The Art of Deploying Data Mining and Machine Learning in Developing and Managing Deepwater Turbidite Gas Assets

Many oil and gas operators have challenges in deepwater turbidite gas asset's reservoir management plan (RMP) readiness due to lack of experience and very limited analog field data. The objective of this article is to demonstrate how data analytics workflow, comprising of data mining and machine learning-based global deepwater turbidite gas field benchmarking and lessons learned, to identify field performance and mitigate subsurface challenges in developing and managing deepwater turbidite gas assets. To mine turbidite field data from around the world, a customized R script was constructed using optical character recognition, regular expression (regex), rule-based logic to extract subsurface and surface data attributes from unstructured data sources. All extracted contents were transformed into a properly structured query language (SQL) database relational format for the cleansing process. Having established the turbidite assets repository, exploratory data analysis (EDA) was then employed to discover insight datasets. To analyze the field performance, the number of wells needed to deplete the field was identified using support vector regression, subsequently, K-means clustering was used to classify the reservoirs productivity. The results of field benchmarking analysis from EDA are deployed in a fit-for-purpose dashboard application, which provides an elegant and powerful framework for data management and analytics purposes. The analytic dashboard which was developed to visualize EDA findings will be presented in this article. The productivity of deepwater turbidite gas reservoirs has been classified based on the maximum gas flow rate and estimated ultimate recovery per well. This result help in identifying the high-rate, high-ultimate-recovery (HRHU) reservoirs of a deepwater turbidite gas field. The regex pattern for 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 identify the issues, impacts, and mitigation plan for dealing with identified risks based on best practices from a global project point of view. Deployment of this novel workflow provides insight for better decision-making and can be a prudent complementary tool for de-risking subsurface uncertainties in developing and managing deepwater turbidite gas assets. The findings from this study can be used to develop the framework that captures current best-practices in the formulation and execution of a RMP including monitoring and benchmark of asset performance in deepwater turbidite gas fields.