CN110502569A - A Visual Analysis Method for Standard Well Screening Based on Discrete Selection Model - Google Patents
A Visual Analysis Method for Standard Well Screening Based on Discrete Selection Model Download PDFInfo
- Publication number
- CN110502569A CN110502569A CN201910758272.0A CN201910758272A CN110502569A CN 110502569 A CN110502569 A CN 110502569A CN 201910758272 A CN201910758272 A CN 201910758272A CN 110502569 A CN110502569 A CN 110502569A
- Authority
- CN
- China
- Prior art keywords
- standard
- logging
- wells
- attribute
- standard well
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000012216 screening Methods 0.000 title claims abstract description 47
- 238000004458 analytical method Methods 0.000 title claims abstract description 33
- 230000000007 visual effect Effects 0.000 title claims abstract description 28
- 238000000034 method Methods 0.000 claims abstract description 54
- 238000005070 sampling Methods 0.000 claims abstract description 35
- 230000015572 biosynthetic process Effects 0.000 claims abstract description 26
- 230000008569 process Effects 0.000 claims abstract description 18
- 238000013461 design Methods 0.000 claims abstract description 13
- 230000003044 adaptive effect Effects 0.000 claims abstract description 12
- 230000008859 change Effects 0.000 claims abstract description 9
- 230000002452 interceptive effect Effects 0.000 claims abstract description 4
- 238000012800 visualization Methods 0.000 claims description 15
- 238000005516 engineering process Methods 0.000 claims description 12
- 239000011159 matrix material Substances 0.000 claims description 9
- 238000013507 mapping Methods 0.000 claims description 7
- 230000000694 effects Effects 0.000 claims description 5
- 238000001914 filtration Methods 0.000 claims description 3
- 230000009286 beneficial effect Effects 0.000 abstract description 2
- 238000005755 formation reaction Methods 0.000 description 23
- 238000010586 diagram Methods 0.000 description 10
- 238000001514 detection method Methods 0.000 description 5
- 238000005457 optimization Methods 0.000 description 4
- 239000003245 coal Substances 0.000 description 3
- 238000011156 evaluation Methods 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 2
- 230000003993 interaction Effects 0.000 description 2
- 230000035699 permeability Effects 0.000 description 2
- 239000003208 petroleum Substances 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 239000011435 rock Substances 0.000 description 2
- RHZUVFJBSILHOK-UHFFFAOYSA-N anthracen-1-ylmethanolate Chemical compound C1=CC=C2C=C3C(C[O-])=CC=CC3=CC2=C1 RHZUVFJBSILHOK-UHFFFAOYSA-N 0.000 description 1
- 239000003830 anthracite Substances 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 238000010835 comparative analysis Methods 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 238000013079 data visualisation Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000005553 drilling Methods 0.000 description 1
- 230000002708 enhancing effect Effects 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 238000003384 imaging method Methods 0.000 description 1
- 238000012886 linear function Methods 0.000 description 1
- 230000003334 potential effect Effects 0.000 description 1
- 238000007781 pre-processing Methods 0.000 description 1
- 238000011158 quantitative evaluation Methods 0.000 description 1
- 230000003252 repetitive effect Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000002269 spontaneous effect Effects 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/26—Visual data mining; Browsing structured data
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/29—Geographical information databases
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/02—Agriculture; Fishing; Forestry; Mining
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Databases & Information Systems (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Business, Economics & Management (AREA)
- Data Mining & Analysis (AREA)
- General Engineering & Computer Science (AREA)
- Mining & Mineral Resources (AREA)
- Economics (AREA)
- Animal Husbandry (AREA)
- Marine Sciences & Fisheries (AREA)
- Life Sciences & Earth Sciences (AREA)
- Remote Sensing (AREA)
- Health & Medical Sciences (AREA)
- Agronomy & Crop Science (AREA)
- General Health & Medical Sciences (AREA)
- Human Resources & Organizations (AREA)
- Marketing (AREA)
- Primary Health Care (AREA)
- Strategic Management (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
本发明公开一种基于离散选择模型的标准井筛选可视分析方法,包括:利用自适应蓝噪声采样模型保持标准井的总体空间分布;设计基于动态规划算法的多尺度地层匹配模型,从不同属性的角度度量测井的相似程度;集成专家用户的先验知识,设计基于离散选择模型的标准井筛选方法,最大化属性相似度与标准井之间的效用;设计迭代交互式的标准井筛选方法,支持用户根据先验知识更改标准井,迭代式地更新离散选择模型,优化标准井筛选过程和结果。本发明在综合考虑测井空间分布及多维属性信息的基础上,实现可视分析驱动的有监督标准井筛选,所获得的标准井能够极大限度地代表周围测井,有利于后续地层智能匹配精度和效率的提升。The invention discloses a visual analysis method for standard well screening based on a discrete selection model, comprising: maintaining the overall spatial distribution of standard wells by using an adaptive blue noise sampling model; Measure the similarity degree of well logging from the angle of analysis; integrate the prior knowledge of expert users, design a standard well screening method based on discrete selection model, maximize the utility between attribute similarity and standard wells; design an iterative interactive standard well screening method , which allows users to change standard wells based on prior knowledge, iteratively update discrete selection models, and optimize standard well screening processes and results. Based on the comprehensive consideration of logging spatial distribution and multi-dimensional attribute information, the present invention realizes the screening of supervised standard wells driven by visual analysis, and the obtained standard wells can represent the surrounding well logging to the greatest extent, which is beneficial to subsequent formation intelligent matching Increased accuracy and efficiency.
Description
技术领域technical field
本发明涉及一种基于离散选择模型的标准井筛选可视分析方法,属于石油勘探、图形学与可视化技术领域。The invention relates to a standard well screening visual analysis method based on a discrete selection model, belonging to the technical fields of petroleum exploration, graphics and visualization.
背景技术Background technique
三维地震波数据和一维测井数据是地质构造解释领域常见的两种数据类型,大量的研究工作围绕上述两种数据类型开展地质构造解释工作。地震波数据是在借助专有设备记录地下波形信号并对其进行一系列预处理后得到,通过时间切片、体积提取、等分析方法帮助用户实现地质构造解释。例如,Patel等学者设计了基于二维切片的地震波数据分析方法(Patel D,Giertsen C,Thurmond J,et al.The seismic analyzer:interpreting andillustrating 2D seismic data[J].IEEE transactions on visualization&computergraphics.2008,14(6):1571-1578.),用户可以对地震波数据层位结构进行预分解,并采用变形纹理和纹理传输函数等绘制算法增强显示地质构造特征,从而提高对地质构造的解释精度,减少地质解释人员的工作量。然而,事实中诸多因素都影响着地震波数据的生成和处理过程,如复杂的地形条件,有限的设备条件和不可避免的计算误差,这些都给地质构造解释带来了很大的不确定性(Zhou B,Hatherly P,Sun W.Enhancing the detection ofsmall coal structures by seismic diffraction imaging[J].International Journalof Coal Geology,2017,178(6):1-12.)。Three-dimensional seismic wave data and one-dimensional well logging data are two common data types in the field of geological structure interpretation. A large number of research work focuses on the above two data types to carry out geological structure interpretation work. Seismic wave data is obtained after recording the subsurface waveform signal with the help of proprietary equipment and performing a series of preprocessing on it. It helps users to interpret geological structures through analysis methods such as time slicing, volume extraction, and so on. For example, Patel et al. designed a seismic wave data analysis method based on two-dimensional slices (Patel D, Giertsen C, Thurmond J, et al. The seismic analyzer: interpreting and illustrating 2D seismic data [J]. IEEE transactions on visualization & computer graphics. 2008, 14 (6): 1571-1578.), the user can pre-decompose the horizon structure of the seismic wave data, and use drawing algorithms such as deformation texture and texture transfer function to enhance the display of geological structure features, thereby improving the interpretation accuracy of geological structures and reducing geological Explain the workload of the staff. However, many factors in fact affect the generation and processing of seismic wave data, such as complex terrain conditions, limited equipment conditions and inevitable calculation errors, which bring great uncertainty to the interpretation of geological structures ( Zhou B, Hatherly P, Sun W. Enhancing the detection of small coal structures by seismic diffraction imaging[J]. International Journal of Coal Geology, 2017, 178(6): 1-12.).
与间接获得的地震波数据不同,一维测井数据直接从实际测井中采集获取,多种与地质结构密切相关的属性被详细记录下来。例如,自然电位属性可以记录地层与泥浆间发生的电化学作用和动电学作用,用于确定渗透性岩层和水淹层(Li J,Liu D,Yao Y,etal.Evaluation of the reservoir permeability of anthracite coals bygeophysical logging data[J].International Journal of Coal Geology,2011,87(2):121-127.)。声波时差属性是通过声波在地层的传播速度来确定岩层孔隙度,从而识别岩性(Tang X M,Zheng Y,Patterson D.Processing array acoustic-logging data to imagenear-borehole geologic structures[J].Geophysics,2007,72(2):87-97.)。基于多属性测井数据进行地层对比是地质构造解释的重要步骤,在地质勘探领域受到越来越多的关注,如基于交叉优化算法的双井匹配算法,基于规则的专家系统算法等。Different from the seismic wave data obtained indirectly, the one-dimensional logging data is directly collected from the actual logging, and a variety of attributes closely related to the geological structure are recorded in detail. For example, the spontaneous potential property can record the electrochemical and electrokinetic interactions between the formation and the mud, which can be used to determine the permeability of rock formations and water-flooded layers (Li J, Liu D, Yao Y, et al. Evaluation of the reservoir permeability of anthracite coals by geophysical logging data[J]. International Journal of Coal Geology, 2011, 87(2):121-127.). The property of acoustic transit time is to determine the porosity of the rock formation by the propagation velocity of the acoustic wave in the formation, so as to identify the lithology (Tang X M, Zheng Y, Patterson D. Processing array acoustic-logging data to imagenear-borehole geologic structures [J]. Geophysics, 2007 , 72(2):87-97.). Stratigraphic correlation based on multi-attribute logging data is an important step in geological structure interpretation, and has received more and more attention in the field of geological exploration, such as dual-well matching algorithm based on cross-optimization algorithm, rule-based expert system algorithm, etc.
因此,从原始数量众多的测井中筛选具有代表性的标准井子集(Ren Z H,Zhang WC,Zhu X M,et al.Method to Determine the Collar Section Depth in Standard WellLogging[J].Petroleum Drilling Techniques,2014(6):68-72.),进行精确而细致的地层对比,可以快速了解局部范围内的地质条件,进而有效指导大规模原始测井的地层匹配,避免大量重复的人工标记过程,一定程度上提升了地质勘探与开发的效率。然而,标准井的筛选依托于专家先验知识,尤其是传统的标准井选择过程极大地依赖于人工标记,筛选过程不仅复杂而耗时,且往往不能考虑测井的空间分布和多属性的相关性,这给后续的地质构造解释带来很大的不确定性,难以提升标准井选择的效率与精度。因此,为了克服人工选择标准井的局限性,综合考虑测井的空间分布和多维属性的相关性,提出一种基于离散选择模型的有监督式标准井标准井筛选可视分析方法。Therefore, a representative subset of standard wells was selected from the original large number of well logs (Ren Z H, Zhang WC, Zhu X M, et al. Method to Determine the Collar Section Depth in Standard WellLogging [J]. Petroleum Drilling Techniques, 2014 (6):68-72.), accurate and detailed stratigraphic comparison can quickly understand the geological conditions in the local area, and then effectively guide the stratigraphic matching of large-scale original logging, avoid a large number of repetitive manual marking processes, and to a certain extent It has improved the efficiency of geological exploration and development. However, the selection of standard wells relies on the prior knowledge of experts. In particular, the traditional standard well selection process relies heavily on manual marking. The screening process is not only complicated and time-consuming, but also often fails to consider the spatial distribution of well logs and the correlation of multiple attributes. This brings great uncertainty to the subsequent interpretation of geological structures, and it is difficult to improve the efficiency and accuracy of standard well selection. Therefore, in order to overcome the limitation of manual selection of standard wells, and comprehensively consider the spatial distribution of logging and the correlation of multi-dimensional attributes, a visual analysis method for supervised standard well screening based on discrete selection model is proposed.
发明内容SUMMARY OF THE INVENTION
本发明的目的是提供一种基于离散选择模型的标准井筛选可视分析方法,从而在数据可视化过程中,实现有监督式的标准井筛选,减少了误差和不确定性,提升了测井数据可视化效率。The purpose of the present invention is to provide a visual analysis method for standard well screening based on discrete selection model, so as to realize supervised standard well screening in the process of data visualization, reduce errors and uncertainties, and improve logging data Visualize Efficiency.
为实现上述目的,本发明所采用的技术方案是:一种基于离散选择模型的标准井筛选可视分析方法,具体包括如下步骤:In order to achieve the above purpose, the technical solution adopted in the present invention is: a visual analysis method for standard well screening based on discrete selection model, which specifically includes the following steps:
(1)基于多维测井数据,分别提取测井的空间分布特征和多维测井属性的地层关联关系:在确定标准井筛选比例的前提下,利用自适应蓝噪声采样算法获得满足局部空间分布的泊松盘;同时,设计基于动态规划算法的多尺度地层匹配模型,从不同属性的角度量化度量测井属性相似程度。(1) Based on the multi-dimensional logging data, the spatial distribution characteristics of the logging and the stratigraphic correlation of the multi-dimensional logging attributes were extracted respectively: on the premise of determining the standard well screening ratio, the adaptive blue noise sampling algorithm was used to obtain the spatial distribution of the wells. At the same time, a multi-scale formation matching model based on dynamic programming algorithm is designed to quantitatively measure the similarity of logging attributes from the perspective of different attributes.
(2)根据专家先验知识获得标准井样本集,利用离散选择模型最大化步骤1所述测井属性相似程度与所述标准井筛选之间的效用,遍历所述自适应蓝噪声采样算法获得的泊松盘,根据所述离散选择模型筛选标准井,并获得标准井筛选信息。(2) Obtain the standard well sample set according to the prior knowledge of experts, use the discrete selection model to maximize the utility between the similarity degree of logging attributes described in step 1 and the standard well screening, and traverse the adaptive blue noise sampling algorithm to obtain of Poisson disk, standard wells are screened according to the discrete selection model, and standard well screening information is obtained.
(3)根据所述标准井筛选信息,通过可视化分析技术引导用户交互式指定或改变标准井,自适应更新所述标准井样本集信息,优化离散选择模型参数,重新筛选,获得满足用户当前需求和经验的标准井。(3) According to the standard well screening information, guide the user to interactively designate or change standard wells through visual analysis technology, adaptively update the standard well sample set information, optimize discrete selection model parameters, and re-screen to meet the current needs of users. and standard well of experience.
进一步地,步骤(1)中,所述利用自适应蓝噪声采样算法获得满足局部空间分布的泊松盘的方法,具体为:Further, in step (1), the method for obtaining a Poisson disk satisfying local spatial distribution by using an adaptive blue noise sampling algorithm is specifically:
1)根据测井的空间位置,利用蓝噪声采样算法获得满足局部空间分布的泊松盘,每个泊松盘内部只允许采样一个测井;1) According to the spatial location of the logging, the blue noise sampling algorithm is used to obtain a Poisson disk that satisfies the local spatial distribution, and only one log is allowed to be sampled inside each Poisson disk;
2)利用核密度估计算法评估所述泊松盘内测井的空间分布情况,计算得到所述测井的空间分布密度值,进而自适应地更新所述泊松盘半径的大小,保持所述测井的空间分布特征。2) Use the kernel density estimation algorithm to evaluate the spatial distribution of the log in the Poisson disk, calculate the spatial distribution density value of the log, and then adaptively update the size of the Poisson disk radius to maintain the Spatial distribution characteristics of well logging.
进一步地,步骤(1)中,所述设计基于动态规划算法的多尺度地层匹配模型的方法,具体为:Further, in step (1), the method for designing a multi-scale formation matching model based on a dynamic programming algorithm is specifically:
1)根据所述多维测井数据,采用中值滤波平滑各条测井属性曲线,非线性处理所述多维测井数据并根据离散结果归一化到0和1之间;1) According to the multi-dimensional logging data, use median filtering to smooth each logging attribute curve, nonlinearly process the multi-dimensional logging data, and normalize between 0 and 1 according to the discrete results;
2)根据所述平滑测井曲线,采用活度函数对测井进行地层层位边界的识别与划分;2) According to the smooth logging curve, use the activity function to identify and divide the stratigraphic horizon boundary for logging;
3)通过动态规划算法求解两井之间的最佳层位匹配序列,将各测井属性上最佳匹配序列对应的地层厚度求和得到两井之间的量化属性差异。3) The optimal horizon matching sequence between the two wells is solved by the dynamic programming algorithm, and the stratum thickness corresponding to the best matching sequence on each logging attribute is summed to obtain the quantitative attribute difference between the two wells.
进一步地,步骤(2)中,所述利用离散选择模型最大化步骤1所述测井属性相似程度与所述标准井筛选之间的效用的方法,具体为:Further, in step (2), the method of using the discrete selection model to maximize the utility between the similarity degree of the logging attributes described in step 1 and the standard well screening is specifically:
1)根据所述满足局部空间分布的泊松盘,利用专家先验知识获得人工标记的标准井样本集;1) According to the Poisson disk that satisfies the local spatial distribution, use the prior knowledge of experts to obtain a standard well sample set that is manually marked;
2)根据所述泊松盘内测井间的多维属性相似度设计固定效用函数,采用多项回归模型模拟随机误差分布,联立二者获得总效用函数,用以计算不同用户选择不同测井作为标准井的概率;2) Design a fixed utility function according to the multi-dimensional attribute similarity between the Poisson disk logs, use a multinomial regression model to simulate the random error distribution, and combine the two to obtain a total utility function, which is used to calculate the different logging selected by different users. The probability of being a standard well;
3)根据所述标准井样本集,利用离散选择模型自动计算出所述总效用函数中的相应参数并遍历所述自适应蓝噪声采样算法获得的泊松盘,进而选择出局部空间内属性组合最大化用户效用的标准井。3) According to the standard well sample set, the discrete selection model is used to automatically calculate the corresponding parameters in the total utility function and traverse the Poisson disk obtained by the adaptive blue noise sampling algorithm, and then select the attribute combination in the local space. Standard wells to maximize user utility.
进一步地,步骤(3)中所述通过可视化分析技术引导用户交互式指定或改变标准井的方法,具体为:Further, the method of guiding the user to interactively specify or change the standard well through the visual analysis technology described in step (3) is specifically:
1)设计多种可视化方案协同展示测井数据信息:将测井的实际空间位置映射到地图中,保留其空间分布特征的可视化结果;根据不同属性测井数据,设计属性曲线图,将局部空间内不同测井的同一属性数据统计在指定属性空间中,采用色调映射技术将所述标准井与非标准井进行区分;利用方差量化所述测井曲线间的差异,统计获得局部空间内标准井的总属性差异直方图,保留属性特征的对比度。1) Design a variety of visualization schemes to collaboratively display logging data information: map the actual spatial location of logging to the map, and retain the visualization results of its spatial distribution characteristics; The same attribute data of different logs in the interior are counted in the specified attribute space, and the standard wells and non-standard wells are distinguished by tone mapping technology; the difference between the logging curves is quantified by variance, and the standard wells in the local space are obtained by statistics. The total attribute difference histogram of , preserving the contrast of attribute features.
2)根据所述可视化结果,设计标准井筛选可视化方案,统计每个局部空间各属性上特征匹配结果,获得多维属性层位匹配关联矩阵;根据层位匹配结果设置筛选圆环,在地图上直观对比并增强显示局部空间内测井间的属性差异。2) According to the visualization results, design a standard well screening visualization scheme, count the feature matching results on each attribute of each local space, and obtain a multi-dimensional attribute horizon matching correlation matrix; set a screening ring according to the horizon matching results, which is intuitive on the map Contrast and enhance the display of property differences between logs in a local space.
3)根据所述属性差异的对比,支持用户交互分析和替换标准井,进而更新样本,根据新的样本集优化离散选择模型参数,重新计算和筛选满足用户需求的标准井。3) According to the comparison of the attribute differences, support user interactive analysis and replacement of standard wells, and then update samples, optimize discrete selection model parameters according to the new sample set, and recalculate and screen standard wells that meet user needs.
与现有技术相比,本发明的有益效果是:在标准井筛选过程中,不仅综合考虑测井的空间分布、测井数据的多维属性相关性,还量化评估领域专家的先验知识,进而利用可视分析技术实现有监督式的标准井筛选,有效降低了人工标记工作量并提升了标准井筛选的精度与效率。综上所述,本发明方法中标准井选择模型中的的参数调整过程简单易行,测井数据中的特征信息通过多种协同可视设计得到快速且直观的展示,并构建便捷的多功能可视分析系统,支持标准井的动态更新与优化,可以明显提高标准井选择过程中的分析效率,提升标准井选择的精度,能够有效满足用户的实时应用需求。Compared with the prior art, the present invention has the beneficial effects that: in the standard well screening process, not only the spatial distribution of well logging and the multi-dimensional attribute correlation of logging data are comprehensively considered, but also the prior knowledge of experts in the field of quantitative evaluation is quantified. The use of visual analysis technology to achieve supervised standard well screening effectively reduces the workload of manual marking and improves the accuracy and efficiency of standard well screening. To sum up, the parameter adjustment process in the standard well selection model in the method of the present invention is simple and easy to implement, the feature information in the logging data can be displayed quickly and intuitively through a variety of collaborative visual designs, and a convenient multi-functional The visual analysis system supports the dynamic update and optimization of standard wells, which can significantly improve the analysis efficiency in the process of standard well selection, improve the accuracy of standard well selection, and can effectively meet the real-time application needs of users.
附图说明Description of drawings
图1为本发明方法的流程示意图;Fig. 1 is the schematic flow chart of the method of the present invention;
图2为蓝噪声采样模型示意图,其中,(a)为初始示意图,(b),(c)为标准井选择过程示例图;Figure 2 is a schematic diagram of the blue noise sampling model, wherein (a) is the initial schematic diagram, (b), (c) are example diagrams of the standard well selection process;
图3为基于动态规划算法的多尺度地层匹配模型示意图;3 is a schematic diagram of a multi-scale formation matching model based on a dynamic programming algorithm;
图4为系统界面图(地理视图),其中,(a)为信息视图,(b)为采样面板,(c)为选择优化面板,(d)为地理视图,(e)为多维属性视图,(f)为地层匹配视图,(g)为地层关联矩阵视图,(h)为总属性差异视图;Figure 4 is a system interface diagram (geographic view), wherein (a) is an information view, (b) is a sampling panel, (c) is a selection optimization panel, (d) is a geographic view, and (e) is a multi-dimensional attribute view, (f) is the stratigraphic matching view, (g) is the stratigraphic correlation matrix view, and (h) is the total attribute difference view;
图5为给定采样率下所选标准井的属性评估结果图,其中,(a)为所选标准井的地图投影结果,(b)为标准井的测井属性曲线绘制结果,(c)为标准井在多维属性相似性表现,(d)为标准井的总属性差异结果,(e)为当前计算得到的用户偏好系数结果,(f)为测井间的实际地层关联程度。Figure 5 is a graph of the property evaluation results of the selected standard wells at a given sampling rate, in which (a) is the map projection result of the selected standard wells, (b) is the logging property curve drawing results of the standard wells, (c) is the multi-dimensional attribute similarity performance of standard wells, (d) is the total attribute difference result of standard wells, (e) is the currently calculated user preference coefficient result, and (f) is the actual formation correlation degree between wells.
图6为更新样本后标准井的对比分析示意图,其中,(a)为样本更新前的用户偏好系数结果,(b)为样本更新前的标准井在多维属性上的地层关联结果,(c)为样本更新后重新计算的用户偏好系数结果,(d)为样本更新后的标准井在多维属性上的地层关联结果。Figure 6 is a schematic diagram of the comparative analysis of the standard wells after updating the samples, in which (a) is the user preference coefficient result before the sample is updated, (b) is the stratigraphic correlation result on the multi-dimensional attributes of the standard wells before the sample is updated, (c) is the recalculated user preference coefficient result after the sample is updated, (d) is the stratigraphic correlation result on the multi-dimensional attributes of the standard well after the sample update.
具体实施方式Detailed ways
下面结合附图,对本发明的基于离散选择模型的标准井可视分析方法作进一步的说明。The method for visual analysis of standard wells based on the discrete selection model of the present invention will be further described below with reference to the accompanying drawings.
如图1为本发明一种基于离散选择模型的标准井筛选可视分析方法的流程图,该方法具体为:Figure 1 is a flowchart of a visual analysis method for standard well screening based on discrete selection model of the present invention, and the method is specifically:
(1)基于多维测井数据,分别提取测井的空间分布特征和多维测井属性的地层关联关系:在确定标准井筛选比例的前提下,利用自适应蓝噪声采样算法获得满足局部空间分布的泊松盘;基于实际的复杂地质条件,设计基于动态规划算法的多尺度地层匹配模型,从不同属性的角度量化度量测井属性相似程度。(1) Based on the multi-dimensional logging data, the spatial distribution characteristics of the logging and the stratigraphic correlation of the multi-dimensional logging attributes were extracted respectively: on the premise of determining the standard well screening ratio, the adaptive blue noise sampling algorithm was used to obtain the spatial distribution of the wells. Poisson disk; based on the actual complex geological conditions, a multi-scale formation matching model based on the dynamic programming algorithm is designed to quantitatively measure the similarity of logging attributes from the perspective of different attributes.
如图2为蓝噪声采样模型示意图,所述利用自适应蓝噪声采样算法获得满足局部空间分布的泊松盘的方法,具体为:Figure 2 is a schematic diagram of a blue noise sampling model, and the described method for obtaining a Poisson disk that satisfies local spatial distribution by using an adaptive blue noise sampling algorithm is specifically:
1)根据测井的空间位置,利用蓝噪声采样算法获得满足局部空间分布的泊松盘,每个泊松盘内部只允许采样一个测井;1) According to the spatial location of the logging, the blue noise sampling algorithm is used to obtain a Poisson disk that satisfies the local spatial distribution, and only one log is allowed to be sampled inside each Poisson disk;
2)利用核密度估计算法评估所述泊松盘内测井的空间分布情况,计算得到所述测井的空间分布密度值,进而自适应地更新所述泊松盘半径的大小,保持所述测井的空间分布特征。2) Use the kernel density estimation algorithm to evaluate the spatial distribution of the log in the Poisson disk, calculate the spatial distribution density value of the log, and then adaptively update the size of the Poisson disk radius to maintain the Spatial distribution characteristics of well logging.
首先,wi表示一口测井,表示一个以wi为中心,ri=r/f(wi)为半径的采样泊松圆盘,参数r表示由用户交互定义的采样率,函数f(w)表示根据测井空间分布进行估计计算的核密度函数,计算过程如公式(1);First, w i represents a log, Represents a sampled Poisson disk with wi as the center and ri =r/f( wi ) as the radius, the parameter r represents the sampling rate defined by the user interaction, and the function f(w) represents the spatial distribution of logging. Estimate the calculated kernel density function, and the calculation process is as formula (1);
公式(1)中,Kh表示一个带宽为h的高斯核函数,f(wi)表示测井wi的核密度估计值。在本方法中,规定每一个采样圆盘仅允许选择一口井作为标准井;同时做出如下定义:检测圆环表示以wi为中心,ri为内径且di=2ri为外径的环形区域,Q表示人工选择的采样井队列,SPQ表示Q队列中采样井所对应采样盘中的所有测井的集合。In formula (1), K h represents a Gaussian kernel function with bandwidth h, and f( wi ) represents the kernel density estimation value of logging wi . In this method, it is stipulated that only one well is allowed to be selected as a standard well for each sampling disk; meanwhile, the following definitions are made: detection ring represents an annular area with wi as the center, ri as the inner diameter and d i = 2ri as the outer diameter, Q represents the manually selected sampling well queue, SP Q represents all logging wells in the sampling tray corresponding to the sampling wells in the Q queue collection.
其次,将所有测井都标记为“活跃”。随机选择其中一个井作为样本,并将其添加到队列Q中。迭代取样过程中,将SPQ中的井更改为“不活跃”。如图2(a)所示,从队列Q中随机选择一口样本井w0,确定其采样圆盘和检测圆环如图2(b)所示,从中随机选择一口“活跃”井w2作为可能的采样井进行检测,如果覆盖了队列Q中标记的任何采样井,如图2(c)所示,“活跃”井w2将更改为“不活跃”,并将在中重新选择另一个“活跃”井w3继续检测,直到找到满足条件的测井,将其标记为采样井并添加到Q队列中。如果w0周围没有有效的样本井,则将其从Q中移除。重复采样过程中,直到所有测井都变成非活跃状态。Second, mark all logs as "active". One of the wells is randomly selected as a sample and added to queue Q. During iterative sampling, the wells in SP Q were changed to "inactive". As shown in Fig. 2(a), randomly select a sample well w 0 from the queue Q to determine its sampling disk and detection ring As shown in Figure 2(b), from An "active" well w 2 is randomly selected as a possible sampling well for detection, if covers any sampling wells marked in queue Q, as shown in Figure 2 (c), the "active" well w2 will be changed to "inactive" and will be Re-select another "active" well w 3 to continue detection until a log that meets the conditions is found, mark it as a sampling well and add it to the Q queue. If there are no valid sample wells around w0, it is removed from Q. Repeat the sampling process until all logs become inactive.
所述设计基于动态规划算法的多尺度地层匹配模型的方法,具体为:The method for designing a multi-scale formation matching model based on a dynamic programming algorithm is specifically:
1)根据所述多维测井数据,采用中值滤波平滑各条测井属性曲线,以获得相应的平滑测井曲线,非线性处理所述多维测井数据并根据离散结果归一化到0和1之间;1) According to the multi-dimensional logging data, median filtering is used to smooth each logging attribute curve to obtain the corresponding smooth logging curve, and the multi-dimensional logging data is processed nonlinearly and normalized to 0 and 0 according to the discrete results. between 1;
2)根据所述平滑测井曲线,采用活度函数对测井进行地层层位边界的识别与划分;2) According to the smooth logging curve, use the activity function to identify and divide the stratigraphic horizon boundary for logging;
3)通过动态规划算法求解两井之间的最佳层位匹配序列,将各测井属性上最佳匹配序列对应的地层厚度求和得到两井之间的量化属性差异。3) The optimal horizon matching sequence between the two wells is solved by the dynamic programming algorithm, and the stratum thickness corresponding to the best matching sequence on each logging attribute is summed to obtain the quantitative attribute difference between the two wells.
所述设计基于动态规划算法的多尺度地层匹配模型的方法的具体过程如图3所示,第一步,对原始测井属性曲线进行平滑处理,第二步,采用活度函数对各条平滑测井曲线进行层位识别,活度函数方程如公式(2)所示:The specific process of the method for designing the multi-scale formation matching model based on the dynamic programming algorithm is shown in Figure 3. The first step is to smooth the original logging attribute curve, and the second step is to use the activity function to smooth each line. The logging curve is used to identify the horizon, and the activity function equation is shown in formula (2):
其中,Ei表示在深度i处的活跃值。yj表示深度范围[i-L,i+L]中的测井属性值,L为半窗口长度。如果井深i处的活跃值大于给定的阈值,则将相应的深度i作为边界来识别地层。第三步,根据目标井划分好的地层层位,利用基于动态规划的两井匹配方法查找出目标井之间的最优匹配路径,以得到目标井之间的最佳匹配序列。假设给定具有m层地层的井A和具有n层地层的井B,可根据全局消耗函数寻找它们之间的最有匹配路径,如公式(3)所示:where E i represents the active value at depth i. y j represents the logging attribute value in the depth range [iL, i+L], and L is the half-window length. If the active value at well depth i is greater than a given threshold, the formation is identified using the corresponding depth i as a boundary. The third step is to find out the optimal matching path between the target wells by using the two-well matching method based on dynamic programming according to the formation horizons of the target wells, so as to obtain the best matching sequence between the target wells. Assuming that a well A with m layers and a well B with n layers are given, the best matching path between them can be found according to the global consumption function, as shown in formula (3):
其中,C(Ai,Bj)表示(A1,B1)到(Ai,Bj)的匹配路径上的差异之和;d(Ai,Bj)是两个地层(井A第i层和井B第j层)之间所有特征差异的总和,用于度量Ai和Bj的相似性。而两井中缺失地层的差异表示为g(Ai)或g(Bj)。采用动态规划方法求解C(Ai,Bj)最小值问题,得到地层对比的最佳序列。最后,将最佳匹配序列对应的两个目标井的地层厚度求和,得到目标井之间的多维属性差异。Among them, C(A i , B j ) represents the sum of the differences on the matching paths from (A 1 , B 1 ) to (A i , B j ); d(A i , B j ) is the two formations (well A The sum of all feature differences between layer i and well B (layer j), used to measure the similarity of A i and B j . The difference between the missing formations in the two wells is expressed as g(A i ) or g(B j ). The problem of minimum value of C(A i , B j ) is solved by dynamic programming method, and the optimal sequence of stratigraphic comparison is obtained. Finally, the formation thicknesses of the two target wells corresponding to the best matching sequence are summed to obtain the multi-dimensional attribute difference between the target wells.
(2)在传统的地质解释过程中,标准井总是根据领域专家先验知识人工选择,不能考虑测井的空间分布和多属性的相关性,容易产生较大的误差和不确定性,严重影响地质构造的后续解释。在本发明方法中,引入一种离散选择模型,提出基于离散选择模型的标准井监督选择可视化框架,能够考虑测井空间分布和多种属性的相关性:基于用户偏好和效用最大化理论构建效用函数,面向多维属性数据设置并计算个人偏好系数,采用离散选择模型将多维属性测井匹配结果与测井的局部区域分布相关联。(2) In the traditional geological interpretation process, standard wells are always selected manually according to the prior knowledge of domain experts, and the spatial distribution and multi-attribute correlation of well logging cannot be considered, which is prone to large errors and uncertainties. Affect subsequent interpretation of geological formations. In the method of the present invention, a discrete selection model is introduced, and a standard well supervision selection visualization framework based on the discrete selection model is proposed, which can consider the spatial distribution of well logging and the correlation of various attributes: construct utility based on user preference and utility maximization theory It sets and calculates the personal preference coefficient for multi-dimensional attribute data, and uses the discrete choice model to correlate the multi-dimensional attribute logging matching results with the local area distribution of logging.
根据专家先验知识获得标准井样本集,利用离散选择模型最大化步骤1所述测井属性相似程度与所述标准井筛选之间的效用,遍历所述自适应蓝噪声采样算法获得的泊松盘,根据所述离散选择模型筛选标准井,并获得标准井筛选信息。Obtain the standard well sample set according to the prior knowledge of experts, use the discrete selection model to maximize the utility between the similarity degree of logging attributes described in step 1 and the standard well screening, and traverse the Poisson obtained by the adaptive blue noise sampling algorithm disk, screen standard wells according to the discrete selection model, and obtain standard well screening information.
所述利用离散选择模型最大化步骤1所述测井属性相似程度与所述标准井筛选之间的效用的方法,具体为:The method of using the discrete selection model to maximize the utility between the similarity degree of the logging attributes described in step 1 and the standard well screening is specifically:
1)根据所述满足局部空间分布的泊松盘,利用专家先验知识获得人工标记的标准井样本集;1) According to the Poisson disk that satisfies the local spatial distribution, use the prior knowledge of experts to obtain a standard well sample set that is manually marked;
2)根据所述泊松盘内测井间的多维属性相似度设计固定效用函数,采用多项回归模型模拟随机误差分布,联立二者获得总效用函数,用以计算不同用户选择不同测井作为标准井的概率;2) Design a fixed utility function according to the multi-dimensional attribute similarity between the Poisson disk logs, use a multinomial regression model to simulate the random error distribution, and combine the two to obtain a total utility function, which is used to calculate the different logging selected by different users. The probability of being a standard well;
3)根据所述标准井样本集,利用离散选择模型自动计算出所述总效用函数中的相应参数并遍历所述泊松盘,进而选择出局部空间内属性组合最大化用户效用的标准井。3) According to the standard well sample set, the discrete selection model is used to automatically calculate the corresponding parameters in the total utility function and traverse the Poisson disk, and then select standard wells whose attribute combination in the local space maximizes the user's utility.
首先,为了能够较好的保证标准井的空间分布均匀性,将每口测井视作一个备选方案,将每个泊松盘内的测井集视作一个相对独立的标准井备选方案集,邀请领域专家基于自身先验知识面向部分测井集进行人工标识标准井,获得实际选择的标准井样本集。First, in order to better ensure the spatial distribution uniformity of standard wells, each logging well is regarded as an alternative, and the logging set in each Poisson disk is regarded as a relatively independent standard well alternative. Invite domain experts to manually identify standard wells for some logging sets based on their prior knowledge, and obtain the actually selected standard well sample set.
然后,基于离散选择模型,利用用户偏好和效用最大化理论构建效用函数,模拟计算不同用户选择不同测井作为标准井所形成的总效用,如公式(4)所示:Then, based on the discrete choice model, the utility function is constructed by using user preference and utility maximization theory, and the total utility formed by different users choosing different logging wells as standard wells is simulated and calculated, as shown in formula (4):
Uqi=Vqi+εqi (4)U qi =V qi +ε qi (4)
其中,Uqi表示选择备选方案“i”对用户“q”可能带来的总效用,Vqi表示备选方案的预期效用值,εqi表示随机残差,用于量化平均效用值与实际效用值的偏差。Among them, U qi represents the total utility that the selection of alternative “i” may bring to user “q”, V qi represents the expected utility value of the alternative solution, and ε qi represents the random residual, which is used to quantify the difference between the average utility value and the actual utility value. Bias in utility value.
由于测井数据是多维属性数据,我们假定固定效用函数V为多变量线性函数,如公式5所示:Since the logging data is multidimensional attribute data, we assume that the fixed utility function V is a multivariate linear function, as shown in Equation 5:
Vqi=βTxqi (5)V qi = β T x qi (5)
其中,β=[β1,β2,...,βn]T表示用户对n个数据属性相应的偏好系数。x=xqi1,xqi2,...,xqin则表示备选方案在各属性上的总相似程度,可根据步骤2计算得到。然后,采用多项Logit模型模拟随机残差ε的密度分布,如式6所示:Among them, β=[β 1 ,β 2 ,...,β n ] T represents the user's preference coefficient corresponding to n data attributes. x=x qi1 , x qi2 ,...,x qin represents the total similarity degree of the alternatives on each attribute, which can be calculated according to step 2. Then, the multinomial Logit model is used to simulate the density distribution of the random residual ε, as shown in Equation 6:
进而将公式(5)、公式(6)代入总效用公式(4),模拟计算在给定方案集中用户“q”选择方案“i”的概率,也即是备选方案“i”的效用大于给定方案集中其他所有备选方案效用的概率,如等式7所示:Then, formula (5) and formula (6) are substituted into the total utility formula (4), and the probability of user “q” choosing plan “i” in a given plan set is simulated and calculated, that is, the utility of alternative plan “i” is greater than The probability of the utility of all other alternatives in a given set of alternatives is given by Equation 7:
最后,将所得的标准井样本集代入公式(7),可以通过回归自动计算出用户偏好系数“β”,并将其更新到总效用函数中,帮助用户在目标数据集中选择属性组合最大化其效用的标准井。按照上述过程,利用离散选择模型最大化测井属性相似度与标准井之间的效用,实现了有监督的标准井筛选。Finally, by substituting the obtained standard well sample set into formula (7), the user preference coefficient "β" can be automatically calculated through regression and updated into the total utility function, helping users to select attribute combinations in the target data set to maximize their Utility standard well. Following the above process, a discrete selection model is used to maximize the utility between log attribute similarity and standard wells, enabling supervised standard well screening.
(3)根据所述标准井筛选信息,通过可视化分析技术引导用户交互式指定或改变标准井,自适应更新所述标准井样本集信息,优化离散选择模型参数,进而重新筛选获得满足用户当前需求和经验的标准井。(3) According to the standard well screening information, guide the user to interactively specify or change standard wells through visual analysis technology, adaptively update the standard well sample set information, optimize the discrete selection model parameters, and then re-screen to meet the current needs of users and standard well of experience.
所述通过可视化分析技术引导用户交互式指定或改变标准井的方法,具体为:The method for guiding users to interactively specify or change standard wells through visual analysis technology is specifically:
1)设计多种可视化方案协同展示测井数据信息:图4所示的系统主视图中,将测井的实际空间位置映射到地图中,保留其空间分布特征的可视化结果;根据不同属性测井数据,设计属性曲线图,将局部空间内不同测井的同一属性数据统计在指定属性空间中,采用色调映射技术将所述标准井与非标准井进行区分;利用方差量化所述测井曲线间的差异,统计获得局部空间内标准井的总属性差异直方图,保留属性特征的对比度;1) Design a variety of visualization schemes to collaboratively display logging data information: in the main view of the system shown in Figure 4, the actual spatial position of logging is mapped to the map, and the visualization results of its spatial distribution characteristics are preserved; logging according to different attributes Data, design attribute curve graph, count the same attribute data of different logging in the local space in the specified attribute space, use tone mapping technology to distinguish the standard well from non-standard well; use variance to quantify the difference between the logging curves Statistically obtain the total attribute difference histogram of standard wells in the local space, and retain the contrast of attribute features;
2)根据所述可视化结果,设计标准井筛选可视化方案:统计每个局部空间各属性上特征匹配结果,获得多维属性层位匹配关联矩阵;根据层位匹配结果设置筛选圆环,在地图上直观对比并增强显示局部空间内测井间的属性差异;2) According to the visualization results, design a standard well screening visualization scheme: count the feature matching results on each attribute of each local space to obtain a multi-dimensional attribute horizon matching correlation matrix; set a screening ring according to the horizon matching results, which is intuitive on the map Contrast and enhance the display of property differences between logs in local space;
3)根据所述属性差异的对比,支持用户交互分析和替换标准井,进而更新样本,根据新的样本集优化离散选择模型参数,重新计算和筛选满足用户需求的标准井,具体过程如图6所示。3) According to the comparison of the attribute differences, support user interactive analysis and replacement of standard wells, and then update samples, optimize discrete selection model parameters according to the new sample set, recalculate and screen standard wells that meet user needs, the specific process is shown in Figure 6 shown.
图4展示了完整的系统界面图。其中,图4(a)是信息视图,显示基本的数据集信息,如数据集中的测井总数、当前筛选出的标准井总数、当前选中的标准井序号以及当前划分的局部区域数;图4(b)是采样面板,用以调整采样率,确定采样井;图4(c)是选择优化面板,提供样本集的更新功能,计算并显示当前的用户各属性偏好系数;图4(d)是地理视图,测井被视为圆点,根据其经纬度值投影在地图上,在地图上直观呈现测井的空间分布情况;图4(e)是多维属性视图,根据原始测井数据描述不同测井属性的曲线变化;图4(f)是地层匹配示意图,直观展示任意两井间的地层匹配情况;图4(g)是地层关联矩阵视图,用以呈现局部范围内多井之间的地层匹配结果,每个矩阵分别代表一种测井属性,每一行或列表示选定局部范围内的一口测井,在矩阵单元格中用颜色映射技术表示一对测井之间的属性相似程度,从左上角到右下角的对角线上的单元格则表示局部范围内每口测井与其他测井之间的总相似程度;图4(h)是标准井总属性差异直方图,通过计算各属性在测井曲线上的方差得到,采用不同的色调映射方案分别显示标准井与非标准井总属性差异。Figure 4 shows the complete system interface diagram. Among them, Figure 4(a) is an information view, which displays basic data set information, such as the total number of well logs in the data set, the total number of standard wells currently screened, the number of the currently selected standard wells, and the number of currently divided local areas; Figure 4 (b) is the sampling panel, which is used to adjust the sampling rate and determine the sampling wells; Figure 4(c) is the selection optimization panel, which provides the update function of the sample set, and calculates and displays the current user attribute preference coefficients; Figure 4(d) is a geographic view, and logging is regarded as a dot, which is projected on the map according to its latitude and longitude values, and the spatial distribution of logging is visually displayed on the map; Figure 4(e) is a multi-dimensional attribute view, which is described differently according to the original logging data. Curve changes of logging attributes; Fig. 4(f) is a schematic diagram of formation matching, which visually shows the formation matching between any two wells; Fig. 4(g) is a view of formation correlation matrix, which is used to present the relationship between multiple wells in a local area. Stratigraphic matching results, each matrix represents a logging attribute, and each row or column represents a log in the selected local area, and the color mapping technique is used in the matrix cells to represent the attribute similarity between a pair of logs , the cells on the diagonal from the upper left corner to the lower right corner represent the total similarity between each log and other wells in the local range; Fig. 4(h) is the histogram of the total attribute difference of standard wells, through The variance of each attribute on the logging curve is calculated, and different tone mapping schemes are used to display the total attribute difference between standard wells and non-standard wells.
图5出示了给定采样率下所选标准井的属性评估结果图,可以帮助用户评价本文说明方法所选标准井的精度。其中,图5(a)是用户根据自身需求确定采样率后,在当前用户偏好系数下,利用公式(1)所获得标准井集合,其在地图上的投影结果与原始测井空间分布的保持了较好的一致性,说明本文发明方法所选标准井能够满足的测井的局部空间分布特征;图5(b)是给定局部空间内标准井与非标准井的测井属性绘制结果,由于复杂地质条件造成了层位偏移,地理位置相近的测井的属性也难以很好拟合;图5(c)是给定局部空间内标准井与非标准井在多维测井属性上的差异表现结果,每一个扇形区域表示一种测井属性,每一层圆环表示一口非标准井,通过环形直方图的分布,可以增强显示标准井与非标准井之间的相似程度;图5(d)是给定局部空间内标准井与非标准井在各个属性维度上的差异总和,通过计算测井属性数据的方差得到,可以通过直方图快速感知标准井在不同测井属性上的相似性表现;图5(e)是利用公式(4)-(7)所示的本发明方法设计的离散选择模型计算得到的用户偏好系数结果展示,采用不同的色彩映射方案增强显示系数的正负性,快速了解当前用户对不同属性的偏好情况;图5(f)是利用公式(2)、公式(3)所示的本发明方法所设计的地层匹配模型计算得到的实际地层匹配结果,在给定局部范围内,采用矩阵图的形式展示任意两井之间的关联,利用色调映射技术呈现两井之间的实际地层关联程度,可以详细且直观地展示给定局部空间内测井在多维属性上的相似程度。Figure 5 shows the property evaluation results of the selected standard wells at a given sampling rate, which can help users evaluate the accuracy of the selected standard wells in the method described in this paper. Among them, Fig. 5(a) shows the standard well set obtained by formula (1) under the current user preference coefficient after the user determines the sampling rate according to their own needs, and the projection result on the map remains the same as the original logging spatial distribution. It shows that the standard wells selected by the method of this paper can satisfy the local spatial distribution characteristics of well logging; Fig. 5(b) is the plotting result of the logging attributes of standard wells and non-standard wells in a given local space. Due to the horizon shift caused by complex geological conditions, it is difficult to fit well logging properties with similar geographical locations; Fig. 5(c) shows the multi-dimensional logging properties of standard wells and non-standard wells in a given local space. Difference performance results, each fan-shaped area represents a logging attribute, and each layer of rings represents a non-standard well. Through the distribution of the annular histogram, the similarity between standard wells and non-standard wells can be enhanced; Figure 5 (d) is the sum of the differences between standard wells and non-standard wells in each attribute dimension in a given local space. It is obtained by calculating the variance of logging attribute data, and the similarity of standard wells in different logging attributes can be quickly perceived through the histogram. Figure 5(e) is a display of the user preference coefficient results calculated by the discrete selection model designed by the method of the present invention shown in formulas (4)-(7), and different color mapping schemes are used to enhance the positive and negative values of the display coefficients. properties, to quickly understand the current user's preference for different attributes; Figure 5(f) is the actual formation matching result calculated by the formation matching model designed by the method of the present invention shown in formula (2) and formula (3). In a given local area, the relationship between any two wells is displayed in the form of a matrix diagram, and the actual stratigraphic correlation degree between the two wells is presented by using tone mapping technology, which can display the multi-dimensional logging in a given local space in detail and intuitively. similarity in properties.
图6出示了更新用户偏好系数前后,标准井与非标准井的多维属性差异比较图,也即是展示了更新用户偏好系数前后,标准井与给定局部空间内非标准井的属性差异情况,以及标准井在多维测井属性上的偏好表现。通过如图4所示的可视分析系统,用户可以获取如图5所示的标准井筛选信息,从而可以根据自身需求的变化交互式更改标准井的选择,并将其作为新样本纳入标准井样本集中。然后通过点击选择面板中的“更新”按钮,重新修正并生成新的用户偏好系数,以满足用户实时应用需求的变化。如图6(a)所示,用户原来较多关注属性COND、AC,较少关注属性SP,后来根据自身需求的变化而极为关注属性SP。在用户交互式更改样本集后,如图6(c)计算得到的新偏好系数是符合用户的需求偏好,说明本发明方法能够有效反映用户对测井属性的实时需求偏好,并据此更新标准井筛选结果。同时,图6(b)与图6(d)展示了本发明方法所提供的标准井选择结果在地层关联矩阵中的变化情况,不难发现,所选标准井在各个属性上的相似性表现与用户在各个属性上的偏好正负性表现出较强的一致性。综合观察图6,可以看出本发明方法获得的结果图像,可以直观且快速的实现标准井与非标准井之间的差异分析,通过可视分析技术支持用户对标准井样本集的进行更新或优化以快速获取新的标准井集合,高效满足用户应用需求的实时变化,有效提高标准井选择的效率与精度;Figure 6 shows the comparison of multi-dimensional attribute differences between standard wells and non-standard wells before and after updating the user preference coefficient, that is, it shows the attribute difference between standard wells and non-standard wells in a given local space before and after updating the user preference coefficient. And the preference performance of standard wells on multi-dimensional logging attributes. Through the visual analysis system shown in Figure 4, users can obtain the standard well screening information shown in Figure 5, so that they can interactively change the selection of standard wells according to their own needs, and incorporate them into standard wells as new samples sample set. Then, by clicking the "Update" button in the selection panel, re-correct and generate a new user preference coefficient to meet the changes of the user's real-time application requirements. As shown in Fig. 6(a), the user originally paid more attention to the attributes COND and AC, and less attention to the attribute SP, and later paid great attention to the attribute SP according to the change of their own needs. After the user interactively changes the sample set, the new preference coefficient calculated as shown in Figure 6(c) is in line with the user's demand preference, indicating that the method of the present invention can effectively reflect the user's real-time demand preference for logging attributes, and the standard is updated accordingly. Well screening results. At the same time, Fig. 6(b) and Fig. 6(d) show the variation of the standard well selection results provided by the method of the present invention in the formation correlation matrix. It is not difficult to find that the similarity performance of the selected standard wells in each attribute It shows strong consistency with the positive and negative preferences of users on each attribute. 6, it can be seen that the result image obtained by the method of the present invention can intuitively and quickly realize the difference analysis between standard wells and non-standard wells, and support users to update or update the standard well sample set through visual analysis technology. Optimized to quickly acquire a new set of standard wells, efficiently meet real-time changes in user application requirements, and effectively improve the efficiency and accuracy of standard well selection;
与传统人工选择标准井可视化过程相比,本发明方法的最大优势是提出基于离散选择模型的标准井筛选可视分析方法,综合考虑了测井数据的空间分布特征、多维属性相关性以及领域专家的先验知识,通过构建便捷的多功能可视分析系统实现有监督的标准井筛选,有效地提升了标准井选择的效率以及精度。Compared with the traditional visual process of manual selection of standard wells, the biggest advantage of the method of the present invention is that it proposes a visual analysis method for standard well screening based on discrete selection model, which comprehensively considers the spatial distribution characteristics of well logging data, multi-dimensional attribute correlation and domain experts. By building a convenient multi-functional visual analysis system to realize supervised standard well screening, the efficiency and accuracy of standard well selection are effectively improved.
Claims (5)
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201910758272.0A CN110502569A (en) | 2019-08-16 | 2019-08-16 | A Visual Analysis Method for Standard Well Screening Based on Discrete Selection Model |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201910758272.0A CN110502569A (en) | 2019-08-16 | 2019-08-16 | A Visual Analysis Method for Standard Well Screening Based on Discrete Selection Model |
Publications (1)
Publication Number | Publication Date |
---|---|
CN110502569A true CN110502569A (en) | 2019-11-26 |
Family
ID=68588141
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN201910758272.0A Pending CN110502569A (en) | 2019-08-16 | 2019-08-16 | A Visual Analysis Method for Standard Well Screening Based on Discrete Selection Model |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN110502569A (en) |
Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2017062009A1 (en) * | 2015-10-08 | 2017-04-13 | Halliburton Energy Services Inc. | Mud pulse telemetry preamble for sequence detection and channel estimation |
CN106951581A (en) * | 2017-01-24 | 2017-07-14 | 同济大学 | Commercial complex simulator |
CN108562950A (en) * | 2017-12-11 | 2018-09-21 | 中国石油天然气集团公司 | Method for intelligently dividing stratum horizon based on logging information |
CN109740427A (en) * | 2018-11-26 | 2019-05-10 | 浙江财经大学 | A Visual Analysis Method for Standard Well Screening Based on Blue Noise Sampling |
CN109763814A (en) * | 2019-01-09 | 2019-05-17 | 浙江财经大学 | Stratum matching visual analysis method based on multi-dimensional logging data |
-
2019
- 2019-08-16 CN CN201910758272.0A patent/CN110502569A/en active Pending
Patent Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2017062009A1 (en) * | 2015-10-08 | 2017-04-13 | Halliburton Energy Services Inc. | Mud pulse telemetry preamble for sequence detection and channel estimation |
CN106951581A (en) * | 2017-01-24 | 2017-07-14 | 同济大学 | Commercial complex simulator |
CN108562950A (en) * | 2017-12-11 | 2018-09-21 | 中国石油天然气集团公司 | Method for intelligently dividing stratum horizon based on logging information |
CN109740427A (en) * | 2018-11-26 | 2019-05-10 | 浙江财经大学 | A Visual Analysis Method for Standard Well Screening Based on Blue Noise Sampling |
CN109763814A (en) * | 2019-01-09 | 2019-05-17 | 浙江财经大学 | Stratum matching visual analysis method based on multi-dimensional logging data |
Non-Patent Citations (1)
Title |
---|
周志光 等: "基于蓝噪声采样的多维标准井筛选可视分析", 《计算机辅助设计与图形学学报》 * |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
US11693140B2 (en) | Identifying hydrocarbon reserves of a subterranean region using a reservoir earth model that models characteristics of the region | |
US11486230B2 (en) | Allocating resources for implementing a well-planning process | |
Stephen et al. | Multiple-model seismic and production history matching: a case study | |
CN101932954B (en) | Subsurface prediction method and system | |
US11815650B2 (en) | Optimization of well-planning process for identifying hydrocarbon reserves using an integrated multi-dimensional geological model | |
CN110579802B (en) | High-precision inversion method for physical property parameters of natural gas hydrate reservoir | |
CN109763814B (en) | A visual analysis method of formation matching based on multi-dimensional logging data | |
US20220237891A1 (en) | Method and system for image-based reservoir property estimation using machine learning | |
US11221425B1 (en) | Generating a model for seismic velocities in a subsurface region using inversion with lateral variations | |
CA3176475A1 (en) | Subsurface lithological model with machine learning | |
WO2016126453A1 (en) | Seismic attributes derived from the relative geological age property of a volume-based model | |
CN109740427B (en) | Standard well screening visual analysis method based on blue noise sampling | |
WO2022159698A1 (en) | Method and system for image-based reservoir property estimation using machine learning | |
Korjani et al. | Reservoir characterization using fuzzy kriging and deep learning neural networks | |
Friedel et al. | Hybrid modeling of spatial continuity for application to numerical inverse problems | |
US20230125277A1 (en) | Integration of upholes with inversion-based velocity modeling | |
Gilder et al. | Geostatistical Framework for Estimation of VS 30 in Data‐Scarce Regions | |
CN110502569A (en) | A Visual Analysis Method for Standard Well Screening Based on Discrete Selection Model | |
US20240210580A1 (en) | Near Surface Modeling and Drilling Hazard Identification for a Subterranean Formation | |
Waggoner | Lessons learned from 4D projects | |
Hou et al. | Uncertainty analysis and visualization of geological subsurface and its application in metro station construction | |
CN115880455A (en) | Three-dimensional intelligent interpolation method based on deep learning | |
Stephen | Seismic history matching with saturation indicators combined with multiple objective function optimization | |
Silvestrov et al. | Evaluating imaging uncertainty associated with the near surface and added value of vertical arrays using Bayesian seismic refraction tomography | |
Degterev | Multivariate Spatial Temporal Model of Gas Dynamic in Underground Gas Storage Based on Saturation Parameter from Well Logging Data |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
PB01 | Publication | ||
PB01 | Publication | ||
SE01 | Entry into force of request for substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
RJ01 | Rejection of invention patent application after publication |
Application publication date: 20191126 |
|
RJ01 | Rejection of invention patent application after publication |