Compositional Changes During Vapex (Vapour Extraction) Operations in Heavy Oil Pools
Bibliographic record
Abstract
Abstract During Vapex (Vapour Extraction) operations for heavy oil recovery, a condensable solvent (such as propane or CO2) is injected into the reservoir via a horizontal injector and mobilized oil is drained via a horizontal producer placed directly underneath it. The solvent is chosen such that it is close to its dew point under reservoir conditions. Mixing with this solvent significantly reduces viscosity of the heavy oil. Theoretical treatments assume the oil to be 'black' i.e. no changes to the oil occur, other than viscosity reduction due to localized dissolution of the solvent. However, one observes several compositional changes during Vapex experiments in the laboratory, especially when working with conventional heavy oils such as those from the Lloydminster Area of Canada. Via physical model studies involving different heavy oils, it was seen that compositional changes occur in the oil being produced as well as, in the oil still resident within the model. These include solvent extraction of vaporizable components of the heavy oil, especially in the early stages of Vapex; subsequent produced oil was seen to be progressively heavier. These effects are more than compensated if de-asphalting of the oil occurs, as was observed in many laboratory Vapex experiments. Since the process is dynamic (unsteady state), oil quality and rates change with time. These changes may also affect price one obtains for the oil produced (function of API gravity and sulfur/ metal contents). Regarding deasphalting of the oil produced, it makes a lot of technical and economic sense to focus on ways of improving oil extraction rates down-hole by partially upgrading the oil in-situ and, on improving commodity quality in surface facilities once the heavy oil-solvent mixture has been produced, prior to its shipment to the refinery/ up-grader. Various aspects of compositional changes during Vapex are discussed using data from physical models; glass micro-models and MRI Images obtained during different Vapex experiments. Introduction In Vapex (Vapour Extraction) operations for heavy oil recovery, a condensable solvent (e.g. propane or CO2) is injected into the reservoir via a horizontal injector and mobilized oil is drained via a horizontal producer placed directly underneath it. The solvent is chosen such that it is close to its dew point under reservoir conditions and resulting solvent-oil mixture in vicinity of the vapour chamber, has significantly lower viscosity as compared to the native oil. The main driving mechanism is gravity to help drain the oil thus mobilized1 (having reduced viscosity) as shown in Figure 1. Theoretical treatments of Vapex assume the oil to be 'black', i.e. no changes to oil occur, other than viscosity reduction due to localized dissolution of the solvent. However, one observes several compositional changes occurring in the laboratory during Vapex, especially when working with conventional heavy oils such as those from the Lloydminster area. These include progressive extraction (into the injected solvent) of light hydrocarbon components of oil and asphaltene deposition. Upon contact with the solvent vapour, vaporizable components of oil are extracted into the vapour phase and/or transfer of some of the solvent into the oil phase occurs.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".