History matching of naturally fractured reservoirs based on the recovery curve method

被引:11
|
作者
Ghaedi, Mojtaba [1 ]
Masihi, Mohsen [1 ]
Heinemann, Zoltan E. [2 ]
Ghazanfari, Mohammad Hossein [1 ]
机构
[1] Sharif Univ Technol, Dept Chem & Petr Engn, Tehran, Iran
[2] Univ Leoben, A-8700 Leoben, Austria
关键词
naurally fractured reservoir; dual porosity; matrix-fracture interaction; history matching; neighborhood-Bayes algorithm; genetic algorithm; WATER/OIL/GAS TRANSFER-FUNCTIONS; NEIGHBORHOOD ALGORITHM; GEOPHYSICAL INVERSION; PROPER USE; SIMULATION; UNCERTAINTY; IMBIBITION; MODEL;
D O I
10.1016/j.petrol.2014.12.002
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
The discrete fracture network (DFN) and Multiple-Continua concept are among the most widely used methods to model naturally fractured reservoirs. Each faces specific limitations. The recently introduced recovery curve method (RCM) is believed to be a compromise between these two current methods. In this method the recovery curves are used to determine the amount of mass exchanges between the matrix and fracture mediums. Two recovery curves are assigned for each simulation cell, one curve for gas displacement in the presence of the gravity drainage mechanism, and another for water displacement in the case of the occurrence of the imbibition mechanism. These curves describe matrix-fracture mass transfer more realistically and therefore can be of great use when obtaining historical production data. This paper presents the potential of the RCM within the framework of history matching of naturally fractured reservoirs to determine the appropriate recovery curves. In particular, the phase contact positions of a sector model, extracted from a real fractured reservoir, are matched throughout the production history using the RCM. Therefore, the main contribution of this work is using the historical data of contact positions to obtain a better description of the matrix-fracture communication. The distribution of an ensemble of the history matched models was illustrated by Boxplot, and the Genetic Algorithm (GA) and the Neighborhood-Bayes technique (NAB) were utilized for optimization and uncertainty quantification, respectively. The calculated recovery curves results are in good agreement with the production history of the model and the Bayesian credible interval (P10-P90) of the proposed method is satisfactory. This work also confirmed the potential applicability of the combined GA and NAB method for optimization and uncertainty quantification purposes. (C) 2014 Elsevier B.V. All rights reserved.
引用
收藏
页码:211 / 221
页数:11
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