A hybrid fuzzy logic/genetic algorithm model based on experimental data for estimation of cuttings concentration during drilling

Adequate hole cleaning is a key to economic drilling operations. However, cuttings transport during drilling is affected by different operational parameters, geometric parameters and, cuttings and drilling fluid related parameters which makes the cuttings transport phenomenon a complicated modeling exercise. This complexity has led to the use of computational intelligence (CI) methods such as fuzzy logic and genetic algorithm (GA). A total of 11 parameters are considered in the current work while developing Mamdani type fuzzy logic model using 702 experimental observations. The developed FL model (FL2) is further combined with GA to optimize fuzzy rule weights which has led to the development of a hybrid CI model termed FL3 in this paper. The performance of FL3 over two test datasets is compared with FL2 and a previously published FL model (Chowdhury and Hovda, 2022) termed FL1 (built on 509 experimental observations and 195 fuzzy rules). Model comparison using three goodness of fit metrics (R2, RMSE and MAE) shows that the hybrid CI model, FL3, outperforms both FL1 and FL2 over both test datasets. R2 value of FL3 over both test datasets is 'very good' following (Moriasi et al., 2007). The difference of FL3 cuttings concentration (Cc) estimates and the measured Cc values is less than 1% for more than two thirds of the experimental data for both test datasets collected at two different geographical locations by two different research groups - TUDRP and SINTEF. The reasons behind FL3 outperforming FL1 and FL2 are data randomization, modified fuzzy sets for eccentricity and drillstring rotation based on the exploratory data analysis published earlier (Chowdhury et al., 2023), use of more experimental data and the optimization of fuzzy rule weights by GA.