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Record W1992683908 · doi:10.2118/162845-ms

Using Microseismicity to Understand Subsurface Fracture Systems and Increase the Effectiveness of Completions: Eagle Ford Shale, TX

2012· article· en· W1992683908 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
KeywordsMicroseismGeologyHydraulic fracturingSeismologyFracture (geology)Oil shaleFault (geology)Focal mechanismTectonicsSource rockPetrologyShale gasPetroleum engineeringMining engineeringGeotechnical engineeringPaleontology

Abstract

fetched live from OpenAlex

Summary Existing natural fractures often have a significant impact on both stimulation and production of oil and gas wells. Effective exploitation of unconventional reservoirs requires the understanding of the local tectonic history and the present day stress regime. Signal strength, high quality reflection seismic, microseismic imaging, and moderate structural complexity of the liquids-rich gas and tight oil Eagle Ford shale makes it an ideal place to study hydraulic fracturing in tight rocks. Microseismic monitoring results showed clear structural trends relating to reactivation of existing faults and fractures, and rock failure mechanisms determined through source mechanism inversions. These results provided critical information to the operator for optimizing the hydraulic fracture design. Microseismic data collected using 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 were 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 different failure mechanism for fractures than for reactivated faults. Microseismicity with a NE-SW orientation are interpreted to be related to either induced or reactivated fractures. Microseismicity also formed trends that are contiguous across more than one wellbore in a ENE-WSW direction. These trends are interpreted to have formed as a result of fault reactivation. Source mechanisms from faulting parallal 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 orientations of the fault-related trends are parallel to extensional Gulf of Mexico growth faulting. The microseismicity trends associated with fracturing form at an angle of approximately 25° to the faulting trends and are parallel to SHmax. Microseismicity trends associated with faults are used to project where faults will intersect adjacent wells. Identification of these faults in the reservoir via microseismic mapping allow operators to modify their treatment parameters and stage spacing in order 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.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.625
Threshold uncertainty score0.937

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.031
GPT teacher head0.242
Teacher spread0.211 · 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