Predictive analytics and statistical learning for waterflooding operations in reservoir simulations
Document Type
Conference Proceeding
Publication Date
12-1-2019
Abstract
Recent improvements in technology and computational power have increased interest in the application of data driven modeling (DDM) in petroleum industry. Recovery process evaluation using numerical reservoir simulators are always time consuming and computational intensive with many assumptions and uncertainty involved and inefficient for fast decision making. Thus, DDM have been adopted as an alternative tool to predict production performance under waterflooding which is one of the most important techniques for improving oil recovery. A synthetic waterflooding dataset including production profile, operational parameters, reservoir properties and well locations is constructed using the numerical reservoir simulator. Exploratory data analysis provides several insights into the non-intuitive factors in building the reservoir model. K-means clustering analysis is performed to identify internal groupings among producers. Artificial neural network (ANN) and support vector regression (SVR) are used to decipher the nonlinear relationships between input attributes and waterflooding production. The trained models are subsequently used to predict cumulative oil and watercut on the unseen samples. Clustering analysis reveal that distance to the free water level has a dominant effect and the clustering assignment is controlled by the interplay among input attributes characterizing reservoir properties and relative well locations. Good agreements between predicted outputs from models and simulation targets present the satisfactory generalization performance and predictive capabilities of ANN and SVR methods. ANN model with one output provides the most accurate prediction result on the test data. SVR models provide similar but slightly worse forecast than ANN models. Proposed methodologies in this work can be utilized as a surrogate or complementary model to analyze and predict recovery process in other reservoirs fast and efficiently.
Publication Source (Journal or Book title)
Proceedings - 18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019
First Page
1511
Last Page
1516
Recommended Citation
Liao, X., & Tyagi, M. (2019). Predictive analytics and statistical learning for waterflooding operations in reservoir simulations. Proceedings - 18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019, 1511-1516. https://doi.org/10.1109/ICMLA.2019.00249