基于非开挖随钻检测系统与随机森林的地层岩性识别

徐晗; 姚孔轩; 程丹仪; 宋强银; 马志明; 朱旭明; 乌效鸣; 赵官慧; 蔡晓春

doi:10.19509/j.cnki.dzkq.2021.0039

基于非开挖随钻检测系统与随机森林的地层岩性识别

doi: 10.19509/j.cnki.dzkq.2021.0039

基金项目:

国家自然科学基金项目 41731284

详细信息

作者简介:
徐晗(1994-), 男, 现正攻读地质工程专业博士学位, 主要从事地下空间工程研究工作。E-mail: xuhancug@163.com

通讯作者:
乌效鸣(1956-), 男, 教授, 主要从事岩土钻掘与工程浆液研究。E-mail: xmwu5610@163.com

中图分类号: P618.4
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- 文章访问数: 631
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- 被引次数: 0
出版历程
- 收稿日期: 2021-01-30

Stratigraphic lithology identification based on no-dig Logging While Drilling system and random forest

摘要

摘要: 通过自主研发设计的非开挖随钻检测系统，采集非开挖钻进参数，进行非开挖钻进实时地层岩性识别，为非开挖施工提供安全信息保证。针对非开挖工程工勘资料缺乏，掘进地层岩性难以判断的问题，提出了一种基于非开挖随钻检测系统实时采集数据，利用随机森林算法建立地层识别模型，通过模型去识别未知地层，并将识别结果可视化展示。通过非开挖随钻检测系统在工程现场的实际应用，获得了包括钻速、扭矩、转速、拉力、泵压、泵量等钻进敏感参数作为训练样本，利用随机森林算法对采集的钻进参数进行训练，构造决策树与随机森林，对钻进参数进行分类，建立了以典型非开挖地层岩性分类为目标的分类模型，分别确定了杂填土、黏土、粉细砂、砾石和淤泥的地层分类标签。进一步，基于机器学习的分类结果，利用PCA主成分分析将地层识别特征降维至三维，实现了地层岩性识别结果的三维展示。将预测模型应用于实际工程，以验证其有效性。结果表明，该方法能在非开挖实时钻进条件下快速识别钻进地层，识别正确率高达92%。该研究成果通过采集导向随钻参数，识别非开挖掘进段地层岩性，为非开挖扩孔阶段钻具选型、泥浆设计等提供了重要信息。
- 非开挖 /
- 随钻检测 /
- 随机森林 /
- 地层识别
Abstract: Through the self-developed and designed no-dig Logging While Drilling(LWD) system, it can collect the parameters of no-dig drilling, identify the real-time formation lithology of no-dig drilling, and provide safety information guarantee for no-dig construction.In view of the lack of prospecting data in no-dig engineering, it is difficult to determine the lithology of the excavation stratum.A real-time data acquisition system based on the no-dig LWD system is proposed.The random forest algorithm is used to establish the stratum identification model, and identify the unknown strata.The identification results are displayed visually.Through the practical application of the detection while drilling system in the engineering field, the drilling sensitive parameters including Rate of Penetration(ROP), torque, rotation speed, pulling force, pump pressure and pump volume are obtained as training samples.The random forest algorithm is used to train the collected drilling parameters, and the decision tree and random forest are constructed to classify the drilling parameters.A classification model aiming at the classification of typical no-dig strata is established, and the classification labels of miscellaneous fill, clay, silty fine sand, gravel and silt are determined respectively.Furthermore, based on the classification results of machine learning, PCA principal component analysis is used to reduce the dimension of strata recognition features to three-dimensional, and realize the three-dimensional display of formation lithology identification results.The prediction model is applied to practical engineering to verify its effectiveness.The results show that the method can quickly identify the drilling formation under the condition of no-dig real-time drilling, and the recognition accuracy is as high as 92%.The research results provide important information for the selection of no-dig mud and no-dig reaming stage.
- no-dig /
- logging while drilling /
- random forest /
- strata recognition

HTML全文

图 1 非开挖随钻检测系统框架

Figure 1. Frame diagram of no-dig detection while drilling system

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图 2 扭矩检测原理图

Figure 2. Schematic diagram of torque detection

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图 3 泥浆压力传感器

Figure 3. Mud pressure sensor

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图 4 随钻检测系统剖面图

Figure 4. Profile chart of LWD system

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图 5 随钻检测系统实物图

Figure 5. Physical diagram of LWD system

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图 6 随机森林算法实现流程^[13]

Figure 6. Implementation process of random forest algorithm

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图 7 地层识别模型其中一棵决策树

Figure 7. A decision tree in stratum recognition model

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图 8 PCA降维后分类结果展示

Figure 8. Display of classification results after PCA dimension reduction

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图 9 奉新液化气站等杆配变增容工程工程地质横剖面图

Figure 9. Cross section of engineering geology of equal pole distribution transformer capacity expansion project in Fengxin lpg station

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图 10 地层岩性特征数据PCA展示图

Figure 10. PCA display diagram of stratigraphic lithology characteristic data

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表 1 随钻数据举例

Table 1. Examples of data while drilling

识别特征	钻速/(m·h^-1)	扭矩/(N·m)	转速/(r·min^-1)	轴向力/MPa	泥浆压力/MPa
杂填土	7.0	13 025	120	6.0	4.0
	6.8	12 661	115	5.9	3.9
	7.2	13 212	126	6.1	4.1
	7.0	13 174	124	6.2	4.1
	7.3	13 300	136	6.4	4.3
黏土	10.0	3 830	130	4.0	2.6
	12.0	4 042	140	4.4	2.9
	13.0	4 217	150	4.6	2.8
	12.0	3 794	142	4.2	2.7
	9.0	3 835	122	3.8	2.3
粉细砂	8.0	7 014	120	5.0	3.0
	7.0	6 056	110	4.1	2.1
	7.4	6 537	115	4.5	2.5
	7.2	6 208	112	4.2	2.2
	8.6	7 615	126	5.6	3.6
砾石	2.0	15 363	100	8.0	4.2
	2.1	15 507	105	8.1	4.2
	2.2	15 766	110	8.3	4.2
	2.4	15 982	120	8.3	4.3
	1.8	15 006	90	7.9	4.1
淤泥	5.0	10 892	140	2.0	4.0
	5.2	11 254	142	2.0	4.1
	5.1	11 146	138	2.0	3.9
	4.8	10 793	130	2.0	3.8
	5.4	11 404	150	2.0	4.4

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表 2 扭矩数据运算展示节选

Table 2. Excerpt of torque data operation display

编号	扭矩/(N·m)	地层岩性
1	12 661	杂填土
2	13 174	杂填土
3	4 042	黏土
4	3 794	黏土
5	7 014	粉细砂
6	6 537	粉细砂
7	15 507	砾石
8	15 982	砾石
9	11 254	淤泥
10	10 793	淤泥

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表 3 模型精确度验证数据

Table 3. Validation data of model accuracy

识别特征	钻速/(m·h^-1)	扭矩/(N·m)	转速/(r·min^-1)	轴向力/MPa	泥浆压力/MPa	工勘岩性	岩性识别	识别结果
1	10.0	4 102	139	4.0	2.6	黏土	粉质黏土	正确
2	2.0	17 100	98	7.0	4.1	砾石	弱胶结砾岩	正确
3	5.0	11 100	140	1.9	4.0	淤泥	淤泥	正确
4	7.1	13 061	122	5.9	4.1	杂填土	杂填土	正确
5	7.9	6 899	114	5.0	3.0	粉细砂	粉砂	正确
6	13.0	4 200	148	4.5	2.9	黏土	粉质黏土	正确
7	1.8	15 831	108	7.8	4.0	砾石	中胶结砾岩	正确
8	5.1	11 156	144	2.0	4.1	淤泥	淤泥	正确
9	6.9	12 856	118	5.8	4.0	杂填土	杂填土	正确
10	8.3	7 320	126	5.2	3.3	粉细砂	粉细砂	正确
11	9.0	3 825	121	3.8	2.2	黏土	粉质黏土	正确
12	2.1	15 930	113	8.2	4.2	砾石	杂填土	错误

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