Hydraulic fracturing is an important measure for the development of tight reservoirs. In order to describe the distribution of hydraulic fractures, micro-seismic diagnostic was introduced into petroleum fields. Micro-seismic events may reveal important information about static characteristics of hydraulic fracturing. However, this method is limited to reflect the distribution area of the hydraulic fractures and fails to provide specific parameters. Therefore, micro-seismic technology is integrated with history matching to predict the hydraulic fracture parameters in this paper. Micro-seismic source location is used to describe the basic shape of hydraulic fractures. After that, secondary modeling is considered to calibrate the parameters information of hydraulic fractures by using DFM (discrete fracture model) and history matching method. In consideration of fractal feature of hydraulic fracture, fractal fracture network model is established to evaluate this method in numerical experiment. The results clearly show the effectiveness of the proposed approach to estimate the parameters of hydraulic fractures.

The distribution of fractures is highly uncertain in naturally fractured reservoirs (NFRs) and may be predicted by using the assisted-history-matching (AHM) that calibrates the reservoir model according to some high-quality static data combined with dynamic production data. A general AHM approach for NFRs is to construct a discrete fracture network (DFN) model and estimate model parameters given the observations. However, the large number of fractures prediction required in the AHM process could pose a high-dimensional optimization problem. This difficulty is particularly challenging when the fractures form a complex multi-scale fracture network. We present in this paper an integrated AHM approach of NFRs to tackle these challenges. Two essential ingredients of the method are (1) a 2D fractal-DFN model constructed as the geological simulation model to describe the complex fracture network, and (2) a mixture of multi-scale parameters, built according to the fractal-DNF model, as an inversion parameter model to alleviate the high-dimensional optimization burden caused by complex fracture networks. A reservoir with a multi-scale fracture network is set up to test the performance of the proposed method. Numerical results demonstrate that by use of the proposed method, the fractures well recognized by assimilating production data.

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The fracturing technique is widely used in many fields. Fracture has a greater impact on the movement of fluids in formations. Knowing information about a fracture is key to judging its effect, but detailed information about complex fracture networks is difficult to obtain. In this paper, we propose a new method to describe the shape of a complex fracture network. This method is based on microseismic results and uses the L-system to establish a method for characterizing a complex fracture network. The method also combines with decomposition to construct a new method called the multiobjective fracture network inversion algorithm based on decomposition (MOFNIAD). The coverage of microseismic monitoring results and the degree of fitting of production data are the two objective functions of the inversion fracture network. The multiobjective fracture network inversion algorithm can be optimized to obtain multiple optimal solutions that meet different target weights. Therefore, this paper established a multischeme decision method that approached the ideal solution, sorting technology and AHP to provide theoretical guidance for finding a more ideal fracture network. According to the error of microseismic monitoring results, we established two cases of fracture to verify the proposed method. Judging from the results of the examples, the fracture network finally obtained was similar to actual fractures.