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Abstract

Fracture prediction at borehole and distribution in spatiality are crucial for evaluating and developing a fractured reservoir. It is difficult to characterise fractures properly because the extremely heterogeneity and complexity of them.This paper presents an integrated approach for fracture prediction and fracture modelling for a naturally fractured buried-hill carbonate reservoir in Jingbei Oilfield. The work we have done is providing an enhanced workflow combining three dimensional seismic data volume, conventional well logs and core data. In order to characterize the fractures comprehensively, we divided the fracture into two types that are large-scale fracture (LSF) and small-scale fracture (SSF).The ant tracking attribute is used to detect the large-scale fractures.Small-scale fractures are firstly observed from the core. By analyzing the core data, then the fracture density and dip angle are evaluated. Additionally, we predict the spatial distribution of the fracture density comparing the core data and well logging data. In this prediction process, artificial neural network algorithm is used to create a numerical model.Different methods are used to build the Discrete Fracture Network (DFN) model for different types of fractures deterministically stochastically.The result shows that the workflow of fracture prediction and modeling is effective for the naturally fractured buried-hill carbonate reservoir.

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/content/papers/10.3997/2214-4609.201602395
2016-12-05
2020-04-08
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http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.201602395
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