Reconhecimento de fáceis em perfis geofísicos de poços com rede neural competitiva

Abstract

The description of a depositional system based on the recognition of sedimentary facies is critical to the oil industry to characterize the petroleum system. In the absence of these facies description by cores or outcrop, we present a methodology based on intelligent algorithm able to identify facies of interest in wireline logs. This methodology uses a competitive neural network to extract geological information from the physical properties mapped in the M-N plot. The competition among neurons identifies the facies of interest, which have been previously identified in a cored borehole in other non-cored boreholes in the same oil field. The purpose of this methodology is to encode and transmit the geological information gained in cored boreholes to non-cored wells and thus achieve the geological interpretation of the facies of interest in an oil field. This methodology has been evaluated with synthetic data and actual wireline logs from two cored boreholes drilled in the Namorado oil field, Campos Basin, Brazil.