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bsee-data-extractor

Extract and normalize BSEE production data across WAR and APD records.

2|Updated Dec 6, 2022
Search Tags:#normalization#data-extraction#production-data#bsee#war#apd#oil-gas
One-click install
npx skills add https://github.com/vamseeachanta/worldenergydata --skill bsee-data-extractor
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: bsee-data-extractor
Source: https://github.com/vamseeachanta/worldenergydata/tree/main/.claude/skills/bsee-data-extractor
Command: npx skills add https://github.com/vamseeachanta/worldenergydata --skill bsee-data-extractor

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires requests, pandas, and includes scripts (resource) components.

What problem does it solve?

BSEE data users face fragmentation across production, WAR, and APD datasets. This skill consolidates data from multiple sources into a consistent, cache-friendly format for fast analysis.

Core Features & Use Cases

  • Normalize production data by API number and block, extract WAR and APD records, and build reliable production histories for oil and gas analyses.
  • Cache results to support offline workflows and repeated queries across time ranges and fields.
  • Use cases include economic evaluation, safety tracking, and field development planning.

Quick Start

Query a well's production history by API number or fetch WAR and APD records for a field, then cache results for reuse.

Frequently Asked Questions about bsee-data-extractor

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
What is the best way to normalize BSEE production data by API number?▼

To extract BSEE WAR and APD records, query the datasets by API numbers and block scope. The skill consolidates fragmented production data and caches the results to support offline workflows and repeated queries across time ranges.

Can I cache BSEE production histories for repeated field analyses?▼

Yes, you can cache BSEE production histories to support offline workflows and repeated queries. The caching layer stores extracted WAR and APD records, enabling fast end-to-end analysis across time ranges and fields without re-fetching data.

How do I extract offshore oil and gas production data using Python?▼

Extracting offshore oil and gas production data in Python involves using requests and pandas to query BSEE datasets by API number and block. The skill normalizes WAR and APD records into a structured format for economic evaluation and field development planning.

Does this BSEE data extraction method work for block scope queries?▼

BSEE data extraction is suited for economic evaluation, safety tracking, and field development planning. It normalizes fragmented WAR and APD datasets into a consistent format, supporting offshore oil and gas analyses with cached production histories.

Why does BSEE production data need normalization across WAR and APD datasets?▼

BSEE production data needs normalization across WAR and APD datasets because the information is fragmented across multiple sources. Normalization consolidates records by API number and block into a consistent, cache-friendly format for reliable analysis.

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