Bibliographic record
Abstract
Abstract Sajaa gas field is one of the oldest gas fields in the Northern Emirates and Petrofac currently holds accountability for the Sajaa asset wells. This mature asset suffered from declining reservoir pressure and increased problems with liquid loading where entrained liquid dropped out in the vertical well bore, thereby restricting the flow of gas and fluids to the topside processing facility. A detailed study was conducted to understand the different techniques available for overcoming this liquid loading problem which impeded gas production rates. After studying several methods to overcome this liquid loading issue, foamer injection was suggested for the wells which had intermittent flow patterns. The foamer application was also chosen as one of the cheapest and easiest means for overcoming liquid loading issues. Foamer treatment for Sajaa fields was initiated two years ago and a substantial increase in gas production was recorded for most of the wells with this foamer application. The foamer product selection was carried out based on modeling and subsequent lab tests. Close monitoring of well behavior to the foamer application was conducted and detailed case histories were developed for some of the wells. A tremendous improvement in gas production rate was noted for one of the wells in Sajaa field – ‘Sajaa S-36’ (9⅝" completion). Unlike most of the other Sajaa wells, S-36 was fitted with capillary injection tubing where the foamer chemical was injected downhole through the ¼" tubing. The well performance was monitored for a period of one year and sufficient data was collected highlighting the foamer performance. This paper presents the results of the foamer downhole injection campaign and gives an overview of the treatment methodology. An insight into the factors influencing the foamer performance, selection methods and the optimization techniques employed during the foamer injection trial are also presented.
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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".