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Exporting and analizing Petrel's data to Python

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Manuel David Soto. MSc in Geological Sciences, University of Texas at Austin, USA.

About this notebook

Petrel is a powerful and widely used program by geoscientists and engineers who work with subsurface data. With it you can make from a simple map map from well markers to a complex model of a reservoir with its corresponding surfaces, faults and fluids. Despite these tremendous capabilities of Petrel, certain simple data analysis tasks, such as a histogram or regression, are complicated, cumbersome or incomplete. Although there are plugins that solve these shortcomings of Petrel, Python is a flexible and free alternative for analysis of data residing in Petrel.

The objective of this Notebook is to show through an example how Python can be used as a complement to Petrel's data analysis tasks. The example compares thickness obtained in Petrel from tops and from a seismic surface, and then finds the best regression between them.

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Here is a list of libraries necessary for this notebook:

numpy
matplotlib
pandas
scikit-learn

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GitHub - cabinhero/petrel2python: Exporting and analizing Petrel's data to Python · GitHub
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Exporting and analizing Petrel's data to Python
Notebook creator
Manuel David Soto. MSc in Geological Sciences, University of Texas
at Austin, USA.
About this notebook
Petrel is a powerful and widely used program by geoscientists and engineers who work with subsurface data. With it you can make from a simple map map from well markers to a complex model of a reservoir with its corresponding surfaces, faults and fluids. Despite these tremendous capabilities of Petrel, certain simple data analysis tasks, such as a histogram or regression, are complicated, cumbersome or incomplete. Although there are plugins that solve these shortcomings of Petrel, Python is a flexible and free alternative for analysis of data residing in Petrel.
The objective of this Notebook is to show through an example how Python can be used as a complement to Petrel's data analysis tasks. The example compares thickness obtained in Petrel from tops and from a seismic surface, and then finds the best regression between them.
Libraries for notebook
Here is a list of libraries necessary for this notebook:
numpy
matplotlib
pandas
scikit-learn

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GitHub - cabinhero/petrel2python: Exporting and analizing Petrel's data to Python · GitHub
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Exporting and analizing Petrel's data to Python
Notebook creator
Manuel David Soto. MSc in Geological Sciences, University of Texas
at Austin, USA.
About this notebook
Petrel is a powerful and widely used program by geoscientists and engineers who work with subsurface data. With it you can make from a simple map map from well markers to a complex model of a reservoir with its corresponding surfaces, faults and fluids. Despite these tremendous capabilities of Petrel, certain simple data analysis tasks, such as a histogram or regression, are complicated, cumbersome or incomplete. Although there are plugins that solve these shortcomings of Petrel, Python is a flexible and free alternative for analysis of data residing in Petrel.
The objective of this Notebook is to show through an example how Python can be used as a complement to Petrel's data analysis tasks. The example compares thickness obtained in Petrel from tops and from a seismic surface, and then finds the best regression between them.
Libraries for notebook
Here is a list of libraries necessary for this notebook:
numpy
matplotlib
pandas
scikit-learn

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Exporting and analizing Petrel's data to Python
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GitHub - cabinhero/petrel2python: Exporting and analizing Petrel's data to Python · GitHub
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Exporting and analizing Petrel's data to Python
Notebook creator
Manuel David Soto. MSc in Geological Sciences, University of Texas
at Austin, USA.
About this notebook
Petrel is a powerful and widely used program by geoscientists and engineers who work with subsurface data. With it you can make from a simple map map from well markers to a complex model of a reservoir with its corresponding surfaces, faults and fluids. Despite these tremendous capabilities of Petrel, certain simple data analysis tasks, such as a histogram or regression, are complicated, cumbersome or incomplete. Although there are plugins that solve these shortcomings of Petrel, Python is a flexible and free alternative for analysis of data residing in Petrel.
The objective of this Notebook is to show through an example how Python can be used as a complement to Petrel's data analysis tasks. The example compares thickness obtained in Petrel from tops and from a seismic surface, and then finds the best regression between them.
Libraries for notebook
Here is a list of libraries necessary for this notebook:
numpy
matplotlib
pandas
scikit-learn

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GitHub - cabinhero/petrel2python: Exporting and analizing Petrel's data to Python · GitHub
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Exporting and analizing Petrel's data to Python
Notebook creator
Manuel David Soto. MSc in Geological Sciences, University of Texas
at Austin, USA.
About this notebook
Petrel is a powerful and widely used program by geoscientists and engineers who work with subsurface data. With it you can make from a simple map map from well markers to a complex model of a reservoir with its corresponding surfaces, faults and fluids. Despite these tremendous capabilities of Petrel, certain simple data analysis tasks, such as a histogram or regression, are complicated, cumbersome or incomplete. Although there are plugins that solve these shortcomings of Petrel, Python is a flexible and free alternative for analysis of data residing in Petrel.
The objective of this Notebook is to show through an example how Python can be used as a complement to Petrel's data analysis tasks. The example compares thickness obtained in Petrel from tops and from a seismic surface, and then finds the best regression between them.
Libraries for notebook
Here is a list of libraries necessary for this notebook:
numpy
matplotlib
pandas
scikit-learn

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GitHub - cabinhero/petrel2python: Exporting and analizing Petrel's data to Python · GitHub
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Exporting and analizing Petrel's data to Python
Notebook creator
Manuel David Soto. MSc in Geological Sciences, University of Texas
at Austin, USA.
About this notebook
Petrel is a powerful and widely used program by geoscientists and engineers who work with subsurface data. With it you can make from a simple map map from well markers to a complex model of a reservoir with its corresponding surfaces, faults and fluids. Despite these tremendous capabilities of Petrel, certain simple data analysis tasks, such as a histogram or regression, are complicated, cumbersome or incomplete. Although there are plugins that solve these shortcomings of Petrel, Python is a flexible and free alternative for analysis of data residing in Petrel.
The objective of this Notebook is to show through an example how Python can be used as a complement to Petrel's data analysis tasks. The example compares thickness obtained in Petrel from tops and from a seismic surface, and then finds the best regression between them.
Libraries for notebook
Here is a list of libraries necessary for this notebook:
numpy
matplotlib
pandas
scikit-learn

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Exporting and analizing Petrel's data to Python
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GitHub - cabinhero/petrel2python: Exporting and analizing Petrel's data to Python · GitHub
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Exporting and analizing Petrel's data to Python
Notebook creator
Manuel David Soto. MSc in Geological Sciences, University of Texas
at Austin, USA.
About this notebook
Petrel is a powerful and widely used program by geoscientists and engineers who work with subsurface data. With it you can make from a simple map map from well markers to a complex model of a reservoir with its corresponding surfaces, faults and fluids. Despite these tremendous capabilities of Petrel, certain simple data analysis tasks, such as a histogram or regression, are complicated, cumbersome or incomplete. Although there are plugins that solve these shortcomings of Petrel, Python is a flexible and free alternative for analysis of data residing in Petrel.
The objective of this Notebook is to show through an example how Python can be used as a complement to Petrel's data analysis tasks. The example compares thickness obtained in Petrel from tops and from a seismic surface, and then finds the best regression between them.
Libraries for notebook
Here is a list of libraries necessary for this notebook:
numpy
matplotlib
pandas
scikit-learn

About
Exporting and analizing Petrel's data to Python
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