The Effect of Production-Logging-Tool Data on Scale-Squeeze Lifetime and Management of Scale Risk in Norwegian Subsea Production Wells: A Case Study
Bibliographic record
Abstract
Summary Because of the higher cost of scale management for subsea (SS) operations compared with platform or onshore fields, and because of the more limited opportunities for interventions, it is becoming increasingly important to obtain and use real production data from wells rather than estimated zone flow contribution from simple permeability (k) and height (h) models for scale-squeeze-treatment design. In this paper I discuss how scale-squeeze treatments were designed (coreflood evaluation of inhibitor retention/release) and deployed for three SS heterogeneous production wells. A permeability model and a layer-height model were initially developed for each well using detailed geological log data, estimated water/oil-production rates, and the predicted water-ingress location within the wells. Two wells were each treated three times using bullhead scale-squeeze treatments, with effective scale control being reported over the designed lifetime. A production log was acquired before the fourth squeeze campaign of these two wells. This information was incorporated into the squeeze simulation to allow review of the ongoing third squeeze and enhance design accuracy for the upcoming fourth squeezes. A third well was treated twice before production-logging data became available, and the performance of treatments to this well is also assessed. The production-logging-tool (PLT) data proved very important in changing the understanding of fluid placement and the water-ingress location during production, resulting in changes to the isotherm values used to achieve effective history match to the inhibitor returns (with PLT data incorporated in all three wells), and most significantly affecting the squeeze lifetimes. It was possible to significantly extend the treatment lifetime of two of the wells (cumulative produced water to minimum inhibitor concentration), while the treatment life of one well was greatly reduced because of the PLT-data-modified model predictions. In this paper I outline the process of reservoir/near-wellbore modeling that is used for most initial squeeze-treatment service companies deployed in the North Sea. I will highlight in detail the value that PLT data can provide to improve the effectiveness of squeeze treatments in terms of understanding of fluid placement during squeeze deployment and water-ingress location within heterogenous production wells. The intention of this paper is to highlight the value that these types of data can provide to improve scale management (squeeze treatment and water shutoff) such that the value created more than offsets the cost of acquiring such information for SS production wells.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".