Reducing User Bias in X-ray Computed Tomography-Derived Rock Parameters through Image Filtering
Reducing User Bias in X-ray Computed Tomography-Derived Rock Parameters through Image Filtering
复制标题
通过图像滤波减少 X 射线计算机断层扫描得出的岩石参数中的用户偏差
DOI：
10.1007/s11242-021-01690-3
复制
发表时间：
2021
影响因子：
2.7
通讯作者：
Ellis, Brian R.
中科院分区：
文献类型：
--
作者：
Thompson, Ellen P.;Tomenchok, Kira;Olson, Tyler;Ellis, Brian R.
摘要准确的孔隙空间表示对于预测流体在地下多孔介质中的流动是至关重要的。孔隙体积分数、几何形状和拓扑结构决定了孔隙尺度上的运移特征，并被用来对储集层动态进行放大预测。X射线计算机层析成像(XCT)可以无损地对岩心样品进行三维成像，因此可以提供关于就地孔隙网络的有价值的信息，但将XCT数据集分割成孔隙和矿物空间并不是一件容易的事情。在这项研究中，在基于机器学习的分割训练类定义之前，对岩心样本的XCT数据集应用了三种过滤器(对比度增强、降噪和射束硬化校正)。比较了有无过滤的分割数据集得到的孔隙率，并与实验值进行了验证。当所有三个过滤器都使用时，XCT得到的孔隙率具有较小的方差，更接近实验数据。以一个岩心样品为例，比较了孔径分布和模拟渗透率与实验数据的关系。使用OpenFOAM对通过孔隙网络的流动进行计算流体动力学模拟表明，当所有三种过滤器都被应用时，渗透率值的一致性得到了改善。这表明，在机器学习训练类定义之前应用这些过滤器可以提高分割结果的重复性，减少用户偏差，从而增加对数字派生岩石参数的置信度。可靠的初始孔隙度和渗透率数据对于改进广泛地下系统中的流体传输和命运预测至关重要。文章要点。过滤后的数据集在训练类别定义中受用户偏差的影响较小分割前的图像过滤提高了模拟渗透率的一致性图像过滤提高了数字派生岩石参数的重复性
AbstractAccurate representation of pore space is essential for predicting fluid flow through subsurface porous media. Pore volume fraction, geometry, and topology determine transport characteristics at the pore scale and are used to make upscaled projections about reservoir behavior. X-ray computed tomography (XCT) allows for nondestructive 3D imaging of rock core samples and can therefore provide valuable information about the pore network in situ, but segmentation of XCT datasets into pore and mineral space is not trivial. In this study, three filters (contrast enhancement, noise reduction, and beam hardening correction) were applied to XCT datasets of rock core samples prior to training class definition for machine learning-based segmentation. Porosities derived from segmented datasets with and without filtering were compared and were validated with experimental values. XCT-derived porosity had reduced variance and was closer to experimental data when all three filters were applied. A case study of one rock core sample compared pore size distribution and simulated permeability to experimental data. Computational fluid dynamics simulations of flow through the pore network using OpenFOAM showed improved consistency in permeability values when all three filters had been applied. This suggests that the application of these filters prior to machine learning training class definition can improve the reproducibility of the segmentation results and reduce user bias, thereby increasing confidence in digitally derived rock parameters. Reliable initial porosity and permeability data are critical for improving fluid transport and fate projections in a broad range of subsurface systems.Article Highlights.Filtered datasets were less affected by user bias in definition of training classesImage filtering before segmentation improved consistency of simulated permeabilityImage filtering improved reproducibility of digitally derived rock parameters
影响因子：
3.4
作者：
Kathleen Sell;E. Saenger;A. Falenty;M. Chaouachi;D. Haberthür;F. Enzmann;W. Kuhs;M. Kersten
通讯作者：
Kathleen Sell;E. Saenger;A. Falenty;M. Chaouachi;D. Haberthür;F. Enzmann;W. Kuhs;M. Kersten
3D X 射线显微断层扫描的统计分割和孔隙度量化
DOI：
--
发表时间：
2011
期刊：
Optical Engineering + Applications
影响因子：
--
作者：
D. Ushizima;D. Parkinson;P. Nico;J. Ajo;A. MacDowell;B. Kocar;W. Bethel;J. Sethian
通讯作者：
J. Sethian
影响因子：
48
作者：
Schindelin, Johannes;Arganda-Carreras, Ignacio;Frise, Erwin;Kaynig, Verena;Longair, Mark;Pietzsch, Tobias;Preibisch, Stephan;Rueden, Curtis;Saalfeld, Stephan;Schmid, Benjamin;Tinevez, Jean-Yves;White, Daniel James;Hartenstein, Volker;Eliceiri, Kevin;Tomancak, Pavel;Cardona, Albert
通讯作者：
Cardona, Albert