Gas Hydrate Saturation Estimates In The Muli Permafrost Area Considering Bayesian Discriminant Functions

In reservoir evaluation, it is essential to estimate the saturation levels of natural gas hydrates. The conventional algorithms of gas hydrate saturation based on geological experience or physical equations are simple multiphysical models that often provide varying results in the absence of core testing, and more complicated is to consider the complexity and instability of gas hydrates reservoirs in permafrost areas. Based on Bayesian statistical theory and three reasonable assumptions of the ideal saturation model, we combined a Bayesian discriminant function and a hyperplane equation to derive and verify two new saturation algorithms: the Saturation algorithm from the Bayesian discriminant function considering a linear correlation (SBDF) and the Saturation algorithm from the Bayesian discriminant function considering the conventional saturation (SBDF-CS). In experiments based on theoretical model data and field logging data from the sandstone and mudstone layers in the Muli permafrost area, the SBDF and SBDF-CS algorithms yielded maximum average saturation error less than 11.1% and 12.2%, only through logging data without core test calibration. The SBDF algorithms rapidly and objectively utilize representative sample data with multi-physical signals. The SBDF algorithm can be used for the initial saturation estimation of gas hydrate in permafrost areas, and the SBDF-CS algorithm is more competent for the accurate calculation of saturation in the comprehensive research.