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A field scale productivity analysis is required for the development of gas hydrate in marine sedimentary layers to verify the field applicability of production techniques and to improve productivity. In this study, the productivity resulting from the application of depressurization using multi-wells for the development of gas hydrate in the Ulleung Basin, East Sea of Korea, was determined. A numerical analysis model reflecting the conditions of candidate sites for the Ulleung Basin was constructed, and the productivity and dissociation behavior were comparatively analyzed. The pressure propagation and gas hydrate dissociation region by the multi-wells were wider and the productivity was higher than that of a single well. Different depressurization effects according to the spacing of multi-wells affected productivity. The results provide basic data for productivity analysis when establishing a field test production plan for the Ulleung Basin. [ABSTRACT FROM AUTHOR]

In this study, we propose a novel workflow to predict the production of existing and new multi-wells. To perform reliable production forecasting on heterogeneous shale formations, the features of these formations must be analyzed by classifying the formations into various groups; the groups have different production characteristics depending on the key factors that affect the shale formation. In addition, the limited data obtained from nearby existing multi-wells should be used to estimate the production of new wells. The key factors that affect shale formation were derived from the correlation and principal component analysis of available production-related attributes. The production of existing wells was estimated by classifying them into groups based on their production characteristics. These classified groups also identified the relationship between hydraulic fracturing design factors and productivity. To estimate the production of new wells (blind wells), we generated groups with different production characteristics and leveraged their features to estimate the production. Probabilistic values of the group features were entered into the input layer of the artificial neural network model to consider the variation in the production of shale formations. All the estimated productions exhibited less error than the previous analytical results, suggesting the utilization potential of the proposed workflow.

In this study, we propose a novel workflow to predict the production of existing and new multi-wells. To perform reliable production forecasting on heterogeneous shale formations, the features of these formations must be analyzed by classifying the formations into various groups; the groups have different production characteristics depending on the key factors that affect the shale formation. In addition, the limited data obtained from nearby existing multi-wells should be used to estimate the production of new wells. The key factors that affect shale formation were derived from the correlation and principal component analysis of available production-related attributes. The production of existing wells was estimated by classifying them into groups based on their production characteristics. These classified groups also identified the relationship between hydraulic fracturing design factors and productivity. To estimate the production of new wells (blind wells), we generated groups with different production characteristics and leveraged their features to estimate the production. Probabilistic values of the group features were entered into the input layer of the artificial neural network model to consider the variation in the production of shale formations. All the estimated productions exhibited less error than the previous analytical results, suggesting the utilization potential of the proposed workflow. [ABSTRACT FROM AUTHOR]