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Record W4233010869 · doi:10.2118/2007-189

Numerical Simulation of In Situ Combustion Experiments Operated under Low Temperature Conditions

2007· article· en· W4233010869 on OpenAlexafffund
B. Sequera, R.G. Moore, S.A. Mehta, M.G. Ursenback

Bibliographic record

VenueCanadian International Petroleum Conference · 2007
Typearticle
Languageen
FieldEngineering
TopicCombustion and flame dynamics
Canadian institutionsUniversity of Calgary
FundersUniversity of CalgaryU.S. Department of Energy
KeywordsIn situCombustionMaterials scienceComputer simulationEnvironmental scienceComputer scienceNuclear engineeringProcess engineeringAutomotive engineeringSimulationChemistryEngineeringPhysicsMeteorology

Abstract

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Abstract During In-Situ Combustion (ISC) processes, different chemical reactions occur depending on the temperature level. In heavy oils and bitumens Low Temperature Oxidation (LTO) reactions dominate below 300 °C, increasing the density and viscosity and producing coke which could prevent the success of ISC. Above 350 °C, combustion reactions dominate, known as High Temperature Oxidation (HTO), producing carbon oxides and water. Numerical models tend to include only thermal cracking and HTO reactions, as LTO reactions are not well understood. In the present work, ISC experiments operated under LTO were simulated, using Saturates, Aromatics, Resins and Asphaltenes (SARA) fractions to characterize the Athabasca bitumen. Concentration profiles and coke deposition for individual temperatures were matched for isothermal experiments from 60 °C to 150 °C. Based on these results, Ramped Temperature Oxidation (RTO) experiments were then modelled, incorporating the heat of reaction at LTO. Different reaction models were studied to match temperature profiles along the reactor, oxygen consumption, coke formation and fluids production. This research will greatly increase the understanding of LTO reactions occurring in Athabasca bitumen during ISC and contribute to the creation of a reliable numerical model that predicts ISC performance under ideal (HTO) and, importantly, non-ideal (LTO) temperature conditions. Introduction In Situ Combustion (ISC) is a promising but complex oil recovery process in which thermal energy is generated inside the reservoir due to combustion reactions between the heaviest fractions of the oil and an injected oxygen containing gas. For heavy oils and bitumens, ignition temperatures above 350 °C are required to promote the combustion reactions (HTO). At lower temperatures other types of reactions predominate, involving the addition of oxygen to the bitumen, producing heavier oxidized compounds. The low temperature oxidation (LTO) reactions are detrimental to oil production hence ISC processes are designed to operate under the high temperature combustion regime (HTO). However, LTO reactions occur if the air flux becomes too low to sustain the combustion reactions, leading to lower than estimated production yields. It has been proven in laboratory experiments that oil recovery is considerably reduced when Low Temperature Oxidation reactions occur to some extent, compromising the success of the ISC. Even though the adverse effect that LTO reactions could have on ISC processes has been shown by different authors [1–3] most numerical simulations of ISC still exclude them [4–6]. Generally, numerical simulations rely only on the high temperature reactions consisting of deposition of coke due to thermal cracking and coke combustion, which leads to unrealistic predictions. The complexity of LTO reactions and the lack of a thorough understanding of their effect on ISC is the main reason for their exclusion in numerical models. Despite all the efforts of studying the kinetics of LTO, there is still a lack of comprehensive kinetic models able to represent the main effect of these reactions on ISC processes. Belgrave et al.[7] were one of the first to include LTO reactions in ISC numerical models.

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 score0.071
Threshold uncertainty score0.983

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 head0.014
GPT teacher head0.259
Teacher spread0.246 · 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

Citations2
Published2007
Admission routes2
Has abstractyes

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