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Record W4239060778 · doi:10.2118/2006-086

Observations of Heavy Oil Primary Production Mechanisms From Long Core Depletion Experiments

2006· article· en· W4239060778 on OpenAlexafffund
N.N. Goodarzi, A. Kantzas

Bibliographic record

VenueCanadian International Petroleum Conference · 2006
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsUniversity of Calgary
FundersCanada Research ChairsPorous Media Laboratory
KeywordsProduction (economics)Primary (astronomy)Core (optical fiber)Environmental scienceComputer sciencePhysicsTelecommunicationsEconomics

Abstract

fetched live from OpenAlex

Abstract Foamy oil solution gas drive mechanisms are complex and our knowledge and understanding is limited despite the extensive studies in the literature. In order to advance our understanding of heavy oil solution gas drive mechanisms, long core depletion experiments were designed. These experiments were performed on sand-filled or glass beadsfilled tubes that are x-ray transparent and have pressure transducers along their length. The novelty of the experiments is the length (of over 18 m) that they extend and the duration of the experimental runs. The results of the longer experiments should be able to provide data that bridge the gap between the field scale and the shorter laboratory experiments that have been performed in the past. Thus production, pressure transient, and saturation data are presented in this "extended" scale. In addition, CT scanner images are expected to provide information about the evolution of gas. Introduction Sand production increases the permeability of the unconsolidated sand reservoirs through the establishment of wormholes. These higher permeability regions in combination with heavy oil solution gas drive, recover heavy oil through primary production (CHOP). A better understanding of the fluid-rock interaction can be established by studying the effect of permeability and geometry of the experiments. Bubble growth in a porous medium is initially controlled by the geometry of the pores, the pore walls and capillary forces(1). Dumore(2) compared two different permeability sand packs and saw that the gas remained dispersed for longer in the high-permeability sand pack. Wall and Khurana(3) observed that lower permeability cores resulted in higher gas saturation within the core. Therefore, they suggested that the free gas saturation depends on capillarity. Sarma and Maini(4) found that although higher production was obtained with a higher permeability core, the general trend for pressure and production as a function of time were the same as the lower permeability core. Firoozabadi et al. (5) observed lower supersaturations and lower critical gas saturation were obtained from lower permeability depletion experiments. Tang et al. (6) saw that poorly packed areas had higher gas saturation as a result of lower capillary forces in higher porosity areas. In higher permeability porous media, trapping due to capillary forces is lower as a result of larger pore sizes. Therefore, the flow of the fluid is less hindered, giving the newly nucleated gas less time to grow within the pores before it begins to move with the oil. This causes the gas to remain dispersed within the oil for longer time before the gas coalesces, compared to lower permeability sand packs. High depletion rates are necessary when field observations of heavy oil solution gas drive are reproduced in the laboratory. As a result there have been numerous investigations in the literature that study the effect of the depletion rate(7–10). High depletion rates are considered representative of near wellbore behavior while low depletion rates represent field conditions. By increasing the length of the sandpack to a much larger scale it should be possible to capture the pressure, saturation and production behavior both near and further from the well bore in a single experiment.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.200
Threshold uncertainty score0.988

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 head0.039
GPT teacher head0.253
Teacher spread0.214 · 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 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

Citations3
Published2006
Admission routes2
Has abstractyes

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