dGB Earth Sciences - Software

Technology	OpendTect Suite of Software Products					Info
	OpendTect	OpendTect Pro	Geoscience Bundle	Machine Learning Bundle	Other Plugins

Data IO
SEG-Y, LAS, ASCII	✔	✔	✔	✔		SEG-Y manipulator to edit non-standard SEG-Y
Use SEG-Y "as is"	✔	✔	✔	✔		No data duplication
Batch Well and LAS import/export	✖	✔	✔	✔		Simple Multi-well and bulk import
Access to Petrel*	✖	✔	✔	✔		Direct link or copy data both ways
Access to SeisWorks	✖	✖	✖	✖	✔	Copy data both ways
Export to GIS applications	✔	✔	✔	✔	✔	GeotiffExport, free plugin by Wayne Mogg
Viewers
2D Viewers	✔	✔	✔	✔		Wiggle + variable density
3D Viewers	✔	✔	✔	✔		Multiple scenes in different domains (time, depth, flattened, ...)
Prestack viewers	✔	✔	✔	✔		In 3D scene and flat viewers
Stereo viewing	✔	✔	✔	✔		Red-Cyan or Quad Buffer
Volume rendering	✔	✔	✔	✔		Interactive opacity editing
RGB(A) blending	✔	✔	✔	✔		Volumes, horizons, sections
Logplots	✔	✔	✔	✔		Basic log viewer (and Bokeh log plot with Pro License)
Cross-plot	✔	✔	✔	✔		Multi-dimensional cross-plot tool (and Bokeh log crossplot with Pro license)
Rose diagrams	✖	✖	✔	✔		Faults & Fractures
Seismic wavefield displays	✖	✖	✖	✖	✔	Seismic Terrain Explorer
Auto Tracking (2D, 3D, faults, objects, ...)
3D Horizon tracker	✔	✔	✔	✔		Amplitude & Similarity (dip guided using Dip Steering license)
2D Horizon tracker	✔	✔	✔	✔		Amplitude & Similarity (dip guided using Dip Steering license)
3D Horizon Unconformity tracker & Inversion+ tracker	✖	✖	✔	✔		Inversion-based flattening & combined with similarity tracking (Inversion+)
Seismic facies tracker	✖	✔	✔	✔		Thalweg tracker
3D Object tracker	✔	✔	✔	✔		Voxel Connectivity Filter
Attributes & Filters(open source)
Interactive multi-volume attribute analysis	✔	✔	✔	✔		On-the-fly parameter testing
"Standard" attributes and filters	✔	✔	✔	✔	✔	Energy, Similarity, Instantaneous Attributes, Sweetness, …
Texture attributes	✔	✔	✔	✔		GLCM based texture attributes for geo-morphologies
Spectral Decomposition	✔	✔	✔	✔		Short window FFT, CWT, EMD, EEMD and CEEMD
Mathematics & Logics	✔	✔	✔	✔		Functions, operations, "IF... THEN... ELSE..." etc.
Attributes from attributes	✔	✔	✔	✔		Create your own attributes by chaining attributes
4D Volume alignment	✔	✔	✔	✔		Match delta and Delta resample attributes
Pattern recognition	✔	✔	✔	✔		Fingerprint attribute
AVO and AVA attributes	✔	✔	✔	✔	✔	Intercept, Gradient and derivatives + AVOAtrrib plugin by Wayne Mogg
Ridge filters	✔	✔	✔	✔		Ridge enhancement filter
Post-stack "broadband"	✖	✖	✖	✖	✔	Frequency Shaping + PostDeGhost
Wavelet shaping	✔	✔	✔	✔	✔	Colop, free plugin by Peter Zahuczki
General Computing
Distributed computing & parallelization	✔	✔	✔	✔		For batch processing and fast on-the-fly computation
Utilities
Sharing 3D PDF images	✖	✔	✔	✔		Grabs a 3D scene and saves it in pdf format
Creating PowerPoint presentations	✔	✔	✔	✔		Python PowerPoint Presentation Maker
Python R&D environment	✖	✖	✔	✔		Machine Learning
2D seismic Mistie correction	✔	✔	✔	✔	✔	Mistie, free plugin by Wayne Mogg
Wells and Modeling
Stratigraphic model	✔	✔	✔	✔		Optional model ties stratigraphy to markers and horizons
Wells, markers, logs	✔	✔	✔	✔		IO and basic editing
Synthetic-to-seismic well tie	✔	✔	✔	✔		Supports stretch & squeeze
Wavelet estimation	✔	✔	✔	✔		Stochastic & deterministic; Synthetic wavelets
Pre-stack synthetics	✖	✖	✔	✔		Zoeppritz solution through "flat-earth": P, S, and multiples
Cross-sections, wedge models, volume synthetics	✖	✖	✔	✔		SynthRock plugin; Gassmann fluid replacement
Stochastic pseudo-well modeling	✖	✖	✔	✔		SynthRock plugin
Well Log Correlation	✖	✖	✔	✖		Well Correlation Panel to QC and edit markers
Rock Physics Library	✔	✔	✔	✔		Compute new logs using "standard" or private equations
Machine Learning log-log prediction	✖	✖	✔	✔		Ensemble, SVM, CNN, …
General Interpretation
Velocity Model Buliding	✔	✔	✔	✔		3D model builder and velocity conversion tools
Time Depth Conversion	✔	✔	✔	✔		Two-way on-the-fly conversion
Stratigraphic flattening	✔	✔	✔	✔		Flattens a 3D scene on a given horizon
Manual interpretation	✔	✔	✔	✔		Points, bodies, polygons, fault sticks, fault planes, ...
Mapping, gridding, contouring
Gridding, contouring	✔	✔	✔	✔	✔	Inverse distance, curvature, triangulation + free plugins by Wayne Mogg
Dip-steered gridding	✖	✖	✔	✔		Inverse distance interpolation along seismic dip
Basemap + mapping	✖	✔	✔	✔		Improves OpendTect's user interaction
Seismic Sequence Stratigraphic functionality
2D/3D Global Interpretation	✖	✖	✔	✖		2D and 3D HorizonCube
2D/3D Wheeler transformation	✖	✖	✔	✖		Flattening of seismic using HorizonCube events
2D/3D systems tracts interpretation	✖	✖	✔	✖		SSIS plugin
3D Body Extraction	✖	✖	✔	✖		Extract 3D bodies from horizon geometries and attributes
Depositional Attributes	✖	✖	✔	✖		HorizonCube density and thickness attributes
Multi-horizon visualization & manipulation	✖	✖	✔	✖		2D and 3D HorizonCube sliders
Attributes & Filters (commercial)
Structurally Oriented Filters	✖	✖	✔	✔		Dip-Steered Median Filter, Fault Enhancement Filter, ...
Resolution enhancement filters	✖	✖	✖	✖	✔	Spectral Blueing
Fault Attributes	✖	✖	✔	✔		Thinned Fault Likelihood, Dip-Steered Similarity, …
Fracture Attributes	✖	✖	✔	✔	✔	Curvature attributes, fracture density, fracture proximity + FracTex
Stratigraphic Attributes	✖	✖	✔	✔		Dip-steered attributes: texture, volume statistics, ...
Hydrocarbon Anomaly Enhancement	✖	✖	✔	✖		Fluid Contact Finder, Seismic Feature Enhancement*
Footprint removal	✖	✖	✖	✖	✔	MGS-Destriping
Machine Learning regression	✖	✖	✔	✔		Image-to-image via CNN, Unet, …
Machine Learning object detection	✖	✖	✔	✔		Chimneys, Faults, Salt, Facies, … via MLP, CNN, …
Machine Learning segmentation	✖	✖	✔	✔		3D seismic facies via UVQ
Machine Learning waveform segmentation	✖	✖	✔	✔		Horizon-based seismic facies maps via UVQ
Quantitative Interpretation functionality
Coloured Inversion - AI, EI, EEI	✖	✖	✖	✖	✔	Seismic Coloured Inversion
Net Pay Prediction	✖	✖	✖	✖	✔	Seismic Net Pay
Seismic consistent Low Frequency Model Building	✖	✖	✔	✖		HorizonCube guided low frequency model
Deterministic Inversion AI, EI, EEI	✖	✖	✖	✖	✔	Deterministic Inversion + Bayesian Linear Inversion (also for EI and EEI)
Stochastic Inversion AI, EI, EEI	✖	✖	✖	✖	✔	Multi-Point Stochastic Inversion
HitCube inversion	✖	✖	✔	✔		SynthRock matching stochastic synthetics and real seismic
Machine Learning Inversion	✖	✖	✔	✔		Rock property prediction via MLP, CNN, Ensemble, ...
Bayesian Inversion	✔	✔	✔	✔	✔	Predicts rock properties from Probability Density Functions
Potential Field Modelling	✖	✖	✖	✖	✔	Xfield

	Linux 64 bit RHEL^*/Rocky Ubuntu OpenSUSE	Windows Windows 10/11 64 bit	macOS macOS 12/13/14
OpendTect	✔	✔	✔
OpendTect Pro	✔	✔	✔
Dip Steering	✔	✔	✔
HorizonCube	✔	✔	✔
Sequence Stratigraphic Interpretation System	✔	✔	✔
Faults & Fractures	✔	✔	✔
Machine Learning	✔	✔	Neural Networks only
Well Correlation Panel	✔	✔	✔
SynthRock	✔	✔	✔
Fluid Contact Finder	✔	✔	✔
Velocity Model Building	✔	✔	✔
Seismic Spectral Blueing	✔	✔	✖
Seismic Coloured Inversion	✔	✔	✖
Seismic Net Pay	✔	✔	✖
Seismic Feature Enhancement	✔	✔	✖
Frequency Shaping	✔	✔	✖
PostDeGhost	✔	✔	✖
Workstation Access	✔	✖	✖
XField	✖	✔	✖
Deterministic Inversion	✔	✔	✖
Stochastic Inversion	✔	✔	✖
MGS Destriping	✔	✔	✖
Fractex	✔	✔	✖
Bayesian Linear Inversion	✔	✔	✖
Surface Segments	✖	✔	✖

* OpendTect Pro 6.6 has been certified for RHEL 8
* OpendTect Pro 7.0 has been certified for RHEL 8 & RHEL 9
For more information about supported Linux distributions, graphics cards and other system requirements please take a look at the Administrator's Manual for OpendTect 7.0.7 or for OpendTect 6.6.10.

Windows

Minimum

Version:	10/11, older Windows versions are not officially supported, but may work.
CPU:	Intel/AMD, 64 bits
GPU:	Basic Intel graphics cards or Nvidia (e.g. recent GeForce/Quadro/NVS series) graphics cards; AMD graphics cards may work.
Memory:	DDR4 memory, 16 GB of RAM, OpendTect itself needs at least 2 GB RAM. Therefore, 16 GB will almost certainly be the absolute minimum.
Storage:	Hard Disk

Version:	10/11, older Windows versions are not officially supported, but may work.
CPU:	Intel/AMD processor with 64 bit support, 3+ GHz multi-core. Note that OpendTect uses all processors if necessary. The more cores and speed, the better. OpendTect will automatically use multiple threads in many situations. This depends on the type of attribute, display, etc. We put a lot of effort to get time-consuming tasks multi-threaded.
GPU:	Nvidia (e.g. recent main-stream up to high-end GeForce series) graphics cards. Quadro or NVS series cards could give the bit extra you want. In doubt, buy the best GeForce card you can find. When buying a laptop make sure that it has a Nvidia chipset.
Memory:	DDR4 or DDR5 memory, on the safe side don't go for less than 32 GB. Buy as much memory that you can afford and fits in the system. The big clients for example use nothing less than 512 GB.
Storage:	SSD is best, other good options are Hard Disk and Network Drive. This is usually under-valued, but it's often the crucial performance component. SSD disks will give a tremendous boost in performance; essentially, data on SSD disks loads almost as fast as pre-loaded, in-memory data. Performance could be miserable if data needed to stream through (relatively) slow disks and/or networks.

For Machine Learning

Version:	10/11, older Windows versions are not officially supported, but may work.
CPU:	Intel, 64 bits for when using Python environment Intel™ Math Kernel - MKL for Machine Learning using CPU only. AMD, 64 bits should be fine when using the Python Environment with CUDA 11.3 for Machine Learning on the GPU. Ideally you want the system to be expendable to 4 GPUs. The CPU will need to support all GPUs. Important to look for is how many PCIe lanes the CPU supports and how many PCIe lanes are needed for the system's number of GPUs and M.2 NVMe SSDs. We recommend to get a CPU with at least 8 cores, 16 threads and 40 PCIe lanes.
GPU:	Nvidia, GeForce or Quadro series. The GPU needs to be fast enough and able to fit the model and data batch in memory. When in doubt choose the one with more memory. Other things to look for is the number of CUDA cores, tensor cores and GB memory bandwidth per second. We recommend the following cards: Turing architecture cards (CUDA 10 and later) Nvidia GeForce RTX 2080 Ti with 11 GB DDR6 memory and 4352 CUDA Cores Nvidia Quadro RTX 6000 with 24 GB DDR6 memory and 4608 CUDA Cores Nvidia Quadro RTX 8000 with 48 GB DDR6 memory and 4608 CUDA Cores Ampere architecture cards (CUDA 11.1 and later) Nvidia GeForce RTX 3080 Ti with 12 GB DDR6 memory and 10240 CUDA Cores Nvidia GeForce RTX 3090 with 24 GB DDR6 memory and 10496 CUDA Cores Nvidia A40 with 48 GB DDR6 memory and 10752 CUDA Cores Ada Lovelace architecture cards (CUDA 11.8 and later) Nvidia GeForce RTX 4070 Ti with 12 GB DDR6 memory and 7680 CUDA Cores Nvidia GeForce RTX 4080 with 16 GB DDR6 memory and 9728 CUDA Cores Nvidia GeForce RTX 4090 with 24 GB DDR6 memory and 16384 CUDA Cores
Memory:	DDR4 or DDR5 memory, on the safe side don't go for less than 32 GB. Buy as much memory that you can afford and fits in the system.
Storage:	The best choice is M.2 NVMe SSD that is big enough for the data. The advantage of M.2 NMVe SSD is that it is plugged into the motherboard and is super fast. Other options are SATA SSD, Hard Disk and Network Drive. Performance could be miserable if data needed to stream through (relatively) slow disks and/or networks.

Please note that...

for best performance OpenGL drivers should be up-to-date. For Machine Learning on GPU we provide a Python package with CUDA 11.3. Please see this table on the Nvidia CUDA Toolkit documentation page for the minimum compatible driver version.
the CUDA 10 Python environment is now obsolete and will no longer receive security updates. Users are encouraged to replace it with CUDA 11. Alternatively, you may decide for the CPU-only Python environment.
4K/8K screens are not fully supported yet. This depends on the scaling factor. We are working on a fix. Please see the FAQ Visualization for a possible workaround.
Windows needs to be updated with the latest updates from Microsoft.

Linux

Minimum

Modern Linux distro:	We have tested: RHEL/CentOS 7.2 and higher Ubuntu 20.04 and higher OpenSUSE Leap 15.4 and higher Other distros will probably work, possibly with a small tweak
CPU:	Intel/AMD processor with 64 bit support
GPU:	Basic Intel Graphics cards or Nvidia (e.g. recent GeForce/Quadro/NVS series) graphics cards; AMD graphics cards may work.
Memory:	DDR4 memory, 16 GB of RAM, OpendTect itself needs at least 2 GB RAM. Therefore, 16 GB will almost certainly be the absolute minimum.
Storage:	Hard Disk

Modern Linux distro:	We have tested: RHEL/Rocky Linux 8.0 and higher Ubuntu 22.04 and higher OpenSUSE Leap 15.4 and higher
CPU:	Intel/AMD processor with 64 bit support, 3+ GHz multi-core. Note that OpendTect uses all processors if necessary. The more cores and speed, the better. OpendTect will automatically use multiple threads in many situations. This depends on the type of attribute, display, etc. We put a lot of effort to get time-consuming tasks multi-threaded.
GPU:	Nvidia (e.g. recent main-stream up to high-end GeForce series) graphics cards. Quadro or NVS series cards could give the bit extra you want. In doubt, buy the best GeForce card you can find. When buying a laptop make sure that it has a Nvidia chipset.
Memory:	DDR4 or DDR5 memory, on the safe side don't go for less than 32 GB. Buy as much memory that you can afford and fits in the system. The big clients for example use nothing less than 512 GB.
Storage:	SSD is best, other good options are Hard Disk and Network Drive. This is usually under-valued, but it's often the crucial performance component. SSD disks will give a tremendous boost in performance; essentially, data on SSD disks loads almost as fast as pre-loaded, in-memory data. Performance could be miserable if data needed to stream through (relatively) slow disks and/or networks.

For Machine Learning

Modern Linux distro:	We have tested: RHEL/Rocky Linux 8.0 and higher Ubuntu 22.04 and higher OpenSUSE Leap 15.4 and higher
CPU:	Intel, 64 bits for when using Python environment Intel™ Math Kernel - MKL for Machine Learning using CPU only. AMD, 64 bits should be fine when using the Python Environment with CUDA 11.3 for Machine Learning on the GPU. Ideally you want the system to be expendable to 4 GPUs. The CPU will need to support all GPUs. Important to look for is how many PCIe lanes the CPU supports and how many PCIe lanes are needed for the system's number of GPUs and M.2 NVMe SSDs. We recommend to get a CPU with at least 8 cores, 16 threads and 40 PCIe lanes.
GPU:	Nvidia, GeForce or Quadro series. The GPU needs to be fast enough and able to fit the model and data batch in memory. When in doubt choose the one with more memory. Other things to look for is the number of CUDA cores, tensor cores and GB memory bandwidth per second. We recommend the following cards: Turing architecture cards (CUDA 10 and later) Nvidia GeForce RTX 2080 Ti with 11 GB DDR6 memory and 4352 CUDA Cores Nvidia Quadro RTX 6000 with 24 GB DDR6 memory and 4608 CUDA Cores Nvidia Quadro RTX 8000 with 48 GB DDR6 memory and 4608 CUDA Cores Ampere architecture cards (CUDA 11.1 and later) Nvidia GeForce RTX 3080 Ti with 12 GB DDR6 memory and 10240 CUDA Cores Nvidia GeForce RTX 3090 with 24 GB DDR6 memory and 10496 CUDA Cores Nvidia A40 with 48 GB DDR6 memory and 10752 CUDA Cores Ada Lovelace architecture cards (CUDA 11.8 and later) Nvidia GeForce RTX 4070 Ti with 12 GB DDR6 memory and 7680 CUDA Cores Nvidia GeForce RTX 4080 with 16 GB DDR6 memory and 9728 CUDA Cores Nvidia GeForce RTX 4090 with 24 GB DDR6 memory and 16384 CUDA Cores
Memory:	DDR4 or DDR5 memory, on the safe side don't go for less than 32 GB. Buy as much memory that you can afford and fits in the system.
Storage:	The best choice is M.2 NVMe SSD that is big enough for the data. The advantage of M.2 NMVe SSD is that it is plugged into the motherboard and is super fast. Other options are SATA SSD, Hard Disk and Network Drive. Performance could be miserable if data needed to stream through (relatively) slow disks and/or networks.

Please note that...

OpendTect may work when using the Nouveau driver, however for best performance the Nvidia driver should be installed. The nouveau driver does not support CUDA.

Gallium3D drivers are not supported.

for best performance OpenGL drivers should be up-to-date. For Machine Learning on GPU we provide a Python package with CUDA 11.3. Please see this table on the Nvidia CUDA Toolkit documentation page for the minimum compatible driver version.

the CUDA 10 Python environment is now obsolete and will no longer receive security updates. Users are encouraged to replace it with CUDA 11. Alternatively, you may decide for the CPU-only Python environment.

low-level GPUs keep showing poor performances through the generations. Shading functionality requires special GPU features, present in the main-stream and high-end GeForce, Quadro and NVS cards. Nevertheless, under Linux, only Nvidia provides drivers capable of running the shading feature. If you can't see any colors on graphic elements, try disabling shading (Utilities > Settings > Look and Feel).

4K/8K screens are not fully supported yet. This depends on the scaling factor. We are working on a fix. Please see the FAQ Visualization for a possible workaround.

Linux 64 bits releases require the libstdc++ library to be present on the system. In the table you can see the minimum libstdc++ library version that is needed:

OpendTect version	libstdc++ library needed
6.4	6.0.19 or newer
6.6	6.0.21 or newer
7.0	6.0.28 or newer

Linux distros will need to have the XCB libraries installed. For check and installation instructions please see Installing OpendTect on Linux.

OpendTect is known to work under RHEL, CentOS, Ubuntu, OpenSUSE and other distributions, as well as earlier versions of the main distributions, too. Fedora usage is not recommended - although it may work it's the only distro that regularly fails to work in combination with OpendTect. This is probably because the graphics vendors do not support it well in terms of drivers.

OpendTect Pro 7.0 has been certified for RHEL 8 & RHEL 9

macOS

Minimum

Version:	macOS 12 (Monterey)
CPU:	mac/Intel or mac/ARM processor with 64-bit support
GPU:	Basic Intel, AMD or Apple graphics card, f.i. the Intel HD Graphics 4000.
Memory:	16 GB of RAM
Storage:	Hard Disk

Version:	macOS 13 (Ventura) / 14 (Sonoma)
CPU:	mac/Intel or mac/ARM processor with 64-bit support
GPU:	Intel, AMD or Apple graphics card
Memory:	Don't go for less than 32 GB RAM. Buy as much memory that you can afford and fits in the system.
Storage:	SSD is best. Other good options are Hard Disk and Network Drive.

For Machine Learning

We can provide packages to test Machine Learning on macOS.

Please note that...

mac/PowerPC support is NOT available.
mac/Intel emulation in Rosetta 2 is not supported.
a 3-button mouse is highly recommended.

Cloud

Amazon Web Services (AWS)

OS / AMI:	Amazon Linux 2023 is not supported, official AWS documentation says that AL2023 is cloud-centered and optimized for Amazon EC2 usage and currently does not include a graphical or desktop environment.
Architecture:	For Linux and Windows: The 64-bit (x86) architecture is supported. Note that the 64-bit (Arm) architecture is not supported.
Instance Type:	We tested OpendTect on the following instance types: g3s, g5
GPU:	We recommend either creating the instance with an AMI that comes with the NVIDIA driver pre-installed or installing the NVIDIA GRID driver on an instance that comes with a NVIDIA GPU. Please note that the basic Microsoft render driver is not supported.
Display:	We recommend to use NICE DCV for the remote display. More information and the downloads for Server and Client can be found on the NICE DCV website.

Please note that...

We have known clients using OpendTect on AWS, Azure and Google Cloud. The requirements are no different compared to the on-prem requirements. Same OS, amount of memory and same graphics requirements apply.

OpendTect

More information

Interpretation

Attributes

Data IO

Free Gems

Spectral Decomposition

Rock physics library

Distributed computing

Presentation Maker

Free Third Party Plugins

Pre-stack viewers

Special Attributes