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Record W4395008413 · doi:10.2118/218175-ms

Experimental Investigation of Air Flux Impact On Reactions Occurring During In-Situ Combustion in Dolomite Reservoirs - Implications for Air Injection Strategies

2024· article· en· W4395008413 on OpenAlexaff
Rita Fazlyeva, Reza Fassihi, D. G. Mallory, R.G. Moore, M.G. Ursenbach, S. A. Mehta, Аlexey Cheremisin

Bibliographic record

VenueSPE Improved Oil Recovery Conference · 2024
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsDolomiteCombustionFlux (metallurgy)In situPetroleum engineeringEnvironmental scienceMaterials scienceGeologyMechanicsMineralogyChemistryMeteorologyMetallurgyPhysicsPhysical chemistry

Abstract

fetched live from OpenAlex

Abstract One of the key undertakings during the energy transition is the assurance of process efficiency in oil and gas operations. By streamlining and optimizing different aspects of production operations, the overall carbon footprint can be reduced. Newly obtained laboratory data on the air injection process could potentially help with making this process more efficient. Historically, the transition from low-temperature range (LTR) to high-temperature range (HTR) during heavy oil in-situ combustion (ISC) has been attributed solely to oil characteristics. However, our research challenges this conventional perspective, underscoring the pivotal role of air flux rates in governing these reaction regime shifts. This study aims to deepen our understanding of the thermal behavior of heavy oil within dolomite reservoirs during ISC. It also shows how to integrate the calorimetry tools to obtain new information on this process. Multiple tests were conducted at a reservoir pressure of 1,740 psig (13 MPag), involving variations in the initial mass of oil and dolomite samples, as well as air injection rates. We utilized both the Calvet C600 and Accelerated Rate Calorimeters (ARC). These units were equipped with mass flow controllers (MFCs) to ensure precise air supply, effluent gas analyzers for product gas component analysis, and wet test meters (WTMs) for measuring produced gas volume. Post-test mass differentials of samples were analyzed extensively. Calvet C600 data demonstrated that the rate of air injection significantly impacts the mode of oxidation and combustion reactions. High air injection rates seem to primarily induce LTR, which is unfavorable for field operations. This observation is reinforced by consistent gas analysis results, showing lower oxygen conversion to CO2 and CO, reduced oxygen utilization, and increased oxygen consumption during low-temperature oxidation (LTO) and water formation reaction in the LTR regime. Conversely, lower air injection rates seem to lead to a shift toward HTR reactions. Cross plots of oxygen uptake versus heat release further confirm these trends, with ARC tests yielding values of 8,000 to 13,000 J/g of oxygen uptake, compared to 13,000 to 16,000 J/g in the Calvet C600 tests. Our innovative approach allows for a comprehensive comparative analysis and result validation between ARC and Calvet C600. We were able to expand the range of applicability of reaction kinetic parameters to optimize combustion processes and ensure safety measures. Our findings also suggest the need to incorporate a mass transfer coefficient into reaction schemes to better model oxygen uptake rates at varying air fluxes. This coefficient should depend on the oxygen uptake rate at different temperatures. The new application of Calvet C600 and ARC in tandem offers a robust data-gathering approach for the in-situ combustion process. Our findings challenge traditional notions of the use of high air flux and emphasize the significance of a proper air flux during the initial phase of a new air injection project and its variation as the project expands.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.850

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 communication0.0000.001
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 head0.026
GPT teacher head0.303
Teacher spread0.276 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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