水平井井地模型人工智能预测方法研究与应用

doi:10.13809/j.cnki.cn32-1825/te.2025.05.015

Abstract

Abstract:

Horizontal well drilling has become an important method for oil companies to enhance single-well production in tight and unconventional oil and gas reservoirs. However, due to the complex spatial relationship between the wellbore trajectory of horizontal wells and the formation layers, traditional vertical well analysis methods cannot be effectively applied. Accurately describing the spatial combination relationship between the wellbore trajectory, the target layer, and the surrounding rock is a primary task in horizontal well logging interpretation. To address this issue, the mainstream approach is to construct an initial stratigraphic model based on a pilot well and then adjust the model segment by segment using forward modeling constraints from logging data. However, this process is time-consuming and requires numerous repetitive forward modeling calculations for different wells in the same area. Therefore, in the processing and interpretation of horizontal well logging data, developing a reasonable well-formation model is essential. This model enables an accurate description of the spatial relationship between the wellbore and the formation interfaces, including the distance between the wellbore position and formation interfaces and the angle between the wellbore axis and the formation normal direction. At the same time, logging data analysis methods based on machine learning and artificial intelligence (AI) technologies have been applied to various aspects of logging data interpretation by training intelligent models. With the support of AI technologies, it is expected to overcome the bottlenecks of traditional methods. To this end, the study proposed an automated horizontal well logging interpretation method based on multi-model integration and deep neural networks. First, a theoretical model was constructed incorporating different wellbore trajectories and formation combination relationships, and a logging response sample library was generated. Then, machine learning models such as eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Categorical Boosting (CatBoost) were integrated, and their prediction results were further fused using a multi-layer perceptron (MLP). Finally, intelligent automatic recognition of the geometric relationship between the well trajectory and the surrounding rock was carried out using actual logging data. Case analysis showed that this method accurately captured the complex logging response characteristics of horizontal wells while significantly improving interpretation speed and accuracy. The proposed method meets the demand for rapid analysis of multiple wells in similar geological environments and provides an efficient, intelligent approach to horizontal well logging interpretation.

Key words: horizontal wells, well logging interpretation, artificial intelligence, deep learning, formation modelling

CLC Number:

TE243.1

LI Yutao,LI Chaoliu,WEI Xingyun, et al. Research and application of artificial intelligence-based prediction method for horizontal well-formation modelling[J]. Petroleum Reservoir Evaluation and Development, 2025, 15(5): 858-871.

Figures/Tables 15

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层号	厚度/m	岩性	自然伽马/API	密度/（g/cm³）	深侧向电阻率/（Ω·m）	浅侧向电阻率/（Ω·m）
1	13.5	泥岩	122.3	2.52	17.5	22.1
2	5.5	砂岩	24.8	2.41	22.9	26.1
3	2.8	粉砂质泥岩	87.1	2.64	22.5	26.2
4	3.5	砂岩	31.6	2.48	25.6	28.3
5	2.2	泥岩	113.4	2.61	16.4	20.3

倾角θ/（°）	λ_S	λ_M	模型数量/个
70~75	1.00	1.2	50
75~80	1.00	1.3	50
80~85	1.00	1.4	50
85~90	1.00	1.5	70
70~75	1.05	1.2	50
75~80	1.05	1.3	50
80~85	1.05	1.4	50
85~90	1.05	1.5	70
70~75	1.10	1.2	50
75~80	1.10	1.3	50
80~85	1.10	1.4	50
85~90	1.10	1.5	70
70~75	1.15	1.2	50
75~80	1.15	1.3	50
80~85	1.15	1.4	50
85~90	1.15	1.5	70
70~75	1.20	1.2	50
75~80	1.20	1.3	50
80~85	1.20	1.4	50
85~90	1.20	1.5	70

算法名称	基本原理	优势	适用的目标变量
极端梯度提升树（eXtreme Gradient Boosting，XGBoost）	基于梯度提升决策树通过迭代添加决策树优化性能，目标函数包括损失函数和正则化项	快速的训练速度和较高的预测精度，良好的泛化能力	Y₂、Y₄、Y₅、Z₃、Z₄、Z₅
随机森林（Random Forest，RF）	集成多棵决策树提高模型的鲁棒性和泛化能力，每棵树在随机选择的样本和特征上训练，减少过拟合风险	有效减少过拟合，提高预测稳定性	Y₁、Y₂、Y₃、Y₄、Y₅、Z₁、Z₂、Z₃、Z₄、Z₅
支持向量机（Support Vector Machine，SVM）	在高维空间中构建最优超平面，通过优化数据点到超平面最小距离提高模型的鲁棒性	能处理高维数据，具有良好的泛化能力	不适用
轻量级梯度提升机（Light Gradient Boosting Machine，LightGBM）	通过对特征值进行直方图离散化提高训练速度和效率，同时保持高预测精度	训练速度快，能处理大规模数据集，预测精度高	Y₁、Y₃、Z₁、Z₂
分类提升机（Categorical Boosting，CatBoost）	优化的梯度提升算法，通过目标编码和其他优化技术提高对类别数据的处理能力	有效处理类别特征，提高预测精度和效率	Y₁、Y₂、Y₃、Y₄、Y₅、Z₁、Z₂、Z₃、Z₄、Z₅
极限学习机（Extreme Learning Machine，ELM）	通过随机生成的隐藏层提高训练速度和预测性能，无需迭代训练，具有很高的计算效率	训练速度快，大多数情况下预测精度高	不适用

目标变量	模型	MSE值	决定系数（R²）	MAE值
Y₁	RF	0.008 6	0.998 3	0.007 8
Y₂	RF	0.021 6	0.990 1	0.014 2
Y₃	RF	0.010 3	0.998 1	0.013 1
Y₄	RF	0.003 9	0.998 4	0.007 8
Y₅	XGBoost	0.000 7	0.997 4	0.005 3
Z₁	RF	0.024 3	0.998 6	0.097 0
Z₂	RF	0.000 7	0.999 8	0.097 0
Z₃	RF	0.000 5	0.998 8	0.009 9
Z₄	XGBoost	0.000 4	0.998 1	0.014 6
Z₅	RF	0.000 1	0.999 1	0.009 4

隐藏层节点数	激活函数	学习率	训练时间/s	验证集MSE值	验证集MAE值	验证集R²
16	ReLU	10^-4	115	0.020 5	0.120 1	0.995 0
32	ReLU	10^-4	132	0.016 0	0.105 0	0.997 8
64	ReLU	10^-4	204	0.008 2	0.007 1	0.999 1
128	ReLU	10^-4	310	0.007 7	0.006 8	0.999 2
64	Sigmoid	10^-4	220	0.010 3	0.011 4	0.996 5
64	Tanh	10^-4	215	0.016 5	0.010 8	0.997 2
64	ReLU	10^-5	250	0.009 1	0.008 3	0.999 5
64	ReLU	10^-3	150	0.013 4	0.115 0	0.995 2

Research and application of artificial intelligence-based prediction method for horizontal well-formation modelling

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