We present a novel and flexible method to optimize the phase response of reflective metasurfaces towards a desired scattering profile. The scattering power is expressed as a spinchain Hamiltonian using the radar cross section formalism. For metasurfaces reflecting an oblique plane wave, an Ising Hamiltonian is obtained. Thereby, the problem of achieving the scattering profile is recast into finding the ground-state solution of the associated Ising Hamiltonian. To rapidly explore the configuration states, we encode the Ising coefficients with quantum annealing algorithms, taking advantage of the fact that the adiabatic evolution efficiently performs energy minimization in the Ising model. Finally, the optimization problem is solved on the D-Wave 2048-qubit quantum adiabatic optimizer machine for binary phase as well as quadriphase reflective metasurfaces. Even though the work is focused on the phase modulation of metasurfaces, we believe this approach paves the way to fast optimization of reconfigurable intelligent surfaces that are modulated in both amplitude and phase for multi-beam generation in and beyond 5G/6G mobile networks.

No abstract

No abstract

This paper is based on the work of a multi-disciplinary team, formed to evaluate the economic potential, with associated risks, of waterflooding the Valhall Field. The evaluation includes selection of facility concept and quantification of probabilistic project economics. The decision analysis is performed using economic models, tornado diagrams, decision trees and monte carlo simulations and presented as cumulative probability functions. The paper outlines the evaluation process from screening to selection of final concept and discusses uncertainties, risks and upside potential associated with waterflooding the Valhall chalk reservoir. This decision risk analysis process gives a simple method for ranking project uncertainties and helps the team to focus on key project drivers which lead to a better understanding of project risks and potential risk mitigation. Introduction The Valhall field is a high porosity naturally fractured chalk reservoir located 290 km offshore, in the southwest comer of the Norwegian North Sea. The field was discovered in 1975, with first oil in October 1982. Production drilling is currently ongoing from 2 drilling rigs, and is expected to result in peak production from Valhall in 1999 from a total of 49 wells. The Original Oil In Place is approximately 2350 mmstb, located in two main reservoir layers, the Tor formation and the Hod formation. Owing to the weakness of the high porosity chalk and the very low original net stress, the Tor formation exhibits exceptional drive energy through pore collapse and compaction. The expected field recovery factor under primary depletion is close to 25%. In 1989, the reservoir pressure declined below the bubble point in the central areas of the field. A single well water injection pilot was then implemented to give critical information on the viability of water injection. Three years of pilot operation and subsequent analysis of results gave valuable experience and information for evaluating the potential of a full field waterflood at Valhall. Although water injection has been studied repeatedly for the Valhall field, it has never been found economically attractive. This is mainly due to the very efficient recovery achieved through pressure depletion, which is essentially lost under a pressure maintenance scheme. In 1996 a multi-disciplinary study team was formed with the objective of evaluating the best economic concept for waterflooding the Tor formation. The Study Process The project had the following main objectives:–Identify the most attractive development option.–Assess economic potential.–Recommend future actions on Waterflood Project risk and upside potential were identified as key decision criteria. The process of decision risk analysis was divided in to four steps (Figure 1): Step 1: Framing the problem. The project team defined the problem and boundaries. The project risks, dependencies, strategies and development options were identified and models for the project evaluation were established. Step 2: Deterministic Analysis. Having framed the problem, it was important to screen the parameters. This enabled the team to focus and target resources on the key project drivers. P. 863^

No abstract