vamseeachanta Field Analyzer Skill
Deepwater field-specific analysis for major GOM developments
When to Use This Skill
Use this skill when you need to:
- Analyze specific deepwater fields (Anchor, Julia, Jack, St. Malo)
- Aggregate production by field across multiple wells/leases
- Compare field performance and economics
- Build field-level type curves
- Track development history and milestones
Core Pattern
"""
ABOUTME: Field-level analysis for major GOM deepwater developments
ABOUTME: Aggregates wells by field and provides field-specific analytics
"""
from dataclasses import dataclass, field
from typing import List, Dict, Optional
import pandas as pd
@dataclass
class FieldDefinition:
"""Definition of a GOM field."""
name: str
operator: str
development_type: str # FPSO, TLP, SPAR, SUBSEA
water_depth_ft: float
first_production: str # YYYY-MM
api_numbers: List[str] = field(default_factory=list)
lease_numbers: List[str] = field(default_factory=list)
blocks: List[str] = field(default_factory=list)
# Known Lower Tertiary fields
LOWER_TERTIARY_FIELDS = {
"ANCHOR": FieldDefinition(
name="Anchor",
operator="Chevron",
development_type="FPSO",
water_depth_ft=5200,
first_production="2024-08",
blocks=["GC 807", "GC 808"]
),
"JACK": FieldDefinition(
name="Jack",
operator="Chevron",
development_type="SPAR",
water_depth_ft=7000,
first_production="2014-12",
blocks=["WR 759"]
),
"ST_MALO": FieldDefinition(
name="St. Malo",
operator="Chevron",
development_type="SPAR",
water_depth_ft=7000,
first_production="2014-11",
blocks=["WR 678"]
),
"JULIA": FieldDefinition(
name="Julia",
operator="ExxonMobil",
development_type="SUBSEA",
water_depth_ft=7000,
first_production="2016-10",
blocks=["WR 627"]
),
"KASKIDA": FieldDefinition(
name="Kaskida",
operator="BP",
development_type="TBD",
water_depth_ft=5800,
first_production="TBD",
blocks=["KC 292"]
)
}
class FieldAnalyzer:
"""
Analyze GOM fields by aggregating well-level data.
Supports predefined fields and custom field definitions.
"""
def __init__(self):
self.field_definitions = LOWER_TERTIARY_FIELDS.copy()
def add_field(self, definition: FieldDefinition) -> None:
"""Add custom field definition."""
self.field_definitions[definition.name.upper()] = definition
def get_field_wells(self, field_name: str) -> List[str]:
"""Get API numbers for wells in a field."""
field_def = self.field_definitions.get(field_name.upper())
if not field_def:
raise ValueError(f"Unknown field: {field_name}")
# Query BSEE for wells in field blocks
from worldenergydata.bsee.data import query_wells_by_block
all_apis = []
for block in field_def.blocks:
apis = query_wells_by_block(block)
all_apis.extend(apis)
return list(set(all_apis))
def aggregate_field_production(
self,
field_name: str,
start_date: str = None,
end_date: str = None
) -> pd.DataFrame:
"""
Aggregate production across all wells in a field.
Returns monthly field-level production totals.
"""
from worldenergydata.bsee.data import get_production_data
field_def = self.field_definitions.get(field_name.upper())
if not field_def:
raise ValueError(f"Unknown field: {field_name}")
# Get production for each well
well_dfs = []
for api in self.get_field_wells(field_name):
try:
df = get_production_data(api_number=api)
df["api_number"] = api
well_dfs.append(df)
except Exception:
continue
if not well_dfs:
return pd.DataFrame()
# Combine and aggregate
combined = pd.concat(well_dfs, ignore_index=True)
# Group by month
combined["year_month"] = combined["date"].dt.to_period("M")
field_production = combined.groupby("year_month").agg({
"oil_bbl": "sum",
"gas_mcf": "sum",
"water_bbl": "sum",
"api_number": "nunique"
}).rename(columns={"api_number": "active_wells"})
field_production["field_name"] = field_name
field_production = field_production.reset_index()
field_production["date"] = field_production["year_month"].dt.to_timestamp()
return field_production
def compare_fields(
self,
field_names: List[str],
metric: str = "oil_bbl"
) -> pd.DataFrame:
"""
Compare production across multiple fields.
Aligns by months on production for fair comparison.
"""
field_dfs = []
for name in field_names:
df = self.aggregate_field_production(name)
if not df.empty:
# Add months on production
df = df.sort_values("date")
df["months_on_prod"] = range(len(df))
df = df[["months_on_prod", metric, "field_name"]]
field_dfs.append(df)
if not field_dfs:
return pd.DataFrame()
# Pivot for comparison
combined = pd.concat(field_dfs)
comparison = combined.pivot(
index="months_on_prod",
columns="field_name",
values=metric
)
return comparison
def field_economics_summary(self, field_name: str) -> Dict:
"""
Generate economics summary for a field.
Returns key metrics and KPIs.
"""
field_def = self.field_definitions.get(field_name.upper())
production = self.aggregate_field_production(field_name)
if production.empty:
return {}
total_oil = production["oil_bbl"].sum()
total_gas = production["gas_mcf"].sum()
peak_oil = production["oil_bbl"].max()
months_producing = len(production)
return {
"field_name": field_name,
"operator": field_def.operator,
"development_type": field_def.development_type,
"water_depth_ft": field_def.water_depth_ft,
"first_production": field_def.first_production,
"cumulative_oil_mmbbl": total_oil / 1_000_000,
"cumulative_gas_bcf": total_gas / 1_000_000,
"peak_oil_bopd": peak_oil / 30,
"months_producing": months_producing,
"active_wells": production["active_wells"].iloc[-1] if len(production) > 0 else 0
}
YAML Configuration Template
# config/input/field-analysis.yaml
metadata:
feature_name: "field-analysis"
created: "2025-01-15"
# Fields to analyze
fields:
- name: "ANCHOR"
include_forecast: true
- name: "JACK"
include_forecast: true
- name: "ST_MALO"
include_forecast: true
# Analysis options
analysis:
aggregate_by: "month"
calculate_type_curve: true
compare_vs_plan: false
# Custom field definitions (optional)
custom_fields:
- name: "MY_FIELD"
operator: "Operator Name"
development_type: "SUBSEA"
water_depth_ft: 6000
blocks: ["GC 100", "GC 101"]
output:
format: "html"
path: "reports/fields/"
include_comparison_chart: true
CLI Usage
# Analyze single field
python -m worldenergydata.field_analyzer \
--field ANCHOR \
--output reports/anchor_analysis.html
# Compare multiple fields
python -m worldenergydata.field_analyzer \
--compare JACK ST_MALO JULIA \
--metric oil_bbl \
--output reports/lt_comparison.html
Best Practices
- Use official BSEE block designations for field definitions
- Validate well assignments periodically as new wells come online
- Exclude wells with anomalous data (testing, workovers)
- Align comparison by months on production, not calendar date