Improving Understanding of Complex Fracture Geometry of the Canadian Horn River Shale Gas Using Unconventional Fracture Propagation Model in Multi-Staged Horizontal Wells
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Bibliographic record
Abstract
Abstract Hydraulic fracturing has become an important component of well completion in unconventional reservoir development and contributes to over 37% of the overall well construction spend. It has also, been seen as the most significant contributor to return on investment in unconventional reservoir exploitation. Until recently, field operation has been based on "trial and error" approach while modeling has been based hitherto on software used basically for the conventional reservoir fracture simulation. Hydraulic fracturing in shale gas reservoirs has often resulted in complex fracture network, as evidenced by microseismic monitoring. The nature and degree of fracture complexity must be clearly understood to optimize stimulation design and field development planning; completion strategy and operations planning. Unfortunately, the existing planar fracture models used in the industry today are not able to simulate complex fracture networks. A recently developed unconventional complex fracture propagation model (UFM) is able to simulate complex fracture network propagation in a formation with pre-existing natural fractures. Multiple fracture branches can propagate simultaneously and intersect, dilate or cross each other. This paper presents an integrated approach to optimize 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 operator and supported by the service provider, the operator could now make more informed design decisions and understand the interaction between the shale, the hydraulic and pre-existing natural fracture network, and reduce costs. The data incorporated into the study from both vertical and horizontal wells included geophysical, geological, petrophysical and geomechanical data integrated into a 3D earth model. Engineering data such as DFIT (measurement made from small volume of water pumped into target formation) derived fracture closure pressure, production and pressure data from the horizontal well in the pad were used for calibration and constraining of the model. 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 numerical simulator and further calibrated with current production history of the candidate 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. The prediction of the model (rates, cumulative and pressure) matched very rapidly and more closely with the observed production from the candidate well, improving confidence on the methodology utilized and results obtained. As a result of the project, the team is now able to run different hydraulic fracture design scenarios including stress shadow between stages validated using microseismic, stress shadow between offset wells, tuning factors not only on the geomechanics side but also in the treatment schedule 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. The result of the study also opened up new way of estimating the drainage area over a period of time and could be used when considering well spacing, placement and density during the field development planning. Based on these findings, the 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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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 it