SVM算法在渤海湾盆地南堡凹陷火山岩储层流体预测中的应用

doi:10.13809/j.cnki.cn32-1825/te.2023.02.006

摘要/Abstract

摘要：

火山岩储层受岩相、岩性、储集空间类型等多因素影响，流体识别难度大，是测井解释的难题之一，亟需建立一种方便快捷识别方法。为此，针对渤海湾盆地南堡凹陷火山岩储层特征，采用机器学习的SVM（支持向量机）算法对未知储层进行流体预测。研究表明：①综合应用岩心、测井、录井等资料对流体敏感特征参数寻优，单信息敏感参数为声波时差、补偿密度、深侧向电阻率，多信息融合参数为自然伽马相对值、全烃比值、烃气密度指数、烃气湿度指数，以上7种参数参与模型建立；②使用SVM算法进行火山岩流体预测，将储层流体分为油层、油水同层和水层3类，选取测井、录井敏感参数，训练可靠样本库，预测库正判率达90 %。SVM算法预测应用表明，SVM算法计算复杂程度低，泛化推广能力强，可快速识别火山岩流体性质，为油气成藏规律分析和地质储量动用开发提供可靠依据。

关键词: SVM算法, 储层特征, 流体预测, 火山岩, 南堡凹陷

Abstract:

Volcanic rock reservoirs are affected by many factors such as lithofacies, lithology, and reservoir space types, and fluid identification is difficult, which is one of the difficulties in well logging interpretation. It is urgent to establish a convenient and quick identification method. For this reason, the SVM（Support Vector Machine） algorithm of machine learning is used to predict the fluids of unknown reservoirs for the volcanic rock reservoirs in the Nanpu Sag of the Bohai Bay Basin. The research shows that: ① Comprehensive application of core, well logging, mud logging and other data to optimize fluid sensitive characteristic parameters, single information sensitive parameters are acoustic time difference, compensation density, resistivity, multi-information fusion parameters are natural gamma relative value, total hydrocarbon Ratio, hydrocarbon gas density index, hydrocarbon gas humidity index, the above seven parameters participate in the model establishment; ②Using the SVM algorithm for volcanic fluid prediction, the reservoir fluid is divided into three types: oil layer, oil-water layer and water layer. Sensitive parameters of well logging and mud logging are selected, and a reliable sample library is trained. The correct judgment rate of the prediction library reaches 90 %. The prediction application of SVM algorithm shows that it has low calculation complexity and strong generalization ability, which can quickly identify the fluid properties of volcanic rocks and provide a reliable basis for the analysis of oil and gas accumulation rules and the production and development of geological reserves.

Key words: SVM algorithm, reservoir characteristics, fluid prediction, volcanic rocks, Nanpu Sag

中图分类号:

TE19

张莹,曲丽丽,朱露,张艳,韩思洋,曾诚. SVM算法在渤海湾盆地南堡凹陷火山岩储层流体预测中的应用[J]. 油气藏评价与开发, 2023, 13(2): 181-189.

ZHANG Ying,QU Lili,ZHU Lu,ZHANG Yan,HAN Siyang,ZENG Cheng. Application of SVM algorithm in fluid prediction of volcanic reservoirs in Nanpu Sag, Bohai Bay Basin[J]. Reservoir Evaluation and Development, 2023, 13(2): 181-189.

图/表 11

图1

表1

图2

表2

图3

图4

表3

火山岩储层流体预测参数"

特征参数	$A C (μ s / f t ?)$	$D E N (g / c m 3 ?)$	$R L L D (Ω · m)$	$Δ G R$	全烃比值	烃气密度指数	烃气湿度指数
相关系数	0.43	0.48	0.36	0.38	0.48	0.55	0.42

表3

表4

SVM识别火山岩流体性质训练数据库"

训练数据
井号	$A C (μ s / f t ?)$	$D E N (g / c m 3 ?)$	$R L L D (Ω · m)$	△GR	全烃比值	烃气湿度指数	烃气密度指数	试油结论
NP11-A1	91.72	2.57	12.30	0.18	12.12	36.16	6.11	油层
NP11-A2	88.56	2.41	14.50	0.25	13.47	52.59	4.47	油层
NP11-A3	88.20	2.26	11.30	0.32	25.70	45.51	5.31	油层
NP11-A4	96.00	2.30	25.00	0.31	26.43	43.53	4.74	油层
NP11-A5	82.40	2.40	20.00	0.14	43.94	31.84	3.83	油层
NP11-A6	84.30	2.47	9.80	0.29	110.41	40.22	3.78	油层
NP11-A7	78.70	2.48	4.10	0.22	5.55	51.81	6.08	油水同层
NP11-A8	95.66	2.14	4.80	0.16	6.00	86.65	10.00	油水同层
NP11-A9	73.40	2.64	8.80	0.23	11.50	23.44	5.50	油水同层
NP11-A10	83.33	2.39	7.80	0.30	7.04	34.52	5.77	油水同层
NP11-A11	87.00	2.30	10.50	0.29	10.50	79.08	9.25	油水同层
NP11-A12	88.81	2.54	7.60	0.16	9.73	33.44	5.43	油水同层
NP11-A13	85.23	2.35	6.50	0.31	8.16	87.39	7.57	油水同层
NP11-A14	90.29	2.48	3.50	0.24	4.42	39.20	7.53	水层
NP11-A15	80.05	2.59	3.50	0.18	2.62	45.24	9.00	水层
NP11-A16	71.54	2.70	6.50	0.25	3.96	50.69	8.33	水层
NP11-A17	87.32	2.35	5.20	0.32	5.16	35.72	6.33	水层
NP11-A18	101.59	2.20	2.50	0.31	4.91	32.87	7.74	水层
NP11-A19	95.18	2.20	6.50	0.22	6.28	24.33	7.28	水层
NP11-A20	100.24	2.33	6.00	0.16	7.68	33.41	7.77	水层

表4

表5

SVM识别火山岩流体性质预测数据库"

预测数据
井号	$A C (μ s / f t ?)$	$D E N (g / c m 3)$	$R L L D (Ω · m)$	△GR	全烃比值	烃气湿度指数	烃气密度指数	试油结论	预测结果	对比
NP11-B1	100.00	2.26	9.80	0.11	15.57	27.61	5.45	油层	油层	正确
NP11-B2	97.00	2.22	15.00	0.26	19.60	49.71	5.92	油层	油层	正确
NP11-B3	83.20	2.47	10.90	0.19	13.92	40.70	5.06	油层	油层	正确
NP11-B4	93.06	2.38	13.20	0.21	36.33	30.13	4.10	油层	油层	正确
NP11-B5	83.12	2.48	21.30	0.28	104.96	23.56	4.89	油层	油层	正确
NP11-B6	74.50	2.42	6.50	0.35	5.25	32.30	6.34	油水同层	油水同层	正确
NP11-B7	80.45	2.42	5.10	0.34	5.64	131.54	7.54	油水同层	油水同层	正确
NP11-B8	85.74	2.50	8.10	0.09	6.22	161.15	11.22	油水同层	油层	错误
NP11-B9	86.73	2.39	10.50	0.24	7.02	73.04	6.56	油水同层	油水同层	正确
NP11-B10	82.12	2.45	6.50	0.17	7.42	65.82	8.50	油水同层	油水同层	正确
NP11-B11	77.37	2.50	5.80	0.23	8.94	17.44	4.92	油水同层	油水同层	正确
NP11-B12	91.64	2.35	9.60	0.30	5.09	119.40	10.00	油水同层	油水同层	正确
NP11-B13	81.58	2.26	3.50	0.37	2.66	38.53	7.14	水层	水层	正确
NP11-B14	91.06	2.59	4.50	0.36	3.56	25.60	8.58	水层	水层	正确
NP11-B15	71.23	2.65	3.60	0.11	3.29	52.00	9.00	水层	水层	正确
NP11-B16	75.23	2.55	5.30	0.26	4.56	34.87	6.84	水层	水层	正确
NP11-B17	98.76	2.39	5.50	0.19	5.21	43.03	11.20	水层	油水同层	错误
NP11-B18	91.53	2.11	3.50	0.25	5.29	30.05	8.38	水层	水层	正确
NP11-B19	101.01	2.44	4.10	0.35	6.54	30.78	9.00	水层	水层	正确
NP11-B20	96.13	2.34	3.66	0.29	4.35	29.50	6.32	水层	水层	正确

表5

图5

图6

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层位	SiO₂	TiO₂	Al₂O₃	Fe₂O₃	FeO	MnO	MgO	CaO	Na₂O	K₂O	P₂O₅	CO₂	H₂O
馆陶组	47.69	2.26	14.29	7.94	3.95	0.16	8.55	6.27	3.15	1.25	0.34	0.66	3.60
	45.64	2.72	15.91	6.42	4.24	0.19	8.75	7.68	3.15	1.45	0.66	0.66	2.89
	46.71	1.92	15.98	4.54	5.87	0.14	4.91	10.21	2.74	0.72	0.35	3.36	2.50
	46.02	2.23	14.86	5.00	7.74	0.09	5.25	8.51	2.30	0.42	0.27	3.60	3.86
	48.67	1.82	14.97	4.01	6.93	0.10	4.71	9.10	2.29	0.51	0.32	3.26	3.12
	45.91	1.85	15.50	2.69	7.28	0.12	4.11	9.05	3.80	0.56	0.35	5.14	3.51

岩性分类		电性响应
岩性分类		自然伽马(API)	声波时差(μs/ft)	密度(g/cm³)
火山熔岩	玄武岩	（30~45）/41	（55~70）/60	（2.3~2.8）/2.55
火山碎屑岩	凝灰岩	（70~90）/82	（68~72）/71	（2.2~2.5）/2.33
火山碎屑沉积岩	凝灰质砂岩	（68~102）/72	（69~96）/79	（2.3~2.6）/2.47