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Record W1975970372 · doi:10.2118/162845-pa

The Use of Microseismicity To Understand Subsurface-Fracture Systems and To Increase the Effectiveness of Completions: Eagle Ford Shale, Texas

2013· article· en· W1975970372 on OpenAlexaff
John P. Detring, Sherilyn Williams‐Stroud

Bibliographic record

VenueSPE Reservoir Evaluation & Engineering · 2013
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsMicrosemi (Canada)
Fundersnot available
KeywordsMicroseismGeologySeismologyHydraulic fracturingOil shaleFocal mechanismShale gasFracture (geology)TectonicsPetrologyPetroleum engineeringMining engineeringGeotechnical engineeringPaleontology

Abstract

fetched live from OpenAlex

Summary Existing natural fractures often have a significant impact on both the stimulation and the production of oil and gas wells. The effective exploitation of unconventional reservoirs requires understanding of the local tectonic history and the present-day stress regime. Signal strength; high-quality reflection seismic, microseismic imaging; and the moderate structural complexity of the liquids-rich gas and tight oil Eagle Ford Shale make it an excellent place to study hydraulic fracturing in tight rocks. Microseismic-monitoring results showed clear structural trends relating to the reactivation of existing faults and fractures, and rock-failure mechanisms determined through source mechanism inversion of events. These results provided critical information to the operator for optimizing the hydraulic-fracture design. Microseismic data collected by use of a surface array allowed the full geometry of the result to be viewed with no directional bias. The geometry of the microseismicity trends related to fracturing developed during the stimulation treatment was representative of the true geometry of the structure. The large aperture and wide azimuth of the monitoring array facilitated the determination of source mechanisms from every event detected, which provided full coverage of the focal sphere of each source mechanism. The events identified two different source mechanisms, indicating a failure mechanism for fractures that is different from that for reactivated faults. Microseismicity with a northeast/southwest (NE/SW) orientation is interpreted to be related to either induced or reactivated faults or fractures. Microseismicity also formed trends that are contiguous across more than one wellbore in an east-northeast/ west-southwest (ENE/WSW) direction. These trends are interpreted to have formed as a result of fault reactivation. Source mechanisms from fracturing parallel to SHmax have failure planes that strike NE/SW with normal dip-slip failure on steeply dipping planes. Those from fault reactivation have strike-slip failure on ENE/WSW-striking failure planes. The NE/SW-striking, dip-slip fractures are parallel to extensional Gulf of Mexico (GOM) growth faulting, and the ENE/WSW-striking, strike-slip faults are at an angle of approximately 25° to the dominant fracturing trends. Microseismicity trends associated with faults are used to project where faults will intersect adjacent wells. The identification of these faults in the reservoir by means of microseismic mapping allows operators to modify their treatment parameters and stage spacing to avoid geologic hazards. The operator combines the treatment-pump parameters for the wells with the additional structural understanding gained from the analysis of fracture trends and source mechanisms to identify zones that should be avoided in subsequent treatments. In addition, the mapped microseismicity provides critical information that was used to modify well spacing for subsequent wells, thereby optimizing the completion plan and dramatically cutting costs.

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.

How this classification was reachedexpand

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)0.002
metaresearch head score (Gemma)0.001
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 score0.076
Threshold uncertainty score0.560

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.034
GPT teacher head0.255
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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2013
Admission routes1
Has abstractyes

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