Abstract:

This talk presents an end-to-end in-house subsurface workflow integrating domain expertise and digital analytics for compartmentalized gas fields in the Gulf of Thailand. The workflow automates key processes—subsurface interpretation, well targeting, and production forecasting—to reduce effort, improve consistency, and support better decisions across the field development lifecycle.

The workflow begins by defining subsurface planning requirements and standardizing procedures to align with business objectives. Fit-for-purpose data analytics are then applied to automate and accelerate each stage.

Automated subsurface interpretation uses trap-specific algorithms based on contour–fault geometry and probabilistic HCCH scenarios to generate consistent prospect maps for closure, nose, and ramp traps. This reduces interpretation time, improves reproducibility, and ensures potential prospects are not overlooked.

Well targeting and platform optimization apply mixed-integer linear programming (MILP) to reduce the number of wellhead platforms, shorten well paths, and maintain full resource coverage, lowering development costs while improving operational efficiency.

Production forecasting and optimization combine decline curve analysis with constrained linear programming to maximize condensate production, minimize water output, and schedule drilling and interventions within export and network limits.

This integrated workflow enables the new perspective of subsurface routines, shifting effort from manual work to strategic decision-making. It combines domain expertise, advanced analytics. Powered by HPC and integrated with PETREL, DSG, and Excel, the workflow combines domain expertise with advanced analytics to enable faster and more effective E&P decisions.

Biography:

Peerapong Ekkawong is a professional petroleum engineer with an M.S. in Petroleum Engineering from Texas A&M University. He specializes in integrating data science and AI/ML into practical subsurface engineering. His multidisciplinary expertise spans the full subsurface domain, including reservoir modeling, production optimization, automated interpretation, database management, and software development.

Currently serving as Head of Subsurface Data Analytics at PTTEP, Peerapong leads subsurface data research and drives digital transformation in the upstream sector. His mission is to embed data-driven technologies into core subsurface domain to enhance efficiency and maximize hydrocarbon recovery.