Understanding Fracture Geometry of the Canadian Horn River Shale Gas via an Unconventional Complex Fracture Propagation Model in Multi-Staged Pad with 9 Horizontal Wells
Bibliographic record
Abstract
Abstract Hydraulic fracturing in shale gas reservoirs has often resulted in complex fracture network growth, as evidenced by microseismic monitoring. The nature and degree of fracture complexity must be clearly understood to optimize stimulation design and completion strategy. Unfortunately, the existing single planar fracture models used in the industry today are not able to simulate complex fracture networks. A recently developed unconventional complex fracture propagation model is able to simulate complex fracture network propagation in a formation with pre-existing natural fractures. Multiple fracture branches can propagate simultaneously and intersect/cross each other. This paper presents an integrated operator, non-operator and service provider's approach to optimize future hydraulic fracture design by fully integrating all the data captured in the Canadian Horn River shale. Based upon insight from the study, which was initiated by the non-operator, continued by the operator and supported by the service provider in two different countries, the operator and non-operator needed to make more informed design decisions and understand the interaction between the shale, the hydraulic and pre-existing natural fracture network and reduce costs. Data were captured from reference vertical wells and a multi-well pad. The data incorporated into the study included geophysical, geological, petrophysical, geomechanical and engineering such as dfit (small volume of water pumped into target formation) derived fracture closure pressure, production and pressure data from the horizontal wells in the pad. A generation of 2D natural fracture network is also included in the paper by defining natural fracture parameters such as length, orientation, spacing, friction coefficient, cohesion, and toughness which are almost entirely validated using lab data and geomechanical interpretation. The complex hydraulic fracture simulation results calibrated with microseismic and fracturing treatment data were incorporated into a shale gas, numerical simulator and further calibrated with current production history of the candidate multi-wells. The results of the hydraulic fracture, natural fracture and reservoir models were utilized to understand the fracture propagation mechanism in the Canadian Horn River shale gas formation. As a result of the project, the team is now able to run different hydraulic fracture design scenarios and assess the impact that each key design parameter has over the candidate well's long term production using a numerical simulator with a unique gridding process. Based on these findings, the operator and non-operator now have an insightful tool that could be used as the building block for future optimization of the fracture design
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".