Abstract: A method for dynamically changing functions of a vehicle, or one more vehicles, via cloud generated lambda or a non-transitory computer-readable storage medium on which is recorded instructions, includes: accessing a cloud application, via other communication protocols; generation of the lambda; implementing software services on a server or other remote devices; accessing a database via the cloud application; and transferring the lambda to one or more vehicles. The software services may include other communication protocols; a configuration manager; a database manager; and a trigger manager. The software services may further include a rule manager; a data collector; an action manager; and a status handler. The methods may further include configuration parameters, implemented for the monitoring and execution of the function on the vehicle, services and applications, implemented for the monitoring and execution of the function on the vehicle, or implementing configurations, via the cloud application.

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: 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.