It’s nearly time for the 2013 Esri Petroleum GIS Conference, or “the PUG”. This year the conference is being held May 7-9, and is back in the George R. Brown Convention Centre in Houston, Texas.

Last year before the conference I posted a blog that looked at the PUG papers back to 2009 and classified them based on their general topic.

This year, with many thanks to Charles Fried (former PUG Steering Committee Chairman) who kindly provided the historical data, I’ve been able to update the analysis with the papers presented from 2000 to 2012. Note that I’ve excluded Esri demonstrations and workshop sessions from the entire analysis.

First up, a quick reminder of the categories again:

• HSE– Health, Safety and Environment
• DMT– Data Management and Technology (includes 'Databases', 'Geodesy'&'Imagery' categories)
• LMG– Land Management
• EXP– Exploration (includes 'Geoscience'&'Workflow and Analysis' categories)
• PIP– Pipeline Integrity and Risk Management
• IFO/PRD– iField Operations and Production Optimization  

The results are shown in the table and series of graphs below.

Table 1: Analysis Data

Table 1 shows the raw data for the analysis. Note the growth in the size of the PUG in 2005, where the number of user papers doubled. This is a reflection of how Esri have grown the event over time, and it would be interesting to compare the numbers of papers against conference delegate numbers over the years - next year’s update perhaps!

Number of Papers

Figures 1 and 2 plot the number of papers in each category.

Figure 1: Number of PUG Papers by Category

Figure 2: Number of PUG Papers by Year

You can see that the PUG has historically been dominated by papers on Technology (DMT), which is perhaps unsurprising, not only because Esri is a technology company, but also because the category itself is very wide, including the Esri technology itself, plus Databases, Data Management, Geodesy, Imagery, and Development.

Exploration (EXP) and Pipeline (PIP) have historically been secondary interest areas, although in recent years papers on HSE and Production (IFO/PROD) have become more frequent. Note the spike in HSE papers at the 2011 PUG which followed the Deepwater Horizon oil spill, and the subsequent dip in HSE papers in 2012. It’ll be interesting to see how big this track is at this year’s PUG.

What’s surprising to me is that papers on Land (LMG) are still in a minority, especially given how many companies in the Vendor Fair/EXPO seem to specialise in this market.

Categories by Percentage

Figures 3 and 4 plot the % of sessions by category.

Figure 3: % of PUG Papers by Category

Figure 4: % of PUG Papers by Year

These graphs suggest that since the PUG started getting bigger around 2005 the proportion of papers in the Exploration (EXP) track has been steadily declining. This is a great shame, as those talks have often been among the most interesting, at least for a lapsed geoscientist like me!

Analysis aside, if you work with GIS in the E&P space but you’ve never been to the PUG (which has been going since 1990/91), then you should check it out – it’s a great conference for catching up on the latest developments from Esri, and seeing how the industry is using the ArcGIS platform.

This year Exprodat will be exhibiting at booth #723 where we’ll be offering a 10% discount on our Houston ArcGIS training event in August, as well as showing new technology including our own software and some examples of what you can do with ArcGIS Online.

If you’re coming please do drop by and say hello!

Posted by Chris Jepps, Technical Director, Exprodat.

One of the key features of a GIS is the ability to view spatial data at any scale. To do this you need to understand what information should be displayed at what scale. This understanding is useful both for working within the GIS and for creating usable output, such as hardcopy maps or PDF map books.

When working with geological data, there may be instances where having (selected) detailed information, such as that relating to individual geological units, shown on a regional map is of use, but generally it will be hidden.

It is possible within most GIS applications to set up scale ‘filters’ on selected layers, to enable them to be hidden at certain scales. This capability allows the GIS user to compile data into a single GIS project that can be used at a range of scales, allowing that GIS project to be used for a variety of purposes.

This blog considers what sorts of geological data might be compiled into a GIS project, and at what scales these data might be generally displayed.

Large scale means big, right?

A large scale map is one that shows small areas in detail, e.g. a town plan. Conversely a small scale map shows large areas in less detail, e.g. a world map.

• Large scale = small area
• Small scale = large area

Scale and geological mapping

Small scale geological maps (1:50,000 and smaller) are suitable for researching issues such as the prospectivity of a region, or regional scale structures, e.g. a fold belt.

Intermediate scale geological maps (1:50,000 to 1:5,000) include more detailed information such as the outlines of prospects; regional-scale strike and dips; and larger structural elements that control hydrocarbon accumulations (e.g. individual anticlines).

Large scale maps (1:5,000 and larger) offer details that would crowd-out a map of smaller scale, such as sampling sites, dip and strike markings, small faults, outcrops. These maps are generally used once a prospect has been identified and geologists have been into the field to gather data. Their scale and content are designed to allow the size and shape of a hydrocarbon or mineral body to be defined and understood.

The following table illustrates suggested scale ranges for different types of feature, and the maps which follow it illustrate the effect of applying these scale ranges to the data layers with a single GIS project, and then viewing the project at a range of scales – the rectangle on each map indicates the map area displayed at the next scale.

Setting scale dependencies in ArcMap

The maps shown above use the scale dependency functionality in ArcMap to control the layers displayed at each scale. As well as making the display clearer, using scale dependency also generally increases the performance of the map, as less data has to be displayed each time it is refreshed.

To set scale dependencies in ArcMap:

1. Right-click the target layer (this can be a group layer) and select Properties.
2. Select the General tab in the Properties dialog and find the Scale Range settings at the bottom.

When scale dependencies are defined, layer entries in the Table of Contents for layers that are outside of their defined scale range will be greyed-out, and the layers won’t be visible in the map.

Summary

Understanding how you can use scale dependent rendering not only allows you to manage data effectively but greatly enhances the value of your map. Controlling what data is shown at what scale enables you to tailor your map for your audience, as well as improve performance.

Posted by Simon Kettle, GIS Consultant, Exprodat.

References

Instituto Geológico y Minero de España, 2013. Mapa geológico de la Península Ibérica, Baleares y Canarias a escala 1/1.000.000 (WMS) http://mapas.igme.es/gis/services/Cartografia_Geologica/IGME_Geologico_1M/MapServer/WMSServer

Instituto Geológico y Minero de España, 2013. Mapa Geológico de España a escala 1/50.000 (WMS) http://mapas.igme.es/gis/services/Cartografia_Geologica/IGME_MAGNA_50/MapServer/WMSServer

Kettle, S., 2012. Resedimented carbonates from the Jurassic of Mallorca. M.Phil. thesis, University of Birmingham.

I have experienced, and no doubt you have too, the tedium of saving out many layer files for the layers you have just created in your ArcMap map document. Having spent perhaps hours perfecting the symbology - getting that labelling to show just as you would like, tweaking the colours so they’re just right - the last thing you want to do is to have to save out what could be tens or even hundreds of layer files. In this blog I show you how 4 lines of Python can save you a whole lot of time.

How do I save a layer file?

ArcMap allows you to save a layer out to a layer file using this workflow:

1) Right click on the layer and select “Save As Layer File…”:

2) A dialog is shown that allows you to navigate to a workspace and save your layer file:

There must be a better way!

Repeating this workflow many times is pretty tedious! The answer? Use Python.

A simple Python script with just four lines of code enables you to save every layer in your map to the same workspace that you saved your map document.

What does the Python script actually do?

Let’s go line-by-line through the script and look at what each line does:

1. This imports the arcpy module so that it can be used in your Python script.

2. Gets a reference to the currently loaded map document (replace CURRENT with a path name to access other map documents).

3. For every layer in the map document, perform the action stated on the next line.

4. Save a copy of the layer to a file with a name that is the layer name listed in the map document, with the suffix “.lyr”. The layers are saved to the same location as the Map Document (MXD).

Summary

This example demonstrates that those GIS professionals who gain an understanding of Python and the arcpy module can look forward to becoming more productive in their use of ArcMap.

I would suggest reading through some of the literature provided on ArcGIS Resources and the Python documentation to gain an insight into using Python to help with your Geoprocessing and other tasks.

Posted by Simon Kettle, GIS Consultant, Exprodat

“All great truths begin as blasphemies,” wrote the playwright George Bernard Shaw. So let’s get the ball rolling with some simple truths about GIS:

A five-year old is better at GIS than most companies.

Geographic information systems have been around for over 40 years. So it may be surprising that even today, many organisations still struggle with the simple concept of extracting value from map-enabled data. Meanwhile, any five-year old can fire up Google Maps and get directions to the nearest toy shop. What’s going on?

On the way to world domination.
(Photo: Microsoft

In the first part of this post I explored some fundamental truths about GIS and why, inspired by the principles of Design Thinking, a good GIS strategy needs to focus on people not technology.

In this second and final part I shall explore how to go about creating a successful GIS strategy, using a simple and effective approach.

Zen and the art of GIS strategy design

Design Thinking shares some similarities with Zen wisdom. Zen is a very simple philosophy which is fiendishly difficult to describe. It is not the purpose of this blog to get into the meaning of life, but Zen offers a number of principles which are useful for designing a GIS strategy:

Beginner’s Mind

This principle is one of the main reasons why companies often ask for outside help when designing a strategy. It is easier for a fresh pair of eyes to look at problems which may be burdened with historical baggage or corporate politics. An independent consultant can draw out true requirements, mediate between different parties and work seamlessly across silos. Besides, a consultant can also bring in fresh perspectives and best practices from across the industry – but, one step at a time, we are getting ahead of ourselves!

Listening and Naturalness

Before even beginning to sketch out a tentative strategy, the principles of Listening and Naturalness need to apply. To design a GIS strategy we spend most of our time onsite simply listening to people. We find out what makes people tick and what their natural preferences are, because not every strategy will work for every type of organisation. Some people need flexibility; others prefer prescriptive processes. Some people yearn for ready-made solutions; others want to take control. Some people just need to visualise data; others require a more analytical workflow. We often find that by simply listening to people the strategy almost designs itself, right there in front of our eyes.

Imperfection

This is a potential stumbling block. It is not easy for people who take pride in their jobs to accept imperfection. The perfect strategy however does not exist, and neither does perfect data or technology. There is always the temptation of delaying a strategic improvement project until a “better time.” It is all too easy to hold back because there’s an organisational issue to be clarified, or the data is not clean enough, or there’s the eternal promise of future technology releases. But as Zen author Alan Cohen once said, “don’t wait until all the conditions are perfect for you to begin – beginning makes the conditions perfect.” It is all about progress, not perfection.

This is also where measurement comes in. To know where you’re going, you first need to know where you are. There are various metrics that can be used for GIS (see for example Exprodat’s Maturity Matrix model). And once you have measured where you are, you can track progress not only against yourself but also compare yourself to industry peers. Such benchmarking can be a very powerful motivator, and provide useful insights as well as best practices (if you are interested in joining our ‘Benchmarking Club’ do get in touch).

Stillness and Simplicity

Last but not least, Stillness and Simplicity drive the actual design of the GIS strategy. Stillness requires that you leave those things alone that don’t need tweaking, and pick your battles carefully. Focus only on the use cases that really matter. This is particularly important for something as versatile as GIS technology. The applications are almost limitless and so you need to prioritise.

There are also many other aspects to consider beyond mere use cases – these include user awareness & training, governance & ownership, data management & automation, data integrity, or technology choices. Unless an organisation is reorganising, or starting from scratch, it should not need major improvements in all of these areas. So you need to target the implementation accordingly.

Introducing some Zen into your GIS landscape.
(Photo: Paul Mannix, Flickr CC)

Keeping it going

These Zen-derived principles should help a great deal in defining a solid GIS strategy. If you ever get stuck along the way, you can always seek inspiration from the company’s business strategy. And if there isn’t one, or it is unclear, you have probably found the root of all problems.

A strategy is only as good as its implementation. The cult novel Zen and the Art of Motorcycle Maintenance observes that true contentment comes from being able to not just ride a bike but also fix it. It is all about balance. If your sole focus is on riding into the sunset you will one day find yourself stranded on a deserted road with a broken bike. On the other hand, someone who is too much into tinkering with the machine might never set off on that big ride.

Similarly, a successful GIS implementation requires a holistic outlook that knows where to go whilst understanding what goes on under the hood, so the journey doesn’t come to an abrupt halt. And like all motorcycles, a GIS needs continuous upkeep and care: not just the tools but the whole system – people, information, technology. Without regular maintenance, the strategy will just gather dust in a file drawer and the GIS system will eventually come to a halt. This may well provide some stillness but it won’t help the advancement of company goals.

Posted by Thierry Gregorius, Principal Strategic Consultant, Exprodat.

References & Bibliography

• Seven Design Principles Inspired by Zen Wisdom, Matthew May, Fast Company.
• What Zen Taught Silicon Valley (And Steve Jobs) About Innovation, Warren Berger,Fast Company.
• Zen and the Art of Motorcycle Maintenance, Robert Pirsig, Vintage Books.
• Alan Cohen quote from http://zenquotes.org/alan-cohen-quotes/

In a previous blog, we showed how to plot dip and strike readings taken by a field geologist within ArcGIS desktop. This blog looks at acquiring and displaying geotagged photos within ArcGIS desktop, another useful tool in the field geologists’ arsenal.

Now, where was that again?

Geotagging allows you to automatically capture the location information for a photo, meaning that you need never wonder where-on-earth it was taken – you’ll know. In ArcGIS desktop 10.1, spatialising and hyperlinking to these geotagged photos is straightforward.

You say 'fromage', I say...

Most modern cameras and smartphones have the ability to geotag photos. The process uses the onboard GPS to capture where the camera is when the photo is taken, saving that information into the photo’s metadata. Some cameras allow additional parameters such as camera bearing to be captured, further enhancing the utility of the photos.

You can view this information if you right-click on a geotagged photo in Windows Explorer and view the GPS section in the Details tab of its Properties, as shown below.

Take a look in the manual for your camera/smartphone to find out how to enable geotagging – it may be switched on by default (checking some old photos should allow you to work if it’s already on). For an iPhone 4, the smartphone used to capture the example photos used in this blog, it can be enabled via the Location Services section in Settings:

If you need more functionality it’s worth noting that camera and smartphone apps and other specialist devices are available that provide more capabilities than simple location capture.

Can you tell where it is yet?

To add the photos to your map, follow the workflow defined below:

1) Copy your photos to a suitable folder and rename as you see fit – the name will be available as an attribute, so making it descriptive of the location is a good idea.

2) In ArcCatalog, open ArcToolbox, and locate the Photos section in Data Management Tools.

3) Open the GeoTagged Photos to Points tool.

4) Populate the parameters – basically you’re going to import the location of each photo to a points featureclass. Click OK to run the import.

5) The new feature class generated will contain a location point for each photo which will have attributes including the path to the photo’s disk location (which we will use to hyperlink to the photo from the point, in a later step), the photo’s name and the date and time at which it was acquired.

6) If you then add the point feature class to ArcMap, you will see the locations of the photos:

7) To hyperlink to a photo from its associated location point you will need to enable hyperlinks on the feature class. To do this, do the following:

• Open the Layer Properties for the layer and go to the Display tab.
• In the Hyperlinks section, check the Support Hyperlinks using field checkbox and select the Path field using the drop down.
• Close the Layer Properties dialog.

8) You should now be able to select the Hyperlink tool from the Tools toolbar and click on a point to open the associated photo in your default image viewing application. It’s like you never left the field!

Summary

Geotagging is simple to enable and instantly makes your photo libraries far more useful when used in a GIS.

Similar but more advanced functionality is available for video files – the ArcGIS Full Motion Video Add-In permits you to view suitably attributed video feeds within ArcGIS Desktop, and allows you to capture geographic features directly from the video frames.

Posted by Ian Peebles, GIS Consultant, Exprodat.

Postscript: some additional notes and resources, courtesy of Willy Lynch, Mining Industry Specialist, ESRI Energy-Mining Industry Team:

• ArcPhoto: a set of geoprocessing tools and ArcMap user interface enhancements to enable the quick import of digital photography into the ArcGIS framework.
• ArcPhoto technical tip: see article approximately 3/4 of the way down the page.
• Adding non-geotagged photos: when photos are not Geotagged, one option is to use ArcGIS Explorer to Geotag the photos.

It can be difficult, when you have a lot of data, to find exactly that you want. There may also be data hiding somewhere in your system that you never even knew existed. So how can you find this data easily?

I still haven’t found what I’m looking for

One answer is to use Voyager. This desktop application comes in a variety of editions, including a free one. It is capable of indexing, amongst other things, local and network drives, finding geographic data as well as document data. It also provides a range of other data management tools – see the Voyager website for more details.

(I’ve been) searchin’ so long

In the interests of transparency, I should state that Exprodat has recently become a reseller for the Voyager software, which we believe is a very useful addition to the toolkit for GIS users.

Seek and ye shall find

In this blog I’m going to look at the basic search functionality that is provided, once you have created your initial index. All the functionality discussed is available in the free version of Voyager.

1. Searching for data by name

Once you have indexed your data, you can search the index using keywords - in the example below I have searched for well data. The list on the left hand side provides details of the datasets found, as well as a thumbnail view of them, whilst the map view to the right shows their geographic extents.

For more advanced users, the query syntax is simple but powerful to use, allowing you to quickly find the exact items you’re looking for.

2. Searching for data by type

You can also find restrict your search to specific data type - if you want to know all the ArcMap Map Documents (.MXDs) that are currently on your system, you can simply filter your results by selecting the Map subtype:

3. Searching for data by location

If you want to see all the data you have within a specific geographical area, you can change to the Map view and define the area within which to search for datasets.

Great, I’ve found some data – now what can I do with it?

Voyager provides tools for each listed dataset, allowing you to download the data files you’ve found or open them in the currently registered application for their data type.

There is also a Voyager ArcMap Toolbar that provides search functionality directly from ArcMap, and which can be installed as part of the default installation – this enables you to directly add search results to your ArcMap session.

Summary

Voyager provides simple-to-configure and simple-to-use search capabilities, amongst many other tools. We will take a look at some of its other capabilities in future blogs, but we recommend that you download the free version and give it a spin – it’s likely that it will make your (GIS) life a little or a lot easier.

Posted by Ellie Hunt, GIS Consultant, Exprodat

I’ve got this map, but it’s a PDF…

How many times have you found a PDF containing data that you’re really interested in using in your latest project, but for which you cannot locate the source GIS or CAD datasets, even after spending hours trawling through your file system?

Have you reluctantly decided to give up the search and opt to manually recreate the data by converting the map to an image, which you’ve then georeferenced, and from which you’ve then digitised the information you required?

This can take hours to do and no matter how well you digitise, you will never be able to achieve the same accuracy as the source data had.

Alternatively, have you spent hours cleaning the result of an automatic vectorisation tool?

I favour the Third Way

There is, thankfully, another way! A more accurate and efficient way . . .

If the PDF file contains vector data (rather than simply an image of the map), then it should be possible to extract that data. Then, providing you can accurately locate, with real world coordinates, a minimum of (preferably) 4 points marked on the map, you should be able to convert the PDF-based vector data back to properly georeferenced vector data in your GIS or CAD software.

To do this, you need one of the numerous third-party PDF to CAD conversion software packages that are available. For the workflow defined in this blog (see below), I’ve used Aide’s PDF to DXF convertor. The trial version allows for 20 free conversions.

1) Convert your PDF to a DXF, using Aide.

2) Load the DXF into ArcMap using the Add Data button.

3) Export the relevant layer(s) from the DXF to feature class(es). In the example, all the relevant data is in the Polyline layer.

4) Add the exported features to the map. This is a good time to tidy up your map. Select the features you want to retain and delete the rest using the Edit tool.

5) Now you’ve got a clean dataset to work with and can geo-reference it using the Spatial Adjustment toolbar. See our previous blog post Georeferencing Vector Data to see how to do this.

6) Further steps might include adding appropriate attributes to the separate features, if required.

That’s it! We have just recovered valuable data that we thought we had lost. Quickly and more importantly, accurately! Let’s just make sure we put it somewhere really safe this time…

Summary

It’s hopefully not the sort of thing you’re going to need to do on a daily basis, but the software described and the workflow defined may help to save you from a sticky situation, so it’s one to file and (this is the important bit) remember.

Posted by Dhowal Dalal, GIS Consultant, Exprodat

Another spreadsheet of coordinates!

When planning seismic lines it's typical to have a spreadsheet of start and end point locations, representing the seismic lines to be shot in the survey. In ArcMap it is possible to generate a set of lines from these points using a single tool – the XY To Line tool.

Workflow

Prerequisites: In order to ensure that the lines that you generate in ArcMap are accurately located, you need to know the coordinate reference system in which the start and end points are supplied.

The XY To Line tool requires that the input spread sheet has 5 columns of data, as illustrated in the screenshot below. The column names don’t have to match those that are shown, but the names shown are clear and concise and hence we would recommend their use. These columns of data are:

1) Line number – a unique identifier (LINE). This can be text-based (eg, ‘Line 1’), or numeric (as shown in the screenshot).
2) Start point: X coordinate (XSTART)
3) Start point: Y coordinate (YSTART)
4) End point: X coordinate (XEND)
5) End point: Y coordinate (YEND)

Figure 1. Spreadsheet layout

Open ArcMap and add the appropriate work sheet from your spread sheet to the data frame – this will add it as a table to your ArcMap document. Note that the Table of Contents view will change from List by Drawing Order to List by Source, allowing you to see the newly added table in the Table of Contents – it will have the same name as the work sheet you selected from the spread sheet.

Once you’ve successfully added the work sheet, open Arc Toolbox and navigate to the following location:

Data Management Tools > Features > XY To Line

Figure 2. Tool location

Double-click the tool to open it, complete the tool parameters as appropriate (as illustrated in the screenshot shown below) then click OK to run the tool and wait for the process to complete.

Notes on the tool parameters

Input table– select the table that you added to your ArcMap document.

Start/End X/Y– select the appropriate field for each of these values – note that only numeric fields are listed in the drop-downs.

Line Type– there are a number of ways of constructing a line between two points on a spheroidal (or spherical) surface. A discussion of the differences is outside the scope of this blog - the help shown in the XY to Line tool gives further information. For most purposes the default option, GEODESIC, will provide the most accurate result.

ID– select, if desired, the appropriate field to add as a unique identifier for each line – note that both numeric and text fields are listed in the drop-down.

Spatial Reference– select the coordinate reference system in which the start/end points are defined. Failing to do this correctly will lead to your generated lines being inaccurately located.

Figure 3. Tool parameters

Et voilà!

Once the tool completes, the newly created polyline feature class will be added to your ArcMap document, ready to be symbolised to your requirements.

Figure 4. Polyline features drawn in ArcMap

Thanks to Fiona Buckingham and Devlyn Robson for their comments on this article.

Posted by Simon Kettle, GIS Consultant, Exprodat.

Have you ever wanted to hide sections of the layers on your map but not wanted to clip your data to do so?

For example, you might want to show all of the 2D seismic available within your company’s license blocks, but hide the data when it is over another company’s blocks, as illustrated in the screen capture below.

Example showing difference between original and masked data.

Looks great, how do I do it?

You can use Definition Queries to build mask layers that allow you to hide areas of your data, without having to edit your original feature classes. To do this, use the following workflow:

1) Create a copy of the layer which you will be using as a mask, by copy/pasting it into your data frame - in this example I use an offshore licenses layer (represented by polygons) to mask a 2D seismic lines layer, so I make a copy of the offshore license layer.

2) Use the Definition Query tab in the Layer Properties dialog to exclude your area of interest - for example, "OPERATOR" <> 'YOURCOMPANY' - from the copied layer. The new layer now has ‘holes’ in it where your licenses are and, if placed above the 2D seismic lines layer in your Table of Contents, it will mask out the 2D seismic lines in other companies’ license blocks. At this point you have achieved the objective, as shown in the screen grab below.

The result - 2D seismic lines masked using a license layer with a definition query applied.

But wait, there’s more!

The technique described above is great if you simply want to mask everything in the layers below the masked layer. However, if you wish to have more freedom in how you order your layers, it is possible to apply the mask to a specific layer (or layers), using Advanced Drawing Options.

To demonstrate this functionality, I have chosen to mask the 2D seismic lines so that only the seismic lines available in ‘current and never previously licensed’ blocks are visible. To do this, use the following workflow:

1) Create a Definition Query on the license layer which excludes the current and never previously licensed areas - e.g. "COMMENTS" <> 'Current (never prev. licensed) area'

2) Right-click on the Data Frame title and select Advanced Drawing Options from the context menu:

The dialog shown below will appear:

3) Check the Draw using masking options specified below box, then:

a. Select the layer to use as the mask.

b. Select the layer(s) that you wish to mask.

c. Click Apply to view the result.

4) The result is a masked layer that you can move around in the Table Of Contents, without having to worry about obscuring information information in other layers that you want to appear on your final map. Note that you can now switch off, in the Table Of Contents, the layer that you are using as the mask.

Posted by Devlyn Robson, GIS Trainer, Exprodat.

The GeoEvent Processor is an extension for ArcGIS for Server version 10.2 that processes real-time data – for instance, vessel or vehicle position information. In addition to allowing the display of real-time data in, um, real-time, the GeoEvent Processor provides GeoFence and other functionality that enables alerts to be automatically generated – for instance, you might want to know when a vessel or vehicle entered or exited a specific area, or when it went it ‘off-route’ – these alerts can be published in a number of ways, including by text and instant message.

The screengrab below shows a simple example for monitoring the location of vehicles in a study area. Having the location feeds from the different vehicles is essential, and there are a variety of means to generate and receive these, from radio beacons to mobile phones. The technology that you can employ will vary with your budget and where you are in the world – we’ll look to cover these in a future blog.

Landsat 8 Imagery from USGS.

Getting to grips with it

So, you’ve installed the GeoEvent Processor and you’re eager to experiment with it. There are some sample files that you can use with the GeoEvent Simulator, but they don’t cover your area or represent the sort of data that you’re anticipating receiving (or you need something to convince your managers to invest in tracking technology!). What to do? Well, you could embark, as I did, on a voyage through a set of functionality that I’d previously heard of but never used – linear referencing.

For my purposes, I wanted to generate a simulated GPS feed that represented a vehicle travelling along a road, at variable speed, with pause points – something that would look vaguely convincing when used as a feed for the GeoEvent Processor. So, first up, I digitised my road, using a satellite image for a randomly selected area as a guide. I then created a point feature class and added points along the road, at which various events would happen – the vehicle speeding up from 30mph to 50mph, the vehicle stopping for 5 minutes, that sort of thing. I then faced the problem of how to interpolate the positions between my points, so that a convincing set of GPS points would be generated. It was at this point that I started looking at linear referencing.

Linear referencing, put simply, uses distance along a line to describe the location of features or events. Using the geometry of the line, the exact coordinates of the features can be extracted. As I ‘knew’ where my events happened, I could extract the distance along the route at which they occurred and then use that information to interpolate the intervening sample locations. All I needed to do was to use the appropriate ArcToolbox functions and then write some Python…

Converting lines to routes

Using the Create Routes tool in ArcToolbox, I created a route from my digitized road. An interesting point to note is that the direction of the route generated may not be the same as the direction of the road that you digitized – there is a control in the Tool that allows you to define which corner of the bounding box to use as the ‘origin’ location – you need to ensure that this is set to the corner closest to the start of your route.

Converting events to referenced points

Armed with my new route, I then used the Locate Features Along Routes tool to generate an Event Table, shown below, containing the measure locations for my events along the route – the tool added the RID (Route ID) and MEAS (MEASure) fields to the input table.

Note that my measure values increase from a negative value towards zero as I failed to pick the correct bounding box corner when generating the route. I worked around this by simply multiplying the measures by -1 to get the route to go ‘in the right direction’.

Python

Having generated the table shown above, I was able to generate the interpolated points between my events using Python. My script looped through the events, calculating the time at which each would occur, and generated points to fill in the gaps, at specified time interval (5 seconds in this example). The new table contained measure distances for each interpolated location, as well as a time, an elapsed time and a speed (the speed is given in metres per second, as I’ve not gotten around to adding a scaling factor back in).

Converting events to point locations

Having generated my interpolated locations, I added this table back to ArcMap and then used the Graph function to confirm that the way in which the speed of the vehicle changed was reasonable (perhaps not entirely realistic – the accelerations and decelerations are linear, but it’s good enough):

Using the Make Route Event Layer tool I converted the values in my table to points on a map. I then added the XY coordinates to the points feature class that I created from the Event Layer. I now had all the data I needed for my simulated GPS feed.

Using the GeoEvent Simulator to feed the GeoEvent Processor

Next, I saved the attribute data out as a CSV file. I then loaded this file into Excel to add a SHAPE field into which I concatenated the XY coordinates – this step is necessary as otherwise, in my experience, the GeoEvent Processor doesn’t correctly generate the point location.

Having done that, my CSV file was ready for use in the GeoEvent Simulator, which comes with the GeoEvent Processor:

The final result

I created an input, a service and an output in the GeoEvent Processor, then pressed play on the GeoEvent Simulator and watched as my simulated points streamed through into my output Feature Service. A quick bit of coding using the JavaScript API then allowed me to visualise the points as they came in, in ‘real time’.

Landsat 8 Imagery from USGS.

Having generated a feed, it was then time to look at GeoFences, but a discussion of that will have to wait for another blog.

Posted by Ross Smail, Research & Development Manager, Exprodat.

Stereo viewing, or Stereoscopy, has long been used by geoscientists to create the illusion of depth. Traditionally, overlapping air photographs (“stereopairs”) have been viewed with a stereoscope, where the stereo effect is created from a slight offset in the viewpoint of each of the stereopair photographs.

Investigating an area in “3D” affords a greater understanding of the processes involved that have shaped the area’s topography. This in turn can help geoscientists understand the nature of the underlying geology. Recent developments in technology, such as the “Visualization Walls” used by major Oil and Gas companies’ E&P departments, allow geoscientists to be immersed in their data. The use of special glasses permits them views of their data from the surface, or subsurface, in 3D, and they can investigate the data by roaming around inside it.

Can we make a visualization wall with ArcGIS?

Not quite, but we can create worthwhile “2.5D” perspective views, and also generate pseudo-stereo “anaglyph” images. For stereo anaglyphs, ArcGIS has good 3D capability using the ArcScene software, which is part of the 3D Analyst extension. It tends to be underused in E&P since many geoscientists use other software for the generation of 3D surfaces. In order to create and view stereo images with ArcGIS, we need a grid of elevation or depth values, and standard stereo red and blue glasses.

In the following example, you can see how we can use freely-downloadable DEM and satellite imagery to create a 3D visualisation of an area. The greyscale image is from an ASTER DEM; the colour image is an enhanced satellite image from Landsat 8. These data are from the Zagros fold belt in Iran, an area rich in oil fields.

Creating stereo images

To view a stereo image in ArcScene:

1) Load the grid, in this case a TIF file, containing the elevation (DEM) values.

2) Now select Floating on a custom surface and use the DEM as the source for the elevation data.

3) Improve the view by changing the vertical exaggeration. Go to the Scene Properties and try a couple of values. In this example, vertical exaggeration has been set to a value of 2 to emphasise the geomorphology of the scene. Additionally, an appropriate color ramp has been chosen to emphasise topographic detail.

Though the perspective now looks correct, it is still diplayed on a flat screen in 2.5D, which means it gives the impression of 3D, without the depth effect created when using stereopairs in a stereoscope.

4) Load the satellite image. To drape it over the DEM, use the DEM as the source for the height data by setting the Floating on a custom surface value to the DEM again. This gives the scene a more realistic view. You will need to uncheck the DEM so that only the Landsat image is displayed.

An image like this example shows a wealth of detail for a geoscientist wanting to study the geology and geomorphology of an area.

A View of Depth: from 2.5D to 3D

To go a step further and produce the impression of depth similar to a 3D TV or a 3D cinema effect, you need to convert the scene into anaglyphs.

1) Use View> View Settings and set Viewing characteristics > Projection to Stereo View.

2) Now use the dropdown option in Stereo view preferences> Method and select Red/Blue Anaglyphs which, as the name suggests, creates a red image and a blue image, displayed with a slight offset.

Viewing in stereo

By default, ArcScene places the blue anaglyph to the left and the red anaglyph to the right. If your stereo glasses are not in the same orientation, switch the order of display of the anaglyphs. When you have it correctly set it should produce an enhanced 3D depth effect, with the image appearing to stand out from the screen.

Can we improve the stereo effect?

To get the best from this you may need to adjust the Eye separation which is just below Stereo view preferences. I found that with this set to 1.6 and the ArcScene window maximised, the effect really worked with the image appearing to float in front of the screen, in 3D.

Another way to ‘fine tune’ is to move the Parallax slider to the right to increase the movements of scene objects relative to the observer, or to the left to decrease them. Eventually you will find the perfect set up for your own eyes.

Whilst this is not an immersion viewing system, for a quick and easy to create 3D effect on a desktop, with only ArcScene, stereo glasses and a minimum of set-up, it is a useful function for virtual field mapping - and it’s fun to use!

Posted by Mike Phillips, Senior GIS Consultant, Exprodat.

References, acknowledgements, further information:

• Stereo glasses image from USGS
• DEM from ASTER DEM
• Landsat Imagery
• Related Exprodat course: ArcGIS 3D Surface Analysis for Petroleum

Confirmation Bias: a tendency of people to favour information that confirms their beliefs or hypotheses: Wikepedia*.

It’s a terrible disease for the petroleum explorer. Succumbing to confirmation bias often leads to overconfidence in one’s ability to predict outcomes, technical risk underestimation and poor decision making. Wishful thinking syndrome is also a common side effect, and the affected often tend to herd together. Do you know someone you work with that suffers from these symptoms? We may be able to help!

http://colleensharen.wordpress.com/2011/12/22/decision-making-confirmation-bias/

I’ve always believed that the best geoscientists are ones that are aware of this trait, and consciously attempt to address it. One of the key root causes is our inability to process large volumes of information; we tend to quickly sift out and discard the stuff that’s contradictory or difficult to reconcile with our working hypotheses. How often have you heard someone say “I prefer vendor A’s data in this area because it’s ‘better’?”

Do they really mean ‘better’ in a scientific sense, or do they mean it matches their models more closely than other data might?

This is where GIS has a crucial role to play in petroleum exploration. There’s no other tool that allows you to integrate so many disparate sources of information in one place, and make some sense of it: 1, 10, 100 information sources, GIS takes it all in its stride and allows us to analyse relationships and patterns graphically, intuitively, in a spatial environment. So that takes care of the issue around processing large volumes of information. Or at least, that’s the way it should work.

We have an application called Team-GIS Exploration Analyst. One of the modules is for acreage analysis and ranking. It lets you choose multiple criteria and quantitatively ‘score’ and rank acreage based on them. This could include things like depth to reservoir, distance to facilities, presence of particular plays, prospect volumes, water depth and so on – basically any decision criteria that can be mapped (which is most of them).

The idea is you generate these models, test your preconceptions and existing hypotheses based on all the data available to you, and correlate your ‘gut feel’ and experience with hard information.

Example scenario: our quantitative ranking shows a block as ‘red’, but we thought it was ‘green’ – so why is that? Review, dive down in to the data in GIS and evaluate the cause. Ah! Our GDE maps are out, new well data has increased the reservoir risk around the block, so the model is updated and we’re off again.

Great! No more subjective decision making when you’re filtering your blocks for the next licensing round! No more waiting for the old timer in the corner to point the gnarled finger at a map and say ‘apply for that one’, and hope he’s right! We get consistency in analysis across the company, technical risk is understood and great exploration results will surely follow.

Well, as it turns out, what we often see is something quite different. The tool is used, but to confirm and support decisions at the end of the process, rather than inform and challenge existing models at the start. Even with all the information available, and a way of making sense of it, the confirmation bias is still at work.

But guess what? The best geoscientists we work with and the companies that seem to be most successful are the ones that work the other way. They use GIS to embrace information, develop and test new exploration models, challenge existing hypotheses, then calibrate those with experience.

It’s a tougher path to follow, but it’s the one that leads to new insights and ultimately should improve exploration performance.

So the next time you sit in a meeting and see someone present a bid proposal or farm-in recommendation, think about the confirmation bias and question what’s been done to address it.

Perhaps GIS might help.

Posted by Gareth Smith, Managing Director, Exprodat.

References:

* http://en.wikipedia.org/wiki/Confirmation_bias

How to calculate the true distance over a surface

A point-to-point distance taken from a map does not provide a true measurement of the distance between those points; I have to take the intervening topography into account, as shown below.

To calculate the 3D length of a line (in ArcMap, with the 3D Analyst extension) the first thing I need to do is to create a new line feature class and enable Z-Values on its geometry:

I can then create lines representing the features that I'm interested in deriving accurate measurements information for, e.g. the 2D seismic lines shown below:

Running the tool…

The Add Surface Information tool in the 3D Analyst Toolbox enables me to use the underlying DEM to add an assortment of attribute data to the line feature class.

In this example I am only adding the surface length when associated with the underlying DEM. However, if I’ve created the line as a multipart feature I can also add the min, max and mean elevation value and slope values:

The resulting measurements are shown below, note the difference in value for 2D and 3D line lengths:

The next step…

Of course, the 3D Analyst extension allows me to do more than use calculate lengths - using the 3D analyst toolbar I can create a vertical profile of a line to visualise changes in topography along that line.

To do this, I click the Layer drop-down arrow on the 3D Analyst toolbar and select the surface that I want to profile, then click the Interpolate Line button.

This tool allows me to digitize the proposed route:

• First I click the surface and digitize the line I want to profile. When I have finished, I simply double-click to stop digitizing.

Note: multiple lines can be drawn and compared in the graph.

• Now I've got my line, I click the Create Profile Graph button profile graph button to construct a graph.

Note: I can right-click the graph to view and change its properties, such as the graph type used.

This article touches on only a small amount of what can be achieved using the 3D analyst. Further analysis for the above scenario could include using the Raster Surface toolset for calculating aspect and slope angle which can be important factors when acquiring seismic.

Posted by Simon Kettle, GIS Consultant, Exprodat.

Many thanks to the following colleagues who helped review this article: Devlyn Robson, Mike Phillips and Fiona Buckingham.

• Related Exprodat course: ArcGIS 3D Surface Analysis for Petroleum

Can the Renewable Energy industry leverage the lessons learnt by its Oil & Gas brother?

Last year the Renewable Energy Focus magazine published the feature "Oil, Gas and Wind: offshore harmony?" which argues that there are opportunities for skills transfer between the Renewable Energy (RE) and Oil & Gas (O&G) sectors. The feature highlights various areas where this could happen (assets installation and maintenance; Health, Safety & Environment; and integrated operations) and suggests transferability of strategic skills between the sectors in areas such as deployment of business models, funding and contractual mediation. In my view the list can also be extended to include other relevant components of an energy project, including the processes required to effectively manage spatial data.

RE and O&G industries both rely heavily on available or newly acquired data to drive strategic decisions and support the development of projects from inception to decommissioning. Both are data-driven, and decision making is (or should be) supported by data and information extracted from it rather than intuition or feelings (on this subject you may want to check Gareth Smith’s recent blog on confirmation bias).

The O&G industry has been dealing with the challenge of extracting business intelligence from large amounts of available data for a long time. The typical “landscape” for O&G companies can be simply summarised: loads of data everywhere that needs to be visible, accessible, usable & re-usable throughout the project’s lifecycle. Through the implementation of strong data management processes some of the most successful O&G companies have, over time, shown a significant reduction in the time spent by their staff in basic tasks such as looking for data, in favour of extracting value from it.

The development lifecycle of a Renewable Energy (RE) project is, in many aspects very similar to its O&G counterpart (Figure 1).

Figure 1 – Example of RE project lifecycle and areas that the GIS can support

Renewables projects require the capture, storage, manipulation and sharing of large quantities of varied data. This includes environmental; coastal process; weather station and seabed surveys; front end engineering and design; and human impact studies. Regional legislation, such as that in the UK, also requires that robust data management procedures which are future proof and scalable, are used to handle these datasets. Keeping control of the vast amount and variety of datasets generated within a given project is a challenge where the experience of the O&G industry can be looked at for inspiration and lessons learnt.

Where GIS is recognised as part of the core of the industry application portfolio, it becomes an enabler not only for data integration, visualisation and dissemination, but also in complementing traditional data management systems. GIS can become “the platform” to provide organisations with an intuitive access point to the company’s data assets allowing, at the same time, advanced use in areas such as sophisticated data analysis, predictive modelling and strategic decision support. GIS can provide the framework to deliver a potential step change resulting in improved communication, collaboration and information dissemination within and outside the company.

In my view a number of lessons learnt in the O&G industry could prove to be valuable for the RE industry - based on Exprodat’s experience in working with O&G clients, I would argue that the key lessons are:

• Clear governance for data under the spatial data management domain, with clear definition of roles and responsibilities.
• Implementation and capture of all critical spatial data in a centrally-managed corporate database.
• Clear separation between project and corporate data, with clear processes for migrating project-related data to the corporate level upon a project’s conclusion.
• Definition and utilisation of data and metadata standards, models and processes.

Our experience is that, where there is a clear strategy for the management of spatial data, data becomes a value generator for the company, providing critical information in an accessible format, in a timely fashion. This in turn reduces the technical and operational risk uncertainty and accelerates the decision-making processes through improved access and use of quality spatial information.

Posted by Paola Peroni, Senior GIS Consultant, Exprodat.

I was very interested by a recent blog by David Bamford on the subject of behavioural economics. The bottom line is that buyers of technology are fickle and not altogether objective when it comes to buying new technology, for a variety of very human reasons:

• Benefits are only realised sometime in the future
• Benefits are uncertain: the product might not work as expected
• Benefits are usually qualitative: it’s difficult to enumerate the value and make absolute comparisons between different options
• Buyers (over)value the items in their possession more than prospective items
• Buyers are loss-averse.

And likewise for the purveyors of technology: their view of the value of their offerings is often very different to their prospective buyers.

As the owner of a small company that develops technology, this all certainly rings true! The initial development to early market acceptance/pay-back phase for new technology can take several years. That's a big investment in time and capital for a small company.

I've also definitely noticed a reduction in 'IT' projects risk tolerance in our clients over the past 5-10 years. It seems to me that many E&P companies are trying to squeeze all the risk out of IT spend decisions and projects. If only they were all as diligent at reducing Exploration risk where the real money is spent, but that's another discussion!

I agree that technology selection and implementation needs to be handled with due diligence and managed properly. But are companies 'throwing the baby out with the bath water' and forgetting how to experiment and innovate?

Some of our best products have come from working closely with clients that are prepared to take a risk on new technology or different approaches. Not everything works, but when it does, the value often far outweighs the downside from the other less successful experiments. These clients are increasingly rare. Most now look for tried & tested solutions, expecting others to have taken the risk out of the decision for them.

In the long run, I feel there's a real danger that the smaller innovators will cease to feed through next generation technologies and ideas, because of this 'perfect storm' of behavioural economics and IT risk aversion in the petroleum sector. There is of course a flipside: The technology business also needs to take a more realistic view of the value of the products they develop, regulate their marketing hyperbole and build confidence in the buyer community.

Posted by Gareth Smith, Managing Director, Exprodat.

When you want to run the same geoprocessing tool on many feature classes, one option is to right click the tool and select the batch option, but it can be tedious to add all your datasets to the list - the entry columns have to be widened to check that the paths have been entered correctly, and the parameters still need to be set for each row even if they are identical. Dragging and dropping, or copying and pasting paths takes time. If there is an error in one row, sometimes you will not be aware until the tool fails and has to be re-run.

Our goal: Write once, process many

A few lines of Python can be of help. Here is an example of a python code block that loops through all the files in the folder ‘C:\Inputs’, performs the ‘Dissolve (Data Management)’ tool on them, and then writes the output to ‘C:\Outputs’.

The rest of this blog reviews the simple script above and looks at variations of it that can assist your batch processing.

We’d suggest that you write your Python script in IDLE or an equivalent Python Integrated Development Environment (IDE), and then copy and paste it into the Python Command window in ArcCatalog to allow the geoprocessing results to be recorded within your Arc session.

Step 1: Setting the workspace

The first line of our script defines the environment workspace parameter, which points at the folder or geodatabase that contains the input datasets.

The ‘r’ prefix makes Python treat the string it’s being given as a ‘raw’ string, meaning that it doesn’t attempt to analyse the string for what are called ‘backslash escape sequences’ – in this case this means you don’t have to change the folder path to include double back slashes instead of single ones.

Step 2: Looping through the input datasets

Next we use a ‘for’ loop to retrieve the datasets from the workspace:

For feature classes or shapefiles:

For rasters:

Note that ‘fc’ and ‘raster’ are arbitrary names - the script would work if all instances of fc were replaced with fc2.

Asides

1. Filtering the input datasets

If you want to only process datasets that contain a certain set of characters within the file name you can use the wild card. For example, to list all the datasets that have “Name” in the middle of the dataset name, you can use:

2. Setting indents for loops

Python uses whitespace to control loops. Once a loop has been defined, each line of the content of that loop has to be entered on an indented line, as shown in the screengrab below. To resume the script outside the loop a new line would be entered at the same level of indentation as the ‘for’ loop.

Step 3: Defining the output dataset names

The easiest way to define a meaningful dataset output name is to use the input name, which is held in the ‘fc’ variable in our examples. Unfortunately, when the input is a shapefile, the variable ‘fc’ will include the ‘.shp’ suffix. Happily, we can use the arcpy.Describe function to retrieve the suffix-less name for the input dataset, and then use this to create our new output name.

The Describe function works for all the types of data that can be used in ArcMap, so this line of our script can cope with (almost) anything we fire at it. As we’re writing the output to the same folder as we’re using for the input, we’ll need to make sure that the output name is different to the input one - in our script, ‘_Dissolved’ has been added as a suffix.

Last step: Running the geoprocessing tool

In our example, we’re running the dissolve tool on each input dataset. If we consult the help for the tool, we see that it requires an ‘in_features’ parameter, which corresponds to our ‘fc’ variable and an ‘out_feature_class’ parameter, which is the ‘outfc’ variable we have just defined. The other parameters are optional, and aren’t used in our script.

And that’s that – we’ve explained our script. Below are a few more ideas, that allow you to tailor the script to your requirements.

Going further

1. Defining another location for the output

If we want to write the output to a different folder, we can set the variable for that folder outside the loop. Note that we need to add ‘\\’ (double backslash) so that we can join the name of the file to the end of the folder – if we don’t, then no backslash will be added at all, and things will get a little confusing.

Note that because the output is being written to another folder, we can simply use the input dataset name for the output.

2. Using counters to name the output dataset

If we want to use a counter to name our output datasets - eg, create datasets with names like File1, File2, File3, then we need to enter a new variable, before the ‘for’ loop, to hold the count. We can then append this to our output dataset name by converting it from a number to a string using str(inputNumber), as shown below (if you don’t do the conversion, you’ll get an error). The last code line shown below increments the counter by 1, so that the next iteration of the loop creates the next dataset name in the sequence.

Conclusion

Using Python code from ArcCatalog can speed up batch processing for many ESRI tools; provide an amount of flexibility on output dataset names; and maintain a record of what we’ve done in the geoprocessing history.

If you’re interested in Python why not check out our previous blog post on Creating Layer Files with Python.

Posted by Tanya Knowles, GIS Technician, Exprodat.

This training tip follows on from our previous blog "Top 10 ArcGIS Printing Checks", and introduces some of ArcGIS 10.1’s dynamic legends capabilities.

The problem

You’ve got a good map, but your legend is stopping it from being a great map:

In older versions of ArcGIS, you could either put up with the deficiencies, or spend time manually adjusting your legend - reducing the font size to fit the labels in (but making them too small to read); converting the elements to graphics, allowing you to amend them (but losing their dynamic link with the data).

The solution

ArcGIS 10.1 provides a suite of legend formatting capabilities that allow you to easily create accurate and attractive legends.

Labelling

If your labels are too long, you can ‘wrap’ them: right-click the legend and open up the Legend Properties dialog, then select the Layout tab. Under the Text Wrapping section tick the box for Wrap Labels, and then type in the width you want for your label (and the width for the description, if displayed). Applying this setting will cause the labels for each feature to be wrapped so the width you’ve specified is never exceeded.

Visible features

A legend should only show relevant information - you don’t want to show items in the legend if they are not shown on your map. ArcGIS 10.1 introduces a handy option to allow your legend to only show the classes that are visible in the current map extent. For example, you might have a pipeline layer in your map which has many pipeline types, only a few of which are actually displayed in the map that you’re creating – using this option will remove the pipelines types that aren’t in the map from your legend, making it shorter and more accurate.

To enable this option select the Items tab within the Legend Properties, click Select All to select all the layers in the legend, tick the Only show classes… option in the Map Extent Options section, and then finally click Apply. Note that there is an additonal option that allows show a count of the features shown in your map to be displayed in the legend.

Alignment

To correctly align the items of your legend, select the desired items, then do either of the following:

• Right-click and choose the Align option from the context menu.

• Snap your features to a guideline –you create these by clicking on a specified place along the ruler.

Note that depending on your needs, you may find that you need to convert grouped legend items to graphics, then ungroup them, so that you can select only those items that you want to reposition.

The Result

A clear, easily understood and neat legend!

Watch this blog as a video: Improving the Appearance of Legends in ArcGIS

Posted by Fiona Buckingham, GIS Trainer, Exprodat.

In our earlier Exprodat blog Creating Geological Block Diagrams, we showed how to create block diagrams using satellite imagery and a digital elevation model. In this blog I look at how to make block diagrams directly from gridded surfaces, creating visual representations of the blocks. The workflows described require ArcGIS Desktop along with the 3D Analyst extension.

Study area

My study area (red box on the map below) is in the Viking Graben Province (yellow polygon) of the North Sea Graben System, in the northern North Sea. The green polygons in the map represent oil fields.

Input grids

I took four TIFF grids for the study area, representing a sequence from youngest (top of the Eocene) to oldest (base of the Cretaceous), with the Paleocene layer between them. The grids are shown below, coloured so that red represents structural highs and blue represents structural lows.

Filling the gaps: from TIFF to TIN to Blocks

I displayed the grids in a perspective view in ArcScene (supplied with 3D Analyst) by setting the base heights of each grid:

• Use Layer Properties > Base Heights > Elevation from surfaces
• Select the radio button for Floating on a custom surface: where the surface you choose is the grid itself. With the correct base heights set, the separation, and therefore thicknesses, can be seen between each grid surface.

Next I wanted to fill the spaces between each layer in order to produce a block unit for each geological period and epoch; this also returns values for the volumes of each block unit. The first step was to convert the TIF grids to ArcGIS TINs (Triangulated Irregular Network), using the Raster to TIN tool:

Under ArcToolbox > 3D Analyst Tools > Triangulated Surface, I used the Extrude Between tool to generate block units between each layer, using the Study Area polygon to limit the extent of the block created.

Running the tool three times resulted in three block units representing the Eocene, Palaeocene and Cretaceous volumes in the study area.

Displaying the block diagram

I then displayed the block units in ArcScene - displaying each block unit in turn allows for a detailed look at their general profile, and some top and base structural detail (below, left). Switching on all block units displays a block diagram (below, right).

Come, look inside

I then made the Cretaceous block semi-transparent, so that I could view the base of the block, and added oil reservoir accumulations (again processed using the Extrude Between tool to place them at the Base Cretaceous) trapped at the contact between the Upper Jurassic and the Base Cretaceous seal along fault footwall topography.

I then added the wells in the area and some borehole data, allowing us to partly validate our blocks – the boreholes intersect the oil fields that they’re tapping!

Is it valid?

A comparison of my block diagram with a published geoseismic cross section is shown below - the two profiles are not in exactly the same location, but they are close enough to allow us to compare them visually. As you can see, the shape of the Lower Cretaceous contact with the Upper Jurassic oil window is a good match, as are the thicknesses of the Eocene and Palaeocene units – the block diagram we have created appears to reflect reality.

Conclusion

I believe that the examples above show how useful 3D Analyst and ArcScene can be for creating and interacting with meaningful representations of subsurface geology.

In a subsequent blog, I will take these examples further and show how to create cross sections and fence diagrams.

Posted by Mike Phillips, Senior GIS Consultant, Exprodat.

References, acknowledgements, further information

• Millennium Atlas sub-surface grids: http://www.expgeo.co.uk
• Geoseismic cross section from 'Isaksen et al. 2002. Hydrocarbon System Analysis in a Rift Basin with Mixed Marine and Nonmarine Source Rocks: The South Viking Graben, North Sea. The American Association of Petroleum Geologists Bulletin, v. 86 no. 4 p. 557-59'.
• Related Exprodat course: ArcGIS 3D Surface Analysis for Petroleum

It is possible to configure various aspects of ArcGIS Desktop, such as the storage location of corporate map templates, by amending Windows registry settings. Further settings can be configured by placing files into the individual users’ profile folders. These capabilities allow for the efficient enterprise-wide deployment of ArcGIS Desktop, ensuring that each user has access to the same corporate resources.

Such resources may be frequently modified, so it is convenient to store these in a network location, allowing the user to automatically pick up the latest version of the resource when they next access it.

This two part blog provides details of how to configure access to various corporate resources. In Part 1 we will look at configuring map templates, style files and coordinate reference systems.

In Part 2 (to be published later this week) we will cover toolboxes and metadata style sheets and also briefly mention the Advanced Settings utility that is provided as part of the ArcGIS Desktop installation.

Windows Registry Settings

The Windows Registry is a collection of configuration settings in Microsoft Windows that is used to store much of the information and settings for software programs, hardware devices, user preferences, operating system configurations, and much more. It is accessed and configured using the Registry Editor program (regedit.exe), a free registry editing utility included with every version of Microsoft Windows.

Note: It is strongly recommended that you back up the registry before making any changes as an incorrect change to your computer's registry could render your computer inoperable.

The following examples are based on an ArcGIS 10.x installation on a Windows 7 machine.

1. Map Templates:

1.1. The following registry setting controls the default folder from which templates are retrieved for display in the ArcMap 10.x Getting Started dialog:

1.2. To change this location to a network location containing corporate templates, Open Registry Editor, navigate to the registry key TemplateDir, double click to open it and change the Value data to your desired path. If this is a network location, use UNC paths.

1.3. To verify the change, Open ArcMap - the templates folder will now be visible in the Getting Started dialog. This registry change can now be deployed across the entire organisation.

2. Style Files:

2.1. The following registry setting controls the default Style folder in ArcGIS:

2.2. To deploy corporate GIS Symbology from a folder or network location, navigate to the above registry folder.

2.3. Create a new Key called Styles. This might not already exist so we need to create it, as shown below.

2.4. Create a new String Value in the Styles key created above. Set the value Name using numbers, sequentially from 0. Multiple styles can be added and will be ordered using the Name field. This is the order that will appear in ArcMap Style Manager.

In the Data field, enter the entire path to the style file. For network folders, use UNC paths.

2.5. To verify the change, Open ArcMap, and navigate to the Style Manager - the styles we added above should be visible. This registry change can now be deployed across the entire organisation.

3. Coordinate Reference Systems (.prj) and Custom Transformations (.gtf):

3.1. ArcMap 10.x can store commonly used projections in a Favourites folder. To deploy commonly used or custom reference systems, copy a folder containing projection files ( .prj) to the users’ coordinate system folder located at:

3.2. In the example below, a folder named Corporate Coordinate Reference Systems containing two prj files was deployed. Multiple folders can also be created, if need be.

3.3. Similarly, any custom transformations (.gtf) can be stored in:

3.4. To verify the change, open ArcMap, then open the Data Frame Properties dialog. Next, click the Coordinate System tab and the coordinate systems added above should be visible. This change can now be deployed across the entire organisation.

In the second and final part of this blog, we will look at configuring access to toolboxes, metadata style sheets and also the Advanced Settings utility that is provided as part of the ArcGIS Desktop installation.

Happy customising!

Posted by Dhowal Dalal, GIS Consultant, Exprodat.