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Record W2988738291 · doi:10.2118/198919-ms

Foam Formulation for High Temperature SAGD Applications

2019· article· en· W2988738291 on OpenAlexaff
Lukemon A. Adetunji, Amos Ben‐Zvi, Alex Filstein

Bibliographic record

VenueSPE Thermal Well Integrity and Design Symposium · 2019
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsCenovus Energy (Canada)
Fundersnot available
KeywordsMethaneResidual oilPetroleum engineeringMaterials scienceEnhanced oil recoveryWaste managementEnvironmental scienceProcess engineeringChemical engineeringChemistryEngineering

Abstract

fetched live from OpenAlex

Abstract In SAGD operations, the steam chamber temperature can be as high as 250°C. Foam formulations that can withstand such high temperature are required for field deployment of foam as conformance control technique. An evaluation of 12 surfactant formulations for their viability as steam foam candidate at chamber temperatures as high as 250°C is presented. Unlike other studies in the literature that used nitrogen for foam generation, methane was employed in this project. This project reveals that strong foam can be formed at 250°C. Also, foam is only generated within a specific range of methane mass quality. Stronger foam was formed when steam and methane were injected simultaneously into the core. Lastly, the existence of residual oil in the core reduced the foam strength. We analyzed the effect of a few parameters on foam strength. Parameters considered include concentration of surfactant, presence of steam as well as methane, mass quality of methane, and residual oil saturation. Laboratory analyses comprising thermal stability, solubility, foam height, adsorption, and coreflood, were conducted. The learnings from this project has the ability to accelerate the application of foam systems in high-temperature SAGD operations to increase conformance, maximize steam usage, and ultimately lead to overall reduction in GHG emissions from SAGD projects.

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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.677
Threshold uncertainty score0.517

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.012
GPT teacher head0.219
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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations5
Published2019
Admission routes1
Has abstractyes

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