A method comprises sampling, at a first sampling rate for a first time window, data values of at least one operational parameter of equipment. The method comprises sampling, at a second sampling rate for a second time window, the data values of the at least one operational parameter, wherein the second sampling rate is different from the first sampling rate. The method comprises classifying, using a machine learning model and the data values in the first time window and the second time window, an operational mode of the equipment into different failure categories.

A system can control a transmission of a pressure signal subsea into a flowline comprising a fluid. The system can receive sensor data indicating one or more properties of a first reflection signal corresponding to the pressure signal in the flowline. The system can adjust a model based on the one or more properties of the first reflection signal. The model can be configured for determining a presence of a material deposition in the flowline. The system can determine, based on a second reflection signal and the adjusted model, a presence of the material deposition in the flowline. The system can output a command configured to initiate a remediation operation to reduce the material deposition in the flowline.

An example computer-implemented method for transmitting data from a downhole location to the earth's surface. The method includes sensing, with one or more sensors, sensor data downhole, the sensor data comprising a plurality of data value sets. The method further includes assigning at least one data value of each of the plurality of data value sets to each of a plurality of time levels or depth levels to generate a data block. The method further includes compressing, with a first processor in the drilling assembly, the data block by a block-based compression technique to generate compressed data. The method further includes transmitting, with a telemetry system, the compressed data from the downhole location to the surface. The method further includes decompressing, with a second processor at the surface, the compressed data to generate decompressed data values. The method further includes controlling the drilling assembly based on the decompressed data values.

An apparatus, methods and systems for data collection related to an oil and gas process and disclosed. A system may include a multi-sensor acquisition component including a plurality of inputs and a plurality of outputs, a sensor data storage profile circuit structured to determine a data storage profile including a data storage plan for the plurality of inputs, a sensor communication circuit structured to interpret a plurality of inputs, a sensor data storage implementation circuit structured to store at least a portion of the inputs in response to the data storage profile, a data analysis circuit structured to analyze the plurality of inputs and determine a data quality parameter, and a data response circuit structured to adjust at least one of the data storage profile and the data collection routine in response to the data quality parameter.

A platform for facilitating development of intelligence in an Industrial Internet of Things (IIoT) system generally includes a plurality of distinct data-handling layers having an industrial monitoring systems layer that collects data from or about a plurality of industrial entities in an industrial environment; an industrial entity-oriented data storage systems layer that stores the data collected by the industrial monitoring systems layer; and an adaptive intelligent systems layer that facilitates the coordinated development and deployment of intelligent systems in the IIoT system; wherein the adaptive intelligent systems layer includes an adaptive edge compute management system that adaptively manages edge computation, storage, and processing in the IIoT system.

A computer-implemented method may include receiving test data representing a cutter/rock interaction for a cutter/rock pair; calibrating an analytical model to represent the cutter/rock interaction mechanism for a cutter/rock pair; applying the calibrated analytical model to expand the test data to form one of a plurality of expanded test datasets; generating a first neural network model, of a plurality of first neural network models, representing cutter/rock interaction between a plurality of cutters of different cutter sizes and a particular rock type, wherein the first neural network is generated using the plurality of expanded test datasets as training input; generating a second neural network model using the plurality of first neural network models as training input, wherein the second neural network model represents non-tested cutter/rock interactions between a plurality of cutters of different cutter sizes and a plurality of rock types.

Methods and systems for a data marketplace in a conveyor environment includes a self-organizing data marketplace. The self-organizing data marketplace includes at least one data collector and at least one corresponding conveyor in an industrial environment, wherein the at least one data collector is structured to collect detection values from at least one sensor of a power roller of the at least one corresponding conveyor; a data storage structured to store a data pool comprising at least a portion of the detection values; a data marketplace structured to self-organize the data pool; and a transaction system structured to interpret a user data request, and to selectively provide a portion of the self-organized data pool to a user in response to the user data request.

In accordance with some embodiments of the present disclosure, systems and methods for a nonlinear toolface control system for a rotary steerable drilling tool is disclosed. The method includes determining a desired toolface of a drilling tool, calculating a toolface error by determining a difference between a current toolface and the desired toolface, generating a model to describe the dynamics of the drilling tool, modify the model, based on at least one intermediate variable, to create a modified model, calculating a correction to reduce the toolface error, the correction based on the modified model, transmitting a signal to the drilling tool such that the signal adjusts the current toolface based on the correction, and drilling a wellbore with a drill bit oriented at the desired toolface.

Examples of techniques for monitoring and controlling the pH of a drilling fluid are disclosed. In one example implementation, a system may include a first sensor to sense a first pH-value and an associated first temperature of the drilling fluid prior to being heated by a drilling fluid heater and a second sensor to sense a second pH-value and an associated second temperature of the drilling fluid subsequent to being heated by the drilling fluid heater. The system may also include a controller comprising a memory having computer readable instructions and a processing device for executing the computer readable instructions. The computer readable instructions include receiving the first pH-value and first temperature from the first sensor, receiving the second pH-value and second temperature from the second sensor, and determining an amount of additive to add to the drilling fluid to maintain a desired pH-value at the second temperature.

A portable device to drill holes has a platform. A plurality of wheel sets is coupled to the platform. A drive system is used for driving the plurality of wheels. An attachment mechanism is positioned on an underside of the platform for securing the device to a surface. A control board is used for controlling the operation of the device. A drill spindle assembly is coupled to the platform. A drill feed assembly is coupled to the drill spindle assembly for raising and lowering the drill spindle assembly. A plurality of sensors are operable to sense one or more magnets disposed below the surface. A drive table is used for positioning the drill spindle assembly in an XY plane based on an output of said plurality of sensors.