As reservoir pressures decrease in maturing gas wells, liquid drop-out forms an increasing restriction on gas production. Even though virtually all of the world's gas wells are either at risk of or suffering from liquid loading, the modeling of liquid loading behavior is still quite immature and the prediction of the minimum stable gas rate not very reliable. Many wells start liquid loading at gas rates well above the values predicted by classic steady state prediction models such as Turner. The loading point is strongly dependent on inclination angle, flow regime transitions and the interaction between tubing outflow behavior and the reservoir IPR. In the paper, the behavior of different natural gas wells and of an air-water test setup are analyzed. Simulations were performed using both commercially available software and dedicated dynamic models. The onset of liquid loading and the dynamic behavior of a flooded well during a restart were predicted. These were then compared to actual production data. The influence of the reservoir parameters and of the tube inclination were of special interest. The influence of dynamic disturbances on the stability are not taken into account by the classic prediction models. Systems with high permeable reservoirs are less able to cope with disturbances. This leads to higher critical rates for those systems. This corresponds to data from field observations. A maximum in the critical velocity is observed around an inclination of 50° with a critical rate 40% higher than for a vertical well. To solve this, relations found from flooding experiments are used to modify the current prediction models. Based on the current work an adaptation to the Turner equation, which takes the inclination effects into account, is proposed. For the observed natural gas wells and for the airwater experiments the modified Turner equation predicts the observed loading points within 20% accuracy. Introduction Liquid loading, that is the process when the gas is no longer able to lift liquid to the surface, is a major limiting production factor for maturing gas wells. Solutions such as gas lift, soap injection, velocity string or plunger lift are required to solve this problem. Accurate predictions of the onset of the liquid loading process allow for better planning and choosing the right countermeasure. Currently, the most widely used model is still the classic Turner criterion, which is based on a force balance on a falling droplet, although it is known to not always be correct. In laboratories, liquid loading occurs due to the drainage of the liquid film which is present at the tubing walls in annular flow (Belt 2008, Westenende 2008). In practice the production decline may also be due to other mechanisms, which may be difficult to distinguish. The main mechanisms for the production decline are thought to be:Film drainage,System instability,Flow regime change (Toma 2007). In film drainage the force balance on the liquid film results in a part of the liquid film with a negative (downwards) velocity. System instability occurs when the inflow performance relation (IPR, reservoir curve) intersects the tubing performance curve (TPC) to the left of the minimum in the tubing curve. In practice the liquid drainage point may be to the left or to the right of the TPC minimum. The system stability is also governed by the pressure drop as is the force balance across the liquid film. The flow regime change is a separate mechanism and is less determined by gravity but is more influenced by increased hold up and wave formation. The flow regime change itself is more likely a result than an initiator. Slug formation can occur when the liquid hold up increases. This increase is expected to be caused by the negative liquid film velocity. Therefore, these three mechanisms may interact and coincide in field cases and the direct cause of a production decline may be difficult to detect.

Real-time monitoring of downhole oil, gas and water flows in wells can significantly improve the production performance of these wells when this flow rate information is used to manipulate inflow control valves. An example of this is the allocation of a gas or water cone to its entrance point in a multilateral well, allowing to close down the individual well where the gas or water cone occurs, instead of closing down the complete well.Downhole monitoring of flows can be done via direct measurement. However, downhole multiphase metering is either expensive, inaccurate, or too difficult due to the harsh conditions. An alternative is to use softsensors. Softsensors estimate downhole holdups and flow rates from (relatively) cheap and reliable conventional downhole meters, such as pressure and temperature measurements, and a dynamic multiphase flow model connecting these measurements with the quantities of interest.Soft-sensing has already been investigated before for unilateral wells in Bloemen et al. (2004) and Leskens et al. (2008). In the second of these references, the simultaneous estimation of downhole oil, water and gas flows from downhole pressure and temperature measurements is considered. It is shown there that this estimation is badly conditioned (i.e. badly observable) and, thereby, not feasible in a practical situation. Using a similar approach and focussing on gas-lift wells, in Bloemen et al. (2004) it is suggested that soft-sensing with only downhole pressure and temperature measurements should work for the case that only a liquid and gas flow are estimated.In this paper, within the same soft-sensing framework as used in the mentioned two references, solutions are sought for soft-sensing of multilateral wells, both for the two-phase (gas and liquid) and three-phase (oil, water and gas) case.For that purpose, first, the question is addressed whether the unilateral two-phase case truly can be solved using only downhole pressure and temperature measurements. If so, the multilateral two-phase case is automatically solved with the corresponding soft-sensing solution simply consisting of a collection of unilateral two-phase sensors, one for each branch. It is shown that this solution is indeed feasible.After that, the three phase case is addressed. It is shown that for this case soft-sensing of multilateral wells is not possible, even when adding surface measurements and even though, as also shown here, it is possible for the unilateral well case when adding such measurements.

The mission of QuTech is to bring quantum technology to industry and society by translating fundamental scientific research into applied research. To this end we are developing Quantum Inspire (QI), a full-stack quantum computer prototype for future co-development and collaborative R&D in quantum computing.A prerelease of this prototype system is already offering the public cloud-based access to QuTech technologies such as a programmable quantum computer simulator (with up to 31 qubits) and tutorials and user background knowledge on quantum information science (www.quantum-inspire.com). Access to a programmable CMOS-compatible Silicon spin qubit-based quantum processor will be provided in the next deployment phase. The first generation of QI's quantum processors consists of a double quantum dot hosted in an in-house grown SiGe/ 28 Si/SiGe heterostructure, and defined with a single layer of Al gates.Here we give an overview of important aspects of the QI full-stack. We illustrate QI's modular system architecture and we will touch on parts of the manufacturing and electrical characterization of its first generation two spin qubit quantum processor unit. We close with a section on QI's qubit calibration framework. The definition of a single qubit Pauli X gate is chosen as concrete example of the matching of an experiment to a component of the circuit model for quantum computation.

Product development Given the recent breakthroughs in quantum technology development in R& D labs all over the world, the perspective of high-tech companies has changed. Product development is initiated next to the existing research and technology development activities. Quantum computer product roadmap Considering the quantum computer as a product requires standardization and integration of all its building blocks and a mature supply chain that can provide high-quality components and can ensure security of supply. The product development approach puts focus on functionality and performance requirements of the product and uses state-of-the-art technology to build the product. Based on the expected requirements of future products it is possible to outline a product development roadmap. It is expected that a fully functional quantum computer will be available within a decade from now, and will be used by the High Performance Computing (HPC) market, where it will replace (part of) the supercomputers that are currently used for complex calculations and data management. In the short term, a partly functional quantum computer will be available and of interest to the R&D market, which has a need for such a product to expedite their quantum technology developments. ImpaQT project In this paper, we present the product development approach and roadmap for quantum computers, based on superconducting circuits as an example. A group of companies in the Dutch quantum ecosystem (Quantum Delta) have joined forces and have started the ImpaQT project. The companies of the ImpaQT consortium form a local supply chain for key components of quantum computers. This paper shows that quantum community has reached the next level of maturity and that the quantum computer as a commercial product looks set to become a reality.

With an increasing number of smart well applications being installed in the field, more knowledge is required to optimize their operation. This paper compares the benefits of various wellhead gas coning control strategies to optimize production of a thin oil rim. This study is performed within the "Integrated System Approach Petroleum Production (ISAPP)" knowledge center of TNO, TU Delft and Shell. For this study a field case model is used, which has been validated with field data. The field case is a thin oil rim with a horizontal well. Due to the location of the horizontal well in the oil rim, the well is particularly susceptible to gas coning. Besides gas coning, wax precipitation is a second production constraint. This makes this well challenging to operate. Different production strategies are investigated and compared against each other: intermittent production and continuous production with pressure differential control. The results of the different production strategies are presented by analyzing the advantages and disadvantages for the different gas coning control strategies, satisfying the given constraint of gas influx. This study reveals the difference in the cumulative production between the two strategies. The use of a closed loop control strategy can lead to a larger oil production in the same amount of time. This paper shows the viability of using dynamic simulation models to quantitatively assess the benefits of various production optimization strategies. This allows operators to compare emerging smart well technologies, and increase trust in specific technologies that could be of an added value to their operation. Even though much has been published about the potential benefits of a smart field philosophy, few published field cases are available. This paper offers a field case testimony of the comparison of various feedback control strategies for purpose of production optimization. Introduction With increasing knowledge and improving technologies, more complex reservoirs (with respect to location and dimensions) can be explored and produced. This brings new challenges in exploration, drilling and production. Furthermore, existing reservoirs require new insights to be able to increase ultimate recovery. Dedicated simulation software tools can offer these new insights by helping to understand production instabilities and test new control strategies to avoid instabilities and to optimize production. The field under investigation has most of its wells drilled with long laterals in a thin oil rim, making them particularly susceptible to gas coning. Gas coning is a phenomenon where the gas oil contact of a reservoir slowly moves towards a well as a result of high drawdown. Eventually, the free gas is being drawn into the well, see Figure 1. Furthermore, the reservoir temperature is low enough to cause wax deposition. At high production rates, a well will suffer a large gas influx, which cannot be handled by the topside equipment. For low production rates, a well will suffer increased wax deposition due to the lower fluid temperature [Nennie, 2008]. Therefore, due to gas coning and wax deposition, some of the wells are operated intermittently. Goal of this study is to determine whether instead of the intermittent production continuous production is more beneficial and if so, quantifying the difference between these control strategies.