Development of a Software System for Drilling Optimization using Surface and Downhole Drilling Data Modeling a Human Process for Drilling Optimization

Abstract

Software systems are developed with the intent of providing methods to perform specific business tasks. However, in actual use cases, additional human processes or supplemental software are often required to complete the business process. In ideal cases, the software would guide the user through the entire business process and significantly aid in the required decision making or analysis to complete the process. This paper will present the development of a new drilling optimization software system ergonomically modeled on key business and analytical processes. These key processes have been organically developed over a number of years for a commercial drilling optimization service using data from downhole drilling dynamics recorders. A multi-disciplinary project team including business unit managers, project coordinators, data analysts, software architects and developers was assembled. The project team designed the new system to ensure it would fit the human processes that had historically been used to support the commercial service. The new system models all of the key business processes associated with the commercial drilling optimization service. These processes include job setup, mobilization of tools, assignment of field personnel to the job, acquisition of all required data in the field, and reporting tools for field personnel. The system also provides the user with an intuitive suite of data analysis and drilling optimization techniques. These include automated processes for handling, merging, analyzing, and presenting the surface drilling parameters and recorded downhole drilling dynamics data. Additionally, the user is provided with recommended data analysis and optimization routines from the system based on data entered throughout the business processes from job setup to field operations and based on trends identified in the surface parameters and recorded downhole drilling dynamics data imported into the system. Case studies demonstrating the value-added to the users of the new system will be presented.

Real-Time Drilling Parameter Optimization Model Based on the Constrained Bayesian Method

“…Over the last century, scholars and major drilling service companies have conducted exploratory studies on drilling parameter optimization. Currently, the optimization of drilling parameters is primarily divided into two categories: one is to establish a single objective optimization model of drilling parameters [2][3][4][5], taking a single-unit footage cost or mechanical specific energy as the objective function, and pursuing cost minimization or energy efficiency maximization. The second is to establish a multi-objective optimization model of drilling parameters to determine the optimal cooperation between them [6][7][8].…”