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Artificial neural network and response surface methodology for modeling oil content in produced water from an Iraqi oil field
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ABSTRACT<p>The majority of the environmental outputs from gas refineries are oily wastewater. This research reveals a novel combination of response surface methodology and artificial neural network to optimize and model oil content concentration in the oily wastewater. Response surface methodology based on central composite design shows a highly significant linear model with P value &lt;0.0001 and determination coefficient R2 equal to 0.747, R adjusted was 0.706, and R predicted 0.643. In addition from analysis of variance flow highly effective parameters from other and optimization results verification revealed minimum oily content with 8.5 ± 0.7 ppm when initial oil content 991 ppm, temperature 46.4 °C, pressure 21 Mpa, and flowrate 27,000 m3/day which is nearly closed to suggested oily content 8.5 ppm. An artificial neural network (ANN) technique was employed in this study to estimate the oil content in the treatment process. An artificial neural network model was remarkably accurate at simulating the process under investigation. A low mean squared error (MSE) and relative error (RE) equal to 1.55 × 10−7 and 2.5, respectively, were obtained during the training phase, whilst the testing results demonstrated a high coefficient of determination (R2) equal to 0.99.</p>
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Publication Date
Sun Mar 29 2020
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Using Different Methods to Predict Oil in Place in Mishrif Formation / Amara Oil Field
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The reserve estimation process is continuous during the life of the field due to risk and inaccuracy that are considered an endemic problem thereby must be studied. Furthermore, the truth and properly defined hydrocarbon content can be identified just only at the field depletion. As a result, reserve estimation challenge is a function of time and available data. Reserve estimation can be divided into five types: analogy, volumetric, decline curve analysis, material balance and reservoir simulation, each of them differs from another to the kind of data required. The choice of the suitable and appropriate method relies on reservoir maturity, heterogeneity in the reservoir and data acquisition required. In this research, three types of rese

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Publication Date
Thu May 18 2023
Journal Name
Journal Of Engineering
Spatial Prediction of Monthly Precipitation in Sulaimani Governorate using Artificial Neural Network Models
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ANN modeling is used here to predict missing monthly precipitation data in one station of the eight weather stations network in Sulaimani Governorate. Eight models were developed, one for each station as for prediction. The accuracy of prediction obtain is excellent with correlation coefficients between the predicted and the measured values of monthly precipitation ranged from (90% to 97.2%). The eight ANN models are found after many trials for each station and those with the highest correlation coefficient were selected. All the ANN models are found to have a hyperbolic tangent and identity activation functions for the hidden and output layers respectively, with learning rate of (0.4) and momentum term of (0.9), but with different data

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Publication Date
Sun Jan 01 2023
Journal Name
Journal Of Engineering
Artificial Neural Network Models to Predict the Cost and Time of Wastewater Projects
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Infrastructure, especially wastewater projects, plays an important role in the life of residential communities. Due to the increasing population growth, there is also a significant increase in residential and commercial facilities. This research aims to develop two models for predicting the cost and time of wastewater projects according to independent variables affecting them. These variables have been determined through a questionnaire distributed to 20 projects under construction in Al-Kut City/ Wasit Governorate/Iraq. The researcher used artificial neural network technology to develop the models. The results showed that the coefficient of correlation R between actual and predicted values were 99.4% and 99 %, MAPE was

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Publication Date
Wed Feb 01 2023
Journal Name
Journal Of Engineering
Vertical Stress Prediction for Zubair Oil Field/ Case Study
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Predicting vertical stress was indeed useful for controlling geomechanical issues since it allowed for the computation of pore pressure for the formation and the classification of fault regimes. This study provides an in-depth observation of vertical stress prediction utilizing numerous approaches using the Techlog 2015 software. Gardner's method results in incorrect vertical stress values with a problem that this method doesn't start from the surface and instead relies only on sound log data. Whereas the Amoco, Wendt non-acoustic, Traugott, average technique simply needed density log as input and used a straight line as the observed density, this was incorrect for vertical computing stress. The results of these methods

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Publication Date
Sat Dec 01 2012
Journal Name
Journal Of Engineering
Development an Anomaly Network Intrusion Detection System Using Neural Network
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Most intrusion detection systems are signature based that work similar to anti-virus but they are unable to detect the zero-day attacks. The importance of the anomaly based IDS has raised because of its ability to deal with the unknown attacks. However smart attacks are appeared to compromise the detection ability of the anomaly based IDS. By considering these weak points the proposed
system is developed to overcome them. The proposed system is a development to the well-known payload anomaly detector (PAYL). By
combining two stages with the PAYL detector, it gives good detection ability and acceptable ratio of false positive. The proposed system improve the models recognition ability in the PAYL detector, for a filtered unencrypt

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Publication Date
Mon Oct 30 2023
Journal Name
Iraqi Journal Of Science
Biostratigraphy of Hartha Formation from Selected Wells in Nasiriyah Oil Field, Southern Iraq
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     The Hartha Formation has been investigated from a biostratigraphic view in three subsurface sections in the Nasiriyah Oil field, wells Ns1, Ns3, and Ns4, South of Iraq. Hartha Formation is composed of limestone and has various areas of intense dolomitization alternating with marly limestone. The formation ranges in thickness from 126 to 182 meters. Thirteen large and small benthic foraminifer species and genera are identified from Hartha Formation. Based on the large benthic foraminifer's assemblage, one distinct biozone was recognized after an examination of the paleontological datum in the investigated area showed that the studied wells contained a diversity of foraminiferal species, the larger foraminifers biozone was propose

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Publication Date
Thu Jun 30 2022
Journal Name
Iraqi Journal Of Science
3D Geological Modelling for Asmari Reservoir In Abu Ghirab Oil Field
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    Building a geological model is an essential and primary step for studying the reservoir’s hydrocarbon content and future performance. A three-dimensional geological model of the Asmari reservoir in Abu- Ghirab oil field including structure, stratigraphy, and reservoir petrophysical properties, has been constructed in the present work. As to underlying Formations, striking slip faults developed at the flank and interlayer normal. Abu Ghirab oilfields are located on the eastern anticlinal band, which has steadily plunged southward. 3D seismic interpretation results are utilized to build the fault model for 43 faults of the Asmari Formation in Abu Ghirab Oilfield. A geographic facies model with six different rock facies types

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Publication Date
Tue Feb 28 2023
Journal Name
Iraqi Geological Journal
3D Geological Model for Zubair Reservoir in Abu-Amood Oil Field
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The Zubair reservoir in the Abu-Amood field is considered a shaly sand reservoir in the south of Iraq. The geological model is created for identifying the facies, distributing the petrophysical properties and estimating the volume of hydrocarbon in place. When the data processing by Interactive Petrophysics (IP) software is completed and estimated the permeability reservoir by using the hydraulic unit method then, three main steps are applied to build the geological model, begins with creating a structural, facies and property models. five zones the reservoirs were divided (three reservoir units and two cap rocks) depending on the variation of petrophysical properties (porosity and permeability) that results from IP software interpr

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Publication Date
Thu Jun 30 2022
Journal Name
Iraqi Journal Of Science
3D Geological Modelling for Asmari Reservoir In Abu Ghirab Oil Field
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    Building a geological model is an essential and primary step for studying the reservoir’s hydrocarbon content and future performance. A three-dimensional geological model of the Asmari reservoir in Abu- Ghirab oil field including structure, stratigraphy, and reservoir petrophysical properties, has been constructed in the present work.

As to underlying Formations, striking slip faults developed at the flank and interlayer normal. Abu Ghirab oilfields are located on the eastern anticlinal band, which has steadily plunged southward. 3D seismic interpretation results are utilized to build the fault model for 43 faults of the Asmari Formation in Abu Ghirab Oilfield. A geographic facies model with six different rock facies type

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Crossref (1)
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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
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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

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