Numerical Modeling of Heavy-Oil Recovery Using Electromagnetic Radiation/Hydraulic Fracturing Considering Thermal Expansion Effect
Bibliographic record
Abstract
Production of heavy oil from deep/tight formation using traditional technologies (“cold” production, injection of hot steam, etc.) is ineffective or inapplicable. An alternative is electromagnetic (EM) heating after fracturing. This paper presents the results of a numerical study of heavy oil production from a well with hydraulic fracture under radiofrequency (RF) EM radiation. Two parameters ignored in our previous modeling studies, namely adiabatic effect and the thermal expansion of oil, are considered in the new formulation, while high gradients of pressure/temperature and high temperature occur around the well. The mathematical model calculates the distribution of pressure and temperature in the system of “well-fracture-formation.” The distribution of thermal heat source is given by the Abernetty expression. The mathematical model takes into account the adiabatic effect and the thermal expansion of heavy oil. The latter makes a significant contribution to heavy oil production. Multistage heavy production technology with heating is assumed and several stages are recognized: stage 1: “Cold” heavy oil production, stage 2: RF-EM heating, and stage 3: RF is turned off and “hot” oil production continues until the flow rate reaches its initial (before heating) value. These stages are repeated starting from the second stage. Finally, RF-EM heating technology is compared to “cold” production in terms of additional oil production and economics. When producing with RF-EM heating with power 60 kW (50 days in the second stages), the oil rate increased several times. Repeated RF-EM heating (25 days in the fourth stage) doubled the production rate. Near-well region temperature increased by ∼82 °C in the second stage with RF-EM heating. Temperature increased by ∼87 °C in the fourth stage with repeated RF-EM heating and production cycles. Economic analysis and evaluation of energy balance showed that the multistage production technology is more efficient; i.e., the lower the payback period, the greater the energy balance. With the increase in pressure difference, the payback period and energy balance increased linearly.
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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) | 0.001 | 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 | 0.001 |
| 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".