Real-Time Production Optimization of Oil and Gas Production Systems: A Technology Survey
- Hans P. Bieker (Norwegian U. of Science & Tech) | Olav Slupphaug (ABB Process Automation) | Tor A. Johansen (Norwegian U. of Science & Tech)
- Document ID
- Society of Petroleum Engineers
- SPE Production & Operations
- Publication Date
- November 2007
- Document Type
- Journal Paper
- 382 - 391
- 2007. Society of Petroleum Engineers
- 5.2.2 Fluid Modeling, Equations of State, 4.1.6 Compressors, Engines and Turbines, 4.1.2 Separation and Treating, 5.5 Reservoir Simulation, 5.6.4 Drillstem/Well Testing, 3.1.6 Gas Lift, 5.5.8 History Matching, 5.3.2 Multiphase Flow, 4.3.4 Scale, 4.6 Natural Gas, 6.5.2 Water use, produced water discharge and disposal, 4.1.5 Processing Equipment, 4.2 Pipelines, Flowlines and Risers, 1.8 Formation Damage, 5.4.2 Gas Injection Methods, 2.3 Completion Monitoring Systems/Intelligent Wells, 4.1.4 Gas Processing, 2.4.3 Sand/Solids Control
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- 2,052 since 2007
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This is a noncritical survey of key literature in the field of real-time production optimization of oil and gas production. The information flow used for optimization of the system is described. The elements in this description include data acquisition, data storage, processing facility model updating, well model updating, reservoir model updating, production planning, reservoir planning, and strategic planning. Methods for well prioritization, gas lift optimization, gas or water injection optimization, and model updating are discussed in the view of the information flow described. Challenges of real-time production optimization are also discussed.
In the daily operation of an oil and gas production system, many decisions (an element of a solution) have to be taken affecting the volumes produced and the cost of production. These decisions are taken at different levels in the organization, but eventually they will reach the physical production system. Fig. 1 gives an overview of a physical production system. For such production systems, the decisions are related to the choke or valve openings, compressor, and pump settings at every instance of time.
An objective function is a single-valued and well-defined mathematical function mapping the values of the decision variables into a performance measure. Examples of such performance measures are the total oil production rate, net present value (profit), or the recovery of the reservoir. In the efforts toward better performance of the production system, a question to be answered is which decisions are better to maximize or minimize the objective function. In the process of making good decisions, information about the production system is used. This information may include the physical properties such as pipe diameters and lengths, or it may include measurements from the production system.
The environment in which the production of oil and gas is obtained is constantly changing. This will affect the value of the performance measure of the decisions used. For example, if the cooling capacity of the production system is an operational bottleneck, this may no longer be the case if the seawater temperature drops or another pump in the cooling system is started. Incidents in the production system may also affect the value of the performance measure of the decisions. A partial shutdown of the production system because of maintenance will most likely also affect system bottlenecks.
Real-time optimization (RTO) is a method for complete or partial automation of the process for making good or optimal decisions. The term "optimal?? is defined below. By continuously collecting and analyzing data from the production system, optimal decisions may be found. Either these settings are then implemented directly in the production system or they are presented to an operator or engineer for consideration. If the settings are implemented directly, the RTO is said to be in a closed loop. RTO defined by Saputelli et al. (2003a) reads: "a process of measure-calculate-control cycles at a frequency, which maintains the system's optimal operating conditions within the time-constant constraints of the system??.
The main aim of RTO is to improve the utilization of the capacity of a production system to obtain higher throughput or net present value. The idea is to operate the production system, at every instant of time, as near to the desired optimum as possible (Sequeira et al. 2002). To achieve this, a model of the production system is optimized to furnish an optimal solution. The model is continuously being updated by measurements from the production system to fit the actual input-output behavior of the processing facilities, wells or network, and reservoir better.
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Alarcón, G.A., Torres, C.F., and Gómez, L.E. 2003. Global Optimization ofGas Allocation to a Group of Wells in Artificial Lift Using NonlinearConstrained Programming. J. of Energy Resources Technology 124(4): 262-268.
Bailey, W.J., Couët, B., and Wilkinson, D. 2005. Field Optimization ToolforMaximizing Asset Value. SPEREE 8 (1): 7-21.SPE-87026-PA. DOI: 10.2118/87026-PA.
Barnes, D.A., Humphrey, K., and Muellenberg, L. 1990. A Production Optimization System forWestern Prudhoe Bay Field, Alaska. Paper SPE 20653 presented at the SPEAnnual Technical Conference and Exhibition, New Orleans, Louisiana. DOI:10.2118/20653-MS.
Benders, J.F. 1962 Partitioning Procedures For Solving Mixed-VariableProgramming Problem. Numerische Mathematik..
Bieker, H.P., Slupphaug, O., and Johansen, T.A. 2007. Well Management Under Uncertain Gas/or Water/Oil Ratios. Paper SPE 106959 presented at the SPE Digital EnergyConference and Exhibition, Houston, Texas, 11-12 April. DOI:10.2118/106959-MS.
Brouwer, D.R., and Jansen, J.D. 2002. Dynamic Optimization of WaterfloodingWith Smart Wells Using Optimal Control Theory. SPEJ 9(4): 391-402. SPE-78278-PA. DOI: 10.2118/78278-PA.
Brouwer, D.R., Jansen, J.D., van der Starre, S., and van Kruijsdijk, C.P.2001. Recovery Increase ThroughWaterflooding With Smart-Well Technology. Paper SPE 68979 presented at theSPE European Formation Damage Conference, The Hague, 21-22 May. DOI:10.2118/68979-MS.
Brouwer, D.R., Nævdal, G., Jansen, J.D., Vefring, E.H., and van Kruijsdijk.C.P. 2004. Improved ReservoirManagement Trough Optimal Control and Continuous Model Updating. Paper SPE90149 presented at the SPE Annual Technical Conference and Exhibition, Houston,26-29 September. DOI: 10.2118/90149-MS.
Buitrago, S., Rodríguez, E., and Espin, D. 1996. Global Optimization Techniques in GasAllocation for Continuous Flow Gas Lift Systems. Paper SPE 35616presented at the SPE Gas Technology Symposium, Calgary, 28 April-1 May. DOI:10.2118/35616-MS.
Clay, R.M., Stoisits, R.F., Pritchett, M.D., Rood, R.C., and Cologgi, J.R.1998. An Approach to Real-TimeOptimization of the Central Gas Facility at the Prudhoe Bay Field. PaperSPE 49123 presented at the SPE Annual Technical Conference and Exhibition, NewOrleans, Louisiana, 27-30 September. DOI: 10.2118/49123-MS.
Cullick, A.S., Heath, D., Narayanan, K., Jay, A., and Kelly, J. 2003. Optimizing Multiple-Field Schedulingand Production Strategy With Reduced Risk. Paper SPE 84239 presented at theSPE Annual Technical Conference and Exhibition, Denver, 5-8 October. DOI:10.2118/84239-MS.
Cullick, A.S., Johnson, D., and Shi, G. 2006. Improved and More-Rapid HistoryMatching With a Nonlinear Proxy and Global Optimization. Paper SPE 101933presented at the SPE Annual Technical Conference and Exhibition, San Antonio,Texas, 24-27 September. DOI: 10.2118/101933-MS.
Dutta-Roy, K. and Kattapuram, J. 1997. A New Approach to Gas-Lift AllocationOptimization. Paper SPE 38333 presented at the SPE Western RegionalMeeting, Long Beach, California, 25-27 June. DOI: 10.2118/38333-MS.
Fang, W.Y. and Lo, K.K. 1996. AGeneralized Well-Management Scheme for Reservoir Simulation. SPERE11 (2): 116-120. SPE-29124-PA. DOI: 10.2118/29124-MS.
Gómez, V. 1974. Optimization of Continuous Flow Gas Lift Systems. Mastersthesis, University of Tulsa, Tulsa.
Grothey, A. and McKinnon, K. 2000. Decomposing the optimization of a gaslifted oil well network. Edinburgh, Scotland: University of Edinburgh.
Handley-Schachler, S., McKie, C., and Quintero, N. 2000. New Mathematical Techniques for theOptimisation of Oil and Gas Production Systems. Paper SPE 65161 presentedat the SPE European Petroleum Conference, Paris, 24-25 October. DOI:10.2118/65161-MS.
Horst, R. and Tuy, H. 1996. Global Optimization: Deterministic Approaches.3rd ed. Springer-Verlag, Berlin.
Jansen, B., Dalsmo, M., Nokelberg, L., et al. 1999. Automatic Control of Unstable GasLifted Wells. Paper SPE 56832 presented at the SPE Annual TechnicalConference and Exhibition, Houston, 3-6 October. DOI: 10.2118/56832-MS.
Kanu, E.P., Mach, J., and Brown, K.E. 1981. Economic Approach to Oil Productionand Gas Allocation in Continuous Gas Lift. JPT 33 (10):1887-1892. SPE-9084-PA. DOI: 10.211/9084-PA.
Koninckx, J., 1988 On-line Optimization of Chemical Plants Using SteadyState Models. PhD thesis. University of Maryland.
Kosmala, A., Aanonsen, S., Gajraj, A., et al. 2003. Coupling of a Surface Network WithReservoir Simulation. Paper SPE 84220 presented at the SPE AnnualTechnical Conference and Exhibition, Denver, 5-8 October. DOI:10.2118/84220-MS.
Kosmidis, V.D., Perkins, J.D., and Pistikopoulos, E.N. 2004. Optimization ofWell Oil Rate Allocations in Petroleum Fields. Industrial & EngineeringChemistry Research 43 (14): 3513-3527.
Kosmidis, V.D., Perkins, J.D., and Pistikopoulos, E.N. 2005. A Mixed IntegerOptimization Formulation for the Well Scheduling Problem on Petroleum Fields.Computers & Chemical Engineering 29 (7): 1523-1541.
Lechner, J.P. and Zangl, G. 2005. Treating Uncertainties inReservoir-Performance Prediction With Neural Networks. Paper SPE94357 presented at the SPE Europec EAGE Annual Conference, Madrid, Spain, 13-16June. DOI: 10.2118/94357-MS.
Lo, K.K. and Holden, C.W. 1992. Use of Well Management Schemes forRate Forecasts. Paper SPE 24071 presented at the Western Regional Meeting,Bakersfield, California, 30 March-1 April. DOI: 10.2118/24071-MS.
Mayhill, T.D. 1974. SimplifiedMethod for Gas-Lift Well Problem Identification and Diagnosis. Paper SPE5151 presented at the 1974 49th Annual Fall Meeting of the Society of PetroleumEngineers of AIME, Houston, 6-9 October. DOI: 10.2118/5151-MS.
McKie, C.J.N., Rojas, E.A., Quintero, N.M., Fonseca, J.R.C., and Perozo,N.J. 2001. Economic Benefits FromAutomated Optimization of High-Pressure Gas Usage in an Oil ProductionSystem. Paper SPE 67187 presented at the SPE Production and OperationsSymposium, Oklahoma City, Oklahoma, 24-27 March. DOI: 10.2118/67187-MS.
Mochizuki, S., et al. 2004. Real-Time Optimization:Classification and Assessment. SPEPO 21 (4): 455-466.SPE-90213-PA. DOI: 10.2118/90213-PA.
Narayanan, K., Cullick, A.S., and Bennett, M. 2003. Better Field Development DecisionsFrom Multiscenario, Interdependent Reservoir, Well, and FacilitySimulations. Paper SPE 79703 presented at the SPE Reservoir SimulationsSymposium, Houston. 3-5 February. DOI: 10.2118/79703-MS.
Naus, M.M., Dolle, N., and Jansen, J.D. 2004. Optimization of Commingled ProductionUsing Infinitely Variable Inflow Control Valves. SPE Advanced TechnicalSeries 21 (2): 293-301. SPE-90959-PA. DOI: 10.2118/90959-PA.
Nemhauser, G.L. and Wolsey, L.A. 1998. Integer and CombinatorialOptimization. John Wiley & Sons: New York.
Nocedal, J. and Wright, S.J. 1999. Numerical Optimization. New York:Springer.
Qin, S. and Badgwell, T. 1997. An overview of industrial modelpredictive control technology. Paper presented at the FifthInternational Conference on Chemical Process Control.
Radin, R.L. 1998. Optimization in Operations Research. Upper SaddleRiver, NJ: Prentice Hall.
Raghuraman, B., Couët, B., and Savundararaj, P. 2002. Valuation of Technology andInformation for Reservoir Risk Management. Paper SPE 77424 presented at theSPE Annual Technical Conference and Exhibition, San Antonio, Texas, 29September-2 October. DOI: 10.2118/77424-MS.
Saputelli, L., Malk, H., Canelon, J., and Nikolaou, M.. 2002. A Critical Overview of ArtificialNeural Network Applications in the Context of Continuous Oil FieldOptimization. Paper SPE 77703 presented at the SPE Annual TechnicalConference and Exhibition, San Antonio, Texas, 29 September-2 October. DOI:10.2118/77703-MS.
Saputelli,, L., Mochizuki, S., and Hutchins, L., et al. 2003a. Promoting Real-Time Optimization ofHydrocarbon Producing Systems. Paper SPE 83978 presented at the SPEOffshore Europe Conference, Aberdeen, 2-5 September. DOI: 10.2118/83978-MS.
Saputelli, L., Nikolaou, M., and Economides, M.J. 2003b. Self-Learning ReservoirManagement. SPEREE 8 (6): 534-547. SPE-84064-PA. DOI:10.2118/84064-PA.
Saputelli, L., Nikolaou, M., and Economides, M.J., 2006. Real-Time ReservoirManagement: A Multi-Scale Adaptive Optimization and Control Approach.Computational Geosciences, 10 (1): 61-96.
Schulze-Riegert, R.W., Haase, O., and Nekrassov, A. 2003. Combined Global and LocalOptimization Techniques Applied to History Matching. Paper SPE 79668presented at the SPE Reservoir Simulation Symposium, Houston, 3-5 February.DOI: 10.2118/79668-MS.
Sequeira, S.E., Graells, M., and Luis, P., 2002. Real-Time Evolution forOn-line Optimization of Continuous Processes. Industrial & EngineeringChemistry Research 41 (7): 1815-1825.
Söderstrom, T. and Stoica, P., 1988. System identification. UpperSaddle River, NJ: Prentice-Hall.
Stoisits, R.F., Crawford, K.D., MacAllister, D.J., McCormack, M.D., Lawal,A.S., and Ogbe, D.O. 1999. Production Optimization at theKuparuk River Field Utilizing Neural Networks and Genetic Algorithms. PaperSPE 52177 presented at the SPE Mid-Continent Operations Symposium, OklahomaCity, Oklahoma, 28-31 March. DOI: 10.2118/52177-MS.
Sudaryanto, B. and Yortsos, Y.C. Optimization of Displacements inPorous Media Using Rate Control. 2001. Paper SPE 71509 presented at the SPEAnnual Technical Conference and Exhibition, New Orleans, Lousiana, 30September-3 October. DOI: 10.2118/71509-MS.
Thiele, M.R. and Batycky, R.P. 2003. Water Injection Optimization Using aStreamline-Based Workflow. Paper SPE 84080 presented at the SPE AnnualTechnical Conference and Exhibition, Denver, 5-8 October. DOI:10.2118/84080-MS..
Torn, A. and Zilinskas, A., 1989. Global Optimization. New York City:Springer-Verlag.
Urbanczyk, C.H. and Wattenbarger, R.A. 1994. Optimization of Well Rates Under GasConing Conditions. SPE Advanced Technology Series 2 (2):61-68. SPE-21677-PA. DOI: 10.2118/21677-PA.
Vazquez, M., Suarez, A., Aponte, H., Ocanto, L., and Fernandes, J. 2001. Global Optimization of Oil ProductionSystems: A Unified Operational View. Paper SPE 71561 presented at the SPEAnnual Technical Conference and Exhibition, New Orleans, Louisiana, 30September-3 October. DOI: 10.2118/71561-MS.
Wang, P. 2003. Development and Applications of Production OptimizationTechniques for Petroleum Fields. PhD thesis, Stanford University, Stanford,California.
Wang, P. and Litvak, M. 2004. Gas Lift Optimization for Long-TermReservoir Simulations. Paper SPE 90506 presented at the SPE AnnualTechnical Conference and Exhibition, Houston, 26-29 September. DOI:10.2118/90506-MS.
Wang, P., Litvak, M., and Aziz, K. 2002. Optimization of Production Operationsin Petroleum Fields. Paper SPE 77658 presented at the SPE Annual TechnicalConference and Exhibition, San Antonio, Texas, 29 September-2 October. DOI:10.2118/77658-MS.
Williams, H.P. 1999. Model Building in Mathematical Programming.Chichester: John Wiley & Sons.
Wolsey, L.A. 1998. Integer Programming. New York: Wiley-InterScience.
Xiong, Q. and Jutan, A. 2003. Continuous optimizationusing a dynamic simplex method. Chemical Engineering Science58 (16): 3817-2828. DOI: 10.1016/S0009-2509(03)00236-7.
Yeten, B., Durlofsky, L.J., and Aziz, K. 2002. Optimization of Smart WellControl. Paper SPE 79031 presented at the SPE International ThermalOperations and Heavy Oil Symposium and International Horizontal Well TechnologyConference, Calgary, 4-7 November. DOI: 10.2118/79031-MS.
Zangl, G., Graf, T., and Al-Kinani, A. 2006. Proxy Modeling in ProductionOptimization. Paper 100131 presented at the SPE Europec/EAGE AnnualConference and Exhibition, Vienna, Austria, 12-15 June. DOI:10.2118/100131-MS.