Simulation Analysis of the Effects of Fractures Geometrical Properties on the Performance of Vapor Extraction (VAPEX) Process
Bibliographic record
Abstract
Abstract Vapor Extraction (VAPEX) a new developed Heavy Oil Recovery (HOR) method, has been extensively studied, both theoretically and experimentally, in conventional sandstone models. However, the applicability of this process on naturally fractured reservoirs has not been addressed yet. The objective of the present work is to evaluate the effect of fractures geometrical properties such as fracture orientation, length of extension, discontinuity in both Up Wells Region (UWR) and Far Wells Region (FWR) and vertical fractures location on VAPEX performance. Additionally, the effect of horizontal and vertical fractures dispersion in different fracture density, dispersion scheme and fractures networking on the performance of VAPEX process was assessed. Fractures discontinuity effect has been also scrutinized in the presence of parallelepiped networked fractures which confirmed the results in the case of either horizontal or vertical fractures systems. It was found that VAPEX process performance is enhanced in the case of longer vertical fractures, lower horizontal fractures dispersion, shorter horizontal fractures extension and lower vertical fractures discontinuity. Vertical fractures dispersion scheme also affects the performance. The ultimate oil recovery in the case of networked fractures will be higher than conventional model if the horizontal fractures have poor connectivity to each other. Introduction In thermal recovery methods, as far as the reservoir's oil is heated up, main fraction of the thermal energy is used up in heating the formation itself. For the reservoirs with very thin pay zone, bottom water zone and/or overlying gas zone, excessive depths and high water saturation the economical applicability of the thermal methods is doubtful. Additionally, application of thermal processes in carbonate reservoirs with low vertical fracture permeability, low porosity and low thermal conductivity where the heat capacity per unit volume of contained oil is high is not economically feasible. With respect to current available recovery technologies, these reservoirs are categorized as problematic reservoirs (1–4). For example in the case of high pressure reservoirs, as an advantage of solvent-base technique, hydrocarbon mixture vapors with non-condensable gases are employed instead of pure solvent to ensure the solvent will be vapor in reservoir condition (5). Some of the other main advantages of solvent-based process over that of thermal methods are less energy consumption, less environmental pollution, and lower capital cost. Furthermore, the hydrocarbon solvents are soluble only in the oil then the possibility of solvent loss to the formation and its confining environment is vanished. Therefore, the solvent-based process appears to be the only EOR technology that can be economically justifiable for problematic heavy oil reservoirs (6). On the other hand, immiscible secondary recovery methods, even when successful, may leave significant amounts of oil in the reservoir due to the presence of capillary forces which are responsible for the entrapment of the oil (7–8). Miscible secondary/tertiary recovery processes, in theory, are considered to be very efficient because they eliminate capillary forces. In the absence of capillary pressure, no interface exists between miscible fluids of different composition.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".