Petrel seismic survey design plugin schlumberger software. One of the important aspects of the pattern recognition is its. After obtaining an ftp password, post stack data, well logs, production history, and gis data can be downloaded from the internet. During the crisp seismic survey in 2011 we collected an 11 x 55 km grid of 3d seismic reflection data and highresolvability em122 multibeam data, with coverage extending from the incoming. Pattern recognition is the process of recognizing patterns by using machine learning algorithm. We will use omni 3d seismic survey design software to demonstrate acquisition concepts such as geometry layout, azimuth, fold, and offset. Development of reservoir characterization models using core, well log, and 3d seismic data and intelligent software. Modeling conditional distributions of facies from seismic. Intelligent seismic inversion workflow for highresolution. He has performed pioneering work on seismic elastic modeling, seismic pattern recognition and artificial intelligece, reservoir monitoring, and induced seismicity. The direct impact on the teaching phase is that a given pattern must be taught in all of its variable positions so that it can be reliably detected in the acquired image. The classification was done with four attribute signature locations. The kmeans algorithm jancey, 1966 is one of the first pattern recognition algorithms which was considered to analyze the seismic data, and it is used up to now coleou et al.
About us acteq 3d seismic survey design software and services. Apr 22, 2020 radexpro is a seismic processing software on windows. Each survey is represented by several attributes, f 1, f 2 f f for example, the attributes might include the amplitude, hilbert transform, envelope, phase, frequency, etc. In this case, the analysis was performed with field data from western venezuela. Principal component analysis pca and self organizing maps soms provide multiattribute analyses that have proven to be an excellent pattern recognition approach in the seismic interpretation workflow. I would like to ask, how hard is it to implement usable pattern recognition system in 3d space. Introduction to 2d3d seismic data acquisition and processing. Unsupervised neural networksdisruptive technology for. The variation of seismic data from one window to another. Welllogging data of strata is taken as time series.
The seismic data used for the generation of the proposed dataset is a public 3d seismic survey called netherlands offshore f3 block which is available at the open seismic repository. Aimachine learning ml promises to alleviate the repetitive nature of this task. For simplicity, many existing algorithms have focused on recognizing rigid objects consisting of a single part, that is, objects whose spatial transformation is a euclidean motion. Excel 20032007 spreadsheet for quality control of 3d seismic survey geometry for geophysicists and geologists. Geologic pattern recognition from seismic attributes. Seismic pattern recognition has been developing quietly but steadily for twenty years, and the first practical applications are now appearing. Fred has served the seg as president and has also served on numerous seg committees including the research committee and global affairs committee.
Wavelet transform with generalized beta wavelets for seismic timefrequency analysis. Seismic facies analysis is considered as a technique for mapping geological changes using seismic data. They originate from image analysis and are based on the grey level cooccurrence matrix glcm, which describes the relationship between pixels and was developed to capture the. The som is a powerful cluster analysis and pattern recognition method developed by prof. A pattern recognition approach for automatic horizon. Pdf seismic facies analysis based on kmeans clustering.
Multiple available methods deterministic, stochastic and advanced neural network that integrate all the available log and seismic attributes data are available. You can quickly and effectively analyze and monitor their seismic acquisition projects. Di2018 developing a seismic pattern interpretation network spinet for automated seismic interpretation 1810. The suite features guided workflows, interactive world maps, 3d imaging, and a 2d color mapping capability that is integrated and interactive with 3d imaging. Omni 3d is a powerful program designed for seismic survey design and modeling. The program can be used for the planning, execution and analysis of land, marine, transition zone, vsp and multicomponent surveys. Intelligent seismic inversion workflow for highresolution reservoir characterization.
Rockpredictor uses a 3d geocellular grid, seismic attributes and well data to propagate in 3dimensions, key rock properties such as facies, toc, brittleness, porosity and natural fracture density. But no matter how new and sophisticated the algorithm, seismic pattern recognition rests on an old and simple foundation. Free automated pattern recognition software that recognizes over 170 patterns works on win xp home edition, only, including chart patterns and candlesticks, written by internationally known author and trader thomas bulkowski. Imagine something like drawing number 2 and the program can tell, that it is a number 2 but in 3d and realtime. Williamsongeological pattern recognition and modeling with a general. The kmeans algorithm jancey, 1966 is one of the first pattern recognition algorithms which was considered to analyze the seismic data, and it is. Seismic attributes a promising aid for geologic prediction. The petrel ssd plugin enables users to plan, edit, execute, and analyze marine, land, obc, and vsp surveys. These technologies are simple, intuitive, and have rapid calculating abilities but a poor forecasting repeatability. Today, paradise distils a variety of information from many attributes simultaneously at full seismic resolution, i. Such a generic texture model schedule can be extended to include all possible deformational and depositional features gao 2004, 2011.
Pattern recognition by dtw and series data mining in 3d stratum modelling and 3d visualization abstract. This paper presents an introduction to pattern recognition, a summary of previous applications in seismic processing, and several new pattern recognition approaches. Seismic attribute characteristics are often associated with reservoir lithology and hydrocarbon potential. Seismic interpretation requires the repetitive application of pattern and texture recognition of seismic images, informed by the geologic understanding of a skilled interpreter. Theres a bit of a gap in the literature on forward modelling of glacial features, and i would quite like to have a go at some forward 2d modelling of what features. Seismic geometric decomposition sgd is an exclusive technique that captures the internal architecture of the seismic reflectors to produce a series of high definition seismic volumes that can be used to improve the delineation of the fault system, the external and internal reservoir architecture and to recognize reflectivity patterns for geomorphological analysis. As a result, stemmer imaging has developed its cvb polimago software package that has been designed specifically for recognition of objects in various poses. The previous screening assumption prevents recognizing and hence utilizing these patterns of seismic data. The tremendous amount of samples from numerous seismic attributes exhibit significant organizational structure.
Dec 17, 2014 cutlines for a dense 3d survey at surmont field, alberta, canada. The algorithm is applicable to differentiate multiple seismic patterns, which provide an avenue for full pattern recognition by building multiple texture calibration templates models. Threedimension 3d modeling and visualization of stratum plays important role in seismic active fault detection, of course in geoinformation science. Seismic geometric decomposition seismic processing. At the conclusion of 3d data processing, the area spanned by a 3d seismic image is divided into a grid of small, abutted subareas called stacking bins. These tools are primarily used to locate complex objects for guiding a gantry, stage, or robot, or for directing subsequent measurement operations. In recent years, the number of seismic attributes and the size of seismic data have been increased.
Oct 07, 2010 the som is a powerful cluster analysis and pattern recognition method developed by professor teuvo kohonen of finland during the 1970s and 80s. Dynamic warping of seismic images dave hale center for wave phenomena, colorado school of mines, golden co 80401, usa. The petrel seismic survey design ssd plugin is a powerful survey design tool fully integrated into the petrel environment. Seismic attribute selection for machinelearningbased facies analysis. Acteq 3d seismic survey design software and services 3d. Basemap, shapefiles, mapping improves the user interaction in opendtect.
Literature on 3d seismic cubes and deep learning github. Numerous acquisition geometries are supported, including 2d and 3d narrow, wide, and multi and full azimuth. These developments continued into the new millennium, with enhanced visualization and 3d computa. From regional exploration to reservoir development and production optimization, geophysics is a critical tool to solve the most complex structural and stratigraphic challengesincluding advanced seismic processing, depth imaging, and 3d, 2d, and prestack seismic interpretation as well as advanced quantitative interpretation. I have the opportunity to work on realtime pattern recognition in 3d space, but i have no previous experiences. The som is a powerful cluster analysis and pattern recognition method developed by professor teuvo kohonen of finland during the 1970s and 80s. Fred aminzadeh has made significant contributions to the field of exploration geophysics and seismic signal processing through his numerous books, patents, and publications, with a focus on technical advances in discipline boundaries of applied geophysics, petroleum engineering, computer science, and electrical engineering. Supervised seismic facies analysis based on image segmentation. Som on a 3d seismic survey consisting of a large number. After a period of quality assurance of contractor software for seismic processing, he became responsible for geophysics in the shell learning centre. Omni 3d seismic survey design software helps you create optimal 2d and 3d designs for land, marine, oceanbottom cable obc, transition zone, vertical seismic profile vsp, and multicomponent surveys.
Paradise executes and manages workflows based on advanced pattern recognition methods, including selforganizing maps som and principal component analysis pca. Some applications of pattern recognition to oil and gas. In the case study shown above, we present results based on som on a 3d seismic survey consisting of a large number of seismic attributes. During the crisp seismic survey in 2011 we collected an 11 x 55 km grid of 3d seismic reflection data and highresolvability em122 multibeam data, with coverage extending from the incoming plate to the outershelf. The application of pattern recognition to oil and gas prospection is very recent, and results are still sparse. In this chapter, the basic concepts of pattern recognition is introduced, focused mainly on. Pdf application of deep learning in first break picking. Each trace in a 3d seismic data volume is positioned so that it passes vertically through the midpoint of a stacking bin. The dataset consists of 384km 2 of time migrated 3d seismic data, with 651 inlines and 951 crosslines, located at the north sea, netherlands offshore figure 1.
For this discussion, seismic data are represented by a 3d seismic survey data volume regularly sampled in location x or y and in time t or in depth z. Current issue society of exploration geophysicists. Mil includes two tools for performing pattern recognition. Machine learning deals with the construction and study of systems that can learn from data, rather than follow only explicitly programmed instructions whereas pattern recognition is. Theres a bit of a gap in the literature on forward modelling of glacial features, and i would quite like to have a go at some forward 2d modelling of what features would look like. Therefore, the interpretation of seismic facies has. Som is a powerful nonlinear cluster analysis and pattern recognition approach that helps interpreters identify patterns in their data, some of which can relate to desired geologic characteristics. Oct 10, 2018 the company launched the paradise multiattribute analysis software in 20, which uses machine learning and pattern recognition to extract greater information from seismic data. The teapot dome 3d survey is a land 3d data set from wyoming provided by the u.
Steve carlson senior geophysical advisor ecopetrol. It is well suited for indepth hruhr marine seismic processing, realtime marine 2d3d seismic qc, onboard fast track processing, land and marine offline seismic qc, complete processing of nearsurface seismic data reflection, refraction, tomography, masw and vsp processing. In this paper, an interpreter computer interactive software, named seisart, is introduced which is employed for seismic facies analysis. In this paper we propose to relate seismic data to facies or petrophysical properties through a colocated window of seismic information instead of the single colocated seismic datum. Developing a seismic texture analysis neural network for. In forested areas, a designer may choose a pattern that minimizes the number of trees that need to be felled. Poststack hydrocarbon prediction methods include pattern recognition, neural networks, and spectral properties. Unsupervised neural networks disruptive technology for. Neural network technology is used today in financial services software, pattern recognition systems, and many other settings. We employ a seismic metaattribute workflow to detect and analyze probable faults and fluidpathways in 3d within the sedimentary section offshore southern costa rica. Pattern recognition can be defined as the classification of data based on knowledge already gained or on statistical information extracted from patterns andor their representation. Geo expro seismic interpretation with machine learning. Where land access is easier, designers may opt for a pattern that is efficient for the recording crew to deploy and pick up receivers.
Pattern or pattern recognition is the process of taking in raw data and taking an action based on the category of the pattern duda et al. Tesseract is a powerful integrated survey design package for land, marine, seabed and mixed mode projects. Unsupervised learning endeavors to bring out hidden patterns or structure in the volume of input data through pattern recognition algorithms, without any prior knowledge about the desired output. Therefore, the emerging machine learning techniques, particularly the convolutional neural networks, appear most suitable for tackling the problem of annotating various patterns existing in a seismic dataset therefore, implementing deep neural networks into 3d seismic interpretation is the current research focus in the community. Oct 23, 2018 building a global seismic texture interpretation network october 23, 2018 the primary goal of seismic interpretation is to understand seismic signals, categorize them into various patterns, connect each pattern with a specific depositional event, and finally reconstruct the geologic history. Dr smith has a good track record of understanding how the industry works with new technology. Comparison and joint evaluation of timelapse pressure and seismic tomography.
The method of recognizing a 3d object depends on the properties of an object. Hi, phd student based in the uk, mostly a geologistpetrophysicist but also interpreting 2d and 3d seismic data over glacial sediments. A 2d seismic line is treated as a 3d survey of one line. After his return to the netherlands, he headed a team for the development of 3d interpretation methods using multiattribute statistical and pattern recognition analysis on workstations. There are a number of ways to lay out sources and receivers for a 3d seismic survey. Pattern recognition by dtw and series data mining in 3d. Cutlines for a dense 3d survey at surmont field, alberta, canada.
831 868 329 818 781 17 1165 1423 340 495 601 1543 1060 1232 410 751 196 1408 313 1471 1378 620 832 410 716 932 587 1063 699 980 723 324 711 845 129 536 987