Fuzzy Logic Applied to Lithofacies and Permeability Forecasting Case Study: Sandstone of Naricual Formation, El Furrial Field, Eastern Venezuela Basin

Abstract

Abstract Lithological description and permeability values estimation using core-derived information of wells with just electric logs is an old problem in reservoir characterization. In essence, the problem consists in finding some explicit relation between log and core data in those wells that contain both types of information. Then, describe reservoir features (derived from core data) of wells with log information only. There are many methodologies (and concepts) that have attacked this subject with major or minor success: statistical techniques, hydraulic units, petrofacies, geostatistical methods, neural nets, fuzzy logic, etc. Applied fuzzy logic is the matter of this article. Fuzzy logic allows quantifying qualitative information and modeling complex not lineal relations. It invites the use of "partial truths" between the "completely false" and "completely true" alternatives and allows the consideration of the whole spectrum of possibilities between these alternatives. When it is applied to reservoir characterization, it accepts the premise that any interpretation is possible although some interpretations are more likely than others. Consequently, values can be proposed to the whole gamut of variations present in the prediction of rock parameters. In this paper, fuzzy logic theory is applied in order to establish a narrow relation between well logs and the seven rock types (lithofacies) of the sedimentological model that describes a very complex reservoir in eastern Venezuela. Core analysis of five wells and the established fuzzy relations are used to get the lithofacies description and possible values of permeability in eighteen wells of the same area having only electrical logs. On the other hand, the efficiency of the algorithm here developed is verified against a control well with both log and core data. In addition, sensibility analysis was done considering the number of cores involved in the data base, and taking into account the capability of this approach describing from the most basic lithology (sand or shale), to the most detailed seven categories above mentioned. The results are very satisfactory and open the possibilities for future research and applications.