Leveraging Designed Simulations and Machine Learning to Develop a Surrogate Model for Optimizing the Gas–Downhole Water Sink–Assisted Gravity Drainage (GDWS-AGD) Process to Improve Clean Oil Production
气和井下水汇辅助重力泄油（GDWS-AGD）过程解决了无限作用含水层包围的储层中气体水淹的局限性，特别是水锥现象。GDWS-AGD技术减少了油井产水量，提高了气体注入能力，并优化了采收率，特别是在高水锥的储层中。GDWS-AGD过程安装了两个7英寸的生产套管，两侧各有一个。然后，完成两个2-3/8英寸的水平油管。一个油管在油-水界面（OWC）区域上方生产油，另一个在下方排水。井筒中的液压封隔器将两个完井分隔开。水汇完井使用电潜泵防止水穿过油柱并进入水平油生产射孔。
为了提高南鲁迈拉油田非均质上层砂岩产油区的采收率，该油田具有强大的含水层和大边水驱，使用组合储层流动模型进行了GDWS-AGD过程评估，并与GAGD过程进行了比较，预测期为10年。结果表明，GDWS-AGD方法在累积油产量上超过GAGD 2.75亿桶，采收率提高了4.7%。基于10年的预测，GDWS-AGD过程可以在1.5年内生产出同样数量的油。此外，通过GAGD和GDWS-AGD过程计算了不同油价（每桶10美元至100美元）下的净现值（NPV）。GDWS-AGD方法在所有油价范围内的NPV方面都优于GAGD。当油价跌至每桶10美元以下时，GAGD技术变得不经济。
实验设计-拉丁超立方抽样（DoE-LHS）和径向基函数神经网络（RBF-NNs）用于确定影响GDWS-AGD过程效果的最佳操作决策变量，并构建代理元模型。决策变量包括控制注入和生产的井限制。最佳方法将回收因子提高了1.7525%，超过了GDWS-AGD过程的基础案例。使用GDWS-AGD，含水率和锥进倾向显著降低，储层压力也随之降低，这些都有助于提高气体注入能力和石油采收率。GDWS-AGD技术比GAGD过程更多地提高了油产量和NPV。最后，与GAGD相比，GDWS-AGD技术在采油和收入方面提供了显著的改进，特别是在具有强大水含水层的储层中。
CMG软件应用情况：
在本研究中，CMG-GEM软件用于预测未来10年的GDWS-AGD过程的储层生产。该软件用于模拟储层流动，并通过历史拟合来验证储层模型的准确性。CMG-GEM软件还用于执行复杂的油藏模拟，以评估设计的实验并计算储层的流动响应。此外，CMG-GEM软件用于模拟GDWS-AGD过程，并与GAGD过程进行比较，以评估两种过程在油藏采收率和平均储层压力方面的效果。
Figure 2. Diagrammatic representation of the Gas and Downhole Water Sink–Assisted Gravity Drainage method.
Abstract
The Gas and Downhole Water Sink–Assisted Gravity Drainage (GDWS-AGD) process addresses gas flooding limitations in reservoirs surrounded by infinite-acting aquifers, particularly water coning. The GDWS-AGD technique reduces water cut in oil production wells, improves gas injectivity, and optimizes oil recovery, especially in reservoirs with high water coning. The GDWS-AGD process installs two 7-inch production casings bilaterally. Then, two 2-3/8-inch horizontal tubings are completed. One tubing produces oil above the oil–water contact (OWC) area, while the other drains water below it. A hydraulic packer in the casing separates the two completions. The water sink completion uses a submersible pump to prevent water from traversing the oil column and entering the horizontal oil-producing perforations. To improve oil recovery in the heterogeneous upper sandstone pay zone of the South Rumaila oil field, which has a strong aquifer and a large edge water drive, the GDWS-AGD process evaluation was performed using a compositional reservoir flow model in a 10-year prediction period in comparison to the GAGD process. The results show that the GDWS-AGD method surpasses the GAGD by 275 million STB in cumulative oil production and 4.7% in recovery factor. Based on a 10-year projection, the GDWS-AGD process could produce the same amount of oil in 1.5 years. In addition, the net present value (NPV) given various oil prices (USD 10–USD 100 per STB) was calculated through the GAGD and GDWS-AGD processes. The GDWS-AGD approach outperforms GAGD in terms of NPV across the entire range of oil prices. The GAGD technique became uneconomical when oil prices dropped below USD 10 per STB. Design of Experiments–Latin Hypercube Sampling (DoE-LHS) and radial basis function neural networks (RBF-NNs) were used to determine the optimum operational decision variables that influence the GDWS-AGD process’s performance and build the proxy metamodel. Decision variables include well constraints that control injection and production. The optimum approach increased the recovery factor by 1.7525% over the GDWS-AGD process Base Case. With GDWS-AGD, water cut and coning tendency were significantly reduced, along with reservoir pressure, which all led to increasing gas injectivity and oil recovery. The GDWS-AGD technique increases the production of oil and NPV more than the GAGD process. Finally, the GDWS-AGD technique offers significant improvements in oil recovery and income compared to GAGD, especially in reservoirs with strong water aquifers.
Keywords:
gas injection; downhole water sink; GDWS-AGD process; field-scale evaluation; recovery optimization
作者单位：
- 伊拉克巴士拉巴士拉石油公司
- 美国路易斯安那州立大学石油工程系，巴吞鲁日，路易斯安那州，美国
