The following study will look at the issue of the dealignment of the trajectory when drilling vertically (a fact), where measurement and process errors are still the primary source of error that can easily lead to the inclination angle having overshot the desired bounds. The current methods, such as the Extended Kalman Filters (EKFs), can incorrectly estimate non-Gaussian noises, unlike the classical particle filters (PFs), which are unable to handle significant measurement errors appropriately. We will solve these problems by creating a new deviation correction mechanism using a dung beetle optimizer particle filter (DBOPF) with a superior Model Predictive Controller (MPC). The DBOPF makes use of the prior knowledge and optimization process to enhance the precision of state estimation and is superior in noise reduction to traditional filters. The improved MPC introduces flexible constraints and weight adjustments in the form of a sigmoid function that enables solutions when the inclination angle exceeds the threshold, and priorities are given to control objectives dynamically. The simulation outcomes indicate that the approach is more effective in the correction of the trajectory and control of inclination angle than the conventional MPC and other optimization-based filters, such as the PSO and SSA, in the presence of the noisy drilling environment.

Drilling optimization seeks to enhance the efficiency of drilling operations by fine-tuning adjustable factors like weight on bit (WOB); the goal is to boost the rate of penetration during drilling and decrease overall well expenses. It is crucial to efficiently and precisely manage weight on bit (WOB) to fine-tune drilling parameters promptly. Drilling optimization focuses on adjusting controllable variables, such as weight on the bit and bit rotation speed, to achieve the highest possible drilling rate during operations. Consequently, it is necessary to conduct a comparative analysis of ML models to evaluate practitioners in picking the appropriate predictive model. This research employs four machine learning methods to forecast weight on bit: Random Forest (RF), K-Nearest Neighbors (KNNs), Gradient Boosting Regression (GBR), and Decision Tree (DT). Machine learning techniques are being evaluated using datasets sourced from well drilling data in Western Sudan, marking the first instance of such data being utilized for this purpose. The key accomplishment of this study is the automation of predicting weight on bit by utilizing machine learning techniques tailored to our datasets. The findings indicated that among the algorithms tested, Random Forest stood out as the most dependable, displaying a prediction accuracy of 98% and a lower RMSE value of 1.015. In contrast, KNN, GBR, and DT achieved accuracies of 91.40%, 80.66%, and 100.00% respectively, with RMSE values of 2.008, 3.011, and 6.27 on the testing dataset, correspondingly. At last, this research is acknowledged as a groundbreaking effort in the field, utilizing machine learning techniques to predict weight on bit occurrences. Consequently, this study presents a publicly available dataset containing details about drilled wells in the Sudanese oil and gas sector. This dataset is meant to be used for upcoming experiments, validating algorithms, and for analytical purposes.