-
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.
-
2.发明公开公开(公告)号：US20230392498A1
公开(公告)日：2023-12-07
申请号：US17833873
申请日：2022-06-06
CPC分类号： E21B49/0875 , E21B47/10 , E21B2200/22
摘要： A system can collect a first set of equipment data and emissions data from a first hydrocarbon operation location. The system can train at least one machine-learning model to estimate an emission factor of at least one equipment component of the first hydrocarbon operation location using the first set of equipment data and the emissions data of the first hydrocarbon operation location. The system can then apply the at least one machine-learning model to a second set of equipment data to estimate total emissions over a predetermined amount of time at a second hydrocarbon operation location.
-
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.
-
公开(公告)号：US20230194738A1
公开(公告)日：2023-06-22
申请号：US17553091
申请日：2021-12-16
CPC分类号： G01V1/30 , G06N20/00 , E21B49/00 , G01V2210/61 , E21B2200/20 , E21B2200/22
摘要： The disclosure presents processes to select cartographic reference system (CRS) recommendations from a CRS model where the CRS recommendations are matched to received seismic data. A learning mode can be used to build the CRS model where seismic data is matched to CRS. The learning mode can be automated using natural language processing system to parse the meta data for the seismic data, such as the name, area, or code, or label. The CRS model can be updated using an output from a user system, such as when a user manually matches a CRS to seismic data. The matched seismic data to CRS, e.g., seismic data-CRS match, can be used as input to a user system or a computing system, such as a borehole operation system.
-
5.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.
-
-
-
-
搜索结果
5 条记录 (耗时 0.014 秒)
