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Record W2020545498 · doi:10.2118/2009-164

Field Measurements of Low Energy Oil Sands Mobility

2009· article· en· W2020545498 on OpenAlexaboutno aff
Shengli Yao, Duilio Raffa, Guohui Tian, D.F. Raffa

Bibliographic record

VenueCanadian International Petroleum Conference · 2009
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental scienceField (mathematics)Petroleum engineeringGeology

Abstract

fetched live from OpenAlex

Abstract The bitumen contained in Axe Lake Discovery in Saskatchewan has very high viscosity, well over 10 million centipoises under original reservoir condition. Since bitumen viscosity decreases with increasing temperature even at low temperatures, and since the Axe Lake reservoir is highly permeable and extremely coarse grained, preliminary reservoir simulations indicated that it is possible that effective, early time, communication between production wells at the bottom of the reservoir can be established by efficient low energy heating and intermittent water pressure pulsing. In order to evaluate the heating concept, we measured the response of the reservoir to heating with down-hole heaters at low temperatures (100 °C). A description of the field test is provided. Different analytical formulations and simulation models were used to analyze the results of the field test and the analyses were compared to prior published work. Field results were also used to determine effective thermal conductivities and effective mobilities at low temperatures for different grid sizes using Cartesian, radial and hybrid simulation grids. A comparison to analytical heat transfer formulations under different boundary conditions is discussed. Calibrated simulations show that not only can the heater be used to preheat the formation but also as an efficient bitumen viscosity reduction tool to increase bitumen mobility during the full bitumen production life of the reservoir. Moreover, high temperature heaters can be combined with cold water injection to generate steam in the reservoir directly. The paper concludes with a simple analysis which shows that such a combined technology can produce bitumen more efficiently and economically when compared to conventional SAGD. Introduction Axe Lake Discovery is located in northwestern Saskatchewan, about 90 km northeast of Fort McMurray. The bitumen reservoirs are buried about 200m below the surface. The bitumen is quite heavy and highly viscous, with a viscosity of over 10 million centipoises at original reservoir condition. Since bitumen viscosity decreases when the temperature rises, thermal technologies such as steam and hot water injection have become the preferred means for bitumen recovery. However, the bitumen reservoir at Axe Lake is relatively shallow and the efficient recovery of bitumen may be not possible using high pressure steam and hot water injection. Preliminary reservoir simulations indicated that it is possible to establish effective communication between producers at early time by efficient low energy heating and intermittent water pressure pulsing. Measuring the response of the reservoir to heating with downhole heaters at low temperature 100 °C, a two-stage test was planned and the second stage is currently being implemented. Different analytical formulations and simulation models were used to analyze the results of the measurement. The effective thermal conductivity and mobility of the bitumen at low temperature will be obtained from the results. Simulation studies also indicated that it was possible to obtain bitumen recovery effectively and economically using high temperature down-hole heater combined with cold water injection. This method takes advantage of simple and safe surface facilities and without losing heat outside the wells when compared to conventional steam injection from the surface.

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)0.000
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 score0.010
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.014
GPT teacher head0.202
Teacher spread0.188 · 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".

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Citations0
Published2009
Admission routes1
Has abstractyes

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