Porosity prediction using Fuzzy SVR and FCM SVR from well logs of an oil field in south of Iran

• Autorzy: Moosavi Nastaran , Bagheri Majid , Nabi-Bidhendi Majid , Heidari Reza

Identyfikatory

DOI

10.1007/s11600-022-00944-y

• Języki publikacji: EN

Abstrakty

EN

Identification of petrophysical parameters including porosity plays an important role to evaluate hydrocarbon reservoirs. A precise prediction of porosity in oil and gas reservoirs may prevent lots of costs before drilling operations. Porosity obtained from core analysis in laboratory is the most reliable one, while they are very expensive and not always accessible. Inappropriate or missing data in under-survey locations are a key challenge for reservoir engineers. In this paper, support vector regression (SVR) is used to estimate porosity in one of the oil fields in south of Iran. SVR creates models due to structural risk minimization methods which help us to produce models with better generalization and less risk of overfitting. Definitely, measured data are always contaminated with noise. One of the common methods to reduce noise and outliers in data is to process them before using them to train the algorithm; during processing, outliers and some noisy data can be suppressed from data, while it is not always easy to distinguish real data from noise. In this paper, we modified SVR to Fuzzy SVR and Fuzzy C Means (FCM) SVR, which are used to decrease effect of noise on model, and then by adding artificial noise including random noise and outliers to data we investigated how these two methods respond to presence of noise. The results show the presence of noise and outliers in data can alter the center locations and distribution of data points in clusters in FCM SVR. Similarly, it can change the variance of Gaussian membership function we used for Fuzzy SVR, but overall, the results show Fuzzy SVR model is notably more robust against noise compared to FCM SVR. Correlation coefficient (CC) calculated between model and core data decreased from 78 to 67% after noise added to data in FCM SVR model, however, calculated CC for Fuzzy SVR remained almost steady altering from 87 to 86%. Subsequently, calculated root mean square error (RMSE) between models and core data increased from 0.0376 to 0.03827 for Fuzzy SVR, while RMSE jumped from 0.0448 to 0.0517 for FCM SVR.

Słowa kluczowe

EN

structural risk minimization; support vector regression; fuzzy SVR; fuzzy C means SVR

PL

minimalizacja ryzyka strukturalnego; regresja wektorów nośnych

• Wydawca: Instytut Geofizyki PAN ; Springer
• Czasopismo: Acta Geophysica
• Rocznik: 2023
• Tom: Vol. 71, no. 2
• Strony: 769--782
• Opis fizyczny: Bibliogr. 33 poz.

Twórcy

autor

Moosavi Nastaran

• Department of Earth Sciences, Science and Research Branch, Islamic Azad University, Tehran, Iran

autor

Bagheri Majid

• Institute of Geophysics, University of Tehran, Tehran, Iran

autor

Nabi-Bidhendi Majid

• Institute of Geophysics, University of Tehran, Tehran, Iran

autor

Heidari Reza

• Department of Earth Sciences, Science and Research Branch, Islamic Azad University, Tehran, Iran

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).