Alerta

USER INTERFACE FOR MATERIALS AND MATERIAL PROPERTIES PREDICTION USING A MACHINE LEARNING MODEL

Absstract of: AU2023409235A1

Machine learning can be used to predict formulations for an output formulation. The machine learning can be implemented by a machine learning model, which employs a forward model and an inverse model. A user interface can be used to gather raw materials selections and output formulation property selections. The selections can be used to generate formulations that comply with selections using the ML model.

TRAINING OF MACHINE LEARNING MODELS FOR WELL TARGET RECOMMENDATION

Absstract of: WO2024064077A1

Disclosed are methods, systems, and computer programs for placing one or more optimal infill well locations within a reservoir. The methods include: generating a first multi-dimensional reservoir model of a first reservoir that is parameterized; assigning well placement data to the first reservoir model to generate a simulation model; applying a stochastic optimization process in a first simulation on the simulation model; determining infill well locations data based on the first simulation; configuring a second multi-dimensional reservoir model based on the infill well locations data; and generating using the second multi-dimensional reservoir model, one or more of: pressure delta data for one or more infill locations associated with a second reservoir, and a simulation opportunity index indicating reservoir properties for the one or more infill locations associated with the second reservoir.

MACHINE LEARNING TRAINING FOR CHARACTERIZING WATER INJECTION AND SEISMIC PREDICTION

Absstract of: WO2024064009A1

A method including receiving a reservoir model of a target underground region. The method also includes extracting, from the reservoir model, a historic pressure distribution in grid cells of the target underground region. The method also includes extracting, from the reservoir model, distances. Each distance represents a distance between a grid cell and a corresponding lineament in the target underground region. The method also includes receiving historic earthquake data of past earthquakes in the target underground region. The method also includes generating a vector. The vector includes features and corresponding values for at least i) the historic pressure distribution, ii) the distances, and iii) the historic earthquake data. The method also includes training a trained machine learning algorithm by recursively executing a machine learning algorithm on the vector until convergence.

AUTOMATED MACHINE LEARNING FAULT MODELING WITH GROUPING

Absstract of: WO2024063797A1

Methods, computing systems, and computer-readable media for a machine learning method of modeling fault-related properties of a geological region are presented. The techniques include: obtaining seismic geological data for a geological region; obtaining from a user identifications of a plurality of faults in the geological region; automatically generating values for descriptors of respective faults of the plurality of faults; automatically partitioning faults of the plurality of faults into a plurality of groups according to the values for the descriptors; obtaining a mapping of respective groups of the plurality of groups to modeling parameter values; applying the mapping to a fault in the geological region outside of the plurality of faults to obtain a modeling parameter value for the fault outside of the plurality of faults; and modeling a fault-related property of the geological region based on the modeling parameter value for the fault outside of the plurality of faults.

MACHINE LEARNING TO ENHANCE SATELLITE TERMINAL PERFORMANCE

Nº publicación: EP4581763A1 09/07/2025

Applicant:

HUGHES NETWORK SYSTEMS LLC [US]
Hughes Network Systems, LLC

Absstract of: US2024396814A1

Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for using machine learning to detect and correct satellite terminal performance limitations. In some implementations, a system retrieves data indicating labels for clusters of network performance anomalies. The system generates a set of training data to train a machine learning model, the set of training data being generated by assigning the labels for the clusters to sets of performance indicators used to generate the clusters. The system trains a machine learning model to predict classifications for communication devices based on input of performance indicators for the communication devices. The system determines a classification for the communication device based on output that the trained machine learning model generates.