Experimental measurement and modeling of water-based drilling mud density using adaptive boosting decision tree, support vector machine, and K-nearest neighbors: A case study from the South Pars gas field

Exact determination of drilling mud weight in order to prevent fracture pressure and at the same time overcoming pore pressure is a key parameter in the drilling of oil and gas wells. Accurate design of drilling mud weight increases drilling safety, reduces mud loss, and also the duration of drilling operations. In this study, five efficient artificial intelligent models including Bayesian ridge regression (BRR), K-nearest neighbors (KNN), support vector machine (SVM), decision tree (DT), and Adaptive Boosting Regressor with Decision Tree (ABRDT) were proposed for estimating mud weight based on a databank of 817 data points from five wells in the South Pars gas field. Variables influencing mud weight include true vertical depth (TVD), hole size, inclination, funnel viscosity, yield point (YP), plastic viscosity (PV), gel strength (measured at 10th second, 10th minute, and 30th minute), API fluid loss (FL), mud type (seawater, KCl-polymer, and salt-polymer), and formation lithology. According to the statistical analysis, the proposed ABR-DT and DT were the most accurate approaches that could predict mud weight with average absolute percent relative errors (AARE) of 0.5159% and 0.7560%, respectively. The accuracy of the models was ranked as: ABR-DT > DT > SVM > KNN > BRR. The sensitivity analysis showed that the predicted mud density is highly influenced by the values of plastic viscosity and true vertical depth. Lastly, the Leverage approach confirmed the validity of the employed data and the applicability area of the proposed ABR-DT model.

Automated summary

Accurate prediction of mud weight is crucial for drilling operations, with ABR-DT and DT models showing the highest accuracy in estimating mud weight.