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

Moosavi Nastaran, Bagheri Majid, Nabi-Bidhendi Majid, Heidari Reza

Acta Geophysica

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2023

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Vol. 71, no. 2

769--782

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.

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Fuzzy support vector regression for permeability estimation of petroleum reservoir using well logs

Moosavi Nastaran, Bagheri Majid, Majid Nabi-Bidhendi, Heidari Reza

Acta Geophysica

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2022

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Vol. 70, no 1

161--172

EN

In this paper we propose a three-step approach to predict permeability. First, by using Electrofacies Analysis (EA), data are classified into several clusters. We take advantage of EA to overcome abrupt changes of permeability which its unpredictability prevents a machine to be learned. EA is also helpful for wells that suffer from core data. Second, fuzzy membership functions are applied on data points in each Electrofacies Log (EL). Third, Support Vector Regression (SVR) is employed to predict permeability using fuzzy clustered data for areas with core missing data. To perform this process, we applied the proposed technique on four well sets of a gas field located in South of Iran; three wells devoted to training and the fourth remained for testing operation. Seven ELs derived using Multi Regression Graph-Based Clustering (MRGC) method. MRGC is able to estimate more appropriate number of clusters without prior knowledge compared to other three algorithms for our case-study area. Then, fuzzy membership functions applied to data. Thereafter, SVR applied to both fuzzy and not-fuzzy ELs. Consequently, the predicted permeability log for both fuzzy and not-fuzzy inputs correlated to real permeability (core data obtained from plugs in laboratory) in the test well. Finally, predicted permeability for each face merged together to make an estimated permeability for the whole test well. The results show that predicted permeability obtained from application of SVR on fuzzy data (FSVR) has a notably better correlation with core data for both clusters individually and the whole data compared to SVR.

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The improvement of the earthquake and microseismic Horizontal-to-Vertical Spectral Ratio (HVSR) in estimating site efects

Sarmadi Mohammad Ali, Heidari Reza, Mirzaei Noorbakhsh, Siahkoohi Hamid Reza

Acta Geophysica

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2021

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Vol. 69, no. 4

1177--1188

EN

The horizontal-to-vertical spectral ratio (HVSR) has been widely used to calculate near-surface site efects including funda mental resonance frequency and soil amplifcation. In this study, we investigated the features of HVSR in order to assess the reliability of this method. The main premise of Nakamura (1989; Nakamura, Y., 2000. Clear identifcation of fundamental idea of Nakamura’s technique and its applications. In: Proceedings of the 12th World Conference on Earthquake Engineer ing. Auckland, New Zealand.) HVSR method is to assume there are little to no amplifcation or de-amplifcation of motion recorded by the vertical component. In this study, we disregarded this assumption and used diferent proposed spectral ratios of earthquake accelerograms to examine and evaluate the accuracy of the HVSR technique. In addition, to increase the accuracy of the results, near-surface attenuation parameter Kappa (K), as an important spectral decay parameter which has infuence on the site efects, has been incorporated in proposed spectral ratios. The efciency of proposed spectral ratios was evaluated using 3948 accelerograms (Includes surface and borehole stations) of 496 earthquakes from 18 KiK-net accelerometery stations of the two regions of Japan with moment magnitudes ranging from 3 to 5. The two selected areas have diferent geological characteristics and diferent site efects parameters. According to the results, The HVSR method is under-determined at stations where the waves are mainly amplifed as they pass through the site. Moreover, in a single sta tion by calculating the amplifcation value on the vertical component, Site efects measurements obtained from surface and borehole data along with classical HVSR technique have a lower mean absolute error than those obtained by using HVSR technique alone.

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Delineation of potential seismic sources using weighted K means cluster analysis and particle swarm optimization (PSO)

Sheikhhosseini Zohreh, Mirzaei Noorbakhsh, Heidari Reza, Monkaresi Hamed

Acta Geophysica

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2021

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Vol. 69, no. 6

2161--2172

EN

Potential seismic sources play an important role in seismic hazard analysis. Identification of seismic sources is generally carried out on the basis of expert judgments, and in most cases, different and controversial results are obtained when several experts are consulted. In fact, the method of source identification is probably an important cause of uncertainty in the seismic hazard analysis. The main objective of this research is to provide an algorithm which combines the weighted K-means clustering analysis and Particle Swarm Optimization in order to automatically identify global optimum clusters by analysing seismic event data. These clusters, together with seismotectonic information, can be used to determine seismic sources. Two validity indexes, Davies–Bouldin's measure and Chou–Su–Lai's measure (CS), are used to determine optimum number of clusters. Study area is located at the longitude of 46°–48° E and latitude of 34°–36° N that is considered as the most seismically active part of Zagros continental collision zone, which has experienced large and destructive earthquakes due to movements of Sahneh and Nahavand segments of Zagros Main Recent Fault. As a result, 7-cluster model which is identified on the basis of DB validity index seems to be suitable for the considered earthquake catalogue, despite some limitations in partitioning.