18 آذر 1400

محمد محمدی باغملایی

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تحصیلات: دکترای تخصصی / -
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مشخصات پژوهش

عنوان Modeling of well productivity enhancement in a gas-condensate reservoir through wettability alteration: A comparison between smart optimization strategies
نوع پژوهش مقالات در نشریات
کلیدواژه‌ها
Gas condensate reservoirsWettability alterationOptimizationTaguchi techniqueHybrid smart model
مجله Journal of Natural Gas Science and Engineering
شناسه DOI https://doi.org/10.1016/j.jngse.2021.104059
پژوهشگران محمد محمدی باغملایی (نفر اول) ، زهرا سخایی (نفر دوم) ، رضا آذین (نفر سوم) ، شهریار عصفوری (نفر چهارم) ، سهراب زنده بودی (نفر پنجم) ، حجت شیری (نفر ششم به بعد) ، ژیلی دووان (نفر ششم به بعد)

چکیده

Wettability alteration of the reservoir rock is considered as one of the promising remedies for the condensate blockage. There are key parameters including wettability state (WS), treatment radius (TR), and treatment time (TT) that considerably influence this treatment process. The objective of this paper is to conduct simultaneous optimization of WS, TR, and TT in a supergiant gas condensate reservoir located in Persian Gulf offshore. For this purpose, two distinct optimization methods are used in this research. The Taguchi design of experiment (DOE) method is first used to find the optimal state with a low number of simulation runs. Afterward, a smart optimization approach is developed through integration of the artificial neural network (ANN) and genetic algorithm (GA) techniques; this hybrid method helps to assess more possible combinations of the three key factors. Also, compositional commercial reservoir simulator ECLIPSE 300 is used to simulate gas production operation, considering different WS, TR, and TT conditions. It was found that both the Taguchi and the hybrid constructed network (ANN-GA) methods lead to the results in agreement with the simulation results so that the magnitudes of average absolute relative error are 0.6397% and 0.0436% for the Taguchi and ANN-GA, respectively, based on gas recovery factor (GRF) data. However, the ANN-GA method estimates a slightly higher GRF at the optimum state, compared to the Taguchi. Nevertheless, Taguchi DOE with only 25 required data points seems a promising and practical option for modeling and optimization purposes. The ANN-GA optimum condition corresponds to 50.64% GRF with intermediate WS, a TR of 12.7 m, and a TT of about 5 months.