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利用GS-LightGBM机器学习模型识别致密砂岩地层岩性

谷宇峰 张道勇 鲍志东 郭海晓 周立明 任继红

谷宇峰, 张道勇, 鲍志东, 郭海晓, 周立明, 任继红. 利用GS-LightGBM机器学习模型识别致密砂岩地层岩性[J]. 地质科技通报, 2021, 40(4): 224-234. doi: 10.19509/j.cnki.dzkq.2021.0416
引用本文: 谷宇峰, 张道勇, 鲍志东, 郭海晓, 周立明, 任继红. 利用GS-LightGBM机器学习模型识别致密砂岩地层岩性[J]. 地质科技通报, 2021, 40(4): 224-234. doi: 10.19509/j.cnki.dzkq.2021.0416
Gu Yufeng, Zhang Daoyong, Bao Zhidong, Guo Haixiao, Zhou Liming, Ren Jihong. Lithology prediction of tight sandstone formation using GS-LightGBM hybrid machine learning model[J]. Bulletin of Geological Science and Technology, 2021, 40(4): 224-234. doi: 10.19509/j.cnki.dzkq.2021.0416
Citation: Gu Yufeng, Zhang Daoyong, Bao Zhidong, Guo Haixiao, Zhou Liming, Ren Jihong. Lithology prediction of tight sandstone formation using GS-LightGBM hybrid machine learning model[J]. Bulletin of Geological Science and Technology, 2021, 40(4): 224-234. doi: 10.19509/j.cnki.dzkq.2021.0416

利用GS-LightGBM机器学习模型识别致密砂岩地层岩性

doi: 10.19509/j.cnki.dzkq.2021.0416
详细信息
    作者简介:

    谷宇峰(1988-), 男, 助理研究员, 主要从事油气储量评审、地层表征和测井解释研究工作。E-mail: aaaaa3377@126.com

  • 中图分类号: P618.13

Lithology prediction of tight sandstone formation using GS-LightGBM hybrid machine learning model

  • 摘要: 以交会图为代表的传统岩性识别图版无法适用于致密砂岩地层,其主要原因是大部分地层岩性的测井响应特征相似度高,难以基于图版分析被有效识别。LightGBM较传统模式识别模型能更高效地解决问题,为此采用该模型识别致密砂岩地层岩性。由于LightGBM在建模时利用了较多的超参数,导致预测结果难以保证为最优,所以采用GS算法进行优化,进而提出GS-LightGBM。实验目的层为姬塬油田西部长4+5段致密砂岩地层。提出模型的预测能力通过设计两个实验来验证。为突出验证效果,实验中加入SVM和XGBoost作为对比模型。实验结果显示,GS-XGBoost和GS-LightGBM的准确率、F1-score和AUC指标相接近,都最高,但GS-LightGBM的计算时间只有GS-XGBoost的约1/23。实验结果表明,GS-LightGBM模型可在不失精度的情况下,能快速给出预测结果,具备了在致密砂岩地层岩性识别研究上的应用价值和推广性。

     

  • 图 1  GS-LightGBM岩性识别计算流程

    Figure 1.  Computing flow of GS-LightGBM used for lithology prediction

    图 2  姬塬油田西部中北部研究区

    Figure 2.  North central study zone in western Jiyuan Oilfield

    图 3  岩性识别三维交会图版

    a.AT90-SP-DEN交会图版;b.AT90-AC-GR交会图版

    Figure 3.  Three dimensional crossplots used for lithology prediction

    图 4  实验1岩性预测结果

    a.GS-SVM模型(P为识别准确率(%)下同);b.GS-XGBoost模型;c.GS-LightGBM模型

    Figure 4.  Results of lithology prediction in the first experiment

    图 5  实验1三种模型部分岩性识别结果柱状图(22xx.~23xx.m)

    Figure 5.  Columns of partial predicted lithologies of three validated models in the first experiment

    图 6  实验1各模型岩性ROC曲线和平均ROC曲线

    a.GS-SVM模型;b.GS-XGBoost模型;c.GS-LightGBM模型;d.各模型平均ROC曲线

    Figure 6.  ROC curve of each predicted lithology and mean ROC curve of all predicted lithologies produced by all validated models in the first experiment

    图 7  实验所得各岩性F1-score和AUC变化趋势

    a.实验所得各岩性F1-score变化趋势;b.实验所得各岩性AUC变化趋势

    Figure 7.  Variations of F1-score and AUC of each predicted lithology in the experiments

    表  1  测井曲线数据统计参数

    Table  1.   Statistical parameters produced by logging data

    测井曲线 Q1 Q3 IQR LIF UIF
    AC/(μs·m-1) 218.42 242.12 35.55 182.87 277.66
    DEN/(g·cm-3) 2.45 2.63 0.26 2.20 2.88
    GR/API 84.82 116.51 47.53 37.29 164.04
    PE/(b·e-1) 2.68 3.43 1.12 1.56 4.56
    AT90/(Ω·m) 7.22 16.11 13.32 -6.10 29.43
    SP/mV 64.83 81.70 25.32 39.51 107.02
    下载: 导出CSV

    表  2  各验证模型、GS优化算法初始参数设置和各验证模型超参数优化结果

    Table  2.   Initial parameter settings of all validated models and GS optimizing algorithm, and optimal results of hyper-parameters of all validated models

    验证模型 SVM XGBoost LightGBM
    参数初始化
    (是否为超参数)
    惩罚系数(c)=1(是);
    核函数=RBF1 (否);
    核函数平滑因子(σ)=0.1 (是)
    S=100 (是);
    η=0.1 (是);
    最大回归树深度
    max_depth=3 (是) 2;
    λ=0.1 (是);
    叶节点最小样本权重之和
    min_chile_weight =0.001 (否) 3;
    最小分裂梯度下降值
    δ=0.001 (否) 4;
    损失函数=对数似然损失函数(否)
    S=100 (是);
    η=0.1 (是);
    max_depth=3 (是);
    λ=0.1 (是);
    叶节点最大数目
    num_leaves=2 (是) 5;
    叶节点样本最少数量
    min_data_in_leaf=5 (是) 6;
    min_chile_weight=0.001 (否);
    桶数max_bin=2 (是) 7;
    每桶最小样本数量
    min_data_in_bin=1 (是) 8;
    损失函数=对数似然损失函数(否)
    GS参数设置
    (左界限,右界限,步长)
    c (1, 100, 0.2);
    σ(0.1, 1, 0.05)
    S (100, 5000, 50);
    η (0.1, 1, 0.05);
    max_depth (3, 10, 1);
    λ (0.1, 10, 0.1)
    S (100, 5000, 50);
    η(0.1, 1, 0.05);
    max_depth (3, 10, 1);
    λ(0.1, 10, 0.1);
    num_leaves (2, 1024, ×2) 9;
    min_data_in_leaf (5, 100, 5);
    max_bin (2, 255, ×2);
    min_data_in_bin (1, 100, 5)
    超参数最优结果 c=9.2;
    σ=0.8
    S=1500;
    η=0.2;
    max_depth=8;
    λ=0.3
    S=1300;
    η=0.35;
    max_depth=7;
    λ=0.3;
    num_leaves=64;
    min_data_in_leaf=20;
    max_bin=8;
    min_data_in_bin=31
    注:1.Radial Basis Function (径向基函数); 2.回归树最大分裂次数;3.如果叶节点中样本二阶导之和小于该值,则剪掉该叶节点;4.如果叶节点中样本对应的梯度下降值之和小于该值,则停止分裂;5.如果叶节点个数超过该值,则停止分裂;6.如果叶节点中样本容量小于该值,则剪掉该叶节点;7.Historgram算法寻找最佳分裂点时所用桶的数量以该值为准;8.如果桶中样本容量小于该值,则弃用该桶;9.“×2”表示以2倍速度增长
    下载: 导出CSV

    表  3  实验中各模型综合评价信息

    Table  3.   Summary of comprehensive evaluation information of all validated models produced in the experiments

    验证
    模型
    GS-SVM GS-XGBoost GS-LightGBM
    准确率/% 平均
    AUC
    计算时间/s 准确率/% 平均
    AUC
    计算时间/s 准确率/% 平均
    AUC
    计算时间/s
    实验1 67.66 0.79 139.87 88.50 0.91 553.31 87.78 0.91 22.95
    实验2 70.53 0.81 175.32 92.81 0.93 859.35 92.61 0.93 37.54
    下载: 导出CSV
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  • 收稿日期:  2020-11-30

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