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Record W2056035984 · doi:10.2118/0314-0136-jpt

The Role of Autonomous Flow Control in SAGD Well Design

2014· article· en· W2056035984 on OpenAlex
Chris Carpenter

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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Petroleum Technology · 2014
Typearticle
Languageen
FieldEngineering
TopicOil and Gas Production Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsInjectorSteam-assisted gravity drainageSteam injectionPetroleum engineeringInflowOil fieldProcess (computing)Process engineeringEngineeringComputer scienceMechanical engineeringGeologyMaterials scienceOil sands

Abstract

fetched live from OpenAlex

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 The paper has not been peer reviewed.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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.796
Threshold uncertainty score0.296

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.003
GPT teacher head0.177
Teacher spread0.175 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it