基于灰色关联与层次分析的脆性指数预测方法——以准噶尔盆地吉木萨尔凹陷芦草沟组致密储层为例

doi:10.11720/wtyht.2023.1242

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Abstract

Owing to the poor physical properties,the tight reservoirs in the Jimusaer Sag can yield industrial oil flow only through hydraulic fracturing.The research on mechanical properties and the brittleness assessment of rocks can provide a certain reference for hydraulic fracturing.This study obtained the mechanical properties of the tight strata in the Jimusaer sag using triaxial mechanical tests and determined the log parameters potentially sensitive to rock brittleness by analyzing the correlation between the sensitivity of the brittleness index and logs.Then,based on the grey correlation theory,this study determined the initial sequence of sensitivity parameters and normalized the parameters selected.Then,it quantitatively correlated the selected parameters with the potential sensitivity to the brittleness index and determined the degrees of correlation and their order.On this basis,this study established a matrix for the pair-wise comparison of the sensitivity parameters using the analytic hierarchy process (AHP) and determined the weight vector.Then,it established the functional relationship model between the brittleness index and the sensitivity parameters,thus developing a new prediction model for the brittleness index.Finally,this study compared the log models with the brittleness model established based on mechanical properties and the brittleness index determined through laboratory tests.The study results are as follows.The tight strata in the Jimusaer Sag have high brittleness,and the comprehensive brittleness index characterized using the whole-process stress-strain curve agreed with the actual brittleness characteristics of rocks.The degree of correlation of the sensitivity parameters determined using the grey correlation method was in the order of natural gamma-ray(GR)>resistivity(R_t)>density(ρ)>neutron(CNL)>sonic interval transit time(ΔT),which had a weight coefficient of 0.33,0.22,0.18,0.16, and 0.11,respectively in the new prediction model.The prediction method proposed in this study was applied to the Lucaogou Formation in the Jimusaer Sag.Compared with that determined through laboratory tests,the brittleness index predicted can reflect the actual brittleness of the formation,exhibiting a high consistency.As shown by the results from well tests,the productivity index of oil was proportional to the brittleness index,and a higher brittleness index was associated a high production capacity after fracturing.Therefore,the new method provides a new approach to brittleness index prediction and guides the parameter selection for the fracturing of reservoirs.

Key words： grey correlation analytic hierarchy process brittleness index tight reservoir Lucaogou Formation

Received: 20 May 2022 Published: 11 October 2023

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	Qing LIU
	Zhen ZHANG
	Shuai YANG
	Feng-Ling LI

Cite this article:

Qing LIU,Zhen ZHANG,Shuai YANG, et al. Method for brittleness index prediction based on grey correlation and analytic hierarchy process:A case study of the tight reservoirs in the Lucaogou Formation of the Jimusaer Sag,Junggar Basin[J]. Geophysical and Geochemical Exploration, 2023, 47(4): 944-953.

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https://www.wutanyuhuatan.com/EN/10.11720/wtyht.2023.1242 OR https://www.wutanyuhuatan.com/EN/Y2023/V47/I4/944

Structural location map of Jimusaer Sag

Physical property distribution of Lucaogou Formation

原理分类	公式	变量说明	获取方法
基干硬度或坚固性	BI₁= (H_m - H)/K	H为硬度,GPa;H_m为微观硬度,GPa;K为体枳模量,GPa	硬度测试
	BI₂ = H/K_IC	K_IC为断裂韧性,MPa·m^1/2	硬度和韧性测试
	BI₃ = HE/ $K I C 2$	E为静态杨氏模量,GPa	陶质材料测试
	BI₄=qσ_c	q为直径小于0.6 mm碎屑百分比,%;σ_c为抗压强度,MPa	兽氏冲击实絵
	BI₅ = c/d	c为裂纹长度,μm;d为韦氏测试特定载荷下贯入尺寸,μm	贯入实验
	BI₆= t_dec/t_inc	t_dec为平均载荷减少时间,s;t_inc为平均载荷增加时间,s
	BI₇ = F_max/P	F_max为试件所受最大载荷,kN;P为相应的贯入深度,mm
基于强度比值	BI₈ = σ_c/σ_t	σ_c为抗压强度,MPa;σ_t为抗拉强度,MPa	单轴抗压測试和巴西劈裂实验
	BI₉ = (σ_c-σ_t)/(σ_c+σ_t)
	BI₁₀ = σ_cσ_t/2
	BI₁₁ = (σ_cσ_t)^0.5/2
基于全应力—应变特征	BI₁₂ = (τ_pτ_r)/τ_p	τ_p为剪切强度峰值,MPa;τ_r为残余剪切强度,MPa	应力—应变测试
	BI₁₃ = ε_r/ε_t	ε_r为可恢复应变,无量纲;ε_t为总应变,无量纲
	BI₁₄= W_r/W_t	W_r为可恢更应变能,J;W_t为总应变能,J
	BI₁₅ =ε_ux·100%	ε_ux为不可恢复轴向应变,无量纲
	BI₁₆ = (ε_p -ε_r)/ε_p	ε_p为应变峰值,无量纲;ε_r为残余应变,无量纲
	BI₁₇ = π/4 +φ/2	φ为内摩擦角,rad	应力应变测试或声波测井数据
	BI₁₈ = sinφ	φ为内摩擦角,rad	应力应变测试或声波测井数据
基于弹性力学参数	BI₁₉ = (E_n+v_n)/2	E_n为归一化杨氏模量,无量纲;v_n为归一化泊松比,无量纲	密度与声波测井
基于岩石矿物组分	BI₂₀ =W_qtz/W_τot BI₂₁ =(W_qtz+W_dot)/W_τot BI₂₂ =(W_QFM+W_Car)W_τot	W_qtz为石英含量,%;W_τot为矿物总量,% W_dot为白云岩含量,% W_QFM为硅酸盐岩含量,%;W_Car为脆性碳酸盐岩含量,%	实验室XRD测试或矿物含量测井

Statistics of brittleness index evaluation method

Calculation method of brittleness index based on stress-strain in whole process

Schematic diagram of brittleness index parameters

Triaxial stress-strain curves of some rock samples

Comprehensive brittleness index of rock samples obtained by experimental method

Morphology of rock sample after compression

Cross plot of logging parameters and brittleness index measured in laboratory

Correlation degree and order of each factor and brittleness index

Comparison matrix scale and its meaning

Judgment matrix

Random consistency values

Comparison of brittleness index prediction

Relation between brittleness index and oil production index per meter

Microseismic monitoring results of hydraulic fracturing

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