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).
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Publication number: 20240062080Abstract: Systems, methods, and computer-readable storage media for aggregating the outputs of multiple machine learning models, then using the output of yet another machine learning model as a multiplier to obtain a final prediction. A system can receiving a plurality of data sets, each data set being associated with at least one data type, and train machine learning models, each model associated with one or more of the different data types. Upon execution, the multiple machine learning models can each produce a prediction which is aggregated together to form an aggregated prediction. The multiplier from the additional machine learning model can then be applied to the aggregated prediction, resulting in a final prediction.Type: ApplicationFiled: August 14, 2023Publication date: February 22, 2024Inventors: Bhaskar Mandapaka, Neil Athavale, Anoop Mohandas, Shashank Panchangam, Ashwani Dev, Carey Hepler, Shiju Zacharia, Chris Wolfi, Smijith Kunhiraman
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Publication number: 20240054440Abstract: Systems, methods, and computer-readable storage media for recommending loads for transport. A system can receive location coordinates for a transport vehicle, and further receive data regarding available loads which can be transported by the transport vehicle. The system can then filter the available loads based at least in part on the location coordinates. The system can also receive at least one carrier profile and at least one shipper profile. Finally, the system can execute a load recommendation algorithm using the preference filtered loads, the at least one carrier profile, and the at least one shipper profile as inputs, resulting in at least one load recommendation score for a load within the preference filtered loads.Type: ApplicationFiled: August 10, 2023Publication date: February 15, 2024Inventors: Madison Strong, Bhaskar Mandapaka, Neil Athavale, Shashank Panchangam, Ashwani Dev, Carey Hepler, Chris Wolfl, Smijith Kunhiraman
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Patent number: 11879316Abstract: 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: GrantFiled: October 4, 2016Date of Patent: January 23, 2024Assignee: Landmark Graphics CorporationInventors: Jeffrey Marc Yarus, Ashwani Dev, Jin Fei, Trace Boone Smith
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Patent number: 11868890Abstract: 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 bType: GrantFiled: April 6, 2022Date of Patent: January 9, 2024Assignees: LANDMARK GRAPHICS CORPORATION, EMC IP HOLDING COMPANY LLCInventors: Chandra Yeleshwarapu, 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, Nagaraj Sirinivasan
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Patent number: 11676000Abstract: 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: GrantFiled: August 31, 2018Date of Patent: June 13, 2023Assignee: Halliburton Energy Services, Inc.Inventors: Ajay Pratap Singh, Roxana Nielsen, Satyam Priyadarshy, Ashwani Dev, Geetha Gopakumar Nair, Suresh Venugopal
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Publication number: 20220307357Abstract: System and methods for tuning equation of state (EOS) characterizations are presented. Pressure-volume-temperature (PVT) data is obtained for downhole fluids within a reservoir formation. A component grouping for an EOS model of the downhole fluids is determined, based on the obtained PVT data. The component grouping is used to estimate properties of the downhole fluids for a current stage of a downhole operation within the formation. A machine learning model is trained to minimize an error between the estimated properties and actual fluid properties measured during the current stage of the operation, where the component grouping for the EOS model is iteratively adjusted by the machine learning model until the error is minimized. The EOS model is tuned using the adjusted component grouping. Fluid properties are estimated for one or more subsequent stages of the downhole operation to be performed along the wellbore, based on the tuned EOS model.Type: ApplicationFiled: June 12, 2020Publication date: September 29, 2022Inventors: Ajay Pratap Singh, Suryansh Purwar, Ashwani Dev, Satyam Priyadarshy
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Publication number: 20220300812Abstract: 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 bType: ApplicationFiled: April 6, 2022Publication date: September 22, 2022Applicants: Landmark Graphics Corporation, EMC IP Holding Company LLCInventors: Chandra YELESHWARAPU, 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, Nagaraj SIRINIVASAN
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Patent number: 11378710Abstract: 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: GrantFiled: July 18, 2018Date of Patent: July 5, 2022Assignee: Landmark Graphics CorporationInventors: Youli Mao, Bhaskar Mandapaka, Ashwani Dev, Satyam Priyadarshy
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Publication number: 20220169440Abstract: A collapsible container that includes a base, a first side panel hingedly attached to the base, a second side panel hingedly attached to the base, a first end panel hingedly attached to the base, with the first end panel including a door, and a second end panel hingedly attached to the base. The collapsible container can further include a top cover. The collapsible container is configured to be positioned in a collapsed position and an assembled position. In an assembled position, the collapsible container is configured to hold items during a domestic or international move.Type: ApplicationFiled: November 30, 2021Publication date: June 2, 2022Inventors: Shiju Zacharia, Smijith Kunhiraman, Patrick Wallace, Ashwani Dev
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Patent number: 11315014Abstract: 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 bType: GrantFiled: August 16, 2018Date of Patent: April 26, 2022Assignee: EMC IP HOLDING COMPANY LLCInventors: 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
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Patent number: 11269100Abstract: 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: GrantFiled: December 21, 2017Date of Patent: March 8, 2022Assignee: Landmark Graphics CorporationInventors: Youli Mao, Raja Vikram Pandya, Bhaskar Mandapaka, Keshava Prasad Rangarajan, Srinath Madasu, Satyam Priyadarshy, Ashwani Dev
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Publication number: 20220034220Abstract: A system for determining real time cluster efficiency for a pumping operation in a wellbore includes a pump, a surface sensor, a downhole sensor system, and a computing device. The pump can pump slurry or diverter material in the wellbore. The surface sensor can be positioned at a surface of the wellbore to detect surface data about the pump. The downhole sensor system can be positioned in the wellbore to detect downhole data about an environment of the wellbore. The computing device can receive the surface data from the surface sensor, receive the downhole data from the downhole sensor system, apply the surface data and the downhole data to a long short-term memory (LSTM) neural network to produce a predicted cluster efficiency associated with operational settings of the pump, and control the pump using the operational settings to achieve the predicted cluster efficiency.Type: ApplicationFiled: November 30, 2018Publication date: February 3, 2022Inventors: Srinath Madasu, Ashwani Dev, Keshava Prasad Rangarajan, Satyam Priyadarshy
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Patent number: 11099289Abstract: 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: GrantFiled: October 4, 2016Date of Patent: August 24, 2021Assignee: LANDMARK GRAPHICS CORPORATIONInventors: Ashwani Dev, Sridharan Vallabhaneni, Raquel Morag Velasco, Jeffrey Marc Yarus
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Publication number: 20200264329Abstract: 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: ApplicationFiled: March 31, 2016Publication date: August 20, 2020Applicant: Landmark Graphics CorporationInventors: Baiyuan GAO, Jesse Methias LOMASK, Ashwani DEV
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Publication number: 20200200931Abstract: 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: ApplicationFiled: December 21, 2017Publication date: June 25, 2020Inventors: Youli Mao, Raja Vikram Pandya, Bhaskar Mandapaka, Keshava Prasad Rangarajan, Srinath Madasu, Satyam Priyadarshy, Ashwani Dev
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Publication number: 20200149354Abstract: 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: ApplicationFiled: August 31, 2018Publication date: May 14, 2020Inventors: Ajay Pratap Singh, Roxana Nielsen, Jr., Satyam Priyadarshy, Ashwani Dev, Geetha Gopakumar Nair, Suresh Venugopal
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Publication number: 20200064507Abstract: 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: ApplicationFiled: July 18, 2018Publication date: February 27, 2020Inventors: Youli Mao, Bhaskar Mandapaka, Ashwani Dev, Satyam Priyadarshy
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Publication number: 20200057675Abstract: 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 bType: ApplicationFiled: August 16, 2018Publication date: February 20, 2020Inventors: 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
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Publication number: 20190277124Abstract: 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: ApplicationFiled: October 4, 2016Publication date: September 12, 2019Inventors: Jeffrey Marc Yarus, Ashwani Dev, Jin Fei, Trace Boone Smith
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Publication number: 20190235106Abstract: 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: ApplicationFiled: October 4, 2016Publication date: August 1, 2019Inventors: Ashwani Dev, Sridharan Vallabhaneni, Raquel Morag Velasco, Jeffrey Marc Yarus