Drilling more efficiently and with less non-productive time (NPT) is one of the key enablers to reduce field development costs. In this work, we investigate the application of a data-driven optimization method called extremum seeking (ES) to achieve more efficient and safe drilling through automatic real-time minimization of the mechanical specific energy (MSE). The ES algorithm gathers information about the current downhole conditions by performing small tests with the applied weight on bit (WOB) and drill string rotational rate (RPM) while drilling and automatically implements optimization actions based on the test results. The ES method does not require an a priori model of the drilling process and can thus be applied even in instances when sufficiently accurate drilling models are not available. The proposed algorithm can handle various drilling constraints related to drilling dysfunctions and hardware limitations. The algorithm’s performance is demonstrated by simulations, where the algorithm successfully finds and maintains the optimal WOB and RPM while adhering to drilling constraints in various settings. The simulations show that the ES method is able to track changes in the optimal WOB and RPM corresponding to changes in the drilled formation. As demonstrated in the simulation scenarios, the overall improvements in rate of penetration (ROP) can be up to 20–170%, depending on the initial guess of the optimal WOB and RPM obtained from e.g., a drill-off test or a potentially inaccurate model. The presented algorithm is supplied with specific design choices and tuning considerations that facilitate its simple and efficient use in drilling applications.

The rate of penetration (ROP) is one of the key parameters related to the efficiency of the drilling process. Within the confines of operational limits, the drilling parameters affecting the ROP should be optimized to drill more efficiently and safely, to reduce the overall cost of constructing the well. In this study, a data-driven optimization method called Extremum Seeking (ES) is employed to automatically find and maintain the optimal weight on bit (WOB) which maximizes the ROP. The ES algorithm is a model-free method that gathers information about the current downhole conditions by automatically performing small tests with the WOB and executing optimization actions based on the test results. In this paper, this optimization method is augmented with a combination of a predictive and a reactive constraint handling technique to adhere to operational limitations. These methods of constraint handling are demonstrated for a maximal limit imposed on the surface torque, but the methods are generic and can be applied to various drilling parameters. The proposed optimization scheme has been tested with experiments on a downscaled drilling rig and simulations on a high-fidelity drilling simulator of a full-scale drilling operation. The experiments and simulations show the method’s ability to steer the system to the optimum and to handle constraints and noisy data, resulting in safe and efficient drilling at high ROP.

The Rate of Penetration (ROP) is one of the key parameters related to the efficiency of the drilling process. Within the confines of operational limits, the drilling parameters affecting the ROP should be optimized to drill more efficiently and safely, to reduce the overall cost of constructing the well. In this study, a data-driven optimization method called Extremum Seeking is employed to automatically find and maintain the optimal Weight on Bit (WOB) which maximizes the ROP. To avoid violation of constraints, the algorithm is adjusted with a combination of a predictive and a reactive approach. This method of constraint handling is demonstrated for a maximal limit imposed on the surface torque, but the method is generic and can be applied on various drilling parameters. The proposed optimization scheme has been tested on a high-fidelity drilling simulator. The simulated scenarios show the method’s ability to steer the system to the optimum and to handle constraints and noisy data.

The Rate of Penetration (ROP) is one of the key parameters related to the efficiency of the drilling process. Within the confines of operational constraints, the drilling parameters affecting the ROP should be optimized to drill more efficiently and safely to reduce the overall cost of constructing the well. In this study, an automatic optimization strategy called Extremum Seeking