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

IPC: E21B44/00 ,  E21B47/12 ,  G06N20/00 ,  G06K9/62

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.

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

IPC: E21B44/00 ,  G01V1/50 ,  G01V1/46 ,  G06K9/62

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.