Obtaining Real-Time Flow Rate, Water Cut, and Reservoir Diagnostics from ESP Gauge Data

Abstract

Routine testing of wells with electric submersible pumps (ESPs) is usually conducted monthly to monitor liquid rates, water cut (WC), and gas/oil ratio (GOR). This monthly testing is the most common form of production and reservoir surveillance and is implemented in even the most mature fields where cost control generally takes precedence over reservoir surveillance. However, this technique has its limitations. The most common limitation is insufficient testing duration to capture a representative sample of reservoir fluids. This testing duration issue is often the case in low-flow rate and deep wells, which require several time-consuming whole or complete liquid holdup periods. Other potential problems include insufficient resolution or repeatability to identify trends in liquid and water-cut rates over short periods of time. To date, the only method for resolving these issues has been to install permanent multiphase meters on each well. Although this method has been implemented in some fields, it is uneconomical for most wells. An analytical method is described for a flow rate calculation that can be implemented in wells produced with ESPs and equipped with downhole gauges and real-time monitoring systems. These downhole gauges and real-time monitoring system provide continuous real-time virtual flow rate measurements and therefore, both liquid and water-cut trends, which deliver the required resolution and repeatability to support both well performance diagnostics and near-wellbore reservoir analysis. This technique, which has the advantage of being valid for both transient and steady-state conditions, provides instantaneous flow rate data when used with real-time data. Case studies presented will illustrate model calibration and its application to back allocation and transient analysis. Examples are provided to show how the data can be used to rapidly identify changes in productivity index and reservoir pressure across the drainage area; thereby, enabling real-time production optimization.

Real-Time Liquid Rate and Water Cut Prediction From the Electrical Submersible Pump Sensors Data Using Machine-Learning Algorithms

“…There have been several attempts to develop a first-principles model that describes ESP operation, due to its widespread use. The main concept underlying virtual flow metering in ESP is to link the pump pressure increase, which is the difference between the intake pressure and the discharge pressure, with the flow and the pump speed while also considering the liquid percentage and gas volume . Earlier attempts for flow rate computation and prediction are displayed in Table , which shows the authors and the summary of their work.…”

A Survey on Distributed Fibre Optic Sensor Data Modelling Techniques and Machine Learning Algorithms for Multiphase Fluid Flow Estimation

“…Several different approaches can be used to measure the multiphase flow, including but not limited to, conventional dedicated hardware-based flow meters [ 7 , 10 , 14 , 15 , 43 ], virtual flow meters [ 17 , 18 , 39 , 42 , 45 , 46 , 47 ], and distributed sensor flow estimations [ 8 , 37 , 40 , 48 ]. This paper focuses on providing a comprehensive review of the last approach, using distributed sensors with physical flow modelling and machine learning algorithms for multiphase flow estimation.…”

Converting ESP Real-Time Data to Flow Rate and Reservoir Information for a Remote Oil Well

“…Liquid rate calculation was based on the power equation described by Camilleri et al (2010) and Camilleri and Zhou (2011). An explanation of the equation is provided in Appendix A.…”

“…Water cut calculation was based on the pressure drop in the production tubing and also tested by Camilleri and Zhou (2011). This provides a measurement of the average mixture fluid density, which can then be translated to a water cut based on the in-situ oil and water densities if one knows the average liquid hold-up in the tubing.…”