Finalize Training Node
Finalizes training table that has undergone all the intended feature engineering in preparation for model training.
Import Seismic Node
Import a post-stack seismic attribute in .sgy format. File headers are expected in SEGY Rev.1 format, but IL/XL X/Y byte locations can be specified for non-standard files.
Feature Engineering Node
Creates and attaches features generated from seismic and data to training table.
This node allows users to run simulations at a trace, section, or volume scale. We recommend to use volume scale simulations sparingly, to save on cloud costs.
This node performs an unsupervised lithology clustering process. Models are logged into the model management database and retrievable for use in real-time or ROP optimizer testing.
Train Ensemble Members node
This node takes your training data and trains a user defined amount of candidate networks. The goal is to later utilize methods model ensembling methods to yield a bootstrap aggregated model that best generatizes to unseen data
Import Horizons Node
Import a bulk horizon file. A minimum of two horizons are required for the QEarth workflow. We currently support Hampson-Russel, Opendtect, and standard column-wise text files
Users can connect various trained models and perform an interactive analysis of the predictions at each well location. Users can then connect the corresponding model simulations to an ensemble node and generate the desired realization.
Hyperparameter Explorer Node
Explores various permutations within hyperparameter space, returning early performance results to guide model design.
This node leverages a ROP targeted QLog model and a lithology clustering model to perform a validation of an ROP optimization process.
Feature Engineering node
This node takes your training data and performs feature scaling and applies Quantico’s proprietary feature engineering methodology to add spatial and physical context.
This node allows users to load well data for training, testing, or blind validation. The data must have been processed using QApp’s internal QLog preprocessing module.