Multiscale data fusion reservoir modeling: The case study of the E3h formation, A gas field, X sag (East China Sea)

Abstract

The study of reservoir characterization is challenging and uncertain owing to the low exploration level of gas field A in the East China Sea shelf basin, in which four exploratory wells have been drilled, the absence of drilling information, and the poor quality of seismic data. In this study, multiscale information is synthesized and refined from several sources, types, and dimensions, including experimental data from the deposition physical simulation experiment, field outcrop, logging curves, and seismic inversion. This study conducts neural network lithofacies prediction research and braided river 3D training image construction and forms a method for geological modeling of deep, heterogeneous, and thick reservoirs in offshore gas fields with few-well conditions by fusing multi-scale information. The lithofacies model is established using the multiple point geostatistical SIMPAT algorithm. The physical model is built using facies control and collaborative constraints. These provide methodological reference and geological support to effectively reduce the exploration risk and uncertainty of reservoir characterization in offshore few-well oil and gas fields.