Drainage Estimation and Proppant Placement Evaluation from Microseismic Data
Why this work is in the frame
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Bibliographic record
Abstract
Abstract In this case study we outline how microseismic analysis can be used to optimize treatment design and determine the portion of the stimulated rock volume that should be productive. To begin, microseismic data was acquired with a permanently installed shallow buried array of geophones during the hydraulic fracturing of 17 wells in the Marcellus Shale. The processed results were used to conduct a multi-disciplinary study integrating geology, geomechanics, reservoir and completion engineering, and ultimately, production data. A stress inversion from focal mechanisms was performed, and correlations were made between hydrocarbon production and microseismic results. That work, in conjunction with the variability in the stimulation approach, was used to optimize the treatment design on an individual wellbore and on a field development scale. Treatment design analysis indicated optimum wellbore spacing, stage spacing and length as well as evaluated the vertical coverage of the treatment within the Marcellus. Incorporating information from source mechanisms, an event magnitude calibrated discrete fracture network (DFN) was modeled taking into account the seismic energy of the events, rock properties, the injected fluid volume and efficiency. Evaluating the placement of proppant inside the DFN enables distinction between the part of the stimulated rock volume (SRV) that contributes to production in the long term, and the part of the reservoir that was affected by the treatment but may not be hydraulically connected over a longer period of time. Finally, the permeability of the stimulated fracture system was calculated from the microseismic results. This allows for the evaluation of the drainage volume and estimation of production.
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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 | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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