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

• It was demonstrated that it is impossible to estimate inflow from the pressure signal generated by the same inflow point.
• The composition change due to inflow is estimated using a simplified engineering approach from the frictional pressure drop variation.
• Developed soft-sensor employs the no-pressure-wave multiphase flow model combined with the extended Kalman filter and uses downhole pressure measurements.
• The soft-sensing approach was tested on a series of simulation-based test cases, which showed reliability of the developed algorithm.

The growing demand for hydrocarbon production has resulted in improved oilfield management using various control and optimization strategies. These strategies increasingly require downhole equipment to obtain real-time oil and gas production rates with sufficient spatial and temporal resolution. In particular, downhole multiphase metering can improve the production of horizontal wells by allocating the zones of oil, gas and water inflow. However, the existing downhole multiphase meters are expensive, inaccurate or accurate only within a limited operating range and therefore such monitoring is unrealistic.To overcome these problems one can use the so-called multiphase soft-sensors, i.e. estimating flow rates from conventional sensors (e.g. pressure gauges) in combination with a dynamic multiphase flow model. This methodology uses inverse modeling concepts to estimate flow rates that are not measured directly. Based on the analysis of the transient pressure response due to a rapid inflow, a real-time estimator is proposed, which uses a dynamic model of the multiphase wellbore flow and information from conventional pressure sensors. The feasibility of the proposed concept is assessed via simulation-based case studies both for noisy synthetic measurements and for artificial data generated by the OLGA simulator.