Optimization of controllable drilling parameters using a novel geomechanics-based workflow

Abstract

Drilling optimization is one of the most important management and engineering objectives in the upstream oil and gas industry, which has been the subject of numerous studies during the last two decades. Although the role of geomechanical parameters has rarely been considered in these studies. Therefore, this study aimed to optimize the controllable drilling parameters using a novel geomechanics-based workflow. For this purpose, data for 4229 m drilling from 4 wells located in one of the oil fields in southwestern Iran were studied. A 1D geomechanical model was developed for each well following the data pre-processing. In the feature selection stage, using the non-dominated sorting genetic algorithm-II (NSGA-II) integrated by the artificial neural network with multilayer perceptron (ANN-MLP), bit diameter (BS), equivalent circulating density (ECD), weight on bit (WOB), revolution per minute (RPM), flow pump rate (FPR), depth (Depth), formation resistivity (RT), static Young's modulus (E), and minimum horizontal stress (Sh) were selected to develop intelligent prediction models for drilling rate of penetration (ROP) and torque (TRQ), so two models ROP and TRQ were then developed using ANN-MLP. Subsequently, the coupling of ROP, TRQ, and mechanical specific energy (MSE) models and the NSGA-II multi-objective optimization algorithm were performed to achieve the best controllable parameters including WOB, RPM, and FPR at any depth (real-time), so that ROP was maximized and TRQ and MSE were minimized. Then, the optimal parameters for each formation thickness, 50 m and 100 m intervals were presented. In addition, the scenario of the set of optimal parameters for each geomechanical unit (GMU) was provided for the first time. Finally, the drilling performance results obtained from the two optimization techniques based on geomechanics and best practices were compared to each other. According to the results, the application of optimal parameters obtained from the best practices significantly leads to a doubling of mechanical specific energy. However, the application of geomechanics-based optimization technique with different scenarios successfully improved the performance of drilling operations; so that in the real-time scenario, the drilling time by 62%, 82%, 56%, and 68% and the MSE by averagely 7%, 29%, 11%, and 15% were reduced for wells A, B, C, and D, respectively. Furthermore, the application of scenarios of GMUs, formation thickness, and 50 m and 100 m intervals had the best performance, respectively. Therefore, the application of geomechanics-based drilling optimization for carbonate formations within the studied parameter ranges is strongly recommended according to the information range in this study.