Rock failure during drilling is an important problem to be solved in petroleum technology. one of the most causes of rock failure is shale chemical interaction with drilling fluids. This interaction is changing the shale strength as well as its pore pressure relatively near the wellbore wall. In several oilfields in southern Iraq, drilling through the Tanuma formation is known as the most challenging operation due to its unstable behavior. Understanding the chemical reactions between shale and drilling fluid is determined by examining the features of shale and its behavior with drilling mud. Chemical interactions must be mitigated by the selection of suitable drilling mud with effective chemical additives. This study is describing t

Shear wave velocity is an important feature in the seismic exploration that could be utilized in reservoir development strategy and characterization. Its vital applications in petrophysics, seismic, and geomechanics to predict rock elastic and inelastic properties are essential elements of good stability and fracturing orientation, identification of matrix mineral and gas-bearing formations. However, the shear wave velocity that is usually obtained from core analysis which is an expensive and time-consuming process and dipole sonic imager tool is not commonly available in all wells. In this study, a statistical method is presented to predict shear wave velocity from wireline log data. The model concentrated to predict shear wave velocity fr

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