An improved wave equation of fractured-porous media for predicting reservoir permeability

The wave characteristics of fractured-porous media can be utilized for permeability identification; however, further research is necessary to enhance the accuracy of this identification. A novel wave equation for fractured-porous media is formulated, and theoretical analysis suggests its effectiveness in accurately identifying reservoir permeability. The proposed methodology establishes a wave equation for fractured-porous media using the volume averaging method and employs finite difference method on staggered grids to calculate wave field dispersion and attenuation, exploring the influence of fracture network structure and confining pressure on the solution of the wave equation. By analyzing the wave equation under various aspect ratios and confining pressure of fractures, it is observed that these factors significantly affect velocity and attenuation, providing valuable insights into seismic response in fractured-porous media. Furthermore, the research findings reveal promising potential in utilizing the new wave equations specific to fractured-porous media for permeability identification purposes. By constructing a three-dimensional fractured-porous network model, the wave equation for permeability identification can examine the correlation between the parameters of the equation and permeability, and establishes an association between fracture parameters and permeability, paving the way for a novel approach to permeability identification

A data-driven method for total organic carbon prediction based on random forests

The total organic carbon (TOC) is an important parameter for shale gas reservoir exploration. Currently, predicting TOC using seismic elastic properties is challenging and of great uncertainty. The inverse relationship, which acts as a bridge between TOC and elastic properties, is required to be established correctly. Machine learning especially for Random Forests (RF) provides a new potential. The RF-based supervised method is limited in the prediction of TOC because it requires large amounts of feature variables and is very onerous and experience-dependent to derive effective feature variables from real seismic data. To address this issue, we propose to use the extended elastic impedance to automatically generate 222 extended elastic properties as the feature variables for RF predictor training. In addition, the synthetic minority oversampling technique is used to overcome the problem of RF training with imbalanced samples. With the help of variable importance measures, the feature variables that are important for TOC prediction can be preferentially selected and the redundancy of the input data can be reduced. The RF predictor is finally trained well for TOC prediction. The method is applied to a real dataset acquired over a shale gas study area located in southwest China. Examples illustrate the role of extended variables on improving TOC prediction and increasing the generalization of RF in prediction of other petrophysical properties

Amplitude Variation with Angle Inversion for New Parameterized Porosity and Fluid Bulk Modulus

Estimating porosity and fluid bulk modulus is an important goal of reservoir characterization. Based on the model of fluid substitution, we first propose a simplified bulk modulus of a saturated rock as a function of bulk moduli of minerals and fluids, in which we employ an empirical relationship to replace the bulk modulus of dry rock with that of minerals and a new parameterized porosity. Using the simplified bulk modulus, we derive a PP-wave reflection coefficient in terms of the new parameterized porosity and fluid bulk modulus. Focusing on reservoirs embedded in rocks whose lithologies are similar, we further simplify the derived reflection coefficient and present elastic impedance that is related to porosity and fluid bulk modulus. Based on the presented elastic impedance, we establish an approach of employing seismic amplitude variation with offset/angle to estimate density, new parameterized porosity, and fluid bulk modulus. We finally employ noisy synthetic seismic data and real datasets to verify the stability and reliability of the proposed inversion approach. Test on synthetic seismic data illustrates that the proposed inversion approach can produce stable inversion results in the case of signal-to-noise ratio (SNR) of 2, and applying the approach to real datasets, we conclude that reliably results of porosity and fluid bulk modulus are obtained, which is useful for fluid identification and reservoir characterization

Rapid Analysis of Alcohol Content During the Green Jujube Wine Fermentation by FT-NIR

The near-infrared (NIR) spectroscopy combined with partial least square regression (PLS) were applied for the prediction of the alcohol content of jujube wine. The NIR spectroscopy was used to collect the spectral data of the jujube wine samples during fermentation and the data were used to establish the quantitative model of alcohol content to achieve rapid on-line detection. The NIR spectroscopy in the range of 950 to 1650 nm from jujube wine were collected and pre-treated by MSC (Multiplicative Scatter Correction) and FD (First Derivative). The alcohol content was measured with alcohol meter. Spectral wavelength selection and latent variables were optimized for the lowest root mean square errors. The results show that the FD - PLS model, which yielded R2 of 0.9246 and RMSEC of 0.6572, is superior to the MSC- PLS model. Results confirmed that NIR spectroscopy is a promising technique for routine assessment of alcohol content of jujube wine and is a viable and advantageous alternative to the chemical procedures involving laborious extractions. The feasibility of the method was thus verified

Fully Connected U-Net and Its Application on Reconstructing Successively Sampled Seismic Data

One of the major hot topics in seismic data processing is the reconstruction of successively sampled seismic data. There are numerous traditional methods proposed for addressing this issue; however, they still have unavoidable drawbacks, such as high computational cost and sensitive tuning parameters. In this study, we suggest a deep learning model for reconstructing successively sampled seismic data, termed fully connected U-Net (FCU-Net). FCU-Net maintains the high-resolution representations by connecting the parallel different-resolution representations and repeating multi-scale fusion. Such a structure allows FCU-Net to successfully extract multi-scale information, which is beneficial for accurate seismic data reconstruction. Additionally, the extending subnetwork of FCU-Net contains a large number of feature channels and sufficient information interaction between different resolution representations via the composite cascades, which contributes to locating successively sampled traces with big gaps and then performing the seismic interpolation. To verify the effectiveness of FCU-Net, we compare it with state-of-the-art networks, i.e., U-Net and HRNet, using synthetic and field examples. The results show that FCU-Net performs best when interpolating successively sampled seismic data, proving its superiority and availability

Effects of High Dietary Starch Levels on the Growth Performance, Liver Function, and Metabolome of Largemouth Bass (<i>Micropterus salmoides</i>)

In this study, we conducted a 16-week feeding trial to investigate the effects of a high-cassava starch diet on growth performance, liver function, and metabolism in largemouth bass (Micropterus salmoides). We formulated five diets containing varying levels of cassava starch: 12%, 9%, 6%, 3%, and 0% (termed M12, M9, M6, M3, and M0, respectively). We distributed these diets among largemouth bass with the initial body weight of 83.33 ± 0.55 g via an in-pond “raceway” aquaculture system. Our findings suggest that high level (12%) of cassava starch dietary inclusion adversely affected growth performance metrics such as weight gain rate and specific growth rate, along with feed utilization efficiency indicators, including protein efficiency, protein deposition rate, and the apparent digestibility of dry matter and protein. This negative impact was accompanied by a decrease in intestinal amylase activity. Through further transcriptomic analysis, we identified several key genes associated with carbohydrate metabolism, which underwent changes influencing liver function. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed the involvement of these differentially expressed genes (DEGs) in the tricarboxylic acid cycle (TCA cycle). Comparative metabolomics analysis further indicated that the M9 group showed significant enrichment in pathways related to amino acid metabolism and alterations in the levels of metabolites involved in carbohydrate metabolism. In conclusion, our study demonstrates that incorporating up to 9% cassava starch in the diet can enhance growth performance in largemouth bass by stimulating digestive enzyme activities and promoting glucose utilization

Data_Sheet_1_Construction of a nomogram to predict the probability of new vertebral compression fractures after vertebral augmentation of osteoporotic vertebral compression fractures: a retrospective study.PDF

ObjectiveThis study aimed to develop and validate a new nomogram model that can predict new vertebral fractures after surgery for osteoporotic compression fractures to optimize surgical plans and reduce the incidence of new vertebral compression fractures.Methods420 patients with osteoporotic vertebral compression fractures were randomly sampled using a computer at a fixed ratio; 80% of the patients were assigned to the training set, while the remaining 20% were assigned to the validation set. The least absolute shrinkage and selection operator (LASSO) regression method was applied to screen the factors influencing refracture and construct a predictive model using multivariate logistic regression analysis.ResultsThe results of the multivariate logistic regression analysis showed a significant correlation between bone cement leakage, poor cement dispersion, the presence of fractures in the endplate, and refractures. The receiver operating characteristic curve (ROC) results showed that the area under the ROC curve (AUC) of the training set was 0.974 and the AUC of the validation set was 0.965, which proves that this prediction model has a good predictive ability. The brier score for the training set and validation set are 0.043 and 0.070, respectively, indicating that the model has high accuracy. Moreover, the calibration curve showed a good fit with minimal deviation, demonstrating the model’s high discriminant ability and excellent fit. The decision curve indicated that the nomogram had positive predictive ability, indicating its potential as a practical clinical tool.ConclusionCement leakage, poor cement dispersion, and presence of fractures in the endplate are selected through LASSO and multivariate logistic regressions and included in the model development to establish a nomogram. This simple prediction model can support medical decision-making and maybe feasible for clinical practice.</p

Data_Sheet_2_Construction of a nomogram to predict the probability of new vertebral compression fractures after vertebral augmentation of osteoporotic vertebral compression fractures: a retrospective study.PDF

ObjectiveThis study aimed to develop and validate a new nomogram model that can predict new vertebral fractures after surgery for osteoporotic compression fractures to optimize surgical plans and reduce the incidence of new vertebral compression fractures.Methods420 patients with osteoporotic vertebral compression fractures were randomly sampled using a computer at a fixed ratio; 80% of the patients were assigned to the training set, while the remaining 20% were assigned to the validation set. The least absolute shrinkage and selection operator (LASSO) regression method was applied to screen the factors influencing refracture and construct a predictive model using multivariate logistic regression analysis.ResultsThe results of the multivariate logistic regression analysis showed a significant correlation between bone cement leakage, poor cement dispersion, the presence of fractures in the endplate, and refractures. The receiver operating characteristic curve (ROC) results showed that the area under the ROC curve (AUC) of the training set was 0.974 and the AUC of the validation set was 0.965, which proves that this prediction model has a good predictive ability. The brier score for the training set and validation set are 0.043 and 0.070, respectively, indicating that the model has high accuracy. Moreover, the calibration curve showed a good fit with minimal deviation, demonstrating the model’s high discriminant ability and excellent fit. The decision curve indicated that the nomogram had positive predictive ability, indicating its potential as a practical clinical tool.ConclusionCement leakage, poor cement dispersion, and presence of fractures in the endplate are selected through LASSO and multivariate logistic regressions and included in the model development to establish a nomogram. This simple prediction model can support medical decision-making and maybe feasible for clinical practice.</p