André Andrade

Porosity images from well logs

Abstract Porosity images are graphical representations of the lateral distribution of rock porosity estimated from well log data. We present a methodology to produce this geological image entirely independent of interpreter intervention, with an interpretative algorithm approach, which is based on two types of artificial neural networks. The first is based on neural competitive layer and is constructed to perform an automatic interpretation of the classical ρ B – φ N cross-plot, which produces the log zonation and porosity estimation. The second is a feed-forward neural network with radial basis function designed to perform a spatial data integration, which can be divided in two steps. The first refers to well log correlation and the second produces the estimation of lateral porosity distribution. This methodology should aid the interpreter in defining the reservoir geological model, and, perhaps more importantly, it should help him to efficiently develop strategies for oil or gas field development. The results or porosity images are very similar to conventional geological cross-sections, especially in a depositional setting dominates by clastics, where a color map scaled in porosity units illustrates the porosity distribution and the geometric disposition of geological layers along the section.

Fracture analysis in borehole acoustic images using mathematical morphology

Fracture analysis is a geological task that treats so-called fracture attributes (location, direction (strike), slope (dip), and aperture) of the fractures that cross the borehole. It can be performed by direct measures on drill cores or interpreted on acoustic or electromagnetic images of the borehole wall. This activity has gained more importance in Brazil with the recent exploration of carbonate reservoirs of the Brazilian pre-salt. The acoustic imaging logging tool creates two images, the amplitude and the travel time. Only the amplitude image, which reflects the acoustic impedance of the borehole wall, is used to perform the fracture analysis. However, some misinterpretations may occur due to the qualitative nature of this interpretation being very dependent on the geologists expertise. Thus, we present a method of performing automation of the fracture analysis using acoustic amplitude images. This article is divided into two parts. In the first part, we present a mathematical model for the acoustic amplitude images along the borehole trajectory crossed by fractures. This model involves all fracture attributes in the generation of the images and is used to validate the results of fracture analysis. The second part presents the method for automatic fracture analysis. This method is composed of two stages. The first one performs fracture identification using an algorithm based on the mathematical morphology, which acts as an edge-detection tool that delimits the fracture region in the acoustic amplitude images. In the second stage, we apply an interpolating polynomial over the image region previously identified as fracture to extract the fracture attributes. The evaluation of this methodology is performed with synthetic images generated by the presented model that supports the results of the automatic fracture analysis performed using real acoustic amplitude images.