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Record W2309414494 · doi:10.2118/175414-ms

Solvent Enhanced Steam Drive: Experiences from the First Field Pilot in Canada

2015· article· en· W2309414494 on OpenAlexafffundabout
Marco Verlaan, R.E. Hedden, Orlando Castellanos Diaz, Vaclav Lastovka, C. A. Giraldo Sierra

Bibliographic record

VenueSPE Kuwait Oil and Gas Show and Conference · 2015
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsShell (Canada)
FundersShell Canada
KeywordsPetroleum engineeringSteam injectionEnvironmental scienceAsphaltCasingSolventOil fieldProduction (economics)EmulsionProcess engineeringWaste managementEngineeringChemistryMaterials scienceChemical engineering

Abstract

fetched live from OpenAlex

Abstract In recent years, the addition of a hydrocarbon condensate (C4 to C20) to steam operations (such as CSS and SAGD) in heavy oil and bitumen reservoirs has emerged as potential technology to improve not only oil recovery and but also energy efficiency. Shell has extended the idea of solvent addition to a steam drive process, applied it for the first time in the Peace River area in Canada, and obtained evidence of oil uplift in the patterns where solvent was injected. However, piloting this new technology in a brown field had many challenges, especially when evaluating its main economic factors: production increase and solvent recovery. To overcome these challenges, emphasis was put on experimental design, data acquisition and quality, and production surveillance. The pilot conditions were designed to increase the probability of success on the two economic factors aforementioned within a short period of time. The assessment of the pilot required that all production streams (emulsion and casing vent gas) were metered and frequently sampled to measure their respective compositions. Cross calibration of metered and sampled water cuts was essential in obtaining conclusive production uplift data. Automatic proportional samplers were successfully deployed under these challenging conditions to obtain representative samples. Due to the overlap of solvent and bitumen components, special attention was taken to allocate hydrocarbon production into bitumen and solvent. New in-house developed algorithms were tested to accurately calculate this split. The addition of a 4 month concentrated slug of solvent in two steam drive patterns resulted in a significant production uplift when compared to two adjacent patterns with steam-only injection. Solvent recovery is still ongoing and exceeds original expectations. Frequent sampling allowed the detection of several trends, including bitumen composition changes during solvent injection and solvent fractionation in the reservoir.

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)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.418
Threshold uncertainty score0.841

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.002
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.026
GPT teacher head0.236
Teacher spread0.209 · 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 designObservational
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

Citations9
Published2015
Admission routes3
Has abstractyes

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