Induced microearthquakes predict permeability...
In this paper, we develop a hybrid machine learning (ML) model to visualize in situ permeability evolution for an intermediate-scale (~10 m) hydraulic stimulation experiment. This model includes an ML model that was trained using the well history of flow rate and wellhead pressure and MEQ (microearthquake) data from the first three stimulation episodes to predict average permeability from the statistical features of the MEQs alone for later episodes. Moreover, a physics-inspired model is integrated to estimate in situ fracture permeability spatially. This method relates fracture permeability to fracture dilation and scales dilation to the equivalent MEQ magnitude, according to laboratory observations. The seismic data are then applied to define incremental changes in permeability in both space and time. Our results confirm the excellent agreement between the ground truth and model- predicted permeability evolution. The resulting permeability map defines and quantifies flow paths in the reservoir with the averaged permeability comparing favorably with the ground truth of permeability.
Source: Hybrid machine learning model to predict 3D in-situ permeability evolution
About this Resource
| Last updated | unknown |
|---|---|
| Created | unknown |
| Name | Induced microearthquakes predict permeability creation in the brittle crust.pdf |
| Format | PDF File |
| License | Creative Commons Attribution |
| Created | 1 year ago |
| Media type | application/pdf |
| has views | False |
| id | 24679133-eb6f-4384-8065-52806de2d8e9 |
| metadata modified | 1 year ago |
| package id | e7669e04-ee4c-4461-8409-013e3cbc9557 |
| position | 3 |
| state | active |
| tracking summary | {'total': 0, 'recent': 0} |