Machine learning has a significant advantage for many difficulties in the oil and gas industry, especially when it comes to resolving complex challenges in reservoir characterization. Permeability is one of the most difficult petrophysical parameters to predict using conventional logging techniques. Clarifications of the work flow methodology are presented alongside comprehensive models in this study. The purpose of this study is to provide a more robust technique for predicting permeability; previous studies on the Bazirgan field have attempted to do so, but their estimates have been vague, and the methods they give are obsolete and do not make any concessions to the real or rigid in order to solve the permeability computation. To

The Cenomanian – Turronian sedimentary succession in the south Iraq oil fields, including Ahmadi, Rumaila, Mishrif and Khasib formations have undergone into high-resolution reservoir-scale genetic sequence stratigraphic analysis. Some oil-wells from Majnoon and West-Qurna oil fields were selected as a representative case for the regional sequence stratigraphic analysis. The south Iraqi Albian – Cenomanian – Turronian succession of 2nd-order depositional super-sequence has been analyzed based on the Arabian Plate chronosequence stratigraphic context, properly distinguished by three main chrono-markers (The maximum flooding surface, MFS-K100 of the upper shale member of Nahr Umr Formation, MFS-K140 of the upper Mishrif carbonate

The paper generates a geological model of a giant Middle East oil reservoir, the model constructed based on the field data of 161 wells. The main aim of the paper was to recognize the value of the reservoir to investigate the feasibility of working on the reservoir modeling prior to the final decision of the investment for further development of this oilfield. Well log, deviation survey, 2D/3D interpreted seismic structural maps, facies, and core test were utilized to construct the developed geological model based on comprehensive interpretation and correlation processes using the PETREL platform. The geological model mainly aims to estimate stock-tank oil initially in place of the reservoir. In addition, three scenarios were applie

    This research includes the use of an artificial intelligence algorithm, which is one of the algorithms of biological systems which is the algorithm of genetic regulatory networks (GRNs), which is a dynamic system for a group of variables representing space within time. To construct this biological system, we use (ODEs) and to analyze the stationarity of the model we use Euler's method. And through the factors that affect the process of gene expression in terms of inhibition and activation of the transcription process on DNA, we will use TF transcription factors. The current research aims to use the latest methods of the artificial intelligence algorithm. To apply Gene Regulation Networks (GRNs), we used a progr

   This research provides a novel technique for using metal organic frameworks (HKUST-1) as a gas storage system for liquefied petroleum gas (LPG) in Iraqi vehicles to avoid the drawbacks of the currently employed method of LPG gas storage. A low-cost adsorbent called HKUST-1 was prepared and characterized in this research to investigate its ability for propane storage at different temperatures (25, 30, 35, and 40 ^oC) and pressures of (1-7) bar. HKUST-1 was made using a hydrothermal method and characterized using powder X-ray diffraction, BET surface area, scanning electron microscopic (SEM), and Fourier Transforms infrared spectroscopy (FTIR). The HKUST-1 was produced using a hydrothermal technique and possesses a high crys

A batch and flow injection (FI) spectrophotometric methods are described for the determination of barbituric acid in aqueous and urine samples. The method is based on the oxidative coupling reaction of barbituric acid with 4-aminoantipyrine and potassium iodate to form purple water soluble stable product at λ 510 nm. Good linearity for both methods was obtained ranging from 2 to 60 μg mL−1, 5–100 μg mL−1 for batch and FI techniques, respectively. The limit of detection (signal/noise = 3) of 0.45 μg mL−1 for batch method and 0.48 μg mL−1 for FI analysis was obtained. The proposed methods were applied successfully for the determination of barbituric acid in tap water, river water, and urine samples with good recoveries of 99.92