The rate of penetration (ROP) is the key parameter to enhance drilling processes as it is inversely proportional to the overall cost of drilling operations. Maximizing the ROP without any limitation can induce drilling dysfunctions such as downhole vibrations. These vibrations are the main reason for bottom hole assembly (BHA) tool failure or excessive wear. This paper aims to maximize the ROP while managing the torque to keep the depth of cut within an acceptable range during the cutting process. To achieve this, machine learning algorithms are applied to build ROP and drilling torque models. Then, a metaheuristic algorithm is used to determine the optimal technical control parameters, the weight on bit (WOB) and revolutions per minute (RPM), that simultaneously enhance the ROP and mitigate excessive vibrations. This paper introduces a new methodology for mitigating drill string vibrations, improving the rate of penetration (ROP), minimizing BHA failures, and reducing drilling costs.

Drill string vibration is one of the limiting factors that affect the maximum drilling performance, and at the same time causes premature failure of drill string components. Optimizing the drilling process in order to enhance efficiency requires a valuable vibration mitigation scheme to increase penetration rate. This article introduces a novel strategy to control drilling parameters to reduce drill string vibration and optimize ROP. In the system development process, various interesting topics have been studied, such as the performance of the controller (using MATLAB Fuzzy Logic toolbox), the application of artificial neural networks in ROP prediction, and drill string modeling. The proposed strategy uses multiple inputs such as surface drilling parameters variation (RPM, WOB, and Torque) together with predictive vibration severity estimate to detect drilling vibrations and adjust related parameters to suppress severe oscillations and avoid unexpected events that lead to non-productive time. The fuzzy logic controller shows overall stability and robustness, the controlled parameters follow the rules used in the fuzzy set which are developed by analyzing data from Algerian oil wells and simulating the "experience and expertise" of decision-makers. The system is multi-objective optimization; can detect inefficiencies, mitigate vibration, and enhance ROP. The artificial neural network ROP model, when simulated using the field data, shows an improvement of ROP by 12% on average across all the drilled formations when compared to the recorded data. A case study is presented to illustrate the application of this method in drilling practice.