- journal_title：Geophysics
- Contributor：Ulrich Zimmer
- Publisher：Society of Exploration Geophysicists
- Date：2011-
- Format：text/html
- Language：en
- Identifier：10.1190/geo2011-0004.1
- journal_abbrev：Geophysics
- issn：0016-8033
- volume：76
- issue：6
- firstpage：WC17
- section：Technical Papers
Microseismic monitoring has become an important part of borehole completions in tight-reservoir formations. Usually, clear objectives for a microseismic survey are set prior to the data acquisition. The possibility of meeting these objectives is determined by the acquisition geometry, the target formation, the completion schedule, and only to a lesser extent, by the data quality itself. Provided is a tutorial on the content and use of prejob modeling and design studies as a tool to anticipate viewing distances, data quantity, location accuracy, event magnitudes, achievable mapping distances, expected waveforms, and noise levels. In addition, potential challenges in meeting the survey objectives can be identified and solutions to these challenges can be devised prior to the survey. For downhole surveys, this involves the evaluation of different sensor array geometries and their impact on the location accuracy in different parts of the expected model. The sensitivity of the event location on the velocity model can be estimated using an initial log-based model. Recently, the detailed characterization of the event mechanism in form of a moment tensor inversion has received increased attention. The accuracy of the inverted moment tensor depends largely on the coverage of the focal sphere, i.e., the distribution of the sensors around the event location. Based on the sensor positions, areas with high- and low-quality moment tensor inversion results can be identified prior to data acquisition through the distribution of the condition number. Depending on the survey objectives and the given constraints, the microseismic design study might show that the survey objectives cannot be met. In this case, it is possible to evaluate alternate technologies, e.g., distributed temperature sensing (DTS), ahead of the project for their potential to meet these challenges.