A Developed Robust Model and Artificial Intelligence Techniques to Predict Drilling Fluid Density and Equivalent Circulation Density in Real-Time

Abstract

When drilling deep wells, it is important to regulate the formation pressure and prevent kicks. This is achieved by controlling the equivalent circulation density (ECD), which becomes crucial in high-pressure and high-temperature wells. ECD is particularly important in formations where the pore pressure and fracture pressure are close from each other (narrow windows). However, the current methods for measuring ECD using downhole sensors can be expensive and limited by operational constraints such as high pressure and temperature. Therefore, to overcome this challenge, two novel models name as ECDeffc.m and MWeffc.m with approach was developed to predict ECD and mud weight (MW) from surface drilling parameters, including standpipe pressure, rate of penetration, drill string rotation, and mud properties. In addition, by utilizing an artificial neural network (ANN) and a support vector machine (SVM), ECD was estimated with a correlation co-efficient of 0.9947 and an average absolute percentage error of 0.23%. Meanwhile, a decision tree (DT) was employed to estimate MW with a correlation coefficient of 0.9353 and an average absolute percentage error of 0.001%. The two novel models were compared with the artificial intelligence (AI) techniques to evaluate the developed models. The results proved that the two novel models were more accurate with the value that obtained from pressure while drilling tools (PWD). These models can be utilized during well design and while drilling operation to evaluate and monitor the appropriate mud weight and equivalent circulation density for saving time and money by eliminating the need for expensive downhole equipment and commercial software.