Factors Influencing the Probability of Hydraulic Fracturing-Induced Seismicity in Oklahoma
Bibliographic record
Abstract
ABSTRACT Injection-induced seismicity became an important issue over the past decade, and although much of the rise in seismicity is attributed to wastewater disposal, a growing number of cases have identified hydraulic fracturing (HF) as the cause. A recent study identified regions in Oklahoma where ≥75% of seismicity from 2010 to 2016 correlated with nearly 300 HF wells. To identify factors associated with increased probability of induced seismicity, we gathered publicly available information about the HF operations in these regions including: injected volume, number of wells on a pad, injected fluid (gel vs. slickwater), vertical depth of the well, proximity of the well to basement rock, and the formation into which the injection occurred. To determine the statistical strength of the trends, we applied logistic regression, bootstrapping, and odds ratios. We see no trend with total injected volume in our Oklahoma dataset, in contrast to strong trends observed in Alberta and Texas, but we note those regions have many more multiwell pads leading to larger cumulative volumes within a localized area. We found a ∼50% lower probability of seismicity with the use of gel compared to slickwater. We found that HF wells targeting older formations had a higher probability of seismicity; however, these wells also tend to be deeper, and we found the trend with well depth to be stronger than the trend with age of formation. When isolated to the Woodford formation, well depth produced the strongest relationship, increasing from ∼5% to ∼50% probability from 1.5 to 5.5 km. However, no trend was seen in the proximity to basement parameter. Based on previously measured pore pressure gradients, we interpret the strong absolute depth relationship to be a result of the increasing formation overpressure measured in deeper portions of the basin that lower the stress change needed to induce seismicity.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".