System Analysis of a Numerical Well Design Optimization Process

The emergence of advanced engineering technologies creates the opportunity to improve existing manual and silo-ed workflows within integrated energy companies. The processes used for drilling engineering have not evolved at the pace of cutting-edge technology advancements over the last 20 years. The most significant shifts in classical well development are standardized design methods, advanced disciplinary analysis, improved knowledge transfer systems, excel-based workflows, and structured employee training. 169As the Industrial Revolution 4.0 progresses, technologies in Model-Based Systems Engineering are emerging to enhance existing well design processes, yet the step change is insufficient to close the technology gap. This research contributes to existing drilling engineering and well design advancements by developing a system optimization architecture for the well design process. A random search algorithm coupled with a stochastic optimization methodology for multi-objective optimization emerges through the relationships defined within a system Design Structure Matrix (DSM). The optimization method includes the evaluation of the algorithms’ computational efficiency, design diversity, and convergence. The development of a numerical solution for well design, will provide the framework necessary to implement advanced analysis of well design that can accurately predict the quality of engineering decision-making.

Name thesis:degree_name

Master

Department dc:contributor.department

System Design and Management Program.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2022

Author dc:creator

• Hicks, Andre

Advisor dc:contributor.advisor

• Rebentisch, Eric S.

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright MIT

Licence dc:rights.uri

• http://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/147556

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/147556