Estimation of rate of penetration considering mechanical, hydraulic, and formation characteristics for mishref formation

Abstract EN

This paper presents a detailed formulation of a rate of penetration (ROP) model, considering many drilling parameters and conditions for obtaining maximum drilling rate as well as minimizing the drilling cost.

A regression analysis technique has been usedfor ROP modeling in Mishref formation.

The data were extracted from routinely available mud and wirelinelogs.

These data includes weight on bit, rotary speed, horse per square inch, and transit time.

For ROP modeling, data of five wells inHalfaya oil field in south Iraq were extracted.

Statistical software called SPSS was used for improving the modeling data and to perform linear and nonlinear multiple regression analysis.

This improving approach included detection the outliers of modeling parameters, grouping the modeling data, moving average and finally applying the regression analysis.

Results of modeling showed that the grouping of modeling data exhibited good convergence with actual data and the overall model of oil field could producegood fitness with the actual data in both cases of linear and nonlinear models.

Also, a good estimation of drilling cost could be obtained when using this This paper presents a detailed formulation of a rate of penetration (ROP) model, considering many drilling parameters and conditions for obtaining maximum drilling rate as well as minimizing the drilling cost.

A regression analysis technique has been usedfor ROP modeling in Mishref formation.

The data were extracted from routinely available mud and wirelinelogs.

These data includes weight on bit, rotary speed, horse per square inch, and transit time.

For ROP modeling, data of five wells inHalfaya oil field in south Iraq were extracted.

Statistical software called SPSS was used for improving the modeling data and to perform linear and nonlinear multiple regression analysis.

This improving approach included detection the outliers of modeling parameters, grouping the modeling data, moving average and finally applying the regression analysis.

Results of modeling showed that the grouping of modeling data exhibited good convergence with actual data and the overall model of oil field could producegood fitness with the actual data in both cases of linear and nonlinear models.

Also, a good estimation of drilling cost could be obtained when using this model.