Microseismic-Derived Expected Ultimate Fracture Half-Height Above/Below Wellbore in Unconventional Stimulated Reservoir Volume in a Multi-F ractured Horizontal 10 Well Pad - Canadian Horn River Basin Case Study
Bibliographic record
Abstract
Summary Over the past decade, microseismic (MS) monitoring has become the primary approach used to gain an in-situ understanding of the rock's response during stimulation. Recently, the utilization of downhole monitoring of treatments has provided an opportunity to investigate ways by which these fractures develop by examining microseismic events recorded during stimulation. In the Horn River, geophysicists and engineers used microseismic field data and its interpretation to constrain reservoir models. Generally, it has been observed that fracture height, width and length vary from formation to formation. In the presence of uncertainty in reservoir models, input data is required for determining the best estimate of a value, and probabilistic methods are used. Risk analysis is a technique used to quantify the impact of uncertainties on output variables, and to determine a range of possible outcomes. In this paper we use Monte Carlo simulation and probability density functions (PDFs) that describe likely values of fracture half-height as an input parameter into reservoir models. PDFs and Cumulative Distribution Functions (CDFs) are used to provide realistic estimates of stimulation parameters, such as stimulated reservoir volume. Microseismic interpretation of events provides an upper bound value for fracture half-height in unconventional shale gas reservoirs. Its CDF for each well on a multi-fractured horizontal well pad provides a better understanding of what the ultimate fracture half-height could be after pressure depletion, and what the resulting implications for well spacing/ placement in pad design are. This facilitates a probabilistic approach to production forecasting and reserves estimation, and allows us to calculate a robust estimate range of estimated ultimate recovery (EUR) and recovery factor (RF). This approach differs from previous work that is based on strong collaborative work between geophysicists and engineers. This paper contributes technically to the knowledge of the unconventional shale oil and gas industry by constraining analytical models that can be used as a quick look/ first pass into numerical simulation, and as estimators of production forecasts and different reserves categories. Furthermore, some of the design considerations of the multi-well pad, in addition to surveillance data – such as production logs, chemical fluid and radioactive tracer – will be addressed, concluding with a discussion of the results.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".