Carbonate matrix stimulation technology has progressed tremendously in the last decade through creative laboratory research and novel fluid advancements. Still, existing methods for optimizing the stimulation of wells in vast carbonate reservoirs are inadequate. Consequently, oil and gas wells are stimulated routinely to expand production and maximize recovery. Matrix acidizing is extensively used because of its low cost and ability to restore the original productivity of damaged wells and provide additional production capacity. The Ahdeb oil field lacks studies in matrix acidizing; therefore, this work provided new information on limestone acidizing in the Mishrif reservoir. Moreover, several reports have been issued on the difficulties encountered during the stimulation operation of the Ahdeb oil field, particularly for the development of the Mishrif reservoir. Since the new core flooding system is built to operate safely and straightforwardly. This study introduced the results of Matrix acidizing experiments, covering the most recent developments in linear core flooding. High-permeability flow pathways are created, and a longer and wider wormhole was generated at a high acid injection rate (6.67 cc/min). The acid efficiency curve yielded the lowest pore volume injected at the breakthrough of the PV_(bt-opt) is 2.73 and the v_(i-opt)=0.6 cm/min; thus, the optimum injection rate that results in an optimal possible wormhole and the least quantity of acid being used for this reservoir is 2.16 cc/min. This research evaluated the impact of matrix acidizing treatment on acoustic characteristics, which studies show are lacking or have never been investigated previously. Furthermore, in the assessment of geomechanical rock properties and elastic and petrophysical parameters before and after acid injection, one of the new concepts discovered during the lab experiment observation of the acoustic waveform before and after acid treatment for the tested rock sample is that the initial arrival time before acid treatment is 21.6 microseconds, with a delay of 31.2 microseconds attributed to the wormhole channel and mineral disintegration. CT-Scan applications in matrix acidizing were investigated in this research; additionally, a 3D view of plug samples was constructed to represent the wormhole extension via CT-processing software. A license of Stimpro Stimulation Software has been used to validate the experimental work to the field scale, making it the most comprehensive instrument for planning and monitoring matrix acid treatment and utilizing actual data to provide a far better knowledge of the well's reaction, with methods that represent the reality of what is happening in the reservoir before, during, and after matrix acid treatments, through the post-treatment skin factor which is the most often utilized statistic for analyzing stimulation treatments and relies on the geometry of the wormholed zone. The acid treatment evaluated for the well AD-12, primarily for the zone Mi4; matrix acid treatments can have their production behavior predicted or matched using the reservoir simulation and production analysis option, employing the numerical simulation license software Petrel (Schlumberger) and Rubis (KAPPA) to determine the efficacy of previous treatments and the economics associated with future treatments. The estimated oil gain volume and percentage for the Mi4 unit in Ad-12 using particularly skin value -3.97 computed from Stimpro software for real stimulation acid job, it is yield enhancement in production of oil gain volume 6154 barrels as well as 105% increase of gain percentage for three months after matrix acidizing.
In this study, gold nanoparticles were synthesized in a single step biosynthetic method using aqueous leaves extract of thymus vulgaris L. It acts as a reducing and capping agent. The characterizations of nanoparticles were carried out using UV-Visible spectra, X-ray diffraction (XRD) and FTIR. The surface plasmon resonance of the as-prepared gold nanoparticles (GNPs) showed the surface plasmon resonance centered at 550[Formula: see text]nm. The XRD pattern showed that the strong four intense peaks indicated the crystalline nature and the face centered cubic structure of the gold nanoparticles. The average crystallite size of the AuNPs was 14.93[Formula: see text]nm. Field emission scanning electron microscope (FESEM) was used to s
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The evolution in the field of Artificial Intelligent (AI) with its training algorithms make AI very important in different aspect of the life. The prediction problem of behavior of dynamical control system is one of the most important issue that the AI can be employed to solve it. In this paper, a Convolutional Multi-Spike Neural Network (CMSNN) is proposed as smart system to predict the response of nonlinear dynamical systems. The proposed structure mixed the advantages of Convolutional Neural Network (CNN) with Multi -Spike Neural Network (MSNN) to generate the smart structure. The CMSNN has the capability of training weights based on a proposed training algorithm. The simulation results demonstrated that the proposed
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The pancreatic ductal adenocarcinoma (PDAC), which represents over 90% of pancreatic cancer cases,
has the highest proliferative and metastatic rate in comparison to other pancreatic cancer compartments. This
study is designed to determine whether small nucleolar RNA, H/ACA box 64 (snoRNA64) is associated with
pancreatic cancer initiation and progression. Gene expression data from the Gene Expression Omnibus (GEO)
repository have shown that snoRNA64 expression is reduced in primary and metastatic pancreatic cancer as
compared to normal tissues based on statistical analysis of the in Silico analysis. Using qPCR techniques,
pancreatic cancer cell lines include PK-1, PK-8, PK-4, and Mia PaCa-2 with differ
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The precise classification of DNA sequences is pivotal in genomics, holding significant implications for personalized medicine. The stakes are particularly high when classifying key genetic markers such as BRAC, related to breast cancer susceptibility; BRAF, associated with various malignancies; and KRAS, a recognized oncogene. Conventional machine learning techniques often necessitate intricate feature engineering and may not capture the full spectrum of sequence dependencies. To ameliorate these limitations, this study employs an adapted UNet architecture, originally designed for biomedical image segmentation, to classify DNA sequences.The attention mechanism was also tested LONG WITH u-Net architecture to precisely classify DNA sequences
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Geographic Information Systems (GIS) are obtaining a significant role in handling strategic applications in which data are organized as records of multiple layers in a database. Furthermore, GIS provide multi-functions like data collection, analysis, and presentation. Geographic information systems have assured their competence in diverse fields of study via handling various problems for numerous applications. However, handling a large volume of data in the GIS remains an important issue. The biggest obstacle is designing a spatial decision-making framework focused on GIS that manages a broad range of specific data to achieve the right performance. It is very useful to support decision-makers by providing GIS-based decision support syste
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