The physical and elastic characteristics of rocks determine rock strengths in general. Rock strength is frequently assessed using porosity well logs such as neutron and sonic logs. The essential criteria for estimating rock mechanic parameters in petroleum engineering research are uniaxial compressive strength and elastic modulus. Indirect estimation using well-log data is necessary to measure these variables. This study attempts to create a single regression model that can accurately forecast rock mechanic characteristics for the Harth Carbonate Formation in the Fauqi oil field. According to the findings of this study, petrophysical parameters are reliable indexes for determining rock mechanical properties having good performance potential. The rock's mechanical characteristics were evaluated using multiple regression analysis (UCS- NPHI, Es- NPHI, VP/VS- NPHI, K- NPHI, and G- NPHI). The UCS-NPHI, obtaining correlation coefficients (R2= 0.74) and for young modulus (R2= 0.85). The expected continuous rock mechanical profile gives a fair indicator of the formation's strength and stability surrounding the wellbore. It can therefore be used to handle problems with wellbore instability, stop sand production, optimize drilling operations, including bit selection and drilling parameters, and perform fracturing operations throughout the Hartha Formation.
Abstract:
We can notice cluster data in social, health and behavioral sciences, so this type of data have a link between its observations and we can express these clusters through the relationship between measurements on units within the same group.
In this research, I estimate the reliability function of cluster function by using the seemingly unrelate
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