An integrated workflow for characterization and simulation of complex fracture networks utilizing microseismic and horizontal core data
An integrated workflow for characterization and simulation of complex fracture networks utilizing microseismic and horizontal core data
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DOI：
10.1016/j.jngse.2016.08.024
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发表时间：
2016-08
影响因子：
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通讯作者：
Jianlei Sun;E. Gamboa;D. Schechter;Zhenhua Rui
中科院分区：
文献类型：
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作者：
Jianlei Sun;E. Gamboa;D. Schechter;Zhenhua Rui
水力压裂处理可能会导致复杂的网络几何形状，难以整合到数值流动模拟器中。为了捕捉这种复杂性，我们提出了一种工作流，它集成了一个受微地震事件和岩心数据约束的半随机离散裂缝网络(DFN)生成器和一个用于显式离散化网络的高效垂直平分线(PEBI)网格生成器。我们应用这个工作流程来评估与DFN相关的不确定性对生产绩效的影响。我们开发了一个DFN模型，该模型通过微地震事件和基于岩心数据的概率密度函数(PDF)的样本裂缝特征来约束天然裂缝的位置。该模型还通过基于地质力学的算法将天然裂缝和水力裂缝连接起来。为了实现高效的裂缝离散化，我们开发了一种PEBI网格技术，该技术能够适应广泛聚集的网络的小角度相交，结合了减少高度倾斜单元的优化算法，并确保了良好的网格质量。最后，为了评估与DFN相关的不确定性对生产的影响，我们实施了一种有效的蒙特卡罗(MC)方法，使流动模拟最小化。我们的集成工作流程提供了一种方法，可以高效地对复杂的DFN系统进行建模和网格划分，以进行数值流体模拟，并结合微地震和岩心数据。此外，这种方法与高效MC技术的实施相结合，可以定量评估DFN相关不确定性对产量预测的影响，这些不确定性来自DFN建模的固有随机性，以及由于裂缝描述不完整而缺乏对PDF参数的准确了解。
Hydraulic fracture treatments may induce complex network geometries that are challenging to incorporate in numerical flow simulators. To capture this complexity, we propose a workflow that integrates a semi-stochastic Discrete Fracture Network (DFN) generator constrained by Microseismic events and core data and an efficient Perpendicular Bisector (PEBI) grid generator for the explicit discretization of the network. We applied this workflow to evaluate the effect of DFN related uncertainties on production performance. We developed a DFN model that constrains the location of natural fractures by microseismic events and samples fracture characteristics from core-data-based Probability Density Functions (PDFs). The model also interconnects natural and hydraulic fractures through a geomechanics-based algorithm. For an efficient fracture discretization, we developed a PEBI meshing technique capable to conform to low-angle intersections of extensively clustered network, incorporating optimization algorithms that reduce highly skewed cells, and ensure good mesh quality. Finally, to evaluate the impact of DFN related uncertainties on production, we implemented an efficient Monte Carlo (MC) methodology that minimizes flow simulations. Our integrated workflow provides a methodology to efficiently model and mesh explicitly complex DFN systems for numerical fluid simulations, incorporating microseismic and core data. Furthermore, this methodology, together with the implementation of an efficient MC technique, allows to evaluate quantitatively the impact on production forecast of DFN related uncertainties, which come from the inherent randomness of the DFN modeling and the lack of accurate knowledge of PDF parameters due to incomplete fracture characterization.