Developing a Robust Surrogate Model of Chemical Flooding Based on the Artificial Neural Network for Enhanced Oil Recovery Implications

Mohammad Ali Ahmadi

doi:10.1155/2015/706897

Abstract

Application of chemical flooding in petroleum reservoirs turns into hot topic of the recent researches. Development strategies of the aforementioned technique are more robust and precise when we consider both economical points of view (net present value, NPV) and technical points of view (recovery factor, RF). In current study many attempts have been made to propose predictive model for estimation of efficiency of chemical flooding in oil reservoirs. To gain this end, a couple of swarm intelligence and artificial neural network (ANN) is employed. Also, lucrative and high precise chemical flooding data banks reported in previous attentions are utilized to test and validate proposed intelligent model. According to the mean square error (MSE), correlation coefficient, and average absolute relative deviation, the suggested swarm approach has acceptable reliability, integrity and robustness. Thus, the proposed intelligent model can be considered as an alternative model to predict the efficiency of chemical flooding in oil reservoir when the required experimental data are not available or accessible.

Highlights

The oil and gas upstream industries are recently encountered with the difficulties and challenges of dealing with hydrocarbon resources whose productions with conventional technologies are following an upward trend of technical limitations
The laboratorial generated outputs are used to conclude two parameters, recovery factor (RF) and net present value (NPV), which are used to evaluate the performance of the chemical flooding which is one of the most popular methods of chemical enhanced oil recovery
The integrity and performance of the proposed predictive approaches in estimating recovery factor (RF) and net present value (NPV) from the literature are described in details

Summary

Introduction

The oil and gas upstream industries are recently encountered with the difficulties and challenges of dealing with hydrocarbon resources whose productions with conventional technologies are following an upward trend of technical limitations. Chemical enhanced oil recovery approaches as one of the most effective subsets of tertiary methods are known as a key to unlock the exploitation of referred resources Different methods for this process have been developed, such as polymer, surfactant/polymer (SP), and alkaline/surfactant/polymer (ASP) flooding. The laboratorial generated outputs are used to conclude two parameters, recovery factor (RF) and net present value (NPV), which are used to evaluate the performance of the chemical flooding which is one of the most popular methods of chemical enhanced oil recovery Having knowledge about these two parameters is essentially vital to make decisions if it is beneficial to run the referred operation. The integrity and performance of the proposed predictive approaches in estimating recovery factor (RF) and net present value (NPV) from the literature are described in details

Data Gathering

Artificial Neural Network and Particle Swarm Optimization

Results and Discussion

Conclusions