Improving Polymerflood Performance Via Streamline-Based Rate Optimization: Mangala Field, India
- Hongquan Chen (Texas A&M University) | Jaeyoung Park (Texas A&M University) | Akhil Datta-Gupta (Texas A&M University) | Sunit Shekhar (Cairn Oil & Gas, Vedanta Ltd.) | Kavish Grover (Cairn Oil & Gas, Vedanta Ltd.) | Joyjit Das (Cairn Oil & Gas, Vedanta Ltd.) | Vivek Shankar (Cairn Oil & Gas, Vedanta Ltd.) | M. Suresh Kumar (Cairn Oil & Gas, Vedanta Ltd.) | Ashish Chitale (Praesagus RTPO)
- Document ID
- Society of Petroleum Engineers
- SPE Improved Oil Recovery Conference, 31 August - 4 September, Tulsa, Oklahoma, USA
- Publication Date
- Document Type
- Conference Paper
- 2020. Society of Petroleum Engineers
- 5.7.2 Recovery Factors, 5.4 Improved and Enhanced Recovery, 5.4.1 Waterflooding, 1.10 Drilling Equipment, 5.5 Reservoir Simulation, 5.3.6 Chemical Flooding Methods (e.g., Polymer, Solvent, Nitrogen, Immiscible CO2, Surfactant, Vapex), 1.10 Drilling Equipment, 5.7 Reserves Evaluation, 5 Reservoir Desciption & Dynamics, 4.3.4 Scale
- EOR, Streamline, Optimization, Polymer flood, Mangala
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- 89 since 2007
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This paper describes a novel streamline-based rate allocation approach to maximize oil recovery from polymerfloods and its application to an ongoing polymerflood in the Mangala field, India which is one of the largest polymerfloods in the world. With over 130 injectors and producers, the field-scale optimization is challenging because of the long simulation times, operational constraints at the well, group and field level and changing field conditions.
The FM-1 unit which is the main producing zone of the Mangala field comprises of multi-layered single storied fluvial channel sands of thickness ranging 3-10m with excellent flow characteristics. The gross thickness of FM-1 is about 90m. The sand is under a five-spot pattern polymerflood with a well spacing of aproximately 200m. A finite difference simulator is used for modeling the polymerflood performance with all relevant physics (mobility control, adsorption, residual resistance factor, polymer rheology/shear thinning, inaccessible pore volume and time-dependent degradation). Streamlines are generated from the flux field extracted from the finite difference simulation and are used to calculate pair-wise efficiency for each injector-producer streamline bundle. The pair-wise efficiency quantifies how much oil is recovered given a barrel of water injection. The streamline-based optimizer iteratively reallocates the fluids to injectors and producers by diverting the injected fluid to high efficiency pairs located in unswept oil regions. After optimization, injection efficiency plots as well as streamline/time of flight plots are used to examine and visualize the flow pattern changes leading to the improved flood performance.
The reservoir model is first calibrated with 10 years of waterflood history. The polymer injection concentration and tapering schemes are determined based on extensive laboratory experiments. The optimization focuses on reallocating the well rates while maintaining the well, group and field level constraints. Two optimization scenarios were studied to examine the potential benefits of rate allocation: (1) optimization of rate allocation for both injectors and producers, (2) optimization of rate allocation for injectors only. Both the scenarios yield improved oil recovery (~5 MMSTB), accelerated production and reduced polymer usage (utility factor). The streamline and injection efficiency plots clearly reveal regions of improved sweep efficiencies, providing an intuitive explanation of the enhanced performance.
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