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Updated: Jul 20, 2025

Microfluidic Devices for Characterizing Pore-scale Event Processes in Porous Media for Oil Recovery Applications
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Microfluidic Devices for Characterizing Pore-scale Event Processes in Porous Media for Oil Recovery Applications

Published on: January 16, 2018

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开发了一个强大的模型和人工智能技术,以实时预测钻井流体密度和同等循环密度

Mohammed Al-Rubaii1, Mohammed Al-Shargabi2, Bayan Aldahlawi1

  • 1Department of Petroleum Engineering, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia.

Sensors (Basel, Switzerland)
|July 29, 2023
PubMed
概括

新的模型使用表面数据预测等效的循环密度 (ECD) 和泥重量 (MW),避免了昂贵的下孔传感器. 这些方法提高了在具有挑战性的井中钻探的安全性和效率.

关键词:
人工智能的人工智能是人工智能.人工神经网络的人工神经网络决策树是一个决策树.钻井效率提高了钻井的效率.同等的循环密度的循环密度.泥的重量 泥的重量支持矢量机器的支持矢量机器

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