Liner-Deployed Inflow Control Devices ICD Production Results in MacKay River SAGD Wells
Bibliographic record
Abstract
Abstract Ever since horizontal drilling became prominent decades ago, it has been the goal of oilfield operators to find an effective mechanism for controlling the toe-to-heel flux into the production liner to delay and/or reduce the inflow of unwanted fluids such as water, gas and steam, in order to maximize sweep efficiency and oil production/recovery. Inflow Control Devices (ICDs) are basically flow restrictors installed along the completion string to alter the pressure drawdown on the reservoir by choking back the high permeability/high mobility zones while allowing more influx from the lower permeability/lower mobility zones. In steam assisted gravity drainage (SAGD) production wells the primary goal is to operate at lower subcool while minimizing live steam production. The pace of ICD technology adoption has accelerated amongst the operators since its benefit was made prominent in the Surmont field (Stalder 2012). PetroChinaCanada (formerly known as Brion Energy) recognized the technical opportunity and commenced implementation of an ICD technology trial at its MacKay River asset. The technology selection, ICDs sizing and performace prediction was conducted in 2013 (Becerra et al). In 2014 two SAGD production wells, each located on different pads, were selected to evaluate if ICDs could have a beneficial impact on performance. The purpose of this paper is to present preliminary results of the two ICD producer wells which, as of November 2017, have shown superior performance to their non-ICD neighboring wells after about 6 months of production.
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".