New Technology to Evaluate Equivalent Circulating Density While Drilling Using Artificial Intelligence

Abstract

Abstract The equivalent circulation density (ECD) is a very important parameter in drilling high pressure high temperature (HPHT) wells and deep water wells. In formations where the margins between the formation pore pressure (PP) and the formation fracture pressure (FP) is narrow, ECD is a key parameter. In these critical operations, the ECD is used to control the formation pressure and prevent kicks. The recent approaches in oilfield depend mainly on using expensive downhole sensors for providing real time values of ECD. These tools also have operational limits which may prevent using it in all downhole conditions. The objective of this paper is to present a new technique for predicting ECD values while drilling without the need for downhole tools. The technique uses surface drilling parameters in a model to evaluate the ECD values precisely using artificial intelligence (AI) techniques. The model was developed using two AI techniques; artificial neural network (ANN) and adaptive neuro fuzzy inference system (ANFIS). Using this technique will save cost and time by eliminating needs for using expensive, complicated downhole tools like measurement while drilling (MWDs) and pressure while drilling (PWD). The results obtained showed that the accuracy of the developed model is very high with error factor less than 0.22 % comparing the actual to predicted ECD values. Implementing this model in well design will have great impact in accurately choosing correct mud weights to safely drill the well. It will also minimize the drilling problems related to ECD such as losses or gains especially in critical area where margins between pore and fracture pressure is very narrow.