A method for predicting the probability of formation of complex hydraulic fracture networks in shale reservoirs: development and application

doi:10.1007/s11707-022-0977-2

Front. Earth Sci.

2023, Vol. 17

Issue (3) : 679-690 https://doi.org/10.1007/s11707-022-0977-2

RESEARCH ARTICLE

A method for predicting the probability of formation of complex hydraulic fracture networks in shale reservoirs: development and application

Xiaona ZHANG^1,², Yanbin YAO^1,^2,³(

), Yongshang KANG^4,⁵

¹. School of Energy Resource, China University of Geosciences, Beijing 100083, China
². Beijing Key Laboratory of Unconventional Natural Gas Geological Evaluation and Development Engineering, China University of Geosciences, Beijing 100083, China
³. Frontiers Science Center for Deep-time Digital Earth, China University of Geosciences, Beijing 100083, China
⁴. College of Geosciences, China University of Petroleum (Beijing), Beijing 102249, China
⁵. State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum (Beijing), Beijing 102249, China

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Abstract

Shales can form a complex fracture network during hydraulic fracturing, which greatly increases the stimulated reservoir volume (SRV) and thus significantly increases oil or gas production. It is therefore important to accurately predict the probability of formation of the hydraulic fracture network for shale gas exploration and exploitation. Conventional discriminant criteria are presented as the relationship curves of stress difference vs. intersection angle. However, these methods are inadequate for application in the field. In this study, an effective and quantitative prediction method relating to the probability of complex fracture network formation is proposed. First, a discriminant criterion of fracture network was derived. Secondly, Monte Carlo simulation was applied to calculate the probability of the formation of the complex fracture network. Then, the method was validated by applying it to individual wells of two active shale gas blocks in the Sichuan Basin, China. Results show that the probabilities of fracture network are 0.98 for well JY1 and 0.26 for well W204, which is consistent with the micro-seismic hydraulic fracturing monitoring and actual gas production. Finally, the method was further extended to apply for the regional scale of the Sichuan Basin, where the general probabilities of fracture network formation are 0.32−1 and 0.74−1 for Weiyuan and Jiaoshiba blocks, respectively. The Jiaoshiba block has, therefore, an overall higher probability for formation of fracture network than the Weiyuan block. The proposed method has the potential in further application to evaluation and prediction of hydraulic fracturing operations in shale reservoirs.

Keywords shale gas complex fracture network shale reservoir Monte Carlo simulation Sichuan Basin

Corresponding Author(s): Yanbin YAO

Online First Date: 30 August 2023 Issue Date: 12 December 2023

Cite this article:

Xiaona ZHANG,Yanbin YAO,Yongshang KANG. A method for predicting the probability of formation of complex hydraulic fracture networks in shale reservoirs: development and application[J]. Front. Earth Sci., 2023, 17(3): 679-690.

URL:

https://academic.hep.com.cn/fesci/EN/10.1007/s11707-022-0977-2
https://academic.hep.com.cn/fesci/EN/Y2023/V17/I3/679

Fig.1 Schematic diagram of (a) simple (bi-wing and symmetric) hydraulic fracture and (b) complex hydraulic fracture network (the blue rectangles in the figure represent hydraulic fractures and the green ones represent natural fractures).

Fig.2 Interaction of the hydraulic fracture (HF) and natural fracture (NF).

Fig.3 Schematic diagram showing the strike and dip angle of the NF and their relationship with principal stress directions (the gray plane represents the NF; AOB is the strike line of the NF; OD is the inclined line of the NF; OD’ is the horizontal projection of the inclined line for the NF; α is the angle between the strike of the NF and the σ_h; β is the dip angle of the NF; θ is the angle between the NF and the σ_v.).

Fig.4 Map showing the study area in the Sichuan Basin (modified from Chen et al. 2014).

Tab.1 Shale tensile strength data of the Lower Longmaxi Formation in the Sichuan Basin

Tab.2 Chi-square test results of S_t for different mathematical distribution types of variables for the Lower Longmaxi Formation in the Sichuan Basin

Tab.3 Input parameters for Monte-Carlo simulation of Well W204 and Well JY1

Fig.5 Cumulative probability curves of Y value of (a) Well W204 and (b) Well JY1.

Fig.6 Directions of hydraulic fractures in (a) well W204 and (b) well JY1 by micro-seismic monitoring.

Fig.7 Contribution rate of each variable to the probability of complex fracture network formation in (a) well W204 and (b) well JY1.

Tab.4 Input parameters for M-C simulation in Weiyuan and Jiaoshiba blocks

Fig.8 Cumulative probability curve of Y value when both α and β are 0° in Weiyuan block.

Tab.5 Comparison of the probabilities of complex fracture network formation between Weiyuan and Jiaoshiba blocks, with respect to different values of α and β

Fig.9 The simulated results of α and β for the formation conditions of complex fracture networks in (a) Weiyuan block and (b) Jiaoshiba block (the gray rectangles in the figure represent the range of α and β when the probability of formation of complex fracture network is greater than 0.9).

Fig.10 Conventional criteria methods for judging the behaviors between HF and NF (The intersection angle in the figure refers to the angle between HF and NF, which is complementary to α).

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