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Gas and Downhole Water Sink-Assisted Gravity Drainage GDWS-AGD EOR Process: Field-Scale Evaluation and Recovery Optimization

机译:气体和井下水槽辅助重力排水GDWS-AGD EOR过程:现场评估和恢复优化

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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.
机译:气体和井下水沉辅助重力泄油(GDWS-AGD)工艺已经发展到克服的气驱油藏工艺具有较强的含水层的限制。这些限制包括高水平的含水和水锥进的高倾向。所述GDWS-AGD过程最小化油的生产井的含水率,提高气体注入性,并且进一步具有很强的水锥进的倾向增强旁路油的回收,尤其是在贮存器。所述GDWS-AGD过程概念性各州双向横向安装两个7英寸生产套管和由两个2-3 / 8英寸的水平油管完成:石油生产上面的油 - 水界面(OWC)和一个OWC下方用于水槽排水。两个结束由外壳内的封隔器液压地隔离。水沉完成时产生一个潜水泵,其防止水通过油柱断裂和进入水平产油穿孔。该GDWS-AGD过程进行了评估,以提高在南方鲁迈拉油田多相上砂岩工资,其中有一个巨大的边水无限活跃的含水层采油。一个组成储层流模型的GDWS-AGD过程中的CO2驱模拟和优化采用。试验设计(DOE)和代理元建模的设计进行积分,以确定影响GDWS-AGD工艺性能的最佳操作的决策参数:最大喷射率和在注入井压力,最大油率和生产井的最小井底压力,和最大水率和在水沉孔最小井底压力。更具体地讲,被用于GDWS-AGD工艺性能的优化拉丁方抽样和径向基神经网络,并建立代理模型,分别。在GDWS-AGD处理结果,水切割和圆锥倾向与贮存器压力一起被显著降低。这导致改善气体注入,提高原油采收率。在油回收进一步改进是通过能源部优化确定操作的决定因素的最优组约束所述油和水的生产用气体注射后实现的。 GDWS-AGD工艺的优势来自于其潜在的可行性,以提高原油采收率,同时减少水锥进,含水率,提高气体注入。这给出了GDWSAGD过程的另一特权以达到在油回收显著改进相比于其它气体注射过程,诸如气体辅助重力泄油(GAGD)过程中,特别是在具有强含水层储层。

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