<p>This dissertation deals with (1) experimental measurements on petrophysical reservoir engineering and morphological properties of Ekofisk chalk, (2) numerical simulation of core flood experiments to analyze and improve relative permeability data, (3) stochastic reconstruction of chalk samples from limited morphological information, (4) extraction of pore space parameters from the reconstructed samples, development of network model using pore space information, and computation of petrophysical and reservoir engineering properties from network model, and (5) development of 2D and 3D idealized fractured reservoir models and verification of the applicability of several widely used conventional upscaling techniques in fractured reservoir simulation. </p><p>Experiments have been conducted on eight Ekofisk chalk samples and porosity, absolute permeability, formation factor, and oil-water relative permeability, capillary pressure and resistivity index are measured at laboratory conditions. Mercury porosimetry data and backscatter scanning electron microscope images have also been acquired for the samples. </p><p>A numerical simulation technique involving history matching of the production profits is employed to improve the relative permeability curves and to analyze hysteresis of the Ekofisk chalk sample. The technique was found to be a powerful tool to supplement the uncertainties in experimental measurements. </p><p>Porosity and correlation statistics obtained from backscatter scanning electron microscope image are used to reconstruct microstructures of chalk and particulate media. The reconstruction technique involves a simulated annealing algorithm, which can be constrained by an arbitrary number of morphological parameters. This flexibility of the algorithm is exploited to successfully reconstruct particulate media and chalk samples using more that one correlation function. A technique based on conditional simulated annealing has been introduced for exact reproduction of vuggy porosity in chalk in the form of foraminifer shells. A hybrid reconstruction technique that initialized the simulated annealing reconstruction with input generated using the Gaussian random field model has also been introduced. The technique was found to accelerate significantly the rate of convergence of the simulated annealing method. This finding is important because the main advantage of the simulated annealing method, namely its ability to impose a variety of reconstruction constraints, is usually compromised by its very slow rate of convergence.</p><p>Absolutely permeability, formation factor and mercury-air capillary pressure are computed from simple network models. The input parameters for the network models were extracted from a reconstructed chalk sample. The computed permeability, formation factor and mercury-air capillary pressure correspond well with the experimental data. The predictive power of a network model for chalk is further extended through incorporating important pore-level displacement phenomena and realistic description of pore space geometry and topology. Limited results show that the model may be used to compute absolute and relative permeabilities, capillary pressure, formation factor, resistivity index and saturation exponent. The above findings suggest that the network modeling technique may be used for prediction of petrophysical and reservoir engineering properties of chalk. Further works are necessary and an outline is given with considerable details.</p><p>Two 2D, one 3D and a dual-porosity fractured reservoir models have been developed and an imbibition process involving water displacing oil is simulated at various injection rates and with different oil-to-water viscosity ratios using four widely used conventional upscaling techniques. The upscaling techniques are the Kyte & Berry, Pore Volume Weighted, Weighed Relative Permeability, and Stone. The results suggest that the upscaling of fractured reservoirs may be possible using the conventional techniques. Kyte & Berry technique was found to be the most effective in all situations. However, further investigations are necessary using realistic description of fracture length, orientation, connectivity, aperture, spacing, etc. </p> / Paper 3,4 and 5 reprinted with kind persmission of Elsevier Science, Science Direct.

<p>We live in a world of becoming. The future is not given, but forms continuously in dynamic processes where path dependence plays a major role. There are many different possible futures. What we actually end up with is determined in part by chance and in part by the decisions we make. To make sound decisions we require models that are flexible enough to identify opportunities and to help us choose options that lead to advantageous alternatives. This way of thinking differs from traditional cost-benefit analysis that employs net present value calculations to choose on purely economic grounds, without regard to future consequences.</p><p>Time and dynamic behaviour introduce a separate perspective. There is a focus on change, and decisions acquire windows of opportunity: the right decision at the right time may lead to substantial change, while it will have little effect if too early or too late. Modelling needs to reflect this dynamic behaviour. It is the perspective of time and dynamics that leads to a focus on sustainability, and thereby the role hydrogen might play in a future energy system. The present work develops a particular understanding relevant to energy infrastructures.</p><p>Central elements of this understanding are:</p><p>- Competition</p><p>- Market preference and choice beyond costs</p><p>- Bounded rationality</p><p>- Uncertainty and risk</p><p>- Irreversibility</p><p>- Increasing returns</p><p>- Path dependence</p><p>- Feedback</p><p>- Delay</p><p>- Nonlinear behaviour</p><p>Change towards a “hydrogen economy” will involve far-reaching change away from our existing energy infrastructure. This infrastructure is viewed as a dynamic set of interacting technologies (value sequences) that provide services to end-users and uphold the required supply of energy for this, all the way from primary energy sources. The individual technologies also develop with time.</p><p>Building on this understanding and analysis, an analytical tool has emerged: the Energy Infrastructure Competition (EICOMP) model. In the model each technology is characterised by a capacity, an ordered-, and an actually delivered volume of energy services. It is further characterised through physical description with parameters like efficiency, time required for extending capacity and improvement by learning. Finally, each technology has an attractiveness, composed of costs, quality and availability, that determines the outcome of competition.</p><p>Change away from our present energy infrastructure into a sustainable one based on renewable energy sources, will entail substantial change in most aspects of technology, organisation and ownership. Central results from the overall work are:</p><p>- <i>Change is dynamic and deeply influenced through situations with reinforcing feedback and path dependence. Due to this, there is a need for long-term perspectives in today's decision making: decisions have windows of opportunity and need to be made at the proper time.</i></p><p>- <i>Strategies aimed at achieving change should team up with reinforcing feedback and avoid overwhelming balancing feedback that counteracts change.</i></p><p>- <i>The EICOMP model is now available as a tool for furthe analysis of our existing energy infrastructure and its dynamic development into possible, alternative energy futures. As the model is intended for practical guidance in decisions, a central practical aim has been to allow it to be used close to where decisions are actually made; i.e. decentralised and locally in firms and in public institutions. In this respect much effort has been made in an attempt to make it transparent and easy to communicate.</i></p><p>- <i>The EICOMP model may be used to analyse situations of reinforcing feedback throughout the alternative energy infrastructures that we may come to have in the future.</i></p>

This dissertation deals with (1) experimental measurements on petrophysical reservoir engineering and morphological properties of Ekofisk chalk, (2) numerical simulation of core flood experiments to analyze and improve relative permeability data, (3) stochastic reconstruction of chalk samples from limited morphological information, (4) extraction of pore space parameters from the reconstructed samples, development of network model using pore space information, and computation of petrophysical and reservoir engineering properties from network model, and (5) development of 2D and 3D idealized fractured reservoir models and verification of the applicability of several widely used conventional upscaling techniques in fractured reservoir simulation. Experiments have been conducted on eight Ekofisk chalk samples and porosity, absolute permeability, formation factor, and oil-water relative permeability, capillary pressure and resistivity index are measured at laboratory conditions. Mercury porosimetry data and backscatter scanning electron microscope images have also been acquired for the samples. A numerical simulation technique involving history matching of the production profits is employed to improve the relative permeability curves and to analyze hysteresis of the Ekofisk chalk sample. The technique was found to be a powerful tool to supplement the uncertainties in experimental measurements. Porosity and correlation statistics obtained from backscatter scanning electron microscope image are used to reconstruct microstructures of chalk and particulate media. The reconstruction technique involves a simulated annealing algorithm, which can be constrained by an arbitrary number of morphological parameters. This flexibility of the algorithm is exploited to successfully reconstruct particulate media and chalk samples using more that one correlation function. A technique based on conditional simulated annealing has been introduced for exact reproduction of vuggy porosity in chalk in the form of foraminifer shells. A hybrid reconstruction technique that initialized the simulated annealing reconstruction with input generated using the Gaussian random field model has also been introduced. The technique was found to accelerate significantly the rate of convergence of the simulated annealing method. This finding is important because the main advantage of the simulated annealing method, namely its ability to impose a variety of reconstruction constraints, is usually compromised by its very slow rate of convergence. Absolutely permeability, formation factor and mercury-air capillary pressure are computed from simple network models. The input parameters for the network models were extracted from a reconstructed chalk sample. The computed permeability, formation factor and mercury-air capillary pressure correspond well with the experimental data. The predictive power of a network model for chalk is further extended through incorporating important pore-level displacement phenomena and realistic description of pore space geometry and topology. Limited results show that the model may be used to compute absolute and relative permeabilities, capillary pressure, formation factor, resistivity index and saturation exponent. The above findings suggest that the network modeling technique may be used for prediction of petrophysical and reservoir engineering properties of chalk. Further works are necessary and an outline is given with considerable details. Two 2D, one 3D and a dual-porosity fractured reservoir models have been developed and an imbibition process involving water displacing oil is simulated at various injection rates and with different oil-to-water viscosity ratios using four widely used conventional upscaling techniques. The upscaling techniques are the Kyte &amp; Berry, Pore Volume Weighted, Weighed Relative Permeability, and Stone. The results suggest that the upscaling of fractured reservoirs may be possible using the conventional techniques. Kyte &amp; Berry technique was found to be the most effective in all situations. However, further investigations are necessary using realistic description of fracture length, orientation, connectivity, aperture, spacing, etc. / Paper 3,4 and 5 reprinted with kind persmission of Elsevier Science, Science Direct.

We live in a world of becoming. The future is not given, but forms continuously in dynamic processes where path dependence plays a major role. There are many different possible futures. What we actually end up with is determined in part by chance and in part by the decisions we make. To make sound decisions we require models that are flexible enough to identify opportunities and to help us choose options that lead to advantageous alternatives. This way of thinking differs from traditional cost-benefit analysis that employs net present value calculations to choose on purely economic grounds, without regard to future consequences. Time and dynamic behaviour introduce a separate perspective. There is a focus on change, and decisions acquire windows of opportunity: the right decision at the right time may lead to substantial change, while it will have little effect if too early or too late. Modelling needs to reflect this dynamic behaviour. It is the perspective of time and dynamics that leads to a focus on sustainability, and thereby the role hydrogen might play in a future energy system. The present work develops a particular understanding relevant to energy infrastructures. Central elements of this understanding are: - Competition - Market preference and choice beyond costs - Bounded rationality - Uncertainty and risk - Irreversibility - Increasing returns - Path dependence - Feedback - Delay - Nonlinear behaviour Change towards a “hydrogen economy” will involve far-reaching change away from our existing energy infrastructure. This infrastructure is viewed as a dynamic set of interacting technologies (value sequences) that provide services to end-users and uphold the required supply of energy for this, all the way from primary energy sources. The individual technologies also develop with time. Building on this understanding and analysis, an analytical tool has emerged: the Energy Infrastructure Competition (EICOMP) model. In the model each technology is characterised by a capacity, an ordered-, and an actually delivered volume of energy services. It is further characterised through physical description with parameters like efficiency, time required for extending capacity and improvement by learning. Finally, each technology has an attractiveness, composed of costs, quality and availability, that determines the outcome of competition. Change away from our present energy infrastructure into a sustainable one based on renewable energy sources, will entail substantial change in most aspects of technology, organisation and ownership. Central results from the overall work are: - Change is dynamic and deeply influenced through situations with reinforcing feedback and path dependence. Due to this, there is a need for long-term perspectives in today's decision making: decisions have windows of opportunity and need to be made at the proper time. - Strategies aimed at achieving change should team up with reinforcing feedback and avoid overwhelming balancing feedback that counteracts change. - The EICOMP model is now available as a tool for furthe analysis of our existing energy infrastructure and its dynamic development into possible, alternative energy futures. As the model is intended for practical guidance in decisions, a central practical aim has been to allow it to be used close to where decisions are actually made; i.e. decentralised and locally in firms and in public institutions. In this respect much effort has been made in an attempt to make it transparent and easy to communicate. - The EICOMP model may be used to analyse situations of reinforcing feedback throughout the alternative energy infrastructures that we may come to have in the future.