Constraining the Complexity of Stimulated Reservoir Volume during Multi-Stage Hydraulic Fracturing of Horizontal Wells through Inter-Well Pressure Hit Modeling
Bibliographic record
Abstract
Abstract To determine the degree of connectivity and complexity of a stimulated fracture network, a prescriptive completion program was undertaken in the Horn River Shale Basin which enabled continuous monitoring of pressure interactions among horizontal wells during multi-stage hydraulic fracturing. This paper introduces a novel approach to characterize the stimulated fracture network, and consequently, to optimize the stimulation, wellbore placement, and re-fracturing designs, by integrating the pressure hits captured from passive wellbores on a pad during fracturing operations. If effective, it may also provide a cost-effective alternative to microseismic monitoring. The workflow initially considers a rigorous data analysis on available pressure hits at each frac-stage in time and space, including the location of pressure events, time of flights to offsetting stimulation, and the magnitude and intensity of pressure hits/falloffs. Streamline simulation, assisted with a hydraulic fracturing module, is then used to match the pressure hits/falloffs in the passive wells. This ultimately provides a dynamic probabilistic 3D map of the fracture network growth, reservoir complexity and inter-well connectivity. The fundamental mechanisms of hydraulic fracturing and the interactions across natural and induced fractures (fracture initiation/propagation/growth) are implemented by means of an advanced coupled hydro-mechanical code, based on distinct element method. Results from initial data analyses were fed into a hydro-mechanical model, which incorporated the physics of the hydraulic fracturing process, in order to reproduce the pressure hit signatures. An assisted streamline-based technique was used to simulate various scenarios of pressure hit responses to construct a database of standard pressure hit/falloff patterns. This database, compiled into a dynamic 3D map, facilitated a probabilistic approach to calculate a robust estimate range of stimulated fracture network of the pad area. This database can be subsequently used in future stimulation and re-fracturing designs. The backbone of the highly complex fracture network, extracted from pressure hit/falloff data, was found to closely align with high-resolution microseismic data. Calibration of the hydro-mechanical model using the pressure hit data provides increased confidence in the use of the model to optimize well placement and hydraulic fracturing designs. In the absence of microseismic data, this unique workflow has the potential to deliver real-time on-site monitoring of fracturing operation at a reduced cost and acceptable accuracy, to provide additional statistics on complexity of stimulated reservoir volume, and to offer a better assessment of the likely range of the induced fracture network among horizontal wells.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".