دانلود مقاله انگلیسی رایگان:غربالگری و بهینه سازی پروژه های سیلی پلیمری با استفاده از پروکسی مبتنی بر شبکه مصنوعی عصبی (ANN) - 2019
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  • Screening and optimization of polymer flooding projects using artificial-neural-network (ANN) based proxies Screening and optimization of polymer flooding projects using artificial-neural-network (ANN) based proxies
    Screening and optimization of polymer flooding projects using artificial-neural-network (ANN) based proxies

    سال انتشار:

    2019


    عنوان انگلیسی مقاله:

    Screening and optimization of polymer flooding projects using artificial-neural-network (ANN) based proxies


    ترجمه فارسی عنوان مقاله:

    غربالگری و بهینه سازی پروژه های سیلی پلیمری با استفاده از پروکسی مبتنی بر شبکه مصنوعی عصبی (ANN)


    منبع:

    Sciencedirect - Elsevier - Journal of Petroleum Science and Engineering, Corrected proof, 106617: doi:10:1016/j:petrol:2019:106617


    نویسنده:

    Qian Sun a,*, Turgay Ertekin b


    چکیده انگلیسی:

    Polymer flooding is one of the most broadly implemented chemical EOR processes due to its low injection cost and successes in oil production increments. This work develops artificial-neural-network based proxies by utilizing synthetic production histories generated from a high-fidelity numerical simulation model. Injectionpattern- based reservoir models are structured to establish the knowledgebase to train the proxies. A forward and an inverse-looking ANN models are structured in this study. The forward-looking expert system are employed as a forecasting and screening tool that is capable to predict time-based project responses. And the inverse-looking ANN predicts the project design schemes that fulfill the expected oil recoveries. The proxies are generalized considering reservoir rock and fluid properties and project design parameters. In this paper, we present results of extensive blind testing applications to confirm the validates of the proxy models. Afterwards, various applications of the expert systems are discussed. A project screening protocol that couples the expert system and particle swarm optimization (PSO) methodology is presented to maximize the polymer injection projects’ net present value (NPV). Moreover, we propose a robust computational workflow that coupled utilize the inverse and forward-looking proxies to find various polymer injection schemes to fulfill the expected oil production profile, which effectively addresses the issue associated with the existence of non-unique solutions in the inverse design problems. The expert ANN systems and the associated project design workflows provide versatile approaches for the field engineers to obtain quick techno-economical assessments of polymer injection projects.
    Keywords: Artificial neural network | Polymer injection | Optimization | EOR screening | EOR project design


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 17
    حجم فایل: 4001 کیلوبایت

    قیمت: رایگان


    توضیحات اضافی:




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