midstream

name	midstream
description	Guide AI agents through pipeline transportation and storage operations.

Skill: Midstream Pipeline

Guide AI agents through pipeline transportation and storage operations.

Purpose

Support pipeline engineers in ensuring safe and efficient hydrocarbon transportation and storage.

Roles

• Pipeline Engineer - Pipeline design, hydraulics, materials
• Operations Manager - Daily operations, scheduling, nominations
• Integrity Engineer - Inspection, corrosion management, repairs

Data Types

Data	Source	Frequency
Flow rates	SCADA	Real-time (1-min)
Pressure	SCADA	Real-time (1-min)
Temperature	SCADA	Real-time
ILI (Inline Inspection)	Smart pig	5-year cycle
Leak detection	CPM/DDS	Real-time (continuous)
Product quality	Lab	Batch/sample

Workflow

Phase 1: Transport Scheduling

1. Receive nominations (shipper requests)
2. Check pipeline capacity
3. Schedule batches/products
4. Optimize pump/compressor runs
5. Confirm deliveries

Phase 2: Real-Time Monitoring

1. Monitor pressure/flow at key points
2. Track batch locations
3. Detect anomalies (leaks, theft)
4. Adjust operations as needed
5. Communicate with control center

Phase 3: Integrity Management

1. Plan ILI (smart pigging)
2. Analyze inspection data
3. Identify defects (corrosion, cracks, dents)
4. Prioritize repairs
5. Verify repairs

Phase 4: Leak Detection

1. Monitor CPM (Computational Pipeline Monitoring)
2. Analyze mass balance
3. Check pressure/flow deviations
4. Investigate anomalies
5. Emergency response if confirmed

Domain Tasks

These tasks are handled by this skill:

• Pipeline hydraulics calculation
• Leak detection analysis
• ILI data interpretation
• Integrity assessment

Software Tasks

These tasks invoke petropowers:oil-gas-delegation:

• SCADA monitoring dashboard
• Pipeline scheduling system
• ILI data management platform
• Leak detection alerting system

Example Workflows

Pipeline Hydraulics

import math

def pressure_drop_liquid(flow_bpd, diameter_in, length_ft, viscosity_cp, density_api):
    """Calculate pressure drop for liquid pipeline (simplified)"""
    
    # Convert units
    q_bpm = flow_bpd / 1440  # bpd to bpm
    d_ft = diameter_in / 12
    
    # Velocity (ft/s)
    area_sqft = math.pi * d_ft**2 / 4
    velocity = q_bpm / 7.48 / area_sqft  # ft/s
    
    # Reynolds number
    rho = 62.4 * (141.5 / (density_api + 131.5))  # lb/ft³
    re = rho * velocity * d_ft / (viscosity_cp * 0.000672)
    
    # Friction factor (Blasius for turbulent)
    f = 0.079 / re**0.25 if re > 4000 else 64 / re
    
    # Pressure drop (psi)
    delta_p = 2 * f * (length_ft / d_ft) * rho * velocity**2 / (32.2 * 144)
    
    return delta_p

# Example: 12" pipeline, 50,000 bpd, 100 miles
d_psi = pressure_drop_liquid(
    flow_bpd=50000,
    diameter_in=12,
    length_ft=100 * 5280,
    viscosity_cp=5,
    density_api=35
)

print(f"Pressure drop: {d_psi:.0f} psi")

Leak Detection (Mass Balance)

def detect_leak(flow_in, flow_out, pressure_in, pressure_out, 
                linepack_initial, linepack_current, tolerance=0.01):
    """
    Simple mass balance leak detection
    
    Returns: (is_leak, discrepancy)
    """
    
    # Mass balance: In - Out = Linepack change + Leak
    # In SCADA units: bpd
    
    # Calculate linepack change
    linepack_change = linepack_current - linepack_initial
    
    # Discrepancy
    discrepancy = flow_in - flow_out - linepack_change
    
    # Threshold (as fraction of flow)
    threshold = flow_in * tolerance
    
    is_leak = abs(discrepancy) > threshold
    
    return is_leak, discrepancy

# Example
flow_in = 50000  # bpd
flow_out = 49500  # bpd
linepack_initial = 10000  # bbl
linepack_current = 10050  # bbl

is_leak, discrepancy = detect_leak(flow_in, flow_out, 0, 0, 
                                     linepack_initial, linepack_current)

if is_leak:
    print(f"LEAK DETECTED: {discrepancy:.0f} bpd discrepancy")
else:
    print(f"Normal operation: {discrepancy:.0f} bpd discrepancy")

ILI Defect Assessment

def assess_corrosion_defect(depth_percent, length_in, diameter_in, smys_psi, maop_psi):
    """
    Assess corrosion defect using ASME B31G (simplified)
    
    Returns: safe operating pressure
    """
    
    # Depth as fraction
    d = depth_percent / 100
    
    # If defect depth > 80% of wall, fail
    if d > 0.80:
        return 0  # Requires immediate repair
    
    # Length parameter
    L = length_in / math.sqrt(diameter_in)
    
    # B31G (modified) calculation
    # Simplified: safe pressure reduction
    
    a = 0.893 * L / math.sqrt(diameter_in)
    
    if L <= math.sqrt(diameter_in):
        # Short defect
        safe_pressure = maop_psi * (1 - 0.66 * d)
    else:
        # Long defect (more severe)
        safe_pressure = maop_psi * (1 - 0.66 * d) / (1 + 0.66 * d**2)
    
    return safe_pressure

# Example: 30% deep, 6" long corrosion in 12" pipe
safe_p = assess_corrosion_defect(
    depth_percent=30,
    length_in=6,
    diameter_in=12,
    smys_psi=35000,
    maop_psi=1440
)

print(f"Safe operating pressure: {safe_p:.0f} psi")
print(f"MAOP: 1440 psi")
print(f"Status: {  safe_p >=   }")

Batch Tracking

import pandas as pd
from datetime import datetime, timedelta

class BatchTracker:
    def __init__(self, pipeline_length_km, avg_velocity_mps):
        self.pipeline_length = pipeline_length_km
        self.avg_velocity = avg_velocity_mps
        self.batches = []
    
    def inject_batch(self, batch_id, product, volume_bbl, timestamp):
        self.batches.append({
            'batch_id': batch_id,
            'product': product,
            'volume': volume_bbl,
            'inject_time': timestamp,
            'position_km': 0,
        })
    
    def update_positions(self, current_time):
        time_hours = [(current_time - b['inject_time']).total_seconds() / 3600 
                      for b in self.batches]
        
        for i, batch in enumerate(self.batches):
            # Position = velocity * time
            position = self.avg_velocity * 3.6 * time_hours[i] / 1000  # km
            batch['position_km'] = min(position, self.pipeline_length)
    
    def ():
         [b  b  .batches  b[] >= .pipeline_length]


tracker = BatchTracker(pipeline_length_km=, avg_velocity_mps=)

inject_time = datetime(, , , , )
tracker.inject_batch(, , , inject_time)
tracker.inject_batch(, , , inject_time + timedelta(hours=))


tracker.update_positions(inject_time + timedelta(hours=))

 batch  tracker.batches:
    ()

Performance Indicators

KPI	Units	Target
Availability	%	>99%
Leak incidents	per 1000 km-yr	<0.5
ILI compliance	%	100%
On-time delivery	%	>98%
Energy efficiency	kWh/bbl	Minimize

Safety Considerations

Pipeline safety critical areas:

• High consequence areas (populated, environmentally sensitive)
• Leak detection coverage must be continuous
• Emergency shutdown systems tested regularly
• Public awareness programs maintained
• One-call system for excavation permits

Quality Checklist

• Leak detection system operational
• Pressure/flow instruments calibrated
• ILI inspections current (< 5 years)
• Emergency response plan updated
• Control room procedures reviewed

References

• ASME B31.4 (Liquid Pipelines)
• ASME B31.8 (Gas Pipelines)
• API 1160 (Pipeline Integrity Management)
• PHMSA regulations (US DOT)