An Integrated Application of Cluster Analysis and Artificial Neural Networks for SAGD Recovery Performance Prediction in Heterogeneous Reservoirs

Abstract

Evaluation of steam-assisted gravity drainage (SAGD) performance that involves detailed compositional simulations is usually deterministic, cumbersome, expensive (manpower and time consuming), and not quite suitable for practical decision making and forecasting, particularly when dealing with highdimensional data space consisting of large number of operational and geological parameters. Data-driven modeling techniques, which entail comprehensive data analysis and implementation of machine learning methods for system forecast, provide an attractive alternative.In this paper, artificial neural network (ANN) is employed to predict SAGD production in heterogeneous reservoirs, an important application that is lacking in existing literature. Numerical flow simulations are performed to construct a training data set consists of various attributes describing characteristics associated with reservoir heterogeneities and other relevant operating parameters. Empirical Arps decline parameters are tested successfully for parameterization of cumulative production profile and considered as outputs of the ANN models. Sensitivity studies on network configurations are also investigated. Principal components analysis (PCA) is performed to reduce the dimensionality of the input vector, improve prediction quality, and limit over-fitting. In a case study, reservoirs with distinct heterogeneity distributions are fed to the model. It is shown that robustness and accuracy of the prediction capability are greatly enhanced when cluster analysis are performed to identify internal data structures and groupings prior to ANN modeling. Both deterministic and fuzzy-based clustering techniques are compared, and separate ANN model is constructed for each cluster. The model is then verified using a testing data set (cases that have not been used during the training stage).The proposed approach can be integrated directly into most existing reservoir management routines. In addition, incorporating techniques for dimensionality reduction and clustering with ANN demonstrates the viability of this approach for analyzing large field data set. Given that quantitative ranking of operating areas, robust forecasting, and optimization of heavy oil recovery processes are major challenges faced by the industry, the proposed research highlights the significant potential of applying effective data-driven modeling approaches in analyzing other solvent-additive steam injection projects.

Practical Implementation of Knowledge-Based Approaches for SAGD Production Analysis

“…In addition, given the correlation between COP and IOPR, it is possible that IOPR is also related to the other 5 input variables. Techniques such as cluster analysis and principal components analysis can be employed in future studies to identify the internal structures among input variables and propose a reduced set of independent input attributes for ANN modeling (Amirian et al 2014).…”

“…ANN and other data-driven modeling techniques have been applied by our group recently to predict SAGD recovery performance in heterogeneous reservoirs (Amirian et al 2013(Amirian et al , 2014. In those works, experimental design technique and numerical simulations were used to create a synthetic data set for ANN modeling.…”

Mechanisms and Operational Strategies of Multi-Lateral Steam-Assisted Gravity Drainage (SAGD) for Heterogeneous Reservoirs

“…The understanding of the shale barrier configuration is the prerequisite to make strategies to control or break it [2,3]. Researchers have made investigations including fast screening of 3D heterogeneous shale barrier configurations [4], data-driven models for characterizing shale barrier configuration [5], numerical studies of the effects of lean zones and evolution characteristics of the SAGD steam chamber [6], application of cluster analysis and artificial neural networks [7], and data analytics and machine learning to characterize shale barrier configuration [8]. They have carried out a 3D physical simulation on dual horizontal well SAGD in heterogeneous reservoirs [9], classified the impact of thermal and permeability heterogeneity on SAGD performance [10], and proposed methods to control reservoir heterogeneity [11].…”