مقایسه کارکرد شبکه‌های عصبی مرسوم برای برآورد تخلخل در یکی از میدان‌های نفتی جنوب خاوری ایران

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه مهندسی معدن، دانشکده فنی، دانشگاه بین المللی امام خمینی، قزوین، ایران

2 گروه زمین شناسی، دانشکده علوم، دانشگاه بین المللی امام خمینی، قزوین، ایران

3 مدیریت طرح‌های اکتشافی، شرکت نفت فلات قاره ایران، تهران، ایران

چکیده

در صنعت نفت از هوش مصنوعی برای شناسایی روابط، بهینه‌سازی، برآورد و رده‌بندی تخلخل بهره‌گیری می‌شود. یکی از مهم‌ترین مراحل ارزیابی پارامترهای پتروفیزیکی مخزن، شناسایی ویژگی‌های تخلخل است. هدف اصلی این پژوهش مقایسه درستی و تعمیم‌پذیری سه شبکه عصبی چند لایه پیش‌خور (MLFN)، شبکه تابع شعاع مبنا (RBFN) و شبکه عصبی احتمالی (PNN) برای برآورد تخلخل با بهره‌گیری از ویژگی‌های لرزه‌ای است. در این راستا، داده‌های زمین‌شناسی 7 حلقه چاه یک میدان نفتی فراساحلی هندیجان در شمال باختری حوضه خلیج فارس مورد ارزیابی قرارگرفت. امپدانس صوتی با بهره‌گیری از روش وارونگی مبتنی بر مدل برآورد شد و سپس شبکه‌های عصبی یاد شده با بهره‌گیری از ویژگی‌های لرزه‌ای بهینه طراحی شده و با روش رگرسیون گام به گام مورد ارزیابی قرار گرفتند. سرانجام مشخص شد که مدل MLFN برای برآورد تخلخل خوب عمل نمی‌کند. PNN از بهترین دقت کارکرد در درون‌یابی تخلخل برخوردار است، اما تعمیم‌پذیری RBFN بهتر است.
 

کلیدواژه‌ها


عنوان مقاله [English]

Comparison of the Function of Conventional Neural Networks for Estimating Porosity in One of the Southeastern Iranian Oil Fields

نویسندگان [English]

  • Farshad Tofighi 1
  • Parviz Armani 2
  • Ali Chehrazi 3
  • Andisheh Alimoradi 1
1 Department of Mining, Faculty of Engineering, Imam Khomeini International University
2 Department of Geology, Faculty of Sciences, Imam Khomeini International University
3 Head of Exploration Project Management, Iranian Offshore Oil Company
چکیده [English]

In the oil industry, artificial intelligence is used to identify relationships, optimize, estimate and classify porosity. One of the most important steps in evaluating the petrophysical parameters of the reservoir is to identify the porosity properties. The main purpose of this study is to compare the accuracy and generalizability of three multilayer feed neural networks (MLFNs), radius base function networks (RBFNs) and probabilistic neural networks (PNNs) to estimate porosity using seismic properties. In this regard, geological data of 7 wells were evaluated from an offshore oil field in Hindijan in the northwest of the Persian Gulf basin. Acoustic impedance was estimated using model-based inversion method, and then the mentioned neural networks were designed using optimal seismic properties and evaluated by stepwise regression method. Finally, it became clear that the MLFN model did not work well for estimating porosity. PNN has the best performance accuracy in porosity interpolation, but RBFNꞌs generalizability is better.
 

کلیدواژه‌ها [English]

  • Seismic Inversion
  • porosity estimation
  • MLFN
  • RBFN
  • PNN
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