Bibliographic record
Abstract
This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 166266, ’The Role of Autonomous Flow Control in SAGD Well Design,’ by Sudiptya Banerjee, SPE, Robert Jobling, SPE, Tarik Abdelfattah, SPE, and Hang Nguyen, SPE, Baker Hughes, prepared for the 2013 SPE Annual Technical Conference and Exhibition, New Orleans, 30 September-2 October. The paper has not been peer reviewed. One notable improvement originating from recent field experience is the novel use of injection-/inflow-control devices (ICDs) in a conventional steam-assisted-gravity- drainage (SAGD) well pair. The use of a properly designed ICD completion has proved beneficial to the processes of developing the steam chamber and improving the inflow profile of the producing well of the SAGD pair. Work conducted in the Surmont field of Alberta, Canada, provided an excellent starting point to optimize flow-control improvements to the SAGD process. Introduction To obtain high efficiency, the SAGD process demands a high degree of steam conformance along the wellbore. The better the steam conformance, the greater the amount of oil mobilized and the higher the potential for oil recovery. However, conformance should not come at the cost of excessive steam injection. A number of thermal inefficiencies (described in the complete paper) are unavoidable in the SAGD process. These collectively result in a deformed and nonoptimal steam chamber that undermines energy maintenance and lowers the ultimate recovery factor. By incorporation of flow-control devices (FCDs) into the injector or producer well, however, a more ideal, more desirable chamber shape can be realized. Overview of ICD Geometries Fundamentally, all flow-control devices operate by the same mechanism: They provide an additional pressure drop at select points in the completion string to complement the pressure drop of fluid moving through the reservoir as well as internal pressure drops within the completion itself. Despite this fundamental similarity, however, the methods by which different styles of ICDs create this pressure drop vary strongly with design. There are three distinctive categories of passive inflow-/injection-control devices (PICDs) available today. The two most common PICD geometries are orifice/tube/nozzle-based (restrictive) (Fig. 1) and helical-channel/baffled- pathway (frictional) (Fig. 2). The restriction-based PICD uses fluid constriction to generate a differential pressure across the device. This method essentially forces the fluid from a larger area through small-diameter ports, creating flow resistance. A frictional-pathway PICD relies instead on surface friction to generate a similar pressure drop. These designs produce a distributed pressure drop over a relatively long area, as opposed to the instantaneous loss through a restriction-style PICD. When fluid flows through the channel/ channels, fluid rheology and channel characteristics interact to create the designed pressure drop.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".