Abstract
It has been predicted that China has about 80 billion tons of oil equivalent of natural gas hydrate resources in its sea. China has conducted at least six drilling and two trial exploitations in the South China Sea, achieving good results. However, these achievements do not necessarily mean that the research area can support commercial exploitation. Only when the natural gas hydrate resources in the research area reach a certain scale will it be valuable for commercial exploitation. A reliable quantitative prediction method is necessary to clarify the scale of gas hydrate in the research area. However, the classical Wood method’s application to the prediction of suspended hydrate saturation in the Shenhu maritime area of China results in a large prediction error; the analysis shows that an unreliable measurement of reservoir parameters is the main reason for the large prediction error. In order to clarify the influence of reservoir parameters, this paper—by analyzing the measurement sources of the parameters of reservoir parameters, firstly indicates that the inaccurate measurement of three reservoir parameters—matrix composition, porosity, and density—is the main cause of prediction error. Then, using the design of different reservoir parameter measurement schemes to conduct comparative analysis, this paper points out that unreliable porosity and density measurement may lead to large prediction errors, while unreliable measurements of matrix composition have a relatively small impact on prediction accuracy. Further analysis shows that the absolute value of the prediction error caused by uncertainty in synthetic reservoir parameter measurement is sometimes larger than the sum of the absolute prediction error caused by the single parameter measurement uncertainty. In addressing the problem of large prediction errors caused by the inaccurate measurement of reservoir parameters, this paper proposes a hydrate saturation prediction method based on non-hydrate correction—called the “Wood-Bao method”. Simulation and actual data studies show that the prediction effect of this method is superior to that of the Wood method.
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