A New Method of Geological Modeling for the Hydrocarbon Secondary Migration Research - Yong Zhang; Chao Li; Jun Li; Xiaorong Luo; Ming Cheng; Xiaoying Zhang; Bin Lu (Applied Sciences)
Reservoir geological modeling plays a crucial role in characterizing the spatial distribution and heterogeneity of subsurface reservoirs. The exploration of deep oil and gas resources is not only a global trend in the oil industry but also an inevitable choice for China to ensure energy security and achieve sustainable development in the oil and gas industry. Oil and gas exploration and development technologies have also made continuous breakthroughs, providing strong support for the sustained increase in China’s deep and ultra-deep oil and gas production. Deep and ultra-deep oil and gas reservoirs exhibit high levels of heterogeneity, which are governed by the original sedimentation processes and have a significant impact on oil and gas migration and accumulation. However, traditional pixel-based stochastic reservoir modeling encounters challenges when attempting to effectively simulate multiple facies simultaneously or objects with intricate internal hierarchical architectures. To address the characterization of highly heterogeneous deep and ultra-deep oil and gas reservoirs, this study defines unit architecture bodies, such as point bars, braided rivers, and mouth bars, incorporating internal nested hierarchies. Furthermore, a novel object-based stochastic modeling method is proposed, which leverages seismic and well logging interpretation data to construct and simulate reservoir bodies. The methodology is rooted in the unit element theory. In this approach, sedimentary facies models are stochastically constructed by selecting appropriate unit elements from a database of different sedimentary environments using Sequential Indicator Simulation. The modeling process is constrained by time sequence, event, and sedimentary microfacies distributions. Additionally, the porosity and permeability of each microfacies in the reservoir model are quantitatively characterized based on statistics derived from porosity and permeability data of different strata, sedimentary microfacies, and rock facies in the study area. To demonstrate the superiority and reliability of this novel modeling method, a modeling case is presented. The case utilizes braided river unit elements as objects for the stochastic simulation of the target reservoir. The results of the case study highlight the advantages and robustness of the proposed modeling approach.