Abstract: Examples of methods and systems are disclosed. The methods may include receiving seismic data regarding a subsurface region of interest, wherein the seismic data comprises a plurality of time-space waveforms, and generating, using statistical sampling, a plurality of pilot waveforms based on the plurality of time-space waveforms. The methods may also include forming a training seismic dataset comprising an input training dataset and an output training dataset, wherein the input training dataset is based on the plurality of time-space waveforms and the output training dataset is based on the plurality of pilot waveforms. The methods may further include training, using the seismic processing system and the training seismic dataset, a machine-learning (ML) model to predict the output training dataset, at least in part, from the input training dataset.

Abstract: A computer implemented method that enables detection and mitigation of drilling hazards is described. The method includes determining time-domain electromagnetic parameters and obtaining data at multiple heights by at least one unmanned aerial vehicle equipped with at least one receiver as a time-domain electromagnetic transmitter emits electromagnetic radiation based on the time-domain electromagnetic parameters. The method also includes fusing the obtained data to generate a subsurface model.

Abstract: Systems and methods for predicting seismic velocities for a subsurface formation include obtaining seismic data from a subsurface formation. The seismic data includes an amount of information about the seismic formation. A reduced seismic dataset is generated by applying an unsupervised machine learning model to cluster the seismic data. The reduced seismic dataset has less data than the seismic data, and the reduced seismic dataset includes the amount of information about the subsurface formation. Inversion models and forward modeling data are generated by performing a full waveform inversion for the reduced seismic dataset. The inversion models specify velocities in the subsurface formation and the forward modeling data specify response values of the inversion models. A supervised machine learning model is trained using the inversion models and the forward modeling data; and seismic velocities are predicted by providing the seismic data as input to the trained supervised machine learning model.

Abstract: Methods and systems for training a machine learning model to process microseismic data recorded during fracturing of a subterranean geological formation are configured for selecting a volume in the subterranean geological formation, the volume comprising a set of vertices and a center, the set of vertices defining a first dimension; determining seismogram data for sources at the vertices of the volume and at the center of the volume; generating training data from the seismogram data, the training data relating values of seismogram data to values of moment tensor components; training a machine learning model using the training data; and determining, based on the trained machine learning model, a second dimension defined for the set of vertices, the second dimension being a maximum value enabling an accuracy for outputs of the trained machine learning model that satisfies a threshold.

Abstract: A system and methods for determining an updated geophysical model of a subterranean region of interest are disclosed. The method includes obtaining a preprocessed observed geophysical dataset based, at least in part, on an observed geophysical dataset of the subterranean region of interest, and forming a training dataset composed of a plurality of geophysical training models and corresponding simulated geophysical training datasets. The method further includes iteratively determining a simulated geophysical dataset from a current geophysical model, determining a data loss function between the preprocessed observed geophysical dataset and the simulated geophysical dataset, training a machine learning (ML) network, using the training dataset, to predict a predicted geophysical model and determining a model loss function between the current and predicted geophysical models. The method still further includes updating the current geophysical model based on an inversion using the data loss and model loss functions.

Abstract: A system and methods for determining a refined seismic model of a subterranean region are disclosed. The method includes obtaining an observed seismic dataset and a current seismic model for the subterranean region and training a machine learning (ML) network using seismic training models and corresponding seismic training datasets and predicting, using the trained ML network, a predicted seismic model from the observed seismic dataset. The method further includes determining a simulated seismic dataset from the current seismic model and a seismic wavelet, a data penalty function based on a difference between the observed and the simulated seismic datasets and a model penalty function from the difference between the current the predicted seismic models. The method still further includes determining the refined seismic model based on an extremum of a composite penalty function based on a weighted sum of the data penalty function and the model penalty function.

Abstract: Disclosed are methods, systems, and computer-readable medium to perform operations including: receiving for a plurality of common midpoint-offset bins each comprising a respective plurality of seismic traces, respective candidate pilot traces representing the plurality of common midpoint-offset bins; generating, based on the respective candidate pilot traces, a respective plurality of corrected seismic traces for each of the plurality of common midpoint-offset bins; grouping the respective pluralities of corrected seismic traces into a plurality of enhanced virtual shot gathers (eVSGs); generating, based on the plurality of common midpoint-offset bins, a common-midpoint (CMP) velocity model; calibrating the CMP velocity model using uphole velocity data to generate a pseudo-3 dimensional (3D) velocity model; performing, based on the plurality of enhanced virtual shot gathers and the pseudo-3D velocity model, a 1.

Abstract: In some implementations, airborne electromagnetic (AEM) data and seismic data for a geographic region including sand dunes are received, and the AEM data identifies apparent resistivity as a function of depth within the sand dunes. An inversion with cross-domain regularization is calculated of the AEM data and the seismic data to generate a velocity-depth model, and the velocity depth model identifies velocity variations within the sand dunes. A seismic image using the velocity-depth model is generated.

Abstract: The subject matter of this specification can be embodied in, among other things, an unmanned aerial vehicle system includes a first loop airframe structure having a transmitter loop antenna and defining a plane, a second loop airframe structure having a receiver loop antenna having a diameter smaller than the transmitter loop antenna and oriented substantially parallel to the plane, a plurality of vertical thrusters configured to provide lift substantially perpendicular to the plane and elevate the system above a ground surface, at least one lateral thruster configured to provide thrust substantially parallel to the plane, a controller affixed configured to control the plurality of vertical thrusters and the lateral thruster, and an electromagnetic sensing system (such as ground-penetrating radar) configured to transmit electromagnetic signals using the transmitter loop antenna and receive secondary electromagnetic signals of secondary eddy currents caused by interactions between the EM signals and undergroun

Abstract: The subject matter of this specification can be embodied in, among other things, an unmanned aerial vehicle system includes a first loop airframe structure having a transmitter loop antenna and defining a plane, a second loop airframe structure having a receiver loop antenna having a diameter smaller than the transmitter loop antenna and oriented substantially parallel to the plane, a plurality of vertical thrusters configured to provide lift substantially perpendicular to the plane and elevate the system above a ground surface, at least one lateral thruster configured to provide thrust substantially parallel to the plane, a controller affixed configured to control the plurality of vertical thrusters and the lateral thruster, and an electromagnetic sensing system (such as ground-penetrating radar) configured to transmit electromagnetic signals using the transmitter loop antenna and receive secondary electromagnetic signals of secondary eddy currents caused by interactions between the EM signals and undergroun

Abstract: Ground penetrating radar (GPR) measurements from a downhole well tool in a wellbore are obtained to identify length of fractures adjacent the wellbore. A ground penetrating radar transmitter of the downhole tool emits an electromagnetic pulse. The electromagnetic wave of the ground penetrating radar is diffracted on encountering an end or tip of a fracture, which acts as a secondary source. The diffracted signal is then collected by downhole receiver(s) of the downhole tool. Length of the fracture is determined based on the time of travel of the electromagnetic wave from its emission until its collection as a diffracted signal by the downhole receiver(s).

Abstract: Ground penetrating radar (GPR) measurements from a downhole well tool in a wellbore are obtained to identify length of fractures adjacent the wellbore. A ground penetrating radar transmitter of the downhole tool emits an electromagnetic pulse. The electromagnetic wave of the ground penetrating radar is diffracted on encountering an end or tip of a fracture, which acts as a secondary source. The diffracted signal is then collected by downhole receiver(s) of the downhole tool. Length of the fracture is determined based on the time of travel of the electromagnetic wave from its emission until its collection as a diffracted signal by the downhole receiver(s).

Abstract: Ground penetrating radar (GPR) measurements from a downhole well tool in a wellbore are obtained to identify length of fractures adjacent the wellbore. A ground penetrating radar transmitter of the downhole tool emits an electromagnetic pulse. The electromagnetic wave of the ground penetrating radar is diffracted on encountering an end or tip of a fracture, which acts as a secondary source. The diffracted signal is then collected by downhole receiver(s) of the downhole tool. Length of the fracture is determined based on the time of travel of the electromagnetic wave from its emission until its collection as a diffracted signal by the downhole receiver(s).

Abstract: Ground penetrating radar (GPR) measurements from a downhole well tool in a wellbore are obtained to identify length of fractures adjacent the wellbore. A ground penetrating radar transmitter of the downhole tool emits an electromagnetic pulse. The electromagnetic wave of the ground penetrating radar is diffracted on encountering an end or tip of a fracture, which acts as a secondary source. The diffracted signal is then collected by downhole receiver(s) of the downhole tool. Length of the fracture is determined based on the time of travel of the electromagnetic wave from its emission until its collection as a diffracted signal by the downhole receiver(s).

Abstract: Ground penetrating radar (GPR) measurements from a downhole well tool in a wellbore are obtained to identify length of fractures adjacent the wellbore. A ground penetrating radar transmitter of the downhole tool emits an electromagnetic pulse. The electromagnetic wave of the ground penetrating radar is diffracted on encountering an end or tip of a fracture, which acts as a secondary source. The diffracted signal is then collected by downhole receiver(s) of the downhole tool. Length of the fracture is determined based on the time of travel of the electromagnetic wave from its emission until its collection as a diffracted signal by the downhole receiver(s).

Abstract: Ground penetrating radar (GPR) measurements from a downhole well tool in a wellbore are obtained to identify length of fractures adjacent the wellbore. A ground penetrating radar transmitter of the downhole tool emits an electromagnetic pulse. The electromagnetic wave of the ground penetrating radar is diffracted on encountering an end or tip of a fracture, which acts as a secondary source. The diffracted signal is then collected by downhole receiver(s) of the downhole tool. Length of the fracture is determined based on the time of travel of the electromagnetic wave from its emission until its collection as a diffracted signal by the downhole receiver(s).

Abstract: In some implementations, airborne electromagnetic (AEM) data and seismic data for a geographic region including sand dunes are received, and the AEM data identifies apparent resistivity as a function of depth within the sand dunes. An inversion with cross-domain regularization is calculated of the AEM data and the seismic data to generate a velocity-depth model, and the velocity depth model identifies velocity variations within the sand dunes. A seismic image using the velocity-depth model is generated.