Skippy - Downhole Petrophysical Logging Harmonisation

A Python library for harmonising downhole petrophysical logging metadata

Read the detailed documentation and get started at the website:
https://gswa-skippy.readthedocs.io/en/latest/

Scope

Skippy is aimed at geoscience data stakeholders who manage petrophysical logging data in LAS files and databases, and are undertaking data harmonisation efforts. It can be used to create AI/ML ready datasets.

Overview

Skippy is a petrophysical consolidation tool developed by the Geological Survey of Western Australia (GSWA). As a "Cygnet" (GSWA's term for geoscience data harmonisation tools), Skippy processes and standardises logging data from various operators into consistent formats, units, and metadata structures.

Petrophysical logs (typically in .LAS files) record downhole measurements of rock properties. These data are collected, for example, by a drill rig boring a drillhole, and an instrument being lowered through the rock. The instrument may record parameters such as temperature, and measurements that reveal the density or electrical properties of the rock. Large amounts of metadata are also recorded, such as the location, time, and company performing the investigation. Like most geoscience data, these records can be messy, with inadequate or incorrect information as well as variable naming conventions. Skippy exists to impose order on these data, by asserting a number of rules defined in a Subject Matter Expert configuration file.

Key Features

• Harmonises LAS files to consistent standard
• Customisable for Subject Matter Expert (SME) requirements
• Standardises mnemonics, descriptions, and other information
• Harmonises Curve metadata and unit conversion
• Helpful logging system to document code and data issues
• Deviation survey calculations using wellpathpy
• Integration possible with SQL databases

Customisable by Subject Matter Experts

Skippy is developed closely with a petrophysical logging SME, and is designed to be adaptable to new configurations in other scientific contexts or in response to new data governance. The expert config captures the rules to assert on the las file contents. These include:

• Which information to include in ~Well, ~Version, and ~Parameter sections.
• The preferred mnemonics to use for these items.
• The preferred descriptions for these mnemonics.
• Where data used to harmonise the file comes from.

Data Access

WAPIMS (Western Australian Petroleum and Geothermal Information Management System) is the relevant database for GSWA data. If a connection or export of the database tables are available, Skippy can pull updated information from these sources.

Caveat

Some harmonisation steps in Skippy rely on the presence of Well or Parameter items, such as LATI and LONG. Skippy currently relies on WAPIMS to populate these and other mnemonics if they are missing. An advanced installation, external well data source (such as an excel table), or tweaking of the expert config may be required to harmonise your files. Please raise an Issue at our GitHub or contact us so we can improve the handling of these conditions!

License and Acknowledgements

This project utilises lasio https://pypi.org/project/lasio/ for document parsing and wellpathpy https://pypi.org/project/wellpathpy/ to compute deviation paths respectively.
This project uses GSWA's companion package gswa-atratus. It does so without modification to the above packages.

This project is subject to copyright. See COPYING for details.