reservoir-production

name	reservoir-production
description	Guide AI agents through reservoir management and production optimization.

Skill: Reservoir & Production Pipeline

Guide AI agents through reservoir management and production optimization.

Purpose

Support reservoir and production engineers in managing hydrocarbon extraction and optimizing well performance.

Roles

• Reservoir Engineer - Reservoir modeling, forecasting, depletion strategy
• Production Engineer - Well performance, artificial lift, well intervention
• Well Intervention Engineer - Workovers, stimulation, completions

Data Types

Data	Source	Frequency
Oil/gas/water rates	SCADA/Metering	Daily/Real-time
Pressure (BHP, THP)	Downhole gauges	Real-time
Temperature	Downhole gauges	Real-time
Reservoir models	Eclipse, CMG, tNavigator	Updated annually
Well tests	Separators/MPFM	Monthly

Workflow

Phase 1: Reservoir Characterization

1. Integrate seismic, logs, core data
2. Build geological model
3. Upscale to simulation model
4. History match to production data
5. Validate model

Phase 2: Production Forecasting

1. Define development scenarios
2. Run reservoir simulation
3. Generate production forecast
4. Estimate reserves (P10/P50/P90)
5. Economic evaluation

Phase 3: Well Optimization

1. Analyze well performance (inflow performance, vertical lift)
2. Identify production bottlenecks
3. Optimize artificial lift (gas lift, ESP, rod pump)
4. Plan well interventions
5. Monitor results

Phase 4: Reservoir Monitoring

1. Track production vs. forecast
2. Monitor pressure trends
3. Analyze well interference
4. Update model as needed
5. Optimize depletion strategy

Domain Tasks

These tasks are handled by this skill:

• Decline curve analysis
• Inflow performance relationship (IPR)
• Nodal analysis
• Material balance
• Reserves estimation

Software Tasks

These tasks invoke petropowers:oil-gas-delegation:

• Production allocation system
• Reservoir monitoring dashboard
• Automated well testing workflow
• Production database

Example Workflows

Decline Curve Analysis

import numpy as np
import pandas as pd
from scipy.optimize import curve_fit

def arps_decline(t, qi, di, b):
    """Arps decline equation"""
    return qi / (1 + b * di * t)**(1/b)

# Example production data
months = np.arange(1, 61)
actual_rate = 1000 / (1 + 0.1 * months)**(1/0.5)  # D = 0.1, b = 0.5

# Fit decline curve
popt, _ = curve_fit(arps_decline, months, actual_rate, p0=[1000, 0.1, 0.5])

qi, di, b = popt
print(f"Initial rate (qi): {qi:.0f} bpd")
print(f"Initial decline (di): {di:.2f}")
print(f"Arps exponent (b): {b:.2f}")

# Forecast
forecast_months = np.arange(1, 121)
forecast_rate = arps_decline(forecast_months, *popt)

print(f"Forecast at 10 years: {forecast_rate[-1]:.0f} bpd")

Inflow Performance (IPR)

def vogel_ipr(p_res, p_wf, q_test):
    """Vogel IPR equation for undersaturated oil"""
    
    # Calculate productivity index
    pi = q_test / (p_res - p_wf)
    
    # Vogel equation: q/q_max = 1 - 0.2*(p_wf/p_res) - 0.8*(p_wf/p_res)^2
    q_max = q_test / (1 - 0.2*(p_wf/p_res) - 0.8*(p_wf/p_res)**2)
    
    def rate_at_pressure(pwf):
        return q_max * (1 - 0.2*(pwf/p_res) - 0.8*(pwf/p_res)**2)
    
    return rate_at_pressure

# Example: Reservoir pressure 3000 psi, tested at 2000 psi flowing
p_res = 3000
p_wf = 2000
q_test = 500  # bpd

ipr = vogel_ipr(p_res, p_wf, q_test)

# Calculate rate at different flowing pressures
for pwf in [2500, 2000, 1500, 1000]:
    rate = ipr(pwf)
    print(f"Pwf = {pwf} psi: Rate = {rate:.0f} bpd")

Nodal Analysis

def vlp_gas_lift(q, thp, depth, gl_rate, pipe_id=2.992):
    """Simplified vertical lift performance with gas lift"""
    # Simplified: gradient decreases with gas lift
    # rho = f(oil_rate, gas_lift_rate)
    
    # Approximate gradient (psi/ft)
    liquid_rate = q / 24  # bpd to bpm
    glr = gl_rate / liquid_rate if liquid_rate > 0 else 0
    
    # Gradient decreases with GLR
    gradient = 0.35 - 0.001 * glr
    gradient = max(gradient, 0.15)  # minimum gradient
    
    # Calculate BHP
    bhp = thp + gradient * depth
    
    return bhp

# Example: Optimize gas lift
thp = 150  # psi
depth = 8000  # ft
q = 500  # bpd

print("Gas Lift Rate | BHP Required")
print("-" * 30)

for gl_rate in [0, 1, 2, 3, 4]:
    bhp = vlp_gas_lift(q, thp, depth, gl_rate)
    print(f"{gl_rate:.0f} MMscfd | {bhp:.0f} psi")

Material Balance

def material_balance_oil(p, N, pi, bob, ce, cf):
    """Simplified material balance for undersaturated oil reservoir"""
    
    # F = N * (Eo + Ef)
    # F = Np * Bob  (production)
    # Eo = Bob - Boi (oil expansion)
    # Ef = (1-N) * (pi - p) * (ce + cf) / Bob
    
    # Simplified: assume constant Bo
    # Np = N * (pi - p) * ce / Bob
    
    expansion = (pi - p) * ce / bob
    Np = N * expansion
    
    return Np

# Example: 100 MMbbl reservoir
N = 100  # MMbbl
pi = 4000  # psi
p = 3500  # psi current pressure
bob = 1.2  # rb/stb
ce = 15e-6  # 1/psi (compressibility)
cf = 3e-6  # 1/psi (formation compressibility)

Np = material_balance_oil(p, N, pi, bob, ce, cf)
print(f"Cumulative production: {Np:.1f} MMbbl")
print(f"Recovery factor: {Np/N*100:.1f}%")

Performance Indicators

KPI	Units	Target
Uptime	%	>95%
Water cut	%	Varies
Gas/oil ratio	scf/stb	Varies
Drawdown	psi	Optimized
Artificial lift efficiency	%	>80%

Quality Checklist

• Production data validated
• Well tests recent (< 3 months)
• Pressure data calibrated
• Reservoir model history matched
• Forecast assumptions documented

References

• Craft & Hawkins (1991), "Applied Petroleum Reservoir Engineering"
• Arps (1945), "Analysis of Decline Curves"
• Vogel (1968), "Inflow Performance Relationships"
• SPE Reservoir Evaluation & Engineering