基于蓝噪声采样的多维标准井筛选可视分析
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摘要
面向石油矿区大规模的测井数据,有效地抽取少量的钻井(即标准井)进行置信度较高的专家人工匹配,进而对全局钻井进行有监督的自动或半自动匹配,对于石油开采矿区的地质构造精确解释具有重要的意义.然而,标准井筛选是一个复杂而耗时的过程,和钻井的空间分布以及钻井之间的地质特征密切相关.因此,综合考虑钻井的地理空间位置和多维地质属性特征,提出一种基于蓝噪声采样的多维标准井筛选可视分析方法.首先,根据大量钻井的地理空间位置,利用蓝噪声采样算法自适应地确定标准井采样率及其采样范围;然后在标准井的局部采样范围内,设计基于动态规划的地层匹配算法,计算钻井之间的多维属性差异以度量钻井之间的地质特征相似度,进而利用MDS算法对钻井的匹配关系进行降维投影,将钻井的空间分布与多维属性差异协同可视化,支持标准井的自动或交互筛选;进一步设计属性视图和矩阵视图,直观地呈现钻井的原始多维属性数据和匹配关系,引导领域专家对标准井的筛选过程进行探索分析和迭代优化.最后,集成便捷的用户交互模式,开发基于蓝噪声采样的多维标准井筛选可视分析系统,帮助用户交互式地探索和分析多维属性测井数据,在综合考虑钻井空间分布及多维属性特征的基础上,有效筛选具有代表性的标准井,为后续的地质构造解释提供准确而可靠的数据资料和经验支持.大量实验结果进一步验证了文中算法的有效性和实用性.
For large-scale logging data in petroleum mining areas, a small number of wells(i.e., standard wells) can be effectively selected to conduct manual matching with high precision, then supervised automatic or semi-automatic matching of global wells can be performed, which is key step to obtain an accurate interpretation of geological structures. However, the selection of standard wells is a complex process, which is closely related to the spatial distribution and the geological characteristics of wells. Therefore, this paper synthetically considers the wells' geographical locations and various well-logging attributes, and proposes a visual analysis method of standard well selection based on blue noise sampling. First, according to the geo-graphical locations of wells, a blue noise sampling model is used to adaptively determine sampling rate and range of standard wells. Second, within the local sampling range of the standard well, a dynamic planning based stratigraphic matching algorithm is designed to calculate the wells' multi-dimensional attribute difference, which measures the geological characteristic similarity between wells. MDS is further introduced to project the matching relationship of wells, further visualize the spatial distribution and multidimensional attribute differences, and support the automatic or interactive selection of standard wells. Then, an attribute view and a matrix view are designed to intuitively display the original multidimensional well-logging data and matching relationship of wells, and guide the domain experts to conduct in-depth exploration and iterative optimization of the standard well selection. Finally, a visual analysis system of multidimensional standard well selection integrating a convenient interaction model based on blue noise sampling is developed to help users interactively explore and analyze multidimensional well-logging data. On the basis of comprehensive consideration of the spatial distribution and multidimensional attribute information of wells, this system effectively selects a number of representative standard wells, and provides accurate and reliable data materials and experience support for subsequent geological structure interpretation. A large number of experimental results further validate the effectiveness and practicability of the proposed method.
For large-scale logging data in petroleum mining areas, a small number of wells(i.e., standard wells) can be effectively selected to conduct manual matching with high precision, then supervised automatic or semi-automatic matching of global wells can be performed, which is key step to obtain an accurate interpretation of geological structures. However, the selection of standard wells is a complex process, which is closely related to the spatial distribution and the geological characteristics of wells. Therefore, this paper synthetically considers the wells' geographical locations and various well-logging attributes, and proposes a visual analysis method of standard well selection based on blue noise sampling. First, according to the geo-graphical locations of wells, a blue noise sampling model is used to adaptively determine sampling rate and range of standard wells. Second, within the local sampling range of the standard well, a dynamic planning based stratigraphic matching algorithm is designed to calculate the wells' multi-dimensional attribute difference, which measures the geological characteristic similarity between wells. MDS is further introduced to project the matching relationship of wells, further visualize the spatial distribution and multidimensional attribute differences, and support the automatic or interactive selection of standard wells. Then, an attribute view and a matrix view are designed to intuitively display the original multidimensional well-logging data and matching relationship of wells, and guide the domain experts to conduct in-depth exploration and iterative optimization of the standard well selection. Finally, a visual analysis system of multidimensional standard well selection integrating a convenient interaction model based on blue noise sampling is developed to help users interactively explore and analyze multidimensional well-logging data. On the basis of comprehensive consideration of the spatial distribution and multidimensional attribute information of wells, this system effectively selects a number of representative standard wells, and provides accurate and reliable data materials and experience support for subsequent geological structure interpretation. A large number of experimental results further validate the effectiveness and practicability of the proposed method.
引文
[1]Bakker P.Image structure analysis for seismic interpretation[D].Delt:Delft University of Technology,2002
[2]Castanie L,Bosquet F,Levy B.Advances in seismic interpretation using new volume visualization techniques[J].First Break,2005,10(10):69-72
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[17]Liu R,Guo H,Yuan X.Seismic structure extraction based on multi-scale sensitivity analysis[J].Journal of Visualization,2014,17(3):157-166
[18]Rudman A J,Lankston R W.Stratigraphic correlation of well logs by computer techniques[J].American Association of Petroleum Geologists Bulletin,1973,57(13):577-588
[19]Mann C J,Dowell T P L.Quantitative lithostratigraphic correlation of subsurface sequences[J].Computers and Geosciences,1978,4(3):295-306
[20]Startzman R A,Kuo T B.A rule-based system for well log correlation[J].SPE Formation Evaluation,1987,2(3):311-319
[21]Wu X.Automated stratigraphic interpretation of well-log data[J].Geophysics,1987,52(12):101-108
[22]Lapkovsky V V,Istomin A V,Kontorovich V A,et al.Correlation of well logs as a multidimensional optimization problem[J].Russian Geology and Geophysics,2015,56(3):487-492
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[24]Doveton J H.Lateral correlation and interpolation of logs[J].Geologic Log Analysis Using Computer Methods,AAPGComputer Applications in Geology,1994:127-150
[25]Partovi S M A,Sadeghnejad S.Fractal parameters and well-logs investigation using automated well-to-well correlation[J].Computers and Geosciences,2017,103:59-69
[26]Herrera R H,Mirko V D B.A semiautomatic method to tie well logs to seismic data[J].Geophysics,2014,79(3):V47-V54
[27]Luthi S M,Bryant I D.Well-log correlation using a back-propagation neural network[J].Mathematical Geology,1997,29(3):413-425
[28]Fang J H,Chen H C,Shultz A W,et al.Computer-aided well log correlation[J].American Association of Petroleum Geologists Bulletin,1992,76(3):307-317
[29]Lallier F,Caumon G,Borgomano J,et al.Uncertainty assessment in the stratigraphic well correlation of a carbonate ramp:method and application to the Beausset Basin,SE France[J].Comptes Rendus Geoscience,2016,348(7):499-509
[30]Wheeler L,Hale D.Simultaneous correlation of multiple well logs[J].SEG Technical Program Expanded Abstracts,2014:618-622
[31]Shi Y,Wu X,Fomel S.Finding an optimal well-log correlation sequence using coherence-weighted graphs[C]//Proceedings of SEG International Exposition and Annual Meeting.Houston Texas:Society of Exploration Geophysicists,2017:1982-1987
[32]Heck D,Schl?mer T,Deussen O.Blue noise sampling with controlled aliasing[J].ACM Transactions on Graphics,2013,32(3):1-12
[33]Chen Z,Yuan Z,Choi Y K,et al.Variational blue noise sampling[J].IEEE Transactions on Visualization and Computer Graphics,2012,18(10):1784-1796
[34]Yan D M,Wonka P.Gap processing for adaptive maximal poisson-disk sampling[J].ACM Transactions on Graphics,2012,32(5):13-15
[35]Ahmed A G M,Guo J,Yan D M,et al.A simple push-pull algorithm for blue-noise sampling[J].IEEE Transactions on Visualization and Computer Graphics,2017,23(12):2496-2508
[36]Sun X,Zhou K,Guo J,et al.Line segment sampling with blue-noise properties[J].ACM Transactions on Graphics,2013,32(4):1-14
[37]Kwon B C,Verma J,Haas P J,et al.Sampling for scalable visual analytics[J].IEEE Computer Graphics and Applications,2017,37(1):100-108
[38]Chen H,Chen W,Mei H,et al.Visual abstraction and exploration of multi-class scatterplots[J].IEEE Transactions on Visualization and Computer Graphics,2014,20(12):1683-1692
[39]Liu L,Boone A,Ruginski I T,et al.Uncertainty visualization by representative sampling from prediction ensembles[J].IEEETransactions on Visualization and Computer Graphics,2017,23(9):2165-2178
[40]Guo D,Zhu X.Origin-destination flow data smoothing and mapping[J].IEEE Transactions on Visualization and Computer Graphics,2014,20(12):2043-2052
[41]Ebeida M S,Davidson A A,Patney A,et al.Efficient maximal poisson-disk sampling[J].ACM Transactions on Graphics,2011,30(49):1-12
[42]Balzer M,Schl?mer T,Deussen O.Capacity-constrained point distributions:a variant of Lloyd’s method[J].ACM Transactions on Graphics,2009,28(86):1-8
[43]Yan D M,Guo J W,Wang B,et al.A survey of blue-noise sampling and its applications[J].Journal of Computer Science and Technology,2015,30(3):439-452
[2]Castanie L,Bosquet F,Levy B.Advances in seismic interpretation using new volume visualization techniques[J].First Break,2005,10(10):69-72
[3]Cvetkovic M,Velic J,Malvic T.Application of neural networks in petroleum reservoir lithology and saturation prediction[J].Geologia Croatica,2009,62(2):115-121
[4]Wheeler L,Hale D.Simultaneous correlation of multiple well logs[C]//Proceedings of SEG Annual Meeting.Denver:Society of Exploration Geophysicists,2014:618-622
[5]Srivardhan V.Stratigraphic correlation of wells using discrete wavelet transform with ourier transform and multi-scale analysis[J].Geomechanics and Geophysics for Geo-Energy and Geo-Resources,2016,2(3):137-150
[6]Patel D,Giertsen C,Thurmond J,et al.The seismic analyzer:interpreting and illustrating 2D seismic data[J].IEEE Transactions on Visualization and Computer Graphics,2008,14(6):1571-1578
[7]Mc Clymon A F,Green A G,Streich R,et al.Visualization of active faults using geometric attributes of 3D GPR data:An example from the Alpine Fault Zone,New Zealand[J].Geophysics,2008,73(2):B11-B23
[8]Gibson D,Spann M,Turner J.Automatic fault detection for 3Dseismic data[C]//Proceedings of International Conference on Digital Image Computing:Techniques and Applications.Sydney:Dicta Macquarie University Sydney,2003:821-830
[9]Hollt T,Beyer J,Gschwantner F,et al.Interactive seismic interpretation with piecewise global energy minimization[C]//Proceedings of the IEEE Symposium on Pacific Visualization.Los Alamitos:IEEE Computer Society Press,2011:59-66
[10]Attene M,Falcidieno B,Spagnuolo M.Hierarchical mesh segmentation based on fitting primitives[J].Visual Computer,2006,22(3):181-193
[11]Patel D,Bruckner S,Viola I,et al.Seismic volume visualization for horizon extraction[C]//Proceedings of the IEEE Pacific Visualization Symposium.Los Alamitos:IEEE Computer Society Press,2010:73-80
[12]Plate J,Tirtasana M,Carmona R,et al.Octreemizer:a hierarchical approach for interactive roaming through very large volumes[C]//Proceedings of the Symposium on Data Visualisation.Los Alamitos:IEEE Computer Society Press,2002:53-64
[13]Castanie L,Levy B,Bosquet F.Volume explorer:roaming large volumes to couple visualization and data processing for oil and gas exploration[C]//Proceedings of the IEEE Visualization.Los Alamitos:IEEE Computer Society Press,2005:247-254
[14]Ropinski T,Steinicke F,Hinrichs K H.Visual exploration of seismic volume datasets[J].Journal of WSCG,2006:73-80
[15]Lidal E M,Patel D,Bendiksen M,et al.Rapid sketch-based 3Dmodeling of geology[C]//Proceedings of the Workshop on Visualisation in Environmental Sciences.Aire-la-Ville:Eurographics Association Press,2013:1-5
[16]Amorim R,Brazil E V,Patel D,et al.Sketch modeling of seismic horizons from uncertainty[C]//Proceedings of the International Symposium on Sketch-Based Interfaces and Modeling.Aire-la-Ville:Eurographics Association Press,2012:1-10
[17]Liu R,Guo H,Yuan X.Seismic structure extraction based on multi-scale sensitivity analysis[J].Journal of Visualization,2014,17(3):157-166
[18]Rudman A J,Lankston R W.Stratigraphic correlation of well logs by computer techniques[J].American Association of Petroleum Geologists Bulletin,1973,57(13):577-588
[19]Mann C J,Dowell T P L.Quantitative lithostratigraphic correlation of subsurface sequences[J].Computers and Geosciences,1978,4(3):295-306
[20]Startzman R A,Kuo T B.A rule-based system for well log correlation[J].SPE Formation Evaluation,1987,2(3):311-319
[21]Wu X.Automated stratigraphic interpretation of well-log data[J].Geophysics,1987,52(12):101-108
[22]Lapkovsky V V,Istomin A V,Kontorovich V A,et al.Correlation of well logs as a multidimensional optimization problem[J].Russian Geology and Geophysics,2015,56(3):487-492
[23]Smith T F,Waterman M S.New stratigraphic correlation techniques[J].Journal of Geology,1980,88(4):451-457
[24]Doveton J H.Lateral correlation and interpolation of logs[J].Geologic Log Analysis Using Computer Methods,AAPGComputer Applications in Geology,1994:127-150
[25]Partovi S M A,Sadeghnejad S.Fractal parameters and well-logs investigation using automated well-to-well correlation[J].Computers and Geosciences,2017,103:59-69
[26]Herrera R H,Mirko V D B.A semiautomatic method to tie well logs to seismic data[J].Geophysics,2014,79(3):V47-V54
[27]Luthi S M,Bryant I D.Well-log correlation using a back-propagation neural network[J].Mathematical Geology,1997,29(3):413-425
[28]Fang J H,Chen H C,Shultz A W,et al.Computer-aided well log correlation[J].American Association of Petroleum Geologists Bulletin,1992,76(3):307-317
[29]Lallier F,Caumon G,Borgomano J,et al.Uncertainty assessment in the stratigraphic well correlation of a carbonate ramp:method and application to the Beausset Basin,SE France[J].Comptes Rendus Geoscience,2016,348(7):499-509
[30]Wheeler L,Hale D.Simultaneous correlation of multiple well logs[J].SEG Technical Program Expanded Abstracts,2014:618-622
[31]Shi Y,Wu X,Fomel S.Finding an optimal well-log correlation sequence using coherence-weighted graphs[C]//Proceedings of SEG International Exposition and Annual Meeting.Houston Texas:Society of Exploration Geophysicists,2017:1982-1987
[32]Heck D,Schl?mer T,Deussen O.Blue noise sampling with controlled aliasing[J].ACM Transactions on Graphics,2013,32(3):1-12
[33]Chen Z,Yuan Z,Choi Y K,et al.Variational blue noise sampling[J].IEEE Transactions on Visualization and Computer Graphics,2012,18(10):1784-1796
[34]Yan D M,Wonka P.Gap processing for adaptive maximal poisson-disk sampling[J].ACM Transactions on Graphics,2012,32(5):13-15
[35]Ahmed A G M,Guo J,Yan D M,et al.A simple push-pull algorithm for blue-noise sampling[J].IEEE Transactions on Visualization and Computer Graphics,2017,23(12):2496-2508
[36]Sun X,Zhou K,Guo J,et al.Line segment sampling with blue-noise properties[J].ACM Transactions on Graphics,2013,32(4):1-14
[37]Kwon B C,Verma J,Haas P J,et al.Sampling for scalable visual analytics[J].IEEE Computer Graphics and Applications,2017,37(1):100-108
[38]Chen H,Chen W,Mei H,et al.Visual abstraction and exploration of multi-class scatterplots[J].IEEE Transactions on Visualization and Computer Graphics,2014,20(12):1683-1692
[39]Liu L,Boone A,Ruginski I T,et al.Uncertainty visualization by representative sampling from prediction ensembles[J].IEEETransactions on Visualization and Computer Graphics,2017,23(9):2165-2178
[40]Guo D,Zhu X.Origin-destination flow data smoothing and mapping[J].IEEE Transactions on Visualization and Computer Graphics,2014,20(12):2043-2052
[41]Ebeida M S,Davidson A A,Patney A,et al.Efficient maximal poisson-disk sampling[J].ACM Transactions on Graphics,2011,30(49):1-12
[42]Balzer M,Schl?mer T,Deussen O.Capacity-constrained point distributions:a variant of Lloyd’s method[J].ACM Transactions on Graphics,2009,28(86):1-8
[43]Yan D M,Guo J W,Wang B,et al.A survey of blue-noise sampling and its applications[J].Journal of Computer Science and Technology,2015,30(3):439-452