Ensemble Machine Learning: The Latest Development in Computational Intelligence for Petroleum Reservoir Characterization

2013 ◽  
Author(s):  
Fatai A. Anifowose
Keyword(s):  
Machine Learning ◽  
Computational Intelligence ◽  
Reservoir Characterization ◽  
Petroleum Reservoir ◽  
Ensemble Machine Learning

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.

Subsurface Characterization Using Ensemble Machine Learning

2021 ◽  
Author(s):  
Gorka G Leiceaga ◽  
Robert Balch ◽  
George El-kaseeh
Keyword(s):  
Machine Learning ◽  
Reservoir Characterization ◽  
Gamma Ray ◽  
Complex Model ◽  
Scientific Model ◽  
Adequate Model ◽  
Subsurface Characterization ◽  
Ensemble Machine Learning ◽  
Data Driven Approach ◽  
High Level

Abstract Reservoir characterization is an ambitious challenge that aims to predict variations within the subsurface using fit-for-purpose information that follows physical and geological sense. To properly achieve subsurface characterization, artificial intelligence (AI) algorithms may be used. Machine learning, a subset of AI, is a data-driven approach that has exploded in popularity during the past decades in industries such as healthcare, banking and finance, cryptocurrency, data security, and e-commerce. An advantage of machine learning methods is that they can be implemented to produce results without the need to have first established a complete theoretical scientific model for a problem – with a set of complex model equations to be solved analytically or numerically. The principal challenge of machine learning lies in attaining enough training information, which is essential in obtaining an adequate model that allows for a prediction with a high level of accuracy. Ensemble machine learning in reservoir characterization studies is a candidate to reduce subsurface uncertainty by integrating seismic and well data. In this article, a bootstrap aggregating algorithm is evaluated to determine its potential as a subsurface discriminator. The algorithm fits decision trees on various sub-samples of a dataset and uses averaging to improve the accuracy of the prediction without over-fitting. The gamma ray results from our test dataset show a high correlation with the measured logs, giving confidence in our workflow applied to subsurface characterization.

scholarly journals Comparison of Ensemble Machine Learning Methods for Soil Erosion Pin Measurements

2021 ◽  
Vol 10 (1) ◽  
pp. 42
Author(s):  
Kieu Anh Nguyen ◽  
Walter Chen ◽  
Bor-Shiun Lin ◽  
Uma Seeboonruang
Keyword(s):  
Machine Learning ◽  
Soil Erosion ◽  
Ensemble Methods ◽  
Machine Learning Algorithms ◽  
Multivariate Adaptive Regression Splines ◽  
Gradient Boosting ◽  
Support Vector ◽  
Ensemble Machine Learning ◽  
Boosting Method ◽  
Bagging Method

Although machine learning has been extensively used in various fields, it has only recently been applied to soil erosion pin modeling. To improve upon previous methods of quantifying soil erosion based on erosion pin measurements, this study explored the possible application of ensemble machine learning algorithms to the Shihmen Reservoir watershed in northern Taiwan. Three categories of ensemble methods were considered in this study: (a) Bagging, (b) boosting, and (c) stacking. The bagging method in this study refers to bagged multivariate adaptive regression splines (bagged MARS) and random forest (RF), and the boosting method includes Cubist and gradient boosting machine (GBM). Finally, the stacking method is an ensemble method that uses a meta-model to combine the predictions of base models. This study used RF and GBM as the meta-models, decision tree, linear regression, artificial neural network, and support vector machine as the base models. The dataset used in this study was sampled using stratified random sampling to achieve a 70/30 split for the training and test data, and the process was repeated three times. The performance of six ensemble methods in three categories was analyzed based on the average of three attempts. It was found that GBM performed the best among the ensemble models with the lowest root-mean-square error (RMSE = 1.72 mm/year), the highest Nash-Sutcliffe efficiency (NSE = 0.54), and the highest index of agreement (d = 0.81). This result was confirmed by the spatial comparison of the absolute differences (errors) between model predictions and observations using GBM and RF in the study area. In summary, the results show that as a group, the bagging method and the boosting method performed equally well, and the stacking method was third for the erosion pin dataset considered in this study.

Sign in / Sign up

Export Citation Format

Share Document