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Record W2252901019 · doi:10.2118/178457-ms

Slotted Liner Design Optimization for Sand Control in SAGD Wells

2015· article· en· W2252901019 on OpenAlexaboutno aff
Jueren Xie

Bibliographic record

VenueSPE Thermal Well Integrity and Design Symposium · 2015
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsServiceability (structure)EngineeringPetroleum engineeringSteam-assisted gravity drainageFinite element methodTorqueCivil engineeringStructural engineeringOil sandsAsphaltMaterials science

Abstract

fetched live from OpenAlex

Abstract To recover heavy oil and bitumen from the highly unconsolidated reservoirs of the Western Canada oilsands fields using the horizontal well method, and in particular the thermal in situ method of Steam Assisted Gravity Drainage (SAGD), some forms of sand control, such as slotted liners and wire-wrapped screens (WWS), are generally required to limit sand production and maintain well productivity. Over the past two decades, a number experimental and analytical studies have been performed for developing industry guidelines and best practices for the structural and hydraulic design and evaluation of sand control liners under the challenging conditions of SAGD steam injection and production wells. The vast majority of SAGD applications, estimated at about 90% (RPS 2009), employ slotted liners as the sand control method. Design efforts to address the structural and serviceability requirements, however, often oppose one other, presenting significant challenges to the design of slotted liners. For example, in order to maintain sufficient torque, collapse and strain-absorbing capacities, traditional slotted liners are often designed to have low Open Flow Areas (OFA) around 1%, although a larger OFA is desired for production efficiency. This paper reviews current design requirements for SAGD wells, as well as puts forward a new slotted liner design consideration for the optimized performance under installation and operational loads. Using Finite Element Analysis (FEA) modeling, the effect of key design parameters, such as slot length and slot pattern, are studied. This paper focuses on the feasibility study of achieving higher OFA than the currently-used limit, while maintaining sufficient torque, collapse and strain-absorbing capacities under thermal well load conditions.

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.

How this classification was reachedexpand

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.953
Threshold uncertainty score0.979

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 head0.028
GPT teacher head0.220
Teacher spread0.192 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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".

Quick stats

Citations27
Published2015
Admission routes1
Has abstractyes

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