A Consolidated Method for Selecting Drilling Parameters

Abstract

Abstract Over the years, considerable research has been conducted and the results of numerous studies have been published about optimizing drilling operations that maximize the footage drilled and minimize drilling costs. One of the optimization aspects studied is determining which drilling parameters improve drilling efficiency. Many drilling application software packages exist to help engineers simulate the drilling process. These software packages might include the capability to evaluate drilling parameters’ sensitivity; however, many of the software programs only focus on a specific engineering area and rely on the engineer to evaluate the relation between the results of an engineering analysis and deciding. A new approach has been developed that consolidates various engineering analyses into a single workflow to automatically define the optimum drilling parameters. The application provides a drilling parameters roadmap consisting of weight on bit (WOB), surface rpm, and flow rate for efficient drilling execution. This application uses the knowledge of offset wells as a starting point to define the WOB and RPM ranges. Those values are represented as a drilling parameter matrix for input into the model. Then, by using static and dynamic bottomhole assembly (BHA) modeling and hydraulics modeling in addition to predicted rate of penetration (ROP), the application runs various sensitivity analyses to obtain the optimum drilling parameters within rig and downhole equipment specifications. The sensitivity analysis with static BHA modeling provides the buckling check to the maximum WOB proposed while the dynamic BHA modeling provides downhole vibration analysis and predicts the drilling ROP, which is compared with downhole tool specifications. Then, the hydraulic modeling provides validation of hole-cleaning quality for the relation between flow rate and ROP, without exceeding rig pump and circulating system pressure specifications. The application also implements a smart learning process to reduce the number of iterations and computation time. The results of this approach were tested on a field project where implementing the roadmap computed from the application showed excellent results for improving drilling performance.