MINI REVIEW article
Front. Earth Sci.
Sec. Georeservoirs
This article is part of the Research TopicThe State-of-Art Techniques of Seismic Imaging for the Deep and Ultra-deep Hydrocarbon Reservoirs - Volume IIIView all 9 articles
Reservoir Prediction Methods Under Sparse Well Conditions in Offshore Fields: Perspectives and Challenges
Provisionally accepted
- 1China University of Petroleum East China, Qingdao, China
- 2Sinopec Shengli Oilfield Company, Dongying, China
- 3Yangtze University, Jingzhou, China
- 4Offshore Oil Production Plant, Sinopec Shengli Oilfield Company, Dongying, China
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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.
Keywords: offshore hydrocarbon, Reservoir prediction, Sparse well data, DATA FUSION, Stochasticmodeling, AI-driven methods
Received: 07 Jun 2025; Accepted: 30 Jan 2026.
Copyright: © 2026 Wang, Cao, Xie, Zhou, yang and zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Yingchang Cao
Pengfei Xie
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