Estimation of shale volume from well logging data using Artificial Neural Network
- Authors: Duong Hong Vu*, Hung Tien Nguyen
Hanoi University of Mining and Geology, Hanoi, Vietnam
- Keywords: Neural network, Volume of shale, Well logging.
- Received: 11st-Jan-2021
- Revised: 25th-Apr-2021
- Accepted: 21st-May-2021
- Online: 30th-June-2021
- Section: Oil and Gas
The existence of shale has a major effect on reservoir quality because it reduces the rock’s both the porosity and permeability. There are several types of shale, and they can be distributed in the sand in four different ways: laminated, structural, dispersed, or any combination of these. Each of them has various features and physical properties. Therefore, shale volume estimation is one of the most important and challengin tasks to be solved information evaluation. There are many equations proposed to calculate shale volume from Gamma - ray log; however, none of them could be considered the best method that can be applied to all case studies. This study aims to propose a new approach to estimate shale volume from well - logging data. Gamma - ray and other logs were used as input data for an artificial neural network (ANN) to predict the shale volume. We apply this technique to the 1143 data set of the ocean drilling program (ODP) in the East Sea. The authors compared the result to core data and recognized that utilization of several logs and ANN gives a better estimation than conventional methods (more accurate and can reflect the trend of actual shale volume).
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