Stress field inversion using downhole fiber-optic distributed acoustic sensing array during hydraulic fracturing in shale gas reservoir
Bibliographic record
Abstract
ABSTRACT We present a case study using a full-section fiber-optic distributed acoustic sensing (DAS) array to monitor microseismicity during hydraulic fracturing and provide insights into subsurface stress variations. Leveraging DAS data’s high spatial resolution and migration-based techniques, we identify 163 micro earthquakes, 46 of which were selected for moment tensor and stress inversion analysis. Independent inversions explain the source mechanisms of events in fracturing stage 8, showing consistency with in situ stress measurements and alignment with the regional stress field, as validated by the World Stress Map and the pilot hole. Seismic events are categorized into three swarms based on their focal mechanism characteristics. Swarm 1 exhibits significant stress anomalies, likely driven by rapid fluid-induced fracturing which alters local stress conditions. In contrast, Swarms 2 and 3 shows stress alignments with regional trends, indicating shear failure along preexisting faults. We also use focal mechanism tomography (FMT) to estimate pore fluid pressure thresholds for each swarm. Swarm 2 exhibits the lowest excess pore pressure (1.24 MPa), suggesting that high fluid pressure is prone to enhance the fracturing of preexisting faults and induce earthquakes. Our findings provide new insights into the role of hydraulic fracturing in induced seismicity, demonstrating that stress anomalies arise from complex fracture geometries and dynamic pore pressure variations. This study highlights the potential of integrating DAS monitoring with stress inversion and FMT to advance our understanding of shale reservoir geomechanics and induced seismicity.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".