# Oil And Gas Data Manager > A Claude skill from ttracx/oil-and-gas-claude-skills. - **Type:** Skill - **Install:** `agentstack add skill-ttracx-oil-and-gas-claude-skills-oil-and-gas-data-manager` - **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior - **Seller:** [ttracx](https://agentstack.voostack.com/s/ttracx) - **Installs:** 0 - **Category:** [Agent Skills](https://agentstack.voostack.com/c/agent-skills) - **Latest version:** 0.1.0 - **License:** MIT - **Upstream author:** [ttracx](https://github.com/ttracx) - **Source:** https://github.com/ttracx/oil-and-gas-claude-skills/tree/main/skills/oil_and_gas_data_manager ## Install ```sh agentstack add skill-ttracx-oil-and-gas-claude-skills-oil-and-gas-data-manager ``` Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent. ## About # Oil and Gas Data Manager ## What this skill does Identifies oil and gas file types automatically, extracts structured engineering data from them, links extracted content to the current project context using well and asset identifiers, and returns normalized outputs ready for downstream workflows including RAG pipelines, QA generation, engineering summaries, and project dashboards. ## When to use Use this skill whenever: - The user uploads or references oil and gas reports, well logs, spreadsheets, or sensor data files - The task involves classifying, parsing, or extracting data from drilling, completions, petrophysics, production, or directional engineering documents - The user asks to summarize, query, or structure data from multiple engineering file types at once - A project context exists and the user wants incoming files linked to wells or activities ## Trigger phrases - "Analyze these drilling and LAS files for my current well" - "Identify the oil and gas file types in this folder and extract engineering data" - "Pull all completion design parameters from the uploaded reports" - "Find well identifiers and drilling KPIs for the active project" - "Summarize NPT, mud properties, and casing points from the latest DDRs" - "What file types are these and what data can you extract?" - "Parse this morning report and link it to the active well" - "Extract the frac stage data from these completion sheets" ## Inputs - One or more uploaded files (PDF, DOCX, XLSX, CSV, LAS, DLIS, JSON, TXT) - Optional project metadata: well name, API/UWI, pad, field, operator, date range - Optional extraction focus: drilling only, completions only, logs only, etc. ## Outputs - File classification: type, MIME, discipline, document purpose - Structured engineering JSON with all extracted parameters - Project match score with matched identifiers listed - Human-readable summary of extracted content - Quality flags: missing data, unit ambiguities, sanity check failures, OCR limitations - Per-field confidence scores and source references (page, section, depth range) ## Supported file types ### Common documents - PDF well programs, drilling reports, completion reports, mud logs, cementing reports, directional surveys - DOCX procedures and operating manuals - TXT daily drilling notes - CSV exports from vendor systems - XLSX drilling parameters and production sheets ### Oil and gas specific technical files - LAS 2.0 and 3.0 well log files - DLIS log containers - LIS legacy log files - WITSML XML exports - WITS real-time stream data - Trajectory survey CSV or TXT - Mud logging data files - Completion design spreadsheets - Production allocation spreadsheets - PVT lab result tables ### Images and semi-structured sources - Well schematics embedded in PDFs - Daily drilling report scans (OCR-limited extraction flagged) - Cement bond log images in PDFs - Toolface and trajectory chart images ## Detection logic ### Step 1: File signature and extension detection - Inspect file extension - Inspect MIME type - Check for known binary signatures (magic bytes) - Detect structured headers for LAS (~Version, ~Well, ~Curve, ~ASCII), DLIS (STORAGE-UNIT-LABEL), WITSML root elements ### Step 2: Content-based classification Use weighted keyword and pattern matching to infer: - **Discipline**: drilling, completions, production, reservoir, geology, geosteering, petrophysics, HSE - **Document type**: DDR, morning report, well plan, BHA sheet, casing design, mud report, cement job report, directional survey, completion program, frac stage sheet, production test, log file - **Asset identifiers**: field, basin, operator, pad, well name, API/UWI, rig name, report date, service company ### Step 3: Project association Match extracted identifiers against the active project context using: - Well name exact match and common aliases (e.g., 1H vs #1H vs Well-1H) - API/UWI number - Pad or lease name - Basin and field name - Operator and service company names - Date overlap with current project schedule ## Extraction targets ### Drilling - Well name, API/UWI, rig name, spud date - Current depth, measured depth, true vertical depth - Rate of penetration (instantaneous and average) - Weight on bit, rotary speed (RPM), torque - Standpipe pressure, pump rate, flow rate - Mud weight (in and out), mud type, viscosity, PV, YP - Losses and gains with depth and volume - Casing set depths and shoe track details - BHA component list with dimensions - NPT events with duration, cause, and classification - Safety incidents and near misses ### Directional and survey - Survey station: MD, inclination, azimuth - TVD, northing, easting - Dogleg severity (per 30m or 100ft) - Target line entry and exit points - Anticollision notes and separation factors - Toolface and slide sheets when present ### Completions - Stage count, cluster count per stage - Perforation intervals (MD top and bottom) - Casing and tubing OD, weight, grade - Frac fluid type and total volume per stage - Proppant type, mesh size, total mass, max concentration - Treating pressure (ISIP, breakdown, max treating) - Rate schedule (slurry and clean) - Plug type, plug depth, perf gun specs - Packer and liner hanger details ### Logs and petrophysics - Depth track (MD and TVD) - Gamma ray (API units) - Resistivity (deep, medium, shallow) - Bulk density - Neutron porosity - Compressional sonic (DT) - Caliper - Interpreted formation tops and bases - Pay intervals with net pay summary - Lithology flags ### Production - Oil rate (STB/d or m3/d) - Gas rate (MSCF/d or m3/d) - Water rate (STB/d or m3/d) - GOR and water cut - Choke size (fraction or mm) - Tubing head pressure, casing head pressure - Test date and test duration - Allocation method and intervals - Separator conditions ## Output schema ```json { "project_match": { "project_name": "string", "match_confidence": 0.0, "matched_identifiers": ["string"] }, "file_info": { "filename": "string", "file_type": "string", "mime_type": "string", "discipline": "string", "document_type": "string" }, "entity_context": { "operator": "string", "field": "string", "basin": "string", "pad": "string", "well_name": "string", "api": "string", "report_date": "string" }, "extracted_data": { "drilling": {}, "directional": {}, "completions": {}, "logs": {}, "production": {} }, "tables": [ { "name": "string", "columns": ["string"], "rows": [] } ], "references": [ { "source_section": "string", "page_or_depth_range": "string", "confidence": 0.0 } ], "quality_flags": ["string"] } ``` ## Core behaviors 1. **Detect before parsing** — Always classify the file type before attempting extraction. Never assume format from extension alone. 2. **Route intelligently** — Use the routing rules below to send each file to the correct parser. Do not apply a generic text extractor to structured binary formats. 3. **Prefer deterministic extraction** — Use parser libraries and regex patterns first. Use LLM-assisted inference only when structured parsing fails or yields low confidence. 4. **Normalize consistently** — Convert all units to a canonical system (metric or field units based on project preference). Store original values alongside normalized values. 5. **Map aliases to canonical terms** — Treat "WOB", "weight on bit", and "bit weight" as the same field. Maintain an alias dictionary for common oilfield abbreviations. 6. **Track source for everything** — Every extracted value must have a reference: page number, section heading, or depth range. 7. **Flag rather than fabricate** — When a value is ambiguous, missing, or requires OCR, mark it with a quality flag. Never invent or interpolate missing values without stating so. 8. **Score confidence at field level** — Provide a 0.0–1.0 confidence score for each extracted field based on extraction method (structured header vs. regex vs. LLM inference). ## Routing rules | Route to | When | |---|---| | LAS parser | Extension is `.las` OR header contains `~Version`, `~Well`, `~Curve`, `~ASCII` | | DLIS parser | Extension is `.dlis` or `.lis` OR binary signature matches DLIS STORAGE-UNIT-LABEL | | WITSML parser | Extension is `.xml` and root element is `WITSMLComposite` or namespace contains `witsml` | | Drilling report parser | Terms: DDR, daily drilling report, morning report, operations summary, bit record, NPT | | Completion parser | Terms: frac, stage, cluster, perforation, proppant, plug and perf, toe sleeve, stimulation, pump schedule | | Production parser | Terms: oil rate, gas rate, water rate, production test, allocation, separator test, choke | | Survey parser | Terms: survey station, inclination, azimuth, dogleg, MD/TVD table, trajectory | | Spreadsheet parser | Extension is `.xlsx`, `.xls`, `.ods` | | CSV parser | Extension is `.csv` with detected delimiter | | Generic text parser | Fallback for `.txt`, `.docx`, unrouted PDFs | ## Extraction heuristics - Prefer explicit headers and labeled cells over inferred positions - When a number is ambiguous, attach the local label and the full surrounding sentence - Reject impossible values using engineering sanity checks (listed below) - When units are missing from a number, look in column headers, section headers, or nearby text within 200 characters - For LAS files, treat UNITS column in ~Curve section as authoritative ## Engineering sanity checks | Check | Rule | |---|---| | Mud weight | Flag values outside 6–22 ppg (or 0.72–2.64 SG) | | Inclination | Reject values > 180 degrees unless clearly a unit error | | Measured depth | Reject negative values | | TVD > MD | Flag as impossible (TVD cannot exceed MD) | | ROP | Flag values > 1000 ft/hr unless horizontal sliding explanation present | | Stage count | Flag if stage count in header conflicts with count of stage rows in table | | Frac treating pressure | Flag values > 20,000 psi | | Oil/gas rate | Flag values with missing units | | GOR | Flag values > 100,000 SCF/STB without context | | Dogleg severity | Flag values > 20 deg/100ft in build sections | ## Failure handling - **Unknown file type**: Return best-effort classification with reasoning and confidence - **OCR required for scanned PDF**: Mark extraction as limited, return what text is recoverable, do not fabricate values - **Multiple project matches**: Return all candidates ranked by confidence, ask user to confirm - **Partial table parse**: Return raw rows with parsing warnings, do not silently drop data - **Binary format without parser**: Note format is unsupported, list what metadata was recovered from headers - **Contradictory values within one document**: Return both values with source references, flag as conflict ## Example responses ### Classification > Detected 3 oil and gas file types: > - `daily_report_2024-03-15.pdf` → Drilling Daily Report, discipline: drilling > - `A12H_survey_final.las` → LAS Well Log, discipline: petrophysics/directional > - `Stage_Design_A12H.xlsx` → Completion Design Spreadsheet, discipline: completions ### Project match > Matched all 3 files to project **"Eagle Ford Pad A — Well 12H"** with 0.93 average confidence. > Matched identifiers: well name alias "A12H", API 42-123-45678-0000, report date within project window. ### Extraction summary > **Drilling report:** Extracted 14 KPIs including ROP 87 ft/hr, WOB 18 klbs, mud weight 9.8 ppg, 2 NPT events (total 4.5 hrs, bit trip + BHA failure). > > **LAS file:** 8 curves parsed (GR, RT, RHOB, NPHI, DT, CAL, MD, TVD). 3 pay intervals identified between 7,840–8,120 ft MD. > > **Completion spreadsheet:** 32 stages, 4 clusters/stage, avg fluid volume 2,200 bbls/stage, 100-mesh sand 1,200 lbs/ft, ISIP avg 7,450 psi. ## Implementation notes - Use `lasio` for all LAS file parsing - Use `dlisio` for DLIS and LIS containers - Use `pdfplumber` for text and table extraction from PDFs - Use `openpyxl` or `pandas` for Excel files - Use `pydantic` v2 for schema validation after extraction - Use `pint` for unit conversion and normalization - Store both raw extracted values and normalized values in output - Keep project matching logic independent from extraction for easier debugging and testing ## Source & license This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code. - **Author:** [ttracx](https://github.com/ttracx) - **Source:** [ttracx/oil-and-gas-claude-skills](https://github.com/ttracx/oil-and-gas-claude-skills) - **License:** MIT Install and usage instructions live in the source repository linked above. ## Pricing - **Free** — Free ## Security capabilities Automated source analysis of v0.1.0 — what this tool can access: - **Network access:** no - **Filesystem access:** no - **Shell / process execution:** no - **Environment & secrets:** no - **Dynamic code execution:** no *"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.* ## Versions - **0.1.0** — security scan: passed — Imported from the upstream source. ## Links - Listing page: https://agentstack.voostack.com/l/skill-ttracx-oil-and-gas-claude-skills-oil-and-gas-data-manager - Seller: https://agentstack.voostack.com/s/ttracx - Browse the marketplace: https://agentstack.voostack.com/browse --- Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.