https://github.com/paulross/totaldepth
TotalDepth is capable of processing and analysing petrophysical wireline logs.
https://github.com/paulross/totaldepth
oil-exploration oil-wells petrophysics python
Last synced: 2 months ago
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TotalDepth is capable of processing and analysing petrophysical wireline logs.
- Host: GitHub
- URL: https://github.com/paulross/totaldepth
- Owner: paulross
- License: gpl-2.0
- Created: 2017-09-11T10:20:24.000Z (almost 8 years ago)
- Default Branch: master
- Last Pushed: 2025-01-20T14:11:43.000Z (5 months ago)
- Last Synced: 2025-03-30T20:11:16.359Z (3 months ago)
- Topics: oil-exploration, oil-wells, petrophysics, python
- Language: Python
- Homepage:
- Size: 58 MB
- Stars: 37
- Watchers: 8
- Forks: 14
- Open Issues: 6
-
Metadata Files:
- Readme: README.rst
- Changelog: HISTORY.rst
- Contributing: CONTRIBUTING.rst
- Authors: AUTHORS.rst
Awesome Lists containing this project
README
********************
TotalDepth
********************
.. image:: https://img.shields.io/pypi/v/TotalDepth.svg
:target: https://pypi.python.org/pypi/TotalDepth
.. image:: https://img.shields.io/travis/paulross/TotalDepth.svg
:target: https://travis-ci.org/paulross/TotalDepth
.. image:: https://readthedocs.org/projects/TotalDepth/badge/?version=latest
:target: https://TotalDepth.readthedocs.io/en/latest/?badge=latest
:alt: Documentation Status
Petrophysical software capable of processing wireline logs.
* Free software: GPL 2.0
* Documentation: https://TotalDepth.readthedocs.io
Features
========================
* Reads LIS, LAS (1.2, 2.0), RP66V1 (DLIS), Western Atlas BIT, and DAT Mud Log file formats for analysis or conversion to other formats.
* Creates Numpy arrays of log data.
* Plots log data as SVG viewable in any modern browser.
* Plots can be made with a wide variety of plot formats.
* TotalDepth can generate HTML summaries of log data.
* TotalDepth is written in Python so it is fast to develop with.
* Special indexing techniques are used to be able to randomly access sequential files.
Wireline Plots
--------------------------
Here is an example of a LAS file plotted with the Triple Combo plot format as seen in a browser which includes the API header:
.. image:: images/TrippleCombo.png
:alt: Tripple Combo
An example of a High Resolution Dipmeter plotted at 1:25 scale:
.. image:: images/HDT_25_no_hdr.png
:alt: High Resolution Dipmeter
Accessing Frame Data as a ``numpy`` Array
------------------------------------------
Here is an example of accessing RP66V1 (DLIS) data as a numpy array and using ``np.info()`` to describe each array:
.. code-block:: python
import numpy as np
from TotalDepth.RP66V1.core import LogicalFile
# path_in is expected to be the path to a RP66V1 file.
# Try this with an example RP66V1 file at TotalDepth/example_data/RP66V1/data/206_05a-_3_DWL_DWL_WIRE_258276498.DLIS
with LogicalFile.LogicalIndex(path_in) as logical_index:
for logical_file in logical_index.logical_files:
if logical_file.has_log_pass:
for frame_array in logical_file.log_pass:
print(frame_array)
frame_count = logical_file.populate_frame_array(frame_array)
print(
f'Loaded {frame_count} frames and {len(frame_array)} channels'
f' from {frame_array.ident} using {frame_array.sizeof_array} bytes.'
)
for channel in frame_array.channels:
print(channel)
# channel.array is a numpy array
np.info(channel.array)
print()
The output will be something like:
.. code-block:: console
FrameArray: ID: OBNAME: O: 2 C: 0 I: b'2000T' b''
Loaded 921 frames and 4 channels from OBNAME: O: 2 C: 0 I: b'2000T' using 14736 bytes.
class: ndarray
shape: (921, 1)
strides: (4, 4)
itemsize: 4
aligned: True
contiguous: True
fortran: True
data pointer: 0x102a08a00
byteorder: little
byteswap: False
type: float32
class: ndarray
shape: (921, 1)
strides: (4, 4)
itemsize: 4
aligned: True
contiguous: True
fortran: True
data pointer: 0x102a09a00
byteorder: little
byteswap: False
type: float32
class: ndarray
shape: (921, 1)
strides: (4, 4)
itemsize: 4
aligned: True
contiguous: True
fortran: True
data pointer: 0x102a0aa00
byteorder: little
byteswap: False
type: float32
class: ndarray
shape: (921, 1)
strides: (4, 4)
itemsize: 4
aligned: True
contiguous: True
fortran: True
data pointer: 0x102a0ba00
byteorder: little
byteswap: False
type: float32
...
Installing TotalDepth
===================================
To install TotalDepth, run this command in your terminal:
.. code-block:: console
$ pip install TotalDepth
This is the preferred method to install TotalDepth, as it will always install the most recent stable release from PyPi.
If you don't have `pip`_ installed, this `Python installation guide`_ can guide
you through the process.
From sources
--------------------------------------
If you are using a virtual environment in your virtual environment directory, for example ``~/pyvenvs``:
.. code-block:: console
$ python3 -m venv ~/pyvenvs/TotalDepth
$ source ~/pyvenvs/TotalDepth/bin/activate
(TotalDepth) $
Or if you have a Conda environment (here using Python 3.8, adjust as necessary):
.. code-block:: console
$ conda create --name TotalDepth python=3.8 pip
$ source activate TotalDepth
Install the dependencies, ``numpy`` and ``Cython``:
If you are using a virtual environment:
.. code-block:: console
(TotalDepth) $ pip install numpy
(TotalDepth) $ pip install Cython
Or if you have a Conda environment:
.. code-block:: console
(TotalDepth) $ conda install numpy
(TotalDepth) $ conda install Cython
The sources for TotalDepth can be downloaded from the `Github repo`_.
You can either clone the public repository:
.. code-block:: console
(TotalDepth) $ git clone git://github.com/paulross/TotalDepth.git
Or download the `tarball`_:
.. code-block:: console
(TotalDepth) $ curl -OL https://github.com/paulross/TotalDepth/tarball/master
Once you have a copy of the source, you can install it with:
.. code-block:: console
(TotalDepth) $ cd TotalDepth
(TotalDepth) $ python setup.py install
Install the test dependencies and run TotalDepth's tests:
.. code-block:: console
(TotalDepth) $ pip install pytest
(TotalDepth) $ pip install pytest-runner
(TotalDepth) $ python setup.py test
Credits
---------
This package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template.
.. _Cookiecutter: https://github.com/audreyr/cookiecutter
.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage
.. _pip: https://pip.pypa.io
.. _Python installation guide: http://docs.python-guide.org/en/latest/starting/installation/
.. _Github repo: https://github.com/paulross/TotalDepth
.. _tarball: https://github.com/paulross/TotalDepth/tarball/master