#6369. Joint event location and velocity model update in real-time for downhole microseismic monitoring: A deep learning approach

November 2026publication date
Proposal available till 21-06-2025
4 total number of authors per manuscript0 $

The title of the journal is available only for the authors who have already paid for
Journal’s subject area:
Computers in Earth Sciences;
Information Systems;
Places in the authors’ list:
place 1place 2place 3place 4
FreeFreeFreeFree
2350 $1200 $1050 $900 $
Contract №6369.1 Contract №6369.2 Contract №6369.3 Contract №6369.4
1 place - free (for sale)
2 place - free (for sale)
3 place - free (for sale)
4 place - free (for sale)

Abstract:
Accurate event location in downhole microseismic monitoring depends largely on how accurate the velocity model is reconstructed. On the flipside, construction of an accurate velocity model is always an uphill task marred with numerous uncertainties. However, with proper inversion approach, both the model and the location of the events can be jointly evaluated. We propose, in this study, a deep learning approach for locating microseismic events and performing velocity model update, in real-time, efficiently and accurately. Both tasks were considered as a multi-dimensional and non-linear regression problem and a multi-layer two-dimensional (2D) convolutional neural network (CNN) was designed to perform the inversions. In training the neural network, low signal-to-noise ratio (SNR) synthetic microseismic data were used to mimic field data.
Keywords:
Deep learning; Event location; Microseismic monitoring; Neural network; Velocity model

Contacts :
0