Abstract: A rear axle center (RAC) locating system may include a tractor and a RAC location acquisition unit. The tractor may include a rear axle having a center, a global positioning system (GPS) antenna offset from the rear axle, and inertial measurement units. The RAC location acquisition unit may include a processing unit and a non-transitory computer-readable medium containing instructions to direct the processing unit to determine a geographic location of the GPS antenna based upon signals received by the GPS antenna and determine a geographic location of the center of the rear axle based upon the geographic location of the GPS antenna and combined data from the inertial measurement units.

Abstract: Methods, computing systems, and computer-readable media for interpreting seismic data, of which the method includes receiving seismic data representing a subterranean volume, and determining a feature-likelihood attribute of at least a portion of a section of the seismic data. The feature-likelihood attribute comprises a value for elements of the section, the value being based on a likelihood that the element represents part of a subterranean feature. The method also includes identifying contours of the subterranean feature based in part on the feature-likelihood attribute of the section, and determining a polygonal line that approximates the subterranean feature.

Abstract: 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.

Abstract: Embodiments of the present disclosure are directed towards systems and method for characterizing wettability. Embodiments may include acquiring, using at least one processor, multi-physics data and transforming the multi-physics data into transformed data that is more sensitive to wettability than the multi-physics data. Embodiments may further include processing, using a data processing engine, the acquired and transformed data using at least one of a data correlation technique and an inversion technique.

Abstract: A vehicle identification system configured to identify a type of a target vehicle includes a first reflector configured to be disposed on an outer surface of the target vehicle, a second reflector configured to be disposed on the outer surface of the target vehicle and spaced apart from the first reflector, and a vehicle identification apparatus configured to identify the type of the target vehicle based on light reflected by the first reflector and the second reflector. The vehicle identification apparatus includes: a laser sensor configured to emit light toward the target vehicle and detect a first reflected light reflected by the first reflector and a second reflected light reflected by the second reflector, and a processor configured to identify the type of the target vehicle based on an intensity of each of the first reflected light and the second reflected light.

Abstract: A failure detection system detects a failure of a sensor that detects a state of a semiconductor manufacturing apparatus. The failure detection system includes a generation unit configured to generate times-series data of information on a detection value of the sensor during a determination period, a calculation unit configured to calculate a regression line of the times-series data, and a failure determination unit configured to determine whether the sensor has failed based on a slope of the regression line.

Abstract: Examples describe systems and methods for iteratively determining a signal strength map for a wireless access point (AP) aligned to position coordinates. An example method includes receiving logs of data from devices. For a plurality of iterations, the method includes selecting a set of logs of data having an amount of GPS being less than a given amount of GPS in a previously selected set, determining estimates of signal strength maps for the wireless AP aligned to position coordinates based on the selected set and on given signal strength maps due to a previous iteration, and performing a simultaneous localization and mapping (SLAM) optimization of the possible locations of the wireless AP based on the given signal strength maps and the estimates of the signal strength maps. Based on the iterative optimizations, an output signal strength map is provided for the wireless AP aligned to position coordinates.

Abstract: A method and apparatus for automated seismic interpretation (ASI), including: obtaining trained models comprising a geologic scenario from a model repository, wherein the trained models comprise executable code; obtaining test data comprising geophysical data for a subsurface region; and performing an inference on the test data with the trained models to generate a feature probability map representative of subsurface features. A method and apparatus for machine learning, including: an ASI model; a training dataset comprising seismic images and a plurality of data portions; a plurality of memory locations, each comprising a replication of the ASI model and a different data portion of the training dataset; a plurality of data augmentation modules, each identified with one of the plurality of memory locations; a training module configured to receive output from the plurality of data augmentation modules; and a model repository configured to receive updated models from the training module.

Abstract: Systems and methods include a method for generating a real-time reservoir porosity log. Historical gas-porosity data is received from previously-drilled and logged wells. The historical gas-porosity data identifies relationships between gas measurements obtained during drilling and reservoir porosity determined after drilling. A gas-porosity model is trained using machine learning and the historical gas-porosity data. Real-time gas measurements are obtained during drilling of a new well. A real-time reservoir porosity log is generated for the new well using the gas-porosity model and real-time gas measurements.

Abstract: A baseline unit for use in a sensor system, the sensor system comprising N force sensors which output N sensor signals, respectively, where N?1, the baseline unit configured to: monitor a measure of a rate of change of a sensor test signal, the sensor test signal being one of said N sensor signals or a signal derived from one or more of said N sensor signals; and in dependence upon the measure of the rate of change, control a stored baseline setting to control how a baseline test signal is calculated from said N sensor signals using a baseline-calculation method, the baseline-calculation method configured by the currently-stored baseline setting.

Abstract: Systems and methods for interactions with power station noise patterns are disclosed. A system can include a data collector communicatively coupled to a plurality of input channels, wherein at least one of the plurality of input channels is operatively coupled to a vibration detection facility structured to detect a noise pattern of a power station, a library structured to store the detected noise pattern, an interface circuit structured to make the noise pattern available to a noise pattern marketplace, the noise pattern marketplace including a plurality of noise patterns from a plurality of power stations; and a user interface for accessing at least one of the plurality noise patterns of the noise pattern marketplace.

Abstract: Systems and methods for simulating subterranean regions having multi-scale fracture geometries. Non-intrusive embedded discrete fracture modeling formulations are applied in conjunction with commercial simulators to efficiently and accurately model subsurface transient flow characteristics in regions having complex hydraulic fractures, complex natural fractures, or a combination of both, and geometries including corner point grids.

Abstract: Systems and methods for selecting a logging deployment option are provided. One embodiment of a method includes providing a user interface that provides at least one user option regarding well data about a well, mud data about mud, and logging tools configuration data about logging tools at the well, receiving the well data about the well, the mud data about mud, and logging tools configuration data about logging tools at the well, and determining whether at least one of the well data, the mud data, or logging tools configuration data includes a trigger for determining a desired logging option. In response to determining that, at least one of the well data, the mud data, or logging tools configuration data includes a trigger for determining the desired logging option, selecting the desired logging option and providing the desired logging option for display.

Abstract: A method includes receiving a model representing a real subsoil geological formation. The model includes a stratigraphic layer, which includes a shore line dividing the stratigraphic layer into a continental zone and a marine zone. First and second flow speed fields are received, with the first flow speed field representative of a continental domain for the stratigraphic layer, and the second flow speed field representative of a marine domain for the stratigraphic layer. A global flow speed field is determined and includes a weighted combination of the first and second flow speed fields for each position in the stratigraphic layer. Weights of the combination are based on a distance of the position to the shore line and whether the position is within the continental zone or the marine zone. The real subsoil geological formation for the stratigraphic layer is determined based on the determined global flow speed field.

Abstract: Seismic exploration of an underground formation uses seismic excitations to probe the formation's properties such as reflectivity that can be imaged using reverse time migration. Using an equal area spherical binning at reflection points improves and simplifies RTM imaging together with adaptability to the data acquisition geometry, while overcoming drawbacks of conventional cylindrical binning.

Abstract: A method for providing sensor data, including providing a maximum measuring range for the sensor, providing a first measuring range which is within the maximum measuring range, providing the sensor data in a data structure having a size that corresponds to the first measuring range, providing a second measuring range which is different from the first measuring range and is within the maximum measuring range, adapting the provided sensor data of the first measuring range for the second measuring range so that the adapted sensor data are provided in an expanded data structure having a size that corresponds to the maximum measuring range, and the provided sensor data being arranged as a function of the difference of the size between the maximum and the second measuring range and the size of the second measuring range within the expanded data structure, and the sensor-data-free sections being filled with values.

Abstract: A compass includes a magnetic sensing element in the form of a coil surrounding magnetic material. Electric current is supplied to the coil in opposite directions, depending on the state of switches operated at times T0, for a first direction, and T5, for a reverse current direction. A voltmeter measures voltages across the coil, namely at least V1 at time T1, after T0, and V2 at time T2, after T5, with T1?T0=T2?T5=predetermined ?T. A processor indicates V1?V2, the magnitude and sign of which indicate the strength and direction of the earth's magnetic field respectively.

Abstract: A stemming system includes a computing device and a stemming device. The computing device includes data processing hardware and memory hardware in communication with the data processing hardware. The data processing hardware includes a transmitter and a receiver. The stemming device is communicatively-coupled to the computing device. The stemming device includes a base portion and a valve-engaging portion. The valve-engaging portion includes a transducer that obtains a measurement communicated to the receiver of the computing device. The measurement includes at least one physical parameter associated with installing a tire-wheel assembly valve to a wheel throughout a process of disposing the valve within a valve hole of the wheel. The processor analyzes a data signature associated with the measurement for determining if the valve has been adequately or inadequately installed by the stemming device.

Abstract: A method is described in which a plurality of magnetic field norms is obtained using a plurality of magnetic field sensor units, each of the plurality of magnetic field sensor units being arranged to measure a magnetic field at a different height while being mounted to an object, each of the plurality of magnetic field norms being a norm of the magnetic field measured by a corresponding one of the plurality of magnetic field sensor units; and the plurality of magnetic field norms is matched with a magnetic field map of an indoor area including a plurality of sub-areas to estimate a location of the object as one of the plurality of sub-areas.

Abstract: Disclosed are systems and methods for obtaining input data comprising properties associated with at least one parent well and a child well associated with the at least one parent well, dividing the input data into a training data subset, a validation data subset, and a test data subset, selecting at least one machine learning model using the training data subset, the validation data subset, and the test data subset based on k-fold cross validation, tuning hyper parameters for each of the at least one machine learning model, and generating a learning output using the at least one machine learning model and the hyper parameters for each of the at least one machine learning model, the learning output indicating a test root-mean-square error (RMSE) and a training RMSE.