Parametric Probabilistic Models for Fluid Diffusivity Inversion and Forward Microseismic Generation Using Seismicity Rates
- Qi Li (University of Calgary) | Roberto Aguilera (University of Calgary)
- Document ID
- Society of Petroleum Engineers
- SPE Western Regional Meeting, 22-26 April, Garden Grove, California, USA
- Publication Date
- Document Type
- Conference Paper
- 2018. Society of Petroleum Engineers
- 5.1.5 Geologic Modeling, 0.2.2 Geomechanics, 2 Well completion, 0.2 Wellbore Design, 3 Production and Well Operations, 3 Production and Well Operations, 2.4 Hydraulic Fracturing
- Probabilistic Discriminative Model, Reservoir Characterization, Forward Microseismic Generation, Fluid Diffusivity Inversion, Probabilistic Generative Model
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- 68 since 2007
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Fluid diffusivity inversion and injection-induced microseismicity provide useful means of evaluating unconventional reservoirs. When taking into consideration geomechanics, linear poroelasticity equations provide the key connection between diffusion and microseismicity. This work explores microseismicity generation from a probabilistic point of view, embedding uncertainty assessment in its quantification. Bayesian framework serves as the base for probabilistic analysis.
The primary objective of this work is to develop a new microseismicity probabilistic model framework that can be used for uncertainty quantification in data analysis as well as to provide forward modeling of microseismicity. Results are tested against real data of Horn Rives shales in Western Canada, and show good prediction of the number of actual microseismicity occurrences against time.
The novel probabilistic model is derived from Directed Graphic Model using a statistical learning framework. Stochastic Poisson process combined with a specific rate model is integrated to generate a likelihood function. Both, parameter inference and microseismic event forecast are assisted by Bayesian theorem. The model has an intrinsic statistical learning root, which specifically uses observed microseismic data to update model parameters and then is applied for microseismicity prediction.
The model is extended to take into account basic geomechanical principles. Dieterich (1994) relative seismicity rate model is integrated into the aforemantioned probabilistic model. We use a synthetic case to demonstrate how the probabilistic model can be connected with stress and pressure information from geomechanical calculations.
The novelty of this study is the development of a probabilistic microseismic prediction model which obeys rate-and-state law based relative seismicity rate constitutive equations. The model inherently considers time dependence of nucleation and fault geomechanics. It can be used for planning purposes in the pre-hydraulic fracturing stage.
|File Size||1 MB||Number of Pages||17|
Broccardo, M., Mignan, A., Wiemer, S., Stojadinovic, B., & Giardini, D. (2017). Hierarchical Bayesian Modeling of Fluid-Induced Seismicity. Geophysical Research Letters. https://doi.org/10.1002/2017GL075251
Dieterich, J. (1994). A constitutive law for rate of earthquake its application to earthquake clustering production and Haberman, Alternate assumptions to (2) might be. Journal of Geophysical Research: Solid Earth, 99(B2), 2601–2618. https://doi.org/10.1029/93JB02581
Dinske, C., & Shapiro, S. A. (2013). Seismotectonic state of reservoirs inferred from magnitude distributions of fluid-induced seismicity. Journal of Seismology, 17(1), 13–25. https://doi.org/10.1007/s10950-012-9292-9
Rudnicki, J. W. (1986). Fluid mass sources and point forces in linear elastic diffusive solids. Mechanics of Materials, 5(4), 383–393. https://doi.org/10.1016/0167-6636(86)90042-6
Scholz, C. H. (1998). Earthquakes and friction laws. Nature, 391(6662), 37–42. https://doi.org/10.1038/34097
Segall, P., & Lu, S. (2015). Injection-induced seismicity: Poroelastic and earthquake nucleation effects. Journal of Geophysical Research: Solid Earth, 120, 1–22. https://doi.org/10.1002/2015JB012060.Received
Shapiro, S. A., Dinske, C., Langenbruch, C., & Wenzel, F. (2010). Seismogenic index and magnitude probability of earthquakes induced during reservoir fluid stimulations. Leading Edge, 29(3), 304–309. https://doi.org/10.1190/1.3353727
Shapiro, S. A., Dinske, C., & Kummerow, J. (2007). Probability of a given-magnitude earthquake induced by a fluid injection. Geophysical Research Letters, 34(22), 1–5. https://doi.org/10.1029/2007GL031615
Yousefzadeh, A., Li, Q., Virues, C., & Aguilera, R. (2016a). Identification of Activated Fracture Networks Using Microseismic Spatial Anomalies, b-values, and Magnitude Analyses in Horn River Basin. SPE Hydraulic Fracturing Technology Conference. https://doi.org/10.2118/179153-MS
Zöller, G. (2013). Convergence of the frequency-magnitude distribution of global earthquakes: Maybe in 200 years. Geophysical Research Letters, 40(15), 3873–3877. https://doi.org/10.1002/grl.50779