MétaCan
Menu
Back to cohort
Record W2019504247 · doi:10.2118/01-03-01

Modelling Cold Production for Heavy Oil Reservoirs

2001· article· en· W2019504247 on OpenAlexaboutno aff
E.S. Denbina, Richard Baker, G.G. Gegunde, A.J. Klesken, S.F. Sodero

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2001
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsnot available
Fundersnot available
KeywordsPetroleum engineeringProduction (economics)ProductivityOil productionProduction rateEnhanced oil recoveryEnvironmental scienceProcess (computing)Lead (geology)WellboreReservoir engineeringPetroleum reservoirPetroleumGeologyProcess engineeringEngineeringComputer science

Abstract

fetched live from OpenAlex

Abstract The term "Cold Production" refers to the use of operating techniques and specialized pumping equipment to aggressively produce heavy oil reservoirs. This encourages the associated production of large quantities of the unconsolidated reservoir sand, creating a modified wellbore geometry that could include "wormholes", dilated zones, or possibly cavities. As well, produced oil in the form of an oil continuous foam resembling chocolate mousse, suggests a foamy solution gas drive occurs in situ. This leads to anomalously high oil productivity and recovery because free gas stays entrained in the foam, thereby sustaining reservoir pressure. In a recent paper(1), the mechanisms that lead to this increased productivity were outlined and the suitable reservoir types conducive to cold production techniques were identified. In this paper, these mechanistic concepts are extended to practical, intuitive modelling techniques that can be applied to existing "black oil" reservoir simulators by appropriate alterations to the input data. Importantly, these techniques have beenound to match actual cold production behaviour in applicable Western Canadian conventional heavy oil reservoirs. With a history matched model, these techniques can be used to extend the cold production scenario into the future, providing better estimates of ultimate recovery. As well, sensitivities to the process can be investigated, including exploring sensitivities to various reservoir and operating parameters (e.g., reservoir pressure, production rate strategies) and examining the impact of a preceding cold production primary depletion on subsequent secondary and tertiary recovery processes. Introduction In approximately the last ten years, many authors have written about the phenomena involved in producing heavy oil by solution gas drive. Their work has been inspired by field observations of cold production in some of the heavy oil reservoirs in Canada and enezuela, where unexpectedly high oil rates and recoveries, as well as low gas-oil ratios, have been attained. This work has included laboratory investigations of fluid and rock properties (including geomechanical studies of the so-called wormholingeffects), conceptual postulation of mechanisms in the context of actual field behaviour, as well as some attempt to mathematically capture and numerically model these mechanisms. Perhaps one of the first to set forth the mechanisms and possible mathematics was Smith(2) at Husky, who also appears to be one of the first investigators to note that the anomalous production enhancement must arise from a combination of geomechanical and fluid effects (i.e., results cannot be "excused as high permeability channels resulting from sand production"). These two categories of mechanisms"geomechanical effects and fluid effects?are the subject of the mechanisms proposed in the literature for explaining the cold productionperformance of heavy oil reservoirs. Geomechanical Effects Productivity of heavy oil wells experiencing cold production is typically much higher than would be expected"actual productivity exceeds radial Darcy flow predictions (using typical oil viscosities and air permeabilities) by factors of four to 10.

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)0.001
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 score0.160
Threshold uncertainty score0.319

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.022
GPT teacher head0.243
Teacher spread0.220 · 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

Citations12
Published2001
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Canadian Petroleum TechnologySame topicReservoir Engineering and Simulation MethodsFrench-language works237,207