Abstract: Certain example embodiments relate to automated data preparation techniques usable to improve machine learning (ML) pipelines. Different error detectors are executed on a dirty dataset to identify which records therein include errors. Each record that has been identified by at least a threshold number of the error detectors as including an error is marked as erroneous. The dirty dataset's records are divided into clean and dirty fractions. If a data exclusion error has emerged, the process is repeated. Otherwise, a new set of data samples is generated by applying a variational autoencoder (VAE) to the clean fraction, the dirty dataset is augmented with the new set of data samples, and the augmented dirty dataset is provided for training of the ML model. It thus becomes possible to better training an ML model without having to repair data determined to include errors.

Abstract: Certain example embodiments relate to meta-learning based error detection. Base classifiers are provided for historical attributes in historical datasets. Each is trained to indicate dirtiness of a value for the associated historical attribute. Clusters and a clustering model are generated using historical clustering features determined for each historical attribute, which are then associated with the clusters. For each dirty attribute in a dirty dataset, corresponding dirty clustering features are determined. The dirty attributes are assigned to the clusters using the corresponding determined dirty clustering features and the clustering model. The base classifiers associated with the clusters to which the dirty attributes were assigned are retrieved. Dirty features are extracted from the dirty dataset, and selectively modified. The extracted dirty features are applied to the retrieved the base classifiers to determine meta-features. A meta-classifier is trained using labeled meta-features.

Abstract: The multifunction wellbore conditioning tool (100, 200, 300) is installed in the bottom hole assembly of a drill string and performs the functions of a cutting tool, keyseat wiper, roller reamer, keyseat wiper, and stabilizer during drilling operations, precluding the need for multiple different tools in the drill string. The tool (100, 200, 300) has a working sleeve (114, 214, 314) captured concentrically on the driveshaft (102, 202, 302) between opposed spring sets (134, 136; 234, 236; 334, 336). The sleeve (114, 214, 314) remains rotationally stationary about the rotating driveshaft (102, 202, 302) when in its central or neutral position on the driveshaft (102, 202, 302). When the sleeve (114, 214, 314) shifts axially on the driveshaft (102, 202, 302) it engages a clutch mechanism that allows it to rotate with the driveshaft (102, 202, 302) so that the rotating working sleeve (114, 214, 314) conditions the borehole.

Abstract: The multifunction wellbore conditioning tool (100, 200, 300) is installed in the bottom hole assembly of a drill string and performs the functions of a cutting tool, keyseat wiper, roller reamer, keyseat wiper, and stabilizer during drilling operations, precluding the need for multiple different tools in the drill string. The tool (100, 200, 300) has a working sleeve (114, 214, 314) captured concentrically on the driveshaft (102, 202, 302) between opposed spring sets (134, 136; 234, 236; 334, 336). The sleeve (114, 214, 314) remains rotationally stationary about the rotating driveshaft (102, 202, 302) when in its central or neutral position on the driveshaft (102, 202, 302). When the sleeve (114, 214, 314) shifts axially on the driveshaft (102, 202, 302) it engages a clutch mechanism that allows it to rotate with the driveshaft (102, 202, 302) so that the rotating working sleeve (114, 214, 314) conditions the borehole.