliographic record
Abstract
Abstract Is it a good idea to drill dual-lateral wells in a differentially depleted carbonate formation? The question is difficult. However, a state-of-the-art production-logging tool brought an answer that sanctioned the re-entry drilling program of this offshore field in the Persian Gulf. The new-technology production services platform equipped with eight electrical e-probes for flow- imaging capability was run on coiled tubing for the first time in this offshore field to characterize the production of a new dual-lateral well. The main objective was to evaluate the total contribution of the horizontal drains. However, since the well was perforated in the different layers crossed by the well path, another critical objective was to determine the need for a stimulation program or a new completion design. Reservoir pressure in the different layers was expected to be non-uniform, so an additional objective of the logging operation was to obtain information on the differential depletion. Production logging in highly deviated wells is difficult with standard sensors. One reason is that a conventional tool using differential pressure cannot measure fluid density. Moreover, flow regimes can be extremely complex, giving rise to phenomena such as water re-circulation, which is common in slanted wells and impossible to measure with a simple production logging string. The data showed that both legs are contributing equally to the total production and observed water re-circulation on the bottom of the well. The perforations are not contributing to the total flow. Moreover, a crossflow observed during shut-in conditions suggested different pressure levels in the two layers. This paper describes the logging operation and discusses how the results helped optimize the re-development plan of this aging field.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".