Patents by Inventor Hiren Maniar

Hiren Maniar 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: 11989648
    Abstract: A training log is selected from a plurality of well logs. A log window of a plurality of log windows is selected from the training log. A positive window is generated from the log window. A negative window is selected from the training log. A siamese neural network (SNN) is trained that includes a first self attention neural network (ANN) and a duplicate self attention neural network with the log window, the positive window, and the negative window, to recognize a similarity between the log window and the positive window and to differentiate against the negative window.
    Type: Grant
    Filed: September 11, 2020
    Date of Patent: May 21, 2024
    Assignee: SCHLUMBERGER TECHNOLOGY CORPORATION
    Inventors: Mandar Shrikant Kulkarni, Hiren Maniar, Aria Abubakar
  • Publication number: 20230341577
    Abstract: A method includes receiving seismic training data comprising a plurality of images each including a plurality of traces, predicting a location of a feature in at least some of the plurality of traces based on a location of an amplitude peak therein, applying labels to the locations, classifying pixels of the plurality of images as representing the feature or not representing the feature, using a semantic segmentation model, adjusting the labels based on the classification of the pixels, training, using the adjusted labels and the seismic training data, a machine-learning model to identify the feature, and identifying the feature in a different seismic data set using the trained machine-learning model.
    Type: Application
    Filed: October 7, 2020
    Publication date: October 26, 2023
    Inventors: Sunil Manikani, Karan Pathak, Gayatri Novenita, Hiren Maniar, Aria Adubakar
  • Publication number: 20230281507
    Abstract: A method includes receiving an input dataset representing one or more physical characteristics of a volume, generating an embedding by reducing a dimensionality associated with the input dataset using a trained machine learning model, comparing the embedding with a plurality of other embeddings generated by reducing a dimensionality of other datasets representing one or more physical characteristics of other volumes, selecting one or more of the other embeddings of the one or more other datasets based at least in part on comparing, and estimating one or more attributes of the volume based at least in part on the one or more other datasets corresponding to the selected one or more of the other embeddings.
    Type: Application
    Filed: April 29, 2021
    Publication date: September 7, 2023
    Inventors: Ranjit Ramchandra VHANAMANE, Hiren MANIAR, Sami SHEYH HUSEIN, Aria ABUBAKAR
  • Publication number: 20230273338
    Abstract: A method for correlating well logs includes receiving a well log as input to a first machine learning model that is configured to predict first markers in the well log based at least in part on a global factor of the well log, receiving the well log as input to a second machine learning model that is configured to predict second markers in the well log based at least in part on local factors of the well log, generating a set of predicted well markers by merging at least some of the first markers and at least some of the second markers, and aligning the well log with respect to one or more other well logs based at least in part on the set of predicted well markers.
    Type: Application
    Filed: July 26, 2021
    Publication date: August 31, 2023
    Inventors: Mandar Shrikant KULKARNI, Purnaprajna Raghavendra MANGSULI, Hiren MANIAR, Aria ABUBAKAR
  • Publication number: 20230128933
    Abstract: A method for digitizing image-based data includes receiving an image file including one or more target objects, generating an intermediate image by removing noise from the image file using a denoising machine learning model, identifying the one or more target objects included in the intermediate image using an object segmentation machine learning model, discretizing the one or more target objects that were identified using the trained object segmentation machine learning model, and storing the one or more target objects that were discretized in a data file, visualizing the one or more target objects, or both.
    Type: Application
    Filed: March 2, 2021
    Publication date: April 27, 2023
    Inventors: Atul Laxman KATOLE, Hiren MANIAR, Denzil Francis CRASTA, Preeti GUPTA, Aria ABUBAKAR, Aaron SCOLLARD, Ekansh VERMA, Pumaprajna MANGSULI
  • Publication number: 20230122128
    Abstract: A method includes receiving geophysical data representative of a geophysical structure; providing the geophysical data as one or more input data to a neural network; training the neural network to reconstruct the geophysical structure that was received and provide one or more uncertainty metrics for one or more features of the geophysical structure that is reconstructed; reconstructing, using the neural network that has been trained, the geophysical structure; and determining, using the neural network that has been trained, the one or more uncertainty metrics by implementing a second drop out condition on the one or more nodes of the one or more hidden layers of the neural network. The training is performed at least partially by implementing a first drop out condition on one or more nodes of one or more hidden layers of the neural network to randomly set an output of the one or more nodes to zero.
    Type: Application
    Filed: March 9, 2021
    Publication date: April 20, 2023
    Inventors: Xiaoli CHEN, Tao ZHAO, Hiren MANIAR, Aria ABUBAKAR
  • Publication number: 20230109902
    Abstract: A machine-implemented method, at least one non-transitory computer-readable medium storing instructions, and a computing system are provided for attenuating noise. A computing system receives a seismic image and generates a first image using a first neural network configured to identify low-frequency ground roll in a seismic image, and a second image using a second neural network configured to identify reflections in the seismic image. A combined image is generated by combining the first image and the second image. The first neural network and the second neural network are adjusted to reduce a difference between the combined image and the seismic image using frequency constraint to guide separation of the seismic image into the first image and the second image.
    Type: Application
    Filed: March 22, 2021
    Publication date: April 13, 2023
    Inventors: Haibin DI, Nicolae MOLDOVEANU, Hiren MANIAR, Aria ABUBAKAR
  • Publication number: 20230026857
    Abstract: A method for seismic processing includes extracting, using a first machine learning model, one or more seismic features from seismic data representing a subsurface domain, receiving one or more well logs representing one or more subsurface properties in the subsurface domain, and predicting, using a second machine learning model, the one or more subsurface properties in the subsurface domain at a location that does not correspond to an existing well based on the seismic data, the one or more well logs, and the one or more seismic features that were extracted from the seismic data.
    Type: Application
    Filed: January 11, 2021
    Publication date: January 26, 2023
    Inventors: Haibin DI, Xiaoli CHEN, Hiren MANIAR, Aria ABUBAKAR
  • Publication number: 20220327324
    Abstract: A method includes receiving well log data comprising a plurality of well logs, identifying one or more sections of one or more well logs of the plurality of well logs that have substantially complete data, training a reconstruction neural network to reconstruct incomplete well logs based on the one or more sections of the one or more well logs that have substantially complete data, and reconstructing one or more incomplete well logs of the plurality of well logs using the reconstruction neural network.
    Type: Application
    Filed: September 3, 2020
    Publication date: October 13, 2022
    Inventors: Xiaoli Chen, Hiren Maniar, Aria Abubakar
  • Publication number: 20220099855
    Abstract: A method can include receiving a first trained machine model trained via unsupervised learning using unlabeled seismic image data; receiving labeled seismic image data acquired via an interactive interpretation process; and building a second trained machine model, as initialized from the first trained machine model, via supervised learning using the received labels, where the second trained machine model predicts stratigraphy of a geologic region from seismic image data of the geologic region.
    Type: Application
    Filed: January 13, 2020
    Publication date: March 31, 2022
    Inventors: Zhun Li, Haibin Di, Hiren Maniar, Aria Abubakar
  • Publication number: 20210270983
    Abstract: A method includes determining a top of salt (TOS) surface in a seismic volume based on a crossline direction of the seismic volume and an inline direction of the seismic volume. The method also includes determining a binary mask based upon the TOS surface. The method also includes sampling seismic data in the seismic volume to obtain a training seismic slice. The method also includes sampling the binary mask to obtain a mask slice. The method also includes selecting a first coordinate in the training seismic slice to produce a first tile. The method also includes selecting a second coordinate in the mask slice to produce a second tile. The method also includes generating or updating a model of the seismic volume based upon the first tile and the second tile.
    Type: Application
    Filed: June 26, 2019
    Publication date: September 2, 2021
    Inventors: Anisha Kaul, Cen Li, Hiren Maniar, Aria Abubakar
  • Publication number: 20210089892
    Abstract: A training log is selected from a plurality of well logs. A log window of a plurality of log windows is selected from the training log. A positive window is generated from the log window. A negative window is selected from the training log. A siamese neural network (SNN) is trained that includes a first self attention neural network (ANN) and a duplicate self attention neural network with the log window, the positive window, and the negative window, to recognize a similarity between the log window and the positive window and to differentiate against the negative window.
    Type: Application
    Filed: September 11, 2020
    Publication date: March 25, 2021
    Inventors: Mandar Shrikant KULKARNI, Hiren MANIAR, Aria ABUBAKAR
  • Publication number: 20200040719
    Abstract: The disclosure relates to a method for performing a drilling operation in a subterranean formation of a field. The method includes obtaining, prior to the drilling operation, a target well data set specifying a target well to be drilled, selecting, from a set of existing wells, a number of analog wells that satisfy a pre-determined similarity criterion with respect to the target well, generating, from a number of analog well data sets of the analog wells, a training data set for the target well, where the training data set includes a rate-of-penetration (ROP) profile for each analog well, generating, using a machine-learning algorithm and based on the training data set, a drilling model that predicts the ROP profile of the target well, and performing, based on the drilling model, modeling of the drilling operation to generate a predicted ROP profile of the target well.
    Type: Application
    Filed: October 5, 2016
    Publication date: February 6, 2020
    Inventors: Hiren Maniar, Sunil Garg, Juan Fernando Corrales Estrada, Henry Martinez