Abstract: This specification describes workflows for, but is not limited to, performing full waveform inversion (FWI) to build high resolution velocity models to improve the accuracy of seismic imaging of a subterranean formation. This specification describes processes to automatically edit and enhance S/N quality of seismic data (such as land seismic data) to prepare the datasets for FWI. The methods for automatic corrections and pre-processing include: automatic iterative surface-consistent residual statics calculation, automatic rejection of anomalous traces (such as dead traces), and the automatic correction of surface-consistent amplitude anomalies (such as by scalar or deconvolution approaches). The operations include automatic “muting” of noise before first arrivals.

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: Systems and methods for removing galvanic distortion caused by near-surface inhomogeneities from surface-to-borehole (STB) measurements are disclosed. Corrected STB measurements may provide for a representation of the resistivity of an oil-bearing reservoir and may be used to determine movement of a waterfront within the reservoir caused by waterflooding of the reservoir.

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

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: A method may include obtaining various seismic traces for a geological region of interest. The method may further include determining an offset attribute and an azimuthal attribute. The method may further include determining, using the offset attribute and the azimuthal attribute, a virtual trace bin for the geological region of interest. The method may further include generating a virtual trace using a subset of the seismic traces and corresponding to the virtual trace bin. The method may further include generating a velocity model for the geological region of interest using a virtual shot gather including the virtual trace and various virtual traces. A respective virtual trace among the virtual traces may correspond to a respective virtual trace bin among various virtual trace bins. The method may further include generating a seismic image of the geological region of interest using the velocity model.

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: Methods and systems for identifying near-surface heterogeneities in a subterranean formation using surface seismic arrays can include: recording raw seismic data using sensors at ground surface; applying a band bass filter to the raw seismic data using a central frequency; picking a phase arrival time for the filtered data; generating an initial starting phase velocity model for tomographic inversion from the raw seismic data; applying tomographic inversion to the filtered data to generate a dispersion map associated at the central frequency; repeating the applying a band bass filter, picking a phase arrival time, generating an initial starting velocity model, and applying tomographic inversion steps for each of a set of central frequencies; and generating a three-dimensional dispersion volume representing near-surface conditions in the subterranean formation by combining the dispersion maps.

Abstract: A method for determining a separation distance between a working head in a machine for laser processing a material and a surface of the material includes generating a measurement beam of low coherence optical radiation, leading the measurement beam towards the material and a reflected or diffused measurement beam towards an optical interferometric sensor arrangement in a first direction of incidence, generating a reference beam of low coherence optical radiation, leading the reference beam towards the optical interferometric sensor arrangement in a second direction of incidence superimposing the measurement and reference beams on a common region of incidence, detecting a position of a pattern of interference fringes between the measurement and reference beams on the common region of incidence, and determining a difference in optical length between the measurement and reference optical paths based on the position of the pattern of interference fringes along an illumination axis.

Abstract: A method and a system for determining relative position of an element of an optical system of an assembly for processing or measuring an object along a measurement line, involve generating a measurement beam and a reference beam of low coherence optical radiation. The measurement and reference beams, alternately or in combination, have a main beam and a multiplexed additional beam. The measurement beam, led toward the element of the optical system, and back-reflected, is superimposed on the reference beam in a region of common incidence of an interferometric optical sensor arrangement. Position or frequency of a main interference fringe pattern and an additional interference fringe pattern is detected.

Abstract: A method for determining local position of an optical element associated with an optical path for transporting a laser beam in a working head of a machine for laser processing a material, includes generating a measurement beam of low coherence optical radiation traveling a measurement optical path, leading the measurement beam towards the optical element and the reflected or diffused measurement beam towards an optical interferometric sensor arrangement, generating a reference beam of low coherence optical radiation traveling a reference optical path and leading the reference beam towards the interferometric optical sensor arrangement, superimposing the measurement and reference beams on a common region of incidence, detecting a position of a pattern of interference fringes between the measurement and reference beams, and determining a difference in optical length between the measurement and reference optical paths as a function of the position of the interference pattern along an illumination axis, or of the

Abstract: In seismic imaging, accurate velocity functions (velocity model) defining seismic velocity as a function of depth in the earth are required. The velocity model is obtained as a result of seismic surveying. Delayed travel times in near surface refraction seismic surveys, an effect known as shingling, can result from an anomalous condition, seismic velocity decreasing with depth. Inclusion of such delayed travel times in a tomographic process for seismic imaging would otherwise cause large errors in determination of a seismic velocity model for seismic imaging of subsurface features. At locations (source-receiver offset) in the survey where the shingling occurs, the velocity inversions are identified. The undesirable effects the delayed travel times caused by the velocity inversions are removed from the survey dataset.

Abstract: An example method is for producing a seismic wave velocity model of a subsurface area. The method includes receiving, by a processor of a computing system, from a seismic receiver, seismic data input comprising a recorded seismic wave field. The method includes receiving, by the processor, an initial 1D velocity model of the subsurface area. The method includes determining, by the processor, a Laplace-Fourier transform of the recorded seismic wave field. The method includes regenerating, by the processor, the current 1D velocity model to generate inverted data representing the subsurface area. The method may include performing, by the processor, an upscaling of a plurality of 1D velocity models to produce a 3D velocity model.

Abstract: This specification describes workflows for, but is not limited to, performing full waveform inversion (FWI) to build high resolution velocity models to improve the accuracy of seismic imaging of a subterranean formation. This specification describes processes to automatically edit and enhance S/N quality of seismic data (such as land seismic data) to prepare the datasets for FWI. The methods for automatic corrections and pre-processing include: automatic iterative surface-consistent residual statics calculation, automatic rejection of anomalous traces (such as dead traces), and the automatic correction of surface-consistent amplitude anomalies (such as by scalar or deconvolution approaches). The operations include automatic “muting” of noise before first arrivals.

Abstract: A variable-frequency light source is configured to emit a light beam and modulate a frequency of the light beam. A fiber optic cable is attached to the variable frequency light source. The fiber optic cable is configured to receive the light beam at an inlet and pass the light beam to an exit. Multiple optical detectors are attached to the fiber optic cable. Each of the optical detectors is configured to detect a specified frequency of light that is backscattered through the fiber optic cable. An actuation mechanism is attached to the fiber optic cable. The actuation mechanism is configured to deform the fiber optic cable in response to a stimulus.

Abstract: An exemplary dissolvable assembly is provided. The dissolvable assembly includes a skeleton made from a first material, and a body made from a second material, wherein the first material and the second material dissolve at different rates.

Abstract: Methods for full waveform inversion (FWI) in the midpoint-offset domain include using a computer system to sort seismic traces into common midpoint-offset bins (XYO bins). For each XYO bin, a linear moveout correction is applied to a collection of seismic traces within the XYO bin. The collection of seismic traces is stacked to form a pilot trace. The computer system determines a surface-consistent residual static correction for each seismic trace. The computer system determines that the surface-consistent residual static correction for each seismic trace is less than a threshold. Responsive to the determining that the surface-consistent residual static correction is less than the threshold, the computer system stacks the collection of seismic traces to provide the pilot trace. The computer system groups the pilot traces for the XYO bins into a set of virtual shot gathers. The computer system performs one-dimensional FWI based on each virtual shot gather.

Abstract: A method for a physics-driven deep learning-based inversion coupled to fluid flow simulators may include obtaining measured data for a subsurface region, obtaining prior subsurface data for the subsurface region, and obtaining a physics-driven standard regularized joint inversion for at least two model parameters. The method may further include obtaining a case-based deep learning inversion characterized by a contracting path and an expansive path. The method may further include forming the physics-driven deep learning inversion with the physics-driven standard regularized joint inversion, the case-based deep learning inversion, and a coupling operator based on a penalty function. The method may further include forming a feedback loop between the physics-driven standard regularized joint inversion and the case-based deep learning inversion for re-training the case-based deep learning inversion. The method may further include generating an inversion solution for reservoir monitoring.

Abstract: Naturally-occurring, electromagnetic signals generated by interaction of solar wind with earth's magnetosphere adjacent a borehole are measured by an electromagnetic sensor positioned adjacent the borehole in the hydrocarbon-bearing formation. Electromagnetic signals generated within the borehole are measured over a period of time by a borehole sensor positioned within the borehole. The electromagnetic signals change over the period of time due to variations in fluid distributions within the hydrocarbon-bearing formation. Electromagnetic changes to the electromagnetic signals generated within the borehole and to the passive, naturally-occurring electromagnetic signals over the period of time are determined by one or more processors. A computational model of the hydrocarbon-bearing formation is generated based in part on the electromagnetic changes.

Abstract: Seismic data from seismic exploration surveys are mapped into a hypercube of bins or voxels in a four-dimensional space (X, Y, Offset, and Azimuth) according to Common Mid-Point (or CMP) between source and receivers. The mapped data from individual voxels or bins is then analyzed by multimodal statistics. Robust estimates of first break picks are obtained from the analysis. The first break picks are then used to as seed inputs for autopicking iteration, which proceeds to convergence. Estimates of confidence levels in the data are provided for re-picking to reduce computer processing time in successive autopicking iterations. Analysis is provided of different seismic attributes such as azimuthal velocity variations indicative of anisotropy, positioning errors of sources/receivers, geometry errors, and three dimensional distribution of inversion residuals. Analysis is also performed of standard deviation of the travel time data useful for estimating data errors in the inversion covariance matrix.