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Najmeh Jafarzadeh

Bio: Najmeh Jafarzadeh is an academic researcher from University of Tehran. The author has contributed to research in topics: Sedimentary depositional environment & Reservoir modeling. The author has an hindex of 1, co-authored 2 publications receiving 8 citations.

Papers
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Journal ArticleDOI
TL;DR: In this article, a 3D model of reservoir facies was obtained in each reservoir zone by using variogram analysis and sequential indicator simulation (SIS), and the results from this stage in combination with identified petrofacies were used for the construction of a reservoir static model.

20 citations

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TL;DR: The role of clay minerals in the diagenesis of the sandstones with increasing burial depth was revealed by SEM and XRD analysis in this article, where the core-based porosity and permeability data in relation to the sedimentary and diagenetic processes influencing each lithofacies.

3 citations


Cited by
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Journal ArticleDOI
TL;DR: In this paper, a comprehensive petrophysical evaluation in an appraisal well of the field resulted in interpretation of lithology and main reservoir quality parameters (porosity and permeability) using conventional and advanced magnetic resonance (NMR) logs.

23 citations

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TL;DR: In this article, the NMR-T2 distribution diagrams were inverted to the synthetic capillary pressure and relative permeability curves and the results were compared to the laboratory derived MICP and Kr curves and a satisfactory agreement was achieved between them.

20 citations

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TL;DR: Zhang et al. as mentioned in this paper investigated the lithological, sedimentological and geochemical characteristics of Lower Jurassic strata (Ziliujing Formation) in the Sichuan Basin to identify the lithofacies and understand the depositional environments and differences in organic matter accumulation of these nonmarine fine-grained sediments.

12 citations

Journal ArticleDOI
TL;DR: Li et al. as mentioned in this paper proposed a framework of active learning and semi-supervised learning for lithology identification based on improved naive Bayes (ALSLINB) for mining logging data.
Abstract: Lithology identification is the basis of energy exploration and reservoir evaluation, intelligent and accurate identification of underground lithology is a key issue. The establishment of a machine learning lithology identification model using logging data is a hot research direction in recent years. However, the logging data has a high degree of non-linearity and multi-response characteristics, and there are insufficient numbers of labeled samples in the training data set. These will eventually affect the modeling accuracy and may cause over-fitting. Therefore, a framework of active learning and semi-supervised learning for lithology identification based on improved naive Bayes (ALSLINB) is proposed. The contributions are fourfold: (i) The Gaussian mixture model (GMM) based on the EM algorithm is used to estimate the probability density of the log data, which fits the probability distribution of the nonlinear multi-response log data. (ii) A framework combining active learning (AL) and semi-supervised learning is proposed for the expansion of labeled samples in the training data set. (iii) The application of pseudo-labeling detection technology can effectively improve the authenticity of pseudo-label samples. (iv) Different from the general deterministic lithology identification method, the result of the ALSLINB algorithm corresponds to the probability score, which provides an auxiliary basis for the prediction result. Finally, the ALSLINB algorithm is applied to two different data sets for a large number of experiments and compared with the related baseline methods to verify its effectiveness and generalization ability. The result proves that the ALSLINB algorithm can complete the lithology recognition task well and has high accuracy and robustness, which provides a new direction for intelligent lithology identification.

12 citations

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TL;DR: In this paper, an improved approach for application of Geomechanical Units (GMUs) classification in reducing the uncertainty of wellbore stability models was proposed, and the optimal drilling direction and safe mud window were modeled in the Iranian Offshore oilfield, Persian Gulf basin.

9 citations