Strong heterogeneity, complicated lithology, and chaotic seismic reflection characteristics are all common features of glutenite reservoirs. It is challenging to pinpoint the interior lithology and quantitatively describe the heterogeneity of the single-stage glutenite. To explore the distribution and superimposition features of subaqueous fans, the upper Es4 in the Y229 region of Dongying sag was used as an example. We have developed a multitrend fusion constraint modeling method based on deposition patterns. First, the truncated Gaussian simulation method is used to establish the sedimentary subfacies model of the subaqueous fan. Second, one lithologic probability volume is generated according to the proportion of various lithology of different sedimentary subfacies. Then, the lobes model is constructed using the object-based simulation method and the quantitative parameters from outcrop and flume sedimentation simulations. In addition, a different lithology probability volume is determined based on how far a particular lobe is from the centerline. The two probability volumes are combined to create the integrated lithology probability volume, which reflects the planar trend and the internal differences of different lobes. To constrain lithologic modeling, the integrated lithology probability volume is used. By comparing the model results with a single-trend constraint, the findings indicate that the multitrend integration constraint modeling method may more accurately depict the internal variability of the glutenite reservoir. In addition, the lithologic model built on this foundation is consistent with the depositional model.

The Lower Shihezi Formation of the Daniudi gas field in the Ordos Basin is a typical reservoir of a braided river system in an alluvial plain, characterized by extensive braided river development, parallel development from the near source to the center of the basin, and frequent interweaving and cut stacking, as well as a complex deposition process that has seen frequent river channel changes. The braided river belt, braided channel, channel bar inside the river, and interlayer within the channel bar constitute a hierarchical and complicated architectural feature, which poses a great challenge to accurately characterize this type of reservoir for modeling. We proposed a hierarchical, level-by-level embedding, and progressive multiple-point geostatistical modeling strategy that is refined layer by layer according to a 5–3 level architectural unit hierarchy, with the modeling results of each level providing constraints for the next level modeling. The hierarchical geological model based on the combination of qualitative architectural anatomy and quantitative pre-architecture unit scale is critical in guiding the efficient development of the remaining gas in the braided river reservoir in Daniudi.

Beach-bar sand in lacustrine facies represents one of the most significant reservoirs. Depending on the depositional characteristics, it can be further divided into two different sedimentary microfacies, beach sand and the bar sand. Favorable reservoirs are often developed in bar sand. The lower section of the upper part of the 4th member of the Shahejie Formation in the Gao89-1 block is a typical nearshore shallow water beach-bar deposit. Oil distribution is influenced by lithofacies and physical properties. In order to better characterize the heterogeneity within beach-bar sandbodies, a modeling method based on the depositional mode and sandbody volume is proposed. Firstly, a sandbody model is established. On this basis, an algorithm for distinguishing between beach and bar sand based on vertical thickness is proposed. The model is post processed based on the sandbody volume to remove unreasonable sandbodies. The method allows for a more realistic three-dimensional geological model of the beach-bar sands in the study area than the classical two-point geostatistical, object-based, and multi-point simulation method. A facies-controlled modeling approach is used to establish a petrophysical property model on this foundation; the result shows that the property models better reflect the characteristics of the petrophysical distribution in the Gao89-1 block.

The sandy braided river depositional system developed in the Lower Shihezi Formation of the Daniudi gas field, Ordos Basin. It has the characteristics of frequent migration and oscillation of braided channels and large well spacing, making it challenging to portray the braided river sand body in this area, bringing uncertainty to the 3D geological modeling of the reservoir. This study takes the primary gas reservoir H1 member as an example. It establishes a quantitative geological knowledge database for the reservoir by statistically fitting the correlation equations between the braided channel and channel bar in the planes and profiles to reduce the uncertainty of reservoir modeling. This study combines the multisource and multiscale information from modern sedimentation, field outcrops, and tank simulation experiments of the braided river. From the data, the distribution intervals for the thickness and width of the braided channel sand body in the H1 member are 1–22 m and 7–320 m, respectively, and the thickness, width, and length distribution intervals of the sand body in the channel bar are 3–30 m, 80–1500 m, and 240–4200 m, respectively. A 3D training image is established using the object-based simulation method based on the H1 member’s well data and combining the quantitative parameters of various microfacies in the geological knowledge database. The multiple-point geostatistical modeling method is applied to establish a sedimentary microfacies model. The model’s uncertainty is reduced through multi-information fusion constraint modeling, providing a reliable basis for guiding the prediction of the remaining gas in the Daniudi gas field.

The reservoir in focus has braided river delta front deposition, with multiple periods of submerged distributary channels within the reservoir. It also displays frequent cutting and stacking with local-connecting characteristics. Forecasting the sand distribution characteristics between wells in this type of reservoir brings a significant challenge for modeling. The data- and knowledge-driven modeling method proposed is applied to the Sangtamu Oilfield as an example. Obtain channel-scale information from the geological knowledge database. Geological expertise is applied to interpret the characteristics of channel distribution. These results are employed as conditional data in the process of geological modeling. By combining the expertise of experts with multi-sources of geoscientific data, this method can obtain accurate and reliable spatial information about the channel. This information is crucial for stochastic simulation between wells and enables to minimize uncertainty in predicting results. The method is useful for 3D modeling of similar sedimentary bodies or well-sparse areas.