Application of Genetic Algorithm for Geochemical Data Analysis in a Southwestern Oil Field of Iran
Subject Areas :
1 -
Keywords: Genetic algorithm, mutation, crossover, chromosome, geochemical data, petrophysical data,
Abstract :
Total Organic Carbon (TOC) is the most effective factor for evaluating formations prone to hydrocarbon production. Measuring TOC requires expensive and time-consuming geochemical tests, which are typically performed on a limited number of samples. This parameter is derived from Rock-Eval pyrolysis data and plays a crucial role in exploratory objectives and assessing the hydrocarbon potential of a formation. The primary aim of this study is to estimate the geochemical parameter TOC from petrophysical data, which are currently available for all drilled wells at relatively low cost and time. Since the relationship between geochemical and petrophysical data is nonlinear and involves certain complexities, interdisciplinary methods have been employed to establish this correlation. In this regard, the present study applies genetic algorithm operators, such as crossover and mutation, to generate new generations and select elite offspring. For each potential solution, a chromosome is defined, and the genetic algorithm calculates weighting coefficients for the petrophysical data. Using these coefficients alongside well logging data, TOC can be estimated for other wells in the field.
