Reservoir engineering library for production analysis, forecasting, and economic evaluation
Project description
ResSmith
ResSmith is a comprehensive reservoir engineering library with a strict 4-layer architecture, designed to work seamlessly with the Smith ecosystem (plotsmith, anomsmith, geosmith, timesmith). ResSmith provides tools for production analysis, decline curve analysis, forecasting, economic evaluation, and fundamental reservoir engineering calculations.
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
Decline Curve Analysis
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}")
Reservoir Engineering
from ressmith.primitives import (
linear_ipr, vogel_ipr, perform_nodal_analysis,
calculate_pvt_properties, solution_gas_drive_material_balance
)
# IPR calculation
rate = vogel_ipr(
reservoir_pressure=5000,
flowing_pressure=3000,
productivity_index=1.0,
bubble_point_pressure=3000
)
# PVT properties
pvt = calculate_pvt_properties(
pressure=5000,
temperature=200,
api_gravity=35,
gas_gravity=0.7
)
# Nodal analysis
result = perform_nodal_analysis(
reservoir_pressure=5000,
productivity_index=1.0,
wellhead_pressure=500,
tubing_depth=5000
)
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
Decline Curve Analysis
- ArpsHyperbolicModel - Hyperbolic decline (0 < b < 1)
- ArpsExponentialModel - Exponential decline (b=0)
- ArpsHarmonicModel - Harmonic decline (b=1)
- LinearDeclineModel - Simple linear decline
- Multiple advanced decline models (Power Law, Duong, Stretched Exponential, etc.)
Reservoir Engineering
- IPR (Inflow Performance Relationship) - Linear, Vogel, Fetkovich, Composite, Joshi horizontal, Cinco-Ley fractured well models
- VLP (Vertical Lift Performance) - Tubing performance, nodal analysis, choke performance, artificial lift optimization
- RTA (Rate Transient Analysis) - Flow regime identification, permeability estimation, fracture analysis, SRV calculation
- Material Balance - Solution gas drive, water drive, gas cap drive, p/Z method for gas reservoirs
- PVT Correlations - Standing, Vasquez-Beggs, Lee-Gonzalez-Eakin, Beggs-Robinson correlations for oil, gas, and water properties
Core Capabilities
- Fit decline models to production data
- Generate forecasts with configurable horizons
- Evaluate economics (NPV, IRR, cashflows)
- Batch processing for multiple wells
- Portfolio analysis and risk reporting
- Statistical forecasting methods
- Panel data analysis with fixed effects
- Report generation (HTML/PDF)
- 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.
Reservoir Engineering Modules
ResSmith includes comprehensive reservoir engineering capabilities:
IPR (Inflow Performance Relationship)
Calculate well deliverability using industry-standard IPR models:
- Linear IPR for single-phase flow
- Vogel IPR for solution gas drive reservoirs
- Fetkovich IPR for two-phase flow with decline
- Composite IPR for layered reservoirs
- Joshi model for horizontal wells
- Cinco-Ley model for fractured wells
VLP (Vertical Lift Performance)
Analyze well performance and optimize production:
- Tubing performance curves
- Nodal analysis (IPR-VLP intersection)
- Choke performance
- Artificial lift optimization (ESP, gas lift, rod pump)
RTA (Rate Transient Analysis)
Analyze production data to characterize reservoirs:
- Flow regime identification (linear, bilinear, boundary-dominated, transient)
- Permeability estimation from production data
- Fracture half-length estimation
- Stimulated Reservoir Volume (SRV) calculation
Material Balance
Analyze reservoir drive mechanisms:
- Solution gas drive (depletion drive)
- Water drive with aquifer influx models (Fetkovich, Carter-Tracy)
- Gas cap drive
- Gas reservoir p/Z method
- Drive mechanism identification
PVT (Pressure-Volume-Temperature)
Calculate fluid properties using industry correlations:
- Standing and Vasquez-Beggs correlations for oil FVF and solution GOR
- Gas Z-factor (Standing-Katz, Hall-Yarborough)
- Oil and gas viscosity (Beggs-Robinson, Lee-Gonzalez-Eakin)
- Water properties (FVF and viscosity)
Examples
See examples/ directory:
basic_fit_forecast.py- Fit model and generate forecastbasic_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.
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