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- W4387446968 abstract "Abstract This study introduces a novel machine learning-based framework aimed at optimizing well placement for shale development. Traditional well-placement techniques, often reliant on physics-based modeling or empirical assumptions, have proven to be time-consuming and prone to suboptimal results. To address these limitations, we propose a Deep Convolutional Neural Networks (DCNN) based workflow that leverages context-specific algorithms for accurate subsurface-driven infill planning optimization. The framework was validated on real-world data from 2 fields of 630 wells in the Permian basin, successfully replicating the validation dataset with 94% accuracy and reducing the time required for analysis by over 85% compared to conventional methods. Furthermore, the framework demonstrated improved Estimated Ultimate Recovery (EUR) and effective reserve management, emphasizing its potential to enhance the economic viability of shale production." @default.
- W4387446968 created "2023-10-10" @default.
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- W4387446968 date "2023-10-09" @default.
- W4387446968 modified "2023-10-11" @default.
- W4387446968 title "Deep-Learning-Based Approach for Optimizing Infill Well Placement" @default.
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- W4387446968 doi "https://doi.org/10.2118/215072-ms" @default.
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