Abstract:
Drilling efficiency is the capacity of a drilling operation to extract natural resources from
subterranean reservoirs with maximum effectiveness. Drilling efficiency is crucial to the
success of extraction operations, as it influences every aspect of the cost of the operation to
the quantity and quality of the extracted natural resources. The effectiveness of a drilling
operation is greatly affected by several factors, one of which is the particle size of the
material being drilled. Drilling engineers must pay close attention to particle size
characteristics, which characterize the size, shape, and density of the cuttings produced
during drilling. Efficiency in drilling significantly impactsthe drilling process, which in turn
affects the extraction technique. Since drilling is a complicated process used to recover
precious natural resources, ineffective drilling will add additional expense and time to the
project. The influence of particle size variables in drilling can be evaluated using machine
learning by analyzing large datasets of drilling parameters and particle properties. In this
study, machine learning algorithms are used to examine the relationship between particle
size parameters and drilling performance, yielding insights into the optimal particle size
parameters for a wide range of formations and drilling scenarios. To determine the
characteristics of drilling and particle size most closely connected, three distinct machine
learning techniques are used in this investigation. Among these methods, Random Forest
shows the strongest correlation between these traits. This approach is used to anticipate
particle size parameters for absent data points within the typical range for drilling
parameters. The study can be used to evaluate the drilling performance and efficiency. A
similar strategy can be implemented in formations with identical geological factors using
the most applicable machine learning model.
