Porosity prediction from X-ray computed tomography logs (RHOB and PEF) using Artificial Neural Networks (ANN)
Andrés Felipe Ortiz- Edwar Hernando Herrera
- Nicolás Santos
Published 2020-09-30
Keywords
- CT,
- Rock images,
- Well logs,
- High-resolution logs,
- Artificial Intelligence
How to Cite
Copyright (c) 2020 Boletín de Geología
This work is licensed under a Creative Commons Attribution 4.0 International License.
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Abstract
This work presents a method for rock porosity prediction from the X-ray computed tomography (CT) logs obtained using a double energy approach, bulk density (RHOB) and photoelectric factor (PEF). The proposed method seeks to correlate the known porosity from the Routine Core Analysis (RCAL) with RHOB and PEF high-resolution logs, as the response of these two measurements depends on the volumetric quantity of different rock materials and of the volume of its porous space. Artificial Neural Networks (ANNs) are trained so they can predict porosity from CT logs at a high resolution (0.625 mm). The ANNs validation and regression plots show that porosity predictions are good. High-resolution porosity models linked to CT images could contribute to enhancing the petrophysics model as they allow a more refined identification of intervals of interest due to the detailed measurement.
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References
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