Patents by Inventor Mohamed-Rabigh Khodja

Mohamed-Rabigh Khodja has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Patent number: 9720130
    Abstract: A Bayesian methodology is described for designing experiments or surveys that are improved by utilizing available prior information to guide the design toward maximally reducing posterior uncertainties in the interpretation of the future experiment. Synthetic geophysical tomography examples are used to illustrate benefits of this approach.
    Type: Grant
    Filed: November 18, 2013
    Date of Patent: August 1, 2017
    Assignee: SCHLUMBERGER TECHNOLOGY CORPORATION
    Inventors: Hugues A. Djikpesse, Mohamed-Rabigh Khodja, Michael David Prange
  • Patent number: 9146330
    Abstract: Complex-valued sensitivity data structures corresponding to respective candidate survey settings are provided, where the sensitivity data structures relate measurement data associated with a target structure to at least one parameter of a model of the target structure. Based on the sensitivity data structures, a subset of the candidate survey settings is selected according to a criterion for enhancing resolution in characterizing the target structure.
    Type: Grant
    Filed: March 15, 2012
    Date of Patent: September 29, 2015
    Assignee: WesternGeco L.L.C.
    Inventors: Hugues A. Djikpesse, Michael David Prange, Mohamed-Rabigh Khodja, Sebastien Duchenne, Henry Menkiti
  • Publication number: 20140081575
    Abstract: A Bayesian methodology is described for designing experiments or surveys that are improved by utilizing available prior information to guide the design toward maximally reducing posterior uncertainties in the interpretation of the future experiment. Synthetic geophysical tomography examples are used to illustrate benefits of this approach.
    Type: Application
    Filed: November 18, 2013
    Publication date: March 20, 2014
    Applicant: SCHLUMBERGER TECHNOLOGY CORPORATION
    Inventors: HUGUES A. DJIKPESSE, MOHAMED-RABIGH KHODJA, MICHAEL DAVID PRANGE
  • Patent number: 8589078
    Abstract: A Bayesian methodology is described for designing experiments or surveys that are improved by utilizing available prior information to guide the design toward maximally reducing posterior uncertainties in the interpretation of the future experiment. Synthetic geophysical tomography examples are used to illustrate benefits of this approach.
    Type: Grant
    Filed: July 22, 2009
    Date of Patent: November 19, 2013
    Assignee: Schlumberger Technology Corporation
    Inventors: Hugues Djikpesse, Mohamed-Rabigh Khodja, Michael David Prange
  • Publication number: 20120250455
    Abstract: Complex-valued sensitivity data structures corresponding to respective candidate survey settings are provided, where the sensitivity data structures relate measurement data associated with a target structure to at least one parameter of a model of the target structure. Based on the sensitivity data structures, a subset of the candidate survey settings is selected according to a criterion for enhancing resolution in characterizing the target structure.
    Type: Application
    Filed: March 15, 2012
    Publication date: October 4, 2012
    Inventors: HUGUES A. DJIKPESSE, MICHAEL DAVID PRANGE, MOHAMED-RABIGH KHODJA, SEBASTIEN DUCHENNE, HENRY MENKITI
  • Publication number: 20110022319
    Abstract: A Bayesian methodology is described for designing experiments or surveys that are improved by utilizing available prior information to guide the design toward maximally reducing posterior uncertainties in the interpretation of the future experiment. Synthetic geophysical tomography examples are used to illustrate benefits of this approach.
    Type: Application
    Filed: July 22, 2009
    Publication date: January 27, 2011
    Applicant: Schlumberger Technology Corporation
    Inventors: Hugues A. Djikpesse, Mohamed-Rabigh Khodja, Michael David Prange