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Record W4236512771 · doi:10.2118/2001-017

3-D Physical Model Studies of Downhole Catalytic Upgrading of Wolf Lake Heavy Oil Using THAI

2001· article· en· W4236512771 on OpenAlexaboutno aff
T.X. Xia, M. Greaves

Bibliographic record

VenueCanadian International Petroleum Conference · 2001
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsnot available
FundersEngineering and Physical Sciences Research Council
KeywordsPetroleum engineeringCatalysisEnvironmental scienceGeologyChemistry

Abstract

fetched live from OpenAlex

Abstract THAI - 'Toe-to Heel Air Injection' is an integrated horizontal wells- in situ process for the recovery and upgrading of heavy crude oil. In addition to thermal upgrading, further upgrading can be achieved by emplacing a catalyst layer around the horizontal producer well, in effect creating a downhole reactor. A series of six catalytic tests was performed in two 3D combustion cells to investigate the degree of upgrading achievable with 10.5 API gravity Wolf Lake oil. The main factors investigated were: the type of HDS catalyst (NiMo, CoMo), extrudate or crushed catalyst, and catalyst loading. Normally, the well configuration was HIHP, i.e. a horizontal injector and a single horizontal producer well in line drive, but one test used a dual producer, HI2HP. The results of these tests show that the basic process is very stable over a 10 hour period, maintaining average peak combustion temperatures of 500 to 600C. Thermal upgrading alone (THAI) achieved a nearly 10 point increase in the API gravity of the produced oil. This was increased by a further 4 to 7 API points using the catalytic process (CAPRI). This indicates that it may be possible to convert a heavy crude oil to almost a light oil product in a one-step in situ reservoir process. The oil recoveries were consistently very high, at around 85 %OOIP, with the produced oil having a viscosity as low as 10 mPas at 20 °C. The process demonstrated robust stability when changes in air injection rate were made and there was also an oil productivity gain when operating at low water air ratios, WAR< 1.0. Introduction Unlike conventional light oil production, the production and transportation of heavy oil is much more difficult, mainly due to its high viscosity at reservoir conditions. In order to process the oil, expensive surface upgrading operations are required to upgrade the produced oil, so that it meets refinery feedstock specifications. Cold production of heavy oil only recovers a small proportion of the original oil in place, typically about 5% to 20%. Surface mining is limited by the reservoir geological characteristics. It is only applied to very shallow deposits, like Athabasca Tar Sands in Canada. When the oil viscosity is very high (>10,000mPas), in the reservoir the oil is virtually immobile. Improved Oil Recovery (IOR) or Enhanced Oil Recovery (EOR) methods are required to increase the oil mobility in order to improve heavy oil production. Steam injection, vapour extraction and in situ combustion (ISC) are three main techniques, achieving high oil recovery of more than 50% OOIP. Steam injection, such as cyclic steam stimulation and steam assisted gravity drainage (SAGD) create no upgrading effect on the produced oil. Vapour extraction (VAPEX) produces an oil with only minimum upgrading, mainly due to rejection of asphaltenes. ISC, or heavy oil air injection (HOAI), is achieved by burning a small fraction of the oil in the reservoir to release the combustion reaction energy between the injected oxygen and hydrocarbons

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.977
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.037
GPT teacher head0.280
Teacher spread0.243 · 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 source (direct Gemma or distilled Codex), 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

Citations27
Published2001
Admission routes1
Has abstractyes

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