Preferred Language
Articles
/
IRems44BVTCNdQwCVldP
Permeability Prediction and Facies Distribution for Yamama Reservoir in Faihaa Oil Field: Role of Machine Learning and Cluster Analysis Approach
...Show More Authors

Empirical and statistical methodologies have been established to acquire accurate permeability identification and reservoir characterization, based on the rock type and reservoir performance. The identification of rock facies is usually done by either using core analysis to visually interpret lithofacies or indirectly based on well-log data. The use of well-log data for traditional facies prediction is characterized by uncertainties and can be time-consuming, particularly when working with large datasets. Thus, Machine Learning can be used to predict patterns more efficiently when applied to large data. Taking into account the electrofacies distribution, this work was conducted to predict permeability for the four wells, FH1, FH2, FH3, and FH19 from the Yamama reservoir in the Faihaa Oil Field, southern Iraq. The framework includes: calculating permeability for uncored wells using the classical method and FZI method. Topological mapping of input space into clusters is achieved using the self-organizing map (SOM), as an unsupervised machine-learning technique. By leveraging data obtained from the four wells, the SOM is effectively employed to forecast the count of electrofacies present within the reservoir. According to the findings, the permeability calculated using the classical method that relies exclusively on porosity is not close enough to the actual values because of the heterogeneity of carbonate reservoirs. Using the FZI method, in contrast, displays more real values and offers the best correlation coefficient. Then, the SOM model and cluster analysis reveal the existence of five distinct groups.

Scopus Crossref
View Publication
Publication Date
Sat Jan 01 2022
Journal Name
Indonesian Journal Of Electrical Engineering And Computer Science (ijeecs)
A new smart approach of an efficient energy consumption management by using a machine-learning technique
...Show More Authors

Many consumers of electric power have excesses in their electric power consumptions that exceed the permissible limit by the electrical power distribution stations, and then we proposed a validation approach that works intelligently by applying machine learning (ML) technology to teach electrical consumers how to properly consume without wasting energy expended. The validation approach is one of a large combination of intelligent processes related to energy consumption which is called the efficient energy consumption management (EECM) approaches, and it connected with the internet of things (IoT) technology to be linked to Google Firebase Cloud where a utility center used to check whether the consumption of the efficient energy is s

... Show More
Crossref
Publication Date
Thu Jun 29 2023
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Determination of Reservoir Hydraulic Flow Units and Permeability Estimation Using Flow Zone Indicator Method
...Show More Authors

   Reservoir characterization plays a crucial role in comprehending the distribution of formation properties and fluids within heterogeneous reservoirs. This knowledge is instrumental in constructing an accurate three-dimensional model of the reservoir, facilitating predictions regarding porosity, permeability, and fluid flow distribution. Among the various methods employed for reservoir characterization, the hydraulic flow unit stands out as a widely adopted approach. By effectively subdividing the reservoir into distinct zones, each characterized by unique petrophysical and geological properties, hydraulic flow units enable comprehensive reservoir analysis. The concept of the flow unit is closely tied to the flow zone indicator, a cr

... Show More
View Publication Preview PDF
Crossref
Publication Date
Sun Jan 22 2023
Journal Name
Mesopotamian Journal Of Big Data
Parallel Machine Learning Algorithms
...Show More Authors

 To expedite the learning process, a group of algorithms known as parallel machine learning algorithmscan be executed simultaneously on several computers or processors. As data grows in both size andcomplexity, and as businesses seek efficient ways to mine that data for insights, algorithms like thesewill become increasingly crucial. Data parallelism, model parallelism, and hybrid techniques are justsome of the methods described in this article for speeding up machine learning algorithms. We alsocover the benefits and threats associated with parallel machine learning, such as data splitting,communication, and scalability. We compare how well various methods perform on a variety ofmachine learning tasks and datasets, and we talk abo

... Show More
View Publication
Scopus (23)
Crossref (15)
Scopus Crossref
Publication Date
Tue Dec 05 2023
Journal Name
Baghdad Science Journal
An Observation and Analysis the role of Convolutional Neural Network towards Lung Cancer Prediction
...Show More Authors

Lung cancer is one of the most serious and prevalent diseases, causing many deaths each year. Though CT scan images are mostly used in the diagnosis of cancer, the assessment of scans is an error-prone and time-consuming task. Machine learning and AI-based models can identify and classify types of lung cancer quite accurately, which helps in the early-stage detection of lung cancer that can increase the survival rate. In this paper, Convolutional Neural Network is used to classify Adenocarcinoma, squamous cell carcinoma and normal case CT scan images from the Chest CT Scan Images Dataset using different combinations of hidden layers and parameters in CNN models. The proposed model was trained on 1000 CT Scan Images of cancerous and non-c

... Show More
View Publication Preview PDF
Scopus (5)
Crossref (3)
Scopus Crossref
Publication Date
Sun Mar 03 2024
Journal Name
Mesopotamian Journal Of Cybersecurity
Using Information Technology for Comprehensive Analysis and Prediction in Forensic Evidence
...Show More Authors

With the escalation of cybercriminal activities, the demand for forensic investigations into these crimeshas grown significantly. However, the concept of systematic pre-preparation for potential forensicexaminations during the software design phase, known as forensic readiness, has only recently gainedattention. Against the backdrop of surging urban crime rates, this study aims to conduct a rigorous andprecise analysis and forecast of crime rates in Los Angeles, employing advanced Artificial Intelligence(AI) technologies. This research amalgamates diverse datasets encompassing crime history, varioussocio-economic indicators, and geographical locations to attain a comprehensive understanding of howcrimes manifest within the city. Lev

... Show More
View Publication
Scopus (17)
Crossref (9)
Scopus Crossref
Publication Date
Sat Sep 30 2017
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Spatial Data Analysis for Geostatistical Modeling of Petrophysical Properties for Mishrif Formaiton, Nasiriya Oil Field
...Show More Authors

Spatial data analysis is performed in order to remove the skewness, a measure of the asymmetry of the probablitiy distribution. It also improve the normality, a key concept of statistics from the concept of normal distribution “bell shape”, of the properties like improving the normality porosity, permeability and saturation which can be are visualized by using histograms. Three steps of spatial analysis are involved here; exploratory data analysis, variogram analysis and finally distributing the properties by using geostatistical algorithms for the properties. Mishrif Formation (unit MB1) in Nasiriya Oil Field was chosen to analyze and model the data for the first eight wells. The field is an anticline structure with northwest- south

... Show More
View Publication Preview PDF
Publication Date
Mon Jun 01 2020
Journal Name
Journal Of Engineering
An An Accurate Estimation of Shear Wave Velocity Using Well Logging Data for Khasib Carbonate Reservoir - Amara Oil Field
...Show More Authors

   

Shear and compressional wave velocities, coupled with other petrophysical data, are vital in determining the dynamic modules magnitude in geomechanical studies and hydrocarbon reservoir characterization. But, due to field practices and high running cost, shear wave velocity may not available in all wells. In this paper, a statistical multivariate regression method is presented to predict the shear wave velocity for Khasib formation - Amara oil fields located in South- East of Iraq using well log compressional wave velocity, neutron porosity and density. The accuracy of the proposed correlation have been compared to other correlations. The results show that, the presented model provides accurate

... Show More
View Publication Preview PDF
Crossref
Publication Date
Sat Dec 01 2012
Journal Name
Journal Of Economics And Administrative Sciences
Robust Estimations of Cluster Analysis: Practical Application in Administrative and Financial Corruption
...Show More Authors

Cluster analysis (clustering) is mainly concerned with dividing a number of data elements into clusters. The paper applies this method to create a gathering of symmetrical government agencies with the aim to classify them and understand how far they are close to each other in terms of administrative and financial corruption by means of five variables representing the prevalent administrative and financial corruption in the state institutions. Cluster analysis has been applied to each of these variables to understand the extent to which these agencies are close to other in each of the cases related to the administrative and financial corruption.           

... Show More
View Publication Preview PDF
Crossref
Publication Date
Tue Jan 01 2019
Journal Name
Spe Europec Featured At 81st Eage Conference And Exhibition
Development of Artificial Neural Networks and Multiple Regression Analysis for Estimating of Formation Permeability
...Show More Authors

View Publication
Scopus (2)
Crossref (1)
Scopus Crossref
Publication Date
Thu Sep 01 2022
Journal Name
Iraqi Journal Of Computers, Communications, Control And Systems Engineering
A Framework for Predicting Airfare Prices Using Machine Learning
...Show More Authors

Many academics have concentrated on applying machine learning to retrieve information from databases to enable researchers to perform better. A difficult issue in prediction models is the selection of practical strategies that yield satisfactory forecast accuracy. Traditional software testing techniques have been extended to testing machine learning systems; however, they are insufficient for the latter because of the diversity of problems that machine learning systems create. Hence, the proposed methodologies were used to predict flight prices. A variety of artificial intelligence algorithms are used to attain the required, such as Bayesian modeling techniques such as Stochastic Gradient Descent (SGD), Adaptive boosting (ADA), Decision Tre

... Show More
View Publication Preview PDF