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: Systems and methods for automated drilling control and optimization are disclosed. Training data, including values of drilling parameters, for a current stage of a drilling operation are acquired. A reinforcement learning model is trained to estimate values of the drilling parameters for a subsequent stage of the drilling operation to be performed, based on the acquired training data and a reward policy mapping inputs and outputs of the model. The subsequent stage of the drilling operation is performed based on the values of the drilling parameters estimated using the trained model. A difference between the estimated and actual values of the drilling parameters is calculated, based on real-time data acquired during the subsequent stage of the drilling operation. The reinforcement learning model is retrained to refine the reward policy, based on the calculated difference. At least one additional stage of the drilling operation is performed using the retrained model.

Abstract: Systems, methods and computer readable storage media for optimizing a determination of a number of grid cell counts to be used in creating the geocellular grid of an earth, geomechanical or petro-elastic model for reservoir simulation. These may involve determining at least one processing time for a simulation; determining a grid cell count to be used in creating a geocellular grid for the simulation based on the at least one processing time and a number of processors to be used for creating the model; creating the geocellular grid using the grid cell count, and generating a model for the simulation using the geocellular grid.

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: A system for controlling operations of a drill in a well environment. The system comprises a predictive engine, a ML engine, a controller, and a secure, distributed storage network. The predictive engine receives a variables associated with surface and sub-surface sensors and predicts an earth model based on the variables, predictor variable(s), outcome variable(s), and relationships between the predictor variable(s) and the outcome variable(s). The predictive engine is also configured to predict a drill path(s) ahead of the drill based on using stochastic modeling, an outcome variable(s), the predicted earth model, and a drilling model(s). The controller is configured to generate a system response(s) based on the predicted drill path(s) and a current state of the drill. The ML engine stores the earth model, the drill path(s), and the variables in the distributed storage network, trains data, and creates the drilling model(s).

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 system, for controlling well site operations, comprising a machine learning engine, a predictive engine, a node system stack, and a blockchain. The learning engine includes a machine learning algorithm, an algorithmically generated earth model, and control variables. The learning algorithm generates a trained data model using the algorithmically generated earth model. The predictive engine includes an Artificial Intelligence (AI) algorithm. The AI algorithm generates a trained AI algorithm using the trained data model and earth model variables using the trained AI algorithm. The system stack is communicable coupled to the predictive engine, the learning engine, the blockchain, sensors, and a machine controller. The blockchain having a genesis block and a plurality of subsequent blocks. Each subsequent block comprising a well site entry and a hash of a previous entry. The well site entry comprises transacted operation control variables.

Abstract: A system for managing well site operations comprising a well site operations module, a chain of blocks of a distributed network, and a sensor bank and control module. The operations module generates earth model variables using a physics model, well log variables or seismic variables, or both, and a trained AI/ML algorithmic model. The chain of blocks comprises a plurality of subsequent blocks. Each subsequent block comprises a well site entry and a hash value of a previous well site entry. A well site entry comprises transacted operation control variables. The well site operations module generates production operation control variables or development operation control variables from earth model variables. The well site entry can also include transacted earth model variables and sensor variables. The sensor bank and control module provides well log variables and the operations module couples control variables to the control module to control well site equipment.

Abstract: A system for managing well site operations, the system comprising executable partitions, predictive engines, node system stacks, and a blockchain. The predictive engines comprise an Artificial Intelligence (AI) algorithm to generate earth model variables using a physics model, well log data variables, and seismic data variables. The node system stacks are coupled to the blockchain, sensors, and machine controllers. Each node system stack comprises a Robot Operating System (ROS) based middleware controller, with each coupled to each partition, each node system stack, each predictive engine, and an AI process or processes. The blockchain comprises chained blocks of a distributed network. The distributed network comprises a genesis block and a plurality of subsequent blocks, each subsequent block comprising a well site entry and a hash value of a previous well site entry. The well site entry comprises operation control variables. The operation control variables are based on the earth model variables.