Diagnostic Fracture Injection Tests Analysis and Numerical Simulation in Montney Shale Formation
Bibliographic record
Abstract
Unconventional oil and gas formations are abundant, have become an increasingly important part of the global energy supply, and are attracting increasing attention from the industry. Predicting key reservoir properties plays a significant role in both geological science and subsurface engineering workflows. With the advent of horizontal well drilling and multiple-stage hydraulic fracturing, the Montney Shale formation is one of the most promising and productive shale plays in Canada. However, very few academic papers discuss its in situ stress, reservoir pressure, and permeability, which are essential for the development of the Montney Shale. The objective of this study is to analyze the geo-stress, the pore pressure, and several key reservoir properties by using diagnostic fracture injection test (DFIT) data from the Montney Shale. One horizontal well from the Wapiti field has been analyzed with a set of DFIT data, and its results show that the general pressure and Gdp/dG responses from Well-A indicate a signature of height recession/transverse storage. In the study, the Tangent Line method, the Compliance method, and the Variable Compliance method have been applied to estimate the key reservoir properties. As a result, the Well-A DFIT analysis estimates that the closure pressure is ranging from 34.367 to 39.344 MPa, contributing to the stress gradient from 14.09 to 16.13 KPa/m for the formation. The pore pressure is ranging from 20.82 to 24.58 MPa, contributing to the pore pressure gradient from 8.54 to 10.07 KPa/m for the formation. The porosity is ranging from 3% to 6%. These reservoir properties are contoured cross the Montney Shale formation. Using the DFIT’s numerical simulation and history matching, the reservoir permeability is 0.024 md, fracture length is 13.44 m, and fracture geometries are analyzed by different models. Moreover, the physics behind the DFIT are analyzed and discussed in detail. For the first time, three different analysis methods have been applied to estimate a series of key reservoir properties for the case wells in the Montney Shale formation. This approach can not only reduce the potential prediction error caused by a single method application but also increase the persuasiveness of the assessment and save time, ensuring the efficient implementation of engineering operations. Given the significance of quantifying in situ stress and reservoir pore pressure in unconventional hydrocarbon exploration and development, this study could help the operator to quickly understand the stress regimes, the fracture geometry, and the formation properties of the Montney Shale formation in the Wapiti field. Furthermore, the interpreted results demonstrated in this paper are adding substantial business value to the asset, especially in terms of improving the hydraulic fracturing design and, thus, accelerating the cashflow from production.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".