Application of Big Data Techniques in a Greenfield Heavy Oil Asset in Kuwait – a Feasibility Study
- Arun Kharghoria (Kuwait Oil Company) | Santiago Gonzalez (Kuwait Oil Company) | Abdullah Abdul Karim Al-Rabah (Kuwait Oil Company) | Alok Kaushik (Shell) | Manu Ujjal (Shell) | Manu Singhal (Shell) | Jacobo Montero (Shell) | Gregorio Gonzalez (Shell) | Mike Cheers (Shell) | Ellen Zijlstra (Shell) | Keith Rawnsley (Shell)
- Document ID
- Society of Petroleum Engineers
- SPE Kuwait Oil & Gas Show and Conference, 13-16 October, Mishref, Kuwait
- Publication Date
- Document Type
- Conference Paper
- 2019. Society of Petroleum Engineers
- Greenfield, Heavy Oil, Random Forest, Machine Learning, Big Data
- 1 in the last 30 days
- 37 since 2007
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The objective of this study was to assess the feasibility of application of analytics techniques in a new heavy oil asset in Kuwait in the following areas: data integration and visualization to support Well, Reservoir and Facility Management (WRFM), understanding well production behavior and their link to reservoir parameters, investigating reasons for sand failure. The study also aimed to highlight focus areas that would facilitate the full field implementation of analytics as a viable WRFM tool.
Due to the green field nature, the current volume of data is relatively small (versus mature assets), and not all the data required is available yet. The study started with formatting, processing and integrating all the available field data into a data management tool. Integrated visualizations were tested to detect early trends of sand and water production. Machine Learning algorithms such as Random Forest, Decision Tree and Neural Network were applied next to the sand prediction problem, focusing on identifying root causes for repetitive sand failures in wells and if possible to predict initial or subsequent sand failures.
The study indicated that integrated visualizations are very promising in support of WRFM in this field in the short term. Early signs of water breakthrough were correlated, particularly in wells with specific combinations of geological features and completion strategies. The sand prediction problem proved to be very challenging to the Machine Learning approaches, with limited success in predicting the historical occurrences. The results indicated that these techniques are likely to be more applicable once the volume of data increases, particularly the higher resolution data (real time data from artificial lift equipment), as well as by incorporating additional data types (sand production measurements during tests, which require additional resources to execute) and other data types available but not tested yet (log data derived parameters). Overall results indicated that analytics should be strongly considered as a valuable tool in the short to medium term in this new field, with efforts in data acquisition and management of the data types identified.
Most of the existing publications on this topic are related to analytics applied in mature fields. This study, conducted in a field still in the early stages, showed the value for early implementation, and highlights that early planning for focused data acquisition can facilitate building initial data-driven models which can be used for prediction purposes. The paper is expected to provide a valuable reference for new heavy oil projects, either under definition or in early production, for the application of data analytics and extend learnings based on machine learning.
|File Size||1 MB||Number of Pages||14|