ERMAI Carbonate PMCD: AI-Enhanced Petrophysical Interpretation for Pressurized Mud Cap Drilling in Complex Carbonate Formations

Résumé fourni par la source

Summary Drilling in complex carbonate formations is challenging due to karst features and extensive fracturing, often causing total mud losses. Pressurized Mud Cap Drilling (PMCD) is commonly employed to maintain wellbore stability in such conditions. However, the continuous seawater pumping during PMCD degrades Logging While Drilling (LWD) data quality, making it unreliable for formation evaluation. To address this, a module leveraging machine learning (ML) and artificial intelligence (AI) was developed to predict synthetic log data under PMCD conditions. By integrating open-hole and cased-hole logs, the Enhance Resource Monetisation through Artificial Intelligence (ERMAI) carbonate PMCD model was created. This paper details the methodology, model development, and field application of this data-driven approach, providing new insights into formation evaluation in complex carbonate environments under PMCD.

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