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- W4306173802 abstract "Shale brittleness is the key parameter to optimize reservoir fracturing modification and improve productivity. However, conventional methods based on rock mechanics and mineralogical parameters are limited by the lack of shear wave velocity and mineral composition data. Machine learning algorithms have been applied in the field of geophysical exploration, but the prediction accuracy of existing algorithms needs to be improved. In this study, a new hybrid model, an integration of the Sparrow Search Algorithm (SSA) and the Extreme Learning Machine (ELM), named SSA-ELM, is proposed for predicting the brittleness index. ELM is performed to create the original brittleness model, meanwhile, the SSA algorithm is utilized to automatically explore the hyperparameters of the model. Additionally, we also use 12 other favorite machine learning algorithms to validate the superiority of the proposed model combined with traditional logs and XRD-derived brittleness. 82 mineral brittleness databases from 5 exploration wells in the Songliao Basin, China were established. The simulation results of two Test wells indicate that the SSA-ELM model has the most accurate predictions and excellent generalization capability. The brittle profiles established based on the SSA-ELM model can effectively guide the fracturing exploration of shale oil reservoirs." @default.
- W4306173802 created "2022-10-14" @default.
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- W4306173802 date "2022-12-01" @default.
- W4306173802 modified "2023-10-16" @default.
- W4306173802 title "A new hybrid method based on sparrow search algorithm optimized extreme learning machine for brittleness evaluation" @default.
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- W4306173802 doi "https://doi.org/10.1016/j.jappgeo.2022.104845" @default.
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