Visualization and Spectral Decomposition

Chapter 4

Visualization and Spectral Decomposition Chapter Outline 4.1. Concepts and Principles of Visualization 4.2. Visualization Techniques 4.2.1. Innervoxel Horizon Autotracking 4.2.2. Target Layer Visualization 4.2.3. Isochronal Data Volume Visualization 4.2.4. Monitoring Profile Visualization 4.2.5. Multiattribute Volume Visualization 4.3. Full 3D Visual Interpretation 4.3.1. Basic Work 4.3.2. Data Volume Browser 4.3.3. Layer Interpretation 4.3.4. ThreeDimensional Visualization 4.3.5. Results Display

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4.4. Virtual Reality Technique 4.4.1. The Composition of a Virtual Reality System 4.4.2. Applications of Virtual Reality Visualization Systems for Oil and Gas Exploration 4.4.3. Oil and Gas Predictive Advantages of the Virtual Reality System 4.4.4. Differences between the Virtual Reality Visualization System and Conventional 3D Visualization 4.5. Spectral Decomposition Technique 4.5.1. Concepts and Usages of Spectral Decomposition 4.5.2. Synopsis 4.5.3. Basic Workflow and Key Parameters 4.6. Examples

Geophysical Exploration Technology. http://dx.doi.org/10.1016/B978-0-12-410436-5.00004-6 Copyright © 2014 Petroleum Industry Press. Published by Elsevier Inc. All rights reserved.

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4.6.1. ThreeDimensional Visualization

4.6.2. Frequency Decomposition

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The three-dimensional (3D) visualization technique, which appeared in the mid-1980s, is a comprehensive technique that integrates computer data processing and image display, along with many other leading-edge technologies. This technique is used to describe geological phenomena and subsurface characteristics based on their 3D seismic data volume. It can help us describe objective data with real-time processing and display and analyze results effectively with static or dynamic images based on the 3D information. When dealing with large amounts of complex data, this technique helps isolate geophysical information so that geologists, geophysicists, and reservoir engineers can “enter” the data volume and describe and characterize stratigraphic features. Based on frequency-spectrum decomposition, the spectral decomposition technique is a special interpretation tool for reservoir characterization that has surfaced in recent years. With the short-time Fourier transform (STFT), it decomposes 3D seismic data into a frequency-tuning data volume, which is closely related to thin-layer interference, seismic wavelets, and stochastic noise.

4.1. CONCEPTS AND PRINCIPLES OF VISUALIZATION Currently, 3D visualization technology is widely used in the oil industry. It can display the seismic data volume from different perspectives via advanced display techniques on real-time graphics workstations. This method can be used to describe subsurface geological characteristics and display geologically anomalous bodies, regardless of whether they are complex structures or stratigraphic sedimentary reservoirs. It is not an analogous technique but a means of displaying data volumes. We can take advantage of large amounts of data, evaluate the authenticity of the information, and extract useful anomalous information in order to understand and analyze the geological events. Threedimensional visualization plays an important role in the communication and integration of different data types. In recent years, the world’s major petroleum geophysical companies, such as Landmark Graphics Corporation, Paradigm Ltd, and Schlumberger Ltd, have developed their own visualization software systems including GeoProbeÒ, VoxelGeoÒ, and GeoVizÒ, respectively. Because computer hardware technology advances very quickly, 3D visualization techniques have become a key method for reservoir prediction and characterization. It can delineate

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channels, deltas, alluvial fans, and the distribution of thick sandstone reservoirs quickly. The 3D data volume, which is the basis of the 3D visualization techniques, can be conventional amplitude data, speed or impedance data after inversion, or attribute data such as the coherent volume and instantaneous amplitude data. In the 3D visual environment, we can identify exploration targets and plan wells easily after making a comprehensive analysis of a variety of data and research results. This technique helps us make a substantial leap from two-dimensional (2D) to 3D data. In general, there are two types of visualizations: plane visualization and 3D visualization. Plane visualization is often used to display seismic profiles, horizons, faults, and log curves, and 3D visualization can display seismic data volume, various types of attribute data, spatial contact relationships of faults, traps, and reservoirs, as well as drilling trajectories based on the concept of the data volume. In practice, “visualization” refers to 3D visualization. Three-dimensional visualization is the expression of a volumetric pixel or volumetric picture element (voxel) cluster. For seismic data, each sampling point is transformed into a voxel and the corresponding seismic trace is equivalent to a volume element column (Figure 4.1); 3D seismic data comprises very large voxel bodies. Each dimensional size of a voxel depends on the distance of two inlines, the distance of two cross-lines, and the sampling rate. A specific voxel is assigned an a value (transparent or opaque level value)

FIGURE 4.1 Voxel.

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and a specific seismic attribute value such as amplitude or coherence. Therefore, a large number of voxels display a 3D data volume, not just a 2D data plane. The color of a voxel’s seismic attribute value is defined by 8-bit numeric range from 128 to 127. So a voxel has a total of 256 faces and each face has different colors. Among them, the smallest value is 128, which represents black and the face 0. Face 256 represents deep red, representing the largest value of 127. Each face represents a seismic attribute range. The transparency of a voxel was defined by an a value from 0 to 1.0, and if the value of a is 1.0, the volume element is completely opaque. If the value of a is 0.0, the volume element is completely transparent. Changing the a value of voxels, we can display some geological bodies opaquely or transparently. While some specific geological bodies are invisible, we are able to highlight characteristics of other visible geological bodies. Three-dimensional visualization is actually a simple, general data processing technique. Combined with image processing, it is able to display and analyze the 3D numerical space with 3D computer graphics. The development of graphics workstations makes it possible to provide the necessary high degree of interaction, computing capability, precise image processing capability, and sufficiently large storage capacity. Most geologists are not familiar with the conception of voxels. In accordance with certain rules, hundreds of millions of simple data units have been arranged in 3D space. Each data unit is considered to occupy a small space unit, the number of physical dimensions of which corresponds to the size of faces. A workstation can display all the units and several voxels can be showed as a pixel on the screen. Data units regularly arranged in 3D space are integrated with each other and produce a complex image that consists of large numbers of pixels. This image can display external surfaces and the content of all voxels simultaneously. In other words, the attributes and transparency of all voxels can be displayed on a specific 3D spatial position at the same time. While transparency is a key parameter, the thickness of a voxel is also important, the value of which helps avoid overlapping display of strata in different periods. Geological target data are assigned to millions of cubic blocks or voxels. The advantage of this process is that such data volume sample points can be arranged to produce a complex image, which can display external surface and internal structure (i.e. the low and high measuring values of an attribute). The biggest advantage of 3D visualization is that interpreters do not have to make a hypothetical model to compare with the data volume, but can characterize and analyze the raw data directly.

4.2. VISUALIZATION TECHNIQUES Seismic exploration technology is the most important technical tool in oil and gas exploration. Seismic data include various geophysical characteristics

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of subsurface geological bodies. Because of the discreteness and limits of information, as well as the restrictions our techniques pose, we were confined to considering and analyzing geological structures, sedimentary characteristics, and reservoir characterization in a 2D spatial perspective for many years. Based on the basic structure of 3D seismic data, the 3D visualization technique enables 3D data volumes to be presented in optimal display images by adjusting parameters, so that researchers can detect geological information from the data volume quickly and accurately. It can greatly improve the ability of analyzing 3D data and studying paleostructure and paleogeomorphology. There are two methods of visualizing data volumes, namely, ray calculation (or image fixation) and objectives fixation (or a combination of both). There are five common methods used for visualization, including innervoxel horizon autotracking, target layer visualization, isochronal data volume visualization, monitoring profile visualization, and multiattribute data volume visualization.

4.2.1. Innervoxel Horizon Autotracking The interpretation before tracking is still conventional, and it compares synthetic seismogram traces with existing log data in order to define the seed voxels of the data volume used for tracking. Thus, according to correlation criteria, automatic tracing can be done by choosing voxels whose attribute values and transparencies are the same as the seed voxels. Voxel tracing tracks data along a real 3D path, and the results of tracing are a data volume rather than an interpretation layer. There are two parameters of voxel automatic tracking, namely, the range of changes in attribute values, and the tracing methods. If a six-direction tracking method is chosen, then a voxel only tracks the nearest voxels (surface contact), while 24-direction tracking traces voxels along the surface to surface, edge to edge, and corner to corner of two neighboring voxels. Taking existing horizon data or quantitative time shifts of horizon data as constraints, data of target layers can be extracted from the entire data volume. By adjusting color, transparency, and illumination parameters, it is more effective to delineate the distribution of geological bodies and ascertain the relationship between the extension direction and cutting relationship of faults.

4.2.2. Target Layer Visualization In the visualization interpretation of faulted target strata, color, lighting conditions, and viewing angles are very important for visualization effects. In geological interpretation, transparency and color are important parameters. Layer flattening visualization is an extension of target strata visualization. After layers are flattened, we can not only investigate high-relief structures but also make visual analysis on large dip angle strata more conveniently and quickly by locking the visual direction of the time window.

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4.2.3. Isochronal Data Volume Visualization Visualization by locking the time window is the easiest and most convenient visualization technique. In the case of subtle layers, it is convenient and effective for the geological evaluation of a formation. We also can scan geological bodies in order to understand the spatial distribution of faults and sedimentary bodies. Its many advantages are as follows: (1) improving the interpretation speed while improving display speed; (2) improving the perspective effect, while an excessive time range of the data volume will worsen the perspective effect; and (3) improving the interpretation accuracy by limiting the picking range of geological data.

4.2.4. Monitoring Profile Visualization The automatic tracing process of complex reservoirs may be interrupted, making it difficult to reach goals, or it may even produce incorrect results, even though there are more seed voxels. In this case, we should add vertical and horizontal seismic profiles that may aid interpreters according to the rules of interpretation or their purpose in complex areas. Often we need several or even dozens of profiles used for animation browsing, or stack a few profiles to highlight amplitudes in order to improve visual resolution and signal-to-noise (S/N) ratio.

4.2.5. Multiattribute Volume Visualization The interpretation of seismic data always gives varied results. Comprehensive visual interpretation of multiple data volumes helps reduce multiple solutions for seismic interpretation, which is an important reason why visual interpretation is superior to traditional interpretation methods. In multiattribute data volume visualization interpretation, one or more data volumes can be used at the same time, such as the wave impedance, Amplitude versus offset (AVO) attribute volume, coherent cube, resistivity data volume, natural gamma data volume, and instantaneous frequency data volume. We can adjust color, transparency, and other parameters for each individual data volume. In the same window, we can complete various visual interpretations such as voxel tracking, locking time windows, or locking horizons. Thus, we can take advantage of a comprehensive interpretation of multiple data volumes and information.

4.3. FULL 3D VISUAL INTERPRETATION Visualization can be simply summarized as two kinds of visualization techniques for different data display methods; namely, volume 3D display and geometry or surface display. Geometry display is still based on traditional 2D data or 3D data display methods. It allows geological interpretation for a 3D data volume and creates a rapid display for 3D interpretation results; then an

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adjustment can be made for interpretation accuracy according to the display results. While volume 3D display can make a 3D visualization for seismic data volume, it is a real 3D display method. Volume 3D display is composed of two parts: one is that 3D computer graphics display techniques are used to complete various image processing, and the other is that we can conduct analysis and research for the 3D visualization display results. Three-dimensional visualization can display all kinds of seismic attribute data quickly on the workstation by processing display parameters such as color, light, transparency, and other parameters. These parameters can directly show the characteristics of structures and reservoirs. Researchers directly study and analyze problems in their geological context in 3D space and then describe and evaluate the structural, sedimentary, and reservoir characteristics of exploration targets. This technique is entirely new and helps geophysicists conduct comprehensive research on geology and geophysics. The basic workflow of 3D visualization (Figure 4.2) can be divided into five key steps, as described below.

4.3.1. Basic Work The fundamentals of this work include having a basic understanding of petroleum geology and establishing 3D seismic data in the study block. We must also analyze the petroleum geological features, layer data, and well logs in order to understand the distribution characteristics of strata and evolution histories of structures. We should also evaluate the data quality and understand the preliminary interpretation results of seismic data and horizon calibration.

FIGURE 4.2 The basic workflow of 3D visualization.

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At the same time, we need to prepare the corresponding 3D data for 3D visualization including amplitude data, impedance inversion data, and data of different attributes. The visual data volume is 3D poststack seismic data.

4.3.2. Data Volume Browser The first step is to set the appropriate proportions of the color bar, line, trace, and time direction, and to adjust the size of the time window for inspecting the data volume. The second step is to inspect the data along inline, cross-line, and time directions to check the data quality and ascertain objectives. The third step is to inspect the data along each line to understand the quality and characteristics of seismic data. At the same time, we should concentrate on discoveries and identification of special phenomena (such as bright spots or flat spots), and ascertain the stratigraphic distribution and development zones (such as salt domes, large fan bodies). Once the research target areas and zones are determined, we can reload the target layers into memory. If the data volume is too large, it is better to resample data with appropriate methods or abandon the data in the shallow and deep parts, which are of no use for ensuring the accuracy of 3D visualization and display speed.

4.3.3. Layer Interpretation Based on the preceding layer interpretation results, we can interpret key layers of the target area by using a waveform automatic tracking technique. There are two criteria for horizon picking: the first criterion is to choose the “seed point”. We should choose the reflecting layers with good reflection characteristics when conducting horizon calibration. The second criterion is that we need to choose the reflecting layers that have good continuity of waveform characteristics and amplitude and can be tracked laterally. The automatic tracking technique should be applied to the seismic data with high S/N ratios.

4.3.4. Three-Dimensional Visualization Three-dimensional visualization is the core of the 3D visualization technique. It includes three parts of 3D display along layers, data volume perspective, and fine description. This technique helps reveal details of depositional characteristics changes and structures that are difficult to identify by traditional interpretation methods. Three-dimensional display along layers is the visualization of target layers. It helps show the target layers in the graphics workstation by adjusting parameters such as color, lighting conditions, and viewing angles. This helps researchers to understand structural characteristics and fault distribution.

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Meanwhile, layer flattening helps us delineate the stratal sedimentary characteristics. We can quickly perform large dip angle stratigraphic visualization with the proper time windows. This 3D display along layers has great advantages for structural and seismic attribute analysis, fault detection, and structural interpretation of large dip angle layers. Data volume perspective is the visualization of an isochronous data volume. Transparency and color are important parameters affecting volume visualization. It is important to determine the visualization parameters and choose reasonable 3D seismic attributes according to specific geological conditions and the quality of the seismic data. Fine description is the fine visual interpretation and description of geological targets, which includes layer interpretation, seed point tracking, and thickness calculation.

4.3.5. Results Display The results display should show the geometrical characteristics and the spatial relationship of interpretation results. The preliminary results help us to discover problems, reevaluate geological targets, and revise drilling design. Currently there are many visual interpretation systems that can meet future 3D interpretation needs, such as the VoxelGeo (Paradigm), GeoProbe (Landmark Graphics), and GeoViz (Schlumberger-GeoQuest). Advanced 3D processing techniques, especially 3D prestack depth migration, help us gather and interpret more accurate seismic data with high S/N ratios. Complex stratigraphic contact relationships, even buried hills, are clearer in the seismic profiles. This enables us to carry out fine interpretation and horizon automatic tracking. For stratigraphic interpretation, time slices are some of the most effective interpretation tools. No doubt a 3D data volume includes much more information than 2D data; 3D data are more scientific, more efficient, and give more reliable results when we conduct structural or stratigraphic interpretation. The evaluation of data volumes is helpful not only for evaluating the quality of a processed data volume but also for future interpretation. It includes the overall structure profiles, stratigraphic changes, and their contact relationships. We should also identify the target layers and their characteristics. The evaluation of discontinuous data in the same area is even more important. It gives interpreters an overview of the structural information, which greatly reduces the possibility of mistakes in data interpretation. Three-dimensional visualization provides objective, efficient, and accurate information when performing 3D data evaluation. We can achieve the best evaluation results from different visual angles using different means of display.

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4.4. VIRTUAL REALITY TECHNIQUE Since the 1990s, scientists and engineers have focused on the virtual reality technique. This technique is a new interface tool for intelligent engineering applications and visualization techniques for large-scale oil exploration and development. This technique generates an artificial virtual environment that consists of 3D computer graphics based on realities programmed into a computer to produce a realistic “virtual environment”. It changes the way engineers process data, especially large amounts of abstract data in a virtual environment. In 1965, Sutherland first proposed the basic ideas of interactive graphic display, feedback devices, and the virtual reality system in an article entitled “Ultimate Display”. Based on a series of achievements on virtual reality systems that had been made since the 1960s, Jaron Lanier formally proposed the term “virtual reality” in the early 1980s. In the 1990s, the rapid development of computer hardware technology and computer software systems made it possible to develop real-time animation consisting of large amounts of sound and image data. Scientists have made great progress on design innovations of interactive systems and input and output devices. After the Society of Exploration Geophysicists Annual Meeting in 1990, geoscientists have emphasized the importance of research on the visualization technique for geophysical petroleum exploration and development. In the past 12 years, visualization techniques have been widely used in 3D seismic interpretation, and visualization in the geoscience field has developed into multidisciplinary, multidimensional, data visualization from 3D seismic data. Many geophysical software companies have developed virtual reality systems. Because of the rapid development of the computer and information technology (IT) industries, the petroleum industry has made great technological strides and has changed workflows considerably for their geological studies. The introduction of high-performance parallel computers has enabled previously time-consuming seismic data processing (such as prestack depth migration) to be widely applied, and has significantly improved the seismic S/N ratio, resolution, and the quality of complex structural imaging. The popularity of workstations makes it possible to conduct seismic interpretation from initial data handling to later computer processing, which helps promote work efficiency, shorten the research cycle, and improve the reliability of outcomes. The virtual reality system is also known as immersive visualization. Observers, operators, and decision makers are immersed in the multidimensional images of digitized information. It improves the analysis and understanding of the information by humanecomputer interaction from the perspective of exact spatial coordinates and omnidirections. In essence, the virtual reality system is an advanced computereuser interface that provides intuitive, natural, and real-time means such as vision,

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FIGURE 4.3 Virtual reality system.

hearing, and touch to the user at once, in order to simplify the user experience and improve the efficiency of the entire system. The purpose of dynamic environment modeling technology is to extract the 3D data from the actual environment, which can be used to create a corresponding virtual environment model. The combination of computer-aided drafting technology and noncontact visual modeling techniques can effectively improve the efficiency of 3D data acquisition (Figure 4.3). From the view of the international development of innovative technologies and IT, one of the key points is the rapid transformation of large computing centers to the Internet-distributed, immersive visualization system center. In a system that integrates computing with network and virtual reality systems, traditional data processing and interpretation analysis with a single workstation have been transitioned to data processing and interpretation analysis with a multidisciplinary team immersed in the visualization of data and interactive 3D images. By way of voice control or various interactions, experts can mobilize and analyze the data, look for distribution characteristics of reservoir traps, compare various risk development models, plan wells and drilling trajectories, and integrate production data with numerical reservoir simulation results. These enable us to find the remaining reservoirs and hidden reservoirs and reduce development costs. In addition, technical experts and decision makers are no longer limited to making decisions by examining reports, atlases, and multimedia introductions. They can examine different ideas of modeling and simulation results to reduce risk and optimize decisions made alongside the professionals (Figure 4.4). With the above advantages, the immersion visualization technology has developed rapidly since the mid-1990s, and the world’s major oil companies and service companies have already built more than 80 visualization centers.

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FIGURE 4.4 Teamwork in a virtual reality system.

4.4.1. The Composition of a Virtual Reality System In general, virtual reality systems for oil and gas exploration and development mainly consist of seven parts, as itemized below: high-performance parallel computers; large-screen special display devices; geological, geophysical, and reservoir engineering networks and databases; seismic interpretation and analysis software systems; reservoir modeling and decision-making risk assessment software systems; virtual reality displays and interactive software and hardware systems; as well as 7. system operation and demonstration halls that have special provisions for power, network transmission, lighting, room height, ventilation equipment, and static magnetic fields. 1. 2. 3. 4. 5. 6.

Virtual reality center hardware and software configurations are customized according to the different requirements of oil and service companies. For different oil and service companies, the virtual reality systems in the IT industry refer to integrating the first, second, and sixth parts listed above. In the oil industry, virtual reality systems are also called 3D visual presentation systems. Some companies integrate virtual reality systems with petroleum technology-applied software, management decision-making software, and 3D visual presentation systems and call them “the virtual reality decision-making system”.

4.4.1.1. Computer Hardware System While dealing with huge data and image processing tasks, virtual reality systems also have to display images of different precision in 3D space. Most companies use the state-of-the-art multiprocessor Silicon Graphics International Corp. (SGI) servers and workstations. Presently, the advanced configuration is the advanced graphics computer system-equipped 16 central processing units based on the SGI ONYX3400 system.

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4.4.1.2. Virtual Reality Display System The display equipment is the key component of a virtual reality system, including the high-end, multi-image channel projection system, such as the Barco projection system and the high-precision display screen system. The high-precision display screen system includes flat, cylindrical, spherical, conical, folded, and cubic screens. 4.4.1.3. Geological, Geophysical, and Reservoir Engineering Networks and Databases If a virtual reality system’s purpose is only for reports, data warehouses, and even data banks, it is not necessarily an important part of the petroleum virtual reality system. But if the system is used for technical operations for oil production projects and immersive interaction management decisions, the network and integrated geological, geophysical, and reservoir engineering databases are essential for the virtual reality system. 4.4.1.4. Seismic Interpretation and Analysis Software Systems Seismic interpretation and analysis software are some of the basic parts of virtual reality systems. Most of the data instances in the virtual reality environment are the processing and interpretation results of these software systems. There are many advanced reservoir characteristic and seismic interpretation and analysis software systems from Landmark, GeoQuest, Paradigm, and geologic computer-aided design (GOCAD). 4.4.1.5. Reservoir Modeling and Decision-Making Risk Assessment Software Systems The GOCAD series, which concerns the modeling and development of evaluation software systems have user and development versions that are available for production applications and secondary development. 4.4.1.6. Virtual Reality Display and Interactive Software and Hardware Systems The main tasks of virtual reality display software systems are to manage the hardware, transfer interaction information, and display all kinds of geoscience information in a virtual reality environment. The key functions include input/output of data (different formats), real-time display of 3D seismic attribute information, slicing 3D data volumes, generating gridlike charts, visualizing drilling trajectory and logging data, and visualizing interactive processing.

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4.4.2. Applications of Virtual Reality Visualization Systems for Oil and Gas Exploration We can use modeling software to generate models in every step of a project workflow. The software automatically records every function and parameter being selected and creates the modules that can be repeated, modified, and connected with other software. Operators can update these processing modules any time, as long as the software is updated. Once a project is completed, hundreds of process modules recorded by the software can be applied to other projects as needed. If the process modules are integrated with exploration and development data, the virtual reality visualization system can semiautomatically or automatically update any data changes, such as permeability, porosity, or saturation parameters, and even the optimal well positions (Figures 4.5 and 4.6).

4.4.2.1. Management Decisions Making oilfield management decisions in a virtual reality system has some unique advantages, as outlined below. 4.4.2.1.1. Establishing the Expert System Each oil field has many experts who are very familiar with the entire field. With modeling software and immersive visualization systems, we can develop a variety of expert subsystems based on the experience of reservoir modeling experts. This way, the development of an oil field will benefit from the unique understanding and experience of both the highly technical and management personnel. 4.4.2.1.2. The Changing Roles of Decision Makers Managers, especially senior managers, have in the past been limited to being observers who do not fully grasp the technicalities of a specific region or even

FIGURE 4.5 Virtual reality system for reservoir studies.

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a single well’s information. Immersive visualization systems fundamentally change this backward management and decision-making process. It allows managers to perform direct manipulation, optimization, and decision-making for the subsurface area with 2D and 3D data. 4.4.2.1.3. Oilfield Management for Petroleum Companies Through a company’s intranet, the entire exploration and development program can be dynamically discussed and demonstrated by people in different locations, just as telemedicine encourages expert consultation by people in different locations.

4.4.2.2. Exploration and Development of Data Bank of Virtual Reality Systems For many years, oilfield companies have concentrated on database integration and sharing of exploration and development data. Because of the rapid development of Internet and intranet technologies, developing a data bank became possible. A number of foreign companies have been launched in the international exploration and development of data bank services. PetroChina will build its own large database. However, because different domestic oil field data are owned by different oil companies, the rise of free trade markets depends on the open-door policy of these companies. 4.4.2.3. Development, Integration, Usage, and Expansion of Immersion Visualization Systems An immersive visualization system typically includes the following nine parts: 1. host computers and auxiliary computers; 2. virtual reality projection and immersive interactive user interfaces;

FIGURE 4.6 Planning wells with a virtual reality system.

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3. network systems, including local area networks (LANs), wide area networks (WANs), and broadband Internet; 4. virtual reality system software; 5. development and application integration platforms for virtual reality systems; 6. virtual reality software packages for processing, interpretation, and reservoir modeling; 7. data warehouses for cross-system sharing of different kinds of data; 8. universal tools for software development; as well as 9. universal algorithms and module libraries, such as signal processing, statistical analysis, and discrete modeling. Depending on the hardware classification, an immersive visualization system mainly depends on the network transmission and visualization. The following five visual systems can transmit multimedia information through the LAN, WAN, and Internet-distributed servers: 1. Wide-screen stereo images of a portable/workstation immersive visualization system. This small-team system is low cost and easy to move. 2. Up-projection immersive visualization system, which is low cost, movable, and typically used for simulating design and simulation. 3. Front-projection fixed-arc immersive visual system, which is particularly applicable when demonstrating to dozens or hundreds of people in a virtual reality environment. 4. Back-projection immersion fixed/moving arc screen visualization system 5. Multiprojection, multidimensional screen visualization system. If we want to display virtual reality 3D images of a project, this different angle projection system is a good choice.

4.4.3. Oil and Gas Predictive Advantages of the Virtual Reality System 4.4.3.1. Prediction Modes A virtual reality system provides unprecedented technological means for hydrocarbon prediction. Figure 4.7 shows an oil and gas ternary prediction mode. In Figure 4.7, the top circle represents oil and gas reservoirs and the right circle represents a prediction model based on data, experience, and theories. The right circle also represents the operators’ detection and forecasting capabilities. The left circle represents the professionals’ prediction ability. The exploration and development models of an oil and gas reservoir result from the three elements adjusting to each other. In a certain, the three elements intersect and produce the seven domains for prediction, as defined below. The first domain represents the oil and gas potential (or the level of exploration and development of a block or an oil field).

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FIGURE 4.7 Multivariate prediction models for oil and gas forecast.

The second domain represents the lack of detection capability. Therefore, we cannot detect a specific reservoir in seismic profiles, but it can be predicted by models such as a residual reservoir. The third domain represents the degree of deviation of prediction models with the characteristics of an oil field. If the area of this domain is smaller, the model is closer to the actual reservoir. The fourth domain represents models that are unrealistic, and it is difficult for users to identify their shortcomings; therefore, people who rely on these models can make inaccurate decisions based on the faulty data. The fifth domain involves a model’s detection capability and represents the amount of deviation of its forecasts from reality. The sixth domain represents a situation where, even in the absence of good predictive models, experience as well as intuitive analysis help us predict reservoirs correctly. The seventh domain includes prediction models that are correct and reservoirs can be detected and identified.

4.4.3.2. Characteristics of Prediction Models Compared with characteristics of these seven domains, the current exploration and development work of PetroChina has the characteristics outlined below: 1. Compared to most international oil companies, PetroChina does not lag behind in hydrocarbon prediction modeling and precise algorithms. Some technologies are advanced, such as seismic attribute extraction, lithology and fluid inversion, and special treatments. 2. Most of our seismic interpretation software, oil and gas forecasting, and modeling software were imported from foreign countries, which lack provisions for secondary development.

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3. A vast amount of work is done by workstations, and project results tend to be a variation on 2D maps. 4. We lack the conditions for multidisciplinary teams to work simultaneously, with most teams completing their work independently. 5. The conventional means of communication to inspect project results are face-to-face meetings and conferences. PetroChina still needs to improve our ability to detect oil and gas, provide an environment for teamwork, create conditions for massive data analysis quickly and easily, and create a study means for comparing model results. In summary, we lack the detection capability showed in Figure 4.7. Immersion visualization systems will enable us to counteract these points mentioned above.

4.4.4. Differences between the Virtual Reality Visualization System and Conventional 3D Visualization Conventional 3D visualization and the virtual reality system for visualization and modeling analysis have several essential differences, as outlined below. 1. With more information, the 3D data are more stereoscopic in the 3D visualization, which gives people greater intuitive understanding. People can make conclusions that are difficult or impossible to obtain under 3D visualization conditions. 2. In the virtual reality environment, users can interact with the data. It is impossible to get the same sense of participation just by a mouse click on a menu or interactive graphics operations in conventional 3D visualization, 3. Conventional 3D visualization is often limited to a 2D computer screen with a single-person operation. The virtual reality system relies on largescreen projection space, and it is ideal for team involvement. In short, there are four advantages of virtual reality visualization. First, the virtual reality system uses various immersive systems such as the cave automatic virtual environment to establish lively work environments, in which users fully focus on the tasks. Users interact with computers through the natural movement of their bodies, such as pointing and crawling. Second, the powerful 3D visualization and highly immersive systems are helpful for users to understand complex 3D data and models. Third, the vast size of the display screen makes it possible for an entire team to cooperate with each other in the virtual data space. Fourth, the new tracking interface improves work efficiency and shortens work cycles. Although this area has enormous potential and the prospect of its application is broad, there are still many unsolved theoretical problems and technical obstacles that have not yet been overcome. In a complete virtual reality environment, the virtual reality system will serve as a powerful multidimensional information processing system that helps geologists and geophysicists generate new ideas and solve problems.

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4.5. SPECTRAL DECOMPOSITION TECHNIQUE The spectral decomposition technique has developed as a descriptive technique for reservoir characteristics based on frequency spectral decomposition.

4.5.1. Concepts and Usages of Spectral Decomposition Frequency decomposition transforms the 3D seismic data into a frequencytuning volume with STFT, and this technique closely relates to thin-layer interference, the seismic wavelet, and random noise. According to the discrete frequency characteristics of the tuning volume of a thin layer, the frequency decomposition technique identifies distribution characteristics of thin layers by analyzing the frequency characteristics of complex formations and regional phase characteristics. It is a new method of seismic interpretation and a powerful imaging and simulation technique for delineating discontinuous geological bodies and stratigraphic thicknesses. The basis of spectral decomposition is that the reflecting signal in the frequency domain indicates the stratigraphic thickness of a thin layer. This method decomposes the virtual reflection signal in each frequency band, which was generated from the reflection interference of the top and bottom of a thin layer. The frequency of the virtual reflection signal corresponds to the two-way time thickness of the thin layer. The seismic wavelet contains many frequency components that are higher than the dominant frequency of seismic waves. The higher frequency components can be used to delineate subtle lithological and stratigraphic characteristics. Spectral decomposition technique depends on the STFT. The frequency response characteristics of the amplitude spectrum corresponding to the longtime window are very different from that corresponding to the short-time window. The frequency responses of seismic traces achieved by long-time window Fourier transform are similar to those of the wavelet. However, the frequency responses of seismic traces achieved by STFT rely on the stratigraphic thickness and physical properties in the short-time window. The time window is shorter, and the geological randomness is smaller. The frequency response of the amplitude spectrum is also different from the frequency response of the wavelet, but is similar to the sum of the frequency responses corresponding to the wavelet and local strata. Landmark Graphics Corporation has made great progress in their spectral decomposition (SpecDecompÒ) software. According to the frequency components of seismic waves, this method can effectively identify thin layers and delineate reflection characteristics of complex geological strata. It helps us understand depositional processes and conduct reservoir evaluation in areas that are difficult using conventional processing and interpretation.

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4.5.2. Synopsis The theoretical basis of the spectral decomposition technique is the tuning effect of thin layers. The tuning amplitude spectrum of thin layers delineates the characteristics of thin layers. With the development of the STFT, seismograms in the time domain are transformed into the frequency-tuning data. The traditional Fourier transform only transforms the entire signal recording into a frequency signal in the time domain. The frequency amplitude reflects the amplitude and phase of each frequency component in the average meaning and entire signal length. Seismic signals of different time lengths have great differences, so we must select an individual signal to perform Fourier transform. To overcome the frequency cutoff effect using the short-time window, we must use a time window function to overcome the time-window effect. That is what we call the STFT. Fourier transform is a kind of integral transform. It transforms a function into another function with different independent variables. For example, a function in the time domain (t) can be transformed into a function in the frequency domain (f), and a function in spatial domain (x) can be transformed into a function in the wave number domain (k). The transform process is reciprocal, as shown in Eqn (4.1) below. gðtÞ

ZþN ¼ Gðf Þei2pft df

(4.1)

N

GðtÞ

ZþN ¼ gðtÞei2pft dt

(4.2)

N

Fourier transformation is also applicable to multivariable functions, and the above formula (Eqn (4.2)) can also be expanded as a 2D transform, as shown in Eqns 4.3 and 4.4 below. " gðx;tÞ ¼

# ZþNZþN

1 ð2pÞ2

Gðk; f ÞeiðkxþftÞ2p dkdf

(4.3)

N N

ZþNZþN Gðk; f Þ ¼

gðx;tÞ eiðkxþftÞ2p dxdt

(4.4)

N N

The calculation precision is only concerned with the length of the data sampling. Some information will be lost because of sampling data truncation and tradeoffs, which affects the calculation accuracy. The time length of seismograms should be “unlimited” in order to meet the accuracy requirements of Fourier transform, but there is an incompatibility between the

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length of the time window for calculation and the computation workload. The longer the length of the time window, the heavier the computation workload. There are differences between characteristics of the frequency spectrums corresponding to the long-length time window and the short one. The amplitude spectrum corresponding to a long-time window is similar to the wavelet spectrum, accompanying the white noise homogenization phenomenon, while a short-time window is only used as a wavelet filter for a single-target geological body, not producing white noise homogenization. By amplitude spectral decomposition using a short-time window, the frequency domain tuning a 3D volume generated from seismic data can be used to identify thin layers and the internal characteristics of geological bodies. In addition, STFT can be used to improve the calculation accuracy of Fourier transform. Discrete fast Fourier transform is commonly used to convert seismic data from the time domain into the frequency domain, using the formula in Eqn (4.5). Gðk; f Þ ¼

M X N X 2p X 2p g ei M mk ei N nf n¼0 n¼0

(4.5)

ðm;nÞ

If using a window function, we can get frequency-tuning data of different time ranges. The amplitude spectrum can be used to identify the time thickness of a formation, and the phase spectrum can be used to detect the discontinuities of geological bodies in the horizontal direction. In order to effectively identify the distribution and internal changes of thin layers, geophysicists always select the frequency slices corresponding to lithologically thin layers in each target formation and get relatively clear tuning images of thin layers within the dominant frequency range.

4.5.3. Basic Workflow and Key Parameters The spectral decomposition technique is designed for detecting thin layers in the 3D seismic work area, using the amplitude spectrum to predict thicknesses. Seismograms are the reflection responses of many thin layers. The interference maps of tuning reflection amplitude spectrum contain the acoustic relationship of each thin layer, and the notch pattern of an amplitude spectrum helps detect the changes in formation thickness. Similarly, the phase instability of a phase spectrum helps us identify stratigraphic discontinuities in the horizontal direction. Combining the interference phenomena of amplitude spectrums and phase spectrums, interpreters can efficiently identify and map underground lithological changes in the 3D seismic work area. The basic workflow is as follows: 1. Load the seismic data in the time domain into the 3D interpretation systems, and identify research horizons and objectives. 2. Conduct frequency analysis and processing parameters tests, in order to determine the time window and frequency parameters. Then convert the

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seismic data corresponding to target layers within the short-time window from the time domain into the frequency domain, and get the resonant volume of target layers. In a frequency slice, distinguish between coherent amplitude changes, which are the interference form of thin layers, from the random noise signals, which are small spots in the interference map similar to relatively poor television signals. Analyze the lateral variation of the target layers by observing the animation in the entire frequency range (such as all frequency slices), according to the depositional model, Calculate the amplitude spectrum and phase spectrum of each sample point in the seismic data and obtain the time data volumes corresponding to each frequency. The notch pattern of an amplitude spectrum helps detect the changes in formation thickness. Similarly, the phase instability of a phase spectrum helps us identify stratigraphic discontinuities in the horizontal direction. In general, after using the resonant volumes of target layers to identify the target layers, we use the discrete frequency data volume to predict reservoirs outside the target area. Interpret and delineate geological targets. The focus should be to calculate the thickness of thin layers and delineate the lateral variation of reservoirs.

When we conduct seismic data processing with spectral decomposition techniques, we can identify strata reliably with a short-time window. The amplitude spectrum of seismic data is not white noise homogenization, so it can be used to distinguish thin layers and the internal features of geological bodies. In addition, because it is very sensitive to small changes in seismic characteristics, the phase spectrum is useful in simulating rock features.

4.6. EXAMPLES 4.6.1. Three-Dimensional Visualization There are five steps in the workflow for 3D visualization, with the most important being the visualization of 3D data volumes. Using the differences in seismic responses for different geological bodies, 3D visualization can identify sandstone body distribution in 3D space in perspective. In practice, we need to make use of each basic step and method flexibly. Below are some examples of how to apply 3D visualization techniques in specific geological conditions.

4.6.1.1. Lithological Reservoirs This study area is located in Sichuan Basin, southwest China. The targets are the 20-m-thick fractured sandstone reservoirs of the Middle Jurassic. The S/N ratio of the seismic data in this area is high, and waveform characteristics are clear (Figure 4.8).

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FIGURE 4.8 Three-dimensional seismic profiles in the work area.

For lithology analysis, we first find geological anomalies in the seismic profiles by inspecting the data volumes. Second, we use cross-sections to determine the time scope corresponding to a geological anomaly and isolate the data volume in the time scope above the standard reflector interface or unconformity that is proximal to and below the anomaly. Third, lock the time window and adjust the color and transparency parameters to find the best time position and proper thickness of the time window for optimally observing geological anomalies and identifying the distribution characteristics of reservoirs. Figures 4.9 and 4.10 show the distribution characteristics of channels in the bottom of the Jurassic Lower Shaximiao Formation. Drilling results confirm that the sandstone thickness is up to 20 m.

FIGURE 4.9 The predictive plane of sandstone body distribution.

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FIGURE 4.10 Three-dimensional display map of a sandstone body.

FIGURE 4.11 Three-dimensional display of structures along a layer and coherent attribute.

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FIGURE 4.12 The imaging of Channel A in the frequency slice of 16 Hz.

4.6.1.2. Distribution Characteristics of Structures and Faults The characteristics of structural traps are obvious. If we display preinterpretation results along a layer plane, the contact relationships between structures and faults are more obvious and clear. Other attributes such as coherence analysis are helpful in verifying interpretation results. Figure 4.11 shows the 3D display of structures along a layer plane and the coherence attribute of the same layer. It also delineates the structural characteristics and distribution of major faults. For different geological bodies, the 3D visualization technique can quickly extract information about the internal structures of a geological body with different visual methods and parameters and quantitatively interpret the

FIGURE 4.13 The imaging of Channel A in the frequency slice of 26 Hz.

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FIGURE 4.14 Fine characterization of rivers by spectral decomposition.

attributes of geological bodies. It can accurately delineate the spatial variation of faults and reservoirs and display the overall appearance and subtle changes in reservoirs. This technique is extremely helpful for quantitative reservoir characterization description.

4.6.2. Frequency Decomposition Figures 4.12 and 4.13 show examples from the Gulf of Mexico. They use spectral decomposition techniques in order to detect the sedimentary

FIGURE 4.15 Response of faults in the phase spectrum of 16 Hz.

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FIGURE 4.16 Response of faults in the phase spectrum of 26 Hz.

phenomena of the Pleistocene Mississippi Delta (Lopez et al., 1997). We can see that frequency slices are more effective in delineating the tiny lateral variations and different depositional characteristics than conventional amplitude and phase slices. Channel A in the 26-Hz frequency slice (Figure 4.13) is clearer than that in the 16-Hz frequency slice (Figure 4.12). Channel B in the

FIGURE 4.17 Response of faults in the conventional phase response of 26 Hz.

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16-Hz frequency slice is more obvious than that in the 26-Hz frequency slice. However, any single frequency slice cannot fully explain depositional changes. An advantage of the spectral decomposition technique is that it allows us to inspect every frequency slice as animation, and we may find subtle changes of acoustic impedance in different frequency slices. Whether Channel A or Channel B, both of them cannot be adequately described by conventional methods (Figure 4.14). The advantage of the phase spectrum is that it can detect discontinuities. From the phase responses of 16 Hz (Figure 4.15) and 26 Hz (Figure 4.16), we find that the phase responses in the position away from faults are stable, while in variable conditions such as faults and other discontinuous phenomena, the phase responses are unstable. The phase spectrum is more useful in delineating faults than conventional phase responses (Figure 4.17).