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 <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.
In this study, Iraqi bentonite taken from Trefawi area/ Al-Anbar region province/ Iraq, was activated to enhance its rheological and filtration properties and increase its quality by decreasing the non-clay minerals (impurities), in order to be used in Iraqi oil companies instead of commercial bentonite. Bentonite was characterized by X-ray diffraction (XRD), X-ray fluorescence (XRF) and particle size distribution (PSD) before and after activation to show the effects on its mineral and chemical properties. The rheological properties of bentonite were enhanced by using different weights (0.4, 0.5, 0.6, 0.7 and 0.8 gm) of sodium carbonate (Na₂CO₃), whereas the filtration properties were enhanced by using different weights (0.5
... Show MoreIn data mining, classification is a form of data analysis that can be used to extract models describing important data classes. Two of the well known algorithms used in data mining classification are Backpropagation Neural Network (BNN) and Naïve Bayesian (NB). This paper investigates the performance of these two classification methods using the Car Evaluation dataset. Two models were built for both algorithms and the results were compared. Our experimental results indicated that the BNN classifier yield higher accuracy as compared to the NB classifier but it is less efficient because it is time-consuming and difficult to analyze due to its black-box implementation.
This research represents a 3D seismic structural study for 602.62 Km2 of Dujaila
Oil Field which is located 55 Km Northwest of Mysan province and 20 Km Southwest
of Ali-AlSharki region within unstable Mesopotamian basin.
Synthetic traces are prepared by using available data of two wells (Du-1, Du-2), in
order to define and pick the reflectors. Two reflectors are picked that represent the top
and bottom of Mishrif Formation, in addition to five units within this Formation are
picked, they named Units 1, 2, 3, 4, and 5.
Time maps for the top and bottom of Mishrif reflectors are drawn to get the
structural picture, these maps show general dip of layers toward NE, and thus, there
are two enclosure domes in the midd
The extracted oil from the Chia seeds white and black were used in the manufacture of certain foods such as mayonnaise. The results of the sensory evaluation showed that the product was acceptable except for the flavor of white chia seed oil. The seeds were fully used in the manufacture of the nutella. The results of the sensory evaluation were encouraging the use of the extracted oil from the black chia seeds in the production of the nutella except the spread property. Chia seeds were also used in the manufacture of pudding. The results of the sensory evaluation showed an excellent and acceptable product of black chia seeds oil can be obtained, while the white seeds did not receive the acceptance in terms of color and flavor.
Yamama Formation is an important sequence in southern Iraq. Petrographic analysis was used to determine and analyze the microfacies and pore types. The diagenetic processes and the impacts on the petrophysical properties of the rocks were also identified. The petrographic identification was based on data of 250 thin sections of cutting and core samples from four wells that were supplied by the Iraqi Oil Exploration Company (O.E.C). The present study focuses on the depositional environment and the microfacies analysis of Yamama Formation. The results revealed several types of microfacies, including peloidal wackestone-packstone, algal wackestone-packstone, bioclastic wackestone-packstone, fo
... Show MoreThis research includes structure interpretation of the Yamama Formation (Lower Cretaceous) and the Naokelekan Formation (Jurassic) using 2D seismic reflection data of the Tuba oil field region, Basrah, southern Iraq. The two reflectors (Yamama and Naokelekan) were defined and picked as peak and tough depending on the 2D seismic reflection interpretation process, based on the synthetic seismogram and well log data. In order to obtain structural settings, these horizons were followed over all the regions. Two-way travel-time maps, depth maps, and velocity maps have been produced for top Yamama and top Naokelekan formations. The study concluded that certain longitudinal enclosures reflect anticlines in the east and west of the study ar
... Show MoreThe aim of this study is interpretation well logs to determine Petrophysical properties of tertiary reservoir in Khabaz oil field using IP software (V.3.5). The study consisted of seven wells which distributed in Khabaz oilfield. Tertiary reservoir composed from mainly several reservoir units. These units are : Jeribe, Unit (A), Unit (A'), Unit (B), Unit (BE), Unit (E),the Unit (B) considers best reservoir unit because it has good Petrophysical properties (low water saturation and high porous media ) with high existence of hydrocarbon in this unit. Several well logging tools such as Neutron, Density, and Sonic log were used to identify total porosity, secondary porosity, and effective porosity in tertiary reservoir. For
... Show MoreThe Hartha Formation is one of the important formations deposited during Late Campanian age.
The present study deals with four boreholes (EB-53, 54, 55 and 56) within the East Baghdad oil field to diagnoses the microfacies and interpret the depositional environments.
Six major microfacies were recognized in the succession of the Hartha Formation. Their characteristic grain types and depositional texture enabled the recognition of paleoenvironment. There are Orbitoides wackestone-packstone , Orbitoides - miliolid wackestone, Peloidal and Pellets - echinoderm wackestone to packstone, Peloidal wackestone to packstone, Pelletal wackestone to packstone, and Planktonic foraminifera wackestone-packstone.
Four assoc
... Show MoreThis study deals with establishing the depositional environment of the Fatha Formation through facies analysis. It also deals with dividing the formation into units based on the rhythmic nature. Data from selected shallow wells near Hit area and deep wells at East Baghdad Oil field are used. Five major lithofacies are recognized in this study, namely, greenish grey marl, limestone, gypsum (and/or anhydrite), halite and reddish brown mudstone (with occasional sandstone).The limestone lithofacies is divided into three microfacies: Gastropods bioclastic wackestone microfacies, Gastropods peloidal bioclastic packstone, and Foraminiferal packstone microfacies.The lithofacies of the Fatha are nested in a rhythmic pattern or what is known as sh
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