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Parametric Probabilistic Models for Fluid Diffusivity Inversion and Forward Microseismic Generation Using Seismicity Rates

机译:使用地震率的流体扩散性反转和前进微震生成的参数概率模型

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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.
机译:流体扩散性反转和注射诱导的微震性提供了评估非传统储层的有用手段。当考虑地质力学时,线性孔弹性方程提供扩散和微震性之间的关键连接。这项工作探讨了概率的观点中的微震性,在其量化中嵌入了不确定性评估。贝叶斯框架用作概率分析的基础。这项工作的主要目标是开发一种新的微震性概率模型框架,可用于数据分析中的不确定量化,以及提供微震性的前向建模。结果是针对加拿大西部喇叭鼠标的真实数据测试的,并显示出对时间的实际微震性次数的良好预测。新颖的概率模型是使用统计学习框架的指向图形模型。与特定速率模型相结合的随机泊松过程集成以产生似然函数。贝叶斯定理辅助参数推断和微震事件预测。该模型具有内在统计学习根,其特异性地使用观察到的微震数据来更新模型参数,然后应用于微震性预测。该模型扩展到考虑到基本的地质力学原则。 Dieterich(1994)相对地震性率模型集成到前述概率模型中。我们使用综合性案例来演示概率模型如何与来自地质力学计算的应力和压力信息连接。该研究的新颖性是开发概率的微震预测模型,其遵循基于率和州的相对地震性率构成型方程。该模型本质上考虑了成核和故障地质力学的时间依赖性。它可用于预防液压压裂阶段的规划目的。

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