Using Distributed Acoustic Sensors to Optimize Production in Intelligent Wells

Abstract

As many fields around the world are reaching maturity, several drilling and completion technologies have been developed to boost production from these fields. Smart completions and distributed measurements are two of the most important tools in this category. This study examined a new smart completion tool, Distributed Acoustic Sensor (DAS) in terms of its utility for inflow monitoring and optimization in smart wells.We began by evaluating the performance of DAS as a tool for measuring downhole multiphase flowrate. A description of the methodology to calculate individual phase flowrates from acoustic signals, caused by pressure fluctuations in the flow, is presented. As DAS provides continuous flow of data from the wellbore, another opportunity emerges to optimize the flow from these smart wells. A methodology to use calculated downhole flowrates to update existing simulation models and perform, near real-time optimization is suggested for wells installed with DAS. As opposed to conventional optimization methods that rely only on reservoir simulation models, this procedure also makes use of real-time flow measurements. The methodology was tested on a synthetic model with encouraging results, where the optimum solutions obtained were in close agreement to the true optimum.The flow profiling procedure was applied to several actual wells. The first well was a single-phase oil producer. This process yielded speed of sound results that matched the fluid properties obtained in the lab. Flowrates from different segments of the well were calculated and results were in close agreement with a surface flowmeter for most sections of the well. More examples were conducted in two-phase flow wells with results being more qualitative and less conclusive. When the optimization procedure was applied for synthetic cases that have wells with similar completions to the tested ones, results showed that significant value could be realized by incorporating real-time measurements in the optimization process.Several advantages could be realized with the application of these methods. First, continuous downhole flow monitoring provides asset managers with more accurate allocation of their wells. Second, more accurate modeling for wellbore flow is possible by using in-situ phase flows to calibrate existing models. With more accurate models, evaluating different flow scenarios is possible before applying them on the field. Finally, quick decisions to change the controls of the well are easier with the described optimization method. By comparison, full reservoir simulation model optimization takes a very long time to make their use practical in everyday applications.

Distributed Fiber Optic Vibration Signal Logging Well Production Fluid Profile Interpretation Method Research

“…The above equation is the pulse width equation and the distance, Z k , between the kth scattering point and the incident end of the pulse light is [21,22]…”

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

“…The flow velocity is defined as the speed of travelling fluids inside the pipe; the flow rate is calculated from the flow velocity by including the flow pipe diameter in the calculation. The flow velocity is derived from the Doppler Effect (or Doppler Shift) principle of and [ 5 ]. It is important to highlight that when a flow approaches a sensor (at a given location), the sound waves that reach the sensor have a shorter wavelength and a higher frequency.…”

“…The flow velocity can be calculated using this phenomenon. It is defined in Equation ( 3 ) where v is the flow velocity and is assumed to be negative, see [ 5 ] for a more comprehensive derivation. …”

“…A typical starting point for production optimization is through continuous monitoring of the downhole production well variables (e.g., Water in Liquid Ratio (WLR), Gas Volume Fraction (GVF), fluid flow rate, water or gas breakthrough, and sand production). Typically, these measurements are then combined with simulations in order to optimize production control parameters (e.g., Inflow Control Valve (ICV) and/or Inflow Control Device (ICD) parameters, pressure setting, and controlling water/gas injection) for stimulating production [ 5 ]. It is a rigorous process with a continuous loop of monitoring, simulating, and optimizing the production of oil, while preventing and addressing anomalies and production disturbances.…”