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Record W2022716917 · doi:10.2118/07-04-02

How Much Oil You Can Get From CHOPS

2007· article· en· W2022716917 on OpenAlexaffabout
Gang Han, Mike Bruno, Maurice B. Dusseault

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2007
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsUniversity of Waterloo
FundersU.S. Department of Energy
KeywordsPermeability (electromagnetism)Petroleum engineeringEnvironmental sciencePorosityOil productionOil fieldLimitingOil viscosityViscosityGeologyGeotechnical engineeringMaterials scienceEngineeringComposite materialChemistry

Abstract

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Abstract Cold Heavy Oil Production with Sand (CHOPS) has been applied with very good success to enhance heavy oil production in Canada, China, Venezuela and Kazakhstan. Based on existing laboratory and field information and case histories, the most important physical processes enhancing cold production have been reviewed, summarized and quantified. Several sanding models are developed, including a new porosity cap model for failure propagation, as well as a semi-analytical elastoplastic stress model coupled with an unsteady pressure model for foamy oils. The mechanisms for oil rate enhancement by CHOPS, such as porosity and permeability enhancement that arise from sand removal, propagation of the elastoplastic (remolded) zone, increase of oil velocity relative to the matrix and the effects of foamy oil behaviour, are quantitatively described and compared. The proposed model can be applied to predict how much additional oil one might expect for a given amount of produced sand. It might also serve as a tool for optimizing cold heavy oil production while nevertheless keeping the sand flux at a low level, which could reduce operating expenses such as limiting sand disposal costs. Introduction There may be more than 0.95 × 1012 m3 of heavy oil in the World(1), compared to 0.28 – 0.37 × 1012 m3 of conventional oil, of which over 40% has already been produced(2). Because of high viscosity, primary recovery factors for heavy oils are generally low; if the viscosity is higher than 10,000 – 20,000 cP in situ and the permeability is less than 5 Darcy, it appears that commercial recovery using any conventional non-thermal method is not possible. With careful design and implementation, various thermal recovery schemes can be effective, but high operational costs restrict their applicability. Though it has been long recognized that "...the maximum recovery of oil from an unconsolidated sand is directly dependent upon the maximum recovery of the sand itself...(3), " CHOPS was not widely implemented with commercial success until advanced pumping systems (such as the progressive cavity pumps) were perfected in the late 1980's for slurries containing sand. Since then, because of reasonable recovery factors (~15 – 20%), production rates (3.2 – 47.7 m3/day), effective sand handling and disposal and no heat costs, CHOPS has grown to provide more than 20% of Canada's oil. In 2002, Canada's oil production from all sources was approximately 460,000 m3/d, of which more than 95,400 m3/d was CHOPS production. Heavy oil reservoirs suitable for CHOPS are located in unconsolidated or weakly consolidated sands where sand mobilization can be easily triggered and sand influx sustained for the productive life of the well. Because of several unique characteristics of unconsolidated heavy oil reservoirs, well productivity may be 10 – 20 times higher in CHOPS wells than predicted by conventional Darcy's law flow equations(4). The mechanisms responsible for the enhanced production rate in CHOPS are(5):Porosity and permeability are enhanced as sand is removed from the formation, along with any mechanical skin that may have developed;The oil flow velocity relative to fixed coordinates is increased if the matrix is partially mobilized. Therefore, production rate increases, as predicted from Darcy's law;

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.834
Threshold uncertainty score0.983

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.004
GPT teacher head0.178
Teacher spread0.174 · 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 designNot applicable
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

Citations33
Published2007
Admission routes2
Has abstractyes

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