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Record W6992922773

Microwave-assisted in-situ catalytic upgrading of heavy oil

2023· other· en· W6992922773 on OpenAlexaboutno aff

Bibliographic record

VenueNottingham ePrints (University of Nottingham) · 2023
Typeother
Languageen
FieldPsychology
TopicMental Health via Writing
Canadian institutionsnot available
Fundersnot available
KeywordsScale (ratio)CombustionOil fieldField (mathematics)Process (computing)Work (physics)Matching (statistics)Full scale
DOInot available

Abstract

fetched live from OpenAlex

This work concerns reservoir simulation of heavy oil recovery and experimental studies of downhole oil catalytic upgrading enhanced with microwaves. Numerical simulations are a key tool to investigate the Toe to Heel Air Injection (THAI) operating mechanism due to the limitations in experimental (3D combustion cell) THAI investigations. In this study, the THAI process was numerically studied through reservoir simulations. The starting point of the numerical simulation study was to develop a new experimental scale THAI model. The new experimental scale THAI model was history-matched against previous 3D combustion tube experiments. The experimental scale THAI model was then scaled up to a field scale model. The field scale THAI models were then used to study two types of THAI well arrangements, namely the direct line drive (DLD) and staggered line drive (SLD). The DLD and SLD well arrangements were compared in terms of sweep efficiency, water cut, and oxygen utilisation. Although, the DLD well arrangement resulted in earlier oil production, it was shown that, in terms of overall performance, the SLD well arrangement is superior to the DLD well arrangement. There are only a limited number of published field scale THAI models in the literature. None of the previously developed field scale THAI models, that considered an underlying bottom water (BW) in the reservoir, was validated against field data. Ab initio field-scale model development using a direct history matching of a field-scale THAI process with BW has never been published by other researchers. This was due to insufficient published data concerning the oil production trends experienced in the field. It was also hypothesised (from former numerical studies) that reducing the air injection rate will lead to lower oil production rates. This work, for the first time, presents a field-scale THAI model that was validated against data obtained from the Kerrobert THAI project in Canada. The model includes the existing relatively thick bottom water in the studied formation. A real life quasi-staggered line drive (QSLD) THAI well configuration was studied through the history-matched model. The history matched model was also used to investigate the effect of the variation in air injection rates on THAI performance in presence of BW. It was demonstrated that extra air ingress from the neighbouring THAI well pair had caused a reduction in oxygen utilisation throughout the process. As a result, the simulated temperature profile declined with an increasing combustion time. An inversely proportional relationship was detected between the coke concentration and the oxygen profile around the HP well. It was found that the size of the steam zone, ahead of the combustion front, differs with variation in air injection rates. It was observed that some of the mobilised oil sank into the BW, leaving a significant amount of oil trapped in the reservoir. To prevent such an event, the location of the HP well was altered as a potential strategy to optimise the THAI efficiency. Consequently, the predicted oxygen utilisation, as well as cumulative oil production, were improved in comparison with the history-matched model. The CAtalytic upgrading PRocess In situ (CAPRI) is an add-on to the THAI process. The horizontal section of the producer well is surrounded by a catalyst layer in the CAPRI process. Further upgrading occurs as the THAI oil flows through the catalyst layer. The history-matched THAI simulation, developed in this study, showed that the in-situ combustion alone may not provide sufficient heating for downhole catalytic upgrading of heavy oil in the THAI-CAPRI process. Therefore, an extra heating source is required for a successful THAI-CAPRI process. As a strategy to provide the needed heating, a microwave heating technique has been proposed in this study. It was demonstrated that heavy oil can be heated directly with microwaves to 425°C, which is the temperature needed for successful catalytic upgrading. Contrary to previous assertions, the microwave heating of heavy oil was done without the need for an additional microwave susceptor. Two commercially available hydrodesulphurization (HDS) and low temperature shift (LTS) catalysts as well as Alumina support, were each used for microwave-assisted catalytic upgrading experiments. The characterisation of the treated oil revealed a considerable upgrading performance in terms of API gravity, reduction in viscosity, and reduction in sulphur content. The characteristics of the THAI process are suitable not only for in-situ upgrading but for hydrogen production downhole. Employing the catalytic add-on of the THAI process (the CAPRI process) could potentially result in even higher in-situ hydrogen production. In this study, a series of microwave-assisted catalytic upgrading experiments were carried out to investigate the hydrogen production potential of the CAPRI process. Two commercial hydrodesulphurization and low shift temperature catalysts were used for the microwave-assisted catalytic hydrogen production experiments. A significant hydrogen content was detected in the gas product using the HDS and LTS catalyst. The produced hydrogen was originated from the catalytic dehydrogenation of heavy oil. The produced hydrogen in-situ within the THAI-CAPRI process can be used to facilitate hydrodesulphurization as well as improving API gravity upgrading by hydrogenation of the treated oil.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.697
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.043
GPT teacher head0.301
Teacher spread0.258 · 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; both teacher heads agree on what is shown here.

Study designOther design
Domainnot available
GenreOther

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
Published2023
Admission routes1
Has abstractyes

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