Browsing by Author "LI, QI"

Now showing 1 - 1 of 1
  • Thumbnail Image
    ItemOpen Access
    Microseismic Based Reservoir Characterization (SBRC): Stimulated Reservoir Volume, Diffusivity, Geomechanics and Probabilistic Modeling
    (2018-04-27) LI, QI; Aguilera, Roberto F.; Aminzadeh, Fred; Lines, Laurence R.; Mehta, Sudarshan A. Raj; Moore, Robert Gordon; Wong, Ron Chik-Kwong
    Microseismic monitoring is a technique that allows examining the 3D growth of a microseismic event cloud stemming from a hydraulic fracturing job. The prevalence of microseismic monitoring has given rise to a new research area referred to as Microseismic Based Reservoir Characterization (SBRC). The primary objective of this thesis is to develop new microseismic interpretation methods with a view to advance practical petroleum engineering aspects of SBRC particularly in tight and shale reservoirs. The major original contributions of this dissertation include: 1. Development of an analytical solution to calculate the relative seismicity rate occurring during a hydraulic fracturing job. Current methods require the use of numerical solutions. 2. Development of a diffusion-based method for calculating the stimulated reservoir volume (SRV) in anisotropic, asymmetric, nonuniform shale petroleum reservoirs. The analytical solution satisfies the requirements of fast implementation, robust application and analytical tractability. Current methods to handle these complexities require the use of numerical solutions. 3. Development of a correlation for calculating Biot coefficient with an emphasis on shale petroleum reservoirs based on knowledge of porosity and permeability. The easy-to-use correlation provides good agreement with previously published direct experimental measurements. This is important as Biot coefficient plays a very important role on stress coupling in microseismicity modeling. 4. Development of a method for calculating large-scale permeability using Mogi’s (1967) empirical rock failure relationship. Large scale permeability plays a very important role on the success or failure of a hydraulic fracturing job. 5. Development of a probabilistic model that combines stochastic process, seismicity rate and statistical learning approach for predicting real time microseismicity occur- rences. This is important in evaluating the ongoing process of a hydraulic fracturing job. 6. Development of a geostatistical simulation algorithm, TopoSim, which integrates topological preserving algorithms and utilizes unsupervised machine learning pro- tocol. The algorithm can be used, for example, in the evaluation of paleochannels in reservoirs of continental origin, and to generate multiple natural fractures network realizations, which can be fed into reservoir and geomechanical simulators. It is concluded that the above original contributions will enhance the SBRC particularly in the case of shale petroleum reservoirs.