Gas hydrate property measurements in porous sediments with resonant ultrasound spectroscopy

McGrail, B. Peter; Ahmed, Sharif; Schaef, Herbert T.; Owen, Antionette T.; Martin, Paul F.; Zhu, Tao

doi:10.1029/2005jb004084

Cited by 6 publications

(5 citation statements)

References 51 publications

(64 reference statements)

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“…To illustrate how the model works, we compare calculated results with the experimental data reported by McGrail et al (2006) 4 . These experiments were conducted with core samples obtained from the Mallik 5L-38 research well and had an estimated pore water salinity of 0.23 M NaCl.…”

Section: Resultsmentioning

confidence: 99%

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A Model to Predict Gas Hydrate Equilibrium and Gas Hydrate Saturation in Porous Media Including Mixed CO2-CH4 Hydrates

Bhangale

Zhu

McGrail

et al. 2006

SPE/DOE Symposium on Improved Oil Recovery

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TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractPhase equilibrium of gas hydrates is altered in porous media as compared to bulk systems. Inside porous media, the thermodynamic potential of chemical components changes due to molecular interactions with pore walls and due to the energy required to maintain capillary equilibrium. A pore-freezing model was developed that can be used to predict gas hydrate equilibrium for pure CH 4 , CO 2 and mixed CH 4 -CO 2 systems in saline pore waters for any given pore size distribution. The important feature of this model is that it takes into consideration salting out phenomena, i.e. increase in salt concentration due to gas hydrate and ice formation. It also gives the gas hydrate saturation in porous medium. A new EOS developed for prediction of bulk gas hydrate equilibrium conditions, based on van der Waals -Platteeuw model, is used in the pore freezing model and can also be used to calculate heats of formation of these hydrates for a given pore size and pressure. The results obtained from the model are compared with experimental results.

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Section: Resultsmentioning

confidence: 99%

“…McGrail et al (2006) 4 then generalized the model to include a statisitical description of the pore size distribution and arrive at the following expression for gas hydrate saturation:…”

Section: Pore Freezing Modelmentioning

confidence: 99%

A Model to Predict Gas Hydrate Equilibrium and Gas Hydrate Saturation in Porous Media Including Mixed CO2-CH4 Hydrates

Bhangale

Zhu

McGrail

et al. 2006

SPE/DOE Symposium on Improved Oil Recovery

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“…(ii) MLMC with homotopy and tgRQI: Next, we consider the homotopy method in the MLMC setting together with tgRQI. We use the conjecture in (51) to set the homotopy parameters such that 1 − t = O(h 2 ), t 0 = 0 and t L = 1. For L = 5, this results in t = {0, 3/4, 15/16, 63/64, 1}.…”

Section: Test Case Imentioning

confidence: 99%

“…Factors such as measurement noise, limitations of mathematical models, the existence of hidden variables, the randomness of input parameters, and other factors contribute to uncertainties in the modelling and prediction of many phenomena. Applications of uncertainty quantification (UQ) specifically related to eigenvalue problems include: nuclear reactor criticality calculations [2,3,25], the derivation of the natural frequencies of an aircraft or a naval vessel [41], band gap calculations in photonic crystals [22,27,55], the computation of ultrasonic resonance frequencies to detect the presence of gas hydrates [51], the analysis of the elastic properties of crystals with the use of rapid measurements [52,61], or the calculation of acoustic vibrations [12,66]. Stochastic convection-diffusion equations are used to describe simple cases of turbulent [24,44,54,63] or subsurface flows [64,67].…”

Section: Introductionmentioning

confidence: 99%

Multilevel Monte Carlo Methods for Stochastic Convection–Diffusion Eigenvalue Problems

Cui,

De Sterck,

Gilbert

et al. 2024

J Sci Comput

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We develop new multilevel Monte Carlo (MLMC) methods to estimate the expectation of the smallest eigenvalue of a stochastic convection–diffusion operator with random coefficients. The MLMC method is based on a sequence of finite element (FE) discretizations of the eigenvalue problem on a hierarchy of increasingly finer meshes. For the discretized, algebraic eigenproblems we use both the Rayleigh quotient (RQ) iteration and implicitly restarted Arnoldi (IRA), providing an analysis of the cost in each case. By studying the variance on each level and adapting classical FE error bounds to the stochastic setting, we are able to bound the total error of our MLMC estimator and provide a complexity analysis. As expected, the complexity bound for our MLMC estimator is superior to plain Monte Carlo. To improve the efficiency of the MLMC further, we exploit the hierarchy of meshes and use coarser approximations as starting values for the eigensolvers on finer ones. To improve the stability of the MLMC method for convection-dominated problems, we employ two additional strategies. First, we consider the streamline upwind Petrov–Galerkin formulation of the discrete eigenvalue problem, which allows us to start the MLMC method on coarser meshes than is possible with standard FEs. Second, we apply a homotopy method to add stability to the eigensolver for each sample. Finally, we present a multilevel quasi-Monte Carlo method that replaces Monte Carlo with a quasi-Monte Carlo (QMC) rule on each level. Due to the faster convergence of QMC, this improves the overall complexity. We provide detailed numerical results comparing our different strategies to demonstrate the practical feasibility of the MLMC method in different use cases. The results support our complexity analysis and further demonstrate the superiority over plain Monte Carlo in all cases.

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“…Another example is the value of the lower quadruple point (ice-water-hydrate-gas) temperature and pressure for CH 4 and CO 2 , and the upper quadruple point (water-hydrate-gas-liquid CO 2 ) for CO 2 hydrate between in-situ and ex-situ conditions; where, the in-situ conditions were determined for a porous media of limited pore-size distribution. In geologic media that have distribution of pore sizes, hydrates would form and dissociate over a range of temperatures and pressures according to the distribution of pore radii and accounting for the impact of salts in the residual pore water (MCGRAIL et al, 2007). The critical conclusion from Goel's (2006) drates in porous media is that to understand the gas exchange technology there is a need for quantitative estimates of formation and dissociation processes in geologic media core samples.…”

Section: Gas Exchangementioning

confidence: 99%

Arctic Energy Technology Development Laboratory

Bandopadhyay¹,

Chamberlin²,

Chaney³

et al. 2008

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The Arctic Energy Technology Development Laboratory was created by the University of Alaska Fairbanks in response to a congressionally mandated funding opportunity through the U.S. Department of Energy (DOE), specifically to encourage research partnerships between the university, the Alaskan energy industry, and the DOE. The enabling legislation permitted research in a broad variety of topics particularly of interest to Alaska, including providing more efficient and economical electrical power generation in rural villages, as well as research in coal, oil, and gas. The contract was managed as a cooperative research agreement, with active project monitoring and management from the DOE.In the eight years of this partnership, approximately 30 projects were funded and completed. These projects, which were selected using an industry panel of Alaskan energy industry engineers and managers, cover a wide range of topics, such as diesel engine efficiency, fuel cells, coal combustion, methane gas hydrates, heavy oil recovery, and water issues associated with ice road construction in the oil fields of the North Slope. Each project was managed as a separate DOE contract, and the final technical report for each completed project is included with this final report.The intent of this process was to address the energy research needs of Alaska and to develop research capability at the university. As such, the intent from the beginning of this process was to encourage development of partnerships and skills that would permit a transition to direct competitive funding opportunities managed from funding sources. This project has succeeded at both the individual project level and at the institutional development level, as many of the researchers at the university are currently submitting proposals to funding agencies, with some success. Executive SummaryThe Arctic Energy Technology Development Laboratory was created by the University of Alaska Fairbanks in response to a congressionally mandated funding opportunity through the U.S. Department of Energy (DOE), specifically to encourage research partnerships between the university, the Alaskan energy industry, and the DOE. The enabling legislation permitted research in a broad variety of topics particularly of interest to Alaska, including providing more efficient and economical electrical power generation in rural villages, as well as research in coal, oil, and gas. The contract was managed as a cooperative research agreement, with active project monitoring and management from the DOE.In the eight years of this partnership, approximately thirty projects were funded and completed. These projects, which were selected using an industry panel of Alaskan energy industry engineers and managers, cover a wide range of topics, such as diesel engine efficiency, fuel cells, coal combustion, methane gas hydrates, heavy oil recovery, and water issues associated with ice road construction in the oil fields of the North Slope. Each project was managed as a separate DOE contract, and the fina...

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Gas hydrate property measurements in porous sediments with resonant ultrasound spectroscopy

Cited by 6 publications

References 51 publications

A Model to Predict Gas Hydrate Equilibrium and Gas Hydrate Saturation in Porous Media Including Mixed CO2-CH4 Hydrates

A Model to Predict Gas Hydrate Equilibrium and Gas Hydrate Saturation in Porous Media Including Mixed CO2-CH4 Hydrates

Multilevel Monte Carlo Methods for Stochastic Convection–Diffusion Eigenvalue Problems

Arctic Energy Technology Development Laboratory

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