Under-compaction and hydrocarbon generation are the main factors affecting pore pressure. The current seismic pore pressure prediction method is to obtain the overpressure trend by estimating the normal compaction trend (NCT) to predict the physical parameters during normal compaction and comparing the measured parameters. However, selecting a single parameter to indicate overpressure may cause insufficient consideration of factors such as hydrocarbon generation. Since hydrocarbon generation requires specific temperature and other conditions, we roughly divide the pore pressure into two parts: under-compaction in the early stage and hydrocarbon generation after reaching the hydrocarbon generation threshold. We propose a petrophysical model for estimating the normal compaction trend before hydrocarbon generation, modify the bulk modulus of the model, and use the bulk modulus method to calculate the pressure generated by under-compaction; the pressure is added to obtain the final pore pressure. In the shale gas work area in the Sichuan Basin, the prediction results are more in line with the actual situation, and the petrophysical analysis shows that the ratio of free hydrocarbon content and kerogen to water is the influencing factor indicating pore pressure. The practicality of the pore pressure prediction formula considering hydrocarbon generation in oil and gas sweet spots is illustrated through an example in the research area.

The classification of shale gas facies from seismic properties is critical for shale gas reservoir characterization. Shale gas facies are affected by many petrophysical properties. Therefore, the characterization of shale facies should be carried out by multiple parameters, which is more reasonable and accurate. However, multi-parameter inversion often leads to unstable results, and coupled properties are generally a way of solving this problem. A Fisher-Bayesian inversion method for estimating shale gas facies is developed in this paper by combining the Fisher projection and Bayesian inversion method. The mathematical method adopted for the inversion is the Bayesian framework. The link between different facies and coupled properties is given by a joint prior distribution. We derive the analytical formulation of the Bayesian inversion under the Gaussian mixture assumption for coupled attributes and different shale gas facies. The proposed approach realizes the fusion of multi-dimensional petrophysical parameters and establishes a shale gas facies prediction method based on coupled properties. The application to real data sets delivers accurate and stable results, where shale gas facies and coupled attributes are accurately predicted and inversed.

A Gaussian mixture Hamiltonian Monte Carlo (HMC) Bayesian method has been developed for the inversion of petrophysical parameters like pyrolysis parameter S1, which is driven by a statistical shale rock-physics model. Pyrolysis parameter S1 can be used to indicate the content of free or adsorbed hydrocarbon in the source rock and it is an important indicator to evaluate the production of shale-oil reservoir. However, most studies on pyrolysis parameters are based on pyrolysis experiments and there is no relevant study to invert pyrolysis parameter S1 from seismic data. Besides, compared with total organic carbon content, pyrolysis S1 is more accurate for evaluating the gas and oil in the shale. Particularly, high values of pyrolysis S1 can directly indicate the content of shale oil. We develop a strategy for assessing shale oil sweet spots through estimating pyrolysis S1 and other petrophysical parameters. Based on the mixture Gaussian assumptions for the prior distribution of the model, we build a joint distribution to link the pyrolysis parameter S1 with elastic attributes, and then derive a formulation to inverse S1 with the Bayesian model. Due to the components of Gaussian mixture, the Hamiltonian Monte Carlo method has been used to sample the posterior distribution. Our study found that the Hamiltonian Monte Carlo method for sampling can improve the efficiency and allow a more robust quantification of the uncertainty; and the application to real seismic data sets shows that the delineation of sweet spots is more accurate combined with pyrolysis S1.

With the development of 5D seismic technology, the stable acquisition of anisotropy information from wide-azimuth seismic data has become a key scientific problem in the seismic inversion of fractured reservoirs. However, existing AVAZ inversion methods suffer from too many parameters to be estimated, large variation in contribution, and unstable inversions. Therefore, we develop an AVAZ inversion method with coherent attribute constraints to solve the problem of unstable inversion of anisotropic parameters. First, we use seismic coherent attributes to build a fracture equivalent probability model containing anisotropic information of the subsurface medium, which is used to simulate large-scale subsurface fractures and faults, thereby improving the reliability and stability of the anisotropic parameter inversion. We carry out the AVAZ inversion method in a Bayesian framework using wide-azimuth seismic data and introduce smoothing background model constraints to reduce the dependence of the inversion on the initial model and improve the stability of the inversion. Moreover, we add Gaussian and fracture probability distribution models to the objective function to improve the reasonableness and stability of the inversion. Then, through the analysis of the contribution of the estimated parameters, we found that the contribution of the isotropic parameters to the reflection coefficient is much larger than that of the anisotropic parameters to the reflection coefficient. Therefore, we developed a stepwise optimization-seeking inversion method for the isotropic and anisotropic parameters, which can reduce the number of the estimated parameters and thus improve the stability of the inversion of the anisotropic parameters. Field data show that this method produces suitable inversion results even at moderate levels of noise. Therefore, we can conclude that the proposed method has good applicability and stability in predicting the anisotropy parameters of fractured shale reservoirs.