Learning From Experience: An Automatic pH Neutralization System Using Hybrid Fuzzy System and Neural Network

Abstract In oil and gas industry, the pH level is one of the most important indicators of mud contamination while drilling. Although the process has simple components, the pH neutralization process is complicated in the mud circulation system. This difficulty is due to the high nonlinearity of the process. In this paper, the fuzzy neural network (FNN) is integrated to the fuzzy logic controller (FLC) to create a system which identifies pH fluctuation into a drilling mud system, assesses and signals its severity. And then, it automatically actuates a chemical treating fluids valve (CTFV), such as Caustic Soda (CS), for neutralizing the drilling fluid pH level while circulating. In this work, an integrated mud circulation lab-scaled system is used to mimic the pH neutralization (e.g., because of the H2S intrusion) while drilling the hydrogen sulfide bearing zones. The system contains several significant nonlinearities such as sudden pH reduction appearances that stretch the ability of simple controllers in achieving satisfactory performance. The results show, the FLC can be used successfully to stabilize any chosen operating point of the system and to deal with nonlinearities (uncertainties of a model). All derived results are validated by computer simulation of a nonlinear mathematical model of the lab-scaled mud neutralization system. The presented FNN in this work demonstrates a reliable response to neutralize the circulated drilling fluid pH, through a quick, safe, and automatic initiation of appropriate treatment.