Modeling of Stress-Dependent Hydraulic Fracturing in a Dynamic Flow Simulation
Bibliographic record
Abstract
Abstract In this work we present a new approach to include the influence of hydraulically induced fractures on the performance of a reservoir. Hydraulic fracturing has become a state-or-the-art completion for all kinds of wells and reservoirs. Especially in the case of low permeable tight gas reservoirs, fracturing is essential for efficient reservoir exploitation. As a result of this, most gas wells are nowadays hydraulically stimulated. However, in the case of low permeable tight gas reservoirs with rather long fractures an appropriate simulation requires a highly-flexible time-dependent adaptive gridding of the model in the vicinity of the created fracture. Our work is based on a recently proposed simulation method which combines a dual continuum approach with a time-dependent highly flexible gridding method in order to accurately simulate the influence of hydraulic fracturing on well and reservoir performance. We extend this method by treating the process of hydraulic fracturing as a part of the simulation. At any given time during the simulation a hydraulic fracture can be initiated for any desired well. The geometry of the fracture is inferred from a given state of stress, injection rate, and elastic rock properties using the Perkins-Kern-Nordgren model. Once the fracture is created the simulation grid is appropriately adopted. We have implemented our approach in a commercial reservoir simulator which provides the necessary gridding flexibility and a dual continuum formulation that allows for the definition of locally restricted dual porosity or dual permeability cells. We will show how the accuracy and flexibility of our method enhances the ability to simulate the performance of a reservoir fracture treatment.
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) | 0.000 | 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 | 0.000 |
| 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".