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Record W1966653694 · doi:10.2118/2007-055

Do Heavy and Medium Oil Waterfloods Differ?

2007· article· en· W1966653694 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueCanadian International Petroleum Conference · 2007
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsSaskatchewan Research Council (Canada)
FundersPetroleum Technology Research Centre
KeywordsCitationPetroleumPetroleum engineeringPetroleum industryComputer scienceEnvironmental scienceLibrary scienceEngineeringGeologyEnvironmental engineering

Abstract

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Abstract To identify the parameters which impact heavy oil waterflood success, we collected production, reservoir, and operating data for 83 western Canadian waterfloods. The waterfloods were classified as either heavy or medium, and separate multivariate analysis models were built for each set. The differences between waterflooding of heavy oils and their medium oil counterparts were substantial and revealing: In terms of operational parameters, incorporating horizontal and directional wells, both producers and injectors, was significantly important to the success of heavy oil waterfloods, but insignificant for medium oils. The two most important reservoir parameters affecting the success of waterflooding medium oils permeability and heterogeneity were insignificant for heavy oils. Introduction Waterflooding is the most common method of enhancing oil production, and is becoming increasingly important in recovering heavy oil. Of the 5201 million m3 of heavy oil in place in Alberta and Saskatchewan, 207 waterflood operations (including 8 abandoned waterfloods) recover more than 24% of that oil in place. Some of the western Canadian heavy oil waterfloods were highly successful, recovering as much as seven times the primary recovery. Other waterfloods fared less well ? eight waterfloods were abandoned in the Saskatchewan Lloydminster region. Despite its prevalence, little is known about how waterflooding heavy oils differs from waterflooding their lighter oil counterparts. There is a substantial body of work on designing, monitoring, and managing waterfloods: however, the problems specific to producing heavy oil by waterflooding are rarely addressed. Some exceptions include five case studies of heavy oil waterfloods, including Forth et al.'s statistical study identifying important parameters in the Golden Lake waterflood.1-5 Two more general studies were Smith's paper on mechanistic aspects of heavy oil waterflooding and Miller's review of the state of the art of waterflooding technology as applied to western Canadian heavy oils.6,7 Miller discussed performance prediction and problems, and offered recommendations to improve performance. In contrast to the case studies, the Saskatchewan Research Council (SRC) performed two statistical studies on a group of heavy oil waterfloods.8,9 Univariate and multivariate analysis were used to highlight the broad themes common to heavy oil waterfloods. Such an approach requires a numerical value corresponding to success, and the SRC has now tested seven such measures. Other authors have also used statistics to assess waterfloods, albeit those producing lighter oils than the western Canadian sites examined in this study. The data mining study of Weiss et al. tested the ratio of secondary production to primary production to analyze Nebraska waterfloods.10 Wu et al. unsuccessfully tried to correlate reservoir parameters with the recovery of 24 west Texas waterfloods.11 McLachlan and Ershagi equated the efficiency of waterfloods to cumulative water/oil ratio.12 Methodology The reservoir data were obtained from documents published by the two provincial regulatory bodies: Saskatchewan Industry and Resources' Reservoir Annual 2002, and Alberta's Energy and Utilities Board (EUB) 2003 Statistical Series.13,14 The production data were obtained from Accumap™.15 Many waterfloods were excluded from the study. We wanted to attribute differences in recovery to the effects of waterflooding, and so excluded operations which had previously used other enhanced oil rec

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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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.223
Threshold uncertainty score0.589

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.018
GPT teacher head0.254
Teacher spread0.236 · 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