Rate of penetration (ROP) is the main function that affects drilling operation economically and efficiently. Many theoretical models reported in the literature were produced to predict ROP based on different parameters. Most of these models used only drilling parameters to estimate ROP. Few models have considered the effects of drilling fluid on ROP using a simulated data or a few real field data. Some of the researchers used artificial intelligence to predict ROP by only one method.
The objective of this research is to predict ROP based on both drilling parameters and mud properties such as weight on bit (WOB), rotary speed (RPM), pump flow rate (Q), standpipe pressure (SPP), drilling torque (τ), mud density (MW), plastic viscosity (PV), funnel viscosity (FV), yield point (YP) and solid (%). More than 400 real field data in shale formation are used to predict ROP using support vector machine (SVM) which is a method of artificial intelligence (AI) and compare it with different mathematical models.
The result showed that support vector machine (SVM) technique outperformed all the theoretical equations of ROP by a high margin as shown by a very high correlation coefficient (CC) of 0.997 and a very low average absolute percentage error (AAPE) of 2.83%.
Drilling operations are very important for petroleum exploration but they are very expensive and requires many economical and safety considerations. Development of oilfield needs an effective drilling process and minimizing the cost of drilling. A lot of efforts have been done to reduce the drilling problems and increase the drilling performance. Usually, as the drilling speed is increased, the drilling cost will be reduced. Rate of penetration (ROP) is the main parameter that affects both the cost and process of drilling.
To obtain the lowest cost and highest efficiency, different parameters should be understood to know their effects on ROP.
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