New Approach to Predict Fracture Pressure Using Functional Networks

Abstract

Abstract Fracture pressure plays a key role in designing the mud weight and the cement slurry density in the drilling operation. Knowing the fracture pressure will eliminate many problems such as loss of circulation and hence reduce the time and the cost of the drilling operation. Many empirical models reported in the literature were used to calculate the fracture pressure based on different parameters. Most of these models used only formation and rock properties to estimate fracture pressure. Other models predicted the fracture pressure based on log data using a few real field data. Artificial intelligence techniques once optimized can be used to predict the fracture pressure with high accuracy. The objective of this research is to predict the fracture pressure based only on surface drilling parameters which are easy to get namely weight on bit (WOB), rotary speed (RPM), drilling torque (τ), rate of penetration (ROP), mud weight (MW) and formation pressure (Pf). More than 4700 real field data points are used to predict fracture pressure using Functional Networks (FN) which is a method of artificial intelligence (AI). Functional Networks (FN) tool was compared with different empirical models. The result showed that FN methods outperformed all the fracture pressure equations by high margin (very high correlation coefficient (R) of 0.986 and a very low average absolute percentage error (AAPE) of 0.201). the developed technique will help the drilling engineers to design the cement slurry and determine the casing setting depth. In addition, the drilling engineers will be able to eliminate the common drilling problems such as loss of circulation.