Benefits of Detailed Compressor Modeling in Optimizing Production from Gas-Lifted Fields Manickam S. Nadar Greg Stephenson
Contents Optimization of Gas-Lifted Fields The Need for Modelling of Gas-Lift Compressors Compressor Models Simple vs. Detailed Implementation and Calibration of Compressor Models Benefits of Modelling Compressors Case Study Conclusions
Optimization of Gas-Lifted Fields Prod Manifold Prod Manifold Lift Gas Manifold Prod Manifold P Water Oil -$ +$ Export Gas +$ P P Lift Gas Q P P P P P Fuel Gas Q External Q Fuel Supply -$
Total System Optimization Optimum separator pressure Optimum gas-lift header pressure Interactions between wells in production gathering network and gas distribution system Optimal allocation of available supply of gas among wells Revenues and costs of the operation considered: Revenue (+$( +$): Oil production, Associated gas production Cost (-$)( ) : Water disposal, Lift gas costs (compression / fuel gas)
Role of Gas-Lift Compressors Compressors - source of high pressure gas for continuous flow gas-lift operations Cost of gas compression is a significant factor in the optimization Gas turbine (GT) driven compressors are dynamic units and they should be optimized as a part of the network
Effects of Compressor Variables Maximum gas throughput is not constant, but depends on: Suction pressure and therefore separator pressure Discharge pressure and therefore gas-lift header pressure (CHP, with P P in the injection line considered) Power available and therefore ambient temperature and condition of the GG All three variables (Psuct, Pdisc and Qg) impact the compression power requirement - should be considered together and not in isolation
Types of Compressor Modelling Can model in different levels of detail Simple compressor model: Power = 0.0845. Z avg. Q gas kη P.T.. k 1 P disc suct k 1 kη 1 Relates compression ratio, horsepower and flow-rate, but cannot model limits on Qgas, Psuct and Pdisc Speed not modelled Gas Turbine not modelled - dynamics associated with the driver not considered
Detailed Compressor Model Models the performance of real machine Operating range - surge and stonewall, and recycle modelled Each compressor stage modelled separately Splits the power from driver for multi-stage compression Efficiency as a function of speed GT modelled separately Can examine effects of degradation Models variation of power with ambient temperature Fuel gas rate calculated and automatically removed from process stream Accessories (gear box, inter-coolers, separators, recycle loop) modelled
Compressor Stage Performance Curves (Assuming constant suction conditions) Surge line Surge region Cannot operate Speed Pd max Pd P Discharge Stonewall N 5 N 3 N 4 Q min Q Q Suction N 1 N 2
Compressor Power Curves (Assuming constant suction conditions) Surge line Speed N 5 N 4 Power N 2 N 3 N 1 Q Suction
Power Required by Compressor P d4 max P d4 P Discharge Q Suction N 1 N 2 N 3 N 4 N 5 N 5 P 4 P 4 min N 3 N 4 Power N 1 N 2 Q 4 min Q 4 Q Suction
Compressor Solution Region Min compressor speed for lowest desired discharge P GT power vs. PT speed (design) Typical operating region Power Compressor min power vs. speed (at surge limit) Actual operating conditions are above this line (for higher values of Q) PT Speed
Model Implementation Recycle loop In Gas Q = 7.484625 MMscf/day In Pres = 107.97 psig In Temp = 124.91 degf In Gas Q = 68.391824 MMscf/day ^Pipe_0_2 Gas Turbine Scrubber Compressor Stage Drive shaft In Pres = 459.65 psig In Temp = 134.00 degf In Gas Q = 75.876465 MMscf/day Gearbox In Gas Q = 1.784185 MMscf/day Fuel Gas Out Pres = 477.66 psig Out Temp = 333.85 degf Out Gas Q = 68.391824 MMscf/day Cooler Out Pres = 1645.00 psig ^Pipe_1_2 Out Temp = 195.00 degf Out Gas Q = 68.391824 MMscf/day ^Junction_0
Model Implementation (Contd.) Construct GT performance surface table using the performance data of the GG in service Speed 2500 3000 3500 4000 5750 Power 10136 11566 10920 10602 10385 10385 11403 10782 10057 9824 9712 9712 12670 10097 9424 9153 9023 9087 13937 9978 9153 8684 8481 8481 15204-1 8959 8481 8208 8129 16471-1 8866 8261 7951 7781 17738-1 -1 8129 7757 7573 19005-1 -1 9138 7687 7440 20272-1 -1-1 7573 7249 22553-1 -1-1 -1 7029
Model Implementation (Contd.) Model each compressor stage using performance curves using polynomial coefficients for head and efficiency Head Coefficient Polytropic Efficiency Q/N (ACFM/RPM) Discharge Pressure Surge Compression Stage Stonewall Suction Flowrate Increasing Speed Q/N (ACFM/RPM)
Calibration of Detailed Compressor Models Gas rates measured at different locations of the network should be consistent Compressor models to be calibrated to the correct SG of gas in the network Gas flow in recycle loops should be modelled flow for surge protection or due to leaking valves? Head coefficient adjusted for pressure rise, and efficiency tuning factor adjusted for power Model variation of power with ambient temperature Example: power loss was 15% for increase in ambient temperature from 70 to 105 F
Case Study Benefits Optimization with detailed compressor models for a large, complex field with 200+ gas lifted wells Direct benefits: 1 3% increase in oil production Up to 14% saving in lift gas; able to reactivate S/I wells Compressor management: Reduced compressor operating costs by 3% Reduced separator pressure (by 5%) and gas lift header pressure (up to 3%)
Benefits of Detailed Compressor Modelling Correctly handles trade-offs between Qgas, Psuct and Pdisc: resulted in lowered Psep and Pdisc Total lift gas available not assumed, but calculated Correctly accounts for changes in power e.g. allowed shutting down a train in winter Shows where the compressor is operating with respect to limits Allows shared understanding between petroleum engineers and compressor O&M staff
Conclusions Compressors are an important component of a gas- lifted production system There are strong interactions between compressor performance and conditions in other parts of the system Therefore, compressor performance needs to be considered and accurately represented when optimising the gas-lift system When this is done, benefits are delivered in terms of increased production, cost savings and increased understanding of total system performance
End of Presentation