Oil reservoir quality assisted by machine learning and evolutionary computation

Abstract

The main target of oil and gas exploration companies is to identify reservoirs and their location with high accuracy. For this purpose, all efforts are applied to reduce uncertainties and risks of water contamination or drilling of dry wells in order to extract as much as possible from the subsurface in the shortest time and at the lowest cost. This chapter shows an alternative for the combination of machine learning techniques, evolutionary computation, and geological interpretations to decrease uncertainties in identifying the location of favorable reservoirs. For this purpose, seismic and well log data from a sand Brazilian field were analyzed. The identification of sandy facies as conducers was made by means of self-organizing maps and extrapolated into signals of seismic data by probabilistic neural networks, converting the image of original amplitude into rock properties. The genetic algorithm was also tested to evaluate different seismic attributes among a group of 37 possibilities to perform the facies prediction task. The image description by multiattributes allowed the definition of the facies distribution modeling. The same process was applied to predict the probability of porosity distribution in seismic data by multilayer perceptron and generalized regression, once again using the genetic algorithm. Through these properties, models from two favorable areas of reservoir were identified in the southwest part of the field. Core description corroborates with the results found by the suggested methodology, indicating its satisfactory application.