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Record W2062926081 · doi:10.2118/02-10-03

Evaluation of the Miscibility and Contribution of Flue Gas to Oil Recovery Under High Pressure Air Injection

2002· article· en· W2062926081 on OpenAlexafffund
O. S. Shokoya, S. A. Mehta, R.G. Moore, Brij Maini, M. Pooladi‐Darvish, Anurug Chakma

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2002
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsUniversity of WaterlooUniversity of Calgary
FundersUniversity of British ColumbiaUniversity of Alberta
KeywordsFlue gasSecondary air injectionPetroleum engineeringEnhanced oil recoveryGas oil ratioLight crude oilMiscibilityChemistryEnvironmental scienceMaterials scienceWaste managementGeologyComposite materialEngineeringOrganic chemistry

Abstract

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Abstract The improvement in the recovery of light oil by high pressure air injection (HPAI) involves a combination of complex processes, each contributing to the overall recovery. One of these processes is the spontaneous ignition of the air-oil mixture with complete oxygen utilization. This process generates flue gases, which are in contact with the reservoir oil at the displacement front. An experimental study was carried out to investigate the mechanism and contribution of miscible displacement, by in situ generated flue gases, to the recovery of light oil in reservoirs undergoing HPAI. The flue gas displacements were carried out on recombined reservoir oil in a slim tube apparatus at a reservoir temperature of 116 º C and pressures ranging from 27.77 MPa (4,028 psi) to 46.06 MPa (6,680 psi). Results show that miscibility could not be achieved between the test oil and flue gases under the test conditions. Experiments conducted between 41.28 MPa (5,987 psi) and 45.04 MPa (6,532 psi), however, gave an indication of near-miscible displacement of the test oil. The flue gases displaced the oil in a forward contacting extraction process, resembling a combined vapourizing/condensing multi-contact gas drive mechanism. The relatively high recovery, high extraction of oil components, and the pattern of flow behind the displacement front, exhibited at high pressures, demonstrate that near-miscible displacement by in situ generated flue gases could significantly contribute to oil recovery in light oil reservoirs undergoing HPAI. Introduction Air injection into high pressure reservoirs is an emerging technology for the enhanced recovery of light oils. It is probably the best hope for improved recovery from the world's ever declining reserves of conventional oil and, in particular, the profitable enhanced recovery of the enormous quantities of residual oil trapped in depleted and mature waterflooded light oil reservoirs(1, 2). Air is the most inexpensive, available gas that can be used to accelerate oil recovery. Air injection is especially applicable in low porosity and low permeability reservoirs where water injectivity is extremely low(3). Apart from providing a better interfacial tension (IFT) response(4), air injectivity is about ten times that of water in terms of reservoir volume(5), making air injection more advantageous than water injection in deep, tight, high pressure reservoirs. A number of successful high pressure air injection projects in light oil reservoirs have been documented in the literature(5–11). Most of these projects have been operating for many years, attesting to their technical and economic success. The improvement in recovery of light oil by HPAI involves a combination of complex processes, each contributing to the overall recovery. These processes include: reservoir pressurization, oil swelling, immiscible gas displacement, and superextraction when operating above the critical point of water. In addition to this, spontaneous oil ignition with complete oxygen utilization(2, 12, 13) and near-miscibility/miscibility of the in situ generated flue gases with the reservoir oil(13) are possible and advantageous processes. Apart from reservoir pressurization, oil swelling, and immiscible gas displacement, the mechanism by which the other processes occur in the reservoir and their contribution to oil recovery have not been discussed in clear terms in the technical literature(14).

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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)0.001
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 score0.615
Threshold uncertainty score0.970

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.009
GPT teacher head0.207
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

Citations16
Published2002
Admission routes2
Has abstractyes

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