Patents by Inventor Ashwani Dev

Ashwani Dev 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).

  • Publication number: 20200264329
    Abstract: Systems and methods for visualizing attributes of multiple fault surfaces in real time by calculating the attributes as each respective fault surface is picked.
    Type: Application
    Filed: March 31, 2016
    Publication date: August 20, 2020
    Applicant: Landmark Graphics Corporation
    Inventors: Baiyuan GAO, Jesse Methias LOMASK, Ashwani DEV
  • Publication number: 20200200931
    Abstract: A method includes receiving a training selection of a first set of faults located in a first subset of a seismic dataset for a subsurface geologic formation, detecting a second set of faults in the seismic dataset based on fault interpretation operations using a first set of interpretation parameters, and determining a difference between the first set of faults and the second set of faults. The method also includes generating a second set of interpretation parameters for the fault interpretation operations based on the difference between the first set of faults and the second set of faults, and determining a feature of the subsurface geologic formation based on fault interpretation operations using the second set of interpretation parameters.
    Type: Application
    Filed: December 21, 2017
    Publication date: June 25, 2020
    Inventors: Youli Mao, Raja Vikram Pandya, Bhaskar Mandapaka, Keshava Prasad Rangarajan, Srinath Madasu, Satyam Priyadarshy, Ashwani Dev
  • Publication number: 20200149354
    Abstract: The subject disclosure provides for a mechanism implemented with neural networks through machine learning to predict wear and relative performance metrics for performing repairs on drill bits in a next repair cycle, which can improve decision making by drill bit repair model engines, drill bit design, and help reduce the cost of drill bit repairs. The machine learning mechanism includes obtaining drill bit data from different data sources and integrating the drill bit data from each of the data sources into an integrated dataset. The integrated dataset is pre-processed to filter out outliers. The filtered dataset is applied to a neural network to build a machine learning based model and extract features that indicate significant parameters affecting wear. A repair type prediction is determined with the applied machine learning based model and is provided as a signal for facilitating a drill bit operation on a cutter of the drill bit.
    Type: Application
    Filed: August 31, 2018
    Publication date: May 14, 2020
    Inventors: Ajay Pratap Singh, Roxana Nielsen, Jr., Satyam Priyadarshy, Ashwani Dev, Geetha Gopakumar Nair, Suresh Venugopal
  • Publication number: 20200064507
    Abstract: A method for determining a position of a geological feature in a formation includes acquiring a seismic dataset, wherein the seismic dataset is based on signals of one or more seismic sensors and determining a set of indicators of candidate discontinuities in the formation based on the seismic dataset. The method also includes labeling a subset of the set of indicators of candidate discontinuities using a neural network with a label based on the set of indicators of candidate discontinuities, wherein the label distinguishes an indicator of a candidate discontinuity between being an indicator of a target discontinuity or being an indicator of a non-target discontinuity and determining the position of the geological feature in the formation, wherein the geological feature in the formation is associated with at least one target discontinuity based on the subset of the set of indicators of candidate discontinuities.
    Type: Application
    Filed: July 18, 2018
    Publication date: February 27, 2020
    Inventors: Youli Mao, Bhaskar Mandapaka, Ashwani Dev, Satyam Priyadarshy
  • Publication number: 20200057675
    Abstract: A computer implemented method, computer program product, and system for managing execution of a workflow comprising a set of subworkflows, comprising optimizing the set of subworkflows using a deep neural network, wherein each subworkflow of the set of subworkflows has a set of tasks, wherein each task of the sets of tasks has a requirement of resources of a set of resources; wherein each task of the sets of tasks is enabled to be dependent on another task of the sets of tasks, training the deep neural network by: executing the set of subworkflows, collecting provenance data from the execution, and collecting monitoring data that represents the state of said set of resources, wherein the training causes the neural network to learn relationships between the states of said set of resources, the said sets of tasks, their parameters and the obtained performance, optimizing an allocation of resources of the set of resources to each task of the sets of tasks to ensure compliance with a user-defined quality metric b
    Type: Application
    Filed: August 16, 2018
    Publication date: February 20, 2020
    Inventors: Jonas F. Dias, Angelo Ciarlini, Romulo D. Pinho, Vinicius Gottin, Andre Maximo, Edward Pacheco, David Holmes, Keshava Rangarajan, Scott David Senften, Joseph Blake Winston, Xi Wang, Clifton Brent Walker, Ashwani Dev, Chandra Yeleshwarapu, Nagaraj Srinivasan
  • Publication number: 20190277124
    Abstract: A method may comprise: modeling a complex fracture network within the subterranean formation with a mathematical model based on a natural fracture network map and measured data of the subterranean formation collected in association with a fracturing treatment of the subterranean formation to produce a complex fracture network map; importing microseismic data collected in association with the fracturing treatment of the subterranean formation into the mathematical model; identifying directions of continuity in the microseismic data via a geostatistical analysis that is part of the mathematical model; and correlating the directions of continuity in the microseismic data to the complex fracture network with the mathematical model to produce a microseismic-weighted (MSW) complex fracture network map.
    Type: Application
    Filed: October 4, 2016
    Publication date: September 12, 2019
    Inventors: Jeffrey Marc Yarus, Ashwani Dev, Jin Fei, Trace Boone Smith
  • Publication number: 20190235106
    Abstract: A multivariate analysis may be used to correlate seismic attributes for a subterranean formation with petrophysical properties of the subterranean formation and/or microseismic data associated with treating, creating, and/or extending a fracture network of the subterranean formation.
    Type: Application
    Filed: October 4, 2016
    Publication date: August 1, 2019
    Inventors: Ashwani Dev, Sridharan Vallabhaneni, Raquel Morag Velasco, Jeffrey Marc Yarus
  • Publication number: 20180306939
    Abstract: Microseismic-event data can be corrected (e.g., to reduce or eliminate bias). For example, a first distribution of microseismic events that occurred in a first area of a subterranean formation can be determined. The first distribution can be used as a reference distribution. A second distribution of microseismic events that occurred in a second area of the subterranean formation can also be determined. The second area of the subterranean formation can be farther from an observation well than the first area. The second distribution can be corrected by including, in the second distribution, microseismic events that have characteristics tailored for reducing a difference between the second distribution and the first distribution.
    Type: Application
    Filed: October 20, 2016
    Publication date: October 25, 2018
    Inventors: Thomas Bartholomew O'Toole, Yevgeniy Zagayevskiy, Raquel Morag Velasco, Ashwani Dev