沉积过程模拟驱动下的深度学习地质建模方法

doi:10.11743/ogg20230119

Abstract

Abstract:

Problems including insufficient quantity and low resolution of data face exploration and development of deep and complex reservoir targets， and the traditional geological modeling methods have been inadequate in terms of technical needs. The intelligent geological modeling represented by deep learning capable of fully integrating multi-scale and multi-dimensional data as well as expert knowledge， is a key research and development direction of geological modeling technology. The study discusses the deep learning-based geological modeling driven by sedimentary process simulation following the comprehensive analysis of the advantages and disadvantages of stratigraphic forward modeling and deep learning-based geological modeling technology. First， forward modeling of sedimentation is carried out based on comprehensive geological analysis， parameter uncertainty is analyzed， and a large amount of geological models are established through parameter disturbance as a training dataset； Second， the geological patterns contained in the learning dataset are learned with the conditional Generative Adversarial Nets （cGAN）， in which the Generative Adversarial Networks （GAN） takes the conditional data such as well and seismic data as the input， and the geological model as the output； Finally， the trained GAN is applied to the real conditional data to obtain the geological model of the target block. The feasibility of this method is verified through testing on the typical geological profiles of the main block of Puguang gas reservoir， and the impact of the training dataset scale on simulation results is analyzed. The combination of sedimentary simulation and deep learning could make up for the shortage of training data and indirectly realize knowledge-driven deep learning-based geological modeling. The method is therefore of great significance to popularization.

Key words: artificial intelligence （AI）, big data, knowledge-driven, Generative Adversarial Networks （GAN）, sedimentary process simulation, deep learning-based geological modeling, Puguang gas reservoir, Sichuan Basin

CLC Number:

TE132.1

Yanfeng LIU, Taizhong DUAN, Yuan HUANG, Wenbiao ZHANG, Meng LI. Deep learning-based geological modeling driven by sedimentary process simulation[J]. Oil & Gas Geology, 2023, 44(1): 226-237.

Figures/Tables 11

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参数类型	具体参数	分析方法
可容空间类	构造沉降曲线	地层回剥法，地震资料解释
	海平面曲线	Haq曲线，岩石的水深指示曲线
	初始地形	标志层厚度，沉积微相对水深指示意义
沉积物供给类	沉积物供给速率，最大产率	地层厚度，井上地层厚度序列
	沉积物供给集中度，透光带厚度	物源方向初始地层厚度变化程度，高能相带厚度
	沉积物供给成分比例，产率下降系数	观测数据中岩性比例，岩性变化频率
沉积物搬运剥蚀类	势能扩散系数	沿沉积物搬运方向的地层厚度变化
	扩散系数变化因子	岩性纵向变化程度
	动能对流系数	沿沉积物搬运方向的岩性变化程度
流体动能分布类	波浪能	水体流速相对大小
	风能	高能和低能相带分布的突变程度
	地形消浪能	沉积相对水体能量的指示意义

序号	参数	最小值	最大值	序号	参数	最小值	最大值
1	AmpSeaL1	40.000	120.000	22	KEng2	1.000	3.000
2	PerdSeaL1	500.000	1500.000	23	KEng3	0.500	1.500
3	AmpSeaL2	20.000	60.000	24	bEng1	0.500	1.500
4	PerdSeaL2	80.000	150.000	25	bEng2	0.040	0.120
5	PerdSeaL3	20.000	60.000	26	bEng3	10.000	30.000
6	LinEuElev3	-55.000	-10.000	27	pPr1	0.350	1.000
7	SubsidenceScale1	0.500	1.500	28	KV1	5.000	15.000
8	PDX2	40.000	120.000	29	KV2	17.000	52.000
9	PDX1	800.000	2200.000	30	KV3	12.000	35.000
10	KDX1	5.000	15.000	31	KW1	0.007	0.022
11	KDX2	25.000	750.000	32	KW2	0.012	0.040
12	Apord1	2.500	7.500	33	KW3	0.007	0.022
13	Apord2	15.000	45.000	34	kR1	0.025	0.000
14	Apord3	7.500	22.000	35	kR2	0.020	0.060
15	Kpord3	0.010	0.030	36	kR3	0.007	0.020
16	Kpord1	0.010	0.030	37	kH1	0.030	0.090
17	Kpord2	0.020	0.070	38	kH2	0.100	0.300
18	Wpord1	30.000	90.000	39	kH3	0.030	0.090
19	Wpord2	15.000	45.000	40	WaveBase1	20.000	60.000
20	Wpord3	35.000	100.000	41	KL1	0.002	0.007
21	KEng1	0.020	0.060

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Deep learning-based geological modeling driven by sedimentary process simulation

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