Cointerpretation of Distributed Acoustic and Temperature Sensing for Improved Smart Well Inflow Profiling

Abstract

Current advances in the well completion technology have allowed for more complex smart well instrumentations with marginal additional cost. As an example, optical fibers can be run along horizontal wells to provide acoustic and temperature data that are distributed both in time and space. With such data at our disposal, an immediate evaluation of the well response is possible as changes in the reservoir or well occur. Most current work in distributed measurements looks at Distributed Acoustic Sensing (DAS) or Distributed Temperature Sensing (DTS) data individually, which limits our inferences about the multiphase flow problem. The objective of this work was to look at the two pieces of data together and determine what improvements can be achieved in multiphase flow problems compared to the conventional methods of looking at each DAS and DTS alone. The study began by evaluating the performance of DAS in analyzing two-phase flow; a process which begins by extracting the speed of sound within the fluid medium from the acoustic signal, then obtaining the phase fraction combination that obtains this speed of sound reading. Another procedure is explained to obtain similar results from DTS measurements. In this case, however, the in-situ phase fractions are correlated to the Joule-Thomson effect as reservoir fluids enters the wellbore. As both these procedures are limited to one- and two-phase flow applications, we extended the solution to work in three-phase flow problems by combining information from DAS and DTS. The flow profiling procedure was applied to two smart wells in the Middle East. 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. In cases where both DAS and DTS were not available for the same well, a commercial compositional and thermal reservoir simulator was used to generate synthetic examples. By applying the developed procedure, we found that cointerpretation of DAS and DTS data yields accurate in-situ three-phase fractions for all ranges of water cuts and gas volume fraction. In comparison, analyzing DAS or DTS individually is usually not sufficient to fully determine a three-phase flow problem.

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.…”

“…DAS systems, began to emerge in the late 2000s, have been used for early-warning system for earthquake and seismic activity monitoring [ 30 ], hydraulic fracture detection [ 29 ], traffic pattern analysis and monitoring [ 31 ], gas leak detection [ 77 ], pipeline surveillance [ 78 ], Vertical Seismic Profiling (VSP) [ 28 ], Steam Assisted Gravity Drainage (SADG) monitoring [ 79 ], and in-well flow profiling that not only used in the laboratory or field trials but also in the real time operations [ 6 , 8 , 32 , 33 ]. Combining DTS and DAS data has also been explored, for example, to address the three-phase flow estimation of oil, water, and gas for the downhole well simulations [ 8 ], which was less accurate and seems unsolvable when only using DAS or DTS alone. Figure 4 shows a sketch of smart wells with several ICVs and fibre-based distributed sensors as a straight-line and helically wound cable around the pipe.…”

“…Distributed fibre optics can be be used to measure these values, with consideration of the complexity of data processing required being dependent on the DS type and the parameter of interest. For example the processing workflow for measuring flow temperature with DTS [75] is relatively simple compared to measuring speed of sound and flow velocity using DAS data [8,9]. The common framework of using physical flow modelling for estimating the multiphase flow from distributed sensor is as follows:…”

“…SoS and J-T coefficient value matching [8,34] 2016 Production oilfield with DAS and synthetic DTS Integral image algorithm for estimating SoS of multiphase fluids and Ability to accurately measure two-phase flows.…”

“…Therefore, it is crucial to have robust, reliable, and accurate monitoring capabilities to achieve the most optimized oil production system. Some of the most powerful approaches for optimizing oil production employ multiphase fluid flow monitoring [6][7][8][9]. Such techniques are used to estimate and monitor the phase-fraction of oil, water, and gas inside the production wells.…”