MétaCan
Menu
Back to cohort
Record W2025407925 · doi:10.2118/132639-pa

Understanding Volumetric Sweep Efficiency in SAGD Projects

2010· article· en· W2025407925 on OpenAlex
R. Baker, C. Fong, C. Bowes, M. Toews

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2010
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsUniversity of Calgary
FundersUniversity of Calgary
KeywordsPetroleum engineeringPetrophysicsSteam injectionEnhanced oil recoveryWork (physics)Volume (thermodynamics)Environmental scienceProcess engineeringGeologyEngineeringMechanical engineeringGeotechnical engineering

Abstract

fetched live from OpenAlex

Abstract An understanding of volumetric sweep and capture efficiency are critical to optimizing SAGD projects. Capture efficiency refers to how much mobilized and heated oil is actually produced. Volumetric sweep efficiency can be estimated reasonably well for SAGD pilots having an abundance of information. A rich data set may include geological, petrophysical, production, injection, pressure, temperature observation and 4D seismic data. With this information, excellent insight into steam chamber development can be assessed. However, determining volumetric sweep and op­timal strategies is a problem where data are sparse. This paper summarizes analytical SAGD surveillance methods that estimate volumetric sweep and presents a work flow that can help optimize SAGD processes with limited data. Although the methods each have their assumptions and are not perfect, there is general agreement. The various techniques are corroborated using public core, injection/production, temperature observation and 4D seismic for the Surmont and Christina Lake pilot projects. Introduction Evaluating reservoir performance through the surveillance of production data is an excellent reservoir management tool. For waterflooding, this can be achieved through a conformance plot(1) that indicates how efficiently net water throughput affects recovery. Associating a water balance with an oil balance enhances the understanding of the influx/efflux of fluids and outer boundary losses out of zones. The plot works well for waterfloods because of the low compressibility of the fluids. For SAGD, a similar surveillance principle using abundant data is applied. However, a material balance alone is not definitive because SAGD is an energy-intensive process where steam is required to reduce the oil viscosity to a point where it will flow. Therefore, accounting for the energy within the SAGD process provides a different perspective for multiple geologies and heterogeneities. Typical objectives of SAGD surveillance using production and monitoring data are to determine: Original oil in place (OOIP) Remaining oil in place (ROIP) Mobile ROIP distribution and current condition (saturation, temperature and pressure) Limiting factors in recovery Potential improvements to economic oil recovery Recovery profile and optimizing recovery factor Operating strategies to achieve better volumetric sweep

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.056
Threshold uncertainty score0.980

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0100.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.032
GPT teacher head0.240
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