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使用机器学习方法对机动底孔组件的实时透率进行预测

Amir Shokry¹, Salaheldin Elkatatny², Abdulazeez Abdulraheem¹

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

Scientific reports

|September 3, 2023

概括

本研究引入了人工智能模型,用于预测电动底孔组件 (BHA) 的钻探透率 (ROP). 这些模型可以准确地实时预测ROP,从而实现优化以降低钻井成本.

科学领域:

石油工程是石油工程中的一个.
人工智能在钻井中的应用

背景情况:

透率 (ROP) 显著影响钻井的经济性和效率.
机动底孔组件 (BHA) 提供了提高ROP和减轻振动的潜力.
有限的研究存在于预测ROP专门用于机动BHA.

研究的目的:

开发和评估人工智能模型,用于预测机动BHA操作中的ROP.
使用表面,下坑和泥参数进行准确的ROP预测.
为了实现钻探参数的实时优化,以提高性能.

主要方法:

人工神经网络 (ANN) 和自适应神经模糊推理系统 (ANFIS) 的应用.
在超过5800个数据点的数据集上进行培训和测试,以70/30的比率.
使用平均绝对百分比误差 (AAPE),根平均平方误差 (RMSE) 和相关系数 (R) 评估模型准确性.

主要成果:

无论是ANN和ANFIS模型都表现出准确预测机动BHA ROP的实时能力.
开发的模型实现了高精度,通过错误指标和相关性得到证实.
这些模型为评估机动BHA性能提供了强大的工具.

结论:

人工智能技术可以有效地预测机动BHA ROP.
提出的模型有助于实时优化钻探参数,以实现最大的ROP.
优化的ROP可以降低钻井成本,并尽量减少开孔暴露时间.

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相关概念视频

Motor Units

Motor Units

A motor unit consists of two main components: a single efferent motor neuron (i.e., a neuron that carries impulses away from the central nervous system) and all of the muscle fibers it innervates. The motor neuron may innervate multiple muscle fibers, which are single cells, but only one motor neuron innervates a single muscle fiber.