Well placement optimization enhancement by implementation of similarity-based mating scheme in non-dominated sorting genetic algorithm-II

, Article 79th EAGE Conference and Exhibition 2017, 12 June 2017 through 15 June 2017 ; 2017 ; 9789462822177 (ISBN) Rostamian, A ; Sharif University of Technology

2017

Abstract

An important step in the early stages of an oilfield development is the accurate well placement (production and injection) which has a significant impact on productivity and profitability of the reservoir. Well placement optimization is a complicated task as it is a function of several contributing factors including reservoir heterogeneities and economical constraints. The complexity of this task increases when it is considered as a multi-objective problem rather than a single-objective one as it raises the evaluation time. In this work, three mating procedures, as part of multi-objective optimization algorithm utilized for this purpose, are examined in order to improve the algorithm's...

Test case prioritization using test case diversification and fault-proneness estimations

, Article Automated Software Engineering ; Volume 29, Issue 2 , 2022 ; 09288910 (ISSN) Mahdieh, M ; Mirian Hosseinabadi, S. H ; Mahdieh, M ; Sharif University of Technology

Springer 2022

Abstract

Regression testing activities greatly reduce the risk of faulty software release. However, the size of the test suites grows throughout the development process, resulting in time-consuming execution of the test suite and delayed feedback to the software development team. This has urged the need for approaches such as test case prioritization (TCP) and test-suite reduction to reach better results in case of limited resources. In this regard, proposing approaches that use auxiliary sources of data such as bug history can be interesting. We aim to propose an approach for TCP that takes into account test case coverage data, bug history, and test case diversification. To evaluate this approach we...

The development of a novel multi-objective optimization framework for non-vertical well placement based on a modified non-dominated sorting genetic algorithm-II

, Article Computational Geosciences ; Volume 23, Issue 5 , 2019 , Pages 1065-1085 ; 14200597 (ISSN) Rostamian, A ; Jamshidi, S ; Zirbes, E ; Sharif University of Technology

Springer International Publishing 2019

Abstract

A single-objective well placement problem is one of the classical optimization problems in oilfield development and has been studied for many years, by researchers worldwide. However, the necessity to face practical applications and handle insufficient data in a single-objective optimization leads to the introduction of a multi-objective optimization framework, which consequently allows an engineer to manage more information. In this study, for the very first time, a multi-objective well placement optimization framework, based on a Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is utilized with a similarity-based mating scheme. To represent the power of this mating procedure, it is...

Non-dominated ranked based genetic algorithm multi-objective well placement optimization

, Article 81st EAGE Conference and Exhibition 2019, 3 June 2019 through 6 June 2019 ; 2019 ; 9789462822894 (ISBN) Rostamian, A ; Jamshidi, S ; Kamari, M ; Sharif University of Technology

EAGE Publishing BV 2019

Abstract

In the early stages of an oilfield development finding the optimum location for injection and production well is a highly crucial task. Locating these wells in the improper locations will damage the benefit of a company in million-dollars scale. Therefore, well placement should be studied accurately. Regarding to the complicated nature of the field development plan, considering well placement optimization as a single objective optimization problem could not properly satisfy all other objectives which define along with a field development plan, therefore recently, the application of multi-objective well placement optimization is introduced. In this work, for the first time Non-Dominated Rank...

Evaluation of different machine learning frameworks to predict CNL-FDC-PEF logs via hyperparameters optimization and feature selection

, Article Journal of Petroleum Science and Engineering ; Volume 208 , 2022 ; 09204105 (ISSN) Rostamian, A ; Heidaryan, E ; Ostadhassan, M ; Sharif University of Technology

Elsevier B.V 2022

Abstract

Although being expensive and time-consuming, petroleum industry still is highly reliant on well logging for data acquisition. However, with advancements in data science and AI, methods are being sought to reduce such dependency. In this study, several important well logs, CNL, FDC and PEF from ten wells are predicted based on ML models such as multilinear regression, DNN, DT, RT, GBoost, k-NN, and XGBoost. Before applying these models, depth matching, bad hole correction, de-spiking, and preprocessing of the data, including normalization, are carried out. Three statistical metrics, R2, RMSE, and PAP, are applied to evaluate the models' performance. Results showed that RF, k-NN, and XGBoost...

An artificial neural network model for the prediction of pressure filters performance and determination of optimum turbidity for coli-form and total bacteria removal

, Article Journal of Environmental Studies ; Volume 37, Issue 60 , 2012 , Pages 129-136 ; 10258620 (ISSN) Badalians Gholikandi, G ; Hazrati, H ; Rostamian, H ; Sharif University of Technology

2012

Abstract

In water treatment processes, because of complicated and nonlinear relationships between a number of physical, chemical and operational parameters, using analytical models with the ability to capture underlying relationships using examples of the desired input-output mapping is quite suitable. Artificial Neural Networks (ANN) has been increasingly applied in the area of environmental and water resources engineering. The main advantage of Artificial Neural Networks over physical-based models is that they are data-driven. The purpose of this research is to study the performance of pressure filters on turbidity removal from water according to several parameters such as turbidity, filtration...

Development of Multi-objective Optimization Framework for Non-vertical Well Placement

, M.Sc. Thesis Sharif University of Technology Rostamian, Aref (Author) ; Bazargan, Mohammad (Supervisor) ; Jamshidi, Saeed (Supervisor)

Abstract

An important step in the early stages of an oilfield development is the accurate identification of the location of production and injection wells which has a significant impact on productivity and profitability of the reservoir. Recently, operation companies in petroleum industry focus on production optimization, recovery enhancement and cost reduction; as a result, application of non-conventional wells (NCW) or non-vertical wells have dramatically increased. Well placement optimization is a complicated task as it is a function of several contributing factors including reservoir heterogeneities and economic constraints. The complexity of this task increases when it is considered as a...

Duopoly Equilibrium in Social Network Markets

, M.Sc. Thesis Sharif University of Technology Rostamian, Mahdieh (Author) ; Ramezanian, Rasol (Supervisor) ; Abam, Mohammad Ali (Supervisor)

Abstract

Economics have been thought about market, trade and money in many eras. During years, several issues regarding markets and making economic decisions have become more complicated, due to population growth and changes in market structure.we are going to discuss the classic model of Cournot in social networks in two competence companies which both produce identical products. Each individual considers a value for each product. lf friend it an individual buy a product then it can effect the individual's value of product. In this model, each company decides on quantity of its products and the selling price of products is calculated based on the amount of all productions. In Cournot model, at the...

Deep Zero-shot Learning

, M.Sc. Thesis Sharif University of Technology Shojaee, Mohsen (Author) ; Soleymani, Mahdieh (Supervisor)

Abstract

In some of object recognition problems, labeled data may not be available for all categories. Zero-shot learning utilizes auxiliary information (also called signatures) describing each category in order to find a classifier that can recognize samples from categories with no labeled instance. On the other hand, with recent advances made by deep neural networks in computer vision, a rich representation can be obtained from images that discriminates different categorizes and therefore obtaining a unsupervised information from images is made possible. However, in the previous works, little attention has been paid to using such unsupervised information for the task of zero-shot learning. In this...

Multi-Modal Distance Metric Learning

, M.Sc. Thesis Sharif University of Technology Roostaiyan, Mahdi (Author) ; Soleymani, Mahdieh (Supervisor)

Abstract

In many real-world applications, data contain multiple input channels (e.g., web pages include text, images and etc). In these cases, supervisory information may also be available in the form of distance constraints such as similar and dissimilar pairs from user feedbacks. Distance metric learning in these environments can be used for different goals such as retrieval and recommendation. In this research, we used from dual-wing harmoniums to combining text and image modals to a unified latent space when similar-dissimilar pairs are available. Euclidean distance of data represented in this latent space used as a distance metric. In this thesis, we extend the dual-wing harmoniums for...

Fabrication and Characterization of Thermoplastic Starch Based Nanocomposite for Bone Scaffold

, M.Sc. Thesis Sharif University of Technology Mahdieh, Zahra (Author) ; Bagheri, Reza (Supervisor)

Abstract

This project aimed to fabricate the bone scaffolds with applying thermoplastic starch-based nano-biocomposites. The starting materials for this scaffold are as follows: thermoplastic starch, ethylene vinyl alcohol as the polymer matrix and nanoforsterite as the ceramic reinforcing phase. Furthermore, vitamin E was used as antioxidant for preserving starch against thermo-mechanical degradations. Likewise, 3D pore structure was developed using azo-dicarbonamide and water in injection moulding process. With blending thermoplastic starch and ethylene vinyl alcohol, some thermoplastic starch’s properties including degradation rate and water absorption were modified. In addition, having...

Unsupervised Domain Adaptation via Representation Learning

, M.Sc. Thesis Sharif University of Technology Gheisary, Marzieh (Author) ; Soleymani, Mahdieh (Supervisor)

Abstract

The existing learning methods usually assume that training and test data follow the same distribution, while this is not always true. Thus, in many cases the performance of these learning methods on the test data will be severely degraded. We often have sufficient labeled training data from a source domain but wish to learn a classifier which performs well on a target domain with a different distribution and no labeled training data. In this thesis, we study the problem of unsupervised domain adaptation, where no labeled data in the target domain is available. We propose a framework which finds a new representation for both the source and the target domain in which the distance between these...

Deep Learning for Multimodal Data

, M.Sc. Thesis Sharif University of Technology Rastegar, Sarah (Author) ; Soleymani, Mahdieh (Supervisor)

Abstract

Recent advances in data recording has lead to different modalities like text, image, audio and video. Images are annotated and audio accompanies video. Because of distinct modality statistical properties, shallow methods have been unsuccessful in finding a shared representation which maintains the most information about different modalities. Recently, deep networks have been used for extracting high-level representations for multimodal data. In previous methods, for each modality, one modality-specific network was learned. Thus, high-level representations for different modalities were extracted. Since these high-level representations have less difference than raw modalities, a shared...

Deep Learning For Recommender Systems

, M.Sc. Thesis Sharif University of Technology Abbasi, Omid (Author) ; Soleimani, Mahdieh (Supervisor)

Abstract

Collaborative fltering (CF) is one of the best and widely employed approaches in Recommender systems (RS). This approach tries to fnd some latent features for users and items so it would predict user rates with these features. Early CF methods used matrix factorization to learn users and items latent features. But these methods face cold start as well as sparsity problem. Recent years methods employ side information along with rating matrix to learn users and items latent features. On the other hand, deep learning models show great potential for learning effective representations especially when auxiliary information is sparse. Due to this feature of deep learning, we use deep learning to...

Adversarial Networks for Sequence Generation

, M.Sc. Thesis Sharif University of Technology Montahaei, Ehsan (Author) ; Soleymani, Mahdieh (Supervisor)

Abstract

Lots of essential structures can be modeled as sequences and sequences can be utilized to model the structures like molecules, graphs and music notes. On the other hand, generating meaningful and new sequences is an important and practical problem in different applications. Natural language translation and drug discovery are examples of sequence generation problem. However, there are substantial challenges in sequence generation problem. Discrete spaces of the sequence and challenge of the proper objective function can be pointed out.On the other, the baseline methods suffer from issues like exposure bias between training and test time, and the ill-defined objective function. So, the...

Improving Sampling Efficiency of Probabilistic Graphical Models

, M.Sc. Thesis Sharif University of Technology Mahdieh, Mohsen (Author) ; Beigy, Hamid (Supervisor)

Abstract

Deep learning methods have become more popular in the past years. These methods use complex network architectures to model rich, hierarchical datasets. Although most of the research has been centered around Discriminative models, however, recently a lot of research is focused on Deep Generative Models. Two of the pioneering models in this field are Generative Adversarial Networks and Variational Auto-Encoders. In addition, knowing the structure of data helps models to search in a narrower hypothesis space. Most of the structure in datasets are models using Probabilistic Graphical Models. Using this structural information, one can achieve better parameter estimations. In the case of...

Improving Graph Construction for Semi-supervised Learning in Computer Vision Applications

, M.Sc. Thesis Sharif University of Technology Mahdieh, Mostafa (Author) ; Rabiee, Hamid Reza (Supervisor)

Abstract

Semi-supervised Learning (SSL) is an extremely useful approach in many applications where unlabeled data can be easily obtained. Graph based methods are among the most studied branches in SSL. Since neighborhood graph is a key component in these methods, we focus on methods of graph construction in this project. Graph construction methods based on Euclidean distance have the common problem of creating shortcut edges. Shortcut edges refer to the edges which connect two nearby points that are far apart on the manifold. Specifically, we show both in theory and practice that using geodesic distance for selecting and weighting edges results in more appropriate neighborhood graphs. We propose an...

Multi-label Classification by Considering Label Dependencies

, M.Sc. Thesis Sharif University of Technology Farahnak Ghazani, Fatemeh (Author) ; Soleymani, Mahdieh (Supervisor)

Abstract

In multi-label classification problems each instance can simultaneously have multiple labels. In these problems, in addition to the complexities of the input feature space we encounter the complexities of output label space. In the multi-label classification problems, there are dependencies between different labels that need to be considered. Since the dimensionality of the label space in real-world applications can be (very) high, most methods which explicitly model these dependencies are ineffective in practice and recently those methods that transform the label space into a latent space have received attention. A class of these methods which uses output space dimension reduction, first...

Adaptation for Evolving Domains

, M.Sc. Thesis Sharif University of Technology Bitarafan, Adeleh (Author) ; Soleymani Baghshah, Mahdieh (Supervisor)

Abstract

Until now many domain adaptation methods have been proposed. A major limitation of almost all of these methods is their assumption that all test data belong to a single stationary target distribution and a large amount of unlabeled data is available for modeling this target distribution. In fact, in many real world applications, such as classifying scene image with gradually changing lighting and spam email identification, data arrives sequentially and the data distribution is continuously evolving. In this thesis, we tackle the problem of adaptation to a continuously evolving target domain that has been recently introduced and propose the Evolving Domain Adaptation (EDA) method to classify...

Cancer Prediction Using cfDNA Methylation Patterns With Deep Learning Approach

, M.Sc. Thesis Sharif University of Technology Mahdavi, Fatemeh (Author) ; Soleymani Baghshah, Mahdieh (Supervisor)

Abstract

Liquid biopsy includes information about the progress of the tumor, the effectiveness of the treatment and the possibility of tumor metastasis. This type of biopsy obtains this information by doing diagnosis and enumerating genetic variations in cells and cell-free DNA (cfDNA). Only a small fraction of cfDNA which might be free circulation tumor DNA (ctDNA) fragments, has mutations and is usually identified by epigenetic variations. On the other hand, the use of liquid biopsy has decreased, and tumors in the final stages are often untreatable due to the low accuracy in prediction of cancer. In this research, the aim is to predict cancer using cfDNA methylation patterns. We obtain these...