TY - THES

T1 - Application of Best Practices for Well Construction Using State of the Art Drilling Software

AU - Berdiev, Timur

N1 - embargoed until null

PY - 2020

Y1 - 2020

N2 - Companies involved in the upstream sector are highly interested in improving their business processes. One of the ways to achieve it is to optimize the most investment intensive activity – the well construction. During the drilling phase, the largest cost factors are time-dependent and the earlier the well is put into production the faster the return of investments, therefore the main driver is the time. The optimization of cycle times must not jeopardize the safety of the process and quality of the well. Therefore, the purpose of the thesis is to develop a solution for a multivariate well construction optimization problem. The approach to the problem starts with describing well-known industrial methods for well construction improvement. At first drilling process is described from the project management aspect. Roles of proper planning, execution, and monitoring components in delivering the high quality well on time are elaborated. Next, the drilling data management is emphasized as a vital component for achieving efficiency gains during well construction. The constituent topics, including data acquisition, storage, quality control, retrieval, are described. For enhancing the planning phase drilling data analysis tools utilizing historical data have to be implemented. They include productive time and process control analysis, non-productive time (NPT) analysis, best composite time (BCT), learning curve analysis, benchmarking. To complement the traditional improvement methods the idea of combining the PDCA (Plan-Do-Check-Act) methodology with real-time time component is presented. That resulted in the developed framework for continuous improvement. The real-time components are state-of-the-art drilling software: Automated Drilling Performance Management (APDM), Simulation Module, and Mechanical Drilling Optimization Module. Their principles and functionality are described in the thesis as well as the data flow model for such a setup. A combination of traditional data analysis methods with the developed framework is aimed at the reduction of non-productive time (NPT) and invisible lost time (ILT), thus solving the problem of multivariate well construction process optimization. Additionally, the workflow for crew and equipment related operations improvement is developed as a part of the continuous improvement framework. The result is the standardization of these types of operations as a best practice. Finally, the application of the suggested improvement framework to optimize the BHA Run operation is described as well as the deployment of performance enhancement workflow for slip-to-slip connection operation is presented utilizing the video analysis software. The developed framework can be implemented by companies to optimize well construction process and achieve performance gains and higher profitability.

AB - Companies involved in the upstream sector are highly interested in improving their business processes. One of the ways to achieve it is to optimize the most investment intensive activity – the well construction. During the drilling phase, the largest cost factors are time-dependent and the earlier the well is put into production the faster the return of investments, therefore the main driver is the time. The optimization of cycle times must not jeopardize the safety of the process and quality of the well. Therefore, the purpose of the thesis is to develop a solution for a multivariate well construction optimization problem. The approach to the problem starts with describing well-known industrial methods for well construction improvement. At first drilling process is described from the project management aspect. Roles of proper planning, execution, and monitoring components in delivering the high quality well on time are elaborated. Next, the drilling data management is emphasized as a vital component for achieving efficiency gains during well construction. The constituent topics, including data acquisition, storage, quality control, retrieval, are described. For enhancing the planning phase drilling data analysis tools utilizing historical data have to be implemented. They include productive time and process control analysis, non-productive time (NPT) analysis, best composite time (BCT), learning curve analysis, benchmarking. To complement the traditional improvement methods the idea of combining the PDCA (Plan-Do-Check-Act) methodology with real-time time component is presented. That resulted in the developed framework for continuous improvement. The real-time components are state-of-the-art drilling software: Automated Drilling Performance Management (APDM), Simulation Module, and Mechanical Drilling Optimization Module. Their principles and functionality are described in the thesis as well as the data flow model for such a setup. A combination of traditional data analysis methods with the developed framework is aimed at the reduction of non-productive time (NPT) and invisible lost time (ILT), thus solving the problem of multivariate well construction process optimization. Additionally, the workflow for crew and equipment related operations improvement is developed as a part of the continuous improvement framework. The result is the standardization of these types of operations as a best practice. Finally, the application of the suggested improvement framework to optimize the BHA Run operation is described as well as the deployment of performance enhancement workflow for slip-to-slip connection operation is presented utilizing the video analysis software. The developed framework can be implemented by companies to optimize well construction process and achieve performance gains and higher profitability.

KW - Well Construction

KW - Drilling Software

KW - Well Construction

KW - Drilling Software

M3 - Master's Thesis

ER -