Prediction of Oil and Gas Reservoir Properties using Support Vector Machines
Authors: Anifowose Fatai Adesina , Ewenla AbdlAzeem Oyafemi , Eludiora Safiriyu Ijiyemi 
Published: March 2015

Abstract
Artificial Intelligence techniques have been used in petroleum engineering to predict various reservoir properties such as porosity, permeability, water saturation, lithofacie and wellbore stability. The most extensively used of these techniques is Artificial Neural Networks (ANN). More recent techniques such as Support Vector Machines (SVM) have featured in the literature with better performance indices. However, SVM has not been widely embraced in petroleum engineering as a possibly better alternative to ANN. ANN has been reported to have a lot of limitations such as its lack of global optima. On the other hand, SVM has been introduced as a generalization of the Tikhonov Regularization procedure that ensures its global optima and offers ease of training.
This paper presents a comparative study of the application of ANN and SVM models in the prediction of porosity and permeability of oil and gas reservoirs with carbonate platforms. Six datasets obtained from oil and gas reservoirs in two different geographical locations were used for the training, testing and validation of the models using the stratified sampling approach rather than the conventional static method of data division.
The results showed that the SVM model performed better than the popularly used Feed forward Back propagation ANN with higher correlation coefficients and lower root mean squared errors. The SVM was also faster in terms of execution time.
Hence, this work presents SVM as a possible alternative to ANN, especially, in the characterization of oil and gas reservoir properties. The new SVM model will assist petroleum exploration engineers to estimate various reservoir properties with better accuracy, leading to reduced exploration time and increased production.

1. Introduction
Petrophysical properties such as porosity and permeability are two important properties of oil and gas reservoirs that relate to the amount of fluid in them and their ability to flow. These properties have significant impact on petroleum field operations and reservoir management. They both serve as standard indicators of reservoir quality in the oil and gas industry (Jong-Se, 2005).
Porosity is the percentage of voids and open spaces in a rock or sedimentary deposit. The greater the porosity of a rock, the greater its ability to hold water and other materials, such as oil. It is an important consideration when attempting to evaluate the potential volume of hydrocarbons contained in a reservoir (Schlumberger, 2007a). Permeability is the ease with which fluid is transmitted through a rock's pore space. It is a measure of how interconnected the individual pore spaces are in a rock or sediment (Schlumberger, 2007b). It is a key parameter associated with the characterization of any hydrocarbon reservoir. In fact, many Petroleum Engineering problems cannot be solved without having an accurate permeability value.
Many reports such as Ali (1994) and Mohagheh (1994) have featured the successful application of Artificial Neural Networks (ANN) as the pioneer Artificial Intelligence (AI) technique in oil and gas reservoir characterization over the years. Despite this, ANN has been reported to have some drawbacks (Petrus et al., 1995). The recent introduction of Support Vector Machines (SVM) that is based on the concepts of Tikhonov Regularization and Structural Risk Minimization (SRM) was introduced to overcome some of the limitations of ANN. Many reports such as such as Anifowose and Abdulraheem (2010); and Helmy et al. (2010) have presented SVM as a promising predictive technique in a good number of applications.
This paper focuses on the study and analysis of the comparative performance of ANN and SVM in the prediction of porosity and permeability of some Middle East and American oil and gas reservoirs. To achieve this aim, Section 2 presents a succinct survey of ANN and SVM. Section 3 describes the experimental methodology, structure of datasets and the evaluation criteria for the study. Section 4 presents the results of the study with a detailed discussion while conclusion is presented in Section 5.