A Multi-Criteria Decision Analysis for Rigless Job Scope Prioritization: Framework for Strategic Rigless Well Candidate Selection
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Rigless well intervention is essential for restoring production and maintaining well integrity in upstream oil and gas operations. However, companies often face dynamic environments where multiple intervention candidates arise simultaneously, while execution capacity is limited by budget, resources, or financial constraints. Without a structured prioritization framework, Rigless execution decisions tend to be inconsistent and heavily reliant on judgment, especially under financial pressure or overly aggressive strategies. This research aims to develop a systematic decision‑support framework for prioritizing Rigless well intervention programs by applying the Analytical Hierarchy Process (AHP). The framework integrates multiple technical, operational, economic, and risk‑related criteria to determine the relative priority of Rigless program objectives. Expert judgments are collected and synthesized to establish a clear priority hierarchy among these alternatives including Lost Production Opportunity (LPO) Reduction, Integrity Restoration, Well Development and Stimulation. The results indicate that LPO Reduction emerges as the highest priority due to its direct and immediate impact on production recovery, followed by Integrity Restoration as a critical prerequisite for safe and sustainable operations. Based on these results, a Rigless Prioritization Guideline is also proposed as the primary business solution which translating analytical rankings into practical execution and deferral rules applicable under concurrent candidate conditions and financial or operational constraints. The main contribution of this study is translating analytical prioritization into a practical business decision framework that supports consistent, transparent, and defensible Rigless execution. This framework enables companies to adapt their execution strategy under aggressive targets or financial constraints without adding operational complexity.
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