vamseeachanta Production Forecaster
Forecast oil and gas production using industry-standard decline curve analysis. Supports Arps decline models, type curve generation, and EUR calculations for reserve estimation.
When to Use
- Forecasting future production for oil and gas wells
- Estimating EUR (Estimated Ultimate Recovery)
- Generating type curves for field development planning
- Fitting decline parameters to historical production data
- Comparing well performance across different fields
- Supporting reserve booking and economic evaluation
Core Pattern
Historical Production → Decline Curve Fit → Parameter Estimation → Forecast → EUR
Decline Curve Models
Arps Equations
| Model | Equation | b value | Typical Use |
|---|---|---|---|
| Exponential | q(t) = qi * exp(-Di * t) |
0 | Mature conventional |
| Hyperbolic | q(t) = qi / (1 + b*Di*t)^(1/b) |
0–1 | Most common |
| Harmonic | q(t) = qi / (1 + Di*t) |
1 | Fracture-dominated |
| Modified Hyperbolic | Hyperbolic until D(t) ≤ D_min, then exponential | — | Long-term forecast |
Parameters: qi = initial rate, Di = initial decline rate, b = exponent, t = time.
Unconventional Models
Duong (transient linear flow): q(t) = q1 * t^(-m) * exp(-a/(1-m) * (t^(1-m) - 1))
Stretched Exponential: q(t) = qi * exp(-(t/τ)^n)
See references/examples.md for full class implementations and fitting code.
Key Classes
| Class | Purpose |
|---|---|
DeclineCurveAnalyzer |
Fit decline models to production data; calculate EUR and validation metrics |
ProductionForecaster |
Generate rate/cumulative forecasts from fitted parameters |
TypeCurveGenerator |
Normalize wells, bin by attribute, generate P10/P50/P90 type curves |
ProductionReportGenerator |
Render interactive HTML reports via Plotly |
Quick Start — Single Well
from production_forecaster import DeclineCurveAnalyzer, ProductionForecaster
import pandas as pd
historical = pd.read_csv('data/production/well_a1.csv') # columns: date, rate
analyzer = DeclineCurveAnalyzer(historical)
params, model_type = analyzer.fit_best_model() # picks best RMSE fit
forecaster = ProductionForecaster(params, cumulative_to_date=historical['cumulative'].iloc[-1])
forecast = forecaster.forecast(years=30, economic_limit=25)
print(f"Model: {model_type} | EUR: {forecast.eur_oil/1e6:.2f} MMbbl")
Quick Start — Type Curves
from production_forecaster import TypeCurveGenerator, NormalizationMethod, TypeCurveBinType
import pandas as pd, glob
wells = [pd.read_csv(f) for f in glob.glob('data/production/field_x/*.csv')]
generator = TypeCurveGenerator(wells, metadata=well_metadata)
generator.normalize_wells(method=NormalizationMethod.IP_30)
generator.create_bins(TypeCurveBinType.WATER_DEPTH)
bin_results = generator.generate_bin_type_curves(TypeCurveBinType.WATER_DEPTH)
eur_dist = generator.calculate_eur_distribution(economic_limit=25)
Type Curve Normalization Methods
| Method | Key | Best For |
|---|---|---|
| Peak rate | peak |
Clear IP wells |
| First month avg | first_month |
Consistent start-up |
| 30-day IP | 30_day_ip |
Standard industry definition |
| 90-day IP | 90_day_ip |
More stable baseline |
| Moving average | moving_average |
Noisy data |
Bins: formation, completion, water_depth, lateral_length, proppant, vintage.
Percentiles: P10 = optimistic (90th), P50 = median, P90 = conservative (10th).
CLI Summary
# Fit and forecast
uv run --no-project python -m production_forecaster fit --data well.csv --model hyperbolic
uv run --no-project python -m production_forecaster forecast --config config/forecast.yaml
# Type curves
uv run --no-project python -m production_forecaster type-curve \
--wells data/production/*.csv --normalize 30_day_ip --bin-by water_depth
# EUR
uv run --no-project python -m production_forecaster eur --qi 5000 --di 0.35 --b 0.8 --limit 25
Full CLI options, YAML config templates, and complete class implementations:
→ references/examples.md
Best Practices
Data quality: Clean outliers; use ≥6–12 months of decline data; account for workovers and shut-ins.
Model selection: Exponential for mature conventional; hyperbolic for most unconventional; high b (>1) for extended linear flow. Use modified hyperbolic for long-term forecasts to cap optimism with a terminal decline rate (5–8%/year).
Uncertainty: Always generate P10/P50/P90. Update forecasts quarterly. Document all assumptions for reserve booking.
Data Models (summary)
DeclineParameters(qi, di, b, decline_type, min_rate, d_min, a, m, tau, n)
ProductionRecord(date, oil_rate, gas_rate, water_rate, days_on)
ForecastResult(dates, oil_rates, gas_rates, cumulative_oil, eur_oil, remaining_oil, ...)
TypeCurveResult(months, p10/p50/p90_rates, mean_rates, well_counts, eur_p10/p50/p90, ...)
Full dataclass definitions in references/examples.md §Data Models.
Related Skills
../npv-analyzer/SKILL.md— Economic evaluation with Monte Carlo simulation../bsee-data-extractor/SKILL.md— Extract BSEE production, WAR, and APD data../field-analyzer/SKILL.md— Field-level production analysis../well-production-dashboard/SKILL.md— Production visualization
v2.0.0 (2025-12-30): Initial release. v2.1.0 (2026-03-08): Trimmed to thin guide; bulk content → references/examples.md.