Use of Tracers To Evaluate and Optimize Scale-Squeeze-Treatment Design in the Norne Field
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Summary When squeezing scale inhibitors (SIs) into oil-production wells, the inhibitor should usually be uniformly placed in the open intervals to optimize squeeze lifetime. In wells with varying reservoir quality and/or significant crossflow, however, uniform placement is difficult to obtain. Flow diverters are frequently used to improve the chemical placement. In many cases, it is of great interest to evaluate the squeeze performance and assess the actual placement and back production of inhibitor to gather well information and thereby optimize future squeeze designs. This can be particularly interesting in subsea wells in which other types of data collection, such as production logging, are not feasible because of high intervention costs and high operational risk. This study suggests the use of tracers during squeeze treatments to evaluate the placement as an alternative to running production-logging tools (PLTs). The main purpose of this paper is to demonstrate the applicability of tracers [in this particular study, the injection of a potassium chloride (KCl) slug in a producer well in the Norne field] to evaluate the layer flow-rate profile along the completion interval, which depends on the pressure and geological properties of each layer. The study consists of verifying the layer flow-rate profile predicted by a history-matched reservoir model. On the basis of this layer flow-rate profile, a tracer-injection program is designed, which includes two production stages at different rates. Finally, on the basis of the reservoir-model predictions, it is identified that each layer is at different pressures, which leads to a distinctive return profile. To evaluate the match between the observed data and the simulation data, the layer flow-rate profile from the reservoir model was used to populate a specialized near-wellbore model for scale-squeeze treatments. The match between the observed data and the simulated data was good. However, the near-wellbore model, in particular the layer flow-rate profile, was fine-tuned further. Finally, the fine-tuned near-wellbore model was used to optimize future treatments more accurately with the fine-tuned layer flow-rate profile.
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.
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 it