The investigation of machine learning techniques for addressing missing well-log data has garnered considerable interest recently, especially as the oil and gas sector pursues novel approaches to improve data interpretation and reservoir characterization. Conversely, for wells that have been in operation for several years, conventional measurement techniques frequently encounter challenges related to availability, including the lack of well-log data, cost considerations, and precision issues. This study's objective is to enhance reservoir characterization by automating well-log creation using machine-learning techniques. Among the methods are multi-resolution graph-based clustering and the similarity threshold method. By using cutting-edge machine learning techniques, our methodology shows a notable improvement in the precision and effectiveness of well-log predictions. Standard well logs from a reference well were used to train machine learning models. Additionally, conventional wireline logs were used as input to estimate facies for unclassified wells lacking core data. R-squared analysis and goodness-of-fit tests provide a numerical assessment of model performance, strengthening the validation process. The multi-resolution graph-based clustering and similarity threshold approaches have demonstrated notable results, achieving an accuracy of nearly 98%. Applying these techniques to data from eighteen wells produced precise results, demonstrating the effectiveness of our approach in enhancing the reliability and quality of well-log production.
Sewer system plays an essential task in urban cities by protecting public health and the environment. The operation, maintenance, and rehabilitation of this network have to be sustainable and scientifically. For this purpose, it is crucial to support operators, decision makers and municipalities with performance evaluation procedure that is based on operational factors. In this paper, serviceability and performance indicator (PI) principles are employed to propose methodology comprising two enhanced PI curves that can be used to evaluate the individual sewers depending on operational factors such as flowing velocity and wastewater level in the sewers. To test this methodology; a case study of al-Rusafa in Baghdad city is
... Show MoreSewer system plays an indispensable task in urban cities by protecting public health and the environment. The operation, maintenance, and rehabilitation of this network have to be in a sustainable and scientific manner. For this purpose, it is important to support operators, decision makers and municipalities with performance evaluation procedure that is based on operational factors. In this paper, serviceability and performance indicator (PI) principles are employed to propose methodology comprising two enhanced PI curves that can be used to evaluate the individual sewers depending on operational factors such as flowing velocity and wastewater level in the sewers. In order to test this methodology; a case study of al-Ru
... Show MoreIn this study, we investigate the behavior of the estimated spectral density function of stationary time series in the case of missing values, which are generated by the second order Autoregressive (AR (2)) model, when the error term for the AR(2) model has many of continuous distributions. The Classical and Lomb periodograms used to study the behavior of the estimated spectral density function by using the simulation.
The research aims to improve the performance of the Directorate of Maysan water by reconciling the objectives of the employees of the directorate with the objectives of the Directorate itself, as well as to identify the strengths and weaknesses in the performance of the Directorate (Leadership - Individuals - Knowledge - Operations - Financial) and presented to experts and arbitrators of specialized, and the researchers have relied on the case study methodology as a descriptive approach is comprehensive analysis, and draws on more than one approach, method and scientific design, has been interviewed a number of experts in the Directorate Maysan's water Identify the weaknesses and strengths of the Directorate, the research has rea
... Show MoreWaste is one of the most important problems affecting the city’s environment and its urban landscape, which results from the activities and activities of man and the natural environment. Its sources have varied between residential, commercial, industrial, medical and hazardous, and its spread in cities, on roads and on abandoned open lands, has led to significant negative effects and risks to human health and the environment.
Therefore, there were serious attempts to deal with waste and follow sequential steps that formed a waste management system such as (collection, sorting, transport, then treatment and disposal). Preventing and reducing waste, then recycling and recovering by composting or burning, and ending with bu
... Show MoreSuicidal ideation is one of the most severe mental health issues faced by people all over the world. There are various risk factors involved that can lead to suicide. The most common & critical risk factors among them are depression, anxiety, social isolation and hopelessness. Early detection of these risk factors can help in preventing or reducing the number of suicides. Online social networking platforms like Twitter, Redditt and Facebook are becoming a new way for the people to express themselves freely without worrying about social stigma. This paper presents a methodology and experimentation using social media as a tool to analyse the suicidal ideation in a better way, thus helping in preventing the chances of being the victim o
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