Deployment of Pressure Hit Catalogues to Optimize Multi-Stage Hydraulic Stimulation Treatments and Future Re-Fracturing Designs of Horizontal Wells in Horn River Shale Basin
Bibliographic record
Abstract
Abstract Continuous monitoring of wellhead pressures, captured from passive wellbores on a multi-well pad during fracturing operation, has been used as a cost efficient diagnostic tool to better characterize stimulated fracture network and reservoir drainage in Horn River Shale Basin. This paper aims to generate pressure hit catalogues and to quantify their effectiveness to deliver real-time on-site improvement in completion treatments, parent/child interactions, and re-fracturing designs. Pressure data of a multi-well pad was initially analyzed to establish measurable attributes for identification of hydro-mechanic responses from direct hydraulic communications. A fully-coupled hydro-mechanical code, with explicit inclusion of discontinuities, explored the interaction mechanisms across natural and induced fractures by reproducing observed pressures of passive wells. Employing multivariate experimental design on a subset of pressure data, pressure hit catalogues were generated with notion to the sensitivity of hydro-mechanical properties of fracture/intact rock, geometrical/statistical properties of fracture network, in-situ stresses, and completion design. Using the unexploited subset of pressure data, the practicality of pressure catalogues was verified against coupled simulations. The pre-processing of pressure data, to assign attributes to pressure hits, was found essential for analysis of interference mechanisms during fracture treatments prior to incorporating the data into a coupled simulation. Explicit modeling of discrete fractures allowed to evaluate how key sensitive parameters, mainly fracture/intact rock properties, and altered stress environment due to continuous multi-stage fracturing operation, could affect passive pressure signature. Using a subset of pressure hit data obtained from a multi-well pad in Horn River Shale Basin, the calibrated coupled simulations helped to constrain the statistical complexity of fracture network realizations, which ultimately observed to closely align with available microseismic data. The efforts went into the calibration of pressure/stress shadows, using wellhead pressure data at subsequent stages, were summarized. Given the model size and dealing with too large data sets, history-matching of individual pressure hits proved to be computationally intensive. The steps to generate and employ pressure hit catalogues were also illustrated. The results obtained from application of pressure catalogues on the unexploited subset of pressure data indicated close agreement with coupled numerical simulation. A procedure in potential deployment of pressure hit catalogues for real-time modification of multi-well completion and re-fracturing design was lastly provided. Because of the inherited uncertainty associated with locating the subsurface origin of passive pressure responses, yet measured at wellhead, more robust interpretation techniques are required for a better on-site assessment of fracturing operations. The possibility of employing pressure hit catalogues for evaluation of well interference, parent-child interactions, complex structure of stimulated fractures, and reservoir drainage, is an effort in that direction, aiming to monitor, and if required, to remediate well spacing, completion treatments, and re-fracturing designs.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".