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author | Siyuan Li a, Tianfu Xu a b, Zubin Chen c, Zhenjiao Jiang a b

caption | Efficient fracture network characterization in enhanced geothermal reservoirs by the integration of microseismic and borehole logs data

source (of information etc) | Geothermics

compiler | Geothermal small core (add micro-signal: geothermalAI, can get relevant information)

→This is the Geothermal Energy Online AIgeothermal coreEditor's first1article


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full text guide



A recent study presents an efficient method for characterizing fracture networks from hydraulic fracturing-induced microseismic events in enhanced geothermal reservoirs. The method includes denoising microseismic events using a density-based spatial clustering algorithm (DBSCAN), random fracture localization via a Monte Carlo algorithm, estimation of fracture sizes via planar projection, and determination of the number of fractures via elbow analysis. In this method, the fracture network can be reconstructed based on the source location of the microseismic events as well as the major geostresses detected through downhole logging and the a priori orientation of the initial fractures. The method was validated with a synthetic case consisting of two fractures and microseismic events, where the microseismic events were located with errors as high as 1.01 TP3T of the reservoir depth.The results show that the method is able to accurately determine the number of fractures and reliably estimate the fracture inclination and tilt angle with an error of less than 10°. The maximum error of the estimated fracture size is significantly lower than the noise of the microseismic event source location. In an application to a granitic hot-dry rock reservoir, two major fracture clusters were identified from more than 2,000 microseismic events: one with a large dip angle and a northeast strike, and the other with a small dip angle and a northwest strike. The fracture geometries are robustly interpreted for different a priori strike ranges. In addition, the proposed method is computationally efficient, allowing real-time generation of cracks in minute computation times. The crack network characterization method is based on the spatial distribution of denoised microseismic events. As the error in the positioning of microseismic events increases (given below 40 m in this study), the error in the generation of the fracture network also increases. In addition, microseismic events are weakly related to the hydraulic connectivity of the fracture network and the internal structure of the fractures. In order to improve the quality of the fracture network characterization to better accommodate accurate fluid and heat transport models, there is a need to further optimize the fracture network by fusing microseismic data with hydraulic test data, which will be investigated in the near future.



02

HIGHLIGHT Pictures


Fig. 1 . Algorithm flowchart of characterizing the fracture network from microseismic events cloud.

Fig. 2 . (a) Injection rates during hydraulic fracturing simulation, (b) two stimulated fractures and microseismic events distribution on fracture planes, (c ) and (d) microseismic events with white noises at the scale of 0.5% and 1.0% of burial depth, respectively.

Fig. 3 . Percentage of microseismic events fitted by fractures under varying number of fractures in responses to (a) 0.5% white noises and (b) 1.0% white noises in source locations of microseismic events; (c) and (d) best-fitted fracture networks generated from microseismic events with 0.5% and 1.0% white noises, respectively. TP3T white noises, respectively.

Fig. 4 . Percentage of microseismic events fitted by fractures under varying number of fractures in responses to the prior fracture orientation ranges estimated at (a) ±15° and (b) ±20°; (c) and (d) best-fitted fracture networks generated from microseismic events under varying prior fracture orientation ranges. (c) best-fitted fracture networks generated from microseismic events under varying prior fracture orientation ranges.

Fig. 5 . (a) Original microseismic events induced by hydraulic fracturing, (b) the microseismic events denoised by DBSCAN, (c) elbow analysis of relationship between the number of fractures and percentage of microseismic events fitted by fractures, and (d) the fracture network generated from microseismic events. events.

Fig. 6 . Fracture network characterization under the prior range of fracture strikes reduced by -6° (b), increased by 6° (d) and 12° (f), respectively, and number of fractures (a, c, e) identified stably by elbow method. respectively, and number of fractures (a, c, e) identified stably by elbow method.

Disclaimer: This article is for academic communication and dissemination only, and does not constitute investment advice

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