Preferred Language
Articles
/
7hiURpcBVTCNdQwCM5ZM
Reservoir permeability prediction based artificial intelligence techniques
...Show More Authors

   Predicting permeability is a cornerstone of petroleum reservoir engineering, playing a vital role in optimizing hydrocarbon recovery strategies. This paper explores the application of neural networks to predict permeability in oil reservoirs, underscoring their growing importance in addressing traditional prediction challenges. Conventional techniques often struggle with the complexities of subsurface conditions, making innovative approaches essential. Neural networks, with their ability to uncover complicated patterns within large datasets, emerge as a powerful alternative. The Quanti-Elan model was used in this study to combine several well logs for mineral volumes, porosity and water saturation estimation. This model goes beyond simply predicting lithology to provide a detailed quantification of primary minerals (e.g., calcite and dolomite) as well as secondary ones (e.g., shale and anhydrite). The results show important lithological contrast with the high-porosity layers correlating to possible reservoir areas. The richness of Quanti-Elan's interpretations goes beyond what log analysis alone can reveal. The methodology is described in-depth, discussing the approaches used to train neural networks (e.g., data processing, network architecture). A case study where output of neural network predictions of permeability in a particular oil well are compared with core measurements. The results indicate an exceptional closeness between predicted and actual values, further emphasizing the power of this approach. An extrapolated neural network model using lithology (dolomite and limestone) and porosity as input emphasizes the close match between predicted vs. observed carbonate reservoir permeability. This case study demonstrated the ability of neural networks to accurately characterize and predict permeability in complex carbonate systems. Therefore, the results confirmed that neural networks are a reliable and transformative technology tool for oil reservoirs management, which can help to make future predictive methodologies more efficient hydrocarbon recovery operations.

Crossref
View Publication
Publication Date
Mon Jul 22 2019
Journal Name
Journal Of Petroleum Exploration And Production Technology
Flow units and rock type for reservoir characterization in carbonate reservoir: case study, south of Iraq
...Show More Authors
Abstract<p>The current work is focused on the rock typing and flow unit classification for reservoir characterization in carbonate reservoir, a Yamama Reservoir in south of Iraq (Ratawi Field) has been selected, and the study is depending on the logs and cores data from five wells which penetrate Yamama formation. Yamama Reservoir was divided into twenty flow units and rock types, depending on the Microfacies and Electrofacies Character, the well logs pattern, Porosity–Water saturation relationship, flow zone indicator (FZI) method, capillary pressure analysis, and Porosity–Permeability relationship (R35) and cluster analysis method. Four rock types and groups have been identified in the Yamama formation de</p> ... Show More
View Publication
Scopus (39)
Crossref (30)
Scopus Clarivate Crossref
Publication Date
Wed May 31 2023
Journal Name
International Journal Of Sustainable Development And Planning
Prediction of Formal Transformations in City Structure (Kufa as a Model) Based on the Cellular Automation Model and Markov Chains
...Show More Authors

The research utilizes data produced by the Local Urban Management Directorate in Najaf and the imagery data from the Landsat 9 satellite, after being processed by the GIS tool. The research follows a descriptive and analytical approach; we integrated the Markov chain analysis and the cellular automation approach to predict transformations in city structure as a result of changes in land utilization. The research also aims to identify approaches to detect post-classification transformations in order to determine changes in land utilization. To predict the future land utilization in the city of Kufa, and to evaluate data accuracy, we used the Kappa Indicator to determine the potential applicability of the probability matrix that resulted from

... Show More
View Publication
Scopus (3)
Scopus Crossref
Publication Date
Sat Mar 31 2018
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Static Model of Zubair Reservoir in Luhais Oil Field
...Show More Authors

Static reservoir modeling is the interacting and analysis of the geological data to visualize the reservoir framework by three-dimensional model and distribute the static reservoir properties. The Petrel E&P software used to incorporate the data. The interpreted log data and core report used in distribution of petrophysical properties of porosity, water saturation and permeability for Zubair reservoir in Luhais oil field.

   The reservoir discretized to 274968 cells in increments of 300, 200 and 1 meter in the direction of X, Y, and Z respectively. The geostatistical approach used in the distribution of the properties of porosity and water saturation overall the reservoir units. The permeability has been calculated

... Show More
View Publication Preview PDF
Publication Date
Wed Mar 10 2021
Journal Name
Baghdad Science Journal
Epipelic Algae in Hemren Reservoir , Iraq
...Show More Authors

The study includes the epipelic algae in Hemren reservoir, for the period between Januarys to October 2000 .The samples were collected from three selected sites at north, middle and south of reservoir. A total of 96 taxa of epipelic algae were identified. The diatoms were the dominated by 82 taxa represented 85.4% of the total identified species, followed by blue-green algae (cyanophyta) of 6.3 taxa (6%), and then green algae (chlorophyta) of 5.2 taxa (5%). One species was recorded for each crysophyta, euglenophyta and pyrrophyta. The seasonal variation for the cell density showed two peaks during spring and autumn seasons. Few species were dominated during the most studied period such as Achnanthes minutissima, Navicula cryptocephala

... Show More
View Publication Preview PDF
Crossref
Publication Date
Sat Jan 01 2011
Journal Name
Journal Of Engineering
FILTRATION MODELING USING ARTIFICIAL NEURAL NETWORK (ANN)
...Show More Authors

In this research Artificial Neural Network (ANN) technique was applied to study the filtration process in water treatment. Eight models have been developed and tested using data from a pilot filtration plant, working under different process design criteria; influent turbidity, bed depth, grain size, filtration rate and running time (length of the filtration run), recording effluent turbidity and head losses. The ANN models were constructed for the prediction of different performance criteria in the filtration process: effluent turbidity, head losses and running time. The results indicate that it is quite possible to use artificial neural networks in predicting effluent turbidity, head losses and running time in the filtration process, wi

... Show More
View Publication Preview PDF
Crossref
Publication Date
Sat Dec 02 2023
Journal Name
Journal Of Engineering
Reservoir Sedimentation Assessment Using Geospatial Technology: A case Study of Dukan Reservoir, Sulaimani Governorate, Kurdistan Region, Iraq
...Show More Authors

The accumulation of sediment in reservoirs poses a major challenge that impacts the storage capacity, quality of water, and efficiency of hydroelectric power generation systems. Geospatial methods, including Geographic Information Systems (GIS) and Remote Sensing (RS), were used to assess Dukan Reservoir sediment quantities. Satellite and reservoir water level data from 2010 to 2022 were used for sedimentation assessment. The satellite data was used to analyze the water spread area, employing the Normalized Difference Water Index (NDWI) and Modified Normalized Difference Water Index (MNDWI) to enhance the water surface in the satellite imagery of Dukan Reservoir. The cone formula was employed to calculate the live storag

... Show More
View Publication Preview PDF
Crossref
Publication Date
Mon Aug 01 2022
Journal Name
Baghdad Science Journal
Optimized Artificial Neural network models to time series
...Show More Authors

        Artificial Neural networks (ANN) are powerful and effective tools in time-series applications. The first aim of this paper is to diagnose better and more efficient ANN models (Back Propagation, Radial Basis Function Neural networks (RBF), and Recurrent neural networks) in solving the linear and nonlinear time-series behavior. The second aim is dealing with finding accurate estimators as the convergence sometimes is stack in the local minima. It is one of the problems that can bias the test of the robustness of the ANN in time series forecasting. To determine the best or the optimal ANN models, forecast Skill (SS) employed to measure the efficiency of the performance of ANN models. The mean square error and

... Show More
View Publication Preview PDF
Scopus (24)
Crossref (12)
Scopus Clarivate Crossref
Publication Date
Mon Feb 04 2019
Journal Name
Journal Of The College Of Education For Women
The Musical Intelligence in kindergarten children
...Show More Authors

The childhood of the important stages in which the adopted child's personality, mental and physical, motor and configure , as well as gain experience , it is the basis on which the future life of the child , studies have unanimously competent in the fields of childhood verily the first five years of a child of the most important stages of life and the most fertile life . Because the stage Childhood its an active role as the foundation upon which to build human personality , it was necessary to create all leads to raise the level of intelligence of children , because intelligence is the first element in the organization of thinking makes the child able to do the activities characterized by mental qualities .
Indicated most of the scien

... Show More
View Publication Preview PDF
Publication Date
Fri Nov 01 2013
Journal Name
Radioelectronics And Communications Systems
Erratum to: “BER Performance Enhancement for Secure Wireless Communication Systems Based on DCSK-MIMO Techniques under Rayleigh Fading Channel”
...Show More Authors

There has been a growing interest in the use of chaotic techniques for enabling secure communication in recent years. This need has been motivated by the emergence of a number of wireless services which require the channel to provide low bit error rates (BER) along with information security. The aim of such activity is to steal or distort the information being conveyed. Optical Wireless Systems (basically Free Space Optic Systems, FSO) are no exception to this trend. Thus, there is an urgent necessity to design techniques that can secure privileged information against unauthorized eavesdroppers while simultaneously protecting information against channel-induced perturbations and errors. Conventional cryptographic techniques are not designed

... Show More
View Publication
Scopus Crossref
Publication Date
Sat May 01 2021
Journal Name
Materials Today: Proceedings
WITHDRAWN: Application of net pay for reservoir characterization in carbonate reservoir rock – Case study: South-eastern of Iraq
...Show More Authors

View Publication
Crossref (1)
Crossref