Modelling of Bitumen Ultradispersed Catalytic Upgrading Experiments in a Batch Reactor
Bibliographic record
Abstract
Abstract Large reserves of heavy crude oil and bitumen are waiting for novel technical recovery developments. Nevertheless, the very low API gravity and impurities such as sulphur and nitrogen of the reserves, influences the economics of their industrial utilization. In situ catalytic upgrading of heavy oil and bitumen has been proposed and tested in laboratory scale experiments. Experimental evidence from laboratory scale tests are very promising and this novel technique can play an important role in the exploitation of huge resources of heavy oil and bitumen. Accurate analytical and numerical modeling is essential in order to correctly interpret experimental measurements of the in situ upgrading. This work will enhance the understanding and design of field scale processes. In this paper, simulation results of bitumen ultra-dispersed catalytic upgrading experiments in a batch reactor area reported. The results show that ultra-dispersed catalytic upgrading results in relatively high residue conversion and can potentially increase the API gravity of the produced oil. These results hold significant promise for upgrading heavy crude oils and bitumen using an ultra-dispersed catalyst. Introduction The decline of light oil reserves has made the recovery of heavy oil and bitumen an attractive substitute supply for the world's ever increasing energy demand. Large reserves of heavy crude oil and bitumen are waiting for a novel technical recovery development. The very low API gravity and impurities such as sulphur and nitrogen, in these reserves influences the economics of their industrial utilization. The selection of enhanced oil recovery (EOR) processes to produce such vast resources of heavy crude oil and bitumen depends on technological and economical considerations. Several field scale EOR techniques have been utilized in the past to exploit such huge resources. These EOR techniques include thermal and non-thermal methods. Thermal methods include Steam Assisted Gravity Drainage (SAGD), Cyclic Steam Stimulation (CSS), steam injection, In Situ Combustion (ISC), and their variants. Non-thermal methods include miscible flooding, chemical flooding, and their variants. Canada is producing about 14% of the total oil produced by the EOR methods. These are most commonly recovered using thermal operations.(1) The Energy Information Administration (EIA) states that the sulphur content and density of refineries input in the United States has been steadily increasing.(2) To improve the refineries feedstock input quality, the incoming heavy oil needs to be upgraded prior to conventional refining. Heavy oil and bitumen with low API gravity can be made more valuable if they are upgraded to meet the current conventional refining specifications. Surface upgrading of heavy oil and bitumen which is a capital and energy intensive technology has been practiced in Canada. Due to the high capital investment and energy requirement for surface upgrading downhole upgrading of heavy oil has been proposed.(3), (4), (5) Down-hole upgrading makes it possible to recover and upgrade immense heavy oil resources without using large volumes of water, burning natural gas, or emitting greenhouse gases. In situ upgrading of heavy oil and bitumen has been reported both in field scale operations and in laboratory scale experiments.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".