Evaluation of Gas Hydrate Saturation Based on Joint Acoustic–Electrical Properties and Neural Network Ensemble

Abstract

Natural gas hydrates have great strategic potential as an energy source and have become a global energy research hotspot because of their large reserves and clean and pollution-free characteristics. Hydrate saturation affecting the electrical and acoustic properties of sediments significantly is one of the important parameters for the quantitative evaluation of natural gas hydrate reservoirs. The accurate calculation of hydrate saturation has guiding significance for hydrate exploration and development. In this paper, experiments regarding methane hydrate formation and dissociation in clay-bearing sediments were carried out based on the Ultrasound Combined with Electrical Impedance (UCEI) system, and the measurements of the joint electrical and acoustic parameters were collected. A machine learning (ML)-based model for evaluating hydrate saturation was established based on electrical–acoustic properties and a neural network ensemble. It was demonstrated that the average relative error of hydrate saturation calculated by the ML-based model is 0.48%, the average absolute error is 0.0005, and the root mean square error is 0.76%. The three errors of the ensemble network are lower than those of the Archie formula and Lee weight equation. The ML-based modeling method presented in this paper provides insights into developing new models for estimating the hydrate saturation of reservoirs.

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