Shear and compressional wave velocities, coupled with other petrophysical data, are vital in determining the dynamic modules magnitude in geomechanical studies and hydrocarbon reservoir characterization. But, due to field practices and high running cost, shear wave velocity may not available in all wells. In this paper, a statistical multivariate regression method is presented to predict the shear wave velocity for Khasib formation - Amara oil fields located in South- East of Iraq using well log compressional wave velocity, neutron porosity and density. The accuracy of the proposed correlation have been compared to other correlations. The results show that, the presented model provides accurate

Unconfined Compressive Strength is considered the most important parameter of rock strength properties affecting the rock failure criteria.  Various research have developed rock strength for specific lithology to estimate high-accuracy value without a core.  Previous analyses did not account for the formation's numerous lithologies and interbedded layers. The main aim of the present study is to select the suitable correlation to predict the UCS for hole depth of formation without separating the lithology. Furthermore, the second aim is to detect an adequate input parameter among set wireline to determine the UCS by using data of three wells along ten formations (Tanuma, Khasib, Mishrif, Rumaila, Ahmady, Maudud, Nahr Um

The data preprocessing step is an important step in web usage mining because of the nature of log data, which are heterogeneous, unstructured, and noisy. Given the scalability and efficiency of algorithms in pattern discovery, a preprocessing step must be applied. In this study, the sequential methodologies utilized in the preprocessing of data from web server logs, with an emphasis on sub-phases, such as session identification, user identification, and data cleansing, are comprehensively evaluated and meticulously examined.

    This Paper aims to know the modern approaches of determining the Qiblah and its ruling in Islamic Faqah, as well as to find out the required in the identity of the Qiblah or the eye, and the care of the advanced Jurists in this matter, and to present some of their sayings on the issue. we have followed the Descriptive analytical method of the aspects of the jurists ’difference in what is required when facing the qiblah either the eye or aspect, the approach of several demands branched out from each topic, which were answered in the theoretical framework of the research, and the research concluded with the most important results: The need to receive the eye of the qiblah for the worshiper who is close to it and it is no

Hydrogen sulfide (H₂S) gas sensor based on cobalt–manganese spinel oxide MnCo₂O₄ thin films that were prepared by chemical precipitation and green synthesis methods. Structural, morphological, and compositional characterizations of MnCo₂O₄ thin films were performed using XRD, FE-SEM, EDS, and AFM. It was found through XRD patterns that MnCo₂O₄ was formed in a cubic spinel structure for both methods; however, the chemically precipitated thin films possessed better crystallinity and larger crystallite sizes, whereas the green-synthesized films exhibited slightly lower crystallinities and smaller sizes, as evidenced by the broader peaks observed. In addition, FE-SEM and AFM images revealed that the MnCo₂O₄ films prepared by

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

Generally, statistical methods are used in various fields of science, especially in the research field, in which Statistical analysis is carried out by adopting several techniques, according to the nature of the study and its objectives. One of these techniques is building statistical models, which is done through regression models. This technique is considered one of the most important statistical methods for studying the relationship between a dependent variable, also called (the response variable) and the other variables, called covariate variables. This research describes the estimation of the partial linear regression model, as well as the estimation of the “missing at random” values (MAR). Regarding the

The objective of the research , is to shed light on the most important treatment of the problem of missing values of time series data and its influence in simple linear regression. This research deals with the effect of the missing values in independent variable only. This was carried out by proposing missing value from time series data which is complete originally and testing the influence of the missing value on simple regression analysis of data of an experiment related with the effect of the quantity of consumed ration on broilers weight for 15 weeks. The results showed that the missing value had not a significant effect as the estimated model after missing value was consistent and significant statistically. The results also