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Energies 2018, 11(9), 2311;

Characterization and Prediction of Complex Natural Fractures in the Tight Conglomerate Reservoirs: A Fractal Method

College of Geosciences, Northeast Petroleum University, Daqing 163318, China
Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China
State Key Laboratory of Petroleum Resource and Prospecting in China University of Petroleum, Beijing 100083, China
Department of Overseas Strategy & Production Planning Research in CNPC International Research Center, Beijing 100083, China
Northwest Oil and Gas Field of Southwest Oil & Gas field Company, PetroChina, Jiangyou 621709, China
Authors to whom correspondence should be addressed.
Received: 28 July 2018 / Revised: 20 August 2018 / Accepted: 31 August 2018 / Published: 2 September 2018
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Using the conventional fracture parameters is difficult to characterize and predict the complex natural fractures in the tight conglomerate reservoirs. In order to quantify the fracture behaviors, a fractal method was presented in this work. Firstly, the characteristics of fractures were depicted, then the fracture fractal dimensions were calculated using the box-counting method, and finally the geological significance of the fractal method was discussed. Three types of fractures were identified, including intra-gravel fractures, gravel edge fractures and trans-gravel fractures. The calculations show that the fracture fractal dimensions distribute between 1.20 and 1.50 with correlation coefficients being above 0.98. The fracture fractal dimension has exponential correlation with the fracture areal density, porosity and permeability and can therefore be used to quantify the fracture intensity. The apertures of micro-fractures are distributed between 10 μm and 100 μm, while the apertures of macro-fractures are distributed between 50 μm and 200 μm. The areal densities of fractures are distributed between 20.0 m·m−2 and 50.0 m·m−2, with an average of 31.42 m·m−2. The cumulative frequency distribution of both fracture apertures and areal densities follow power law distribution. The fracture parameters at different scales can be predicted by extrapolating these power law distributions. View Full-Text
Keywords: tight conglomerate; fracture characterization and prediction; fractal method tight conglomerate; fracture characterization and prediction; fractal method

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Gong, L.; Fu, X.; Gao, S.; Zhao, P.; Luo, Q.; Zeng, L.; Yue, W.; Zhang, B.; Liu, B. Characterization and Prediction of Complex Natural Fractures in the Tight Conglomerate Reservoirs: A Fractal Method. Energies 2018, 11, 2311.

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