Effect of Bitumen Viscosity and Bitumen-Water Interfacial Tension on the Efficiency of Steam Assisted Bitumen Recovery Processes
Bibliographic record
Abstract
Abstract In Alberta, Canada bitumen is commercially produced by in-situ processes at about 830,000 bbl/d capacity, production capacity is projected to exceed 5,000,000 bbl/d in the next two decades. Steam assisted gravity drainage (SAGD) process is one of the in-situ bitumen recovery processes; the economics and efficiency of which would be improved by reducing the steam to bitumen ratio. For this purpose, the addition of light hydrocarbon solvents into steam as a solvent to reduce bitumen viscosity has been studied; however, several decades of research efforts has resulted in only limited commercial success. More recently, as an alternative to solvent addition, the use of biodiesel (fatty acid methyl esters) with steam as a surfactant additive reducing bitumen-water interfacial tension was proposed and studied experimentally (Babadagli et al, 2009; Babadagli and Ozum, 2010). In the present study, experiments were performed at typical reservoir pressure conditions to evaluate the performance of solvent (pentane) addition with steam to reduce viscosity and biodiesel addition with steam to reduce bitumen-water interfacial tension to improve bitumen recovery efficiency. The results showed that bitumen recovery efficiency may decrease with the addition of hydrocarbon solvent if the solvent is added after a certain period of steam injection. Solvent addition to the steam was tested at 5% and 15% of bitumen mass dosages. Steam assisted bitumen recovery tests with biodiesel addition under 2-g/kg-bitumen dosage showed an increase in bitumen recovery efficiency. To further investigate the reasons behind the lowered recovery with solvent addition, an analytical model was developed to predict heat transfer and pressure fields in the reservoir causing Darcy flow, and therefore, bitumen mobility. The distribution of the aqueous and solvent phases injected in time and space considering the phase change (steam and solvent condensation) effect with respect to steam chamber were clarified for different injection conditions and scenarios. The results and observation will be useful in defining the appropriate application conditions for both solvent as a viscosity reducer and biodiesel as an interfacial tension reducer additive in SAGD operations.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".