王徐庄油田薄层生物石灰岩小—微裂缝识别及建模

doi:10.7657/XJPG20240605

摘要/Abstract

摘要：

小—微裂缝作为王徐庄油田沙河街组薄层生物石灰岩重要的储集空间之一，因缺乏有效的测量方法和表征技术，导致其研究较为困难，影响了油气开发中流体流动能力的预测。综合岩心、岩石薄片、CT扫描、地层微电阻率扫描成像测井、常规测井等资料，对小—微裂缝的发育情况开展研究。采用PSO-BP神经网络预测研究区裂缝性储集层发育情况及分布特征，提出了离散裂缝网络模拟方法，模拟了小—微裂缝的空间展布。结果表明：小—微裂缝发育的生物石灰岩深、浅电阻率幅差较大；研究区生物石灰岩小—微裂缝较为发育，对改善储集层物性和注水受效方向有重要意义；小—微裂缝受控于断裂带和生物石灰岩沉积微相。油藏数值模拟证实，融合小—微裂缝介质的双孔双渗模型的油水关系动态拟合效果更好。

关键词: 王徐庄油田, 沙河街组, 生物石灰岩, 小—微裂缝, 粒子群优化算法, BP神经网络, 离散裂缝网络模型

Abstract:

Micro-minor fractures represent a key type of reservoir space in the thin biolimestones of the Shahejie formation in the Wangxuzhuang oilfield. Due to the lack of effective measurement methods and characterization techniques, it is challenging to understand these fractures, thereby hindering accurate prediction of fluid flow capacity during oil and gas development. By integrating the data of core samples, thin sections, CT scanning, formation micro-resistivity imaging (FMI) logging, and conventional logging, the development of micro-minor fractures was investigated. With a PSO-BP neural network, the fracture development and distribution in the fractured reservoirs of the study area were predicted. Then a discrete fracture network modeling approach was proposed to simulate the spatial distribution of these fractures. The results show that the biolimestone with developed micro-minor fractures exhibits significant amplitude differences between shallow and deep lateral resistivity readings. Micro-minor fractures are well developed in the biolimestones in the study area, which play a crucial role in improving reservoir physical properties and waterflood response directions. These fractures are controlled by fault zones and sedimentary microfacies of the biolimestone. Numerical simulation confirms that the dual-porosity dual-permeability model incorporating micro-minor fractures can provide a better fit for the dynamic behavior of oil-water relations.

Key words: Wangxuzhuang oilfield, Shahejie formation, biolimestone, micro-minor fracture, particle swarm optimization algorithm, BP neural network, discrete fracture network model

中图分类号:

TE112.23

李云鹏, 林学春, 余星辰, 康志宏, 李佩敬, 王亚静, 祁爱平. 王徐庄油田薄层生物石灰岩小—微裂缝识别及建模[J]. 新疆石油地质, 2024, 45(6): 671-679.

LI Yunpeng, LIN Xuechun, YU Xingchen, KANG Zhihong, LI Peijing, WANG Yajing, QI Aiping. Identification and Modeling of Micro-Minor Fractures in Thin Biolimestones in Wangxuzhuang Oilfield[J]. Xinjiang Petroleum Geology, 2024, 45(6): 671-679.

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井名	地理坐标下黏滞剩磁相对标志线的偏角/（°）	地理坐标下标志线方位角/ （°）	误差置信圆/ （°）	裂缝产状
岐606井	312.9	47.1	10.1	走向为60°，2条直立裂缝
	305.4	54.6	10.0
	282.0	78.0	10.1
岐607井	306.2	53.8	14.6	走向为60°，高角度裂缝
	302.8	57.2	8.6
	293.0	67.0	13.4
岐608井	283.9	76.1	4.7	走向为78°，2条低角度裂缝
	281.5	78.5	6.9
	280.3	79.7	10.7
岐609井	278.0	82.0	4.0	走向为83°，直立裂缝
	276.0	84.0	5.8
	277.0	83.0	4.0
岐613井	245.4	114.6	10.5	走向为117°，2条直立裂缝
	243.3	116.7	8.3
	240.5	119.5	10.0
岐630井	288.1	71.9	12.3	走向为74°，高角度裂缝
	285.1	74.9	8.6
	284.7	75.3	7.9
岐634井	260.0	100.0	6.9	走向为100°，高角度裂缝
	258.3	101.7	8.3
	261.6	98.4	10.9
岐635井	272.3	87.7	9.4	走向为86°，直立裂缝
	273.2	86.8	9.7
	276.5	83.5	11.3
岐637井	99.0	261.0	6.5	走向为257°，直立裂缝
	103.8	256.2	11.1
	107.5	252.5	5.7
岐643井	308.2	51.8	3.0	走向为37°，高角度裂缝
	335.7	24.3	8.1
	325.0	35.0	10.4
岐646井	261.3	98.7	11.6	走向为83°，高角度裂缝
	279.3	80.7	8.8
	291.7	68.3	9.0
岐647井	99.0	261.0	6.5	走向为257°，高角度裂缝
	103.8	256.2	11.1
	107.5	252.5	5.7
岐648井	84.8	275.2	8.6	走向为265°，直立裂缝
	95.1	264.9	11.3
	104.6	255.4	6.3
岐649井	116.4	243.6	10.4	走向为252°，水平裂缝
	105.1	254.9	6.6
	103.9	256.1	10.2
岐652井	130.0	260.0	4.0	走向为261°，2条高角度裂缝
	138.0	252.0	5.8
	119.6	270.4	4.0
岐3井	94.8	265.2	15.2	走向为269°，2条直立裂缝
	52.4	307.6	17.1
	87.3	272.7	11.4
岐5井	199.2	160.8	1.7	走向为160°，高角度裂缝
	199.2	160.8	5.2
	201.0	159.0	1.8