Abstract: A user profile associated with a first user is received. A user prescription associated with the first user is received. A historical interaction of the first user with a display is received. A global vision model is received. One or more display sets to be used on the display is determined based on at least the user profile, the user prescription, the global vision model, and the historical interaction.

Abstract: A system, method and program product for stratigraphic layer identification using a stratigraphic knowledge base for machine learning. Reservoir data includes seismic data and well log data for a reservoir area. The well log data is processed to identify well stratigraphic layer features and the seismic data is processed to identify seismic stratigraphic layer features. A feature matching algorithm based on a stratigraphic knowledge base is selected to match the well stratigraphic layer features to the seismic stratigraphic layer features. The matched features are used to define a stratigraphic layer interpretation for the reservoir area and the interpretation is presented to a user.

Abstract: A user profile associated with a first user is received. A user prescription associated with the first user is received. A historical interaction of the first user with a display is received. A global vision model is received. One or more display setts to be used on the display is determined based on at least the user profile, the user prescription, the global vision model, and the historical interaction.

Abstract: A method for identifying seismic horizons in digitized seismic images includes deploying a swarm of agents at an initial position in a seismic image to be analyzed, where the swarm of agents includes picking agents that define a direction for the swarm and averaging agents that smooth the direction of the swarm, identifying a direction to follow in the seismic image for each picking agent, and advancing each picking agent in the identified direction, and averaging, by the averaging agents, the directions identified by the picking agents, wherein if an information concentration measured by a picking agent at a current time step is greater than a previous time step, the picking agent keeps a previous direction, otherwise the picking agent changes direction according to the average current state of the set of averaging agents within its neighborhood.

Abstract: A system, method and program product for stratigraphic layer identification using a stratigraphic knowledge base for machine learning. Reservoir data includes seismic data and well log data for a reservoir area. The well log data is processed to identify well stratigraphic layer features and the seismic data is processed to identify seismic stratigraphic layer features. A feature matching algorithm based on a stratigraphic knowledge base is selected to match the well stratigraphic layer features to the seismic stratigraphic layer features. The matched features are used to define a stratigraphic layer interpretation for the reservoir area and the interpretation is presented to a user.

Abstract: A method is provided, the method including: displaying an image on a display; detect a user input corresponding to one or more portions of the image; analyzing the user input to determine at least one feature vector corresponding to the user input; and determining a classification for the one or more portions of the image based at least on the at least one feature vector.

Abstract: A method for identifying seismic horizons in digitized seismic images includes deploying a swarm of agents at an initial position in a seismic image to be analyzed, where the swarm of agents includes picking agents that define a direction for the swarm and averaging agents that smooth the direction of the swarm, identifying a direction to follow in the seismic image for each picking agent, and advancing each picking agent in the identified direction, and averaging, by the averaging agents, the directions identified by the picking agents, wherein if an information concentration measured by a picking agent at a current time step is greater than a previous time step, the picking agent keeps a previous direction, otherwise the picking agent changes direction according to the average current state of the set of averaging agents within its neighborhood.

Abstract: A method is provided, the method including: displaying an image on a display; detect a user input corresponding to one or more portions of the image; analyzing the user input to determine at least one feature vector corresponding to the user input; and determining a classification for the one or more portions of the image based at least on the at least one feature vector.