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Record W1992384907 · doi:10.2118/09-03-36

Effect of Foaminess on the Performance of Solution Gas Drive in Heavy Oil Reservoirs

2009· article· en· W1992384907 on OpenAlexafffundabout
Ahmed Alshmakhy, Brij Maini

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2009
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPetroleum engineeringGas oil ratioFossil fuelEnvironmental scienceEnhanced oil recoveryWork (physics)Oil reservesWaste managementChemistryPetroleumGeologyEngineering

Abstract

fetched live from OpenAlex

Abstract Some heavy oil reservoirs under solution gas drive show abnormally high final recoveries. One of the mechanisms to explain these phenomena is the foamy oil flow effect which occurs under certain operating conditions. It has been studied extensively, yet remains poorly understood and difficult to model. The objective of this work was to investigate the effect of oil foaminess on the performance of solution gas drive in heavy oil reservoirs. In this research, the first step was to find a foaming agent that will have a measurable effect on foam stability of a viscous mineral oil. A simple experimental procedure was developed to quantify the oil foaminess in the presence of an added foaming agent. Several depletion tests were conducted with the added foaming agent at different depletion rates using a two metre long sandpack. The experimental results showed that the increased foaminess of oil did not have a significant effect on the solution gas drive performance when the depletion rate was high. However, in a slow depletion test, the effect of oil foaminess was significant. Introduction With high oil prices and the continuous decline of conventional resources, attention is shifting towards heavy oil in many parts of the world. Six to nine trillion barrels, or more than two-thirds of the world's oil resources, are heavy viscous crudes that remain difficult to produce(1). Heavy oil promises to play a major role in the future of the oil industry. Therefore, understanding heavy oil behaviour and improving the recoveries in heavy oil reservoirs is crucial to meeting future energy demand. The high viscosity of heavy oils, typically in the range of 500 to 50,000 cP, results in low recovery factors in primary production. However, some Canadian heavy oil reservoirs produce more than what is expected by the conventional analogs. Primary recovery from these reservoirs could be as high as 15%(2). In conventional solution gas drive, the gas evolves in the pore space and connects with the gas in the other pores forming a free continuous gas resulting in higher gas rates. In heavy oil reservoirs, the gas bubbles tend to remain dispersed within the viscous oil because of the high viscosity, low diffusion rates and higher pressure gradients. This behaviour results in higher oil rates, lower gas-oil ratios and slower pressure decline within the reservoir. The production from this type of reservoir is usually accompanied by sand. The two-phase flow of oil and dispersed gas bubbles is usually referred to as foamy oil flow. Smith(3) appears to be the first researcher who provided an analysis to the anomalous behaviour of the heavy oil reservoirs under solution gas drive using field data. The most common techniques used to produce heavy oil from underground formations involve thermal recovery processes. However, extensive developments in Canada in the period from 1985 to 2005 have resulted in several new heavy oil exploitation technologies. One of the major new technologies in the last two decades is cold heavy oil production with sand (CHOPS)(4).

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.619
Threshold uncertainty score0.760

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.202
Teacher spread0.198 · 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 designBench or experimental
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

Citations13
Published2009
Admission routes3
Has abstractyes

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