Seismic reflection coefficient equation for the fluid-saturated porous reservoirs under the effect of in situ stress is of great importance to broad fields such as geofluid discrimination, in situ stress prediction, and safe production. However, the stress effect on seismic reflection coefficient in porous reservoirs is poorly understood. To fill this knowledge gap, an approximate seismic reflection coefficient equation for fluid-saturated porous reservoirs under horizontal stress was proposed to model the natural effect of horizontal stress on seismic reflection response. We first revisited the acoustoelasticity (AE) theory and used it to characterize the impact of horizontal stress on skeleton anisotropy. Then, the effective elastic stiffness tensor and the corresponding spatial perturbation in the stressed fluid-saturated porous reservoirs were established under the assumption of fluid incompressibility, which were further employed to derive the approximate seismic reflection coefficient equation based on the elastic inverse scattering theory. By comparing our equation to the exact one, we confirmed its validity within the moderate incidence angles and stresses. The effects of horizontal stress on the P-wave amplitude variation with angle and azimuth (AVAZ) characteristics and seismic response were thoroughly investigated. It was shown that the horizontal stress significantly influenced the amplitude magnitude and seismic phases. Furthermore, the derived reflection coefficient equation was inserted into the Bayesian inversion scheme to estimate the geofluid indicator and other elastic parameters. Synthetic test and filed application showed a reasonable agreement between the inverted result and drilling data, which illustrated the feasibility and stability of our inversion method.

The vertically transverse isotropic (VTI) reservoirs subjected to horizontal in situ stress are frequently encountered in the subsurface, which can be approximately treated as an orthorhombic medium in the framework of acoustoelasticity. However, the seismic estimation for anisotropy parameters in such stress-induced reservoirs is still poorly studied. To address this issue, we derive a linearized reflection coefficient equation in stress-induced orthorhombic media by means of the theories of acoustoelasticity and elastic inverse scattering. The acoustoelasticity theory is utilized to characterize the effective elastic stiffness tensor in a stress-induced orthorhombic medium. The introduction of two dimensionless stress-induced anisotropy (SIA) parameters eliminates the need for third-order elastic constants (3oECs). Then, the stiffness perturbation is presented under the weak-anisotropy hypothesis and is substituted into the scattering function to derive the linearized reflection coefficient equation in stress-induced orthorhombic media. The feasibility of our reflection coefficient equation within the range of moderate stress (or moderate SIA) is confirmed by comparing it to the exact solution. Incorporating the wavelet effect, our reflection coefficient equation as a forward operator is utilized to establish a step-wise Bayesian inversion approach to estimate the anisotropy parameters. Specifically, the SIA parameters are inverted from the amplitude differences in seismic data at different azimuths in the first step. Next, the obtained parameters as the prior dataset are input into the second-step procedure to predict the VTI parameters with partial angle-stacked seismic data. Synthetic and field tests illustrate the robustness and effectiveness of our approach.

In hydrocarbon exploration and development, fluid indicators that can directly identify reservoir hydrocarbons from seismic data are of great significance for seismic interpretation in the fracture-induced horizontal transversely isotropic (HTI) reservoirs. In this paper, based on the unified elastic wave theory of the medium, a new anisotropic fluid indicator is constructed incorporating squirt flow effect between the cracks of rocks. The novel anisotropic fluid indicator can better reflect the influence of pore fluid within the rock on wave propagation. Compared with conventional elastic parameters, the new established anisotropic fluid indicator is more sensitive to oil/gas. Subsequently, by combining the perturbation of the elastic stiffness component in fluid-saturated fractured porous media and the inverse scattering function, an anisotropic PP-wave reflection coefficient is derived in terms of an anisotropic fluid indicator incorporating squirt flow effect and fracture weaknesses. The comparison of Rüger’s equation and the new reflection coefficient equation confirms the validity of our equation for parameter estimation. Further our reflection coefficient equation is used to establish an anisotropic frequency-dependent azimuthal amplitude variation with offset (AVO) inversion method to estimate the anisotropic fluid indicator and fracture weaknesses. The feasibility of the inversion method is verified by the field data application in eastern China, which demonstrates that the anisotropic fluid indicator with the squirt flow effect is certainly sensitive to the gas-bearing fractured reservoirs, and can provide a more effective method for fluid identification in gas-fractured reservoirs.

The hydrocarbon-bearing shale reservoirs can be approximately modeled as the transversely isotropic media with the vertical symmetry axis (VTI), usually exhibiting strong anisotropy and significant contrast among the reservoirs. Therefore, the effects of strong anisotropy should be considered when conducting the seismic interpretation for this type of reservoir through the AVO (amplitude variation with offset) technique. However, the existing approximately linear PP-wave reflection coefficient equations derived based on the assumption of weak anisotropy and weak contrast in elastic parameters will create considerable errors in the case of strong anisotropy and contrast. Quadratic approximation has also been proposed in the case of strong contrast in elastic parameters. To overcome the problem of strong anisotropy and contrast in AVO inversion, we propose a novel PP-wave reflection coefficient equation (expressed by Lamé constants) in terms of five reflectivity parameters (i.e., the ratio of the difference to the mean value between the parameters of two half-spaces) based on the quasi-Zoeppritz equation for the VTI media. Combining the novel reflection coefficient equation and Bayesian inversion framework, we constructed the nonlinear inversion objective function. We chose the multivariate Gaussian distribution as the prior function to reduce the inversion multiplicity. The MCMC sampling algorithm was adopted to estimate the five reflectivity parameters for the nonlinear inversion problem.

N6-methyladenine (6mA) is an important DNA modification form associated with a wide range of biological processes. Identifying accurately 6mA sites on a genomic scale is crucial for under-standing of 6mA's biological functions. However, the existing experimental techniques for detecting 6mA sites are cost-ineffective, which implies the great need of developing new computational methods for this problem. In this paper, we developed, without requiring any prior knowledge of 6mA and manually crafted sequence features, a deep learning framework named Deep6mA to identify DNA 6mA sites, and its performance is superior to other DNA 6mA prediction tools. Specifically, the 5-fold cross-validation on a benchmark dataset of rice gives the sensitivity and specificity of Deep6mA as 92.96% and 95.06%, respectively, and the overall prediction accuracy is 94%. Importantly, we find that the sequences with 6mA sites share similar patterns across different species. The model trained with rice data predicts well the 6mA sites of other three species: Arabidopsis thaliana, Fragaria vesca and Rosa chinensis with a prediction accuracy over 90%. In addition, we find that (1) 6mA tends to occur at GAGG motifs, which means the sequence near the 6mA site may be conservative; (2) 6mA is enriched in the TATA box of the promoter, which may be the main source of its regulating downstream gene expression.

MOTIVATION: DNA N4-methylcytosine (4mC) modification is an important epigenetic modification in prokaryotic DNA due to its role in regulating DNA replication and protecting the host DNA against degradation. An efficient algorithm to identify 4mC sites is needed for downstream analyses. RESULTS: In this study, we propose a new prediction method named SOMM4mC based on a second-order Markov model, which makes use of the transition probability between adjacent nucleotides to identify 4mC sites. The results show that the first-order and second-order Markov model are superior to the three existing algorithms in all six species (Caenorhabditis elegans, Drosophila melanogaster, Arabidopsis thaliana, Escherichia coli, Geoalkalibacter subterruneus and Geobacter pickeringii) where benchmark datasets are available. However, the classification performance of SOMM4mC is more outstanding than that of first-order Markov model. Especially, for E.coli and C.elegans, the overall accuracy of SOMM4mC are 91.8% and 87.6%, which are 8.5% and 6.1% higher than those of the latest method 4mcPred-SVM, respectively. This shows that more discriminant sequence information is captured by SOMM4mC through the dependency between adjacent nucleotides. AVAILABILITY AND IMPLEMENTATION: The web server of SOMM4mC is freely accessible at www.insect-genome.com/SOMM4mC. CONTACT: [email protected] or [email protected].

For hybrid electric vehicles, higher depth of discharge (DOD) indicates more use of battery energy, which can supply more inexpensive propulsions than the fossil fuel but accelerate the battery aging, thus leading to the increase in the equivalent battery life loss cost (EBLLC) related to battery aging. While developing an energy management strategy considering the battery aging effect for plug-in hybrid electric vehicles (PHEVs), a tradeoff between energy consumption cost (ECC) and EBLLC should be made to identify the optimal DOD and minimize the total cost (TC). Furthermore, the optimal DOD is changeable with the initial state of charge (SOC) level. To develop a robust controller to deal with varying initial SOCs for PHEVs, this paper proposes a data-driven method, namely, a three-layer artificial neural network (ANN) to realize real-time power distribution including battery life model. Real-world speed profiles and Pontryagin's minimum principle (PMP) are leveraged to identify the optimal DODs and generate the neural network training data based on cases with a range of initial SOCs. The results clearly demonstrate the robustness of the proposed ANN and also indicate that the data-driven method can effectively reduce the total of ECC and EBLLC compared to typical optimization algorithms without a battery aging model, including the dynamic programming, PMP, and the rule-based strategy. In particular, the ANN can reduce the TC by 19.99%, 25.97%, and 33.13%, respectively, for cases with the initial SOC of 0.95, 0.85, and 0.65, compared to the rule-based method. And the TC of the ANN is comparable to the PMP including the battery degradation model. Moreover, the training sample scale on forecasting accuracy and computational efficiency of the ANN is evaluated. Finally, the computational time of these methods is comprehensively discussed to evaluate the time efficiency of the proposed method.