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Record W2072105628 · doi:10.2118/161965-ms

Integrated Microseismic Monitoring for Field Optimization in the Marcellus Shale - A Case Study

2012· article· en· W2072105628 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSPE Canadian Unconventional Resources Conference · 2012
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsMicrosemi (Canada)
Fundersnot available
KeywordsGeophoneMicroseismGeologyBoreholeHydraulic fracturingFracture (geology)SeismologyPassive seismicEconomic geologyEnvironmental geologyOil shalePetroleum engineeringEngineering geologyGeotechnical engineeringTectonicsVolcanism

Abstract

fetched live from OpenAlex

Abstract The work presented in this paper focuses on an integrative analysis of hydraulic fracture treatments conducted in the Marcellus Shale. The treatments have been monitored by a permanently installed array of buried geophones used to detect microseismic events. These event sets were analyzed in conjunction with available data from other sources, such as well logs and well cores, as well as information on reservoir properties, regional and local geology and other sub-surface structural information. Passive seismic data was acquired by an array of 101 permanently installed geophones buried and cemented in place at a depth of 150 ft in purpose-drilled boreholes covering an area of over 18 square miles providing high resolution stimulation monitoring. The permanent installation of geophones below the surface allows for significant increase in signal-to-noise ratio and consistent comparison of hydraulic fracture treatments for any given number of wells under the array footprint. This integrative analysis determined how various factors related to the specific reservoir geology in the Marcellus and to what extent the variability of hydraulic fracture treatments impacted the microseismic results. The next step of the evaluation investigated the relationship between hydrocarbon production and the microseismic results, relative to changes in geology and variability of the stimulation approach. Analysis of stress changes indicated by the microseismic source mechanisms was used to explain the asymmetry of microseismicity about the wellbore. Relationships and statistics of treatment options with respect to the monitoring results were investigated, including the modeled discrete fracture network, the modeled fracture volume, the stimulated reservoir volume, and the cumulative microseismic moment of the event set. The initial production (IP) was compared to reservoir and engineering parameters, such as treatment pressures, sequence of treatments (toe-to-heel vs. zipper-frac), net pressures, and stage spacing, to determine if the variability in the microseismic results is due to engineering differences or to spatially-varying reservoir properties. Simple well test simulations were performed to investigate different fracture and flow models, and compare results to the IP and available reservoir properties.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.072
Threshold uncertainty score0.924

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.025
GPT teacher head0.246
Teacher spread0.221 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it