The most accurate heuristic-based algorithms for estimating the oil formation volume factor

Abstract

There are various types of oils in distinct situations, and it is essential to discover a model for estimating their oil formation volume factors which are necessary for studying and simulating the reservoirs. There are different correlations for estimating this, but most of them have large errors (at least in some points) and cannot be tuned for a specific oil. In this paper, using a wide range of experimental data points, an artificial neural network model (ANN) has been created. In which its internal parameters (number of hidden layers, number of neurons of each layer and forward or backward propagation) are optimized by a genetic algorithm to improve the accuracy of the model. In addition, four genetic programming (GP)-based models have been represented to predict the oil formation volume factor In these models, the accuracy and the simplicity of each equation are surveyed. As well as, the effect of modifying of the internal parameters of the genetic programming (by using some other values for its nodes or changing the tree depth) on the created model. Finally, the ANN and GP models are compared with fifteen other models of the most common previously introduced ones. Results show that the optimized artificial neural network is the most accurate and genetic programming is the most flexible model, which lets the user set its accuracy and simplicity. Results also recommend not adding another operator to the basic operators of the genetic programming.

Keywords

Genetic programming / Neural network / Modeling

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