Lithofacies identification and classification are critical for characterizing the hydrocarbon potential of unconventional resources. Although extensive applications of machine learning models in predicting lithofacies have been applied to conventional reservoir systems, the effectiveness of machine learning models in predicting clay-rich, lacustrine shale lithofacies has yet to be tackled. Here, we apply machine learning models to conventional well log data to automatically identify the shale lithofacies of Gulong Shale in the Songliao Basin. The shale lithofacies were classified into six types based on total organic carbon and mineral composition data from core analysis and geochemical logs. We compared the accuracy of Multilayer Perceptron (MLP), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Random Forest models. We mitigated the bias of imbalanced data by applying oversampling algorithms. Our results show that ensemble methods (XGBoost and Random Forest) have a better performance in shale lithofacies identification than the other models do, with accuracies of 0.868 and 0.884, respectively. The organic siliceous shale proposed to have the best hydrocarbon potential in Gulong Shale can be identified with F1 scores of 0.853 by XGBoost and 0.877 by Random Forest. Our study suggests that ensemble machine learning models can effectively identify the lithofacies of clay-rich shale from conventional well logs, providing insight into the sweet spot prediction of unconventional reservoirs. Further improvements in model performances can be achieved by adding domain knowledge and employing advanced well log data.

Predicting the spatial distribution of braided fluvial facies reservoirs is of paramount significance for oil and gas exploration and development. Given that seismic materials enjoy an advantage in dense spatial sampling, many methods have been proposed to predict the reservoir distribution based on different seismic attributes. Nevertheless, different seismic attributes have different sensitivities to the reservoirs, and informational redundancy between them makes it difficult to combine them effectively. Regarding reservoir modeling, multi-point geostatistics represents the distribution characteristics of the braided fluvial facies reservoirs effectively. Despite this, it is very difficult to build high-quality training images. Hence, this paper proposes a three-step method of predicting braided fluvial facies reservoirs based on probability fusion and multi-point geostatistics. Firstly, similar statistical data of modern sedimentation and field paleo-outcrops were processed under the guidance of the sedimentation pattern to construct reservoir training images suitable for the target stratum in the research area. Secondly, each linear combination of selected seismic attributes was demarcated to calculate the principal component value and work out the elementary conditional probability. Lastly, the PR probability integration approach was employed to combine all conditional probabilities and calculate the joint probability. Then the joint probability was combined with training images to build a reservoir distribution model through multi-point geostatistics. We illustrated the detailed workflow of our new method by applying it to a braided fluvial reservoir modeling case in the Bohai Bay Basin, East China. The new method reduced the error of prediction results by 32% and 46% respectively, and the error of water content by 36.5% and 60.3%. This method is a potentially effective technique to predict and characterize the reservoir spatial distribution and modeling in other oil fields with the same geological background.

Tight oil reservoirs are considered important exploration targets in lacustrine basins. High-quality reservoir prediction is difficult as the reservoirs have complex distributions of depositional facies and diagenesis processes. Previous research has found that the diagenesis process of tight oil sandstones varies greatly in different depositional facies. However, diagenesis variation in different depositional facies is still poorly studied, especially in distributary channels of shallow water delta deposits in lacustrine basins. Based on the description of core samples, the observation of rock slices, the interpretation of well logging data, and the analysis of porosity and permeability data, the differences in the lithofacies types, diagenesis processes, and pore structures of different distributary channels have been clarified. Ultimately, a model of diagenesis and reservoir heterogeneity distribution in the shallow-water delta of Chang 8 Member of the Yanchang Formation in the Caijiamiao area of the Ordos Basin has been established. This research indicates that the main distributary channels in the study area are dominated by massive bedding sandstone lithofacies, while the secondary distributary channels are primarily characterized by cross-bedding sandstone lithofacies. There are significant differences in the compaction, dissolution, and cementation of authigenic chlorite and carbonate among different parts of the distributary channels. Plastic mineral components, such as clay and mica, are abundant in sheet sands, and are more influenced by mechanical and chemical compaction. Influenced by the infiltration of meteoric water and hydrocarbon generation, dissolution pores are relatively well-developed in the underwater distributary channel reservoirs. A large amount of carbonate cementation, such as calcite and siderite, is found within the sandstone at the interface between sand and mud. The occurrence of authigenic chlorite exhibits a clear sedimentary microfacies zonation, but there is little difference in the kaolinite and siliceous cementation among different microfacies reservoirs. Finally, a model of diagenetic differences and reservoir quality distribution within dense sand bodies has been established. This model suggests that high-quality reservoirs are primarily developed in the middle of distributary channels, providing a theoretical basis for the further fine exploration and development of oil and gas in the study area.