Research on the prediction method of 3D reservoir sweet spots distribution of shale oil in shale intercalated layer: A case from the Yanchang Formation of B15 block, Ordos Basin

doi:10.13745/j.esf.sf.2025.7.15

Abstract

Abstract:

Sweet spots in the shale oil reservoirs in intercalated layer of continental basins are characterized by vertical thinness and lateral discontinuity. Therefore, accurately predicting their three-dimensional (3D) distribution is key to improving the horizontal well sweet spot encounter rate and single-well productivity. The Chang 7 Member of the Yanchang Formation in the Ordos Basin contains abundant reservoirs of shale oil in intercalated layer. However, previous development practices have revealed significant variations in the horizontal well sweet spot encounter rate. Under the constraints of low-quality seismic data and large well spacing, a robust method for predicting the 3D spatial distribution of sweet spots at the single-reservoir-layer level has not yet been established. This study takes the B15 block of the Yanchang Formation in the Ordos Basin as its research focus. To address this gap, we propose an intelligent 3D sweet spot prediction approach with multi-dimensional and multi-variable constraints. This method utilizes seismic data as a macroscopic constraint and employs multi-well correlation techniques to predict the planar distribution of sweet spots. Furthermore, it adopts a single-sweet-spot modeling method based on micro-architecture surfaces to achieve 3D modeling of individual sweet spots. The core of this modeling method lies in constructing micro-architecture surfaces for sweet spots using a geometric morphology intelligent construction algorithm under the constraints of the 2D sweet spot distribution. Utilizing an embedding method constrained by stratigraphic stacking patterns, we optimize the top and bottom micro-architecture surfaces and embed the single-sweet-spot models to generate a multi-sweet-spot 3D geological model. This approach effectively reproduces the spatial distribution patterns and complex contact relationships of the sweet spots. Consequently, it can effectively guide horizontal well placement and enhance the sweet spot encounter rate.

Key words: Ordos Basin, shale oil in shale intercalated layer, 3D sweet spot prediction, geological modeling, Yanchang Formation

CLC Number:

P618.13

WAN Xiaolong, WU Shenghe, ZHOU Xinping, XU Zhenhua, FU Jinhua, WANG Zifeng, MA Shuwei, WU Degang, LI Zhen, LIU Mingcheng. Research on the prediction method of 3D reservoir sweet spots distribution of shale oil in shale intercalated layer: A case from the Yanchang Formation of B15 block, Ordos Basin[J]. Earth Science Frontiers, 2025, 32(5): 417-431.

Figures/Tables 18

Fig.1 Map views of tectonic location and well position of B15 block, Ordos Basin. a modified after [1].

Fig.2 Correlation section of single layer in B15 Block, Ordos Basin (section location is shown in Fig.1b)

Table 1 Characteristics of microfaices of B15 Block, Ordos Basin

沉积微相		岩性	砂体厚度	韵律		孔隙度	泥质含量	含油饱和度	GR曲线响应特征
水道		细砂岩和粗粉砂岩为主	1.5~4.0 m	正韵律		>10%	<5%	>55%	高幅度钟型
朵叶体	朵叶主体	细砂岩和粗粉砂岩为主	1.5~4.0 m	反韵律或均质韵律	6%~10%		5%~8%	45%~55%	高幅度漏斗型或箱型
朵叶体	朵叶侧缘	细粉砂岩	0.5~1.5 m	无明显韵律		<6%	>8%	40%~45%	小幅度漏斗型或指型

Fig.3 Core characteristics of microfaices in B15 Block, Ordos Basin

Fig.4 Comprehensive interpretation histogram of sweet spots of Well B15 in B15 Block, Ordos Basin

Fig.5 Statistical histogram of the development degree of sweet spots within different microfacies in B15 Block, Ordos Basin

Fig.6 Workflow diagram of intelligent 3D sweet spot distribution prediction with multi-dimensional and multi-variable constraints

Fig.7 Map views of microfacies (a) and sweet spots (b) of Chang ${{7}_{2}}^{1-5}$ Layer in B15 Block, Ordos Basin

Fig.8 Structure-stratigraphic model of B15 Block, Ordos Basin

Table 2 Workflow of density clustering method with constrained cluster centers

步骤	方法流程
1	输入:待聚类对象点集合P={p₁,p₂,…,p_n}
2	从与优势甜点区域F_i相近的任一对象点p_i开始;
3	寻找合并p_i直接密度可达的对象,并约束这些对象的簇中心点不远离优势甜点区域F_i;
4	如果p_i是一个核心点,则找到了一个聚类,如果p_i是一个边界点(即从p_i没有密度可达的点)则寻找下一个对象点p_i₊₁;
5	循环进行2、3步骤,直到所有点都被处理。

Fig.9 Sketch map of principle and implementation effect of the minimum convex hull set algorithm

Fig.10 Top micro-architecture surface of sweet spot of Chang${{7}_{1}}^{2-7}$ single-layer in B15 Block, Ordos Basin

Fig.11 A schematic illustration of the embedded modeling process for type Ⅰ and type Ⅱ1 sweet spots

Fig.12 Sweet spot model of Chang${{7}_{1}}^{2-7}$ single-layer of B15 Block, Ordos Basin.

Fig.13 3D sweet spot model of B15 Block, Ordos Basin

Fig.14 Statistics diagram of the proportion of different sweet spots in B15 Block, Ordos Basin

Fig.15 Cross-section of microfacies in B15 Block, Ordos Basin (section location is shown in Fig.1b)

Fig.16 Cross-section of the sweet spot model in B15 Block (section location is shown in Fig.1b)

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