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Overlaying Shape Files on Seismic Surveys

In our post, “Closer Look at Coordinate Conversions,” we allude to the capabilities of INTViewer with coordinate system conversions. One benefit of on-the-fly conversions is the ability to see your seismic data in context. In the example below, a time slice is reprojected to the coordinate system used by Google Maps.

 

Side-by-side view of seismic dataset in original CRS projected to a Mercator-type CRS over satellite imagery. Data courtesy of Geophysical Pursuit Inc.

 

Showing satellite imagery is only one example of how you can use INTViewer to verify the geolocation of a seismic survey. INTViewer can visualize much more than seismic, and our customers often use INTViewer to compare seismic survey with shape files.

In the example below, the seismic is delimited in two regions, and each of these regions is delimited by a shape file.

Two shape files overlaid on a time slice layer with Bing Maps in the background.

 

The most basic shape files consist essentially of polygons. Each point of this polygon has coordinates relative to a CRS. The shape files in this example are referencing the NAD27 coordinate system. INTViewer automatically converts NAD27 locations to the CRS used by Google Maps, making it possible to view several datasets in the same map window.

Similar to layers in Adobe Photoshop, each dataset has its own layer. Layering allows you to visualize several objects at one, while keeping independent control of each object. This concept is used across the entire INTViewer experience to allow users to overlay data.

When users start a new session, they typically open the dataset from the File menu. Then, to overlay data, they select the Layer → Add Layer menu. For example, to produce the screenshot below, you would first:

One shape file overlaid on a time slice layer.

 
Open a seismic dataset as a time slice:

Seismic dataset as time slice.

 
Then add a GIS layer:

Adding a GIS Layer

 
INTViewer’s support for shape files goes beyond visualizing simple polygons. The example below describes oil and gas fields West of Norway.

Shape file showing Oil and Gas fields west of Norway.

 
INTViewer also lets users create their own shape file programmatically (see our help site here). Check out the subject of our next post — one of the most interesting uses of shape files in INTViewer—the Mineral Rights plugin. In this plugin, seismic surveys are cut along regions delimited by shape files.

Stay tuned!

Check back soon for more new features and tips on how to use INTViewer or contact us for a demo.


Visualize Microseismic Events with INTViewer Plugins [Walkthrough]

INTViewer is well-known for its seismic analysis capabilities. Among the less well-known plugins, there is a set that always impresses during demos: the microseismic plugins, a set of four plugins that allows INTViewer users to visualize microseismic events.

To download these free plugins from INTViewer, open Tools→Download Plugins and click the Download Plugin link. A wizard will open. Follow this wizard to perform the installation.

INTViewer’s Plugin Store, directly accessible from inside the application.

 

This installation adds a menu item to the File menu. Select File→Open in 3D→Microseismic, then select a dataset. INTViewer supports microseismic files in .CSV (comma-separated values) format. A microseismic dataset is essentially a set of X and Y points, and each point has a timestamp and attribute values. If your dataset is not stored in the .CSV format, it would be easy to plug your own with the INTViewer public API.

There are several ways that microseismic events can be represented in a .CSV file, and a mapping needs to be specified to let INTViewer know how to read this file. There is an Auto Detect button that facilitates that process.

 

INTViewer is able to detect complex data formats, even with the date and time stored in the same column. In this example above, the timestamp section specifies that both date and time are stored in column 1.

The 3D visualization will load after you press the OK button.

Basic visualization of microseismic events in 3D

 

The visualization of microseismic events can have up to 7 dimensions. The first 3 dimensions are X, Y and Z. A 4th dimension is color. In the example above, points are colored by amplitude values. You can visualize a 5th dimension by selecting an attribute to control the size.

Visualization with variable size symbols

 

You can visualize a 6th dimension by selecting an attribute to control the transparency.

Visualization with transparency

 

The last dimension are the symbols themselves. Just like we use color maps to color points, we can use symbol maps to symbolize points.

Visualization with a symbol map

 

Because events are indexed by time, INTViewer makes it easy to reveal the sequence of events for a microseismic dataset. Open Window→Playback

The Playback window

 

The Playback window shows an histogram of events, ordered by timestamp. The longer the bar, the higher the number of events for that timestamp. By pressing the Play buttons, you start the animation of all your microseismic displays.

Interaction between the Playback window and the 3D visualization

 

The playback window is not the only histogram you can visualize. The distribution of any attribute is accessible.

Histogram of the CHI attribute

 

Cross-plotting is also possible. Select two attributes of a microseismic dataset for the X and Y axis, then one for the color.

Cross-Plot between the LTA and CHI attributes, colored by depth

 

The map window has capabilities similar to the 3D window. The color and symbol of points can be controlled by the values of any two attributes.

Example of map visualization

 

The map window is a powerful tool, featuring on-the-fly conversions between coordinate reference systems. If you specified a CRS during the mapping step, you can reproject your data to any other CRS.

All windows can visualize multiple datasets at once. In the example below, we combined a well and microseismic events.

Combining a well trajectory and a microseismic in 3D

 

The XSection window visualization is particularly interesting. It allows you to combine a seismic dataset, a well trajectory, and microseismic events.

Combining a seismic, a well trajectory and a microseismic in a XSection window
The Gamma-Ray (GR) well curve is shown in red

 

The last feature of this walkthrough is INTViewer’s Python scripting. Just like any other data type, you automate the visualization of microseismic events with a few lines of Python. INTViewer has the option to act as a Python server that an external system can easily control. Events can be added programmatically to microseismic datasets. New points are visualized immediately, making INTViewer an option to visualize real-time microseismic data.

We’ve published a few tutorials showcasing how Python can be used to work with microseismic data:

Generating synthetic microseismic data
Creating a sub-selection of a microseismic dataset using a cross-plot trend shape

Ready to learn more? Contact us for a live demo of the microseismic plugins!


New Window System for INTViewer 5.2

If you are already a user of INTViewer, the first thing you will certainly notice when you open INTViewer 5.2 for the first time is the new window system. The way windows are laid out on screen has changed, introducing tabs to browse through these windows.

Side-by-side comparison of the window systems (new “TABS” on the left, legacy “FRAMES” on the right).

 

Before panic sets in, I want to reassure you: The window system you used in previous versions has not been removed. You can actually revert back to it.

Option to control which window system should be used.

 

Now the question becomes: “Which window system works best for my workflow?” As INTViewer is used in many different ways for many different outcomes, it’s difficult to give a definitive answer. Let’s explore what each window system is best for.

The new “TABS” window system is versatile. You can place your data windows anywhere on the INTViewer desktop, not just the middle area. In the example below, the time slice of the survey is displayed on the side. The XSection window is front and center. As you move your cursor in the XSection window, cursor synchronization allows you to locate which slice of the survey you are looking at, but the time slice doesn’t take precious screen space.

Example of showing a time slice as a helper window.

 

As the user base of INTViewer grew, I noticed it was being used to show an increasing number of windows at the same time. Managing all these windows can become a task in itself. The example below shows how we’ve upgraded INTViewer so you can keep dozens of windows while focusing on a few select visualizations.

Example of multiple windows

 

This layout is especially useful if you use INTViewer to QA the result of processing steps. Tabs allow you to keep your content manageable: Each window is quickly accessible, but they don’t overlap. There is even a shortcut to switch between windows. Press Ctrl+Tab at the same time, and the following switch panel will show:

Quick window switch panel

 

Another advantage of the “TABS” window system is that sessions remember the state of each window, not just data windows. When you open a session, the full layout of your INTViewer session is remembered.

With this quick introduction to the “TABS” window system, why would anybody want to revert to the legacy one? In my experience, the “Frames” window system works great on laptops, where screen size comes at a premium and you only need to perform one simple task. INTViewer is a great tool for acquisition QC in the field. It takes seconds to open a dataset and perform a spectrum analysis, making it a good fit when you need the mobility of laptops.

A spectrum next to a seismic dataset

 

INTViewer is also a development platform. Customers develop their own plugin to make INTViewer perform one specific task. For example, if you develop a velocity picking plugin, the layout of your screen will be quite standard: one window shows the original data, another shows the velocity model, and the last one shows the modified data based upon this velocity model. Users spend long hours working only with these three windows, and the user experience needs to be optimized for this work.

The API in the “FRAMES” window system allows programmers to finely control the placement and size of each window. I visited a customer last week that did exactly that for this one plugin. Users of that plugin had a standard workstation with three monitors, and the plugin would make sure that each window would be shown in its own monitor.

Personally, since I work on a desktop PC with two large monitors, I prefer the “TABS” window system. The “TABS” window system has been an option in INTViewer for a few years. It has matured nicely, and after integrating feedback from early adopters, I believe this is now ready for prime time as the default option.

Check back soon for more new features and tips on how to use INTViewer or contact us for a demo.


A Closer Look at Coordinate Conversions

The CRS chooser when you type “World Mercator” in the search box

INTViewer makes coordinate conversions virtually transparent to users. Users pick two Coordinate Reference Systems (CRS), one for their data and one for the visualization map, and the visualization updates automatically.

How does INTViewer do it? The short answer is “it depends”. The long answer is that the strategy used varies based upon the CRSs selected, and the points to convert. To explain this, a better understanding of CRSs is needed: Coordinate Reference Systems are essentially defined as an origin, a bounding box, and a mathematical formula to convert each point to LAT-LONG coordinates.

A well-known CRS is the Mercator projection spanning the entire globe (except the poles) and using LAT-LONG coordinates with Greenwich, UK as its origin. This CRS is known in INTViewer as “WGS 84 / World Mercator”.

There are thousands of well-known CRSs used all over the world; this is only one of them. The European Petroleum Survey Group (EPSG) was formed in 1986, and maintains the list of these CRSs. This group provides a database of CRSs to all subscribers and this database is exported in the form of XML files to INTViewer. When you pick a CRS, you are essentially going through that exported list of CRSs.

When INTViewer converts coordinates between two CRSs, one strategy is to use WGS 84 as a hub. This means that we first convert coordinates from the first coordinate system to the WGS 84 coordinate system, then we convert the resulting coordinates to the second coordinate system. Unfortunately, this cannot be done with the CRS definitions alone; a “transform to WGS 84” needs to be specified for the conversion to WGS 84.

WGS 84 as a transformation hub
WGS 84 as a transformation hub

 

By default, INTViewer doesn’t prompt for that transform even where there are several possible options. INTViewer picks the transform to WGS 84 automatically based upon the bounding box of the points to convert. Each transform has its own “area of use” and INTViewer tries to pick the transform that has the smallest area of use but still contains all points to convert.

The CRS options panel
The CRS options panel

 

Users can elect to pick transforms manually by checking the highlighted option in the CRS options panel.

The “WGS 84 hub” technique is an easy way to convert coordinates between any two CRSs, but this is not the most accurate method, as it can introduce errors of more than 10 meters. For really accurate conversions, NADCON (North American Datum CONversion) and NTV2 (National Transformation Version 2) grids are a better option.

In a few words, NADCON and NTV2 grids allow much more precise conversions between two CRSs that have overlapping bounding boxes. NADCON and NTV2 grids are not defined for the entire planet, only specific areas of interest like North America, Europe, Australia, or the Middle East. As a result, INTViewer will use the WGS 84 technique as a fallback only if NADCON or NTV2 grids are not defined.

North America typically uses two different datums: NAD27 and NAD83. NAD27 and NAD83 are two geodetic reference systems, one created in 1927, the other in 1983. In 1989, U.S. states started defining High Accuracy Reference Networks (HARN) using GPS technology, making it possible to convert coordinates to LAT-LONG without a loss of precision of more than one meter. To take advantage of this precision, INTViewer attempts to use HARN-based conversions when possible.

In conclusion, the strategy to convert coordinates is highly based on the area of use (the bounding box) of the points to convert. If these points are found to be outside of a NADCON or NTV2 area, WGS 84 will be used as a hub. HARN may be used within the NADCON areas.

INTViewer has been evaluated against the OGP Geospatial Integrity of Geoscience Applications (GIGS) compliance guidelines and found to be compliant in 2013. The International Association of Oil&Gas Producers (OGP) created these guidelines to eliminate common failures of geospatial integrity in geoscience software applications.

While not a pure mapping software, INTViewer tries to find the right mix between ease of use and accuracy. The accuracy is good enough that it can be used to manage exclusion zones in seismic data, as demonstrated by the Mineral Rights plugin. As a developer, you can leverage this conversion mechanism in your own plugin without worrying about the implementation details.

Check back soon for more new features and tips on how to use INTViewer or contact us for a demo.


Extend INTViewer with Java

The inspiration for this blog comes from this poster that was recently added to our walls at the office.

So, how does INTViewer help developers empower users?

INTViewer provides many features off-the-shelf, but its extensibility is unique. Developers can customize numerous aspects of INTViewer by extending the INTViewer platform. And one way to extend this platform is to write Java plugins.

Here’s an example: INTViewer 5.2 has a new feature called Auxiliary Widgets which allows content to be displayed above XSection windows. I also mentioned this feature when I introduced the Normalization widget.

An example of the auxiliary widget allowing interactive limits normalization
Because INTViewer is an extensible platform, customers can add their own widget to the set already built in, meaning they can write their own widget for XSection windows.

We maintain a dedicated site for developers to find tutorials and other reference material to learn how to write plugins. For example, here’s the step-by-step guide to help developers add their own auxiliary widget.

The “Statistics” auxiliary widget added by plugin

What’s remarkable about this example is that it only takes three small files to plug this fully functional feature. Meaning that not only is INTViewer an extensible platform, but it is also a platform that is simple to extend. We spent many hours on the public API to make sure it is easy to understand and easy to use, yet powerful.

Now, why is the ability to add content on top of XSection windows valuable? Hardware vendors are one example of a company that would want to leverage this feature. Companies who sell acquisition hardware need to provide a tool for their customers to visualize the proprietary data captured by their instruments. The auxiliary widgets area is an ideal place for showing additional data specific to acquisition hardware. As end users visualize a seismic dataset recently captured, they also visualize vital parameters of the acquisition session without having to open another dialog or window.

Extending INTViewer is a broad topic, impossible to cover in just one post, so check back often for more articles on this topic and more. Or to get started with plugins, check out this architecture article.

Check back soon for more new features and tips on how to use INTViewer or contact us for a demo.