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Record W2077033726 · doi:10.2118/09-02-22

Cold Flow: A Multi-Well Cold Production (CHOPS) Model

2009· article· en· W2077033726 on OpenAlexafffundabout
B. Tremblay

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2009
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsSaskatchewan Research Council (Canada)
FundersPetroleum Technology Research Centre
KeywordsAcreBarrel (horology)Dispersion (optics)Environmental scienceOil productionGas oil ratioRange (aeronautics)Petroleum engineeringHydrology (agriculture)GeologyGeotechnical engineeringEngineeringGeographyArchaeology

Abstract

fetched live from OpenAlex

Abstract A multi-well cold production (CHOPS) model, developed at the Saskatchewan Research Council, was used in conjunction with a commercial reservoir simulator, to predict the effect of well spacing and in-filling on oil recovery in cold production. The oil and sand production from the Lindbergh/Frog Lake cold production wells was history matched initially. The comparative economics of five different well spacings (10 acre, 20 acre, 40 acre, 60 acre and 80 acre) was estimated. The 20 acre spacing was the most economic overall for heavy oil prices varying between C$251/m3 (C$40/barrel) and C$502/m3 (C$80/barrel). The multi-well model was used to estimate the additional oil recovery from in-fill wells at different in-fill times. In-filling a 40 acre spacing well with 10 acre spacing wells was not economic for a Lindbergh/Frog Lake type of reservoir. In-filling the same well with 20 acre spacing wells was economic at higher oil prices in the C$502/m3 range (C$80/barrel). Introduction The main mechanisms which contribute to the success of the cold production process are solution gas drive and sand production. Maini(1) argued that the greater oil recovery for heavy oil compared to light oil was due to the formation of a gas in oil dispersion which he called "foamy oil." This dispersion would have a greater compressibility which would maintain the reservoir pressure for longer times. Firoozabadi(2), based on a series of solution gas drive experiments performed at reservoir-type oil velocities, attributes the greater oil recovery for heavy oil to a higher critical gas saturation and to reduced gas flow leading to reservoir pressure maintenance. Three basic scenarios were proposed in the literature to explain how sand production leads to greater oil recovery:a limited dilated sand region around the wellbore(3),a dilated sand region around the wellbore with wormholes extending into the formation(4) andonly wormholes extending out into the formation(5, 6). The third scenario can best explain the rapid (within a few hours) travel time of a fluorescein tracer dye between an injector and a producer, observed by Amoco in a tracer test(5). Since the concentration of this dye, which is known to adsorb on the surface of a porous medium, did not change at the producer, Squires(5) concluded that an open (sand-free) channel connected the injector and producer. This observation contradicts scenarios 1 and 2 since these scenarios preclude the existence of an open channel throughout the length of the wormholes. Field engineers have observed that they sometimes lose fluid circulation while drilling into cold produced reservoirs. The location at which fluid loss occurs can help in mapping the wormhole network in the field. For example, when Nexen Inc. drilled two horizontal wells in a field after cold production, they observed lost circulation to neighbouring wells at the locations indicated by the ? marks in Figure 1. These 40 acre spacing cold production wells had produced large quantities of sand.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.113
Threshold uncertainty score0.226

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.013
GPT teacher head0.232
Teacher spread0.219 · 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 designSimulation or modeling
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

Citations30
Published2009
Admission routes3
Has abstractyes

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