New Insights on Catalysts-Supported In-Situ Upgrading of Heavy Oil and Hydrogen Generation during In-Situ Combustion Oil Recovery
Bibliographic record
Abstract
Summary As part of greenhouse gas reduction initiatives, there have been many publications on carbon sequestration, reducing the carbon footprint of oil and gas operations, and generating carbonless fuel [e.g., hydrogen (H2)] by means of in-situ processes. In-situ upgrading (ISU) can help with these aspects by converting bitumen and heavy oil into low sulfur, low N2, and low asphaltene products, generating fewer emissions and producing hydrogen as a byproduct, thus helping with utilization of vast resources of energy that would otherwise be wasted due to extreme measures of no fossil fuel policies. In addition, such processes could produce more valuable products, enhanced shipping/pipelining, and less demanding downstream processing. In this paper, we provide new insights into the results of several combustion tube tests that were performed for Alberta Ingenuity Centre for In Situ Energy, using different heavy oils with fresh supported catalysts. The catalysts were placed in the production end of the combustion tube so oil would pass over the catalyst bed before being produced. In practice, solid catalyst particles could be placed into the oil-bearing formation adjacent to the producing wellbore, ensuring that crude oil will flow over the catalysts during oil production. In this paper, we use many laboratory results that have never been published before. The objective is to understand whether using catalysts has merit in our future oil production activities under the current environmental restrictions. A commercial Ni/Mo catalyst was used in these tests. The results of these tests indicated at least temporary significant occurrence of reactions such as hydroprocessing (HP) and hydrotreating reactions, such as hydrocracking, hydrodesulfurization (HDS), hydrodenitrogenation (HDN), and hydrodeoxygenation. They also generated a significant volume of hydrogen in situ. We will discuss the impact of pressure, temperature, water injection, and dispersed vs. supported catalysts on the degree of oil upgrading. Also, the key parameters that could impact in-situ hydrogen generation will be presented. Specifically, the role of reactions such as aquathermolysis, thermal cracking, water-gas shift (WGS, defined later) reaction, and coke gasification will willbe discussed. Note that the products of these reactions could undergo additional methanation (ME) reactions, which could reduce the H2 concentration in the produced gas. Finally, methods of upscaling these results to the field conditions will be presented.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".