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Patent Agency Ranking
公开(公告)号：US11891882B2
公开(公告)日：2024-02-06
申请号：US16932056
申请日：2020-07-17
Applicant: Landmark Graphics Corporation
Inventor： Jiazuo Zhang
Abstract: Disclosed embodiments include methods and systems for classifying test data. In one embodiment a method includes determining one or more variable types in a multivariate test vector within a data set, and for a plurality of machine-learning models, determining a closest match between variable types used by (to train) the machine-learning models and the determined variable types for the test vector. In response to determining a closest match for one machine-learning model, a corresponding machine-learning model is selected and the test vector is classified using the selected model. In response to determining a closest match for multiple machine-learning models, a similarity is determined between a probability distribution for the test data set and the probability distributions for the multiple machine-learning models to generate similarity values for each of the models. In response to one of the similarity values exceeding a threshold value, a machine-learning model is selected that corresponds to the exceeding similarity value and the test vector is classified using the selected model.
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2.发明申请公开(公告)号：US20220004919A1
公开(公告)日：2022-01-06
申请号：US16963313
申请日：2020-02-20
Applicant: Landmark Graphics Corporation
Inventor： Jiazuo Zhang , Graham Baines
Abstract: According to some aspects, machine-learning models can be executed to classify a subsurface rock. Examples include training numerous machine-learning models using training data sets with different probability distributions, and then selecting a model to execute on a test data set. The selection of the model may be based on the similarity of each data point of the test data set and the probability distribution of each training class. Examples include detecting and recommending a pre-trained model to generate outputs predicting a classification, such as a lithology, of a test data set. Recommending the trained model may be based on calculated prior probabilities that measure the similarity between the training and test data sets. The model with a training data set that is most similar to the test data set can be recommended for classifying a physical property of the subsurface rock for hydrocarbon formation.
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3.发明授权公开(公告)号：US11954567B2
公开(公告)日：2024-04-09
申请号：US16963313
申请日：2020-02-20
Applicant: Landmark Graphics Corporation
Inventor： Jiazuo Zhang , Graham Baines
IPC: G06F18/22 , G01V20/00 , G06F18/214 , G06F18/2431 , G06N20/00
CPC classification number: G06N20/00 , G01V20/00 , G06F18/214 , G06F18/22 , G06F18/2431 , G01V2210/66
Abstract: According to some aspects, machine-learning models can be executed to classify a subsurface rock. Examples include training numerous machine-learning models using training data sets with different probability distributions, and then selecting a model to execute on a test data set. The selection of the model may be based on the similarity of each data point of the test data set and the probability distribution of each training class. Examples include detecting and recommending a pre-trained model to generate outputs predicting a classification, such as a lithology, of a test data set. Recommending the trained model may be based on calculated prior probabilities that measure the similarity between the training and test data sets. The model with a training data set that is most similar to the test data set can be recommended for classifying a physical property of the subsurface rock for hydrocarbon formation.
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公开(公告)号：US20230068373A1
公开(公告)日：2023-03-02
申请号：US17446537
申请日：2021-08-31
Applicant: Landmark Graphics Corporation
Inventor： Jiazuo Zhang
Abstract: A method comprises receiving a current dataset for a current time window from at least one sensor in a wellbore created in a subsurface formation, wherein the current dataset comprises values of a number of current features of the subsurface formation at a spatial location in the wellbore. The method includes selecting at least one previous time window from a number of previous time windows that includes a previously cached dataset that was detected by the at least one sensor or a different sensor in the wellbore and that spatially overlaps with the spatial location for the current dataset. The method includes merging the current dataset with the previously cached dataset to create a merged dataset. The method includes selecting a machine learning model from a plurality of machine learning models for the spatial location in the wellbore based on the merged dataset.
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公开(公告)号：US20220018221A1
公开(公告)日：2022-01-20
申请号：US16932056
申请日：2020-07-17
Applicant: Landmark Graphics Corporation
Inventor： Jiazuo Zhang
Abstract: Disclosed embodiments include methods and systems for classifying test data. In one embodiment a method includes determining one or more variable types in a multivariate test vector within a data set, and for a plurality of machine-learning models, determining a closest match between variable types used by (to train) the machine-learning models and the determined variable types for the test vector. In response to determining a closest match for one machine-learning model, a corresponding machine-learning model is selected and the test vector is classified using the selected model. In response to determining a closest match for multiple machine-learning models, a similarity is determined between a probability distribution for the test data set and the probability distributions for the multiple machine-learning models to generate similarity values for each of the models. In response to one of the similarity values exceeding a threshold value, a machine-learning model is selected that corresponds to the exceeding similarity value and the test vector is classified using the selected model.
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