A Procedure for the Configuration of an Inflow Control Device Completion Using Reservoir Modelling and Simulation in the North Amethyst Pool
Bibliographic record
Abstract
Abstract This paper outlines an approach to simultaneously reduce gas and water production through the design and implementation of an inflow control device (ICD) completion for a horizontal production well in the North Amethyst pool. The procedure uses Schlumberger’s Petrel modelling software, Schlumberger’s reservoir simulator, ECLIPSE, and a multi-segmented well (MSW) model to optimally configure an ICD completion within a reservoir model. This approach utilizes the reservoir model to generate ternary plots (oil, gas and water) that represent three-phase movement within the reservoir. The use of MSW enables the dynamic display of a virtual production logging tool (PLT) plot, representing the expected three-phase inflow performance along the wellbore. Both ternary and PLT plots identify the locations of high gas and high water inflow zones along the wellbore. With these zones identified, various configurations of ICD completions are designed to control these breakthrough zones and are then simulated. ICD equipment options, such as reduced nozzle sizes and blank zones, are considered in the design. The simulation results of the various ICD configurations are compared to determine the optimal design. The design objectives are to optimize oil inflow, oil rate, and ultimate recovery by delaying and reducing gas and water production. The produced liquid rate was also optimized with rate sensitivities for each ICD configuration which led to a design that further reduces gas and water 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".