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

doi:10.1007/s11600-022-00944-y

Cited by 8 publications

(5 citation statements)

References 23 publications

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“…To further verify the superiority of the selected method and the fairness of the experiment, this article selects the traditional 1DCNN 36 and SVR 37,38 models as comparative experiments. Aer 60 repeated experiments under different pretreatments and dimensionality reduction, the average output performance indicators of the test set are shown in Table 2.…”

Section: Analytical Methods Papermentioning

confidence: 99%

Research on transformer fault diagnosis based on an IWHO optimized MS1DCNN algorithm and LIF spectrum

Yan¹,

Chen²,

Kan³

et al. 2023

Anal. Methods

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Section: Analytical Methods Papermentioning

confidence: 99%

Research on transformer fault diagnosis based on an IWHO optimized MS1DCNN algorithm and LIF spectrum

Yan¹,

Chen²,

Kan³

et al. 2023

Anal. Methods

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“…Machine learning algorithms can analyze large amounts of data from various sources, such as well logs and seismic surveys, to identify patterns and make predictions(Iturrarán-Viveros and Parra 2014, Elkatatny, Tariq et al 2018). By training these algorithms on historical data sets, they can learn to recognize the relationships between different variables and accurately predict porosity in new reservoirs (Moosavi et al, 2022). This approach has been successfully applied in several carbonate reservoirs around the world, resulting in more accurate predictions of porosity and improved hydrocarbon recovery rates.…”

Section: Introductionmentioning

confidence: 99%

“…For example, the combination of support vector regression (SVR) and fuzzy Logic algorithms shows good performance in estimating the porosity of the reservoir. They employ fuzzy techniques to reduce the noise in datasets (Moosavi et al 2022). Numerous studies indicate combining clustering and prediction algorithms can lead to more accurate predictions and better insights into complex datasets (Ahmadi and Chen 2019).…”

Section: Introductionmentioning

confidence: 99%

Improved Porosity Estimation in Complex Carbonate Reservoirs Using Hybrid CRNN Deep Learning Model

Mehrabi,

Bagheri,

Bidhendi

et al. 2024

Preprint

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An accurate estimation of rock porosity is essential to determine the storage capacity of hydrocarbon reservoirs. As the most realistic technique, the porosity value can be measured directly for core samples. However, laboratory core analysis is costly and sometimes unfeasible, leading to developing other indirect estimation methods from geophysical well logs or seismic data. Besides, estimating porosity in carbonate reservoirs is often difficult due to inherent heterogeneity and complex geological environments. In this paper, we proposed a hybrid deep learning model that integrates the CNN and RNN architectures to predict the porosity in heterogeneous carbonate reservoirs based on well-log data. The novelty of this approach lies in the hybridization of CNN and RNN architectures, which allows the effective extraction and combining of relevant information from the well log data and an accurate estimate of porosity in challenging heterogeneous carbonate reservoirs. The ability of CRNNs to capture the complex spatial dependencies within these reservoirs can lead to more accurate porosity estimations and valuable insights into reservoir characterization. This hybrid deep model consists of several hidden layers, convolutional, pooling, recurrent, and dense layers. The dataset consists of well-log and core analysis from the Iranian carbonate oil field, which was used to build a predictive model. well-log data was used as model input and core data for model validation. We compare the performance of the CRNN model with the MLP model for predicting porosity values from well-log data. Results show the proposed model can accurately predict porosity values at new locations where only well-log data are available, and they outperform conventional neural network models in terms of accuracy and generalization. The correlation coefficient between the model and core data improves from 0.67 for the MLP model to 0.98 for the CRNN model.

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“…Fuzzy Logic Systems (FLSs) offer a non-traditional approach to perceptual modeling and have been successfully applied in the fields of control, information retrieval and system modeling, etc. The related singleton fuzzification works have been widely implemented to build predictive models of coal structures [15][16][17][18][19] and have been integrated into mainstream engineering software, such as Geolog, MATLAB and Python [20][21][22]. Compared to its type-1 fuzzy set (T1-FS) counterpart for rock parameters, the interval type-2 fuzzy logic system (IT2-FLS) in this study shows better performance in four areas, i.e., uncertainty simulation, rule base volume, control surface smoothness and adaptability.…”

Section: Introductionmentioning

confidence: 99%

Coal Structure Prediction Based on Type-2 Fuzzy Inference System for Multi-Attribute Fusion: A Case Study in South Hengling Block, Qinshui Basin, China

et al. 2023

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The accurate prediction of coal structure is important to guide the exploration and development of coal reservoirs. Most prediction models are interpreted for a single sensitive coal seam, and the selection of sensitive parameters is correlated with the coal structure, but they ignore the interactions between different attributes. Part of it introduces the concept of the geological strength index (GSI) of coal rocks in order to achieve a multi-element macroscopic description and quantitative characterization of coal structure; however, the determination of coal structure involves some uncertainties among the properties of coal, such as lithology, gas content and tectonic fracture, due to their complex nature. Fuzzy inference systems provide a knowledge discovery process to handle uncertainty. The study shows that a type-2 fuzzy inference system (T2-FIS) with multi-attribute fusion is used to effectively fuse pre-stack and post-stack seismic inversion reservoir parameters and azimuthal seismic attribute parameters in order to produce more accurate prediction results for the Hengling block in the Shanxi area. The fuzzy set rules generated in this paper can provide a more reliable prediction of coal structure in the GSI system. The proposed system has been tested on various datasets and the results show that it is capable of providing reliable and high-quality coal structure predictions.

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

Cited by 8 publications

References 23 publications

Research on transformer fault diagnosis based on an IWHO optimized MS1DCNN algorithm and LIF spectrum

Research on transformer fault diagnosis based on an IWHO optimized MS1DCNN algorithm and LIF spectrum

Improved Porosity Estimation in Complex Carbonate Reservoirs Using Hybrid CRNN Deep Learning Model

Coal Structure Prediction Based on Type-2 Fuzzy Inference System for Multi-Attribute Fusion: A Case Study in South Hengling Block, Qinshui Basin, China

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