Improving predictability of stimulated reservoir volume from different geological perspectives

• Published in: Marine and petroleum geology 2018-08, Vol.95, p.219-227
• Main Authors: Yang, Sheng, Yi, Yuanyuan, Lei, Zhengdong, Zhang, Yonghua, Harris, Nicholas B., Chen, Zhangxin
• Format: Article
• Language: English
• ISSN: 0264-8172; 1873-4073
• DOI: 10.1016/j.marpetgeo.2018.04.018

A major challenge in the development of unconventional resources is to comprehensively characterize and evaluate hydraulic fracturing outcomes. Microseismic (MS) data are a fundamental element of interpretations and estimates of stimulated reservoir volume (SRV). However, an interpretation based solely on MS data has its limitations because of many disagreements between MS signals and hydraulic fracturing. In this study, detailed core and well log data are applied to generate a rock fracability index, based on a formation's proneness to form hydraulic fractures from different geological perspectives. Near-wellbore zones are first evaluated by the rock fracability index. Then a pixel-based reservoir modeling method is implemented to develop a rock mechanical model of a whole shale layer. Employing a comparison between a rock mechanical model and the distribution of MS events, some mismatched areas are located, and the corresponding MS signal filter threshold criteria are adjusted. As a result, a new MS dataset is generated to develop an improved SRV. This approach is applied in the Biyang shale oil reservoir, China, which was deposited in a continental lacustrine environment with complex laminations and high heterogeneous rock mineral compositions. As compared to the original SRV, the SRV generated by the application of this approach clearly illustrates three major hydraulic fracture networks and can be further applied to model the well production. The proposed approach provides a good and valuable guide to increase the reliability of a SRV. •Modeling Shale Geomechanical Properties by a Rock Fracability Index.•Discovering Corresponding Relationship between Rock Fracability Index and Microseismic Data.•Enhancing SRV by Integrating Microseismic Data with a Reservoir Geomechanical Model.