"""Well performance analysis example.
This example demonstrates comprehensive well performance analysis including
IPR generation, sensitivity analysis, and optimization.
"""
import asyncio
from fastmcp.client import Client
from pyrestoolbox_mcp import mcp
async def well_performance_analysis():
"""Complete well performance analysis workflow."""
# Client context
async with Client(mcp) as client:
print("=" * 80)
print("Well Performance Analysis")
print("=" * 80)
# Reservoir and fluid properties
api = 38.0
degf = 175.0
sg_g = 0.68
rsb = 750.0
pi = 4000.0 # Initial reservoir pressure
pb = 3500.0 # Bubble point pressure
# Well and reservoir parameters
h = 75.0 # Net pay thickness (ft)
k = 150.0 # Permeability (mD)
s = -2.0 # Skin (stimulated well)
re = 1500.0 # Drainage radius (ft)
rw = 0.5 # Wellbore radius (ft)
print("\nReservoir Properties:")
print("-" * 80)
print(f"Oil API: {api:.1f} degrees")
print(f"Temperature: {degf:.1f} degF")
print(f"Gas SG: {sg_g:.3f}")
print(f"Solution GOR at Pb: {rsb:.1f} scf/stb")
print(f"Initial Pressure: {pi:.1f} psia")
print(f"Bubble Point: {pb:.1f} psia")
print("\nWell Properties:")
print("-" * 80)
print(f"Net Pay: {h:.1f} ft")
print(f"Permeability: {k:.1f} mD")
print(f"Skin: {s:.1f}")
print(f"Drainage Radius: {re:.1f} ft")
print(f"Wellbore Radius: {rw:.2f} ft")
# Step 1: Generate IPR curve
print("\nStep 1: Generate IPR Curve")
print("-" * 80)
pwf_values = [pi * (1 - i / 20) for i in range(21)] # 0% to 100% drawdown
qo_result = await client.call_tool(
"oil_rate_radial",
{
"pi": pi,
"pb": pb,
"api": api,
"degf": degf,
"sg_g": sg_g,
"psd": pwf_values,
"h": h,
"k": k,
"s": s,
"re": re,
"rw": rw,
"rsb": rsb,
"vogel": False,
},
)
print(f"\n{'Pwf (psia)':>12} | {'Drawdown (psi)':>15} | {'Rate (STB/day)':>15} | {'PI':>10}")
print("=" * 60)
for pwf, qo in zip(pwf_values, qo_result["value"]):
drawdown = pi - pwf
pi_value = qo / drawdown if drawdown > 0 else 0
print(f"{pwf:12.1f} | {drawdown:15.1f} | {qo:15.2f} | {pi_value:10.2f}")
max_rate = max(qo_result["value"])
print(f"\nMaximum Rate (AOF): {max_rate:.2f} STB/day")
# Step 2: Sensitivity analysis - Permeability
print("\nStep 2: Sensitivity Analysis - Permeability")
print("-" * 80)
k_values = [50, 100, 150, 200, 250]
pwf_test = pi * 0.5 # 50% drawdown
print(f"\nRate vs Permeability at {pwf_test:.0f} psia BHFP:")
print(f"{'Permeability (mD)':>20} | {'Rate (STB/day)':>15}")
print("-" * 40)
for k_test in k_values:
qo_k = await client.call_tool(
"oil_rate_radial",
{
"pi": pi,
"pb": pb,
"api": api,
"degf": degf,
"sg_g": sg_g,
"psd": pwf_test,
"h": h,
"k": k_test,
"s": s,
"re": re,
"rw": rw,
"rsb": rsb,
"vogel": False,
},
)
qo_val = qo_k["value"]
if isinstance(qo_val, list):
qo_val = qo_val[0]
print(f"{k_test:20.0f} | {qo_val:15.2f}")
# Step 3: Sensitivity analysis - Skin
print("\nStep 3: Sensitivity Analysis - Skin Factor")
print("-" * 80)
s_values = [-5, -2, 0, 2, 5, 10]
print(f"\nRate vs Skin at {pwf_test:.0f} psia BHFP:")
print(f"{'Skin':>8} | {'Rate (STB/day)':>15} | {'% of Max':>10}")
print("-" * 40)
max_qo_s = None
for s_test in s_values:
qo_s = await client.call_tool(
"oil_rate_radial",
{
"pi": pi,
"pb": pb,
"api": api,
"degf": degf,
"sg_g": sg_g,
"psd": pwf_test,
"h": h,
"k": k,
"s": s_test,
"re": re,
"rw": rw,
"rsb": rsb,
"vogel": False,
},
)
qo_val = qo_s["value"]
if isinstance(qo_val, list):
qo_val = qo_val[0]
if max_qo_s is None:
max_qo_s = qo_val
pct = (qo_val / max_qo_s) * 100 if max_qo_s > 0 else 0
print(f"{s_test:8.1f} | {qo_val:15.2f} | {pct:10.1f}%")
# Step 4: Compare radial vs linear flow
print("\nStep 4: Radial vs Linear Flow Comparison")
print("-" * 80)
area = 10000 # Cross-sectional area for linear flow (ft²)
length = 1000 # Length for linear flow (ft)
qo_radial = await client.call_tool(
"oil_rate_radial",
{
"pi": pi,
"pb": pb,
"api": api,
"degf": degf,
"sg_g": sg_g,
"psd": pwf_test,
"h": h,
"k": k,
"s": s,
"re": re,
"rw": rw,
"rsb": rsb,
"vogel": False,
},
)
qo_linear = await client.call_tool(
"oil_rate_linear",
{
"pi": pi,
"pb": pb,
"api": api,
"degf": degf,
"sg_g": sg_g,
"psd": pwf_test,
"area": area,
"length": length,
"k": k,
"rsb": rsb,
"vogel": False,
},
)
qo_r = qo_radial["value"]
qo_l = qo_linear["value"]
if isinstance(qo_r, list):
qo_r = qo_r[0]
if isinstance(qo_l, list):
qo_l = qo_l[0]
print(f"\nFlow Geometry Comparison at {pwf_test:.0f} psia BHFP:")
print(f"{'Flow Type':>15} | {'Rate (STB/day)':>15}")
print("-" * 35)
print(f"{'Radial':>15} | {qo_r:15.2f}")
print(f"{'Linear':>15} | {qo_l:15.2f}")
print(f"{'Ratio (R/L)':>15} | {qo_r/qo_l if qo_l > 0 else 0:15.2f}")
# Step 5: Vogel IPR for below bubble point
print("\nStep 5: Vogel IPR (Below Bubble Point)")
print("-" * 80)
pwf_vogel = [pb * (1 - i / 10) for i in range(11)] # From Pb to 0
qo_vogel = await client.call_tool(
"oil_rate_radial",
{
"pi": pi,
"pb": pb,
"api": api,
"degf": degf,
"sg_g": sg_g,
"psd": pwf_vogel,
"h": h,
"k": k,
"s": s,
"re": re,
"rw": rw,
"rsb": rsb,
"vogel": True,
},
)
print(f"\nVogel IPR (Below Bubble Point):")
print(f"{'Pwf (psia)':>12} | {'Pwf/Pb':>10} | {'Rate (STB/day)':>15}")
print("-" * 45)
for pwf, qo in zip(pwf_vogel, qo_vogel["value"]):
ratio = pwf / pb if pb > 0 else 0
print(f"{pwf:12.1f} | {ratio:10.3f} | {qo:15.2f}")
print("\n" + "=" * 80)
print("Well Performance Analysis completed successfully!")
print("=" * 80)
if __name__ == "__main__":
asyncio.run(well_performance_analysis())
