Electro-Acoustic Solvent-Based Method for Enhancing Heavy Oil Recovery
Bibliographic record
Abstract
Abstract A novel multi-physics approach is proposed to enhance performance of solvent extraction methods in heavy oil reservoirs by utilizing the enhanced mixing and spreading during acoustic excitation. In the modeling, the macroscopic flow equation is coupled to the conservative form of the advection-dispersion model while it is linked with an external time harmonic body load in a poroelastic domain. Linking the latter elements of the modeling is fully coupled and both impacts of the pressure load on the rock stress as well as the induced pore pressure by the rock strain are considered. Numerical simulation results are obtained by solving coupled macroscopic equations using the finite element method for a quarter five-spot source-sink geometry. Based on the numerical solutions, normalized concentration profiles of the displacing fluid as well as plots of resident and effluent concentrations are obtained for qualitative and quantitative analyses. Simulation results of the recovery enhancements are compared to conventional solvent-based methods of enhanced heavy oil recovery in terms of energy trade-off. Acoustically assisted solvent flooding reduces the required volume of injected solvent through enhancing dispersive mixing. The acoustic excitation at a relative amplitude of 200, which is applicable to field scale applications, can result in an additional 12 % enhancement in displacing an in-place fluid via an assisted solvent extraction process as compared to the equivalent silent displacement. Such enhancement happens both before and after breakthrough. Higher amplitudes and frequencies and wave propagations transverse to the flow direction increase the enhancement. Combining acoustic stimulations and electromagnetic (EM) heating may further enhance the recovery. The required equations to incorporate EM heating are provided as well. In the proposed multi-physics approach, both the required amounts of solvent and the greenhouse gas (GHG) emissions can be reduced. Electricity usage by the elements of excitations will be the key contributors in reducing GHG emissions’ footprints of heavy oil extraction. The simulation results of the developed model can provide an estimation of input parameters in economic analysis such as the cumulative delivered energy to oil ratio that is an essential component in calculating GHG emissions.
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 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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".