Application of RBF Neural Network in Parameters of Shale Oil Well Logging Evaluation

作者：Lu Zhixin^*; Lu ShuangFang; Chen GuoHui

来源：4th International Conference on Energy and Environmental Protection (ICEEP), 2015-06-02 to 2015-06-04.

• RBF neural network
• porosity
• oil saturation

The attention of Shale reservoirs is continuing to increase at home and abroad, while higher content of oil and gas "dessert district" is the preferred target of shale oil and gas development, therefore increase the shale oil and gas exploration parameters for oil shale continuous evaluation the accuracy requirements. Using conventional logs for continuous evaluation of oil-bearing sandstone argument has been widely accepted, but still exploring of shale evaluation. This paper aims to explore the feasibility of using conventional logging parameters evaluation of shale oil by RBF neural network. The RBF network method for L Wells porosity and oil saturation was evaluated, the results showed that the higher the feasibility of RBF neural network method for porosity and oil saturation evaluation, with high precision, shale oil and gas exploration and development of great significance. Neural network method for shale organic heterogeneity evaluation with a larger application.

• 出版日期2015
• 单位东北石油大学; 中国石油大学（华东）; 中国石油大学（北京）