Patents by Inventor Aria Abubakar

Aria Abubakar 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: 20240232479
    Abstract: An integrated workflow is presented including a suite of data-driven technologies that aims to substantially reduce the cost of monitoring data acquisition, improve the robustness and efficiency of time-lapse data processing procedures to shorten the turnaround time of projects utilizing seismic data for monitoring sub-surface fluid reservoirs. In particular, plumes of subsurface CO2 may be monitored, including CO2 deliberately injected into the sub-surface as a sequestration technique. The workflow may include two parts: (1) cost-effective data acquisition schemes and (2) efficient data processing algorithms. The technology components in the workflow may include deep learning sparse monitoring data reconstruction and optimal acquisition survey design, deep learning deblending of simultaneous source monitoring data, time-lapse data repeatability enforcement through deep learning, and rapid CO2 plume body and property estimation directly from pre-migration monitoring data.
    Type: Application
    Filed: January 9, 2024
    Publication date: July 11, 2024
    Inventors: Wenyi Hu, Son Phan, Cen Li, Aria Abubakar, Zhun Li
  • Patent number: 12026222
    Abstract: A method and apparatus for subsurface data processing includes determining a set of clusters based at least in part on measurement vectors associated with different depths or times in subsurface data, defining clusters in a subsurface data by classes associated with a state mode, reducing a quantity of the subsurface data based at least in part on the classes, and storing the reduced quantity of the subsurface data and classes with the state model in a training database for a machine learning process.
    Type: Grant
    Filed: March 1, 2023
    Date of Patent: July 2, 2024
    Assignee: SCHLUMBERGER TECHNOLOGY CORPORATION
    Inventors: Vikas Jain, Po-Yen Wu, Aria Abubakar, Shashi Menon
  • Publication number: 20240176036
    Abstract: A method includes receiving seismic data and an initial velocity model, generating a first seismic image based at least in part on the seismic data and the initial velocity model, training a machine learning model to predict salt masks based at least in part on seismic images, merging the initial velocity model and the first salt mask to generate a first modified velocity model, generating an updated velocity model based at least in part on the first modified velocity model, generating a second seismic image based at least in part on the updated velocity model, predicting a second salt mask based at least in part on the second seismic image and the updated velocity model, using the trained machine learning model, and merging the updated velocity model and the second salt mask to generate a second modified velocity model.
    Type: Application
    Filed: March 17, 2022
    Publication date: May 30, 2024
    Inventors: Tao Zhao, Chunpeng Zhao, Anisha Kaul, Aria Abubakar
  • Publication number: 20240168194
    Abstract: A method for modeling a subsurface property for a subterranean volume of interest includes receiving input measurement data representing a subterranean volume of interest, predicting a subsurface property based at least in part on the input measurement data using a first machine learning model, predicting a subsurface property model based at least in part on the subsurface property, the input measurement data, or both, using a second machine learning model, predicting synthetic measurement data based at least in part on the subsurface property model using a third machine learning model, a physics-based model, or both, comparing the synthetic measurement data and the input measurement data, and training the first machine learning model, the second machine learning model, or both based at least in part on the comparing.
    Type: Application
    Filed: March 22, 2022
    Publication date: May 23, 2024
    Inventors: Aria Abubakar, Haibin Di, Tao Zhao, Zhun Li, Cen Li
  • 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
  • Patent number: 11965998
    Abstract: A computer-implemented method includes receiving a test seismic dataset associated with a known truth interpretation, receiving one or more hard constraints, training a machine learning system based on the test seismic dataset, the known truth interpretation, and the one or more hard constraints, determining an error value based on the training the machine learning system, adjusting the error value based on one or more soft constraints, updating the training of the machine learning system based on the adjusted error value, receiving a second seismic dataset after the updating the training; applying the second seismic dataset to the machine learning system to generate an interpretation of the second seismic dataset, generating a seismic image representing a subterranean domain based on the interpretation of the second seismic dataset, and outputting the seismic image.
    Type: Grant
    Filed: May 11, 2020
    Date of Patent: April 23, 2024
    Assignee: SCHLUMBERGER TECHNOLOGY CORPORATION
    Inventors: Haibin Di, Cen Li, Aria Abubakar, Stewart Smith
  • 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: 20230205842
    Abstract: A method and apparatus for subsurface data processing includes determining a set of clusters based at least in part on measurement vectors associated with different depths or times in subsurface data, defining clusters in a subsurface data by classes associated with a state mode, reducing a quantity of the subsurface data based at least in part on the classes, and storing the reduced quantity of the subsurface data and classes with the state model in a training database for a machine learning process.
    Type: Application
    Filed: March 1, 2023
    Publication date: June 29, 2023
    Inventors: Vikas Jain, Po-Yen Wu, Aria Abubakar, Shashi Menon
  • 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: 20230105326
    Abstract: Methods and systems for augmented geological service characterization are described. An embodiment of a method includes generating a geological service characterization process in response to one or more geological service objectives and a geological service experience information set. Such a method may also include augmenting the geological service characterization process by machine learning in response to a training information set. Additionally, the method may include generating an augmented geological service characterization process in response to the determination information.
    Type: Application
    Filed: December 5, 2022
    Publication date: April 6, 2023
    Inventors: Shashi MENON, Aria ABUBAKAR, Vikas JAIN, David Furse ALLEN, John RASMUS, John Paul HORKOWITZ, Valerian GUILLOT, Florent D'HALLUIN, Ridvan AKKURT, Sylvain WLODARCZYK
  • Patent number: 11609353
    Abstract: A method and apparatus for subsurface data processing includes determining a set of clusters based at least in part on measurement vectors associated with different depths or times in subsurface data, defining clusters in a subsurface data by classes associated with a state mode, reducing a quantity of the subsurface data based at least in part on the classes, and storing the reduced quantity of the subsurface data and classes with the state model in a training database for a machine learning process.
    Type: Grant
    Filed: September 26, 2018
    Date of Patent: March 21, 2023
    Assignee: Schlumberger Technology Corporation
    Inventors: Vikas Jain, Po-Yen Wu, Aria Abubakar, Shashi Menon
  • Publication number: 20230084403
    Abstract: A method for modeling a subterranean volume includes receiving seismic data comprising a signal, generating a semblance in the frequency-wavenumber domain for the seismic data, wherein the semblance represents a coherence of the signal in the frequency-wavenumber domain, extracting one or more wave energy modes in the semblance using a machine learning model trained to identify dispersion curves in the semblance based on a visible characteristic of the dispersion curves, and generating a model representing surface wave propagation based at least in part on the identified one or more wave energy modes.
    Type: Application
    Filed: January 13, 2021
    Publication date: March 16, 2023
    Inventors: Anisha KAUL, Phillip BILSBY, 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
  • Patent number: 11531131
    Abstract: A method for modeling a subsurface volume includes receiving a plurality of ordered seismic images including representations of objects in the subsurface volume, generating flow fields based on a difference between individual images of the plurality of ordered seismic images, and identifying the objects in the seismic images based on the flow fields and the plurality of ordered seismic images.
    Type: Grant
    Filed: January 15, 2021
    Date of Patent: December 20, 2022
    Assignee: Schlumberger Technology Corporation
    Inventors: Zhun Li, Aria Abubakar
  • Patent number: 11520075
    Abstract: Methods and systems for augmented geological service characterization are described. An embodiment of a method includes generating a geological service characterization process in response to one or more geological service objectives and a geological service experience information set. Such a method may also include augmenting the geological service characterization process by machine learning in response to a training information set. Additionally, the method may include generating an augmented geological service characterization process in response to the determination information.
    Type: Grant
    Filed: June 12, 2019
    Date of Patent: December 6, 2022
    Assignee: Schlumberger Technology Corporation
    Inventors: Shashi Menon, Aria Abubakar, Vikas Jain, David Furse Allen, John Rasmus, John Paul Horkowitz, Valerian Guillot, Florent D'Halluin, Ridvan Akkurt, Sylvain Wlodarczyk
  • 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
  • Patent number: 11428077
    Abstract: A method for drilling includes obtaining formation-measurement pairings, training a machine-learning model using the formation-measurement pairings, receiving measurements obtained by a tool positioned in a well formed in a formation, and generating a formation model of at least a portion of the formation using the machine-learning model and the measurements. The formation model represents one or more physical parameters of the formation, one or more structural parameters of the formation, or both.
    Type: Grant
    Filed: July 5, 2019
    Date of Patent: August 30, 2022
    Assignee: SCHLUMBERGER TECHNOLOGY CORPORATION
    Inventors: Yan Kai Xu, Qingfeng Zhu, Hui Xie, Helen Xiaoyan Zhong, Ettore Mirto, Aria Abubakar, Yao Feng, Ping Zhang