What is it about?

This paper proposes a machine learning-based methodology to identify geomechanical parameters of minifrac tests. This methodology integrates artificial intelligence techniques to match borehole pressure curves. In this context, the genetic algorithm minimizes the error between the observed and the predicted data. Moreover, a gradient boosting machine is adopted to build a surrogate model

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Why is it important?

Predicting the behavior of petroleum reservoirs is challenging and includes, as a crucial step, determining its geomechanical parameters


We aim to contribute to the estimation of geomechanical parameters in the context of petroleum industry based on an artificial intelligence

Rafael Abreu
Pontificia Universidade Catolica do Rio de Janeiro

Read the Original

This page is a summary of: Parameter identification of minifrac numerical tests using a gradient boosting‐based proxy model and genetic algorithm, International Journal for Numerical and Analytical Methods in Geomechanics, November 2023, Wiley,
DOI: 10.1002/nag.3654.
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