EXTRAPOLATION OF SEISMIC DATA TO REDUCE PROCESSING EDGE ARTIFACTS
Examples of methods and systems are disclosed. The methods may include obtaining, using a seismic processor, a training seismic dataset, comprising an input seismic dataset with a first extent and an output seismic dataset with a second extent, wherein the second extent is greater than the first extent. The methods may also include training, using the seismic processor and the training seismic dataset, a machine-learning (ML) network to predict the output seismic dataset, at least in part, from the input seismic dataset.
1 . A method, comprising:
obtaining, using a seismic processor, a training seismic dataset, comprising an input seismic dataset with a first extent and an output seismic dataset with a second extent, wherein the second extent is greater than the first extent; and
training, using the seismic processor and the training seismic dataset, a machine-learning (ML) network to predict the output seismic dataset, at least in part, from the input seismic dataset.
2 . The method of claim 1 , further comprising:
obtaining an observed seismic dataset pertaining to a subsurface region of interest with a third extent; and
predicting, using the seismic processor and the trained ML network, an extended seismic dataset with a fourth extent, at least in part, from the observed seismic dataset, wherein the fourth extent is greater than the third extent.
3 . The method of claim 1 , wherein the training seismic dataset comprises a synthetic seismic dataset.
4 . The method of claim 3 , wherein the synthetic seismic dataset comprises:
a plurality of synthetic events of seismic reflectivity having a geometrical trajectory in space-time; and
at least one seismic wavelet.
5 . The method of claim 4 , wherein the synthetic seismic dataset further comprises random perturbations to at least one of the at least one seismic wavelet and the geometrical trajectory.
6 . The method of claim 1 , wherein the ML network is a convolutional neural network.
7 . The method of claim 1 , wherein training the ML network comprises supervised learning.
8 . The method of claim 2 , wherein the first extent, the second extent, the third extent, and the fourth extent are each a spatial extent.
9 . The method of claim 2 , further comprising:
determining, using the seismic processor, a seismic image of the subsurface region of interest based, at least in part, on the extended seismic dataset; and
determining, using a seismic interpretation workstation, a drilling target in the subsurface region of interest based, at least in part, on the seismic image.
10 . The method of claim 9 , further comprising:
planning, using a wellbore planning system, a planned wellbore trajectory to intersect the drilling target; and
drilling, using a drilling system, a wellbore guided by the planned wellbore trajectory.
11 . A non-transitory computer-readable medium storing computer-executable instructions stored thereon that, when executed by a computer processor, cause the computer processor to perform steps of:
obtaining a training seismic dataset, comprising an input seismic dataset with a first extent and an output seismic dataset with a second extent, wherein the second extent is greater than the first extent; and
training, using the training seismic dataset, a machine-learning (ML) network to predict the output seismic dataset, at least in part, from the input seismic dataset.
12 . The non-transitory computer-readable medium of claim 11 , the steps further comprising:
receiving an observed seismic dataset pertaining to a subsurface region of interest with a third extent; and
predicting, using the trained ML network, an extended seismic dataset with a fourth extent, at least in part, from the observed seismic dataset, wherein the fourth extent is greater than the third extent.
13 . The non-transitory computer-readable medium of claim 11 , wherein the training seismic dataset comprises a synthetic seismic dataset.
14 . The non-transitory computer-readable medium of claim 13 , wherein the synthetic seismic dataset comprises:
a plurality of synthetic events of seismic reflectivity having a geometrical trajectory in space-time; and
at least one seismic wavelet.
15 . The non-transitory computer-readable medium of claim 11 , wherein the ML network is a convolutional neural network.
16 . A system, comprising:
a seismic acquisition system configured to record an observed seismic dataset pertaining to a subsurface region of interest; and
a seismic processor, configured to:
obtain a training seismic dataset, comprising an input seismic dataset with a first extent and an output seismic dataset with a second extent, wherein the second extent is greater than the first extent;
train, using the training seismic dataset, a machine-learning (ML) network to predict the output seismic dataset, at least in part, from the input seismic dataset;
obtain an observed seismic dataset pertaining to a subsurface region of interest with a third extent; and
predict, using the trained ML network, an extended seismic dataset with a fourth extent, at least in part, from the observed seismic dataset, wherein the fourth extent is greater than the third extent.
17 . The system of claim 16 , wherein the training seismic dataset comprises a synthetic seismic dataset.
18 . The system of claim 16 , wherein the ML network is a convolutional neural network.
19 . The system of claim 16 , further comprising:
a seismic processor, configured to determine a seismic image of the subsurface region of interest based, at least in part, on the extended seismic dataset; and
a seismic interpretation workstation, configured to determine a drilling target in the subsurface region of interest based, at least in part, on the seismic image.
20 . The system of claim 19 , further comprising:
a wellbore planning system, configured to plan a planned wellbore trajectory to intersect the drilling target; and
a drilling system configure to drill a wellbore guided by the planned wellbore trajectory.