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استفاده از رگرسیون بردار پشتیبان بر پایه کرنل گاوسی برای مدل‌سازی تخلخل مخزن در یکی از میادین نفتی ایران | ||
| فیزیک زمین و فضا | ||
| مقاله 2، دوره 47، شماره 3، آذر 1400، صفحه 421-432 اصل مقاله (949.01 K) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/jesphys.2021.315671.1007270 | ||
| نویسندگان | ||
| مهدی رفیعی1؛ مجید باقری* 2؛ مجید نبی‌بیدهندی3 | ||
| 1دانشجوی کارشناسی ارشد، گروه فیزیک زمین، مؤسسه ژئوفیزیک، دانشگاه تهران، تهران، ایران | ||
| 2استادیار، گروه فیزیک زمین، مؤسسه ژئوفیزیک، دانشگاه تهران، تهران، ایران | ||
| 3استاد، گروه فیزیک زمین، مؤسسه ژئوفیزیک، دانشگاه تهران، تهران، ایران | ||
| چکیده | ||
| تراوایی، تخلخل و رخساره‌‌‌های رسوبی فاکتور‌‌‌های اساسی مشخصه‌‌‌های مخزنی هستند. تخلخل نمایانگر توانایی سنگ در ذخیره سیالات است. رویکرد‌‌‌های زیادی برای رگرسیون‌‌‌های خطی/غیرخطی از جمله شبکه‌‌‌های عصبی در سال‌‌‌های اخیر بسیار موردتوجه بوده‌‌اند، شبکه عصبی پرسپترون چندلایه (MLP) یکی از این شبکه‌ها می‌‌باشد که توانایی خود را به اثبات رسانده است ولی هرکدام از این روش‌ها معایبی دارند. در این تحقیق روش ماشین بردار پشتیبان (SVM) به‌عنوان روش اصلی برای رگرسیون و تخمین تخلخل مخزن در یکی از مخازن هیدروکربنی به‌کار گرفته شده است. این روش با روش پرسپترون چندلایه مقایسه شده است و نتایج هرکدام موردبررسی قرار گرفته‌‌‌اند. برای این‌کار ابتدا هرکدام از ماشین‌‌‌های موردنظر برای تخمین تخلخل در محل چاه به‌کار گرفته شده‌‌اند و نتایج اولیه باهم مقایسه شدند. نتایج اولیه رگرسیون بردار پشتیبان توانایی بالاتری نسبت به پرسپترون چندلایه نشان داد. برای این‌کار بردار پشتیبان برپایه کرنل‌‌‌های مختلف مورد استفاده قرار گرفت که تابع کرنل گاوسی نتایج بهتری حاصل کرد و نهایتاً برای مدل‌سازی سه‌بعدی تخلخل به‌کار گرفته شد. برای تهیه یک نقشه سه‌بعدی به داده‌‌‌های لرزهای و نشانگرهای استخراج شده مناسب از روی آن نیاز می‌‌باشد. بررسی و مقایسه نتایج نشان داد که هردوی ماشین‌‌‌های پرسپترون چندلایه و بردار پشتیبان از توانایی بالایی برخوردار هستند ولی رگرسیون بردار پشتیبان با توجه به قدرت تخمین بالا نتایج بهتری حاصل کرد. | ||
| کلیدواژه‌ها | ||
| تخلخل؛ رگرسیون؛ شبکه عصبی پرسپترون چندلایه؛ ماشین بردار پشتیبان؛ نشانگرهای لرزهای؛ نگارهای چاه | ||
| عنوان مقاله [English] | ||
| Reservoir porosity modelling using support vector regression based on Gaussian kernel in an oil field of Iran | ||
| نویسندگان [English] | ||
| Mehdi Rafei1؛ Majid Bagheri2؛ Majid Nabi-Bidhendi3 | ||
| 1M.Sc. Student, Department of Earth Physics, Institute of Geophysics, University of Tehran, Tehran, Iran | ||
| 2Assistant Professor, Department of Earth Physics, Institute of Geophysics, University of Tehran, Tehran, Iran | ||
| 3Professor, Department of Earth Physics, Institute of Geophysics, University of Tehran, Tehran, Iran | ||
| چکیده [English] | ||
| Permeability, porosity and sedimentary facies are the main factors of reservoir characteristics. Porosity indicates the ability of a rock to store fluids. So far, many approaches including linear / nonlinear regressions have been developed to predict porosity. Neural networks have received a lot of attention in recent years, and various types of learning machines based on neural networks have been introduced. Multilayer perceptron neural network (MLP) is one of these networks that proven its ability, but each of these methods has disadvantages. In this research, the support vector machine (SVM) method has been used as the main method for regression and estimation of the reservoir porosity in one of the hydrocarbon reservoirs. This method has been compared with the multilayer perceptron method and the results of each have been investigated. The best way to get accurate values of physical properties of reservoir is to measure them directly in the laboratory. However, this method has disadvantages: high cost, time consuming, lack of access to the entire depth of the well. For these reasons, geologists extract core from a number of wells and from a specific range. Geologists generally use a statistical approach involving multiple linear or nonlinear regressions to relate reservoir characteristics to each other (eg, porosity and permeability). In these contexts, a linear or non-linear relationship is assumed between porosity and other reservoir characteristics. However, these techniques are insufficient for certain issues, such as heterogeneous reservoirs. Recently, geoscientists have used artificial intelligence (AI) methods, especially neural networks (NNs), to predict reservoir parameters. Neural networks have been widely used in various fields of science and engineering. To build a three-dimensional model of a reservoir, a thorough knowledge of permeability, porosity and sedimentary facies is required. Well logs and core information are local measurements that do not reflect the behavior of the reservoir as a whole. In addition, well information does not cover the entire field area, while 3D seismic information covers a larger area. Changes in lithology and fluids cause changes in amplitude, wavelet shape, coherence coefficient, and other seismic attributes. These attributes can provide information for building a repository model. The main purpose of this research is to analyze training machines developed by computer scientists to predict reservoir characteristics such as porosity in vertical and lateral directions with the help of well logs and seismic attributes. The aim is to achieve the following steps to estimate a reliable porosity model of the reservoir: Development of a multilayer perceptron (MLP) to estimate the porosity using well logs. Development of a support vector machine (SVM) to estimate the porosity using well logs. Comparing the proposed methods and choosing the best. Estimation of porosity based on seismic attributes using the selected algorithm. Making a three-dimensional model of the reservoir porosity based on the training machine. As it was expected, these computational intelligence approaches overcome the weakness of the standard regression techniques. Generally, the results show that the performances of Support Vector Machine outperform that Multilayer Perceptron neural networks. In addition, Support Vector Regression (SVR) is more robust, easier and quicker to train. Therefore, it could be concluded that the use of SVM technique will be valuable and powerful for geoscientists to model the reservoir properties. | ||
| کلیدواژه‌ها [English] | ||
| Porosity, Regression, Multilayer Perception Neural Network, Support Vector Machine, Seismic attributes, well logs | ||
| مراجع | ||
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