Using Machine Learning Methods to Identify Reservoir Compartmentalization in Mature Oilfields from Legacy Production Data

2021 ◽  
Author(s):  
Kamlesh Ramcharitar ◽  
Arti Kandice Ramdhanie
Keyword(s):  
Machine Learning ◽  
Cognitive Abilities ◽  
Production Data ◽  
Learning Methods ◽  
Structural Time Series ◽  
Machine Learning Methods ◽  
Flow Units ◽  
Reservoir Compartmentalization ◽  
Well Completions ◽  
Production Characteristics

Abstract Despite long production histories, operators of mature oilfields sometimes struggle to account for reservoir compartmentalization. Geological-led workflows do not adequately honor legacy production data since inherent bias is introduced into the process of allocating production by interpreted flow units. This paper details the application of machine learning methods to identify possible reservoir compartments based on legacy production data recorded from individual well completions. We propose an experimental data-driven workflow to rapidly generate multiple scenarios of connected volumes in the subsurface. The workflow is premised upon the logic that well completions draining the same connected reservoir space can exhibit similar production characteristics (rate declines, GOR trends and pressures). We show how the specific challenges of digitized legacy data are solved using outlier detection for error checking and Kalman smoothing imputation for missing data in the structural time series model. Finally, we compare the subsurface grouping of completions obtained by applying unsupervised pattern recognition with Hierarchal clustering. Application of this workflow results in multiple possible scenarios for defining reservoir compartments based on production data trends only. The method is powerful in that, it provides interpretations that are independent of subsurface scenarios generated by more traditional workflows. We demonstrate the potential to integrate interpretations generated from more conventional workflows to increase the robustness of the overall subsurface model. We have leveraged the power of machine learning methods to classify more than forty (40) well completions into discrete reservoir compartments using production characteristics only. This effort would be extremely difficult, or otherwise unreliable given the inherent limitations of human spatial, temporal, and cognitive abilities.

scholarly journals Potential for Vertical Heterogeneity Prediction in Reservoir Basing on Machine Learning Methods

Geofluids ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12 ◽  
Author(s):  
Hongqing Song ◽  
Shuyi Du ◽  
Ruifei Wang ◽  
Jiulong Wang ◽  
Yuhe Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Neural Networks ◽  
Rapid Development ◽  
Petroleum Industry ◽  
Production Data ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Vertical Heterogeneity ◽  
Dynamic Production

With the rapid development of computer technology, some machine learning methods have begun to gradually integrate into the petroleum industry and have achieved some achievements, whether in conventional or unconventional reservoirs. This paper presents an alternative method to predict vertical heterogeneity of the reservoir utilizing various deep neural networks basing on dynamic production data. A numerical simulation technique was adopted to obtain the required dataset, which contains dynamic production data calculated under different heterogeneous reservoir conditions. Machine learning models were established through deep neural networks, which learn and capture the characteristics better between dynamic production data and reservoir heterogeneity, so as to invert the vertical permeability. On the basis of model validation, the results show that machine learning methods have excellent performance in predicting heterogeneity with the RMSE of 12.71 mD, which effectively estimated the permeability of the entire reservoir. Moreover, the overall AARD of the predictive result obtained by the CNN method was controlled at 11.51%, revealing the highest accuracy compared with BP and LSTM neural networks. And the permeability contrast, an important parameter to characterize heterogeneity, can be predicted precisely as well, with a derivation of below 10%. This study proposed a potential for vertical heterogeneity prediction in reservoir basing on machine learning methods.

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