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Record W4253288028 · doi:10.2118/2005-111

Chemically Assisted Ignition Technologies for a Light Oil Air Injection Process

2005· article· en· W4253288028 on OpenAlexaff
Jingrui Li, S.A. Mehta, R.G. Moore, M.G. Ursenbach, E. Zalewski, K. Van Fraassen

Bibliographic record

VenueCanadian International Petroleum Conference · 2005
Typearticle
Languageen
FieldChemistry
TopicSpectroscopy and Chemometric Analyses
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsIgnition systemProcess (computing)Process engineeringSecondary air injectionMaterials scienceEnvironmental scienceAutomotive engineeringPetroleum engineeringComputer scienceEngineeringAerospace engineeringOperating system

Abstract

fetched live from OpenAlex

Abstract Chemically assisted ignition technologies for a light oil(base oil) with a density 876.4kg/m3 (API 30 °) were investigated to increase the effectiveness of ignition of this oil in an air injection process. Several experiments were performed using a Pressurized Differential Scanning Calorimetry (PDSC) thermal analysis technique and an Accelerating Rate Calorimetry (ARC) Tests to examine the relative effects of catalysts and initiators (chemical additives). In the PDSC and ARC tests, the mixtures were subjected to a controlled heating schedule under a constant flow rate of air at 4.14 MPa (600 psig) and 13.8 MPa (2000 psig) pressure respectively. The heat released by the oxidation reactions as a function of temperature was analyzed. In the presence of the metallic catalyst and chemical initiators, the oxidation behavior of the tested oil was dramatically improved. A significant reduction in both the onset temperature of the exotherm of the base oil in the low temperature regions and in the activation energy for the low temperature oxidation reactions was achieved using the chemical additives. For low temperature reservoirs, chemically assisted ignition involving a slug or slugs of chemical additive slug injections has potential for application in air injection based improved oil recovery process. Introduction Air injection has proven itself as a viable process inimproving oil recovery from several light oil reservoirs, and as a result, it has received much interest in recent years [1,2]. When air is injected into a light oil reservoir, exothermic chemical reactions occur between the reservoir oil and oxygen contained in the injected air. The desired reactions are those resulting in heat generation and the production of carbon dioxide. Downstream of the reaction zone, the combustion product gas which is comprised of CO2, CO, N2 combined with the vaporized light hydrocarbon fractions and hot water sweep the oil toward production wells. Air injection for light oil reservoir is a complex process involving simultaneous heat and mass transfer in a multiphase environment coupled with oxidation chemical reactions [3]. Ignition is the first phase of this process [4]. A satisfactory ignition is of prime importance in initiating a successful air injection process [3]. In high temperature reservoirs, the air injection process is initiated by injecting air, which will spontaneously ignite the oil in place[1]. However, in some situations where spontaneous ignition of the reservoir oil is not likely to occur, several artificial means have been implemented. These include external heat injection into the near-wellbore region using downhole electrical heaters and hot gasesgenerated by a gas burner, or the injection of a slug of steam. Artificial ignition is commonly applied in heavy oil reservoirs, but it is highly desirable to avoid having to run heaters or burners when air injection is to be applied in deep, high pressure reservoirs. An excellent historical review of ignition methods is provided by Sstrange et. al[3]. Chemical ignition, which involves the injection of a slug or slugs containing chemicals which have desirable oxidation characteristics into the air injection wells prior to the injection of air.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.272
Threshold uncertainty score1.000

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)0.0010.000

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 head0.017
GPT teacher head0.267
Teacher spread0.250 · 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.

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

Citations7
Published2005
Admission routes1
Has abstractyes

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