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Record W1983337872 · doi:10.2118/165456-pa

Understanding the Steam-Hammer Mechanism in Steam-Assisted-Gravity-Drainage Wells

2013· article· en· W1983337872 on OpenAlexaboutno aff
Mazda Irani

Bibliographic record

VenueSPE Journal · 2013
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsnot available
Fundersnot available
KeywordsSteam injectionSteam-assisted gravity drainagePetroleum engineeringSteam drumAsphaltSuperheated steamEnvironmental scienceSteam explosionDrainageWaste managementOil sandsGeologyEngineeringMaterials scienceBoiler (water heating)Pulp and paper industry

Abstract

fetched live from OpenAlex

Summary Steam-assisted gravity drainage (SAGD) is one of the successful thermal-recovery techniques applied in Alberta oil-sands reservoirs. When considering in-situ production from bitumen reservoirs, viscosity reduction is necessary to mobilize bitumen, thereby flowing toward the production well. Steam injection is currently the most effective thermal-recovery method. Although steamflooding is commercially viable, condensation-induced water hammer (CIWH) resulting from rapid steam-pocket condensation can be a challenging operational problem. In steamflooding, steam is injected through a well down to the reservoir, warming it to temperatures of 150 to 270°C (302 to 518°F) to liquefy the bitumen inside the reservoir (Garnier et al. 2008; Xie and Zahacy 2011). The liquefied bitumen then drains to a lower well through which it is produced to the surface. In this process, steam pockets can become entrapped in subcooled condensate inside either the injection or the production tubing, causing a rapid collapse of the steam pocket. This type of rapid condensation is commonly referred to as "steam hammer." In this study, three different scenarios are explored to better understand steam-hammer situations in SAGD wells. These scenarios are at injectors or producers during the startup phase (or circulation phase), in the injection tubing during the injection phase, and in the production tubing during the injection phase. Modeling each of these scenarios indicates that a steam-hammer occurrence is likely in two of the three scenarios, but that its incidence can be mitigated. The likely scenarios for a steam-hammer occurrence are in either the injection or the production tubing during the startup phase, and in the injection tubing during the injection phase. Steam-hammer occurrences during the circulation period can be controlled by lowering the injection pressure and controlling water drainage into the reservoir. Flow shocks that occur as a result of countercurrent flow limiting (CCFL) are very likely to take place in the injection tubing during the injection phase but can be controlled by injecting at a higher steam quality. The least likely scenario for a steam-hammer occurrence is in the production tubing during the injection phase. This is because the produced (or breakthrough) steam temperature would need to be more than 20°C higher than the produced-liquid temperature to start a water-hammer condition.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.059
GPT teacher head0.267
Teacher spread0.208 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

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

Citations17
Published2013
Admission routes1
Has abstractyes

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