Forecasting seismic activity induced from hydraulic fracturing \noperations
Bibliographic record
Abstract
As the world transitions towards a carbon-neutral economy in order to meet the Paris \nclimate change accords, many countries are utilising natural gas as a transition fuel \nwhile the renewable energy sector continues to develop. As part of this transition \nin the UK, it is the intent that the use of domestic natural gas, including gas from \nunconventional reservoirs such as shale, is fully realised. The extraction of natural \ngas from shale is not without environmental risk and seismic events induced by the \nhydraulic fracturing process are cited as the reason for the current suspension of \nhydraulic fracture operations in the UK. The reactive control approach, of which \nthe ‘traffic light system’ procedures are part of, is the most widely used method to \nforecast seismic events. This ties the likelihood of a seismic event occurring to a \nsingle seismological derived parameter. There have been challenges in this approach, \nand newly developed probabilistic forecasting methods that are capable of predicting \nseismic events likely to occur in the future are still in development and yet to be \nthe primary decision making system to control the injection schedule. The primary \nobjective of this thesis was to research forecasting approaches that alleviates the \ndisadvantages posed by these current methods. A software system was developed \nbased on relating a forecasting model to real-time changes in the fracture network \nfrom the two causes of induced seismicity; an increase in pore-pressure re-activating \nfault lines and the transfer of stress from other seismic events. This software system \nanalyses microseismic records using four geophysical signal analysis methods which \nwhen combined produces two maps updated in real-time; a fracture map highlighting \nhydraulic connections and a Coulomb stress change map. To verify the software \nsystem, the causes of a magnitude 3.9 earthquake on the 12 January 2016 from a \nshale gas production well in Fox Creek, Canada were retrospectively investigated \nand the usage of the system to forecast seismic events evaluated. The fracture map \ngenerated from the microseismic records indicated a hydraulic connection between \nstage 23 of the hydraulic fracture process and a legacy fault line. The input of \nfracture fluid increased the pore-pressure on the fault line, ultimately causing slip \nand the magnitude 3.9 earthquake. There was no evidence to show that static stress \ntransfer from other seismic events in the area was affecting the triggering process. The \nforecast model was validated by comparing the fracture maps in the time leading up to \n12 January event to the forecast model. Although numerous events were positioned to \nbe part of a transition between the hydraulic tensile fractures to the fault line, it was \nnot possible to analyse these events due to the low signal to noise ratio and therefore \nnot viable to fully validate the forecast model with this case study. Further research \nwith different case studies where the acquisition geometry is closer to the events is \nrecommended to fully validate the forecast model before field implementation.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".