Optimizing the trajectory of directional wellbores is essential to minimize drilling costs and the impacts of potential drilling problems. It poses multi-objective optimization challenges. Well-design optimization models initially focus on wellbore-length minimization, but ideally also need to consider minimizing the surface torque during drilling and address, among other constraints, collision avoidance with offset wells. A novel trajectory-optimization model is described that computes the separation factor along the wellbore. It employs a genetic optimization algorithm with an objective function that maximizes the minimum separation factor along the entire length of a wellbore. Plausible well trajectories are identified within a feasible solution space defined by user-identified constraints. The simplicity and effectiveness of the proposed model are demonstrated using a case study involving real well data from the Reshadat oil field offshore southern Iran. In the case considered, a proposed well trajectory is identified as unsafe in terms of its minimum separation factor with an offset well and is re-planned with the proposed model to achieve a safer trajectory.

This study presents a novel approach for optimizing well paths in extended reach drilling (ERD) wells. Different trajectories can be used for ERD wells, each with its pros and cons. Previous research overlooked certain objective functions in single-objective optimization and lacked an autonomous method for selecting the best solution from Pareto optimal solutions in multi-objective optimizations. Furthermore, they lacked comparing different profiles in well design. Risk assessment and operational factors, which greatly influence optimization and drilling success, were insufficiently considered. This study utilized the multi-objective genetic algorithm (MOGA) and the technique for order preference by similarity to an ideal solution (TOPSIS) method to select the optimal well path based on torque, wellbore length, risk (e.g., keyseat), and required tools. First, all possible trajectories were determined, and MOGA identified the optimal path with minimal torque and length. The fuzzy decision-making method automatically selected the best solution from the Pareto optimal solution set. The associated risks and required tools are evaluated for each trajectory. Finally, the TOPSIS method selected the optimal trajectory based on torque, length, risks, and required tools. The case study demonstrated that the undersection path was the most advantageous trajectory for ERD wells, with a 60% closeness to the ideal state. The multiple build trajectory achieved 57% closeness, while the build and hold and double build paths had lower closeness values (43 and 28%, respectively). Consequently, it can be inferred that in the context of ERD wells, it is preferable to carry out the deviation process at deeper depths.

Bentonite-based drilling fluids are used for drilling, where inhibitive fluids are not required. The rheological and the density properties of the drilling fluids are highly affected by high temperature and pressure. Due to high temperature, the clay particles stick together, and the fluid system becomes more flocculated. Poorly designed drilling fluid may cause undesired operational issues such as poor hole cleaning, drill strings sticking, high torque and drag. In this study, the 80 °C thermally stable Herschel Bulkley’s and Bingham plastic yield stresses drilling fluids were formulated based on lignosulfonate-treated bentonite drilling fluid. Further, the impact of a MoS₂ nanoparticle solution on the properties of the thermally stable base fluid was characterized. Results at room temperature and pressure showed that the blending of 0.26 wt.% MoS₂ increased the lubricity of thermally stable base fluid by 27% and enhanced the thermal and electrical conductivities by 7.2% and 8.8%, respectively.

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.