Technology and Applications for the Interpretation and Modeling of Advanced Sand Body Architectures

1. Introduction

As oil and gas fields continue to develop, water cut gradually increases. The primary focus of oil and gas exploration and development is identifying remaining oil and improving oil recovery [1,2,3,4]. Studying sand body architecture is crucial for controlling the distribution of remaining oil in reservoirs during the later stages of oil and gas field development [5,6,7,8,9,10,11,12].

The aim of reservoir architecture research is to characterize the heterogeneity within reservoirs. In oil and gas reservoirs, this research primarily focuses on the remaining oil and gas, thereby enhancing the recovery rates. Since Miall [13] introduced the architectural analysis method, techniques for interpreting both ancient and modern sediments have continuously advanced. As reservoir architecture theory and related disciplines have evolved, the study of reservoir architecture has undergone several transformations: first, from field outcrops and modern sedimentation to subsurface reservoirs [14]; second, through the comprehensive application of new technologies and methods, such as 3D ground-penetrating radar and unmanned aerial vehicle (UAV) imaging, beyond simple profile outcrop measurements [15]; third, the shift from river sedimentary systems to other types of sedimentary systems.

At present, the interpretation of sand body architecture still faces many challenges, such as how to integrate multiple sources of information to establish quantitative architecture patterns, how to use seismic information to accurately predict architecture under the condition of few wells being present in offshore oil and gas fields, and the need to improve the random modeling method of sand architecture.

2. Review of New Advances

Chen et al. [16] utilize advanced 3D architecture modeling techniques, integrated with horizontal well data, to accurately characterize braided river reservoirs in the Ordos Basin. The study reveals complex sand body stacking patterns, providing valuable insights into reservoir heterogeneity and its direct correlation with gas productivity. The findings emphasize the critical role of detailed reservoir architecture analyses in optimizing gas field development strategies and enhancing hydrocarbon recovery efficiency [16].

Liang et al. [17] analyzed the internal structure of the composite channel sand body using a dense well pattern, with the boundaries defined by seismic data. The superposition relationships between single channels in gravity flow reservoirs can be classified into three types: lateral migration, vertical superposition, and isolated [17].

Qiao et al. [18] proposed an uncertainty evaluation method using Bayesian transformation to minimize the uncertainty in calculating the proportion of facies. The prior distribution of facies is segmented into intervals, with each interval individually modeled. Spatial resampling is then performed for each realization to estimate the likelihood of each facies proportion. Finally, Bayesian transformation is applied to derive the posterior distribution of the facies proportion, narrowing the intervals. This process ultimately reduces the uncertainty of the modeling results [18].

Cheng et al. [19] identified three sedimentary microfacies—river channel, estuary dam, and floodplain—in the Su 36-11 braided river reservoir of the Ordos Basin based on well data. A 2D training image database was developed using well profiles and facies maps, and a multi-point modeling method was used to reconstruct a 3D geological model. The model achieved an error rate of less than 10%, providing valuable support for oil and gas reservoir development [19].

Shang et al. [20] presented a system for accurately recognizing core lithology by extracting key features from core images using a ResNet50 convolutional neural network, achieving an accuracy of 91%. The system lays a foundation for identifying subsurface reservoirs and provides critical data for the construction of three-dimensional geological models. This improved the understanding of reservoir heterogeneity and distribution, supporting efficient oil and gas field development [20].

Ma et al. [21] found that volcanic eruption styles and structures strongly affect the distribution patterns of volcanic reservoirs. Using the Zao35 fault block as an example, volcanic rocks formed through faults acting as volcanic conduits are characterized by effusive eruptions, while volcanic vents near faults typically show a beaded distribution pattern. The superposition of multiple volcanic activity phases collectively leads to the formation of volcanic reservoirs, providing a geological foundation for the subsequent development of volcanic oilfields [21].

Zhou et al. [22] identified three sedimentary microfacies—river channels, estuary dams, and floodplains—in the Su 36-11 braided river reservoir of the Ordos Basin using well data. A 2D training image database is created from well profiles and facies maps and a multi-point modeling method is used to reconstruct a 3D geological model. The model error is less than 10%, supporting the development of oil and gas reservoirs [22].

3. Conclusions

The articles in the Special Issue “Technology and Applications for the Interpretation and Modeling of Advanced Sand Body Architectures” highlight that the interpretation and modeling of sand body architecture are crucial for unlocking the remaining oil potential during the later stages of oil and gas field development. This study focuses on effectively integrating well logging data with seismic data under the guidance of quantitative models and quantitatively characterizing the spatial distribution of different configuration unit levels.

Readers of the Special Issue “Technology and Applications for the Interpretation and Modeling of Advanced Sand Body Architectures” can access key data, methods, and challenges related to the configuration and modeling of sand bodies. They can analyze the current research status and make informed judgments about future trends.