In this work we describe a technique developed to improve medium-term prediction methods of monthly smoothed sunspot numbers. Each month, the predictions are updated using the last available observations (see the monthly output in real time at http://sidc.oma.be/products/kalfil). The improvement of the predictions is provided by applying an adaptive Kalman filter to the medium-term predictions obtained by any other method, using the six-monthly mean values of sunspot numbers covering the six months between the last available value of the 13-month running mean (the starting point for the predictions) and the "current time" (i.e. now). Our technique provides an effective estimate of the sunspot index at the current time. This estimate becomes the new starting point for the updated prediction that is shifted six months ahead in comparison with the last available 13-month running mean, and it provides an increase of prediction accuracy. Our technique has been tested on three medium-term prediction methods that are currently in real-time operation: The McNish-Lincoln method (NGDC), the standard method (SIDC), and the combined method (SIDC). With our technique, the prediction accuracy for the McNish-Lincoln method is increased by 17 -30%, for the standard method by 5 -21% and for the combined method by 6 -57%.
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 prediction of the gas production from mature gas wells, due to their complex end-of-life behavior, is challenging and crucial for operational decision making. In this paper, we apply a modified deep LSTM model for prediction of the gas flow rates in mature gas wells, including the uncertainties in input parameters. Additionally, due to changes in the system in time and in order to increase the accuracy and robustness of the prediction, the Ensemble Kalman Filter (EnKF) is used to update the flow rate predictions based on new observations. The developed approach was tested on the data from two mature gas production wells in which their production is highly dynamic and suffering from salt deposition. The results show that the flow predictions using the EnKF updated model leads to better Jeffreys' J-divergences than the predictions without the EnKF model updating scheme.
Reliable energy supply becomes increasingly complex in hybrid energy networks, due to increasing amounts of renewable electricity and more dynamic demand. Accurate modeling of integrated electricity and gas distribution networks is required to quantify operational bottlenecks in these networks and to increase security of supply. In this paper, we propose a hybrid network solver to model integrated electricity and gas distribution networks. A stochastic method is proposed to calculate the security of supply throughout the networks, taking into account the likelihood of events, operational constraints and dynamic supply and demand. The stochastic method is evaluated on a real gas network case study. The calculated security of supply parameters provide insight into the most critical parts of the network and can be used for future network planning. The capabilities of the coupled hybrid energy network simulation are demonstrated on the real gas network coupled to a simplified electricity network. Results demonstrate how combined simulation of electricity and gas networks facilitate the control design and performance evaluation of regional hybrid energy networks.
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