Modeling of Induced Hydraulically Fractured Wells in Shale Reservoirs Using ‘Branched’ Fractals
Bibliographic record
Abstract
Abstract Reservoir simulation has gradually become one of the most advanced methods for historical performance analysis and production forecasting for most conventional reservoirs. Unconventional fractured reservoirs still present a formidable challenge to simulate. Simplified reservoir modeling techniques are widely used at present. The recent development of liquid rich gas shale fields presents a new spectrum of problems for numerical modeling in terms of adequate description of in-situ rock permeability, geometry of the induced hydraulic fractures and long-term retrograde behaviour of gas condensate. Most commonly used simulation models of fractured wells rely on multiple transverse planes each representing a single stage hydraulic fracture to form a network of fractures perpendicular to the wellbore (Vincent, 2011). This approach has difficulties in describing pressure distribution along a single fracture and also within the corresponding drainage area. As a result, the magnitude of pressure depletion and condensate drop-out appear uniform across the fracture-reservoir systems. This can often lead to over-estimation of the rock permeability as well as the contacted reservoir volume. The focus of this research was on the Kaybob rich gas / retrograde gas condensate region of the Duvernay Shale that is currently being developed in Alberta, West Canada. An actual active production well was used to set up simple ‘branched fractal’ simulation models which were aimed to represent alternative topologies of a "typical" induced hydraulic fracture. Fluid properties, well completion information and production data were used to build and history matched the model over the 2-year historical period. The simulations demonstrated that fracture geometry had a substantial impact on predicted condensate rates for the same amount of gas production. Differences in reservoir pressure patterns around the fracture segment and the resulting variations in condensate bank build-up led to a wide range of the predicted condensate recoveries over a typical life time of a shale gas producer. The knowledge gained from this research may provide valuable insight into the optimal fracturing design, including selection of fracture spacing and screening available technology to create the necessary fracture geometry in order to maximize condensate recovery from a rich gas condensate shale field.
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 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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".