Joint inversion of time-lapse crosswell electromagnetic, seismic, and production data for reservoir monitoring and characterization

2012 ◽  
Author(s):  
L. Liang ◽  
A. Abubakar ◽  
T. M. Habashy
Keyword(s):  
Joint Inversion ◽  
Time Lapse ◽  
Production Data ◽  
Reservoir Monitoring

Joint inversion of time‐lapse seismic and production data for Norne field

2011 ◽  
Author(s):  
Amit Suman ◽  
Juan Luis Fernández‐Martínez ◽  
Tapan Mukerji
Keyword(s):  
Joint Inversion ◽  
Time Lapse ◽  
Production Data

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

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1052
Author(s):  
Baozhong Wang ◽  
Jyotsna Sharma ◽  
Jianhua Chen ◽  
Patricia Persaud
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Reservoir Characterization ◽  
Time Lapse ◽  
Production Data ◽  
Oil Saturation ◽  
Ensemble Machine Learning ◽  
Input Parameters ◽  
Saturation Profiles ◽  
Field Production

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.

Dynamic Petrophysics-Applications of Time-Lapse Reservoir Monitoring in Saudi Arabia

2005 ◽  
Author(s):  
Shouxiang Mark Ma ◽  
Raghu Ramamoorthy ◽  
Abdulrasool Al-Hajari ◽  
Oscar Kelder ◽  
Ashok Srivastava
Keyword(s):  
Saudi Arabia ◽  
Time Lapse ◽  
Reservoir Monitoring

Spatially limited tomographic inversion for time-lapse oil reservoir monitoring

2002 ◽  
Vol 33 (1) ◽  
pp. 18-22
Author(s):  
Toshiyuki Yokota ◽  
Akio Nishida ◽  
Shigeharu Mizohata ◽  
Sunao Muraoka
Keyword(s):  
Time Lapse ◽  
Oil Reservoir ◽  
Tomographic Inversion ◽  
Reservoir Monitoring

scholarly journals Joint Inversion of Time-Lapse Seismic Data

2019 ◽  
Author(s):  
Cesar Barajas-Olalde ◽  
Donald Adams ◽  
Lu Jin ◽  
Jun He ◽  
Nicholas Kalenze ◽  
...  
Keyword(s):  
Seismic Data ◽  
Joint Inversion ◽  
Time Lapse
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