Create, visualize, and analyze lithological logs from depth intervals.
npx skills add https://github.com/SteadfastAsArt/geoscience-skills --skill striplog
Please help me install this Agent Skill. Skill: striplog Source: https://github.com/SteadfastAsArt/geoscience-skills/tree/main/striplog Command: npx skills add https://github.com/SteadfastAsArt/geoscience-skills --skill striplog
Create, visualize, and analyze lithological and stratigraphic logs for well data, turning interval descriptions into structured, searchable records.
Provide depth intervals or a CSV describing top, base, and lithology to generate a Striplog and visualize it.
High-intent search queries and answers about installing and using this skill.
To create a lithology log from depth intervals, provide a CSV or textual data containing top, base, and lithology descriptions to generate a structured striplog record. This process transforms raw depth intervals into searchable stratigraphic logs for well-log projects.
Yes, you can export well-log data to CSV, LAS, or JSON formats. This allows the structured lithological and stratigraphic logs to be seamlessly integrated into downstream geoscience workflows and cross-section analysis.
The best way to standardize lithology names and synonyms is by applying a Lexicon-driven standardization process. This ensures data integrity by mapping varied textual descriptions to consistent lithology interpretations across multiple wells.
Yes, this approach calculates net-to-gross (NTG) across multiple wells. It analyzes the created lithological logs to derive NTG values, applying lexicon standardization to maintain consistent interpretation throughout the well-log project.
To visualize lithology columns with legends and hatch patterns, generate a striplog from your depth intervals. The visualization feature renders the lithological data graphically, applying distinct colors and hatch patterns to differentiate stratigraphic units.
Yes, you need pandas to process stratigraphic logs from depth intervals. The data processing relies on pandas for managing the tabular well-log data structure required to create, analyze, and export the lithology records.
SteadfastAsArtLoad LAS or DLIS well logs and perform end-to-end formation evaluation.
SteadfastAsArtParse and normalize LAS well-log files into DataFrames or CSV.
jpfieldingQuery Macrostrat for formation ages, lithology, thickness, and depositional environments.
zlovexforeverInterpret well log data to generate formation evaluation reports with lithology, porosity, and fluid contacts.
