Using Streamline-Derived Injection Efficiencies for Improved Waterflood Management
- Marco R. Thiele (StreamSim Technologies Inc.) | Roderick P. Batycky (StreamSim Technologies Inc.)
- Document ID
- Society of Petroleum Engineers
- SPE Reservoir Evaluation & Engineering
- Publication Date
- April 2006
- Document Type
- Journal Paper
- 187 - 196
- 2006. Society of Petroleum Engineers
- 5.1.2 Faults and Fracture Characterisation, 5.3.1 Flow in Porous Media, 2.3 Completion Monitoring Systems/Intelligent Wells, 4.1.2 Separation and Treating, 5.4.1 Waterflooding, 1.6 Drilling Operations, 5.4.2 Gas Injection Methods, 5.5.8 History Matching, 5.4.7 Chemical Flooding Methods (e.g., Polymer, Solvent, Nitrogen, Immiscible CO2, Surfactant, Vapex), 4.3.4 Scale, 5.5 Reservoir Simulation, 6.5.2 Water use, produced water discharge and disposal, 3.3 Well & Reservoir Surveillance and Monitoring, 5.5.7 Streamline Simulation, 4.1.5 Processing Equipment, 5.3.2 Multiphase Flow, 5.4 Enhanced Recovery
- 13 in the last 30 days
- 1,370 since 2007
- Show more detail
- View rights & permissions
|SPE Member Price:||USD 10.00|
|SPE Non-Member Price:||USD 30.00|
This paper describes a novel approach to predict injection- and production-well rate targets for improved management of waterfloods. The methodology centers on the unique ability of streamlines to define dynamic well allocation factors (WAFs) between injection and production wells. Streamlines allow well allocation factors to be broken down additionally into phase rates at either end of each injector/producer pair. Armed with these unique data, it is possible to define the injection efficiency (IE) for each injector and for injector/producer pairs in a simulation model. The IE quantifies how much oil can be recovered at a producing well for every unit of water injected by an offset injector connected to it. Because WAFs are derived directly from streamlines, the data reflect all the complexities impacting the dynamic behavior of the reservoir model, including the spatial permeability and porosity distributions, fault locations, the underlying computational grid, relative permeability data, pressure/volume/temperature (PVT) properties, and most importantly, historical well rates.
The possibility to define IEs through streamline simulation stands in contrast to the ad hoc definition of geometric WAFs and simple surveillance methods used by many practicing reservoir engineers today. Once IEs are known, improved waterflood management can be implemented by reallocating injection water from low-efficiency to high-efficiency injectors. Even in the case in which water cannot be reallocated because of local surface-facility constraints, knowing IEs on an injector/producer pair allows the setting of target rates to maintain oil production while reducing water production.
We demonstrate this methodology by first introducing the concept of IEs, then use a small reservoir as an example application.
Local areas of water cycling and poor sweep exist as a flood matures. Current flood management is restricted to surveillance methods or workflows centered on finite-difference (FD) simulation, where areas of bypassed oil are identified and then rate changes, producer/injector conversions, or infill-drilling scenarios are tested. However, identifying and testing improved management scenarios in this way can be laborious, particularly for waterfloods with a large number of wells and/or a relatively high-resolution numerical grid.
For mature fields that have potential for improved production without introducing new wells or producer/injector conversions, the main goal is to manage well rates so as to reduce cycling of the injected fluid while maintaining or even increasing oil production.
Reservoir engineers have no easy or automated way to identify injection patterns, well-pair connections, or areas of inefficiency beyond simple standard fixed-pattern surveillance techniques (Baker 1997; Baker 1998; Batycky et al. 2005). Such methods are approximate at best owing to the need to define geometric allocation factors and fixed patterns, which suffer from "out-of-pattern?? flow. These limitations are removed through streamline-based surveillance models (Batycky et al. 2005). By adding a transport step along streamlines, streamline simulation (3DSL 2006) can additionally identify how much oil production results from an associated injector, quantifying the efficiency down to an individual injector/producer pair. It is this crucial piece of information—the efficiency of an injector/producer pair—that allows an improved estimation of future target rates, leading to improved reservoir flood management.
|File Size||2 MB||Number of Pages||10|
3DSL v2.30 User Manual. 2006. Streamsim Technologies Inc., SanFrancisco.
Baker, R.O. 1997. Reservoir Management for Waterfloods. JCPT36 (4):20-24.
Baker, R.O. 1998. Reservoir Management for Waterfloods—Part II. JCPT 37(1): 12-17.
Baker, R.O., Kuppe, F., Chugh, S., Bora, R., Stojanovic, S., and Batycky, R.2002. Full-Field Modeling UsingStreamline-Based Simulation: Four Case Studies. SPEREE5(2):126-134. SPE-77172-PA.
Batycky, R.P., Blunt, M.J., and Thiele, M.R. 1997. A 3D Field-Scale Streamline-BasedReservoir Simulator. SPERE 12 (4):246-254.SPE-36726-PA.
Batycky, R.P., Thiele, M.R., Baker, R.O., and Chugh, S.H. 2005. Revisiting ReservoirFlood-Surveillance Methods Using Streamlines.Paper SPE 95402presented at the SPE Annual Technical Conference and Exhibition, Dallas, 9-12October.
Bratvedt, F., Gimse, T., and Tegnander, C. 1996. Streamline computations forporous media flow including gravity. Transport in PorousMedia 25 (1):63-78.
Brouwer, D.R. and Jansen, J.-D. 2004. Dynamic Optimization of WaterfloodingWith Smart Wells Using Optimal Control Theory. SPEJ 9(4):391-402. SPE-78278-PA.
Culick, S., Heath, D., Narayanan, K., April, J., and Kelly, J. 2004. Optimizing Multiple-Field Schedulingand Production Strategy With Reduced Risk . JPT56(11): 77-83. SPE-88991-MS.
Davidson, J.E. and Beckner, B.L. 2003. Integrated Optimization for RateAllocation in Reservoir Simulation. SPEREE6(6):426-432. SPE-87309-PA.
Flanders, W.A. and Bates, G.R. 1987. Optimizing Reservoir Surveillance byUsing Streamlines and the Microcomputer .Paper SPE 16482 presented atthe SPE Petroleum Industry Application of Microcomputers, Lake Conroe, Texas,23-26 June.
Grinestaff, G.H. 1999. Waterflood Pattern Allocations:Quantifying the Injector to Producer Relationship With StreamlineSimulation.Paper SPE 54616 presented at the SPE Western RegionalMeeting, Anchorage, 26-28 May.
Higgins, R.V. and Leighton, A.J. 1962. A Computer Method to CalculateTwo-Phase Flow in Any Irregularly Bounded Porous Medium.JPT 14 (6):679-683. SPE-243-PA.
LeBlanc, J.L. and Caudle, B.H. 1971. A Streamline Model for SecondaryRecovery. SPEJ 11 (3):7-12. SPE-2865-PA.
Martin, J.C. and Wegner, R.E. 1979. Numerical Solution of Multiphase,Two-Dimensional Incompressible Flow Using Stream-Tube Relationships.SPEJ 19(5):313-323. SPE-7140-PA.
Muskat, M.: The Flow of Homogeneous Fluids Through Porous Media,McGraw-Hill Book Co. Inc., New York City (1937).
Samier, P., Quettier, L., and Thiele, M. 2002. Applications of StreamlineSimulations to Reservoir Studies. SPEREE 5(4):324-332.SPE-78883-PA.
Thiele, M.R. 2003. Streamline Simulation. Keynote address at the 7th Intl.Forum on Reservoir Simulation, Baden-Baden, Germany, 23-27 June.
Thiele, M.R., Batycky, R.P., and Blunt, M.J. 1997. A Streamline-Based 3D Field-ScaleCompositional Reservoir Simulator. Paper SPE 38889 presented at theSPE Annual Technical Conference and Exhibition, San Antonio, Texas, 5-8October.
Thiele, M.R., Batycky, R.P., and Kent, L.T. 2002. Miscible WAG SimulationsUsing Streamlines. Paper presented at the 8th European Conference on theMathematics of Oil Recovery (ECMOR), Freiberg, Germany, 3-6 September.
Thiele, M.R., Batycky, R.P., Blunt, M.J., and Orr, F.M. Jr. 1996. Simulating Flow in HeterogeneousSystems Using Streamtubes and Streamlines . SPERE11(1):5-12. SPE-27834-PA.
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.