使用智能Microchip支撑剂数据的智能裂缝诊断程序

Vuong Van Pham, Amirmasoud Kalantari Dahaghi, S. Negahban, W. Fincham, A. Babakhani
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引用次数: 0

摘要

非常规油气储层的开发需要了解水力裂缝的几何形状和复杂性。目前的裂缝诊断方法包括近井方法(产量和温度测井、示踪剂、井眼成像)和远场技术(微地震裂缝作图)。这些技术提供了间接和/或解释裂缝的几何形状。因此,这些方法都不能对水力裂缝的特征提供完整、详细和准确的描述。该研究提出了一种新方法,利用注入的细尺寸和无电池的Smart MicroChip支撑剂(SMPs)的直接数据来绘制裂缝几何形状。这种新方法可以直接、快速、智能地处理高压高温环境下smp采集的高分辨率地理传感器数据,并使用智能综合裂缝诊断平台(IFDP)绘制裂缝网络图。IFDP是一种闭环架构,基于多维投影、无监督聚类和表面重建。结合仿射变换和浅层人工神经网络控制聚类的随机性。IFDP在3个自行设计的合成裂缝网络的裂缝诊断中证明了其有效性,预测能力达到100%的一致性,“相当满意”到“高度满意”,执行稳健性在85-100%之间。耦合仿射变换与人工神经网络的结合提高了IFDP中无监督聚类的性能。
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Intelligent Fracture Diagnostic Procedure Using Smart Microchip Proppants Data
Unconventional oil and gas reservoir development requires an understanding of the geometry and complexity of hydraulic fractures. The current categories of fracture diagnostic approaches include methods for near-wellbore (production and temperature logs, tracers, borehole imaging) and far-field techniques (micro-seismic fracture mapping). These techniques provide an indirect and/or interpreted fracture geometry. Therefore, none of these methods consistently provides a fully detailed and accurate description of the character of created hydraulic fractures. This study proposes a novel approach that uses direct data from the injected fine size and battery-less Smart MicroChip Proppants (SMPs) to map the fracture geometry. This novel approach enables direct, fast, and smart of the received high-resolution geo-sensor data from the SMPs collected in high pressure and high-temperature environment and maps the fracture network using the proposed Intelligent and Integrated Fracture Diagnostic Platform (IFDP), which is a closed-loop architecture and is based on multi-dimensional projection, unsupervised clustering, and surface reconstruction. Affine transformation and a shallow ANN are integrated to control the stochasticity of clustering. IFDP proves its efficacy in fracture diagnostics for 3 in-house design synthetic fracture networks, with 100% consistency, rated "fairly satisfied" to "highly satisfied" in prediction capability, and between 85-100% in execution robustness. The integration of the couple affine transformation-ANN increases the performance of unsupervised clustering in IFDP.
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