Abstract

SOTO, R; WU, CH. H  and  BUBELA, A. M. DEVELOPMENT OF INFILL DRILLING RECOVERY MODELS FOR CARBONATE RESERVOIRS USING NEURAL NETWORKS AND MULTIVARIATE STATISTICAL AS A NOVEL METHOD. C.T.F Cienc. Tecnol. Futuro [online]. 1999, vol.1, n.5, pp.5-23. ISSN 0122-5383.

This work introduces a novel methodology to improve reservoir characterization models. In this methodology we integrated multivariate statistical analyses, and neural network models for forecasting the infill drilling ultimate oil recovery from reservoirs in San Andres and Clearfork carbonate formations in West Texas. Development of the oil recovery forecast models help us to understand the relative importance of dominant reservoir characteristics and operational variables, reproduce recoveries for units included in the database, forecast recoveries for possible new units in similar geological setting, and make operational (infill drilling) decisions. The variety of applications demands the creation of multiple recovery forecast models. We have developed intelligent software (Soto, 1998), Oilfield Intelligence (Ol), as an engineering tool to improve the characterization of oil and gas reservoirs. Ol integrates neural networks and multivariate statistical analysis. It is composed of five main subsystems: data input, preprocessing, architecture design, graphic design, and inference engine modules. One of the challenges in this research was to identify the dominant and the optimum number of independent variables. The variables include porosity, permeability, water saturation, depth, area, net thickness, gross thickness, formation volume factor, pressure, viscosity, API gravity, number of wells in initial waterflooding, number of wells for primary recovery, number of infill wells over the initial waterflooding, PRUR, IWUR, and IDUR. Multivariate principal component analysis is used to identify the dominant and the optimum number of independent variables. We compared the results from neural network models with the non-parametric approach. The advantage of the non-parametric regression is that it is easy to use. The disadvantage is that it retains a large variance of forecast results for a particular data set. We also used neural network concepts to develop recovery models. The neural network infill drilling recovery model is capable of forecasting the oil recovery with less error variance compared with non-parametric, fuzzy logic and regression models.

Keywords : neural networks; carbonate reservoirs; recovery models; infill drilling.

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