Abstract
The prediction and optimization of drilling rate of penetration (ROP) are among the most effective approaches in improving drilling efficiency. To achieve that, it calls for a well-established prediction model and a well-defined optimization methodology. With the advancement in large dataset acquisition and computational efficient machine learning methods, data-driven ROP prediction has superiority over classical physical models. Furthermore, when the ROP prediction model is trained and validated, it can be used to optimize the controllable parameters, preferably globally, given objective functions and proper constraints. The global optimization of drilling ROP is desirable in the design phase, such that the controllable parameters can be optimized for the whole planned well depth. This provides an optimum plan pushing the limit of drilling efficiency and provides valuable controlling strategies that guide the drilling operations.
The main object of this research is to investigate the global optimization workflow for ROP using prediction models based on machine learning methods. We first present an automated data processing method, dealing with and taking advantage of the variety and a vast amount of the drilling dataset. Then, the deep neural network (DNN) model for ROP predictions is validated and tested. In the trained predictive model, there are three controllable parameters, weight on bit (WOB), drilling string revolution speed (RPM), and drilling fluid flow rates (Q). Next, we choose the genetic algorithm (GA) to search the global optimal parameter combination in the control parameters space. The optimization workflow can be applied for the whole well depth, various segments of depth intervals, and different formation layers, resulting in a combination of controllable parameters for the entire well, for every section of given depth intervals, and for each formation layer, respectively. In summary, the global optimization workflow incorporates end-to-end data processing and promotes improved drilling efficiency. The global optimized results push the limit of drilling efficiency and provide valuable post-drilling analysis and offset drilling design recommendations. However, the extreme optimum results may not be reached in field practice, as more constraints such as formation information need to be applied to make the operation realistic.
