Active Learning Method for Estimating Missing Logs in Hydrocarbon Reservoirs

Abstract

Characterization and estimation of physical properties are two of the most important key activities for successful exploration and exploitation in the petroleum industry. Pore-fluid pressures as well as estimating permeability, porosity, or fluid saturation are some of the important example of such activities. Due to various problems occurring during the measurements, e.g., incomplete logging, inappropriate data storage, or measurement errors, missing data maybe observed in recorded well logs. This unfortunate situation can be overcome by using soft computing approximation tools that will estimate the missing or incomplete data. Active learning method (ALM) is such a soft computing tool based on a recursive fuzzy modeling process meant to model multi-dimensional approximation problems. ALM breaks a multiple-input single-output system into some single-input single-output sub-systems and estimates the output by an interpolation. The heart of ALM is fuzzy measuring of the spread. In this paper, ALM is used to estimate missing logs in hydrocarbon reservoirs. The regression and normalized mean squared error (MSE) for estimating density log using ALM were equal to 0.9 and 0.042, respectively. The results, including errors and regression coefficients, proved that ALM was successful on processing the density estimation. ALM is illustrated by an example of a petroleum field in the NW Persian Gulf .