ressmith 0.2.0

Reservoir decline curve analysis and forecasting for production optimization and economic evaluation

ResSmith

ResSmith is a reservoir decline curve analysis and forecasting library with a strict 4-layer architecture, designed to work seamlessly with the Smith ecosystem (plotsmith, anomsmith, geosmith).

Architecture

ResSmith follows a strict 4-layer architecture with enforced one-way imports:

• Layer 1 (Objects): Immutable dataclasses representing the core domain
• Layer 2 (Primitives): Algorithms and base classes
• Layer 3 (Tasks): Task orchestration
• Layer 4 (Workflows): User-facing functions with I/O and plotting

See ARCHITECTURE_SUMMARY.md for details.

Quick Start

import pandas as pd
from ressmith import fit_forecast

# Load production data
data = pd.DataFrame({
    'oil': [100, 95, 90, 85, 80],
}, index=pd.date_range('2020-01-01', periods=5, freq='M'))

# Fit and forecast
forecast, params = fit_forecast(
    data,
    model_name='arps_hyperbolic',
    horizon=24
)

print(f"Forecast: {forecast.yhat.head()}")
print(f"Parameters: {params}")

Installation

pip install ressmith

Or with optional dependencies:

pip install ressmith[fit]  # Include scipy for optimization
pip install ressmith[viz]  # Include matplotlib (or use plotsmith)

Features

Current Models

• ArpsHyperbolicModel - Hyperbolic decline (0 < b < 1)
• ArpsExponentialModel - Exponential decline (b=0)
• ArpsHarmonicModel - Harmonic decline (b=1)
• LinearDeclineModel - Simple linear decline

Core Capabilities

• Fit decline models to production data
• Generate forecasts with configurable horizons
• Evaluate economics (NPV, IRR, cashflows)
• Batch processing for multiple wells
• Strict type safety with timesmith.typing

Integration with Smith Ecosystem

PlotSmith

Use PlotSmith for all visualization:

from ressmith import fit_forecast
from plotsmith import plot_timeseries

forecast, _ = fit_forecast(data, model_name='arps_hyperbolic', horizon=24)
fig, ax = plot_timeseries(forecast.yhat, title='Production Forecast')

AnomSmith

Share typing from timesmith.typing for consistent data structures across libraries.

GeoSmith

Share typing and integrate spatial analysis for basin-level analysis.

Examples

See examples/ directory:

• basic_fit_forecast.py - Fit model and generate forecast
• basic_economics.py - Evaluate economics for a forecast

Run examples:

python examples/basic_fit_forecast.py
python examples/basic_economics.py

Migration from pydca

This library is being migrated from the pydca (decline-curve) repository. See:

• MIGRATION_PLAN.md - Migration strategy
• MIGRATION_STATUS.md - Current status

Development

# Install in development mode
pip install -e ".[dev]"

# Run tests
pytest

# Run examples
python examples/basic_fit_forecast.py

Requirements

• Python 3.12+
• numpy >= 1.24.0
• pandas >= 2.0.0
• timesmith >= 0.2.0

License

MIT License - see LICENSE file for details.

Contributing

See CONTRIBUTING.md for guidelines.

Related Projects

• PlotSmith - Layered plotting library
• AnomSmith - Anomaly detection
• GeoSmith - Subsurface analysis
• TimeSmith - Time series types