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Record W2042891983 · doi:10.2118/2002-086

Tracking Cold Production Footprints

2002· article· en· W2042891983 on OpenAlexfundaboutno aff
R. P. Sawatzky, D.A. Lillico, Mike London, B. Tremblay, R. M. Coates

Bibliographic record

VenueCanadian International Petroleum Conference · 2002
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsnot available
FundersIndustry Canada
KeywordsProduction (economics)Computer scienceTracking (education)Economics

Abstract

fetched live from OpenAlex

Abstract Cold production is a primary recovery method used by producers in the Lloydminster area to improve recovery rates from heavy oil reservoirs by producing sand aggressively along with heavy oil. It improves oil production rates substantially through regions of increased permeability - wormholes. The process seems to key on the formation and flow of foamy oil into wormholes, as they grow into the reservoir. The wormholes provide significantly increased access to the reservoir. The area drained by a cold production well can be called its footprint. Identifying and mapping cold production footprints is useful in the planning and development of reservoir exploitation strategies for cold production pools. The cold production research group at the Alberta Research Council is constructing reservoir engineering tools, such as field scale numerical simulation models, that can be used to assess cold production footprints. The results generated by the tools will be illustrated with examples from an intensive study of a set of Edam cold production wells. Introduction Cold production is a method for enhancing primary production from heavy oil reservoirs. In the cold production process, sand is produced aggressively along with heavy oil. The process improves oil production rates substantially through regions of increased permeability - wormholes. It seems to key on the formation and flow of foamy oil into wormholes, as they grow into the reservoir. The wormholes provide significantly increased access to the reservoir. The cold production process has been applied with commercial success by producers in the area surrounding Lloydminster. It emerged as a viable commercial technology for the recovery of heavy oil in the mid to late 1980s, with the adaptation of progressive cavity pumps for heavy oil lift operations. By the mid 1990s, cold production had become widespread throughout the Canadian heavy oil industry. Currently, it is established as one of the principal methods for recovering heavy oil from the Western Canadian Sedimentary Basin (WCSB). Production of heavy oil generated by cold production technology in western Canada is roughly 37,000 m3/day, from a producing well count of nearly 6,000, according to production figures for November 2001 (the most recent figures available at the time of writing). This is an unofficial estimate; no official production volumes for cold production have been assembled. The estimate was calculated for a current Alberta Research Council (ARC) internal study; production that could reasonably be attributed to the cold production process was determined from heavy oil sales volumes reported to the Alberta and Saskatchewan regulatory agencies(1). It forms a significant portion of the total production of heavy oil from western Canada (including net in situ bitumen production in Alberta) - approximately 140,000 m3/day according to official National Energy Board estimates for the same period(2). The development of cold production as a successful commercial recovery technology has been field-driven. A substantial body of knowledge and expertise on cold production exploitation strategies and operating practices has been accumulated by Canadian heavy oil producers, through hard-won field experience.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.075

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.004
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.023
GPT teacher head0.217
Teacher spread0.194 · 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

Citations56
Published2002
Admission routes2
Has abstractyes

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