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Record W3133869493 · doi:10.5194/egusphere-egu21-16385

Cascading earthquakes on a fracture network in a geo-energy reservoir

2021· article· en· W3133869493 on OpenAlexaff
Kadek Hendrawan Palgunadi, Alice‐Agnes Gabriel, Dimitry Garagash, P. Martín

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsDalhousie University
Fundersnot available
KeywordsInduced seismicitySeismologyFracture (geology)Fault (geology)GeologyMagnitude (astronomy)Stress (linguistics)Seismic momentStress fieldEarthquake magnitudeGeotechnical engineeringPhysicsStructural engineeringEngineeringScalingGeometryMathematicsFinite element method

Abstract

fetched live from OpenAlex

<p>The increasing rate of induced seismicity in subsurface reservoirs, exceeding occasionally moment magnitude 5, has generated significant attention among earthquake scientists and regulators over the last decade. Fluid injection activity during the operation stage often produces a significant, sometimes even destructive, earthquake. Many approaches have been proposed to monitor, model, and predict the injection-related seismicity to avoid an earthquake larger than a threshold set by the regulator (e.g., Mw 2.0). However, unexpected higher magnitude events occur exceeding what is predicted by empirical models, theoretical relations, or computer simulations. </p><p>Current models do not consider that subsurface reservoirs consist of complex fracture networks characterized by connected and unconnected individual fracture planes, often comprising a larger but inactive fault (unfavorably oriented with respect to regional stress). Fluid injection may then perturb stress conditions and trigger an initial rupture on fractures close to the injection well; this initial event may then dynamically trigger other fractures and potentially generate a large earthquake. </p><p>We inspect conditions leading to induced earthquakes taking into account the complex fracture network intersected to an inactive fault using dynamic earthquake rupture simulations. We generate the fracture network using a nearest-neighbor method following statistical parameters (power-law distribution of fracture length and fracture density) based on field data. There are 134 fractures consisting of 95 connected fractures, 3 fractures connected with at least one fracture, and 38 unconnected fractures. We focus on two fracture populations oriented in strike N110E ± 10° and N210E ± 10°, respectively. The main fault has a depth-dependent dip orientation which results in a listric fault geometry. </p><p>For our dynamic rupture simulations, we use the open-source software SeisSol (https://github.com/SeisSol/SeisSol), apply a laboratory-based rate-and-state with rapid velocity weakening friction law, and assign source radius-dependent characteristic length (L parameter) to the fractures. We vary stress conditions (maximum horizontal orientation, static-pore pressure, and prestress ratio) and conduct an initial static Mohr-Coulomb analysis before running the expensive dynamic rupture simulation. We choose conditions that lead to cascading rupture with (case 1) and without (case 2) the involvement of the main fault. Case 1 has higher artificial overstress within the nucleation area than case 2. Our simulation shows intricate rupture progression over small fractures via rupture branching with the parallel and orthogonal connected fractures. The rupture can also transfer to the unconnected fractures through dynamic triggering from the closest neighboring fracture. Case 1 produces a moment magnitude of Mw 6.36 that is equivalent to case 2. Our preliminary result reveals that connected fractures can generate a significant and potentially large induced earthquake if all fractures are favorable to the stress condition.</p>

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)0.000
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 score0.337
Threshold uncertainty score0.559

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 head0.014
GPT teacher head0.249
Teacher spread0.235 · 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".

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Citations0
Published2021
Admission routes1
Has abstractyes

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