基于热蒸发烃气相色谱谱图数字化原油密度定量计算方法研究

doi:10.3969/j.issn.2096-1693.2026.03.011

摘要/Abstract

摘要：

原油密度作为表征储层流体物性的关键参数，在油气藏综合评价中具有重要指示意义。传统实验室分析方法存在的时效性滞后问题，以及现有随钻判识技术过度依赖岩石热解参数，存在参数有限，信息维度不足等制约，难以满足高效勘探需求。针对以上难题，本文以渤海油田115组典型热蒸发气相色谱谱图样品为研究对象(密度范围0.7542~1.0077 g/cm³)，创新性构建热蒸发烃气相色谱谱图量化表征体系。通过谱图数字化处理提取多维特征参数，结合机器学习深度挖掘谱图特征参数及其衍生变量与原油密度间的内在耦合关系；采用分层随机抽样法将样品数据集划分为训练集(80%)与测试集(20%)，最终建立高精度原油密度预测模型。结果表明，该模型在测试集与实际应用案例中均展现出优异的预测性能，预测值与实测值的平均绝对误差(MAE)小于0.02，具备高预测精度与可靠性。相较于传统方法，该技术无需依赖完井后的现场取样测试，可在钻探过程中基于岩屑样品快速实现原油密度定量计算，为油气勘探现场决策提供关键技术支撑，具有显著的工程应用价值与推广前景。

关键词: 密度预测, 热蒸发烃气相色谱, 谱图数字化, 机器学习, 多元逐步线性回归

Abstract:

As a key parameter characterizing the physical properties of reservoir fluids, crude oil density plays an important indicative role in the comprehensive evaluation of oil and gas reservoirs. Traditional laboratory analysis methods suffer from time lag, while existing logging-while-drilling identification technologies excessively rely on rock pyrolysis parameters, which are limited by insufficient parameters and inadequate information dimensions, thus failing to meet the needs of efficient exploration. To address these challenges, this paper takes 115 sets of typical thermal evaporation of gas chromatography samples from Bohai Oilfield as research objects (density range: 0.7542~1.0077 g/cm³), and innovatively constructs a quantitative characterization system for thermal evaporation hydrocarbon gas chromatography. Multidimensional characteristic parameters are extracted through digital processing of the spectra, and the internal coupling relationship between the characteristic parameters and their derived variables and crude oil density is deeply explored via machine learning. The sample dataset was divided into a training set (80%) and a test set (20%) using the stratified random sampling, and a high-precision crude oil density prediction model was finally established. The results show that the model exhibits excellent prediction performance both on the test set and practical application cases. The mean absolute error between the predicted and measured values is less than 0.02, indicating a high prediction accuracy and reliability. Compared with traditional methods, this technique does not depend on post-completion field sampling and testing, and can quickly realize the quantitative calculation of crude oil density based on cuttings samples during drilling. It provides key technical support for on-site decision-making in oil and gas exploration, with remarkable engineering application value and promation prospects.

Key words: density prediction, thermal evaporation hydrocarbon gas chromatography, chromatogram digitalization, machine learning, multiple stepwise linear regression

中图分类号:

韩学彪, 毛敏, 袁胜斌, 李大冬, 李美俊. 基于热蒸发烃气相色谱谱图数字化原油密度定量计算方法研究[J]. 石油科学通报, 2026, 11(2): 369-381.

HAN Xuebiao, MAO Min, YUAN Shengbin, LI Dadong, LI Meijun. Study on quantitative calculation method for crude oil density based on digitization of thermal evaporation hydrocarbon gas chromatograms[J]. Petroleum Science Bulletin, 2026, 11(2): 369-381.

https://sykxtb.cup.edu.cn/CN/Y2026/V11/I2/369

图/表 13

图1 热蒸发烃气相色谱谱图特征

Fig. 1 Gas chromatogram characteristics of thermally evaporated hydrocarbons

图2 热蒸发烃气相色谱不同谱图特征与原油密度相关性

Fig. 2 Correlation between gas chromatogram characteristics of thermally evaporated hydrocarbons and crude oil density

图3 谱图边缘检测和矢量化

Fig. 3 Chromatogram edge detection and vectorization

图4 谱图坐标轴检测与刻度标定

Fig. 4 Chromatogram axis detection and scale calibration

图5 热蒸发烃气相色谱组分自动定名

Fig. 5 Automatic qualitative identification of gas chromatographic components in thermally evaporated hydrocarbons

图6 热蒸发烃气相色谱谱图不同面积积分示意图

Fig. 6 Schematic diagram of different area integrations for gas chromatograms of thermally evaporated hydrocarbons

图7 热蒸发烃气相色谱谱图等分面积积分示意图

Fig. 7 Schematic diagram of equal-area integration for gas chromatograms of thermally evaporated hydrocarbons

图8 不同标准化比值参数与原油密度相关性分析(表中A₁₃/A₃₄：A₁₃代表1~3等分区间面积和，A₃₄代表3~4等分区间面积和)

Fig. 8 Correlation analysis between area characteristic parameters of chromatograms and crude oil density

表1 多元逐步线性回归分析结果(n=92)

Table 1 Results of multiple stepwise linear regression analysis (n=92)

模型	未标准化系数		标准化系数	t	P	共线性统计
	B	标准误	Beta			容差	VIF
(常量)	0.833	0.013		63.811	0.0000
A₁/(A₃+A₄)	-0.087	0.012	-0.628	-7.463	0.0000	0.891	1.122
(A_异+A_基)/A_正	0.041	0.007	0.507	6.027	0.0000	0.891	1.122
	R²				0.912
	调整R²				0.882
	F			F(2,89)=68.327, P=0.0000
	德宾-沃森(D-W)值				2.069

图9 测试集原油密度的实测值与模型预测值绝对误差分析

Fig. 9 Absolute error analysis between measured and predicted crude oil density in the test set

图10 X井综合柱状图

Fig. 10 Comprehensive histogram of well X

图11 X井1460 m色谱图多面积积分计算示意图

Fig. 11 Schematic diagram of multi-area integration calculation for chromatogram of well X at 1460 m

表2 X井色谱谱图面积计算数据表

Table 2 Area calculation data sheet for chromatogram of well X

深度/m	A₁	A₂	A₃	A₄	A₅	A_正	A_异	A_基	预测密度/(g/cm³)	平均/(g/cm³)
1460	191.51	264.87	220.94	112.84	46.11	208.01	39.35	588.91	0.9069	0.9086
1480	96.87	188.37	317.1	230.27	82.58	280.79	72.01	562.39	0.9102
1490	189.51	266.87	218.94	110.84	45.11	203.79	75.31	552.17	0.9092

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