International Business Machines Corporation

Petrogal Brasil S.a.

Dario Sergio Cersosimo

Renato Fontoura De Gusmao Cerqueira

Emilio Ashton Vital Brazil

Rodrigo Da Silva Ferreira

Joana De Noronha Ribeiro De Almeida

Ulisses Mello

Ana Fucs

Viviane Torres Da Silva

Vinicius Costa Villas Boas Segura

Leonardo Guerreiro Azevedo

Marco Daniel Melo Ferraz

Leonardo Pondian Tizzei

Astrid De Jesus Torres Fernandez

Juliana Jansen Ferreira

Bruno Felix Carvalho

Leonardo Silveira De Albuquerque Martins

Raphael Melo Thiago

Elton Figueiredo De Souza Soares

Storing and retrieving subsurface rock physical property models for well drilling by generating estimated subsurface rock physical property models based on a respective set of seismic data, determining a stratigraphic classification for each model based on seismic interpretation data; storing the models associated classification in a database, determining characteristics of the seismic data used to generate the models, selecting from the database a subset of the models based on a similarity of the stratigraphic classification, determining measures of similarity between the characteristics of the seismic data used to generate each of subset of the models and a new set of seismic data and retrieving an estimated subsurface rock physical property model from the subset models based on the similarity measures.

Storage and retrieval of subsurface rock physical property prediction models using seismic interpretation

Machine teaching in an embodiment can include receiving a user annotated concept for a given image in given context. The given image can be broken into parts and classified. Relationships can be determined associated with the parts. The created relationships can be stored along with the user annotated concept in a knowledge base. One or more similar images can be annotated using the parts and relationships. A second image associated the given context can be retrieved, decomposed into parts. The parts can be classified. Relationships can be determined associated with the second image's parts. Classifications and relationships associated with the second image's parts can be compared with classifications and relationships associated with the given image's parts. Based on comparing, the second image can be annotated with the user annotated concept for the given image.

Machine teaching complex concepts assisted by computer vision and knowledge reasoning

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Lisbon, Portugal

Dario Sergio Cersosimo