Gas hydrates have received considerable attention due to their important role in flow assurance for the oil and gas industry, their extensive natural occurrence on Earth and extraterrestrial planets, and their significant applications in sustainable technologies including but not limited to gas and energy storage, gas separation, and water desalination Given not only their inherent structural flexibility depending on the type of guest gas molecules and formation conditions, but also the synthetic effects of a wide range of chemical additives on their properties, these variabilities could be exploited to optimise the role of gas hydrates This includes increasing their industrial applications, understanding and utilising their role in Nature, identifying potential methods for safely extracting natural gases stored in naturally occurring hydrates within the Earth, and for developing green technologies This review summarizes the different properties of gas hydrates as well as their formation and dissociation kinetics and then reviews the fast-growing literature reporting their role and applications in the aforementioned fields, mainly concentrating on advances during the last decade Challenges, limitations, and future perspectives of each field are briefly discussed The overall objective of this review is to provide readers with an extensive overview of gas hydrates that we hope will stimulate further work on this riveting field

Gas hydrates in sustainable chemistry

The brittleness indices used in rock mechanics and their application in shale hydraulic fracturing: A review

What controls the mechanical properties of shale rocks? – Part II: Brittleness

The impedance inversion technique plays a crucial role in seismic reservoir properties prediction. However, most existing impedance inversion methods often suffer from spatial discontinuities and instability because each vertical profile is processed independently in the inversion. We tested a transform-domain sparsity promotion simultaneous multitrace impedance inversion method to address this issue. The approach was implemented through minimizing a data misfit term and a transform-domain sparsity constraint term that incorporates the (2D or 3D) structural information into the inversion processing. A 2D synthetic data example was applied to mainly explain the roles of the transform-domain sparsity constraint. We determined that the transform-domain sparsity constraint can help in stabilizing the inversion, reducing the influence of high-wavenumber noise on the inverted result, and exploring spatial continuities of structures. Furthermore, a 3D field data example was used to examine the effectiven...

Simultaneous multitrace impedance inversion with transform-domain sparsity promotion

The fluid term in the Biot-Gassmann equation plays an important role in reservoir fluid discrimination. The density term imbedded in the fluid term, however, is difficult to estimate because it is less sensitive to seismic amplitude variations. We combined poroelasticity theory, amplitude variation with offset (AVO) inversion, and identification of P- and S-wave moduli to present a stable and physically meaningful method to estimate the fluid term, with no need for density information from prestack seismic data. We used poroelasticity theory to express the fluid term as a function of P- and S-wave moduli. The use of Pand S-wave moduli made the derivation physically meaningful and natural. Then we derived an AVO approximation in terms of these moduli, which can then be directly inverted from seismic data. Furthermore, this practical and robust AVO-inversion technique was developed in a Bayesian framework. The objective was to obtain the maximum a posteriori solution for the P-wave modulus, S-wave modulus, and density. Gaussian and Cauchy distributions were used for the likelihood and a priori probability distributions, respectively. The introduction of a low-frequency constraint and statistical probability information to the objective function rendered the inversion more stable and less sensitive to the initial model. Tests on synthetic data showed that all the parameters can be estimated well when no noise is present and the estimated P- and S-wave moduli were still reasonable with moderate noise and rather smooth initial model parameters. A test on a real data set showed that the estimated fluid term was in good agreement with the results of drilling.

AVO inversion and poroelasticity with P- and S-wave moduli

Fluid indicators estimated from seismic dataplay important roles in reservoir characterization and prospect identification. Traditionally, there are a variety of fluid indicators proposed, but they are very likely to provide ambiguous results for fluid identification due to the fact that their sensitivity is dependent upon the mixed effect of pore fluid and rock porosity. To raise the sensitivity of fluid indication, we used the effective porefluid bulk modulus as a fluid indicator. Starting with the poroelastic amplitudevariation with offset (AVO) theory and the corresponding rock-physics model with the homogeneous sorting trend, we derived a new AVO approximation that allowed us toestimatethe effective pore-fluidbulkmodulusina direct fashion. The inversion for the fluid indicator is formulated inBayesian framework withthe Cauchydistributionasa prior constraint. We tested the method on synthetic data and analyzed the feasibility and stability of the inversion. A field dataexampleshowsthatthe effective pore-fluidbulkmodulus can reduce the ambiguity caused by the rock porosity and improve the quality of fluid discrimination in a clastic reservoir. Further research needs to be done on the reservoirs that do not fit the rock-physics model without a sorting trend.

Bayesian inversion for effective pore-fluid bulk modulus based on fluid-matrix decoupled amplitude variation with offset approximation

Seismic fluid identification works as an effective approach to characterize the fluid feature and distribution of the reservoir underground with seismic data. Rock physics which builds bridge between the elastic parameters and reservoir parameters sets the foundation of seismic fluid identification, which is also a hot topic on the study of quantitative characterization of oil/gas reservoirs. Study on seismic fluid identification driven by rock physics has proved to be rewarding in recognizing the fluid feature and distributed regularity of the oil/gas reservoirs. This paper summarizes the key scientific problems immersed in seismic fluid identification, and emphatically reviews the main progress of seismic fluid identification driven by rock physics domestic and overseas, as well as discusses the opportunities, challenges and future research direction related to seismic fluid identification. Theoretical study and practical application indicate that we should incorporate rock physics, numerical simulation, seismic data processing and seismic inversion together to enhance the precision of seismic fluid identification.

Research on seismic fluid identification driven by rock physics

Geofluid discrimination plays an important role in the fields of hydrogeology, geothermics, and exploration geophysics. A geofluid discrimination approach incorporating linearized poroelasticity theory and pre-stack seismic reflection inversion with Bayesian inference is proposed in this study to identify the types of geofluid underground. Upon the review of the development of different geofluid indicators, the fluid modulus is defined as the geofluid indicator mainly affected by the fluid contained in reservoirs. A novel linearized P-wave reflectivity equation coupling the fluid modulus is derived to avoid the complicated nonlinear relationship between the fluid modulus and seismic data. Model examples illustrate the accuracy of the proposed linearized P-wave reflectivity equation comparing to the exact P-wave reflectivity equation even at moderate incident angle, which satisfies the requirements of the parameter estimations with P-wave pre-stack seismic data. Convoluting this linearized P-wave reflectivity equation with seismic wavelets as the forward solver, a pragmatic pre-stack Bayesian seismic inversion method is presented to estimate the fluid modulus directly. Cauchy and Gaussian probability distributions are utilized for prior information of the model parameters and the likelihood function, respectively, to enhance the inversion resolution. The preconditioned conjugate gradient method is coupled in the optimization of the objective function to weaken the strong degree of correlation among the four model parameters and enhance the stability of those parameter estimations simultaneously. The synthetic examples demonstrate the feasibility and stability of the proposed novel seismic coefficient equation and inversion approach. The real data set illustrates the efficiency and success of the proposed approach in differentiating the geofluid filled reservoirs.

Geofluid Discrimination Incorporating Poroelasticity and Seismic Reflection Inversion

Young’s modulus and Poisson’s ratio are related to quantitative reservoir properties such as porosity, rock strength, mineral and total organic carbon content, and they can be used to infer preferential drilling locations or sweet spots. Conventionally, they are computed and estimated with a rock physics law in terms of P-wave, S-wave impedances/velocities, and density which may be directly inverted with prestack seismic data. However, the density term imbedded in Young’s modulus is difficult to estimate because it is less sensitive to seismicamplitude variations, and the indirect way can create more uncertainty for the estimation of Young’s modulus and Poisson’s ratio. This study combines the elastic impedance equation in terms of Young’s modulus and Poisson’s ratio and elastic impedance variation with incident angle inversion to produce as table and direct way to estimate the Young’s modulus and Poisson’s ratio, with no need for density information from prestack seismic data. We initially derive a novel elastic impedance equation in terms of Young’s modulus and Poisson’s ratio. And then, to enhance the estimation stability, we develop the elastic impedance varying with incident angle inversion with damping singular value decomposition (EVA-DSVD) method to estimate the Young’s modulus and Poisson’ sr atio. This method is implemented in a two-step inversion: Elastic impedance inversion and parameter estimation. The introduction of a model constraint and DSVD algorithm in parameter estimation renders the EVA-DSVD inversion more stable. Tests on synthetic data show that the Young’s modulus and Poisson’s ratio are still estimated reasonable with moderate noise. A test on a real data set shows that the estimated results are in good agreement with the results of well interpretation.

Xingyao Yin

Papers

AVO inversion and poroelasticity with P- and S-wave moduli

Bayesian inversion for effective pore-fluid bulk modulus based on fluid-matrix decoupled amplitude variation with offset approximation

Research on seismic fluid identification driven by rock physics

Geofluid Discrimination Incorporating Poroelasticity and Seismic Reflection Inversion

Elastic impedance parameterization and inversion with Young's modulus and Poisson's ratio