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De-risking subsurface uncertainties in developing and managing deepwater turbidite gas assets through data analytics

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Abstract

Subsurface uncertainties due to turbidite complexity will impact the recoverable resource and reservoir productivity which leads to decision making and planning of a deepwater field. This paper provides an alternative de-risking technique through data analytics approach in developing and managing deepwater turbidite gas assets based on global deepwater turbidite gas fields benchmarking and lessons learned. To collect data on turbidite fields, data mining was done to extract relevant key information on high-level field information, geological and reservoir properties, production, wells, facility, subsurface and operational challenges, and mitigation plan to manage such challenges from published literature data. All extracted contents were transformed to proper target database relational format for the clean-up process. Having established a turbidite field database, exploratory data analysis was then employed to discover insight the datasets. Furthermore, an analytic dashboard was developed to visualize the findings and perform analysis specifically on field benchmarking. Subsurface challenges, specifically as related to reservoir uncertainties and associated risks, including operational challenges in developing and managing deepwater turbidite gas fields were identified through word cloud recognition. Key subsurface challenges were then categorized and statistically ranked, finally, a decomposition tree was used to comprehensively identify the issues, hypotheses, impacts, and mitigation plan for dealing with identified risks based on best practices from a global project point of view.

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The author would like to thank the management of AEM Energy Solutions for giving permission to publish this paper and their support for this project.

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    Edo Pratama

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Pratama, E. De-risking subsurface uncertainties in developing and managing deepwater turbidite gas assets through data analytics. Arab J Sci Eng 47, 11021–11037 (2022). https://doi.org/10.1007/s13369-021-05590-y

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