| Autor: |
Xiaoqing Hu, Biao Yuan, Rui Zhou, Quanwei Xu |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
2021 4th International Conference on Artificial Intelligence and Big Data (ICAIBD). |
| Popis: |
Intelligent oil and gas system is becoming more and more popular. Carbonate lithology is a crucial part of reservoir characterization. The classification of lithology is an important task in petroleum exploration and engineering since it influences the production efficiency. On this basis, a lithology classification system based on bidirectional gated recurrent unit (BGRU) is designed to classify the lithology by using the logging data collected from Sudong 41–33 Block of Sulige Gas Field. In this study, cross-validation are displayed for verifying the validity of BGRU model. The cross-validation results of three different wells indicate that BGRU model performs better than conventional models. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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