Monitoring, Reporting, and Forecasting Fuel Supply
and Plant Performance
Why Monitor Performance
• To answer the age old question:
• Is the Resource or the Power Plant Responsible
for the Generation Curtailment?
• But a better way to look at this is:
• By monitoring trends in performance of plant
equipment and possible changes in the
resource, problems can be identified and
address before generation curtailment occurs
2
Monitor Performance
• Power plant performance tracking
• Turbo-Generator performance
• Turbine isentropic efficiency*
• Condenser performance tracking *
• Cooling tower performance tracking
• Resource performance
• Well performance tracking
• Forecasting of well workovers/cleanouts
* Examples
3
Monitor Turbine Performance
100
LP steam meter
(reading zero)
Turbine Isentropic Efficiency (%)
95
90
85
80
75
70
Plant Data
65
Design minus 2.5%
Overall Turbine/Generator Design
Design plus 2.5%
Overall Turbine/Generator Efficiency (design) = 81.3%
60
4
Monitor Condenser Model
5
Monitor Condenser Performance
(based on vender’s performance plot)
5.0
y = 1.0886x
4.5
y=x
Condenser Pressure (observed, in Hg)
4.0
3.5
3.0
2.5
2.0
1.5
1.0
0.5
Date prior to turnaround (July 2014-March 2015)
0.0
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
Condenser Pressure (Design, in Hg)
6
Monitor Condenser Performance
(based on vender’s performance plot)
5.0
4.5
y=x
y = 1.0118x
Condenser Pressure (observed, in Hg)
4.0
3.5
3.0
2.5
2.0
1.5
1.0
0.5
Data since turnaround (April-Dec 2015)
0.0
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
Condenser Pressure (Design, in Hg)
7
Monitor Performance
• Resource Performance – Supplement to periodical well tests
• Tracking plant inlet enthalpy*
• Tracking brine chemistry– NCG*
* Examples
8
Monitor Resource Performance
(inlet enthalpy calculated from Heat & Mass Balance)
480
460
Production Enthalpy (BTU/lb)
440
420
400
380
360
340
Calculated from plant data
Model based on daily production rates
320
Upper Control Limit (2 sigma)
Plant Heat&Mass Balance (ave=404.8 BTU/lb)
Lower Control Limit(2 sigma)
300
9
Monitor Resource Performance
(inlet brine chemistry – NCG example)
1,600
Non-Condensable Gas Flow (scfm)
1,400
1,200
1,000
800
600
400
200
Plant Data
Model based on daily production rates
0
10
Monitor Performance
• Well performance tracking
• Extrapolate production data to minimal WHP*
• Minimal plant inlet pressure
• Pressure drop in wellhead valves and production
pipeline
• Extrapolate injection data to maximum WHP*
• Maximum WHP based on frac gradient
• Maximum injection pump pressure
• Pressure drop in injection pipeline
• Decline Curve Analysis of normalized well
performance data*
* Examples
11
Monitor Well Performance
(Extrapolate production data to minimal WHP)
600
Wellhead Pressure (psig)
500
400
300
200
100
0
0
500
1,000
1,500
Production Rate (KPH)
2,000
2,500
3,000
12
Monitor Well Performance
(Extrapolate injection data to maximum WHP)
500
May 13-14, 2012 data
August 2013 data
450
y = 0.7944x - 434.59
y = 0.3103x - 434.59
Feb 9-11, 2015 data
May 5-12 2015 data
July 21-13, 2015 data
400
August 22-25, 2015 data
Wellhead Pressure (psig)
y = 0.5144x - 434.59
August 28, 2015 data
350
300
250
200
150
100
50
0
0
500
1000
1500
2000
2500
3000
Injection Rate (KPH)
13
Monitor Well Performance
(Decline Curve Analysis of normalized well performance data)
3500
Production Capacity (KPH at 375 psi WHP)
3000
2500
2000
1500
1000
500
0
14
Monitor Performance
• Forecasting of well workovers/cleanouts*
* Examples
15
Production Capacity (KPH)
Forecasting of well workovers/cleanouts
Summer Plant Demand
Winter Plant Demand
16