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

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.