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Record W2478881845 · doi:10.2118/08-07-25

Chemically Assisted Ignition Technologies for a Light Oil Air Injection Process

2008· article· en· W2478881845 on OpenAlexaff
J. Li, S. A. Mehta, R.G. Moore, M.G. Ursenbach, E. Zalewski, K. Van Frassen

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2008
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsSecondary air injectionLight crude oilExothermic reactionChemistrySteam injectionCombustionChemical reactionChemical engineeringIgnition systemCatalysisEnhanced oil recoveryPetroleum engineeringWaste managementOrganic chemistryThermodynamics

Abstract

fetched live from OpenAlex

Abstract A light oil (API 30 º) reservoir is an excellent candidate for high pressure air injection, but the oil is not believed to be capable of self-ignition at the reservoir temperature. Several chemical additives and catalysts are studied to evaluate their effectiveness of ignition improvement for this light oil sample. Pressurized Differential Scanning Calorimetry (PDSC) and Accelerating Rate Calorimetry (ARC) experiments are examined in this study. The oil sample, which is mixed with certain catalysts and chemical additives, is subjected to a controlled heating schedule under a constant flow rate of air at 4.14 MPa (600 psig) and 13.8 MPa (2,000 psig) pressure for the PDSC and ARC tests, respectively. The amount and rate of heat released by the oxidation reactions is analyzed for those tests. In the presence of a metallic catalyst and chemical initiators, oxidation behaviour of the oil tested is dramatically improved. Also observed are a significant reduction in the onset temperature of significant exotherm and an increased rate for the release of heat. Introduction Air injection has been proven as a viable process in improving oil recovery from light oil reservoirs, and as a result, it has received much interest in recent years(1, 2). The concept of recovery increment is when air is injected into a light oil reservoir and exothermic chemical reactions occur. The desired reactions result in heat generation and the production of carbon dioxide. Downstream of the reaction zone, the combustion produced gas sweeps oil toward the production wells, combining with light hydrocarbon fractions vapourized by heat released from oxidation reactions. Therefore, incremental oil production is achieved. However, air injection for a light oil reservoir is a complex process involving simultaneous heat and mass transfer in a multiphase environment coupled with oxidation chemical reactions. Ignition is the first phase of this process and a satisfactory ignition is of prime importance in initiating a successful air injection process(2). In high temperature reservoirs, the air injection process is initiated by injecting air, which may spontaneously ignite the oil-in-place(1). However, in some cases, spontaneous ignition of the reservoir oil is not likely to occur so that several artificial means have been implemented(3), including down hole electrical heaters, a gas burner or injection of steam, 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. As a result, chemical ignition is proposed(2). The concept of chemical ignition is where a slug of chemicals with reactive oxidation characteristics is injected into an oil bearing zone prior to the injection of air from an injector. If heat released from an oxidation reaction is continually generated at a rate greater than it is dissipated, starting at the native reservoir temperature, oil can be spontaneously ignited without the application of artificial means. The reactive nature of the base oil present in the ignition zone can be enhanced or stimulated. A spontaneous ignition may occur within the formation. Bednarski(4) reported on a chemical ignition improvement 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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.335
Threshold uncertainty score0.949

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.011
GPT teacher head0.227
Teacher spread0.216 · 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

Citations11
Published2008
Admission routes1
Has abstractyes

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