A Methodological Approach for Reservoir Heterogeneity Characterization Using Artificial Neural Networks

Abstract

ABSTRACT For the past few years Artificial Neural Networks (ANN) have made a strong comeback to the scientific community. They are used in a variety of tasks where adaptive computing can enhance process performance. There has been a handful of papers suggesting the use of artificial neural networks in the petroleum industry1-3. These papers can be classified into two major categories. First category includes papers that recommend the use of ANN in classification of lithologies from well logs. Second category includes papers that employ ANN to pick the proper reservoir model for well testing purposes. This paper introduces a new implementation of the neuro-computing technology in petroleum engineering. It is shown in this study that artificial neural networks possess numerous capabilities, and can be much more useful to petroleum engineers than previously thought. An implementation of artificial neural networks in characterization of reservoir heterogeneity is presented in this paper. A methodology is introduced through which different rock properties in highly heterogeneous reservoirs can be predicted with good accuracy using information deduced from geophysical well logs. Examples of such networks are presented using field data for verification. The underlying reasons (theories) that make achievement of such complex tasks possible are discussed. The notion that artificial intelligence and neural networks in particular have immense potentials in solving complex engineering and scientific problems are addressed. The innovation now lies on the creativity of the researchers to recognize and define petroleum engineering problems that can be addressed by artificial intelligence technology.