Downhole Multiphase Metering In Wells By Means Of Soft-sensing

Abstract

Multiphase flow meters are indispensable tools for achieving optimal operation and control of wells as these meters deliver real-time information about their performance. For example, multiphase flow meters located downhole can improve the production of multilateral and multizone wells by timely allocating the zone where a gas or water cone occurs. However, multiphase meters are either expensive, inaccurate, or cannot be used downhole due to the harsh conditions. An alternative that can be used to overcome these disadvantages is to use multiphase soft-sensors, i.e. to estimate holdups and flow rates from relatively cheap and reliable conventional meters, such as pressure and temperature measurements, and a dynamic model connecting these measurements with the unknown quantities. The aim of this paper is to demonstrate, via two simulation based case studies, some possibilities and limitations of such multiphase soft-sensors. In the first case study the question is adressed whether it is possible to use only downhole pressure and temperatures measurements to estimate in real-time the water, oil and gas flow rates in a well. This question is of practical importance as these measurements are relatively cheap and reliable. The second case addresses the question whether it is possible to allocate the gas cone in a well with multiple inflow points or zones. This question is relevant as the estimated flow rate and holdup profiles can be used to manipulate Inflow Control Valves in such a way that gas breakthrough is prevented. Using amongst others OLGA data as "real-life" data, an additional question addressed here is what the influence is of soft-sensor model error and measurement noise on the quality of the estimates. From the first case study it can be concluded that, due to bad observability, pressure and temperature measurements alone are not sufficient to accurately estimate in real-time well flow composition parameters in a practically relevant situation. The preliminary results discussed in the second case study indicate that a soft-sensing solution to the gas cone allocation problem may very well be feasible. Introduction Motivated by the ever growing discrepancy between demand for and availability of oil and gas and by the improvement and increased availability of downhole measurement and control equipment, the oil and gas industry has recently embraced the "smart wells" philosophy. The main idea of this philosophy can be stated as the improvement of current reservoir management by improving current reservoir and well monitoring and control practice. By doing so, one aims at a higher yield from a given reservoir, on the short-term and/or on the long-term, while simultaneously fulfilling constraints that are imposed out of environmental and (other) operational considerations. Here, the focus is on the improvement of current well monitoring practice. Well monitoring can be defined as real-time measuring or estimating well production performance parameters such as water, oil and gas flow rates. These can be delivered to an operator or a control system to allow for taking steps to improve current well production performance. In particular, monitoring devices located downhole can improve the production of multilateral or multizone wells by determining at which areas/zones of the well which fluids are entering. Even more specific, this knowledge allows for a better handling of gas or water breakthrough. See e.g. Leemhuis et al. (2007).

Soft-Sensing for Multilateral Wells With Downhole Pressure and Temperature Measurements

“…This paper follows the soft-sensing framework set out in Leskens et al (2008) and, before that, in Bloemen et al (2004). In Leskens et al (2008), the simultaneous estimation of downhole oil, water and gas flows from downhole pressure and temperature measurements in a unilateral well is considered.…”

“…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.…”

“…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.…”

Soft-sensors: Model-based estimation of inflow in horizontal wells using the extended Kalman filter

“…Leskens et al [6] considered the simultaneous estimation of downhole oil, water and gas flow rates from downhole pressure and temperature measurements in a single horizontal well using the extended Kalman filter. It has been shown that in case of measurements that are corrupted with noise, obtaining correct estimates is not possible.…”

Soft sensing for two-phase flow using an ensemble Kalman filter

“…Also, the Kalman filter has been used for tuning the parameters of two phase flow models (Lorentzen et al, 2003). Leskens et al (2008) considered the simultaneous estimation of downhole oil, water and gas flow rates from downhole pressure and temperature measurements in a single well. This approach has been extended by de to the multilateral well case both for the two-phase (oil and gas) and three-phase (oil, gas and water) cases.…”