A system for elevator door sensor fusion, fault detection, and service notification is provided. The system includes a processor and a memory including computer-executable instructions that, when executed by the processor, cause the processor to perform operations. The operations include monitoring outputs of a plurality of sensors. It is determined whether the outputs of the plurality of sensors follow an expected pattern. Based at least in part on determining that the outputs of the plurality of sensors do not follow the expected pattern: identifying a sensor in the plurality of sensors that is not following the expected pattern, and transmitting a notification indicating that the sensor is exhibiting unexpected behaviour.

A hybrid sensing apparatus for collecting data inside a well, the apparatus including an optical cable that acquires a first set of data; and an array of discrete probes connected to each other with an electrical cable. The discrete probes are configured to acquire a second set of data. The apparatus further includes an attachment system attached to the discrete probes and configured to hold the optical cable. The attachment system is configured to expose the optical cable to directly contact the well.

In an embodiment, a method is performed by a computer system. The method includes integrating a series of data inputs related to a well. The series of data inputs includes at least one real-time data input and at least one non-real-time data input. The method further includes based, at least in part, on a result of the integrating, facilitating a real-time display of performance data for the well. The real-time display includes information related to at least one of hydraulic surveillance and torque-and-drag surveillance.

In an embodiment, a method is performed by a computer system. The method includes integrating a series of data inputs related to a well. The series of data inputs includes at least one real-time data input and at least one non-real-time data input. The method further includes based, at least in part, on a result of the integrating, facilitating a real-time display of performance data for the well. The real-time display includes information related to at least one of hydraulic surveillance and torque-and-drag surveillance.

A surface wave prospecting method for jointly extracting Rayleigh wave frequency dispersion characteristics in a seismoelectric field. A surface wave prospecting method includes following steps of: acquiring jointly acquired data, where the jointly acquired data includes seismic wave data and electric field data; carrying out jointly imaging processing on jointly acquired data to obtain a superposed frequency dispersion spectrum; carrying out extraction processing on superposed frequency dispersion spectrum to obtain a frequency dispersion curve, outperforming inversion processing on frequency dispersion curve to obtain a stratum structure profile. As seismic wave data and electric field data are adopted to carry out combined imaging processing to obtain superposed frequency dispersion spectrum, multi-mode frequency dispersion curve is extracted, multiplicity of solutions of inversion is greatly reduced during inversion, precision and stability of surface wave prospecting are greatly improved.

An optical seismic surveying system including, a multibeam laser source including a plurality of laser-sources, a Diffractive-Optical-Element (DOE), an imager and a processor. The laser-sources direct respective laser-beams toward a single common focal point. The DOE is located at a single common focal point and configured to split each laser-beam into a plurality of laser-beams, toward an instantaneous area of interest. The laser-beams impinging on the instantaneous area of interest produce a laser spot assemblage including a plurality of laser spots. The imager acquires a plurality of defocused images of speckle patterns produced by diffused reflections of the laser spots. The speckle pattern correspond to a respective laser spot and thus to a respective sensing point in the instantaneous area of interest. The processor determines a relative displacement between corresponding speckle patterns in sequential pairs of images and determines a respective time-signal for each sensing point representing vibrations thereat.

An optical seismic surveying system including, a multibeam laser source including a plurality of laser-sources, a Diffractive-Optical-Element (DOE), an imager and a processor. The laser-sources direct respective laser-beams toward a single common focal point. The DOE is located at a single common focal point and configured to split each laser-beam into a plurality of laser-beams, toward an instantaneous area of interest. The laser-beams impinging on the instantaneous area of interest produce a laser spot assemblage including a plurality of laser spots. The imager acquires a plurality of defocused images of speckle patterns produced by diffused reflections of the laser spots. The speckle pattern correspond to a respective laser spot and thus to a respective sensing point in the instantaneous area of interest. The processor determines a relative displacement between corresponding speckle patterns in sequential pairs of images and determines a respective time-signal for each sensing point representing vibrations thereat.

A method and system of machine learning-augmented geophysical inversion includes obtaining measured data; obtaining prior subsurface data; (a) partially training a data autoencoder with the measured data to learn a fraction of data space representations and generate a data space encoder; (b) partially training a model autoencoder with the prior subsurface data to learn a fraction of model space representations and generate a model space decoder; (c) forming an augmented forward model with the model space decoder, the data space encoder, and a physics-based forward model; (d) solving an inversion problem with the augmented forward model to generate an inversion solution; and iteratively repeating (a)-(d) until convergence of the inversion solution, wherein, for each iteration: partially training the data and model autoencoders starts with learned weights from an immediately-previous iteration; and solving the inversion problem starts with super parameters from the previous iteration.

The disclosure presents a process for sharpening an image data representation of collected measurements from a subterranean formation. The sharpening process utilizes an azimuthal filter applied to azimuthal radial ranges around a borehole to designate azimuthal bins. The azimuthal filter utilizes a set of filter coefficients to modify an azimuthal target bin. The set of filter coefficients is a devolution set as it contains at least one positive and one negative filter coefficient. The filter ratio of positive to negative filter coefficients can be adjusted utilizing the statistical uncertainty of the collected measurements and a targeted filter ratio. In some aspects, an axial filter process, also using a binning methodology, can be applied to the collected measurements, where the azimuthal and axial filtered values can be combined for the final image representation. The azimuthal and axial processes can be executed in serial or parallel process flows.

Processes and systems for correlating well log data sets from well logging passes within a well bore. In some embodiments, a process for well log depth matching can include normalizing a first well log from a first logging pass obtained within a well bore and a second well log from a second logging pass obtained within the well bore, performing a pre-shift, performing feature picking to identify one or more features along the second well log, performing normalized cross-correlation based optimization between the first well log and the second well log to match the one or more features along the second well log to the same one or more features of the first well log and generating a shift table for depth shifting the one or more features of the second well log and the first well log.