Well-to-well correlation and identifying lithological boundaries by principal component analysis of well-logs

摘要

Identifying the location of lithological boundaries is one of the essential steps of reservoir characterizations. The manual well-to-well correlation is usually implemented to identify lithological boundaries. Various automated methods were introduced to accelerate this correlation; however, most of them use single well-log data. As each well-log contains specific information of rock and fluid properties, the simultaneous use of various well-logs can enhance the correlation accuracy. We extend an automatic well-to-well correlation approach from the literature to use the benefits of various well-logs by applying principal component analysis on multiple well-logs of a carbonate reservoir. The extracted features (i.e., mean, coefficient of variation, maximum to minimum ratio, trend angle, and fractal dimension) from a reference well are examined across observation wells. The energy of principal components is evaluated to determine the appropriate number of principal components. We examine three different scenarios of applying principal component analysis and determine the best methodology for wellto-well correlation. In the first scenario, the principal component analysis reduces the dependency of statistical attributes extracted from a single well-log. We then apply principal component analysis on multiple well-logs to extract their features (Scenario II). Finally, we check whether principal component analysis can be applied at multiple steps (Scenario III). The analysis of variance and Tukey are used to compare the accuracy of the scenarios. The results show that identifying lithological boundaries in different wells is significantly improved when the principal component analysis approach combines information from multiple well-logs. Generally, it is concluded that principal component analysis is an effective tool for increasing well-to-well correlation accuracy by reducing the dependency of well-to-well correlation parameters (Scenario I) and the feature extraction from log data (Scenario II & III).