| Autor: |
H. Williams, S. Hageraats-Ponomareva, T. Krasznavolgyi, W. Epping, R. Lamens, S. Davey, J. Przybysz-Jarnut, R. Newport, E. Link |
| Rok vydání: |
2020 |
| Předmět: |
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| Zdroj: |
First EAGE Digitalization Conference and Exhibition. |
| DOI: |
10.3997/2214-4609.202032046 |
| Popis: |
Summary Automated facies identification workflows which use Machine Learning (ML) are publicly available but perform sub-optimally (accuracy in the order of 60%) due to a lack of integration with geological domain knowledge. Existing tools consider well log values mostly on a depth-by-depth basis, using only very basic feature engineering. Our solution aims to integrate ML with well-established geoscience principles (also referred to as geo-rules) such as sequence stratigraphy, proximal-distal trends, and log-trend patterns. Geological knowledge is incorporated into ML to improve the quality and robustness of facies prediction and is captured as additional geologically-inspired features added to the dataset. These features include the mean value and other derived properties of intervals, density-neutron separation, segmentation and wavelet transform. All ML algorithms tested with this augmented set of features show significant improvement in performance metrics as compared to solutions with basic logs only. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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