利用混合模型CRBM-PSO-XGBoost识别致密砂岩储层岩性

doi:10.11743/ogg20210518

摘要/Abstract

摘要：

识别储层岩性是开展地层对比和沉积展布等地质基础研究工作的重要前提。致密砂岩储层多为砂、泥岩薄互层，形成的岩性不仅种类较多，而且大部分岩性的测井响应也较为相似，为相关的岩性识别工作带来了困难。基于机器学习技术在模式识别上具有强大的分析性能，选用了稳定性好且计算效率高的XGBoost模型来解决致密砂岩储层岩性识别问题。该模型在建模过程中需要较多的经验参数参与，且计算速度随着自变量的增加而逐渐降低，为此提出了采用CRBM模型和PSO模型对其进行改进。CRBM模型具备数据提取功能，可从源数据中挖掘出更少但更利于建模的新自变量，而PSO模型可通过迭代计算确定XGBoost所有经验参数的最优值。以姬塬油田西部长4+5段部分取心井资料为基础，通过设计两个实验来验证提出的混合模型的预测能力。为加强验证效果，在实验中加入了PNN和SVM的优化模型进行对比。实验后发现所提出模型的预测准确率最高，均在90%以上。实验结果表明所提出的模型不仅能有效解决致密砂岩储层岩性识别问题，且较以往经典预测模型更具推广应用性。

关键词: XGBoost模型, CRBM模型, PSO模型, 岩性识别, 致密砂岩储层, 姬塬油田, 鄂尔多斯盆地

Abstract:

Lithology identification is universally regarded as a prerequisite for some fundamental geological work such as stratigraphic correlation and analysis of sedimentary system distribution. The tight sandstone reservoirs are generally composed of multiple thin sandstone-mud alternating beds, featuring a variety of lithological types, most of which are similar in log responses, posing great difficulty for lithology identification. Given the powerful analytical performance of machine learning techniques shown on pattern recognition, XGBoost, a stable and highly efficient model is adopted to predict lithology of the tight sandstone reservoirs. However, this model needs many empirical parameters to complete its prediction, and its computing efficiency will be reduced when it deals with more independent variables. The CRBM and PSO models are, therefore, introduced to improve the prediction performance. CRBM is functional to extract features from original data so that fewer but more significant independent variables can be created by the CRBM in modeling. While PSO is effective to determine the optimal values via iterative computation for those empirical parameters used by XGBoost. Based on the data derived from some cored wells of Chang 4+5 Members in western Jiyuan oilfield, two experiments are designed to validate the prediction performance of the hybrid model proposed, CRBM-PSO-XGBoost. Furthermore, two optimized models formed by PNN and SVM are selected to make a comparison, as a way to consolidate the validation effect. The experiment results manifest that the prediction accuracy comes to peak by the proposed model, up to over 90%. In all, the hybrid model proposed in the study, capable of identifying lithology of tight sandstone reservoirs, is of wider application to lithology prediction compared with previous models.

Key words: XGBoost model, CRBM model, PSO model, lithology identification, tight sandstone reservoir, Jiyuan oilfield, Ordos Basin

中图分类号:

TE19

谷宇峰,张道勇,鲍志东. 利用混合模型CRBM-PSO-XGBoost识别致密砂岩储层岩性[J]. 石油与天然气地质, 2021, 42(5): 1210-1222. doi: 10.11743/ogg20210518 Yufeng Gu,Daoyong Zhang,Zhidong Bao. Lithology identification in tight sandstone reservoirs using CRBM-PSO-XGBoost[J]. Oil & Gas Geology, 2021, 42(5): 1210-1222. doi: 10.11743/ogg20210518

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	PNN	SVM	XGBoost
初始参数(是否需要优化)	α=X(0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1)(是)	c=1 (是) 核函数=RBF(否) gamma=1 (是)	D=100 (是) η=0.01 (是) max_depth=3 (是) λ=0.1 (是) min_chile_weight=0.1 (是) δ=0 (是) 损失函数=交叉熵损失函数(否)
PSO参数设定	q=10 t=100 ω_max，ω_min=0.9, 0.4 c₁, c₂=1.5 r₁和r₂，范围[0, 1]
	各优化参数的σ_{max_1}和σ_{max_2}
	α (10^-2, 2×10⁰)	c (10^-3, 10³) gamma (10^-3, 10³)	D (10²0, 10⁴) η (10^-3, 10⁰) max_depth (3×10⁰, 10¹) λ (10⁰, 10²) min_chile_weight (10⁰, 10²) δ (10⁰, 10¹)
	各优化参数的W_max
	α (0.1)	c (10) gamma (10)	D(50) η(0.1) max_depth(5) λ(10) min_chile_weight(10) δ(5)
优化后参数	X=(0.85, 0.83, 0.91, 0.79, 0.98, 0.89, 1.01, 0.87)	c=15 gamma=2.5	D=730 η=0.2 max_depth=5 λ=1.3 min_chile_weight=1.2 δ=0

参数	值
最大迭代次数(K)	100
迭代精度(e)	0.001
可见层神经元个数(n_v)	11
隐含层神经元个数(n_h)	6
动量系数(ξ)	0.9
学习速率(η)	0.1
噪音控制参数(μ)	0.05
噪音方差(σ)	0.2
S函数上渐近线(ϕ_h)	1
S函数下渐近线(ϕ_l)	0
mini-batch学习样本容量(χ)	100

预测模型	实验1		实验2
预测模型	HA井	HB井	HA井	HB井
CRBM-PSO-PNN	79.00/50.269 2	75.33/50.269 1	83.00/120.348 5	80.67/120.348 6
CRBM-PSO-SVM	83.67/25.487 1	77.00/25.487 2	88.33/43.247 3	84.33/43.247 2
CRBM-PSO-XGBoost	92.67/35.983 7	90.33/35.983 6	96.33/54.453 9	93.67/54.453 7

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