Integrated cluster analysis and artificial neural network modeling for steam-assisted gravity drainage performance prediction in heterogeneous reservoirs

Cognitive Data-Driven Proxy Modeling for Performance Forecasting of Waterflooding Process

“…The other issue rather than over-fitting due to the network configuration can be the existence of hidden structures within the data set. In this case, a cluster analysis and principal components analysis can be applied in upcoming research works to detect the internal hidden patterns within the input attributes of the underlying process and make recommendations for dimensionality reduction to have independent input parameters for cognitive data-driven proxy modeling [39].…”

“…In particular, neural networks have been utilized in recent years as a proxy model to predict heavy oil recoveries [34][35][36][37][38][39], to perform EOR (enhanced oil recovery) screening [40][41][42]to characterize reservoir properties in unconventional plays [43], and to evaluate performance of a CO 2 sequestration process [44]. As a data-driven proxy modeling workflow numerical reservoir simulation models are subjected to the commercial simulator to build the comprehensive training data set.…”

“…This representative data set consist of pertinent input variables labelling reservoir uncertainty due to the geological heterogeneities and crucial injection/production constraints with the corresponding desired objective function output values including cumulative oil production profile and ultimate recovery factor for the underlying recovery process. Artificial neural network models are trained employing the representative training data set and eventually applied as a data-driven proxy modeling alternate to forecast the desired objective function target values including cumulative oil production profile and ultimate recovery factor during the SAGD process [38,39].…”

Practical implementation of knowledge-based approaches for steam-assisted gravity drainage production analysis

“…It is obvious that application of ANN in analysis of SAGD process in heterogeneous reservoirs is still lacking. Amirian, Leung, Zanon, and Dzurman (2014) applied ANN to estimate oil production from individual well pairs in layered heterogeneous reservoirs using a synthetic training dataset constructed from experimental design and numerical simulations. Although their results demonstrated significant potential in applying these data-driven approaches for recovery prediction through synthetic dataset, the feasibility of their approaches to actual field data was not demonstrated.…”

Modeling Static VAR Compensators Using Adaptive Neural Network

“…artificial neural network (ANN) is employed to predict steam-assisted gravity drainage production in heterogeneous reservoirs. 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 [15]. The artificial neural network (ANN) models have been used for representing and modelling the velocity distributions of combined open channel flows.…”