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基于Adam-神经网络的致密砂岩脆性评价方法:以南堡凹陷高北边坡为例

万里明 吴均 卢军凯 刘彝 陈勉

万里明, 吴均, 卢军凯, 刘彝, 陈勉. 基于Adam-神经网络的致密砂岩脆性评价方法:以南堡凹陷高北边坡为例[J]. 地质科技通报, 2020, 39(2): 94-103. doi: 10.19509/j.cnki.dzkq.2020.0210
引用本文: 万里明, 吴均, 卢军凯, 刘彝, 陈勉. 基于Adam-神经网络的致密砂岩脆性评价方法:以南堡凹陷高北边坡为例[J]. 地质科技通报, 2020, 39(2): 94-103. doi: 10.19509/j.cnki.dzkq.2020.0210
Wan Liming, Wu Jun, Lu Junkai, Liu Yi, Chen Mian. Brittleness evaluation method of tight sandstone based on Adam-neural network: A case study of a block in Gaobei slope, Nanpu Sag[J]. Bulletin of Geological Science and Technology, 2020, 39(2): 94-103. doi: 10.19509/j.cnki.dzkq.2020.0210
Citation: Wan Liming, Wu Jun, Lu Junkai, Liu Yi, Chen Mian. Brittleness evaluation method of tight sandstone based on Adam-neural network: A case study of a block in Gaobei slope, Nanpu Sag[J]. Bulletin of Geological Science and Technology, 2020, 39(2): 94-103. doi: 10.19509/j.cnki.dzkq.2020.0210

基于Adam-神经网络的致密砂岩脆性评价方法:以南堡凹陷高北边坡为例

doi: 10.19509/j.cnki.dzkq.2020.0210
基金项目: 

国家自然科学基金联合基金项目“超深井井筒安全构建工程基础理论与方法” U1762215

国家自然科学基金联合基金项目“高温高压油气安全高效钻完井工程基础理论与方法 U19B6003-05

国家十三五自然科学基金项目“丛式井缝网构建理论与控制技术” 2017ZX05009

详细信息
    作者简介:

    万里明(1995—),男,现正攻读油气井工程专业硕士学位,主要从事石油工程岩石力学与压裂方面研究工作。E-mail:18811331063@163.com

  • 中图分类号: P588.21+2.3

Brittleness evaluation method of tight sandstone based on Adam-neural network: A case study of a block in Gaobei slope, Nanpu Sag

  • 摘要: 致密砂岩储层脆性评价对于“甜点”区预测和压裂改造都有重要作用。针对目前脆性评价力学机理不足、脆性矿物组分分析准确性不高的问题,提出了一种考虑岩石力学性质、脆性矿物组分和岩石成熟度的Adam-神经网络脆性综合评价方法。根据南堡凹陷高北边坡27块岩样的三轴力学实验结果,分析了岩石应力-应变曲线和破坏形态得出Rickman脆性指数,根据全岩矿物X-衍射实验分析得到反映成熟度的黏土矿物和反映脆性组分的非黏土矿物的含量,然后以反映力学性质的Rickman脆性指数为目标函数,以黏土矿物和非黏土矿物含量为训练参数,通过改进的Adam算法建立神经网络脆性评价模型,最后用测井曲线验证模型的准确性。研究表明,该地区脆性矿物以石英、长石为主,中等脆性程度,岩石区域各向异性较强,测井动态力学参数计算的脆性指数与模型相吻合。该Adam-神经网络算法结合力学、地质和矿物学因素, 可以快速得到更加准确的区域脆性指数,对指导选井选层,压裂施工都有很好的指导意义。

     

  • 图 1  南堡凹陷北部区域构造单元划分图[21]

    Figure 1.  Tectonic unit zoning of the north region of Nanpu Sag

    图 2  南堡凹陷12块岩心应力应变曲线

    Figure 2.  Stress-strain curves of 12 specimens of Nanpu Sag

    图 3  27块岩心的Rickman脆性指数结果

    Figure 3.  The Rickman brittleness index of 27 specimens

    图 4  南堡凹陷致密砂岩典型破坏形式

    Figure 4.  Sketchs of typical failure mode of tight sandstone in Nanpu Sag

    图 5  南堡凹陷27块岩样矿物组成

    Figure 5.  Relative contents of mineral of 27 specimens in Nanpu Sag

    图 6  Adam-神经网络脆性评价示意图

    Figure 6.  Schematic diagram of Adam-neural network brittleness evaluation

    图 7  岩石矿物组分相关性分析(横坐标为相关系数)

    Figure 7.  The correlation analysis of the rock mineral composition

    图 8  脆性评价的Adam-神经网络结构示意图(w、b分别为权重和阈值)

    Figure 8.  The schematic diagram of Adam-neural network for brittleness evaluation

    图 9  神经网络不同学习率收敛图

    Figure 9.  Convergence curves of different learning rates in neural network

    图 10  神经网络不同算法收敛图

    Figure 10.  Convergence curves of different algorithms in neural network

    图 11  脆性指数神经网络测试集结果误差分析

    Figure 11.  Comparison diagram of brittleness index between prodection value and true value

    图 12  南堡凹陷高5区块64井测井曲线脆性预测对比图

    Figure 12.  Brittleness prediction and comparsion diagram from Well 64 logging curve in of Block Gao 5 Nanpu Sag

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  • 收稿日期:  2019-01-13

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