Ensemble Machine Learning Assisted Reservoir Characterization Using Field Production Data–An Offshore Field Case Study

Baozhong Wang, Jyotsna Sharma, Jianhua Chen and Patricia Persaud
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Baozhong Wang: Computer Science and Engineering Division, School of Electrical Engineering and Computer Science, Louisiana State University (LSU), Baton Rouge, LA 70803, USA
Jyotsna Sharma: Department of Petroleum Engineering, Patrick F. Taylor Hall, Louisiana State University (LSU), Baton Rouge, LA 70803, USA
Jianhua Chen: Computer Science and Engineering Division, School of Electrical Engineering and Computer Science, Louisiana State University (LSU), Baton Rouge, LA 70803, USA
Patricia Persaud: Department of Geology and Geophysics, Howe-Russell-Kniffen, Louisiana State University (LSU), Baton Rouge, LA 70803, USA

Energies, 2021, vol. 14, issue 4, 1-20

Abstract: Estimation of fluid saturation is an important step in dynamic reservoir characterization. Machine learning techniques have been increasingly used in recent years for reservoir saturation prediction workflows. However, most of these studies require input parameters derived from cores, petrophysical logs, or seismic data, which may not always be readily available. Additionally, very few studies incorporate the production data, which is an important reflection of the dynamic reservoir properties and also typically the most frequently and reliably measured quantity throughout the life of a field. In this research, the random forest ensemble machine learning algorithm is implemented that uses the field-wide production and injection data (both measured at the surface) as the only input parameters to predict the time-lapse oil saturation profiles at well locations. The algorithm is optimized using feature selection based on feature importance score and Pearson correlation coefficient, in combination with geophysical domain-knowledge. The workflow is demonstrated using the actual field data from a structurally complex, heterogeneous, and heavily faulted offshore reservoir. The random forest model captures the trends from three and a half years of historical field production, injection, and simulated saturation data to predict future time-lapse oil saturation profiles at four deviated well locations with over 90% R-square, less than 6% Root Mean Square Error, and less than 7% Mean Absolute Percentage Error, in each case.

Keywords: reservoir characterization; machine learning; saturation prediction; offshore oilfield; random forest (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
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