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公开(公告)号：US20220205351A1
公开(公告)日：2022-06-30
申请号：US17134738
申请日：2020-12-28
发明人： Shreshth Srivastav , Lloyd Maddock , Misael Luis Santana , Ashish Kishore Fatnani , Shashwat Verma , Sridharan Vallabhaneni
摘要： A drilling data correction system corrects drilling data entries in high-importance drilling data segments using machine learning and rules-based drilling models. A data importance analyzer identifies high-importance data segments in incoming drilling data. The drilling data correction system inputs features of drilling data into machine learning drilling models and rules-based drilling models trained to predict the high-importance data segments. Predictions from the machine learning drilling models and rules-based drilling models are presented to a user based on drilling data prediction criteria. The machine learning drilling data predictions are used to automatically correct the high-importance data segments, or the user chooses between machine learning drilling data predictions and rules-based drilling data predictions to correct the high-importance drilling data segment.
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公开(公告)号：US20210285321A1
公开(公告)日：2021-09-16
申请号：US16878715
申请日：2020-05-20
IPC分类号： E21B47/117 , E21B44/00 , G06N20/00
摘要： A wellbore drilling system can generate a machine-learning model trained using historic drilling operation data for monitoring for a lost circulation event. Real-time data for a drilling operation can be received and the machine-learning model can be applied to the real-time data to identify a lost circulation event that is occurring. An alarm can then be outputted to indicate a lost circulation event is occurring for the drilling operation.
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公开(公告)号：US20220205350A1
公开(公告)日：2022-06-30
申请号：US17134626
申请日：2020-12-28
发明人： Misael Luis Santana , Ashish Kishore Fatnani , Shashwat Verma , Shreshth Srivastav , Sridharan Vallabhaneni
摘要： A drilling data analytics engine disclosed herein automatically corrects drilling data with predictive modeling. A drilling data quality analyzer segregates drilling data into good drilling data and bad drilling data that has missing, incomplete, or incorrect entries. For each bad data entry in the bad drilling data, the drilling data analytics engine preprocess drilling data attribute values for the corresponding task not including the drilling data attribute value for the bad data entry and inputs the preprocessed drilling data attribute values into a trained predictive model. The trained predictive model is trained on good drilling data to estimate values for the drilling attribute corresponding to the bad data entry.
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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
摘要： 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：US20210311221A1
公开(公告)日：2021-10-07
申请号：US16841890
申请日：2020-04-07
发明人： Samiran Roy , Soumi Chaki , Sridharan Vallabhaneni
摘要： Optimizing seismic to depth conversion to enhance subsurface operations including measuring seismic data in a subsurface formation, dividing the subsurface formation into a training area and a study area, dividing the seismic data into training seismic data and study seismic data, wherein the training seismic data corresponds to the training area, and wherein the study seismic data corresponds to the study area, calculating target depth data corresponding to the training area, training a machine learning model using training inputs and training targets, wherein the training inputs comprise the training seismic data, and wherein the training targets comprise the target depth data, computing, by the machine learning model, output depth data corresponding to the study area based at least in part on the study seismic data; and modifying one or more subsurface operations corresponding to the study area based at least in part on the output depth data.
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6.发明申请公开(公告)号：US20190235106A1
公开(公告)日：2019-08-01
申请号：US16331635
申请日：2016-10-04
CPC分类号： G01V1/282 , G01V1/288 , G01V1/301 , G01V1/307 , G01V1/308 , G01V1/42 , G01V1/50 , G01V11/00 , G01V2210/121 , G01V2210/1234 , G01V2210/6122 , G01V2210/6163 , G01V2210/6165 , G01V2210/6167 , G01V2210/6169 , G01V2210/63 , G01V2210/646 , G06Q10/06 , G06Q50/02
摘要： 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：US11795814B2
公开(公告)日：2023-10-24
申请号：US16878715
申请日：2020-05-20
IPC分类号： E21B47/117 , G06N20/00 , E21B44/00 , G06F30/27 , G06T9/00
CPC分类号： E21B47/117 , E21B44/00 , G06F30/27 , G06N20/00 , G06T9/002 , E21B2200/22 , G06T2207/20084
摘要： A wellbore drilling system can generate a machine-learning model trained using historic drilling operation data for monitoring for a lost circulation event. Real-time data for a drilling operation can be received and the machine-learning model can be applied to the real-time data to identify a lost circulation event that is occurring. An alarm can then be outputted to indicate a lost circulation event is occurring for the drilling operation.
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公开(公告)号：US11614557B2
公开(公告)日：2023-03-28
申请号：US16841890
申请日：2020-04-07
发明人： Samiran Roy , Soumi Chaki , Sridharan Vallabhaneni
摘要： Optimizing seismic to depth conversion to enhance subsurface operations including measuring seismic data in a subsurface formation, dividing the subsurface formation into a training area and a study area, dividing the seismic data into training seismic data and study seismic data, wherein the training seismic data corresponds to the training area, and wherein the study seismic data corresponds to the study area, calculating target depth data corresponding to the training area, training a machine learning model using training inputs and training targets, wherein the training inputs comprise the training seismic data, and wherein the training targets comprise the target depth data, computing, by the machine learning model, output depth data corresponding to the study area based at least in part on the study seismic data; and modifying one or more subsurface operations corresponding to the study area based at least in part on the output depth data.
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9.发明申请公开(公告)号：US20220075915A1
公开(公告)日：2022-03-10
申请号：US17016075
申请日：2020-09-09
发明人： Sridharan Vallabhaneni , Samiran Roy , Soumi Chaki , Bhaskar Jogi Venkata Mandapaka , Rajeev Pakalapati , Shreshth Srivastav , Satyam Priyadarshy
摘要： Methods and apparatus for generating one or more reservoir 3D models are provided. In one or more embodiments, a method can include training a first machine learning model to generate one or more integrated enhanced logs based, at least in part, on an integrated data set, wherein the integrated data set includes seismic data and well log data; generating one or more integrated enhanced logs from the first machine learning model; grouping the one or more integrated enhanced logs into an ensemble of integrated enhanced logs to form a static reservoir 3D model of a subterranean reservoir; inputting additional data to the first machine learning model to produce one or more updated integrated enhanced logs; and grouping the one or more updated integrated enhanced logs into an ensemble of updated integrated enhanced logs to form an updated 3D model.
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