Artificial Neural Network Model for Alkali-Surfactant-Polymer Flooding in Viscous Oil Reservoirs: Generation and Application

Si Le Van,Bo Chon

doi:10.3390/en9121081

Abstract

Chemical flooding has been widely utilized to recover a large portion of the oil remaining in light and viscous oil reservoirs after the primary and secondary production processes. As core-flood tests and reservoir simulations take time to accurately estimate the recovery performances as well as analyzing the feasibility of an injection project, it is necessary to find a powerful tool to quickly predict the results with a level of acceptable accuracy. An approach involving the use of an artificial neural network to generate a representative model for estimating the alkali-surfactant-polymer flooding performance and evaluating the economic feasibility of viscous oil reservoirs from simulation is proposed in this study. A typical chemical flooding project was referenced for this numerical study. A number of simulations have been made for training on the basis of a base case from the design of 13 parameters. After training, the network scheme generated from a ratio data set of 50%-20%-30% corresponding to the number of samples used for training-validation-testing was selected for estimation with the total coefficient of determination of 0.986 and a root mean square error of 1.63%. In terms of model application, the chemical concentration and injection strategy were optimized to maximize the net present value (NPV) of the project at a specific oil price from the just created ANN model. To evaluate the feasibility of the project comprehensively in terms of market variations, a range of oil prices from 30 $/bbl to 60 $/bbl referenced from a real market situation was considered in conjunction with its probability following a statistical distribution on the NPV computation. Feasibility analysis of the optimal chemical injection scheme revealed a variation of profit from 0.42 $MM to 1.0 $MM, corresponding to the changes in oil price. In particular, at the highest possible oil prices, the project can earn approximately 0.61 $MM to 0.87 $MM for a quarter five-spot scale. Basically, the ANN model generated by this work can be flexibly applied in different economic conditions and extended to a larger reservoir scale for similar chemical flooding projects that demand a quick prediction rather than a simulation process.

Highlights

After a period of time of producing oil from natural energy reservoirs using primary and secondary extraction methods, enhanced oil recovery (EOR) methods are commonly considered to extract the large amounts of oil often remaining in reservoirs
The results of the ultimate recovery factor ranged from 21.92% to 86.95%, indicating the sufficient alteration of variables, guaranteeing the quality of data set for further processes, such as sensitivity analysis and optimization
An artificial neural network has been successfully generated for chemical flooding using a set of simulation data which referenced the practical situation of a viscous oil field for a quarter five-spot pattern scale

Summary

Introduction

After a period of time of producing oil from natural energy reservoirs using primary and secondary extraction methods, enhanced oil recovery (EOR) methods are commonly considered to extract the large amounts of oil often remaining in reservoirs. According to the reports of the journal Oil & Gas, the successful polymer flooding project in the Pelican Lake field in Canada could profitably produce more than 9540 m3 /day in 2014, whereas the pilot project in the Gudong field recovered approximately 13.4% of the original oil in place (OOIP) in 1998 by ASP flooding [6,7,8]. Like other EOR methods, the successful application of chemical injections still depends on many factors including reservoir conditions, the type of crude oil or operating conditions, and most importantly the economic feasibility of the project

Objectives

Results

Conclusion