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Which operational parameters are best suited to optimize a completion strategy in an unconventional reservoir:
Tops bypassed pay case study analyzes these types of upstream data:
You can have multiple output neurons in a Feed-Forward Back-Propagation Neural Network:
Unsupervised neural networks are called:
When classifying multiple seismic attributes, which dimensionality reduction technique is ideal?
In a drilling optimization data-driven workflow, categorizing the data is critical:
Lithology-Fluids identification analyses include input parameters for training AI models such as:
The six principles of forecasting guarantee accuracy
What does the “I” stand for in the ARIMA forecasting of temporal data?
Which Deep Learning technique is ideally suited to forecast hydrocarbons when dealing with complex temporal datasets:
In the case of regression problems where a quantity is predicted, which is the preferred function to estimate model error?
An epoch in a DL model equals one forward and one backward pass for all training samples:
Digital twins use static, streaming, and historical data to mirror the past and present:
A fully developed digital twin should include each of the following elements:
Reinforcement Learning is a technique that learns from trial and error by interacting with an environment and receiving rewards or penalties
Which element of RL aims to maximize the cumulative rewards gained from the environment:
XAI, or Explainable AI, is critical for SMEs.
Are Shapley Values based on Quantum Theory?
PINNs are used to find solutions and unknown parameters of ordinary differential equations (ODEs) and partial differential equations (PDEs).