Evaluation of a workflow to derive terrestrial light detection and ranging fracture statistics of a tight gas sandstone reservoir analog
Evaluation of a workflow to derive terrestrial light detection and ranging fracture statistics of a tight gas sandstone reservoir analog
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对导出致密气砂岩储层模拟的地面光探测和测距裂缝统计数据的工作流程进行评估
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发表时间：
2018
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通讯作者：
C. Hilgers
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作者：
P. Wüstefeld;Maite de Medeiros;B. Koehrer;Dominik Sibbing;L. Kobbelt;C. Hilgers
了解地下的天然裂缝网络具有很大的挑战性，因为直接的一维钻孔数据无法反映其空间复杂性，而三维地震数据在空间分辨率上有限，无法分辨单个米级裂缝。在这里，我们提出了一个原型的工作流程自动裂缝检测沿着水平扫描线，使用地面光检测和测距（t-激光雷达）。数据来源于德国西北部下萨克森盆地中一个更大规模的宾夕法尼亚期（局部上石炭统）储层露头模拟。该工作流程允许将t-LIDAR数据集成到传统的裂缝建模软件中，用于表征天然裂缝网络的方向和空间分布。该分析概述了从西-西南/东-东北走向的裂缝的横向重新定向，接近一个正常的断层，约600米（1970英尺）的位移，朝着东西走向远离故障。裂缝走廊，10-20米（33-66英尺）宽，存在于无断层的岩石中，平均裂缝密度为3.4-3.9 m−1（11.2-12.8 ft−1）。建立了一个小规模的数字露头模型，作为数据集成的基础。裂缝检测和分析作为随机建模离散裂缝网络的输入，证明了导出数据到标准油气勘探和生产行业方法的可转移性。所提出的t-LIDAR工作流程提供了一个强大的工具，定量空间分析的露头类似物，在天然裂缝网络表征，并丰富了经典的露头调查技术。这项研究可能有助于更好地将露头模拟数据应用于地下天然裂缝性储层，减少这种储层类型在深度表征的不确定性。
Understanding natural fracture networks in the subsurface is highly challenging, as direct one-dimensional borehole data are unable to reflect their spatial complexity, and three-dimensional seismic data are limited in spatial resolution to resolve individual meter-scale fractures. Here, we present a prototype workflow for automated fracture detection along horizontal scan lines using terrestrial light detection and ranging (t-LIDAR). Data are derived from a kilometer-scale Pennsylvanian (locally upper Carboniferous) reservoir outcrop analog in the Lower Saxony Basin, northwestern Germany. The workflow allows the t-LIDAR data to be integrated into conventional reservoir-modeling software for characterizing natural fracture networks with regard to orientation and spatial distribution. The analysis outlines the lateral reorientation of fractures from a west–southwest/east–northeast strike, near a normal fault with approximately 600 m (∼1970 ft) displacement, toward an east–west strike away from the fault. Fracture corridors, 10–20 m (33–66 ft) wide, are present in unfaulted rocks with an average fracture density of 3.4–3.9 m−1 (11.2–12.8 ft−1). A reservoir-scale digital outcrop model was constructed as a basis for data integration. The fracture detection and analysis serve as input for a stochastically modeled discrete fracture network, demonstrating the transferability of the derived data into standard hydrocarbon exploration-and-production-industry approaches. The presented t-LIDAR workflow provides a powerful tool for quantitative spatial analysis of outcrop analogs, in terms of natural fracture network characterization, and enriches classical outcrop investigation techniques. This study may contribute to a better application of outcrop analog data to naturally fractured reservoirs in the subsurface, reducing uncertainties in the characterization of this reservoir type at depth.