致密砂岩储层脆性测井评价方法研究及应用——以鄂尔多斯盆地渭北油田为例

doi:10.11720/wtyht.2021.1335

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Abstract

Tight sandstone reservoirs have the characteristics of strong heterogeneity,poor physical properties,and difficulty in exploration and development.In order to find the high-brittleness section of tight sandstone reservoirs in the Weibei oilfield and fracture this kind of reservoirs,this paper proposes a method based on ANN (artificial neural network) model for shear wave prediction under the condition of lacking suitable brittleness prediction methods for tight sandstone reservoirs in the Weibei oilfield at present.The predicted value is highly consistent with the measured value,and the brittleness index of each well in the study area is calculated by the elastic parameter method further.For the purpose of improving the accuracy of the brittleness index predicted by this method,X-ray diffraction full-rock analysis of fewer wells in the study area is utilized,and it is concluded that quartz and carbonate rocks are the main brittle minerals of the Yanchang formation in the study area."(quartz+carbonate) content/ total minerals " are adopted to calculate the rock brittleness index and then improve the brittleness index predicted by the elastic parameter method.This technique which takes advantage of the balance between the mineral composition method and the elastic parameter method not only improves the prediction accuracy but also makes up for the lack of array acoustic logging and whole rock analysis data.This method was used to predict the brittleness of tight sandstone reservoirs in the WB2 well area of the Yanchang formation in the Weibei oilfield, and high-brittleness sections of WB52 and WB49 were further chosen to be fractured.It is shown that the production stimulation effect was obvious after fracturing,which is of great significance for guiding hydraulic fracturing.The method and process proposed in this paper have strong application and promotion value.

Key words： brittleness index tight sandstone fracturing transformation shear wave prediction elastic parameters artificial neural network mineral fragility index dessert

Received: 29 September 2020 Published: 15 December 2021

ZTFLH:

P631.4

Corresponding Authors: HAN Xiang-Yi E-mail: zhu007yan@163.com;610220744@qq.com

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	Yan ZHU
	Xiang-Yi HAN
	Xin-Xin YUE
	Chun-Feng YANG
	Wen-Xin CHANG
	Li-Juan XING
	Jing LIAO

Cite this article:

Yan ZHU,Xiang-Yi HAN,Xin-Xin YUE, et al. Research and application of brittleness logging evaluation method to tight sandstone reservoirs:Exemplified by Weibei oilfield in Ordos Basin[J]. Geophysical and Geochemical Exploration, 2021, 45(5): 1239-1247.

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https://www.wutanyuhuatan.com/EN/10.11720/wtyht.2021.1335 OR https://www.wutanyuhuatan.com/EN/Y2021/V45/I5/1239

Structure diagram of artificial neural network

Flow chart of ANN shear wave velocity prediction method

Intersection of well logging curves and shear wave velocity in the study area

Comparison of predicted values of S-wave velocity and actual measured values with different wells

Analysis results of shear wave velocity in the target interval of WB48 well

X-ray diffraction data of the whole rock in the Yanchang Formation of WB48 well

Intersection analysis of quartz,feldspar,carbonate and clay minerals with Young's modulus and Poisson's ratio
a—intersection analysis of quartz with Young's modulus and Poisson's ratio;b—intersection analysis of feldspar with Young's modulus and Poisson's ratio;c—intersection analysis of carbonate with Young's modulus and Poisson's ratio;d—intersection analysis of clay minerals with Young's modulus and Poisson's ratio

Intersection diagram of Young’s modulus and Poisson’s ratio

Prediction of brittleness index of tight oil wells in Weibei area

Brittleness prediction results of tight oil wells in Weibei area

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