Gas and Downhole Water Sink-Assisted Gravity Drainage GDWS-AGD EOR Process: Field-Scale Evaluation and Recovery Optimization
- Watheq J. Al-Mudhafar (Louisiana State University) | Andrew K. Wojtanowicz (Louisiana State University) | Dandina N. Rao (Louisiana State University)
- Document ID
- Society of Petroleum Engineers
- SPE Improved Oil Recovery Conference, 14-18 April, Tulsa, Oklahoma, USA
- Publication Date
- Document Type
- Conference Paper
- 2018. Society of Petroleum Engineers
- 5.5 Reservoir Simulation, 2.1.3 Completion Equipment, 5.7.2 Recovery Factors, 2 Well completion, 5.4.2 Gas Injection Methods, 5.4 Improved and Enhanced Recovery, 5.4 Improved and Enhanced Recovery, 5 Reservoir Desciption & Dynamics, 5.7 Reserves Evaluation, 2.2 Installation and Completion Operations
- Downhole Water Sink, Assisted Gravity Drainage, Infinite Edge and/or Bottom Water Drive, Immiscible CO2 Flooding, Enhance Oil Recovery
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The Gas and Downhole Water Sink-Assisted Gravity Drainage (GDWS-AGD) process has been developed to overcome of the limitations of Gas flooding processes in reservoir with strong aquifers. These limitations include high levels of water cut and high tendency of water coning. The GDWS-AGD process minimizes the water cut in oil production wells, improve gas injectivity, and further enhance the recovery of bypassed oil, especially in reservoirs with strong water coning tendencies.
The GDWS-AGD process conceptually states installing two 7 inch production casings bi-laterally and completing by two 2-3/8 inch horizontal tubings: oil producer above the oil-water contact (OWC) and one underneath OWC for water sink drainage. The two completions are hydraulically isolated by a packer inside the casing. The water sink completion is produced with a submersible pump that prevents the water from breaking through the oil column and getting into the horizontal oil-producing perforations.
The GDWS-AGD process was evaluated to enhance oil recovery in the heterogeneous upper sandstone pay in South Rumaila Oil field, which has an infinite active aquifer with a huge edge water drive. A compositional reservoir flow model was adopted for the CO2 flooding simulation and optimization of the GDWS-AGD process. Design of Experiments (DoE) and proxy metamodeling were integrated to determine the optimal operational decision parameters that affect the GDWS-AGD process performance: maximum injection rate and pressure in injection wells, maximum oil rate and minimum bottom hole pressure in production wells, and maximum water rates and minimum bottom hole pressure in the water sink wells. More specifically, Latin hypercube sampling and radial basis neural networks were used for the optimization of the GDWS-AGD process performance and to build the proxy model, respectively.
In the GDWS-AGD process results, the water cut and coning tendency were significantly reduced along with the reservoir pressure. That resulted to improve gas injectivity and increase oil recovery. Further improvement in oil recovery was achieved by the DoE optimization after determining the optimal set of operational decision factors that constrains the oil and water production with gas injection. The advantage of GDWS-AGD process comes from its potential feasibility to enhance oil recovery while reducing water coning, water cut, and improving gas injectivity. That gives another privilege for the GDWSAGD process to reach significant improvement in oil recovery in comparison to other gas injection processes, such as the Gas-Assisted Gravity Drainage (GAGD) process, particularly in reservoirs with strong water aquifers.
|File Size||2 MB||Number of Pages||20|
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