Production-Strategy Insights Using Machine Learning: Application for Bakken Shale
- Guofan Luo (University of Houston) | Yao Tian (University of Houston) | Mariia Bychina (University of Houston) | Christine Ehlig-Economides (University of Houston)
- Document ID
- Society of Petroleum Engineers
- SPE Reservoir Evaluation & Engineering
- Publication Date
- August 2019
- Document Type
- Journal Paper
- 800 - 816
- 2019.Society of Petroleum Engineers
- Bakken, Machine Learning
- 23 in the last 30 days
- 365 since 2007
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Researchers from both industry and academia have intensively studied tight oil resources in the past decade since the successful development of Bakken Shale and Eagle Ford Shale, and have made tremendous progress. It has been recognized that locating the sweet spots in the regionally pervasive plays is of great significance. However, we are still struggling to determine whether the dominant control on shale-well productivity is geologic or technical. Given certain geological properties, what is the best completion strategy?
Most of the previous studies either analyze the completion data alone or divide the entire play into different data clusters by map coordinates and depth, which might neglect the heterogeneity in thickness and reservoir-quality parameters. In our study, we first conducted stratigraphic and petrophysical analyses, using the regional variation in depth, thickness, porosity, and water saturation to capture the regional heterogeneity in the Bakken Shale petroleum system. We selected approximately 2,000 horizontal wells, targeting the Middle Bakken Formation with detailed completion records and initial production dates during 2013 and 2014. Completion data inputs include normalized stage length (NSL), stage counts, normalized volume of fluid (NVF), and normalized volume of proppant (NVP).
We investigated the relationship between the geological and completion features, and its effect on the first year of production. Then, we built a neural-network model to identify the relationship between the first-year oil production and the selected features. We separated the data into three sets for training, validation, and testing. After we trained the model using the training and validation set, we tested the model to estimate its robustness. Through sensitivity analysis, we demonstrated how the completion parameters combined with geological input would affect the production.
The developed technique provides a method to identify the best well location, understand the effectiveness of the completion strategy, and predict the well production. Although the data used came from wells in the Bakken Shale, the methodology applies in a similar way to other tight oil plays.
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