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公开(公告)号：US20180306939A1
公开(公告)日：2018-10-25
申请号：US15560001
申请日：2016-10-20
Applicant: Landmark Graphics Corporation
CPC classification number: G01V1/364 , G01V1/288 , G01V1/308 , G01V1/345 , G01V1/42 , G01V2210/1234 , G01V2210/1299 , G01V2210/1429 , G01V2210/163 , G01V2210/6122 , G01V2210/646 , G06F17/50 , G06Q50/02
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.
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公开(公告)号：US11879316B2
公开(公告)日：2024-01-23
申请号：US16331643
申请日：2016-10-04
Applicant: Landmark Graphics Corporation
Inventor： Jeffrey Marc Yarus , Ashwani Dev , Jin Fei , Trace Boone Smith
CPC classification number: E21B43/26 , E21B43/16 , G01V1/16 , G01V1/288 , G01V1/40 , G01V1/50 , G01V2210/1234 , G01V2210/646 , G01V2210/65 , G01V2210/665 , G06F30/00
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.
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公开(公告)号：US20190277124A1
公开(公告)日：2019-09-12
申请号：US16331643
申请日：2016-10-04
Applicant: Landmark Graphics Corporation
Inventor： Jeffrey Marc Yarus , Ashwani Dev , Jin Fei , Trace Boone Smith
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.
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公开(公告)号：US20220307357A1
公开(公告)日：2022-09-29
申请号：US17293454
申请日：2020-06-12
Applicant: LANDMARK GRAPHICS CORPORATION
Inventor： Ajay Pratap Singh , Suryansh Purwar , Ashwani Dev , Satyam Priyadarshy
IPC: E21B43/16 , E21B47/06 , E21B47/003
Abstract: 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.
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公开(公告)号：US11269100B2
公开(公告)日：2022-03-08
申请号：US16610031
申请日：2017-12-21
Applicant: Landmark Graphics Corporation
Inventor： Youli Mao , Raja Vikram Pandya , Bhaskar Mandapaka , Keshava Prasad Rangarajan , Srinath Madasu , Satyam Priyadarshy , Ashwani Dev
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.
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Patent Agency Ranking
公开(公告)号：US20220034220A1
公开(公告)日：2022-02-03
申请号：US17276985
申请日：2018-11-30
Applicant: Landmark Graphics Corporation
Inventor： Srinath Madasu , Ashwani Dev , Keshava Prasad Rangarajan , Satyam Priyadarshy
Abstract: 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.
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公开(公告)号：US11868890B2
公开(公告)日：2024-01-09
申请号：US17714247
申请日：2022-04-06
Applicant: Landmark Graphics Corporation , EMC IP Holding Company LLC
Inventor： 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
CPC classification number: G06N3/08 , G06F9/48 , G06F9/4843 , G06F9/4881 , G06F9/50 , G06F9/5061 , G06F9/5066 , G06F9/5077 , G06F9/5083 , G06N3/02 , G06N3/04 , G06N3/086 , G06F2209/501 , G06F2209/5011 , G06F2209/5019
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 based on the deep neural network output.
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公开(公告)号：US11378710B2
公开(公告)日：2022-07-05
申请号：US16489286
申请日：2018-07-18
Applicant: Landmark Graphics Corporation
Inventor： Youli Mao , Bhaskar Mandapaka , Ashwani Dev , Satyam Priyadarshy
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.
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公开(公告)号：US11099289B2
公开(公告)日：2021-08-24
申请号：US16331635
申请日：2016-10-04
Applicant: Landmark Graphics Corporation
Inventor： Ashwani Dev , Sridharan Vallabhaneni , Raquel Morag Velasco , Jeffrey Marc Yarus
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. For example, a method may involve modeling petrophysical properties of a subterranean formation, microseismic data associated with treating a complex fracture network in the subterranean formation, or a combination thereof with a mathematical model based on measured data, microseismic data, completion and treatment data, or a combination thereof to produce a petrophysical property map, a microseismic data map, or a combination thereof; and correlating a seismic attribute map with the petrophysical property map, the microseismic data map, or the combination thereof using the mathematical model to produce at least one quantified correlation, wherein the seismic attribute map is a seismic attributed modeled for the complex fracture network.
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公开(公告)号：US20200149354A1
公开(公告)日：2020-05-14
申请号：US16611817
申请日：2018-08-31
Applicant: LANDMARK GRAPHICS CORPORATION
Inventor： Ajay Pratap Singh , Roxana Nielsen, Jr. , Satyam Priyadarshy , Ashwani Dev , Geetha Gopakumar Nair , Suresh Venugopal
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.
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