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In the conventional stochastic inversion method, the spatial structure information of underground strata is usually characterized by variograms. However, effectively characterizing the heterogeneity of complex strata is difficult. In this paper, multiple parameters are used to fully explore the underground formation information in the known seismic reflection and well log data. The spatial structure characteristics of complex underground reservoirs are described more comprehensively using multiple statistical characteristic parameters. We propose a prestack seismic stochastic inversion method based on prior information on statistical characteristic parameters. According to the random medium theory, this method obtains several statistical characteristic parameters from known seismic and logging data, constructs a prior information model that meets the spatial structure characteristics of the underground strata, and integrates multiparameter constraints into the likelihood function to construct the objective function. The very fast quantum annealing algorithm is used to optimize and update the objective function to obtain the final inversion result. The model test shows that compared with the traditional prior information model construction method, the prior information model based on multiple parameters in this paper contains more detailed stratigraphic information, which can better describe complex underground reservoirs. A real data analysis shows that the stochastic inversion method proposed in this paper can effectively predict the geophysical characteristics of complex underground reservoirs and has a high resolution.

Carbonate, tight sandstone, and shale reservoirs have many pore types, and the relationship between the porosity and elastic parameters is extremely discrete due to the complex pore shape. This paper presents a method for predicting reservoir pore types based on pore shape substitution. The pore shape substitution allows for accurately characterizing the changes in the elastic properties of the rock with the changes in pore shape, assuming there are no changes in terms of minerals, porosity, or fluids. By employing a multiple-porosity variable critical porosity model, the effective pore aspect ratio could be inverted from the velocities of the rock. To perform pore shape substitution, we could replace the effective pore aspect ratio with another pore aspect ratio or increase/decrease the volume content of different pore shapes. The reservoir pore types could be evaluated by comparing the differences in the reservoir velocities before and after the substitution of the pore shape. The test results pertaining to the theoretical model and the well logging data indicated that the pore shape substitution method could be applied to characterize pore types in terms of separating the effects of the pore shapes from the effects of the minerals, porosity, or fluids on the velocities.

Understanding the development of Western historiography as an academic field in China is important for advancing and deepening the development of this field. The historical development of the study...

A linearized rock physics inversion method is proposed to deal with two important issues, rock physical model and inversion algorithm, which restrict the accuracy of rock physics inversion. In this method, first, the complex rock physics model is expanded into Taylor series to get the first-order approximate expression of the inverse problem of rock physics; then the damped least square method is used to solve the linearized rock physics inverse problem directly to get the analytical solution of the rock physics inverse problem. This method does not need global optimization or random sampling, but directly calculates the inverse operation, with high computational efficiency. The theoretical model analysis shows that the linearized rock physical model can be used to approximate the complex rock physics model. The application of actual logging data and seismic data shows that the linearized rock physics inversion method can obtain accurate physical parameters. This method is suitable for linear or slightly non-linear rock physics model, but may not be suitable for highly non-linear rock physics model. Key words: rock physics inversion, linearization, physical parameters, rock physics model, Taylor expansion

IntroductionPriming of tumor-specific T cells is a key to antitumor immune response and inflammation, in turn, is crucial for proper T-cell activation. As antigen-presenting cells can activate T cells, dendritic cells (DCs) loaded with tumor antigens have been used as immunotherapeutics against certain cancer in humans but their efficacy is modest. Necroptosis is a form of programmed cell death that results in the release of inflammatory contents. We previously generated mice with DC deficiency in a negative regulator of necroptosis, Fas-associated death domain (FADD), and found that these mice suffer from systemic inflammation due to necroptotic DCs. We hypothesize that FADD-deficient DCs could serve as a better vaccine than wild-type (WT) DCs against tumors.Materials and methodsFADD-deficient and WT mouse DCs loaded with the relevant tumor peptide were injected onto mice before or after the syngeneic tumor challenge. DC vaccinations were repeated two more times and anti-PD-1 antibodies were coinjected in some experiments. Tumor sizes were measured by caliper, and the percentages of tumor-free mice or mice survived were examined over time. The cytometric analysis was carried out to analyze various immune populations.ResultsIn two separate tumor models, we find that mice receiving FADD-deficient DCs as vaccine rejected tumors significantly better than those receiving a WT DC vaccine. Tumor growth was severely hampered, and survival extended in these mice. More activated CD8 T cells together with elevated cytokines were observed in mice receiving the FADD-deficient DC vaccine. Furthermore, we observed these effects were potent enough to protect against tumor challenge postinjection and can work in conjunction with anti-PD-1 antibodies to reduce the tumor growth.ConclusionsNecroptotic-susceptible DCs are better antitumor vaccines than WT DCs in mice. Our findings suggest that necroptosis-driven inflammation by DCs may be a novel avenue to generating a strong adaptive antitumor response in the clinical setting.

With the rise in the widespread application of high-temperature heat pump (HTHP) systems, the system temperature across the large volume, high condensing temperature, and sink unbalanced proportion of heat supply, and other characteristics of the HTHP system need to be optimized as the primary solution. To improve the thermodynamic performance of the system at high temperatures across the process, the effects of the intermediate cooling structure circulation mode and hierarchical heating on the HTHP system were analyzed in this work. Furthermore, the HTHP system was optimized by adjusting the appropriate diversion coefficients in order to improve its coefficient of performance (COP). The optimum value of COP and the corresponding coefficients of diversion for the different process requirements were calculated using R245fa as the refrigerant and perturbing the coefficients of diversion in combination with a simulated annealing algorithm. When the condensing temperature was between 120–140 °C, the COP of the optimized system was observed to be 15.93–20.48% higher than that of the traditional two-stage compression system, and the system was found to exhibit a significant unit heat production and refrigeration capacity.

EGS system is a single-layer heat exchanger system. Because of its output requirements, the temperature drop of the heat exchanger layer is very significant in the expected production stage. At the same time, due to the uncontrollable conditions of the storage tank, the generation of calorific value can not be accurately controlled, so the optimization of the existing EGS heat transfer research is not comprehensive. The solution to this problem is to design a three-tube and six-control EGS technology, which is based on the structure of multi-layer heat exchangers. This technology can control the stratified flow, ensure the medium production, avoid the problem of large temperature gradient in the heat exchanger layer caused by large single layer flow, and provide some reference for conventional and unconventional oil and gas co-production technology.

In this paper we propose Russell fluid factor direct estimation method based on stochastic seismic inversion. It is a Monte Carlo based strategy for non-linear inversion, which can effectively integrate the highfrequency information of well-logging data and have a higher resolution. And the method is formulated in a Bayesian framework. Firstly, we can calculate the Russell fluid factor using well-logging data and get the a priori information of fluid factor through sequential Gaussian simulation (SGS). Then we construct the likelihood function. Finally, we apply Metropolis algorithm in order to obtain an exhaustive description of the posteriori probability density. In this paper, we use the sequential Gaussian simulation (SGS) in a new implementation way, which can improve the computation speed. According to the numerical calculations, we can conclude that the final results match the model and real well-logging data well and have a higher resolution. In addition, Russell fluid factor we inverted is a sensitive indicator for reservoir fluid identification.

This paper gets the Q through the AVF inversion which uses the new frequency dependent absorptive reflectivity linearization and the least-squares method. The new linear formula expands the suggest that elastic overburden overlays a high attenuation target to those that any (elastic or attenuate) overburden overlays a high attenuation target. So the new linear formula makes the expression more reasonable and more universal. Besides that, AVF inversion is unstable because of wavelet and noise. So we develop a method here of casting the AVF inverse problem as a leastsquares minimization problem. The results show that leastsquares AVF inversion can get exact and stable Q.

Porosity and saturation are important parameters to describe the reservoir properties. Here, we develop a method to inverse porosity and saturation within a Bayesian framework using Markov Chain Monte Carlo (MCMC) method. First, we discuss the changes of P-wave velocity, S-wave velocity, density due to changes in porosity and saturation. Then, we build a three-layer model to do the inversion making use of the full Knott-Zoeppritz equation to get the seismic AVO data. The advantages of MCMC methods are: (1) the accuracy of solution is improved with the prior information joined in, and the uncertainty is greatly reduced; (2) solving the nonlinear problem directly, so the process of inversion is to do the seismic forward in each iteration; (3) the dependence of results and initial values is not strong, because the algorithm are not use the objective function with a single optimal solution; (4) optimization process can jump out of local optimum, and ultimately obtain the global optimal solution. By testing a layered model, shows the MCMC method based on Metropolis-Hastings algorithm is available and can obtain good results by random searching solution space.

The conventional method is not performing well in reservoir parameters prediction because of lacking learning samples. The support vector machine method could help us in this situation. We repeat an experiment to verify the excellent generalization ability of SVM. Four applications of real data processing were done by us, and they were all working very well. The result shows that this method would bring us to a nice place.

Seismic attributes have been widely used since their introduction and have already become a valid analytical tool for lithology prediction and reservoir characterization; wavelet transform based on the matching pursuit analysis approach using a specific type of Gabor-atoms, it can extract useful seismic attributes that reflect reservoir property, and can be used to divide favorable reservoir area; model and real data application shows that the proposed method is simple and effective and can be easily used

Seismic data contain a large amount of geological information, clustering analysis is an effective method to analyze seismic data; it has already become a valid analytical tool for lithology prediction and reservoir characterization; unsupervised fuzzy clustering based on fuzzy c-means algorithm is a very important technology in seismic data analysis, it is capable of creating useful character mappings of the data by reducing a large number of attributes down to one that can be visualized on a map; this kind of method can look for the center of each cluster and membership grades which belong to corresponding cluster; model test and actual application have shown the validity of this method

This paper gives a new approach to suppress noise in seismic data. According to the time-frequency local characteristics of wavelet transform and the optimal filtering characteristics of KL transform, we have advanced this approach. On the basis of the fact that the noise energy is different from reflection energy in each frequency range, prestack seismic data is divided into different frequency band by wavelet transform first, and then it is filtered by KL transform. Synthetic and real data processing results show that the method can desirably eliminate noise.

Wavelets are a relatively recent development in applied mathematics. With the local time-frequency of wavelet transforms, we can deal much more with the signal reflected by thin-mutual strata. Therefore, we have put forward a method to distinguish the seismic sequence using the wavelet analysis theory. Through the seismic sequence analysis using wavelet transforms, we can obtain vertical and horizontal phase profiles from real seismic data. We will distinguish deposition cycle and material components in the vertical phase profile, and compare and track deposition sequence and structure interface horizontally in the horizontal phase profile.

On the basis of frequency time (F-T) analysis for a single seismic trace, this paper studies the 3D F-T analysis of a seismic profile using the wavelet transform. Once the seismic profile is transformed into a 3D X-T-F data cube, seismic sequence analysis can be done on the 3D cube. Distinguishing the seismic sequence cycle and determining the sequence interface location can be realized in the F-T slice. Tracing continuity characteristics of the sequence interface and picking geological body boundaries and tectonic faults can be implemented in the X-T slice. It becomes the basis for 4D F-T analysis of the 3D seismic data cube and for quantitative interpretation on 4D X-Y-T-F cubes.