تخمین تراوایی و شبیه‌سازی آن به منظور تعیین ویژگی‌های مخزنی سازند شوریجه در یکی از مخازن شمال شرق ایران

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشکده زمین‌شناسی، پردیس علوم، دانشگاه تهران، ایران

2 گروه علوم زمین، دانشکده علوم طبیعی، دانشگاه تبریز، ایران

چکیده

تراوایی یکی از مهم‌ترین پارامترها در مخازن هیدروکربنی است. درک صحیح از مقدار تراوایی و نحوه توزیع و گسترش آن در فرآیند مدیریت تولید از میدان سودمند است. فرآیند  مغزه‌گیری به‌دلیل محدودیت‌های که وجود دارد برروی تعداد کمی از چاه‌های میدان انجام می‌گیرد درحالی‌که بیشتر چاه‌ها تحت عملیات چاه‌نگاری قرار می‌گیرند. بنابرین یافتن راهی برای تخمین خصوصیات مخزن توسط نگاره‌های چاه‌پیمایی و مدل‌سازی آن در میدان تکنیک با ارزشی است. بنابراین در این پژوهش از روش شبکه عصبی مصنوعی پرسپترون چندلایه (پس انتشار خطا) برای تخمین تراوایی بخش‌های مختلف سازند شوریجه در حوضه رسوبی کپه داغ استفاده شده است. نمودارهای صوتی، نوترون و چگالی و نتایج حاصل از ارزیابی سازند شامل تخلخل و اشباع آب مفید به‌عنوان لایه ورودی و داده تراوایی حاصل از آنالیز مغزه دو چاه نیز به‌عنوان سلول‌های لایه خروجی برای آموزش شبکه مورد استفاده قرار گرفت. پس از آموزش شبکه با داده این دو چاه از داده آنالیز مغزه یک چاه دیگر برای آزمایش شبکه استفاده شد که در مرحله آزمایش شبکه ضریب هبستگی 98% برای تراوایی به‌دست آمد. با استفاده از این شبکه عصبی، تراوایی برای چاه‌های دیگر میدان که فاقد داده مغزه بودند تخمین‌زده شد. بعد از تخمین تراوایی به‌کمک شبکه عصبی نحوه توزیع و گسترش آن به‌کمک الگوریتم مدل‌سازی گوسی متوالی (SGS) در مقیاس میدان مشخص گردید. طبق مدل به‌دست آمده نواحی ماسه‌سنگی که عمدتاً در زون‌های B و D هستند به‌عنوان نواحی مخزنی تفکیک شده‌اند و همچنین نواحی مرکزی و شمال غربی میدان به‌دلیل میانگین تراوایی بالاتر نواحی مستعد برای حفاری‌های بعدی میدان می‌باشند.
 

کلیدواژه‌ها


عنوان مقاله [English]

Permeability Estimation and its Simulation to Determine the Reservoir Characteristics of Shurijeh Formation in One of the Reservoirs of Northeast Iran

نویسندگان [English]

  • Milad Moradi 1
  • Hossain Rahimpour 1
  • Ali Kadkhodaie 2
1 School of Geology, College of Science, University of Tehran, Iran
2 Earth Sciences Department, Faculty of Natural Sciences, University of Tabriz, Tabriz, Iran
چکیده [English]

Permeability is one of the most important parameters in hydrocarbon reservoirs. It is beneficial to have a correct understanding of the permeability and its distribution in the production management process. Due to the limitations, the coring process is performed on a small number of wells in the field, while most of the wells are subjected to well logging operations. Therefore, finding a way to estimate the characteristics of the reservoir by well-logging and modeling it on the field is a valuable technique. Therefore, in this study, the multilayer perceptron artificial neural network method (error back propagation) has been used to estimate the permeability of different parts of the Shurijeh Formation in the Kopeh Dagh sedimentary basin. Sonic logs, neutrons, density and the results of the formation evaluation, including porosity and saturation of useful water as input layer, and permeability data from core well analysis of two wells as output layer cells, were used to train the network. After training the network with the data of these two wells, the core analysis data of another well was used to test the network, which in the network test stage, a correlation coefficient of 98% for permeability was obtained. With the help of this neural network, permeability was estimated for other wells in the field that no core data were obtained from. After estimating the permeability using neural network, its distribution and expansion were determined using Sequential Gaussian Simulation algorithm (SGS) in the field scale. According to the obtained model, the sandstone areas, which are mainly in zones B and D, are separated as reservoir areas and also the central and northwestern areas of the field, due to the higher average permeability, are areas prone to further excavations of the field.

کلیدواژه‌ها [English]

  • Shurijeh Formation
  • Kopet-Dagh
  • Well Loging
  • Multilayer-perceptron Artificial Neural Network (MLP_ANN)
  • Sequential Gaussian Simulation (SGS)
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