INTViewer

Apr 08 2021

Extended Well Data: How to Use the New Well Data Structure in INTViewer 2021

LAS format is the industry-standard format to store and exchange well log curve data. Despite its simplicity and usability, it has some strong limitations: log curves must have the same Z sampling for instance.

The latest INTViewer 2021 introduces a new well data structure, the Extended Well Data (EWD). Like the existing well data based on LAS files, EWD can be displayed in the Well Log Window. They can be displayed in Map views, 3D, and seismic viewers as well (provided the trajectory is well defined).

This new format allows more complex edition operation, heterogeneous log curves in the same well data, and a new time/depth conversion process.

Synthesis Window

The EWD structure allows more flexibility in log and well edition. The EWD Synthesis window exhibits all logs and well metadata.

On this window some actions are available:

Metadata edition
Add curves with formula
Remove curves
Open Well Log Window with selected curves

Curve data can be edited using the Log Curve editor.

Z and values can be edited, samples can be added or removed, and data can be copied in an external spreadsheet, then pasted back in the editor after modification.

Contrary to standard LAS data, EWD allows log curves to have heterogeneous Z columns, and various samples count. Each log curve can then be edited individually without affecting other curves sampling.

To learn more about how to edit wells in INTViewer, check out our video tutorial:

Log Curve Formula

Like with standard LAS-based well data, curves can be added using a formula. Since log curves can have different Z units, users will be forbidden to mix time and depth curves using formulas.

Since curves can be edited individually and have different sampling, the result curve, and all other curves used as input, will be resampled to the sampling of the first data found in the formula (in the image above GR_Time will be used).

Formula editor can be started from the main synthesis window, and also from the popup menu on the EWD node.

Time / Depth Conversion

By defining a curve as Time / Depth law (typically a curve that will have time or depth unit for data values, and opposite unit for Z values), EWD offers the ability to perform Time/Depth conversion. A simple editor to choose the data to convert and the law to use is opened when Time Depth conversion action is launched:

Time / Depth conversion can be performed on logs, but also on Markers. The EWD format can hold several Markers set, each having time or depth unit.

Time/Depth conversion action can be found on the main EWD synthesis window, but it’s also available from the EWD contextual menu in Well Log Window.

The Time/Depth conversion process is a simple sample-by-sample linear interpolation using the Time/Depth law as reference. A more complex process, eventually with parameters, can be added with customization.

LAS Import/Export

EWD can be created from LAS files and exported to LAS files after edition. LAS data can then be imported as EWD in INTViewer 2021. Users can perform various editions, time/depth conversion, and other actions, then export the result in standard LAS to use the data outside INTViewer 2021.

When exporting to LAS, the data domain must be specified (a LAS can only contain data sharing the same Z unit). Users can also choose which data to export. The following screenshot shows an EWD data and the exported LAS side by side.

In the EWD view log curves and markers are in depth, the time depth law is displayed in green. Converted and exported data are in time.

Users have the choice with EWD to display or not in the same view time and depth data.

Customization

The Extended Well Data GUIs can be customized to offer more specific behaviour to the standard EWD functionalities. So a specific plugin will be able to modify:

EWD Synthesis customization
- Specific header panel
- Custom table filters
- Custom table control (available units, domain, validation)
Time/Depth conversion
- Conversion process and parameters can be added

For more information on INTViewer, please visit www.int.com/products/intviewer/

Mar 09 2021

How to Use INTViewer 2021’s New Mapping Capabilities

INTViewer is a platform that allows geoscientists to view seismic data, check for errors, confirm geospatial integrity, perform light processing, and analyze their dataset. INTViewer is specifically designed to enable users to quickly access large datasets—prestack, stack, and 2D— from a laptop in the field to a desktop or remotely via the cloud.

The upcoming release of INTViewer 2021 has new map features including a RemoteMap plugin and support for the import and export of GeoTIFF files.

Users can populate map views with more GIS data(1). The possibility to aggregate several GIS data sources allows users to get a clear understanding of their field.

INTViewer Timeslice — Time slice exported from INTViewer and rendered on top of a satellite view in QGIS.

In the previous version of the RemoteMap plugin, users could use a Web Map Tile Service like Google or Bing to visualize in the background. In the 2021 update, we have added the possibility to set up a custom WMS server. Users can now register their preferred WMS servers in the settings panel and access them in any map view, making it easy to correlate geographic information with their data.

INTViewer Teapot Field — Teapot field, showing study bounds, lithology and faults from USGIN Geology

Using INTViewer, users can also produce georeferenced images by exporting maps to a GeoTIFF image to view in their favorite GIS software.

With these new and improved features, users will be able to get a better understanding of their field, easily and efficiently correlate geographic data, and import and export GeoTIFF files.

For more information on INTViewer, please visit www.int.com/products/intviewer/

1 These features are available via the RemoteMap and GisRaster plugins, available on the update center.

Mar 02 2021

INT’s INTViewer 2021 Release Extends Functionality for Geoscience Data QA/QC from Anywhere

New features expand support for well data analysis, mapping, import/export georeferenced images, more flexible licensing, and more.

Houston, TX —INT is pleased to announce the newest release of INTViewer. This release includes a RemoteMap plugin, support for the import and export of GeoTIFF files, a new extended well data structure, and flexible license borrowing.

“This latest release focuses on giving INTViewer users the ability to create a new Well data model, improved mapping capabilities with new features, and the ability to borrow licenses through a new and improved UI. We believe these additions and improvements will result in significant performance gains for our clients.”

—Laurent Renard, Research and Development Manager at INT, Inc.

RELEASE HIGHLIGHTS:

RemoteMap Plugin — Allows users to overlay data on a background map from Google or Bing maps, set up a custom Web Map Service (WMS) server (authentication not supported), and the ability to overly multiple remote layers.
Import and Export GeoTIFF Files — Users can produce georeferenced images by exporting maps to a GeoTIFF image to view in any GIS software.
Extended Well Data (EWD) — Can be displayed in the Well Log Window, Map views, and 3D and seismic viewers. EWD offers the ability to edit your log curve data and Time/Depth conversion as well as manual editing. EWD can be created from LAS files and exported to LAS files after editing.
License Borrowing — Allows users to perform borrowing licenses through a new UI. This improvement will be useful when a geoscientist needs to take INTViewer on his laptop to work in the field for a limited time.

INTViewer is a software that allows geoscientists to view seismic and well data, check for errors, confirm geospatial integrity, perform light processing, and analyze their datasets. INTViewer is specifically designed to quickly access large datasets—prestack, stack, and 2D—from a laptop in the field to a desktop or remotely via the cloud. INTViewer is customizable to support proprietary and automated workflow via Python script.

Read the press release on PRWeb.

For more information about INTViewer or INT’s other products, please visit www.int.com.

Interested in trying INTViewer?

Request a trial for INTViewer today

Dec 05 2019

A Countdown of INTViewer’s Features for the Cloud

2019 has been a year full of milestones. INT celebrated its 30 years and has made IVAAP available to all members of the OSDU consortium as part its demo release. But this year has seen many more achievements, and among them is the consolidation of INT products as a complete ecosystem, an ecosystem centered around geoscience data, built for the cloud. One of the pieces of this ecosystem is INTViewer. With the new year approaching, let’s count down the ways that the latest iteration of this desktop application facilitates the ingestion of your data to the cloud.

Time slices take less disk space

Seismic datasets take a large amount of space. While storage is “cheap” on the cloud, when individual files take terabytes, creating a copy of that file is not an innocuous decision. Time slices provide an excellent visualization of a seismic survey but transposing a dataset effectively creates a copy of that data and not every workflow requires access to all possible time slices.

INTViewer 2019 now offers an option to choose how many time slices you want to create during transposition. Just a few slices is often enough, especially if you maintain a data library and use INT’s solutions to showcase your data to potential customers. The output of the transposition will be a much smaller file, cheaper to host and faster to upload.

Virtual headers save time and reduce storage costs

This feature was actually added in 2018, but is worth mentioning because of the cost savings. When you use INTViewer to prepare data, you might find that some headers are not populated. For example, for acquisition data, you might know the location of the source and the receivers, but not the offset or the location of the midpoint. These two header values can be calculated, and INTViewer proposes to create so-called “virtual headers” that will store this information.

Creating virtual headers doesn’t modify your SEG-Y file. It doesn’t change the size of the small index file that INTViewer creates to make fast data access possible. Without virtual headers, to show the midpoint or the offset, your only solution would be to rewrite your data. Not only this rewriting operation takes time, but it also creates yet another copy of your data, doubling your storage costs.

Quick validation of your data before you upload it to the cloud

New technologies bring new terminologies. One term in particular that has made its debut in the geoscience community moving to the cloud is the term of “snowball”. A snowball is the physical transport solution that cloud providers offer when the network becomes impractical to move large data files to the cloud. This is a painful process to “ship” your data with a snowball and even when network bandwidth does allow reasonable upload times, there is certainly no time to do it twice.

INTViewer has been designed to allow immediate quality control of your data. Drag and drop your SEG-Y file to INTViewer’s desktop, and you’ll visualize traces immediately. Performing a spectrum analysis is two clicks away. And there is no need to set up a project. After indexing your dataset locally, verifying the location of your data on a map is also instantaneous. This is a simple way to confirm the validity of the location headers and coordinate reference system prior to ingestion.

As you upload more and more data to the cloud, your validation process needs to become systematic. This is where the automation of INTViewer comes in handy. INTViewer is scriptable through Python, allowing you to repeat the exact same validation steps prior to ingesting your data to the cloud.

A more efficient and useful index file

Users of INTViewer are familiar with the .XGY file, an XML file that INTViewer creates during indexing. This file contains the meta data of a SEG-Y file after it’s been indexed. The format of this file has been changed in 2019 in two ways:

The meta-data of an indexed SEG-Y is now visible in the .XGY file. An example of such meta-data is the amplitude statistics (minimum amplitude, maximum amplitude, average, RMS). These statistics used to be stored in the companion binary .IGX file. Exposing these statistics in plain text allows our customers to extract this information in an automated manner just by parsing the .XGY file. This is especially useful if you are building your own data lake.

When indexing a 2D line, INTViewer automatically calculates the trajectory of that line. Likewise, when indexing a post-stack or a pre-stack, INTViewer derives the outline of this survey. This information was always stored in the .XGY file, but its projection to WGS84 was not… until 2019. While it’s also valuable information to extract and store in a proprietary database, cloud solutions such as IVAAP benefit from reading the projected geometry of a dataset instead of having to calculate it. The data loads faster on a map because there is only one file from the cloud to read to get all the meta-data, instead of two with an index from 2018. The number of files to read is important because cloud APIs consume more resources when accessing multiple files compared to the same accesses on a local file system.

Integration with IVAAP through the INTGeo plugins

Historically, the INTGeo plugins of INTViewer were written to access files posted on INTGeoServer. INTGeoServer is a lightweight geoscience server often used in conjunction with INT’s HTML5Viewer. INTViewer has long been able to efficiently visualize seismic and well datasets posted on INTGeoServer.

Likewise, with the release of INTViewer 2019, INTViewer can also access data posted in IVAAP. This means that if you have ingested your seismic datasets to Amazon S3, you can visualize these datasets in INTViewer by just pointing this application to your IVAAP instance. INTViewer is storage-agnostic and its tools (2D, 3D, F-K, Spectrum, etc.) will work without extra steps, as if the data was local.

This capability is quite useful to conclude an ingestion workflow. After you upload one or several datasets, you typically want to verify that your data wasn’t corrupted during this process, or simply that all files were posted. With the INTGeo plugins, you do not need to open IVAAP to perform this step, it can be done from the same desktop application used to flight-test this data prior to ingestion.

IVAAP supports multiple cloud vendors. In addition to Amazon S3, you can visualize data posted both to Microsoft Azure Blob Storage and Google Cloud Storage. If IVAAP has been deployed to access these data stores, you only need your IVAAP credentials and INTViewer to open the datasets they contain. This also applies to all files posted in an IVAAP “geofiles” connector, whether they are seismic (SEG-Y, SEP) or well (LAS, DLIS) files.

This concludes our countdown (happy new INTViewer?). While INTViewer stands on its own as an application for QA and QC, it is also a useful companion to a cloud ingestion workflow in general, and to IVAAP in particular. You reached this far—contact us for a demo or an evaluation!

Aug 09 2019

In Retrospect: 10 Years at INT

This month of July marks a significant personal milestone since I have worked at INT for 10 years. 10 years is a long time, especially in technology where paradigm changes occur approximately every three years. Yes, the word “paradigm” was actually in vogue the year I started at INT—that’s how long it’s been. For this anniversary, I’d like to take you on a chronological tour of my experience.

The Formative Years

The first two years at INT were spent learning the many aspects of the application and the science I was working on: INTViewer and subsurface data. I liked joining a new team and getting acclimated to a new code base. I learned a lot from INTViewer’s architect. For example, he helped me understand the significance of making aspects pluggable. Not only does it serve INTViewer as a platform, but it allows the code to evolve without getting out of control. Following this principle, INTViewer’s code base has been able to grow several folds. And we’ll see that the plugins approach served me well in other projects over the course of 10 years.

Growing with INTViewer and INTGeoServer

After the first two years on the job, I picked up more responsibilities. Becoming the “ultimate resort for INTViewer questions” affected me in a way I didn’t anticipate. When I first started, whenever someone asked me a question I could not answer, my internal dialogue went something like, “I don’t know that part of the system. Who is the best person to ask for help on this?” After two years, this changed to: “I have been in this situation before. I know I will find the answer.” This somewhat irrational belief that I can answer any question thrown my way has helped me quite a bit when it comes to solving problems and helping others. When a coworker has a tough technical question, I didn’t anticipate I would one day answer, “Let’s find out!” with such confidence.

The needs of INT’s customers have changed over 10 years. One particular concern that has been pervasive across that time period is the ubiquity of data. Before “cloud” became the new word in vogue, customers often came to me with this problem: “I have teams all across the world, but I don’t want to maintain a worldwide file system. Visualization needs to be fast for all, without having to duplicate data. What do I do?” It’s out of these conversations that INTGeoServer, another pluggable platform, came to be.

INTViewer had years of experience built in to how to access data files efficiently, but, as a product, it needed to move beyond the file system. This was a complex technical challenge and an opportunity to widen the team’s technical skills.

INT gave me other opportunities to innovate: the integration of Python with INTViewer is quite unique in the market. Looking back, even though the technical solutions to reach “data ubiquity” have changed over the years, even though we introduced new ways to automate geoscience workflows, the fundamental work on geoscience data hasn’t evolved much. While software can be a scary place with its rate of change, I find that the geoscience learnings from my first two years are still relevant.

Building IVAAP and the Future of Ubiquitous Data

The latest evolution of ubiquitous data is cloud-based. The last three years have been a sort of new beginning for me since I’ve been tasked with leading the data side of IVAAP. Most of the IVAAP backend was essentially written from scratch, which is very satisfying as a developer. What is even more satisfying was working with the development team and seeing it grow. Since the backend was written by this team, there is no longer an “ultimate resort for questions” role. With the recent work with the OSDU consortium, I am happy and proud that the architectural decisions we made over the last three years have shown we are going in the right direction. This was recently validated by making IVAAP compatible with the OSDU platform. This work didn’t require any changes to IVAAP’s SDK—IVAAP’s OSDU implementation is actually just a plugin for this backend.

A Developer Culture

Working on INTViewer, INTGeoServer, and IVAAP for a grand total of 10 years, what has made me show up every morning has been the deep technical aspects of the job, the products I have been able to work on, and the people I interact with. INT has been a wonderful opportunity for me because of its technological leadership. If a developer says “I need X to achieve Y,” this gets immediate attention because the company culture is very developer-friendly. If you are a developer at heart like I am, being able to write code all day without interruptions is a significant perk of the job. Frankly speaking, these 10 years were also possible because developers at INT are seen as an investment, not a cost. Unlike other companies, INT has a strong will to weather tough economic cycles without shrinking its staff. I have grown with INT, and we both keep growing together (we are hiring, by the way). As a leader, I strive to help today’s new hires to have the same positive experience I had.