A novel classification method applied to well log data calibrated by ontology based core descriptions
Vinicius Medeiros Graciolli · Americanae (AECID Library) · 2018
A method for the automatic detection of lithological types and layer contacts was developed through the combined statistical analysis of a suite of conventional wireline logs, calibrated by the systematic description of cores. The intent of this project is to allow the integration of rock data into reservoir models. The cores are described with support of an ontology-based nomenclature system that extensively formalizes a large set of attributes of the rocks, including lithology, texture, primary and diagenetic composition and depositional, diagenetic and deformational structures. The descriptions are stored in a relational database along with the records of conventional wireline logs (gamma ray, resistivity, density, neutrons, sonic) of each analyzed well. This structure allows defining prototypes of combined log values for each lithology recognized, by calculating the mean and the variance-covariance values measured by each log tool for each of the lithologies described in the cores. The statistical algorithm is able to learn with each addition of described and logged core interval, in order to progressively refine the automatic lithological identification. The detection of lithological contacts is performed through the smoothing of each of the logs by the application of two moving means with different window sizes. The results of each pair of smoothed logs are compared, and the places where the lines cross define the locations where there are abrupt shifts in the values of each log, therefore potentially indicating a change of lithology. The results from applying this method to each log are then unified in a single assessment of lithological boundaries The mean and variance-covariance data derived from the core samples is then used to build an n-dimensional gaussian distribution for each of the lithologies recognized. At this point, Bayesian priors are also calculated for each lithology. These distributions are checked against each of the previously detected lithological intervals by means of a probability density function, evaluating how close the interval is to each lithology prototype and allowing the assignment of a lithological type to each interval. The developed method was tested in a set of wells in the Sergipe-Alagoas basin and the prediction accuracy achieved during testing is superior to classic pattern recognition methods such as neural networks and KNN classifiers. The method was then combined with neural networks and KNN classifiers into a multi-agent system. The results show significant potential for effective operational application to the construction of geological models for the exploration and development of areas with large volume of conventional wireline log data and representative cored intervals.