Abstract: Systems and methods are disclosed for dynamically smoothing images based on network conditions to adjust a bitrate needed to transmit the images. Content in the images is smoothed to reduce the quantity of bits needed to encode each image. Filtering the images modifies regions including content having a high frequency of pixel variation, reducing the frequency, so the pixel colors in the region appear “smoothed” or homogeneous. In other words, a region of an image showing a grassy lawn has a high frequency of variation from pixel to pixel resulting from the fine detail of separate blades of grass that may be similar in color, but not homogeneous. Encoding the region as a single shade of green (or multi-pixel regions of different shades of green) enables a viewer to recognize it as a grassy lawn while greatly reducing the number of bits needed to represent the region.

Abstract: The disclosed embodiments include reservoir simulation systems and methods to dynamically improve performance of reservoir simulations. The method includes obtaining input variables for generating a reservoir simulation of a reservoir, and generating the reservoir simulation based on the input variables. The method also includes determining a variance of computation time for processing the reservoir simulation. In response to a determination that the variance of computation time is less than or equal to a threshold, the method includes performing a first sequence of Bayesian Optimizations of at least one of internal and external parameters that control the reservoir simulation to improve performance of the reservoir simulation. In response to a determination that the variance of computation time is greater than the threshold, the method includes performing a second sequence of Bayesian Optimizations of at least one of the internal and external parameters.

Abstract: A drilling device may use a concurrent path planning process to create a path from a starting location to a destination location within a subterranean environment. The drilling device can receive sensor data. A probability distribution can be generated from the sensor data indicating one or more likely materials compositions that make up each portion of the subterranean environment. The probability distribution can be sampled, and for each sample, a drill path trajectory and drill parameters for the trajectory can be generated. A trained neural network may evaluate each trajectory and drill parameters to identify the most ideal trajectory based on the sensor data. The drilling device may then initiate drilling operations for a predetermined distance along the ideal trajectory.

Abstract: In various examples, a frame may be encoded as multiple sub-frames. For example, data particularly relevant to conveying visual motion between frames may be encoded in a first sub-frame(s) with remaining data being encoded in a second sub-frame(s). Other information may be included in the first sub-frame(s), such as high entropy data. The high entropy data may be estimated using quantization and dequantization of macroblocks. Packet pacing may be applied at least between the encoded sub-frames. As the first sub-frame(s) may include the most important information for frame updates at the client device, if the second sub-frame(s) is not received and/or displayed the first sub-frame may be displayed providing high quality results. More error correction may be used for the first sub-frame than the second sub-frame to increase the likelihood that the first sub-frame is received at a client device.

Abstract: Provided are a method and apparatus for processing a convolution operation in a neural network. The apparatus may include a memory, and a processor configured to read, from the memory, one of divided blocks of input data stored in a memory; generate an output block by performing the convolution operation on the one of the divided blocks with a kernel; generate a feature map by using the output block, and write the feature map to the memory.

Abstract: Aspects and features of a system for real-time drilling using automated data quality control can include a computing device, a drilling tool, sensors, and a message bus. The message bus can receive current data from a wellbore. The computing device can generate and use a feature-extraction model to provide revised data values that include those for missing data, statistical outliers, or both. The model can be used to produce controllable drilling parameters using highly accurate data to provide optimal control of the drilling tool. The real-time message bus can be used to apply the controllable drilling parameters to the drilling tool.

Abstract: System and methods for simulating fluid flow during downhole operations are provided. Measurements of an operating variable at one or more locations within a formation are obtained from a downhole tool disposed in a wellbore within the formation during a current stage of a downhole operation being performed along the wellbore. The obtained measurements are applied as inputs to a hybrid model of the formation. The hybrid model includes physics-based and machine learning models that are coupled together within a simulation grid. Fluid flow within the formation is simulated, based on the inputs applied to the hybrid model. A response of the operating variable is estimated for a subsequent stage of the downhole operation along the wellbore, based on the simulation. Flow control parameters for the subsequent stage are determined based on the estimated response. The subsequent stage of the operation is performed according to the determined flow control parameters.

Abstract: In various examples, a deep neural network (DNN) based pre-filter for content streaming applications is used to dynamically adapt scene entropy (e.g., complexity) in response to changing network or system conditions of an end-user device. For example, where network and/or system performance issues or degradation are identified, the DNN may be implemented as a frame pre-filter to reduce the complexity or entropy of the frame prior to streaming—thereby allowing the frame to be streamed at a reduced bit rate without requiring a change in resolution. The DNN-based pre-filter may be tuned to maintain image detail along object, boundary, and/or surface edges such that scene navigation—such as by a user participating in an instance of an application—may be easier and more natural to the user.

Abstract: Embodiments of the subject technology for deep learning based reservoir modelling provides for receiving input data comprising information associated with one or more well logs in a region of interest. The subject technology determines, based at least in part on the input data, an input feature associated with a first deep neural network (DNN) for predicting a value of a property at a location within the region of interest. Further, the subject technology trains, using the input data and based at least in part on the input feature, the first DNN. The subject technology predicts, using the first DNN, the value of the property at the location in the region of interest. The subject technology utilizes a second DNN that classifies facies based on the predicted property in the region of interest.

Abstract: A system and method for controlling a drilling tool inside a wellbore makes use of simulated annealing and Bayesian optimization to determine optimum controllable drilling parameters. In some aspects, a computing device generates sampled exploration points using simulated annealing and runs a Bayesian optimization using a loss function and the exploration points to optimize at least one controllable drilling parameter to achieve a predicted value for a selected drilling parameter. In some examples, the selected drilling parameter is rate-of-penetration (ROP) and in some examples, the controllable drilling parameters include such parameters as rotational speed (RPM) and weight-on-bit (WOB). In some examples, the computing device applies the controllable drilling parameter(s) to the drilling tool to achieve the predicted value for the selected drilling parameter and provide real-time, closed-loop control and automation in drilling.

Abstract: Disclosed is an electronic device. The An electronic device including a storage, and a processor configured to perform convolution processing on target data and kernel data based on stride information that indicates an interval at which the kernel data is applied to the target data stored in the storage, in which the processor is further configured to divide the target data into a plurality of pieces of sub-data based on first stride information, perform the convolution processing on the plurality of pieces of sub-data and a plurality of pieces of sub-kernel data respectively corresponding to the plurality of pieces of sub-data based on second stride information that is different from the first stride information, and combine a plurality of processing results, the plurality of pieces of sub-kernel data are obtained by dividing the kernel data based on the first stride information, and the second stride information indicates that the interval at which the kernel data is applied to the target data is 1.

Abstract: A system and method for controlling a drilling tool inside a wellbore makes use of Bayesian optimization with range constraints. A computing device samples observed values for controllable drilling parameters such as weight-on-bit (WOB) and drill bit rotational speed in RPM and evaluates a selected drilling parameter such a rate-of-penetration (ROP) for the observed values using an objective function. Range constraints can be continuously learned by the computing device as the range constraints change. A Bayesian optimization, subject to the range constraints and the observed values, can produce an optimized value for the controllable drilling parameter to achieve a predicted value for the selected drilling parameter. The system can then control the drilling tool using the optimized value to achieve the predicted value for the selected drilling parameter.

Abstract: A system for real-time steering of a drill bit includes a drilling arrangement and a computing device in communication with the drilling arrangement. The system iteratively, or repeatedly, receives new data associated the wellbore. At each iteration, a model, for example an engineering model, is applied to the new data to produce an objective function defining the selected drilling parameter. The objective function is modified at each iteration to provide an updated value for the selected drilling parameter and an updated value for at least one controllable parameter. In one example, the function is modified using Bayesian optimization The system iteratively steers the drill bit to obtain the updated value for the selected drilling parameter by applying the updated value for at least one controllable parameter over the period of time that the wellbore is being formed.

Abstract: A system for determining completion parameters for a wellbore includes a sensor and a computing device. The sensor can be positioned at a surface of a wellbore to detect data prior to finishing a completion stage for the wellbore. The computing device can receive the data, perform a history match for simulation and production using the sensor data and historical data, generate inferred data for completion parameters using the historical data identified during the history match, predict stimulated area and production by inputting the inferred data into a neural network model, determine completion parameters for the wellbore using Bayesian optimization on the stimulated area and production from the neural network model, profit maximization, and output the completion parameters for determining completion decisions for the wellbore.

Abstract: A physics-influenced deep neural network (PDNN) model, or a deep neural network incorporating a physics-based cost function, can be used to efficiently denoise sensor data. To generate the PDNN model, noisy sensor data is used as training data input to a deep neural network and training output is valuated with a cost function that incorporates a physics-based model. An autoencoder can be coupled to the PDNN model and trained with the reduced-noise sensor data which is output from the PDNN during training of the PDNN or with a separate set of sensor data. The autoencoder detects outliers based on the reconstructed reduced-noise sensor data which it generates. Denoising sensor data by leveraging an autoencoder which is influenced by the physics of the underlying domain based on the incorporation of the physics-based model in the PDNN facilitates accurate and efficient denoising of sensor data and detection of outliers.

Abstract: The present disclosure is drawn to material sets and 3-dimensional printing systems that include a fusing agent. One example of a material set can include a fusing agent and a detailing agent. The fusing agent can include water, a carbon black pigment, and a water-soluble co-solvent in an amount from 20 wt % to 60 wt %. The detailing agent can include water and a black dye. In another example, a material set can include a fusing agent and a thermoplastic polymer powder.

Abstract: Geosteering can be used in a drilling operation to create a wellbore that is used to extract hydrocarbons from a defined zone within the subterranean formation. According to some aspects, generating paths for the wellbore may include using path-planning protocols and pure-pursuit protocols. The pure-pursuit protocol may be executed to output a plurality of candidate drilling paths. The output may also include control parameters for controlling the drill bit. A trajectory optimizer may determine a result of multi-objective functions for each candidate path. A cost function may represent a cost or loss associated with a candidate path. Additionally, the trajectory optimizer may perform an optimization protocol, such as Bayesian optimization, on the cost functions to determine which candidate path to select. The selected candidate path may correspond to new control parameters for controlling the drill bit to reach the target location.

Abstract: Certain aspects and features relate to a system for trajectory planning and control for new wellbores. Data can be received for multiple existing wells associated with a subterranean reservoir and used to train a deep neural network model to make accurate well property projections at any other location in the reservoir. A model of features for specific well locations based on seismic attributes of the well location can be automatically generated, and the model can be used in drilling trajectory optimization. In some examples, the system builds a deep neural network (DNN) model based on the statistical features, and trains the DNN model using Bayesian optimization to produce an optimized DNN model. The optimized model can be used to provide drilling parameters to produce an optimized trajectory for a new well.

Abstract: A model optimizer for predicting a drill bit variable can select a model from multiple models based on a learned preference. The preference may be updated according to preference indicator received from a user in response to an output model selection.

Abstract: Aspects of the subject technology relate to systems and methods for predicting physical characteristics of a physical environment using a physical characterization model trained based on simulated states of a modeled physical environment. A physical characterization model can be generated based on a plurality of simulated states of a modeled physical environment. Specifically, the physical characterization model can be trained by mapping simulated spatial properties of the modeled physical environment temporally across the plurality of simulated states of the modeled physical environment. Further, input state data describing one or more input states of a physical environment can be received. One or more physical characteristics of the physical environment can be predicted by applying the physical characterization model to the one or more input states of the physical environment.