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Record W1963622700 · doi:10.2118/2004-067

THAI-CAPRI Process: Tracing Downhole Upgrading of Heavy Oil

2004· article· en· W1963622700 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueCanadian International Petroleum Conference · 2004
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsnot available
FundersEngineering and Physical Sciences Research CouncilResearch Councils UK
KeywordsTracingProcess (computing)Petroleum engineeringComputer scienceEnvironmental scienceGeologyOperating system

Abstract

fetched live from OpenAlex

Abstract Worldwide conventional oil production is expected to peak in the present decade, and the need to reevaluate the potential of heavy oil resources becomes ever more imperative. Steamflooding is reliable, but it is not very energy efficient. It also has large water requirements and does not significantly upgrade oil. In situ combustion (ISC), properly configured and operated holds considerable promise for both mobilizing and upgrading heavy crude oils with less energy expenditure. The THAI-CAPRI process appears to eliminate many of the pitfalls of conventional ISC and yield substantially upgraded oil. Although extensive documentation on the performance of the process is available, only basic upgrading data have been reported. The present study aims to quantify the extent and nature of oil upgrading during an experimental run that incorporates dry and wet phases over each toe-heel air injection (THAI) and THAI with catalyst (CAPRI) modes. Gas, oil, water and solid residue analyses are used to infer mechanisms of upgrading and to begin to gauge the economic (sweep and recovery) and environmental (gas emissions and produced water quality) impact associated with the eventual field operation of the process. Introduction Easily accessible conventional oil reserves need to be supplemented by other hydrocarbon sources during the transition from the fossil to a hydrogen fuel economy, e.g. Haaland et al. (1). In the near term, heavy oil (< 20oAPI) exploitation can compete with conventional oil if new approaches are applied to facilitate its recovery and moderate its quality detractors and environmental impact. Steamflooding is by far the most commonly applied enhanced oil recovery (EOR) method. This approach, however, is energy intensive with up to 50% BTU equivalent of oil recovered required to generate steam (2). Steamflooding imparts only minor improvements in oil quality and has a significant environmental impact (greenhouse gas emissions and water contamination). Other EOR approaches include in situ combustion (fireflooding), gas injection / solvent extraction and microbial treatment. Of these, in situ combustion (ISC) has the greatest potential for both increasing recovering rates and improving the quality of oil. Although ISC has been applied for decades, difficulties in process control, reservoir unsuitability, operational practices and oil combustion characteristics have led to its near extinction as an EOR method (Sarathi, 1998) (3). In the last decade, conceptual, bench, pilot and commercial scale approaches to improve the ISC recovery method have been promoted: Operating strategies for spot pattern and line drive (4, 5). Employment of various well configurations (6–11). "Pressure cycling" (12 – 15). "Combustion override split - horizontal well" or COSH (16). "Toe-to-Heel Air Injection" (THAI) and catalyst additive (CAPRI) (17–20). Downhole or near wellbore approaches to ISC are summarized by Weissman et al. (21). Low API gravity, high viscosity, high sulfur and metals content and high acidity will substantially reduce the price realized for oil produced by any EOR method. In some regions, the refining capacity for heavy oils is limited. Large heavy oil operations, therefore, have moved upgrading capability to the site of heavy production (e.g., northern Alberta).

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.

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 score0.066
Threshold uncertainty score0.658

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 head0.019
GPT teacher head0.257
Teacher spread0.238 · 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