Characteristics and Control Mechanism of Lacustrine Shale Oil Reservoir in the Member 2 of Kongdian Formation in Cangdong Sag, Bohai Bay Basin, China

The significance of lacustrine shale oil has gradually become prominent. Lacustrine shale has complex lithologies, and their reservoir properties are quite various. The multi-scale pore structure of shale controls the law of shale oil enrichment. Typical lacustrine shale developed in the Member 2 of Kongdian Formation in Cangdong sag, Bohai Bay Basin. The lithofacies and multi-scale storage space of this lacustrine shale have been systematically studied. 1. The mineral composition is quite different, and the lithofacies can be summarized into siliceous, carbonate and mixed types. The rock structure can be summarized into laminated, layered, and massive types. 2. The pores are diverse and multi-scale. Interparticle pores contribute the main storage space, especially the interparticle pores of quartz and dolomite. 3. The physical properties of the massive shales is relatively inferior to those of layered and laminatedtypes, and it presents the characteristics of " laminated >layered > massive ". The developed laminae can significantly improve the space and seepage capacity of lacustrine shale. 4. Clay minerals provide the main nano-scale storage space, but they are often filled in pores and reduces the shale brittleness, which have destruction effects.

Lithofacies Types, Assemblage Characteristics, and Sedimentary Evolution Model of Lacustrine Shale in Dongyuemiao Formation of Fuxing Area

The identification and classification of lithofacies’ types are very important activities in shale oil and gas exploration and development evaluation. There have been many studies on the classification of marine shale lithofacies, but research on lacustrine shale lithofacies is still in its infancy. Therefore, in this study, a high-resolution sequence stratigraphic framework is established for the lacustrine shale of the Jurassic Dongyuemiao Formation in the Fuxing area using detailed core observations, thin section identification, XRD analysis, major and trace element analysis, wavelet transform analysis, and detailed identification and characterization of the fossil shell layers in the formation. In addition, the lithofacies’ types and assemblages are identified and characterized, and the lithofacies’ characteristics and sedimentary evolution models in different sequence units are analyzed. The significance of the lithofacies assemblages for shale oil and gas exploration is also discussed. The results show that the shale of the target interval can be divided into 8 parasequence sets; further, 9 types of lithofacies and 6 types of lithofacies assemblages are identified. The 9 lithofacies are massive bioclast-containing limestone shoal facies (LF1), thick-layered fossil shell–containing limestone facies (LF2), layered mud-bearing fossil shell–containing limestone facies (LF3), laminated fossil shell–containing argillaceous shale facies (LF4), laminated fossil shell–bearing argillaceous shale facies (LF5), argillaceous shale facies (LF6), massive storm event–related bioclast-containing facies (LF7), massive argillaceous limestone facies (LF8), and massive mudstone facies (LF9). The sedimentary evolution models of different lithofacies are established as follows: Unit 1 (LF1-LF6) of the Dong-1 Member corresponds to the early stage of a lake transgressive system tract, and Units 2–4 (LF4-LF7) correspond to the middle to late stage of the lake transgressive system tract, which was an anoxic sedimentary environment. The Dong-2 Member (LF7-LF8) and the Dong-3 Member (LF5+LF9) correspond to a lake regressive system tract, which was an oxygen-rich sedimentary environment. Based on the characteristics of the shale lithofacies, sedimentary environment, and the quality of the reservoir, the lithofacies assemblage of LF4–LF7 in Unit 4 is the most favorable type for oil and gas exploration, followed by the lithofacies assemblage in Unit 2; the lithofacies assemblage in the Dong-2 and Dong-3 Members are the worst.

Total Organic Carbon Content Prediction in Lacustrine Shale Using Extreme Gradient Boosting Machine Learning Based on Bayesian Optimization

The total organic carbon (TOC) content is a critical parameter for estimating shale oil resources. However, common TOC prediction methods rely on empirical formulas, and their applicability varies widely from region to region. In this study, a novel data-driven Bayesian optimization extreme gradient boosting (XGBoost) model was proposed to predict the TOC content using wireline log data. The lacustrine shale in the Damintun Sag, Bohai Bay Basin, China, was used as a case study. Firstly, correlation analysis was used to analyze the relationship between the well logs and the core-measured TOC data. Based on the degree of correlation, six logging curves reflecting TOC content were selected to construct training dataset for machine learning. Then, the performance of the XGBoost model was tested using K -fold cross-validation, and the hyperparameters of the model were determined using a Bayesian optimization method to improve the search efficiency and reduce the uncertainty caused by the rule of thumb. Next, through the analysis of prediction errors, the coefficient of determination ( R 2 ) of the TOC content predicted by the XGBoost model and the core-measured TOC content reached 0.9135. The root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) were 0.63, 0.77, and 12.55%, respectively. In addition, five commonly used methods, namely, Δ log R method, random forest, support vector machine, K -nearest neighbors, and multiple linear regression, were used to predict the TOC content to confirm that the XGBoost model has higher prediction accuracy and better robustness. Finally, the proposed approach was applied to predict the TOC curves of 20 exploration wells in the Damintun Sag. We obtained quantitative contour maps of the TOC content of this block for the first time. The results of this study facilitate the rapid detection of the sweet spots of the lacustrine shale oil.