No description available.

<p>In Part 1 T-distributed random fields (TRF) are adressed. The main contribution of this paper is the introduction of a new, analytically tractable, heavy-tailed continuous random field model, namely the TRF model. In Gunning (2002) a related random field model is discussed. Based on a motivating example from a well log from the Gullfaks field in the North Sea, it is demonstrated how the T-distribution is able to describe the variability in the geophysical data better than the frequently used GRF model. This flexibility is obtained by introducing a new parameter, the degrees of freedom. The qualities of TRFs are defined in detail, together with results for hiearchical representations, simulation and estimation of model parameters. It is shown that the TRFs are analytically tractable, with several properties equal, or similar, to the GRF. Also, the TRF includes the GRF as a limiting case when the degrees of freedom parameter goes to infinity. The heavy-tailed effect is observable only cross-realization, and not in-realization, implying that several independent realizations of the random field is needed in order to formally estimate the degree of freedom parameter. Both the TRF and GRF models are estimated on a real well-log observations from the Gullfaks field, and the TRF model appears to be superior to existing Gaussian-based models, as well as L´evy-Stable random field models.</p><p>In Part 2 inversion of well log observations and seismic data is performed. The goal is the elastic parameters, P-wave velocity, S-wave velocity and density. The main contribution of this paper is that a spatially continuous approach is used in the inversion, allowing the information to be included in their exact spatial locations, not having to be forced into a grid representation. Also, scaling differences between well log observations and seismic data are implicitely taken care of. The paper is an extension of parts of the work done by Buland and Omre (2003). A priori, the the elastic parameters are defined as a log-Gaussian random field. This results in a posterior distribution for the log of the elastic parameters being approximately a continuous Gaussian random field. Thus, the result of the inversion is analytically available when performing a linearization of the well log observations. As the posterior pdf is given on a spatially continuous form, grid refining is easy. Also, prediction in any arbitrary location can be performed.</p><p>In Part 3 the problem of discretizing continuous random fields is adressed. The main contribution of this paper are general formulas for representation of any random field, by any set of basis functions. Assume a reparameterization of a continuous random field through a given set of spatial basis functions, and a corresponding set of random parameters. That is, the continuous spatial random field is split into a continuous spatial part, and a stochastic part. It is demonstrated how the stochastic properties of the continuous random field are inherited by the discrete set of random parameters, given the set of spatial basis functions. The results are demonstrated for different sets of basis functions, both orthogonal and non-orthogonal. Wavelet bases and results for GRFs are studied in more detail. See Walter and Shen (2001) for more on orthogonal systems.</p><p>In Paper 4 a data analysis on synthetic production data genereated from a percolation system is performed. The main contribution of this paper is the demonstration of to which extent the knowledge of the breakthrough time increases precision in forecast of oil production. This is done by estimation of the two first moments. Clear trends in the behaviour of the empirical mean and variance, when conditioning on different breakthrough times, are observed. This suggests that there might exist general results being independent of the given percolation system. The two first moments are also introduced into a more formal statistical setting; as parameters in a Gaussian distribution. This can be used to quantitatively compare conditional and unconditional production data, and thus quantifying the increased knowledge of future oil production the breakthrough time provides. See Stauffer and Aharony (1992) for an introduction to percolation theory.</p><p>In summary, Parts 1-3 contains the core of the thesis, focusing on near-well description of reservoirs, and the modelling of elastic material parameters as continuous random fields.</p><p>Part 2 applies the frequently used continuous GRF model in a Bayesian inversion procedure, resulting in an analytical, continuous GRF posterior distribution for the elastic material parameters given both well log observations and seismic data. However, it is a well-known fact that the GRF model is not capable of explaining all the variability in geophysical measurements. Part 1 introduces a new analytically tractable, continuous random field model with a more heavy-tailed pdf, the TRF model. When working with continuous random fields, at some point a discretization must take place. Part 3 adresses the problem of discretizing continuous random fields given a set of basis function. Results for any random field in general, and GRFs in particular, are provided. Finally, Part 4 is an empirical study of synthetic production data generated from a percolation system. It thus differs from the first three parts in several ways, mainly in fact that the full field is considered, and that continuous random fields is not the main concern.</p>

<p>The rate of methane hydrate and natural gas hydrate formation was measured in a 9.5 litre stirred tank reactor of standard design. The experiments were performed to better understand the performance and scale-up of a reactor for continuous production of natural gas hydrates. The hydrate formation rate was measured at steady-state conditions at pressures between 70 and 90 bar and temperatures between 7 and 15 °C. Between 44 and 56 % of the gas continuously supplied to the reactor was converted to hydrate.</p><p>The experimental results show that the rate of hydrate formation is strongly influenced by gas injection rate and pressure. The effect of stirring rate is less significant, and subcooling has no observable effect on the formation rate. Hydrate crystal concentration and gas composition do not influence the hydrate formation rate. Observations of produced hydrate crystals indicate that the crystals are elongated, about 5 μm in diameter and 10 μm long. Analysis of the results shows that the rate of hydrate formation is dominated by gas-liquid mass transfer. A mass transfer model, the bubble-to-crystal model, was developed for the hydrate formation rate in a continuous stirred tank reactor, given in terms of concentration driving force and an overall mass transfer coefficient. The driving force is the difference between the gas concentration at the gas-liquid interface and at the hydrate crystal surface. These concentrations correspond to the solubility of gas in water at experimental temperature and pressure and the solubility of gas at hydrate equilibrium temperature and experimental pressure, respectively. The overall mass transfer coefficient is expressed in terms of superficial gas velocity and impeller power consumption, parameters commonly used in studies of stirred tank reactors.</p><p>Experiments and modeling show that the stirred tank reactor has a considerable potential for increased production capacity. However, at higher hydrate production rates the capacity will be limited by heat transfer in the reactor. For a higher production capacity and in scale-up of the hydrate production process, the upstream gas supply system and the downstream separator must be increased in proportion to the reactor capacity.</p>

No description available.

The rate of methane hydrate and natural gas hydrate formation was measured in a 9.5 litre stirred tank reactor of standard design. The experiments were performed to better understand the performance and scale-up of a reactor for continuous production of natural gas hydrates. The hydrate formation rate was measured at steady-state conditions at pressures between 70 and 90 bar and temperatures between 7 and 15 °C. Between 44 and 56 % of the gas continuously supplied to the reactor was converted to hydrate. The experimental results show that the rate of hydrate formation is strongly influenced by gas injection rate and pressure. The effect of stirring rate is less significant, and subcooling has no observable effect on the formation rate. Hydrate crystal concentration and gas composition do not influence the hydrate formation rate. Observations of produced hydrate crystals indicate that the crystals are elongated, about 5 μm in diameter and 10 μm long. Analysis of the results shows that the rate of hydrate formation is dominated by gas-liquid mass transfer. A mass transfer model, the bubble-to-crystal model, was developed for the hydrate formation rate in a continuous stirred tank reactor, given in terms of concentration driving force and an overall mass transfer coefficient. The driving force is the difference between the gas concentration at the gas-liquid interface and at the hydrate crystal surface. These concentrations correspond to the solubility of gas in water at experimental temperature and pressure and the solubility of gas at hydrate equilibrium temperature and experimental pressure, respectively. The overall mass transfer coefficient is expressed in terms of superficial gas velocity and impeller power consumption, parameters commonly used in studies of stirred tank reactors. Experiments and modeling show that the stirred tank reactor has a considerable potential for increased production capacity. However, at higher hydrate production rates the capacity will be limited by heat transfer in the reactor. For a higher production capacity and in scale-up of the hydrate production process, the upstream gas supply system and the downstream separator must be increased in proportion to the reactor capacity.

In Part 1 T-distributed random fields (TRF) are adressed. The main contribution of this paper is the introduction of a new, analytically tractable, heavy-tailed continuous random field model, namely the TRF model. In Gunning (2002) a related random field model is discussed. Based on a motivating example from a well log from the Gullfaks field in the North Sea, it is demonstrated how the T-distribution is able to describe the variability in the geophysical data better than the frequently used GRF model. This flexibility is obtained by introducing a new parameter, the degrees of freedom. The qualities of TRFs are defined in detail, together with results for hiearchical representations, simulation and estimation of model parameters. It is shown that the TRFs are analytically tractable, with several properties equal, or similar, to the GRF. Also, the TRF includes the GRF as a limiting case when the degrees of freedom parameter goes to infinity. The heavy-tailed effect is observable only cross-realization, and not in-realization, implying that several independent realizations of the random field is needed in order to formally estimate the degree of freedom parameter. Both the TRF and GRF models are estimated on a real well-log observations from the Gullfaks field, and the TRF model appears to be superior to existing Gaussian-based models, as well as L´evy-Stable random field models. In Part 2 inversion of well log observations and seismic data is performed. The goal is the elastic parameters, P-wave velocity, S-wave velocity and density. The main contribution of this paper is that a spatially continuous approach is used in the inversion, allowing the information to be included in their exact spatial locations, not having to be forced into a grid representation. Also, scaling differences between well log observations and seismic data are implicitely taken care of. The paper is an extension of parts of the work done by Buland and Omre (2003). A priori, the the elastic parameters are defined as a log-Gaussian random field. This results in a posterior distribution for the log of the elastic parameters being approximately a continuous Gaussian random field. Thus, the result of the inversion is analytically available when performing a linearization of the well log observations. As the posterior pdf is given on a spatially continuous form, grid refining is easy. Also, prediction in any arbitrary location can be performed. In Part 3 the problem of discretizing continuous random fields is adressed. The main contribution of this paper are general formulas for representation of any random field, by any set of basis functions. Assume a reparameterization of a continuous random field through a given set of spatial basis functions, and a corresponding set of random parameters. That is, the continuous spatial random field is split into a continuous spatial part, and a stochastic part. It is demonstrated how the stochastic properties of the continuous random field are inherited by the discrete set of random parameters, given the set of spatial basis functions. The results are demonstrated for different sets of basis functions, both orthogonal and non-orthogonal. Wavelet bases and results for GRFs are studied in more detail. See Walter and Shen (2001) for more on orthogonal systems. In Paper 4 a data analysis on synthetic production data genereated from a percolation system is performed. The main contribution of this paper is the demonstration of to which extent the knowledge of the breakthrough time increases precision in forecast of oil production. This is done by estimation of the two first moments. Clear trends in the behaviour of the empirical mean and variance, when conditioning on different breakthrough times, are observed. This suggests that there might exist general results being independent of the given percolation system. The two first moments are also introduced into a more formal statistical setting; as parameters in a Gaussian distribution. This can be used to quantitatively compare conditional and unconditional production data, and thus quantifying the increased knowledge of future oil production the breakthrough time provides. See Stauffer and Aharony (1992) for an introduction to percolation theory. In summary, Parts 1-3 contains the core of the thesis, focusing on near-well description of reservoirs, and the modelling of elastic material parameters as continuous random fields. Part 2 applies the frequently used continuous GRF model in a Bayesian inversion procedure, resulting in an analytical, continuous GRF posterior distribution for the elastic material parameters given both well log observations and seismic data. However, it is a well-known fact that the GRF model is not capable of explaining all the variability in geophysical measurements. Part 1 introduces a new analytically tractable, continuous random field model with a more heavy-tailed pdf, the TRF model. When working with continuous random fields, at some point a discretization must take place. Part 3 adresses the problem of discretizing continuous random fields given a set of basis function. Results for any random field in general, and GRFs in particular, are provided. Finally, Part 4 is an empirical study of synthetic production data generated from a percolation system. It thus differs from the first three parts in several ways, mainly in fact that the full field is considered, and that continuous random fields is not the main concern.

<p>Thermophysical properties (dissociation enthalpy, heat capacity, metastability) and compositional properties (hydrate number, free water and fractionation) of natural gas hydrate were studied experimentally on samples that contained large amounts of ice. Methods for continuous hydrate production and sampling, and for quantification of the properties were developed. Hydrate was produced from a natural gas of ethane (5 %mol) and propane (3 %mol) in methane.</p><p>A low temperature scanning calorimetry method was developed to measure dissociation enthalpy, heat capacity, hydrate number and free water (ice). During the analysis, the hydrate samples were pressurized to 1.7 MPa with methane and the system operated between the hydrate equilibrium curves of methane and the hydrate forming natural gas. A sample conditioning procedure eliminated thermal effects of desorption as the ice melted. Desorption occurred since the samples were produced and refrigerated to 255 K under a natural gas pressure of 6-10 MPa, but were analyzed and melted under a methane pressure of 1.7 MPa.</p><p>A low temperature isothermal calorimetry method was developed to quantify the metastability properties. Metastability was confirmed for temperatures up to 268 K and quantified in terms of the low dissociation rate.</p><p>Fractionation data were obtained in the range 3.0 to 7.5 MPa and for subcoolings between 2 and 16 K. High pressure and large subcooling is desirable to suppress fractionation. A fractionation model was proposed. The model coincides with the van der Waals-Platteeuw model for zero subcooling. No fractionation is assumed for hypothetical hydrate formation at infinite driving force (subcooling). Between these two extremes an exponential term was used to describe the fractionation. The model predicted fractionation with an accuracy of about 1%abs corresponding to 1-10%rel.</p>

Thermophysical properties (dissociation enthalpy, heat capacity, metastability) and compositional properties (hydrate number, free water and fractionation) of natural gas hydrate were studied experimentally on samples that contained large amounts of ice. Methods for continuous hydrate production and sampling, and for quantification of the properties were developed. Hydrate was produced from a natural gas of ethane (5 %mol) and propane (3 %mol) in methane. A low temperature scanning calorimetry method was developed to measure dissociation enthalpy, heat capacity, hydrate number and free water (ice). During the analysis, the hydrate samples were pressurized to 1.7 MPa with methane and the system operated between the hydrate equilibrium curves of methane and the hydrate forming natural gas. A sample conditioning procedure eliminated thermal effects of desorption as the ice melted. Desorption occurred since the samples were produced and refrigerated to 255 K under a natural gas pressure of 6-10 MPa, but were analyzed and melted under a methane pressure of 1.7 MPa. A low temperature isothermal calorimetry method was developed to quantify the metastability properties. Metastability was confirmed for temperatures up to 268 K and quantified in terms of the low dissociation rate. Fractionation data were obtained in the range 3.0 to 7.5 MPa and for subcoolings between 2 and 16 K. High pressure and large subcooling is desirable to suppress fractionation. A fractionation model was proposed. The model coincides with the van der Waals-Platteeuw model for zero subcooling. No fractionation is assumed for hypothetical hydrate formation at infinite driving force (subcooling). Between these two extremes an exponential term was used to describe the fractionation. The model predicted fractionation with an accuracy of about 1%abs corresponding to 1-10%rel.

This thesis will investigate the effect of fracture capillary pressure in the fractures, and the effect of non-straight fracture relative permeability lines.

The temporary plug and abandonment (P&amp;A) of old wells, and the following slot recovery, will be important for increasing the average rate of recovery from the Norwegian Continental Shelf (NCS). However, these operations faces challenges like high costs, safety concerns, environmental issues, and rapidly growing demand. Maintaining well integrity may be difficult when re-entering old wells. The demand for increased efficiency may lead operators to compromise on safety to finalize projects in time. This thesis tries to give a broad understanding of the well integrity issues on the NCS, and then tie these ndings around the term slot recovery operations. It will be important to understand the aspects which aects the lifetime of a well. Especially the long-term pressure, temperature and chemical effects on casings/tubings are important aspects to be understood. If this is done, one can increase the material lifetime in a well, and thus be able to re-use more of the casing strings in a slot recovery. These measures will help to keep marginal fields protable for a longer period. The thesis has also kept a strong focus on the challenges regarding the planning phase of a slot recovery operation. Of the essential factors in a slot recovery is to verify the old barrier envelope, and based on these findings create a robust operational plan. One of the mistakes which has been done in several slot recoveries is that the plan is created before any tests have been done. Once the plan is set and signed by the management, changes are harder to implement. Testing of the well often reveals unexpected factors which needs to be taken into consideration when planning an operation. Two separate slot recovery operations were also studied. They were carefully chosen to highlight typical issues with a slot recovery, and thus show that the theory fits with the reality. The requirements and guidelines for wells on the NCS also needs to be more customized for slot recovery operations.