Authors:
G. Agalya, Mohammad Ayaz Ahmad, Yuri Ryagin

Addresses:
Department of Petroleum Engineering, Dhaanish Ahmed College of Engineering, Chennai, Tamil Nadu, India. Department of Mathematics, Physics and Statistics, University of Guyana, Georgetown, Guyana, South America. Department of Low Temperature Physics and Nanoscale Systems, Ural Federal University (UrFU), Yekaterinburg, Sverdlovsk Oblast, Russia.

Abstract:

Surface well testing is an extremely crucial operation in oil and gas production prediction and reservoir characterisation. Yet these operations are usually plagued by inefficiency and incur stratospheric cost overruns and delays. This study proposes a strategic planning model to enhance the productivity of surface well testing operations. The research seeks to optimise resource deployment, streamline workflows, and synchronise real-time information to minimise non-productive time (NPT). The research used mixed methods, combining quantitative evaluation of the past operational record and qualitative information collected from simulated case histories. The research dataset comprised 457 surface well test operations across multiple geologic formations. The main performance indicators (KPIs), such as equipment availability, person-hour effectiveness, and data collection accuracy, were tracked. The main software used for analysis comprised a Python script with a rigorous focus on data analysis and simulation, supported by Tableau for visualisation. The system illustrated here shows a 25% reduction in NPT and a 15% increase in overall operating effectiveness. The research indicates that a data-intensive, high-energy strategic planning framework can be used to effectively reduce average operational holdup at minimum cost, resulting in cost-effective and effective well testing programs. 

Keywords: Surface Well Testing; Strategic Planning; Non-Productive Time (NPT); Operational Efficiency; Data Analytics; Equipment Availability; Testing Operations; Reservoir Data; High-Technology Machinery.

Received on: 24/05/2024, Revised on: 11/08/2024, Accepted on: 29/11/2024, Published on: 03/06/2025

DOI: 10.69888/FTSASS.2025.000532

FMDB Transactions on Sustainable Applied Sciences, 2025 Vol. 2 No. 1, Pages: 48-56

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