Using Microseismicity to Understand Subsurface Fracture Systems and to Optimize Completions: Eagle Ford Shale, TX
Bibliographic record
Abstract
Summary Existing natural fractures often have a significant impact on both stimulatio n and production of oil and gas wells . Effective exploitation of unconventional reservoirs requires the understanding of the loca l tectonic history and the present day stress regime. Signal strength, high quality reflection seismic, microseismic imag ing, and moderate structural complexity of the liquids-rich gas and tight oil Eagle Ford shale makes it an ideal place to study hydrau lic fracturing in tight rocks. Microseismic monitoring results showed clear structural trends relating to reactiv ation 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 using a surface array allowed the full geometry of the r esult to be viewed with no event location bias. The geometry of the microseismicity trends related to fracturing develo ped 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 different failure mechanism for fractures than for reactivated faults. Microseismicity with a NE-SW orientation are interpreted to be related to either induce d or reactivated faults or fractures. Microseismicity also formed trends that are contiguous across more than one wellbore in an ENE-WSW direction. These trends are interpreted to have form ed 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 growth faulting and the ENE-WSW-striking, strike-slip faults are at an angle of approximately 25 o to the dominant fracturing trends. Microseismicity trends associated with faults are used to project where fau lts 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 p ump parameters for the wells with the additional structural understanding gained from the analysis of fracture tren ds 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 com pletion plan and dramatically cutting costs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".