دانلود مقاله انگلیسی رایگان:به سمت مدل های واقع بینانه تر از مخزن با توصیف برش لبه نفوذپذیری با روش MPS و انتخاب مبتنی بر یادگیری عمیق - 2019
بلافاصله پس از پرداخت دانلود کنید
دانلود مقاله انگلیسی یادگیری عمیق رایگان
  • Toward more realistic models of reservoir by cutting-edge characterization of permeability with MPS methods and deep-learning-based selection Toward more realistic models of reservoir by cutting-edge characterization of permeability with MPS methods and deep-learning-based selection
    Toward more realistic models of reservoir by cutting-edge characterization of permeability with MPS methods and deep-learning-based selection

    سال انتشار:

    2019


    عنوان انگلیسی مقاله:

    Toward more realistic models of reservoir by cutting-edge characterization of permeability with MPS methods and deep-learning-based selection


    ترجمه فارسی عنوان مقاله:

    به سمت مدل های واقع بینانه تر از مخزن با توصیف برش لبه نفوذپذیری با روش MPS و انتخاب مبتنی بر یادگیری عمیق


    منبع:

    Sciencedirect - Elsevier - Journal of Petroleum Science and Engineering, 181 (2019) 106135: doi:10:1016/j:petrol:2019:05:086


    نویسنده:

    Arash Azamifarda, Fariborz Rashidib, Mohammad Ahmadia, Mohammadreza Pourfardc, Bahram Dabirb,∗


    چکیده انگلیسی:

    Different sources of data are used to construct a reliable model of reservoir for oil/gas production. This model ought to be matched with the production history of reservoir and also show reliable predictions for future performance. To this end, permeability modeling (characterization of heterogeneity) is crucially important which is proved to be done by Multiple Point Statistics (MPS) recently. Furthermore, deep learning methods are massively used as a promising tool for regression applications. In this study, one MPS method is employed for generating the reservoir realizations. Realizations, alongside their simulation outputs, are utilized for training a convolutional deep network. In this manner, MPS is joined with deep learning to find the most appropriate realization(s) of the reservoir based on the fluid flow simulation. Moreover, unseen MPS realizations as well as another MPS realizations are used to verify the selection ability of trained network. The detailed architecture of convolutional network is illustrated in this study. The purpose of training this network and combination with MPS is to generate the matched realization(s) in history period that also show acceptable reservoir behavior in the future times of reservoir simulation. After training, the actual production data of selected realizations are obtained by simulation the reservoir for history and also future times. The results show that selected realizations efficiently capture the trend of reference behavior. Although these realizations lack identical permeability values, they have same texture of permeability (permeability heterogeneity). Meanwhile, they show acceptable match in reservoir simulation outputs. By proposed workflow, the uncertainty of permeability modeling is considered more exhaustively. It is done by selecting the realizations from enormous possible realizations dataset and providing a deep learning tool which is capable for screening quite large number of realizations. Interesting finding is satisfactory behavior of realization( s) in both history and future periods of reservoir performance.
    Keywords: Reservoir simulation | Permeability modeling | Multiple point statistics | Deep learning | Geostatistic | Convolutional neural network (CNN)


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 12
    حجم فایل: 6670 کیلوبایت

    قیمت: رایگان


    توضیحات اضافی:




اگر این مقاله را پسندیدید آن را در شبکه های اجتماعی به اشتراک بگذارید (برای به اشتراک گذاری بر روی ایکن های زیر کلیک کنید)

تعداد نظرات : 0

الزامی
الزامی
الزامی
rss مقالات ترجمه شده rss مقالات انگلیسی rss کتاب های انگلیسی rss مقالات آموزشی