混合参数用于液体识别,使用一个增强的量子神经网络在一个紧密的水库中
Dejiang Luo1,2, Yuan Liang3, Yuanjun Yang4
1College of Mathematics and Physics, Chengdu University of Technology, Chengdu, 610059, China. luodejiang06@cdut.cn.
Scientific reports
|January 11, 2024
概括
这项研究引入了一个量子神经网络 (QNN),用于在狭窄的水库中改进流体识别. 新的QNN模型提高了分类干,气和气-水层的准确性,为水库分析提供了更智能的解决方案.
科学领域:
- 地质科学是地球科学.
- 人工智能的人工智能
- 量子计算是一种量子计算.
背景情况:
- 在紧密的水库中识别流体特性是具有挑战性的,因为识别率低,记录数据的手动解释不智能.
- 井日志和岩石物理参数在紧密的水库中对不同的流体特性表现出不同的反应和灵敏度.
- 传统的流体识别在水库中的方法往往是低效的,缺乏复杂的地质构成所需的智能.
研究的目的:
- 开发一种智能流体分类方法,用于利用量子神经网络 (QNN) 密集的水库.
- 分析记录响应特征和参数灵敏度,以优化紧密水库中的流体识别.
- 提出和评估一种新的QNN方法,包括混合参数和波形激活功能,以提高分类准确性.
主要方法:
- 记录响应特征和参数灵敏度的分析,用于在紧密的水库中识别流体.
- 基于井日志和岩石物理数据构建各种输入参数集.
- 开发一个量子神经网络模型,集成样本量子状态描述,输入参数灵敏度分析和波形激活函数.
主要成果:
- 不同的输入参数和激活功能显著影响流体分类模型的识别性能.
- 拟议的量子神经网络模型,利用混合参数和波形激活函数,与原始QNN相比,显示出更高的流体识别精度.
- 在位于中国四川盆地的紧密水库中成功识别了干,气和气水共同层.
结论:
- 带有波形激活功能的混合量子神经网络提供了一种高度有效和智能的方法,用于在狭窄的水库中识别流体.
- 拟议的方法显著提高了识别精度,解决了传统手动解释和非智能算法的局限性.
- 这种基于QNN的先进技术显示出在水库工程和勘探中促进和应用的巨大潜力.
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