Optimal mud weight window calculation using machine learning methods, By Ali Ranjbar

Research

Title	Optimal mud weight window calculation using machine learning methods
Type	Presentation
Keywords	Mechanical earth model, stress, safe drilling mud window, well stability, artificial intelligence, daily drilling data
Researchers	Parirokh Ebrahimi (First researcher) , Mohammad Rasul Dehghani Firuzabadi (Second researcher) , Moien Kafi (Third researcher) , Fatemeh Mohammadi nia (Fourth researcher) , Ali Ranjbar (Fifth researcher)

Abstract

One of the controllable parameters which is able to prevent well instability is the drilling mud weight. Therefore, finding the right drilling mud weight, which is also known as the safe drilling mud window, has become one of the challenges in the oil industry and specifically, in the drilling of hydrocarbon wells. To calculate this important parameter, one-dimensional mechanical earth model is usually used by the data obtained from the well logs. This is while using the daily drilling report operation compared to the data obtained from the survey well, in addition to saving time, it is also economical. In this research, the calculation of the safe mud window of the drilling using the daily drilling reports and its estimation using modern artificial intelligence methods such as Artificial Neural Network (ANN) and Multi-Gene Genetic Programming (MGGP) have been discussed. Due to the lighter mathematical calculations and the acceptable accuracy of the results obtained from the equation provided by the MGGP method, the use of this artificial intelligence method in estimating the safe window of drilling mud is suggested in terms of speed, accuracy and efficiency.