Reservoir prediction methods under sparse well conditions in offshore fields: perspectives and challenges

Understanding application of reservoirs prediction methods in offshore hydrocarbon is are increasingly important task. It is made challenging by the sparse well data, low resolution seismic data, and complex geological mode. These data are inherently multimodal and multiscale, and the core of reservoir prediction research lies in how to integrate them through predictive algorithms to generate plausible realizations of the subsurface. To understand the availability of the reservoir prediction methods, we review the state of the field and make recommendations for how to select the method to characterize offshore oilfield reservoirs. The advances of computer hold promise for applying kinds of prediction methods to complete this work. Seismic attribute analysis, stochastic modeling, and AI-driven method play a key role in this effort. With the increasing demand for exploration and development, multiple methods are constructed into a workflow to improve prediction result. Through the comparison and synthesis of existing technologies, this work provides valuable technical guidance for future development and offers important support for the transparent characterization of three-dimensional subsurface geological structures.