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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
Applicant: Landmark Graphics Corporation
Inventor： Shreshth Srivastav , Lloyd Maddock , Misael Luis Santana , Ashish Kishore Fatnani , Shashwat Verma , Sridharan Vallabhaneni
Abstract: 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：US20240185119A1
公开(公告)日：2024-06-06
申请号：US18072990
申请日：2022-12-01
Applicant: Landmark Graphics Corporation
Inventor： Jean-Christophe Wrobel-Daveau , Graham Baines , Graeme Nicoll , Shashwat Verma , Mrigya Fogat
IPC: G06N20/00
CPC classification number: G06N20/00
Abstract: A system can be used to incorporate historical geological data into machine learning techniques. The system can receive historical geological data. The system can pre-process the historical geological data by applying a selected, relative-time pre-processing technique to the historical geological data with respect to time-attributed geological phenomena. The system can train a machine-learning model using the pre-processed historical geological data. The system can apply the trained machine-learning model to generate predictions of geological phenomena. The system can provide a user interface to provide a visualization of the predictions of geological phenomena.
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Patent Agency Ranking
公开(公告)号：US11795814B2
公开(公告)日：2023-10-24
申请号：US16878715
申请日：2020-05-20
Applicant: Landmark Graphics Corporation
Inventor： Shashwat Verma , Sridharan Vallabhaneni , Rune Hobberstad , Samiran Roy
IPC: E21B47/117 , G06N20/00 , E21B44/00 , G06F30/27 , G06T9/00
CPC classification number: E21B47/117 , E21B44/00 , G06F30/27 , G06N20/00 , G06T9/002 , E21B2200/22 , G06T2207/20084
Abstract: 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：US20210285321A1
公开(公告)日：2021-09-16
申请号：US16878715
申请日：2020-05-20
Applicant: Landmark Graphics Corporation
Inventor： Shashwat Verma , Sridharan Vallabhaneni , Rune Hobberstad , Samiran Roy
IPC: E21B47/117 , E21B44/00 , G06N20/00
Abstract: 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：US11630224B2
公开(公告)日：2023-04-18
申请号：US17119181
申请日：2020-12-11
Applicant: Landmark Graphics Corporation
Inventor： Samiran Roy , Shashwat Verma
Abstract: A system is described for determining a likelihood of a type of fluid in a subterranean reservoir. The system may include a processor and a non-transitory computer-readable medium that includes instructions executable by the processor to cause the processor to perform various operations. The processor may receive pre-stack seismic data having seismically-acquired data elements for geometric locations in a subterranean reservoir. The processor may determine, using the pre-stack seismic data, input features for each geometric location and may execute a trained model on the input features for determining a likelihood of a type of fluid in the subterranean reservoir and for determining a list of features affecting the likelihood. The processor may subsequently output the likelihood and the list of features.
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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
Applicant: Landmark Graphics Corporation
Inventor： Misael Luis Santana , Ashish Kishore Fatnani , Shashwat Verma , Shreshth Srivastav , Sridharan Vallabhaneni
Abstract: 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：US20220187484A1
公开(公告)日：2022-06-16
申请号：US17119181
申请日：2020-12-11
Applicant: Landmark Graphics Corporation
Inventor： Samiran Roy , Shashwat Verma
Abstract: A system is described for determining a likelihood of a type of fluid in a subterranean reservoir. The system may include a processor and a non-transitory computer-readable medium that includes instructions executable by the processor to cause the processor to perform various operations. The processor may receive pre-stack seismic data having seismically-acquired data elements for geometric locations in a subterranean reservoir. The processor may determine, using the pre-stack seismic data, input features for each geometric location and may execute a trained model on the input features for determining a likelihood of a type of fluid in the subterranean reservoir and for determining a list of features affecting the likelihood. The processor may subsequently output the likelihood and the list of features.
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