Fractured Reservoir Modeling: Effects of Hydraulic Fracture Geometries in Tight Oil Reservoirs
Bibliographic record
Abstract
Abstract Tight oil production is emerging as an important new source of energy supply and has reversed a decline in U.S. crude oil production and Western Canadian light oil production. At present, combination of the multistage hydraulic fracturing and horizontal wells has become a widely used technology in stimulating tight oil reservoirs. However, the ideal planar fractures used in the reservoir simulation are excessively simplified. Effects of some key fracture properties, such as facture geometry distributions and permeability change, are usually not taken into consideration during the simulation. Over simplified fractures in the reservoir model may fail to represent the complex fractures in reality, leading to significant errors in forecasting the reservoir performance. In this paper, we examined the different fracture geometry distributions and further discussed the effects of geometry distribution on well productions. All fracture geometry scenarios are confined by the microseismic mapping data. To make the result more reliable and relevant, a geo-model was first constructed for a tight oil block in Willesden Green oil field, AB, Canada. The simulation model was then generated based on the geo-model and history-matched. A horizontal well was drilled in the simulation model and different fracture geometry scenarios were analyzed. Results indicate that the simulation results of simple planar fractures overestimate the oil rate and lead to relatively higher oil recoveries. In addition, the effect of hydraulic fracture geometries under the higher fracture conductivity is more significant compared to those under lower fracture conductivity.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".