Integrated Microseismic Monitoring for Field Optimization - Marcellus Shale
Bibliographic record
Abstract
This article, written by Senior Technology Editor Dennis Denney, contains highlights of paper SPE 161965, ’Integrated Microseismic Monitoring for Field Optimization in the Marcellus Shale—A Case Study,’ by Carl W. Neuhaus, SPE, Sherilyn Williams- Stroud, Christine Remington, and William B. Barker, SPE, MicroSeismic, and Keith Blair, Garrett Neshyba, and Taylor McCay, Gastar Exploration, prepared for the 2012 SPE Canadian Unconventional Resources Conference, Calgary, 30 October- 1 November. The paper has not been peer reviewed. This work focused on integrative analysis of hydraulic-fracture treatments conducted in the Marcellus shale. The treatments were monitored by a permanently installed array of buried geophones used to detect microseismic events. Analysis of stress changes indicated by the microseismic-source mechanisms was used to explain the asymmetry of microseismicity about the wellbore. Simple well-test simulations were performed to investigate different fracture and flow models and to compare results with the initial potential and available reservoir properties. Introduction Microseismic monitoring has become a crucial technology in unconventional reservoirs, dramatically improving the industry’s understanding of processes occurring during hydraulic- fracture-stimulation treatments. Improved equipment, signal processing, and data analysis have led to a substantial gain in popularity in recent years. The data set used for this integrated in-depth analysis was recorded near the surface with a permanently installed array of 101 geophones covering an area of more than 18 sq miles around the five treatment wellpads. The geophones are cemented in place in purpose-drilled shallow boreholes to ensure optimal coupling with the Earth. The wide-azimuth, large-aperture, and high-fold geometry allow for a near-constant resolution under the entire array. Surface microseismic monitoring uses full-waveform stacking to determine microseismic-event locations, a concept similar to a dish microphone. The dish, in this case the array of geophones, can be computationally pointed at target locations—a process referred to as beam steering. The Marcellus shale is a black-shale unit of the Hamilton group of the Middle Devonian section of the Appalachian basin. It underlies several states in the northeastern United States with the areal majority found in the states of New York, Pennsylvania, West Virginia, and Ohio, as shown in Fig. 1. The high-gamma-ray area of the Marcellus, with a formation thickness larger than 50 ft, exceeds 34 million acres, with a basal unit containing more than 10% total organic carbon. As with all shale reservoirs, because of its low matrix permeability, hydrocarbon production is controlled by the amount of natural fracturing in the rock. This integrative analysis provided an in-depth investigation of the microseismic response of the formation resulting from hydraulic-fracture stimulation. Microseismic data were analyzed in conjunction with available data from other sources, such as well logs and well cores, as well as information on reservoir properties and regional and local geology.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".