Thursday, 25 August 2016

Testing on Small Volumes, Processing on Large

With the acquisition of ever larger and denser surveys, it can be challenging to efficiently optimise and run processes on your data. GeoTeric solves this problem by allowing you two different methods to rapidly test your processes on small volumes, before running the processing on the full dataset. An example will be shown below using the Noise Expression tool. The steps assume you have the full dataset loaded into GeoTeric.

  1. Visualise the full dataset in the main viewer.
  2. Click the ‘Extents’ button in the bottom right.
  3. Trim the volume to focus on a target area.
  4. Enter a name in the ‘Subset’ box and click ‘Save Subset’.


  1. Open Noise Expression, selecting the subset from the dropdown.
  2. Optimise your process as normal.
  3. Once you are satisfied with the results, hit ‘Generate 3D Volumes’.
  4. Firstly, note the location and name of the batch job. This can be applied to any dataset at a later point. Instructions for this will be given later.
  5. If you wish to apply the current process to the full volume, simply select it from the dropdown at the top.
  6. If you choose the full volume from the dropdown and click ‘Process’, the processing will be applied to that dataset.
  7. This method can be used in Noise Expression, Spectral Expression, HDFD and Fault Expression.


Using the Batch Job

This will allow you to load up any batch job that has been saved previously, and apply it to any datasets you have available in your project. Care should be taken to only apply processes to appropriate datasets.

  1. From the menus in the top left of the main viewing window, select ‘Workflows’ > ‘Processes and Workflows…'
  2. From the top left of the Processes and Workflows window, select ‘File’ > ‘Open…’ (The window is actually titled ‘Batch Processing Framework’)
  3. Browse to the location of your batch job and open it. This will generally be in the ‘batchjobs’ directory within your project.
  4. On the left hand tab, select the input dataset from the drop down.
  5. The output dataset names can also be changed at the bottom of each tab. This is not essential however.


Thursday, 11 August 2016

Combining different Noise Cancellation/Spectral Enhancement volumes using Time Variant

The aim of this blog post is to describe how to combine different Noise Cancellation/Spectral Enhancement volumes using a time variant method.
 
This workflows can be used for:
  1. Creating a combined Noise Cancellation volume in which different noise filter were applied to target different noise contents that vary vertically and can be bounded by a horizon.
  2. Creating a Spectral Enhancement volume with different enhancement parameters that can be divided by a horizon.
The following workflows is for two Noise Cancellation volumes where there is a need to apply a more aggressive noise filter below the horizon and only a gentle filter above the horizon, but the same workflow applies for the Spectral Enhancement volumes too.

Input needed for this workflows are horizons which separate the areas with different noise content vertically, Noise Cancellation volumes, which target the different intervals and a time volume, that is used for the combination. Example in this post will show the workflows to combine 2 areas which is separated by a horizon.
Figure 1: Original Seismic Volume

The first step is to create two Noise cancellation volumes, a gentle noise filter above the horizon and an aggressive filter below the horizon. The next step is to combine these two volumes into one. To combine these two volumes, a time volume is needed, which is unflatten using the horizon and formatted for the combination purpose.

The following is the workflow to create the a formatted Unflatten Time Volume.
  1. Go to Workflows>Processes and Workflows>Processes>Utilities>Time Volume.Input your seismic volume to create the time volume.
    Figure 2: Time volume displayed using the spectrum colour bar, where green is the higher value.  
  2. Use the Horizon Tools to unflatten the Time Volume. Go to Tools> Horizon Tools> Flatten/Unflatten. Toggle Seismic as volume type. Then specify the input horizon, the input volume will be the newly created Time Volume. The output volume will need to be named as Unflatten Time Volume. Change the mode to Un-flatten Horizon and Click Apply. Once you input the horizon, a Set Flattened value, which is the mean of the input horizon, will be displayed at the bottom of the Horizon Tool menu. Write down the value as it will be used to format the Unflatten Time Volume later.
    Figure 3: Unflatten Time Volume   
     
  3. The next step is to format the Unflatten Time Volume so that one of the Noise Cancellation volumes can be assigned to the data above the horizon and the other Noise Cancellation can be assigned to below the horizon. Go to Workflows>Processes and Workflows>Processes>Volume Math>Parser. Input the Unflatten Time Volume, and use the following parser expression: ((im1>0)*(im1-A)) where A is the “set flattened value to” number that was given when unflattening the time volume. Now you have the formatted Unflatten Time Volume.

    Figure 4: Formatted Unflatten Time Volume displayed as Redwhiteblue colourbar. Red are the negatives values, whites are 0 values and blues are the positive values. It will be observed that along the horizon, values are all 0, this will be used as a transition zone between the 2 volumes.
The final steps now is to combine the two noise cancellation volume using the formatted Unflatten Time Volume. To combine the volumes, go to Workflows>Processes and Workflows>Processes>Volume Math>Parser. The first volume should be the formatted Unflatten Time Volume as im1, Gentle Noise Cancellation as im2 and Aggressive Noise Cancellation as im3. The Parser expression used will be:

((im1<0)*im2) + ((im1>100)*im3) + (((im1>=0) & (im1<=100))*(im1*im3/100)) + (((im1>=0) & (im1<=100))*((100-im1)*im2/100))
As a part of QC, you can compare the Combined Noise Cancellation result with the Aggressive and Gentle Noise Cancellation volume, or look at the Difference volume of each of the Noise Cancellation volumes (Original-Noise Cancellation Volume) to see the related noise in the Combined Noise Cancellation volume.
Combining these two volumes in this way, will avoid abrupt changes along the horizon. Instead there will be a transition zone of 100ms taken from both volumes. This is not limited to only two areas of Noise Cancellation, more volumes can be combined, in a sequence, using the same workflow.This workflows can also be used to combine different level of Spectral Enhancement as long as there are horizons that separate the areas with different Spectral Enhancement.

Monday, 1 August 2016

Multi-volume phase alignment QC

GeoTeric offers a number of workflows that allow a QC of the phase alignment between multiple volumes. These workflows can be used to analyse the similarity between angle stacks or 4D vintages. This enables the user to assess the phase alignment of the data to ensure an accurate analysis of the data.

Peaks and troughs replacement
The peaks of stack 1 can be replaced by the data from stack 2. If they are phase aligned, the result will be a peak. If they are misaligned, the result will be a trough. The troughs from stack one will be ignored and replaced by zeros.

This volume can be produced by selecting the two stacks in the Parser and using the expression (im1>0)*im2

The Parser is available in Processes & Workflows -> Processes -> Volume Maths -> Parser.

In a similar way, the troughs of stack 1 can be replaced by the data from stack 2. If they are aligned, the result will be a trough. If they are misaligned, the result will be a peak.

This volume can be produced by using the expression (im1<0)*im2 in the Parser.

Example from the Parihaka dataset, offshore New Zealand. On the left, peaks in the near have been replaced by the far, so troughs indicate misalignment. On the right, troughs in the near have been replaced by the far, so peaks indicate misalignment.

Bedform stack
A much more detailed similarity analysis can be achieved by using a Bedform stack.
The Bedform indicator attribute can be computed for each of the stacks, and then multiple Bedform attributes can be combined using a Parser expression to create the Bedform stack.
To combine three Bedform indicators, we can use the following Parser expressions depending on the data type.

For 32bit:
((((im1>700000000)*im1)+((im1<-700000000)*im1))+(((im2>700000000)*im2)+((im2<-700000000)*im2))+(((im3>700000000)*im3)+((im3<-700000000)*im3)))/3

For 16bit:
((((im1>10000)*im1)+((im1<-10000)*im1))+(((im2>10000)*im2)+((im2<-10000)*im2))+(((im3>10000)*im3)+((im3<-10000)*im3)))/3

For 8bit:
((((im1>170)*im1)+((im1<90)*im1))+(((im2>170)*im2)+((im2<90)*im2))+(((im3>170)*im3)+((im3<90)*im3)))/3

These Parser expressions are simply (im1+im2+im3)/3, but removing the doublet values in each of the Bedform attributes.

If the output is visualised using the Azimuth colour map, good alignment will be seen in black, areas with dispersion along a peak in red, and areas with dispersion along a trough in blue. The lighter the blue and red colours, the worse the alignment.

Example from the Parihaka dataset, offshore New Zealand. The bedform stack has been computed using the near, mid and far angle stacks.

Alignment quantification workflow
To quantify the alignment between two stacks (im1 and im2), the following Parser expression can be applied:

((im1>0)&(im2>0))*1+((im1<0)&(im2<0))*1+
((im1<0)&(im2>0))*-1+((im1>0)*(im2<0))*-1+
((im1=0)&(im2!=0))*-1+((im2=0)&(im1!=0))*-1+
((im1=0)&(im2=0))*1

This will produce a volume with
+1 when there is good phase alignment between the stacks
-1 when there is bad phase alignment between the stacks

Note that any blank areas in any of the stacks will bias the results, so it is recommended to apply this workflow on a subset around the area of interest.

By using the Voxel Pick tool, available in the properties panel when highlighting the volume, a voxel pick can be placed for a +1 value and another one for a -1 value. Going to the Picked voxels tab, the two picked voxels will be displayed. Right clicking on each of them will bring up the Measure option, which allows a measurement of the number of voxels within the dataset that have the value of the picked voxel. Using the default options in the measure tool we can eaily get the number of voxels with a +1 value, and we can repeat the process for the voxels with a -1 value.



This way, as the number of voxels with a +1 value and the number of voxels with a -1 value is known, a simple ratio or alignment percentage can be manually calculated.


If the alignment is close to 50%, that is a bad alignment, as it means a peak in stack 1 has almost the same likelihood of being a peak or a trough in stack 2. Anything close to 90% or 100% means there is a good phase alignment.

Conclusion
Using GeoTeric we are able to check if the stacks are aligned properly or not. If there is a large amount of misalignment, the user should consider reprocessing or recreating the angle stacks before continuing with the AVO or 4D analysis.

Wednesday, 13 July 2016

How to create a structural-topographic horizon map in GeoTeric?

Office walls of exploration teams frequently feature structural-topographic maps of key horizons: topography of the horizon is encoded into colours – usually using a spectrum or a rainbow colour scale – while faults are indicated by e.g. black or red lines. We’ve been recently asked whether such an image could be produced using GeoTeric.

Although GeoTeric offers a wide range of functionalities with horizons, this is not one of the default ones. We can either set Height values in the Viewing Mode roll-down menu, or chose Data mapped and select a Fault Detect volume, but cannot visualise both at the same time. Fortunately, GeoTeric is flexible enough to allow for a workaround, which only requires a horizon and a Fault Detect volume.

The first step is to create a Time Volume, using Processes & Workflows / Processes / Utilities / Time Volume. The input of the process is the Fault Detect volume (or any other volume of the same size and byte type). For the sake of this blog post let’s call the output TimeVolume#1. This process creates a volume where each voxel has a value that is equal to its two-way travel time or depth.

Figure 1 Use the batch processor to create a time volume.

The next step is to embed the detected faults into the TimeVolume, using Processes & Workflows / Processes / Faults / FaultIn. The highest value of the output volume (e.g. FaultIn#1_TimeVolume) represent voxels where there was a fault detected, while all the other voxel values are equal to the TWT/depth.

Figure 2  Embed the detected faults into the Time Volume.

Now have a look at your horizon. In File / Project Manager / Horizons / select the horizon you wish to visualise, and take a note of its time/depth extrema (2111.49 ms and 2706.96 ms in our case). These values will be used to compress the colour scale of the FaultIn#1_TimeVolume.

Figure 3 The Project Manager (hot key: Ctrl+M) is used to establish the extreme of the horizon.

Let’s visualise this volume and select the Body Spectrum colour map. Move the sliders under the colour map and check the numbers next to the Minimum Value and Maximum Value. In this example the top slider was set to 66.11%, the lower one was set to 51.55%, and the Minimum and Maximum Values are 2707.2 and 2111.0, respectively. We are ready to visualise the horizon.

Figure 4 Compress the colourmap so that it reflects the minimum and maximum time/depth values of the horizon.

Add the horizon to the scene, select Data mapped for the Viewing Mode, choose Volume for the Data Mapping Type and select the FaultIn#1_TimeVolume. It is worth turning Interpolation off. Contours can be activated under Contouring.

Figure 5 Properties window of the horizon.


Figure 6 Topographic map of the horizon with detected faults (black) and contour lines (grey).

In case you find the fault lines too thin, use Processes & Workflows / Processes / Faults / FaultTrends with the Thick option to produce a new fault volume, and embed it into the TimeVolume instead of the FaultDetect volume. All the other steps are the same, but please note, this can result in a “crowded” image if there are many faults.

(Gaynor Paton and Peter Szafian)

Thursday, 30 June 2016

Manipulation of Volume Data Values


The blog post this week will show how we can manipulate the data values in a volume with specific emphasis on how this can be applied to geobodies.

If we have a volume with a set of segmented geobodies created using the ‘body labelling’ process (ProcessesandWorkflows: Processes>Geobodies>BodyLabelling); these bodies will be assigned different data values across the dynamic range of the data type based on their size. The largest bodies will be given the highest data values and the smallest the lowest values.  If we are interested in a particular body/bodies we may want to isolate this from the set of geobodies, and also we may want to assign it a particular data value so it is a more user friendly number for application of additional workflows or to be used in modelling software.  We will use an example containing channel geobodies, shown below.

Figure 1. Example Frequency decomposition blend highlighting channels of interest (above). Simple volumetric geobody extraction (below).

The first step to being able to make changes to these values is to identify the specific numerical value of the body or bodies of interest. This can be achieved by using the ‘voxel pick’ tool found in the properties box for any visualised volume. By making a voxel pick onto a body of interest in the main 3D window the numerical data value for that picked voxel, (and hence the whole body in this case), will be displayed.



Figure 2. Properties box for making voxel picks (above) and Two bodies of interest with voxel picks made to identify their data value (below)

We can see the spread of the data values of the volume in the histogram, opened using the – Opacity tab and switching the histogram display to logarithmic display.



Figure 3. Histogram for volume in opacity editor

Once we know the data value(s) of interest we can use the Parser (ProcessesandWorkflows: Processes>VolumeMaths>Parser) to apply syntax to adjust the values. Below are a few common examples of where this can be beneficial and an example parser equation to carry out the function. All examples use a geobody volume created using the body labelling process as input (im1).

1)      To change value of a specific body to a desired value, e.g. for input into modelling software which requires certain values.

(im1=n1)*10

Where image 1 value is equal to n1 set its value to 10



Figure 4. Parser showing syntax to be applied to volume (above). Resulting geobody and corresponding histogram (below)


2)      To isolate one or more bodies

(im1=n1)*10 + (im1=n2)*20

Where im1 value is equal to n1 set it to 10; where im1 value is equal to n2 set to 20; all other values will be set to zero.  This will create a volume with 3 values – 10, 20 and zero (background).





 3)     To give several bodies the same data value

(im1=n1)*10 + (im1=n2)*10 + (im3=n3)*10

Where im1 value is equal to n1 set it to 10, where im2 value is equal to n2 set it to 10, where im3 value is equal to n3 set it to 10.



4)     To change values for groups of bodies

(im1>n1)*10

This will set all bodies above the n1 threshold to a value of 10.

Note: These techniques can be useful for adjusting data values of seismic facies classification volumes created in the ‘Interactive Facies Classification (IFC+) module.

For more information on the parser please refer to the help files which contain detailed information on application of syntax and examples of different uses.

Friday, 17 June 2016

Horizon based geobodies for Gross Rock Volume calculation

Not all geobodies are discrete geological features that can be extracted such as channels or debris flows, sometime more large scale geobodies need to be calculated. This is often the case when analysing large reservoirs where a “tank of sand” model may be applied or if two horizons pinch-out making geobody volumetric estimations difficult. This is an important process as the Gross Rock Volume (GRV) is an essential input to the STOIIP or GIIP calculations.  We can increase the accuracy of our GRV estimation with the use of existing horizons and the Adaptive Geobody tool.
STOIIP = Vb * N/G * ΙΈ * (1 – Sw) * (1/Bo)
STOIIP = GRV * Net/Gross Ratio * Porosity * Oil Saturation * Oil Formation Factor
In this example we want to calculate the total GRV for the area between the Top Pyrenees and Base Clinoform horizons. The rest of the STOIIP calculations components are known from previous studies, but trying to get an accurate GRV is the final piece in the jigsaw.

The first step in the process involves making a horizon based geobody.  This can be achieved by accessing the Horizons tools by left-clicking on a horizon in the project tree, which will subsequently populate the Properties tab with the horizon options.  Select the Tools tab and Launch the horizon tools. 



In this window select the Crop/cut tab and populate the input horizons and input volume accordingly.  It is important to select the Binary sculpting method as this will assign all the voxels between the two horizons as a singular value, thus creating a “geobody” volume.



The newly generated Geobody volume can then be used as the source input for the adaptive geobody process. The Adaptive Geobody tool is located in the Interpret workflow tab at the top of the screen or can be access by right-clicking on a volume in the 3D viewer.  In the Adaptive Geobody tool it is then possible to select voxels that represent the internal and external response of the geobody feature. The values of these voxels are then processed via a probability density function that searches for other areas in the volume with a similar value.  As the geobody volume has assigned the area between the two horizons as a single value it should be possible to easily constrain the growth of the adaptive geobody.



To encourage the growth of the geobody it is possible to increase the Acceptance level quite significantly.  This will not affect the resultant geobody as the value assigned to the internal areas (32767) and the external (-32767) areas are so different. Decreasing the Mesh Granularity can also speed up this process.



Once the geobody has finished adapting it is possible to find the volume of the geobody.  Clicking on the Adaptive Geobody Metrics tab in the geobody properties window will bring into view the metrics for all existing geobodies in the GeoTeric project.  Here it is possible to see values such as the Inline, Crossline and Vertical extent of the geobody along with the total surface area. The penultimate column will show the volume of the geobody, in this case the GRV for the area of interest.  As this is a time project an Internal Velocity value has been assigned to convert the time values to depth.  This can be changed or adjusted by left-clicking on the geobody in the table and adjusting the value in the box below.


This workflow allows the user to create large scale geobodies in a short amount of time with a high degree of accuracy that can be used as direct inputs for GRV values for calculating STOIIP or GIIP, ideally but not restricted to stratigraphic pinch-outs.

Friday, 3 June 2016

Angle Stacks and CMY Blending

CMY Blending is a very powerful Cognitive Interpretation technique in GeoTeric that can be used to reveal faults and stratigraphic or carbonate features. In this example we will look at using Structurally Oriented Semblance on angle stack data from the Taranaki Basin, New Zealand. After conditioning the data (noise attenuation and spectral balancing) for all three angle stacks, we can create SO Semblance attributes. This can be done in the Fault Expression tool or also in the Batch Processor: Processes>Attributes>Edge Attributes>SO Semblance. For more information on edge attributes click here: http://blog.geoteric.com/2013/05/edge-attributes-which-one-should-i-be.html

Quite often, geological features can be masked by a full stack volume. Therefore looking at three angle stacks in single blend can help the interpreter reveal geological features more clearly.


To create a CMY blend using the angle stack SO Semblance volumes, click on Tools>New Colour Blend. The Near, Mid and Far SO Semblance volumes can be put in the respective inputs. After clicking on “set blend”, the result can be previewed on any slice or a horizon. The most crucial step is to select “Uniform Stretch” to make sure the scaling parameters are equal between the volumes blended. The default RGB scheme can then be switched to CMY. This unique ability to preview the colour blend on any slice or horizon prior to actual generation allows the interpreter to save a considerable amount of time and work more efficiently.


In the image below for example, we have the full stack SO Semblance, where the channel edges are not continuous everywhere and some channel features are quite subtle.


While in the CMY blend below, the parts of the channels where the edges appear washed out are more clearly delineated. It also effectively shows where in the blend each angle stack’s contribution is. In the red circle for example the far stack response shows a clearer edge which has been potentially masked by the full stack. 


The unique, quantitative nature of GeoTeric’s colour blends also means that they can be interpreted on directly in GeoTeric without any compromise on resolution, however the user is also free to export them using the various links such as the Link to Petrel or DecisionSpace. The colour blends transferred using the links will retain more of the resolution compared to those that are exported as SEGYs. 






Wednesday, 25 May 2016

GeoTeric @ EAGE 2016, Booth #1340

We are pleased to announce our full booth schedule for EAGE 2016 in Vienna

Daily Booth Talks
Showcasing GeoTeric 2016 in action.

10:00am - Rapid shallow hazard imaging and delineation using Cognitive Interpretation workflows.
11:00am - What’s New in GeoTeric 2016 –Making Your Workflow Easier.
1:30pm - Quantify your colour blends with GeoTeric’s Forward Modelling.
2:30pm - GeoTeric Cognitive Interpretation Services Offering.
3:30pm - How to get the most from colourblends using GeoTeric’s Interpretation Tools.

There are also the free GeoTeric training sessions- see here for details. 


Collaborative Workflow Lunches
See how GeoTeric is used in conjunction with other leading industry applications. If you register in advance, we’ll even get you lunch.

Tuesday @ 12:30pm         - GeoTeric with PaleoScan™, from Eliis.
Wednesday @ 12:30pm    - GeoTeric with prestackPro™, from Sharp Reflections.
Thursday @ 12:30pm        - GeoTeric with GeoScience Ltd.


Conference Talks
We will be presenting three talks at the EAGE Technical Programme. See below for all the details you need to make sure you don’t miss us.

Tuesday 31stMay
“The Effect of Colour Blindness on Seismic Interpretation”
Gaynor Paton
Time –4:20pm
Session –Automated Interpretation
Room –Stolz1

Wednesday 1st June
“Evaluating the Gap between Seismic-scale and Well-scale Observations of Structure –a North Sea Case Study”
Ryan Williams
Time -9:45am
Session –Fractured & Carbonate Reservoirs
Room –Schubert 5
This case study was written in collaboration with GeoScience Ltd.

“The Application of Data Conditioning, Frequency Decomposition and RGB Blending in the GohtaDiscovery (Norway)”
Syed Gilani (DEA E&P Norge AS)
Time –2:20pm
Session –Seismic Reservoir CharacterisationII –From Case Studies to New Advances
Room –Lehar 3
This is a case study that was written in collaboration with Noreco ASA

Thursday 2ndJune
“Effects of Post Stack Seismic Data Conditioning on Impedance Inversion for Reservoir, Brazilian Pre-salt, Santos Basin”
Luis Gomez
Time -3:30pm
Session –Seismic Reservoir Characterisation III –Inversion Case Studies
Room –Lehar 3
This case study was written in collaboration with Petrobras


GeoTeric will be serving drinks and nibbles at the ice breaker so please stop by and say hello. Looking forward to see you there!