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Compressional and shear wave velocities (Vp and Vs, respectively) are important elastic parameters to predict reservoir parameters, such as lithology and hydrocarbons. Due to acquisition technologies and economy, the shear wave velocity is generally lacking. Over the last few years, some researchers proposed deep learning algorithms to predict the shear wave velocity using conventional logging data. However, these algorithms focus either on spatial feature extraction for different physical properties of rocks or on sequential feature extraction in the depth direction of rocks. Only focusing on feature extraction in a direction of rocks might lead to a decrease in prediction accuracy. Therefore, we propose a hybrid network of a two-dimensional convolutional neural network and the gated recurrent unit (2DCNN-GRU), which can establish more complex nonlinear relationships between the input and output data based on the spatial features extracted by 2DCNN and the sequential features extracted by GRU. In this study, two cases are used to validate the reliability and prediction accuracy of the proposed network. Comparing the prediction results of 2DCNN, GRU, and the proposed network, the proposed network shows better performance. Meanwhile, for improving the prediction accuracy of the proposed network, the relationship is analyzed between the prediction accuracy of the proposed network and the length of the input sample. [ABSTRACT FROM AUTHOR]

Secondary pores are the main reservoir space and transportation channel of oil and gas in reef limestone reservoir. At present, the main method of calculating secondary porosity is restricted by the morphological characteristics of porosity spectrum, regional artificial influence, and accuracy of calculation. We present a new method for calculating secondary porosity of reef limestone reservoir by the nuclear magnetic resonance T 2 spectrum which is calibrated by casting thin section. We begin with analyzing and determining the high correlation between the surface porosity of casting thin section and the total porosity. The objective is confirming the feasibility of the method of calculating secondary porosity by using thin-section information calibrate. Then, we use the surface porosity of thin section as the calibrating data and find the T 2 relaxation time corresponding to the best correlation between the secondary porosity and the secondary surface porosity of casting thin section, that is, the T 2 cutoff value of secondary porosity, through the Monte Carlo method. Finally, we calculate the secondary porosity by using the functional relationship between the secondary surface porosity and the surface porosity. The statistical analysis shows that the method of calculating secondary porosity effectively improves the calculation accuracy of secondary porosity. The secondary porosity calculation results have a high correlation with the reservoir productivity. [ABSTRACT FROM AUTHOR]

The shale oil of the Lucaogou Formation in the Jimusaer Sag of the Junggar Basin was divided into two sweet spots for exploration and development. Crude oil in the upper and lower sweet spots comes from the upper and lower source rocks. After years of exploration, it has been found that the crude oil in the lower sweet spot has worse physical properties than that of the upper sweet spot. In this study, through the physical and geochemical analysis of crude oil in the upper and lower sweet spots, combined with the organic petrological observation of the upper and lower source rocks, the cause of the poor physical properties of the crude oil in the lower sweet spot has been identified. n-Alkanes in the saturated hydrocarbons of crude oil in the upper and lower sweet were complete while odd-to-even predominance was evident, indicating that the poor physical properties of the crude oil are unrelated to biodegradation. In addition, the correlation between the biogenic parameters and the physical properties of crude oil was analyzed, finding that the difference in crude oil is mainly related to the composition of biogenic precursors of upper and lower source rocks. Combined with organic petrological observation, the lower source rock was found to be rich in telalginite (green algae), which is therefore the primary reason for the difference in physical properties. In comparing results from the characteristics of crude oil biomarkers from both the upper and lower sweet spots, crude oils in the upper sweet spot are similar to each other, indicating that the enrichment of crude oil has experienced a certain migration. In contrast, the differences in biomarkers between the crude oils of the lower sweet spot were relatively large and changed regularly with depth, suggesting the self-generated and self-stored characteristics of crude oil enrichment. At the same time, it was found that the crude oil in the lower sweet spot is also affected by the maturity of adjacent source rocks under the condition of a consistent parent material source. Overall, it was determined that the lower the maturity of source rocks, the poorer the physical property of the crude oil produced. [ABSTRACT FROM AUTHOR]