Precise forecasting of pore pressures is crucial for efficiently planning and drilling oil and gas wells. It reduces expenses and saves time while preventing drilling complications. Since direct measurement of pore pressure in wellbores is costly and time-intensive, the ability to estimate it using empirical or machine learning models is beneficial. The present study aims to predict pore pressure using artificial neural network. The building and testing of artificial neural network are based on the data from five oil fields and several formations. The artificial neural network model is built using a measured dataset consisting of 77 data points of Pore pressure obtained from the modular formation dynamics tester. The input variables are vertical depth, bulk density, and acoustic compressional wave velocity, with the activation function of tangent sigmoid. The average percent error, absolute average percent error, mean square error, root mean square error, and correlation coefficient (R2) were applied for evaluation. The results revealed that the best artificial neural network structure was (3-8-1), with average percent error, absolute average percent error, mean square error, root mean square error, and correlation coefficient R2 of -0.52, 1.01, 3994, 63.2, and 0.995, respectively. A C++ computer program is provided with a calculation sample to simplify the implementation of the proposed artificial neural network. The dependency degree of pore pressure on each input parameter is investigated, revealing the highest impact of depth on pore pressure prediction. Furthermore, to check the validity of the artificial neural network against the different datasets, the artificial neural network performance was compared with 84 new data points and showed an advantage over the existing models. The very good performance of artificial neural network for different types of oil reservoirs and formations reveals an insignificant effect of lithology on the prediction of pore pressure.
Microservice architecture offers many advantages, especially for business applications, due to its flexibility, expandability, and loosely coupled structure for ease of maintenance. However, there are several disadvantages that stem from the features of microservices, such as the fact that microservices are independent in nature can hinder meaningful communication and make data synchronization more challenging. This paper addresses the issues by proposing a containerized microservices in an asynchronous event-driven architecture. This architecture encloses microservices in containers and implements an event manager to keep track of all the events in an event log to reduce errors in the application. Experiment results show a decline in re
... Show MoreThe purpose of this study is to investigate the research on artificial intelligence algorithms in football, specifically in relation to player performance prediction and injury prevention. To accomplish this goal, scholarly resources including Google Scholar, ResearchGate, Springer, and Scopus were used to provide a systematic examination of research done during the last ten years (2015–2025). Through a systematic procedure that included data collection, study selection based on predetermined criteria, categorisation based on AI applications in football, and assessment of major research problems, trends, and prospects, almost fifty papers were found and analysed. Summarising AI applications in football for performance and injury p
... Show MoreThe most significant function in oil exploration is determining the reservoir facies, which are based mostly on the primary features of rocks. Porosity, water saturation, and shale volume as well as sonic log and Bulk density are the types of input data utilized in Interactive Petrophysics software to compute rock facies. These data are used to create 15 clusters and four groups of rock facies. Furthermore, the accurate matching between core and well-log data is established by the neural network technique. In the current study, to evaluate the applicability of the cluster analysis approach, the result of rock facies from 29 wells derived from cluster analysis were utilized to redistribute the petrophysical properties for six units of Mishri
... Show MoreMultilocus haplotype analysis of candidate variants with genome wide association studies (GWAS) data may provide evidence of association with disease, even when the individual loci themselves do not. Unfortunately, when a large number of candidate variants are investigated, identifying risk haplotypes can be very difficult. To meet the challenge, a number of approaches have been put forward in recent years. However, most of them are not directly linked to the disease-penetrances of haplotypes and thus may not be efficient. To fill this gap, we propose a mixture model-based approach for detecting risk haplotypes. Under the mixture model, haplotypes are clustered directly according to their estimated d
Removing hazardous organic pollutants, such as 4-nitrophenol (4-NP) and Congo red (CR) dyes from aqueous media and CO2 from the atmospheric medium remains a significant challenge. Herein, we report a facile in-situ synthetic approach for fabricating CuO-ZnO heterostructure photocatalysts through the surfactant-assisted co-precipitation method. The catalytic results demonstrate that the Cu1O-ZnO photocatalyst exhibits excellent activity under direct sunlight irradiation, owing to the heterostructure formation between the CuO and ZnO. The Cu1O-ZnO photocatalyst showed higher reaction rate constant (k) values of 0.20 min−1 for 4-NP and 0.09 min−1 for CR compared to previous reports. Additionally, efficient CO2 reduction was also achiev
... Show MoreThe problem of internal sulfate attack in concrete is widespread in Iraq and neighboring countries.This is because of the high sulfate content usually present in sand and gravel used in it. In the present study the total effective sulfate in concrete was used to calculate the optimum SO3 content. Regression models were developed based on linear regression analysis to predict the optimum SO3 content usually referred as (O.G.C) in concrete. The data is separated to 155 for the development of the models and 37 for checking the models. Eight models were built for 28-days age. Then a late age (greater than 28-days) model was developed based on the predicted optimum SO3 content of 28-days and late age. Eight developed models were built for all
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