Building the Fracture Network Model for the Okuaizu Geothermal Field Based on Microseismic Data Analysis

Author:

Darisma Dian1ORCID,Mukuhira Yusuke2ORCID,Okamoto Kyosuke3,Aoyogi Naoki3,Uchide Takahiko4,Ishibashi Takuya3,Asanuma Hiroshi3,Ito Takatoshi2

Affiliation:

1. Tohoku University Graduate School of Environmental Studies: Tohoku Daigaku Daigakuin Kankyo Kagaku Kenkyuka

2. Tohoku University: Tohoku Daigaku

3. National Institute of Advanced Industrial Science and Technology: Kokuritsu Kenkyu Kaihatsu Hojin Sangyo Gijutsu Sogo Kenkyujo

4. AIST: Kokuritsu Kenkyu Kaihatsu Hojin Sangyo Gijutsu Sogo Kenkyujo

Abstract

Abstract

Understanding the flow behavior in a geothermal reservoir is important to manage sustainable geothermal energy extrapolation and contribute to the growth of further geothermal energy usage. Fluid flow in geothermal reservoirs generally occurs in complex existing fracture systems in which reservoirs are situated in highly fractured rock. To model such a fracture system as a discrete fracture model, a fracture’s location and orientation are computed with a statistical process and observational data. In many cases, 1D borehole logging data still has not been well worked. In this study, we use microseismic data to build a fracture network system and extract the detailed position and dimension of fractures. This study uses microseismic data recorded at the Okuaizu Geothermal Field, Fukushima prefecture, Japan, from 2019 to 2021. First, we located the hypocenter locations, removing the effect of uncertainty in velocity structure related geothermal fluids. Then, we relocated and clustered the seismic events based on waveform similarity. Then, we analyze each cluster to define fracture orientation using principal component analysis (PCA) and focal mechanism (FM). We used the P polarity with the S/P ratio as a constraint for a better fault plane solution. With PCA, we also can extract the fracture dimension of each cluster. Our clustering analysis showed that clusters were not always plannar as fractures, and we interpreted them as fracture zones. According to the consistency between the PCA and FM, each cluster/fracture zone was identified into three conceptual models to characterize the fracture network system in this field. The proposed model shows variations in the orientation of small fractures within the fracture zone. We characterized the spatial variation of the fracture distribution and orientations in the reservoir with these models and exhibited the fracture network system of this field. The fracture zone near the injection well has strike N-S, and the dip is above 80°, but the fracture zone in the northeastern part of the injection well has strike in NW-SE with a dip between 60° and 80°. The fracture network system estimated from this study is crucial for robust reservoir modeling as our model is more realistic, observation-orientated, and includes local anomalies of the reservoir properties.

Publisher

Springer Science and Business Media LLC

Reference55 articles.

1. Adler PM, Thovert J-F (1999) Fractures and fracture networks. Springer Science & Business Media

2. AIST (2024) Crustal Stress Database. https://gbank.gsj.jp/crstress/english/. Accessed 29 Feb 2024

3. Interpretation of reservoir creation process at Cooper Basin by microseismic multiplet analysis;Asanuma H;GRC Trans,2009

4. Analysis of Microseismic Events from a Stimulation at Basel, Switzerland;Asanuma H;GRC Trans,2007

5. Interpretation of Reservoir Structure from Super-Resolution Mapping of Microseismic Multiplets from Stimulation at Basel, Switzerland in 2006;Asanuma H;GRC Trans,2008

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