Abstract: Systems and methods include a computer-implemented method for predicting temperatures. Temperature data corresponding to historical drilling operations of a well is collected and stored in a database. The database is split into a training dataset and an evaluation dataset. Space-time-temperature probability models are generated using the training dataset. The space-time-temperature probability models are trained using the evaluation dataset. The space-time-temperature probability models are evaluated to ensure a performance level above a model accuracy threshold. A predicted temperature profile for a new well is generated using the space-time-temperature probability models.

Abstract: A system and method for machine learning with physics-based models to predict multilateral well performance are provided. An exemplary method enables obtaining data associated with well completion, data associated with inflow control valves, and reservoir attributes of multilateral wells. Production scenarios are generated based on the data associated with well completion, the data associated with inflow control valves, and the reservoir attributes of the multilateral wells. The production scenarios are input into a physics-based model of the multilateral wells, and simulation data associated with the multilateral wells output from the physics-based model is obtained. A neural network based machine learning model is trained using the simulation data associated with the multilateral wells and target parameters associated with the multilateral wells, wherein the trained machine learning model is configured to predict multilateral well production parameters.

Abstract: Systems and methods include a computer-implemented method for well models. Historical well data for past operations of a well is received. An expanded minimum/maximum range of values for the historical well data is generated for parameters used in a well model. Object-oriented object instances are generated using the historical well data and the expanded range. Each object instance is an object that includes a specific combination of parameters within the expanded range. The well model is executed using the instances, generating result objects, each representing a result of the well model. A hybrid well model variant of the well model is generated using the object instances and result objects to model combinations of the object instances and the result objects. A data object encapsulating the object instances and result objects is passed by an object-oriented application to a machine learning model in an absence of database tables.

Abstract: Provided are techniques for operating a hydrocarbon well having inflow control valves (ICVs) and an electrical submersible pump (ESP) disposed in a wellbore of the well. Baseline production data is acquired for a relatively small subset of the possible operating configurations of ICVs in the well, the baseline data is assessed to model fluid pressure gradients in the wellbore, and the modeled fluid pressure gradients are used to estimate an ESP intake pressure for one or more configurations of the ICVs, and the ICVs are controlled to operate in accordance with an ICV configuration associated with an estimated ESP intake pressure that is within an operating intake pressure range for the ESP.

Abstract: Sensor data is received from a plurality of sensors contained in an electrical submersible pump (ESP) deployed in a well hole. An early indication of an ESP failure that is imminent is determined in real time using a pattern recognition methodology based on the sensor data and a pattern detection model that correlates historical ESP failures with historical sensor readings. A notification of the imminence of the ESP failure is provided.

Abstract: Provided are techniques for operating a hydrocarbon well having inflow control valves (ICVs) and an electrical submersible pump (ESP) disposed in a wellbore of the well. Baseline production data is acquired for a relatively small subset of the possible operating configurations of ICVs in the well, the baseline data is assessed to model fluid pressure gradients in the wellbore, and the modeled fluid pressure gradients are used to estimate an ESP intake pressure for one or more configurations of the ICVs, and the ICVs are controlled to operate in accordance with an ICV configuration associated with an estimated ESP intake pressure that is within an operating intake pressure range for the ESP.

Abstract: Embodiments provide a system and method for designing automated inflow control devices used in the extraction of hydrocarbons from subterranean formations. The system includes real-time data processing module configured to gather and process well data, the well data comprising logging while drilling data and user inputted data, and an intelligent field restriction module configured to generate one or more optimized inflow control device designs based on the well data gathered and processed by the real-time data processing module. The system further includes an inflow control design module configured to couple the one or more optimized inflow control device designs from the intelligent field restriction module with one or more scenarios to create a set of ranked optimized inflow control device designs, from which a user selects a preferred optimized inflow control device design based on well optimization goals defined in the user inputted data.

Abstract: Calibrated network models for oil production systems are executed using a set of production metrics for a given well. An execution run is initially performed for the given well using a inputs corresponding to a specified time period. Multiple execution runs are performed, iterating on productivity index data for the specified time period, until a simulated production rate converges to a historical production rate corresponding. The productivity index value for the given well at the specified time period is determined based on the converged simulated production rate. The productivity index value represents the rate test conditions at the specified time period. The executing and determining are repeated for additional points in time for the given well to determine productivity index values representing the rate test conditions at respective ones of the additional points in time. A productivity index profile providing productivity index values as a function of time is generated.

Abstract: Sensor data is received from a plurality of sensors contained in an electrical submersible pump (ESP) deployed in a well hole. An early indication of an ESP failure that is imminent is determined in real time using a pattern recognition methodology based on the sensor data and a pattern detection model that correlates historical ESP failures with historical sensor readings. A notification of the imminence of the ESP failure is provided.

Abstract: Embodiments of the invention provide a system and method for designing automated inflow control devices to be used in the extraction of hydrocarbons from subterranean formations. According to at least one embodiment, the system includes real-time data processing module configured to gather and process well data, the well data comprising logging while drilling data and user inputted data, and an intelligent field restriction module configured to generate one or more optimized inflow control device designs based on the well data gathered and processed by the real-time data processing module. The system further includes an inflow control design module configured to couple the one or more optimized inflow control device designs from the intelligent field restriction module with one or more scenarios to create a set of ranked optimized inflow control device designs, from which a user selects a preferred optimized inflow control device design based on well optimization goals defined in the user inputted data.