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Project FORCE ML Competition 2020 Synthetic Models and Wells

General

Country:

N/A

Data summary:

OpendTect project with Synthetic Models and Wells. For image data extraction and training.

OpendTect license information:

License key protected parts of OpendTect (OpendTect Pro and the commercial plugins) can be used on this data set without license keys because the software does not check for license key when it runs on this data set.

Supported for OpendTect:

6.4.6, 6.6.X, 7.0.0 and onwards

Size:

2.2 GB (uncompressed), 1.6 GB (download)

License:

Creative Commons 4.0

Download:

download

Contributors

Competition organiser:

FORCE

Original seismic:

Value added products:

dGB Earth Sciences created the OpendTect project.

FORCE

Equinor created Synthetic Models

Schlumberger created Synthetic Models

dGB Earth Sciences created the OpendTect project.

Acknowledgment

Using the data and value-added products in this project in publications is permitted. We kindly request to be acknowledged in the following manner:

We thank dGB Earth Sciences for making the data available as an OpendTect project via their TerraNubis portal terranubis.com.

Survey Parameters

Survey type:

Only 3D

Inline range and step:

1, 151, 1

Crossline range and step:

1, 589, 1

Z range and step:

0, 3.6, 0.004 Time

Inline bin size (m/line):

18.75

Crossline bin size (m/line):

12.5

Area (sq km):

20.84

More Info

FORCE Machine Learning Competition 2020 information

Here you will find more information about the FORCE Machine Learning Competition 2020.

We show you how to get a headstart with OpendTect Pro 7.0 and how to make and train your own models by using the OpendTect Pro 7.0 Machine Learning plugin:

- the dgbes.com FORCE Machine Learning Competition 2020 webpage

- OpendTect Machine Learning - knowledge base

- the Machine Learning Webinar videos

- download and install OpendTect 7.0

- Join the OpendTect Machine Learning Developers' Community on Discord

- Develop your own models documentation

- Machine Learning Workflow: Wells Log-Log Prediction (Density)

- Machine Learning Workflow: Wells Lithology Classification

- Machine Learning Workflow: Seismic bodies (Supervised 3D)

- Machine Learning Workflow: Seismic Unet 3D Fault Predictor

- Machine Learning Workflow: 3D Seismic + Wells Rock Property Prediction

- Machine Learning Workflow: Seismic Image to Image Faults Prediction

- Machine Learning Workflow: Seismic Image Regression Unet Fill Seismic Traces

- Machine Learning Workflow: Seismic Image Regression Unet Fill Seismic Traces

Available Data

Synthetic Models:

  • Equinor_issap20_AIi.cbvs
  • Equinor_issap20_Fault.cbvs
  • Equinor_issap20_Pp.cbvs
  • Schlumberger_800_SLB_Force_Fault_Model_BlockId.cbvs
  • Schlumberger_800_SLB_Force_Fault_Model_Circular_Noisy_Amplitude.cbvs
  • Schlumberger_800_SLB_Force_Fault_Model_Circular_NoNoise_Amplitude.cbvs
  • Schlumberger_800_SLB_Force_Fault_Model_Circular_VeryNoisy_Amplitude.cbvs
  • Schlumberger_800_SLB_Force_Fault_Model_Flat_Noisy_Amplitude.cbvs
  • Schlumberger_800_SLB_Force_Fault_Model_Flat_NoNoise_Amplitude.cbvs
  • Schlumberger_800_SLB_Force_Fault_Model_Flat_VeryNoisy_Amplitude.cbvs
  • Schlumberger_800_SLB_Force_Fault_Model_Hybrid_Noisy_Amplitude.cbvs
  • Schlumberger_800_SLB_Force_Fault_Model_Hybrid_NoNoise_Amplitude.cbvs
  • Schlumberger_800_SLB_Force_Fault_Model_Hybrid_VeryNoisy_Amplitude.cbvs
  • Schlumberger_800_SLB_Force_Fault_Model_IsFault.cbvs

WellsLogs:

  • 15_9-13
  • 15_9-14
  • 15_9-15
  • 15_9-17
  • 16_10-1
  • 16_10-2
  • 16_10-3
  • 16_10-5
  • 16_11-1_ST3
  • 16_1-2
  • 16_1-6_A
  • 16_2-11_A
  • 16_2-16
  • 16_2-6
  • 16_4-1
  • 16_5-3
  • 16_7-4
  • 16_7-5
  • 16_8-1
  • 17_11-1
  • 25_10-10
  • 25_11-15
  • 25_11-19_S
  • 25_11-24
  • 25_11-5
  • 25_2-13_T4
  • 25_2-14
  • 25_2-7
  • 25_3-1
  • 25_4-5
  • 25_5-1
  • 25_5-3
  • 25_5-4
  • 25_6-1
  • 25_6-2
  • 25_6-3
  • 25_7-2
  • 25_8-5_S
  • 25_8-7
  • 25_9-1
  • 26_4-1
  • 29_3-1
  • 29_6-1
  • 30_3-3
  • 30_3-5_S
  • 30_6-5
  • 31_2-19_S
  • 31_2-1
  • 31_2-7
  • 31_2-8
  • 31_2-9
  • 31_3-1
  • 31_3-2
  • 31_3-3
  • 31_3-4
  • 31_4-10
  • 31_4-5
  • 31_5-4_S
  • 31_6-5
  • 31_6-8
  • 32_2-1
  • 33_5-2
  • 33_6-3_S
  • 33_9-17
  • 33_9-1
  • 34_10-16_R
  • 34_10-19
  • 34_10-21
  • 34_10-33
  • 34_10-35
  • 34_11_1
  • 34_11-2_S
  • 34_12-1
  • 34_2-4
  • 34_3-1_A
  • 34_3-3_A
  • 34_4-10_R
  • 34_5-1_A
  • 34_5-1_S
  • 34_6-1_S
  • 34_7-13
  • 34_7-20
  • 34_7-21
  • 34_8-1
  • 34_8-3
  • 34_8-7_R
  • 35_11-10
  • 35_11-11
  • 35_11-12
  • 35_11-13
  • 35_11-15_S
  • 35_11-1
  • 35_11-6
  • 35_11-7
  • 35_12-1
  • 35_3-7_S
  • 35_4-1
  • 35_6-2_S
  • 35_8-4
  • 35_8-6_S
  • 35_9-10_S
  • 35_9-2
  • 35_9-5
  • 35_9-6_S
  • 35_9-8
  • 36_7-3
  • 7_1-1
  • 7_1-2_S

Map
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    Derivative work

    An important aspect of TerraNubis is the possibility to create derivative work. For example, someone performed a seismic interpretation and makes this interpretation including all data files and interpreted grids available as a project for sale in TerraNubis. The seller permits 3rd parties to create derivative work for this project. This is a free choice for every project on sale. It means that any expert can work on this data set to create additional value-added products without paying the seller. It is explicitly forbidden to use the data for anything else then creating derivative work. The derivative work has to be sold exclusively via TerraNubis and always in combination with the original project.

    Download derivative work agreement

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