CA2921406C - Method for monitoring a well or a reservoir containing a fluid, and apparatus for using the same - Google Patents

Abstract

A method for monitoring the level of fluid in the annulus of a well is proposed. The method comprises the processing of image data generated using distributed acoustic sensing on an optical fiber extending along the well to determine at least one acoustic wave propagation limit in the annulus, and the determining of an estimate of the annulus fluid level based on the determined at least one acoustic wave propagation limit.

Description

METHOD FOR MONITORING A WELL OR A RESERVOIR CONTAINING
A FLUID, AND APPARATUS FOR USING THE SAME
The present disclosure relates to the field of monitoring a well or reservoir containing a fluid.
The present disclosure claims priority benefit from U.S. Provisional Application No. 61/867,335 (filed August 19, 2013).
Distributed acoustic sensor (so-called "DAS") technology offers a way to monitor oil and gas resources, as described in the document: 'Distributed Acoustic .. Sensing ¨ a new way of listening to your well/reservoir', K. Johannessen, B.
Drakeley, M. Farhadiroushan, SPE 149602 (2012). The operating principal for this technology is based on interference effects in optical fiber that are associated with the optical time domain reflectometry, a description of which can be found in:

"Interferometric Optical Time-Domain Reflectometry for Distributed Optical¨Fiber Sensing", S. V. Shatalin, V. N. Treschikov and A. J. Rogers, Appl. Opt., vol.
37, no.
24, pp 5600-5603 (1998). The backscattered centres form low contrast Fabry-Perot interferometers, which are illuminated by optical pulses travelling along the fiber. A
major limitation of many disturbance sensors based on this approach is that they are incapable of determining the full acoustic field ¨ namely the amplitude, frequency and phase of the incident signal.
Improved DAS measurement techniques ¨have been developed with an aim to remove this limitation for determination of the amplitude, frequency and the distance of an acoustic event. Such improved DAS techniques have been applied for downhole measurements of flow, sound and seismic vibration, as described in:
"Distributed Acoustic Sensing - A New Tool for Seismic Applications", T.
Parker, S.V. Shatalin., M. Farhadiroushan, Y. I. Kamil, A. Gillies, D. Finfer, and G.
Efstathopoulos, 74th EAGE Conference & Exhibition incorporating SPE EUROPEC
2012, Y002 (June 2012).
There remains a need for new ways of exploiting data generated by such improved DAS techniques, in particular in the field of monitoring a well or a reservoir containing a fluid.
It is an object of the present subject disclosure to provide improved systems and methods for monitoring a well or a reservoir containing a fluid.
According to one aspect of the present subject disclosure, an apparatus for

2 monitoring a well or a reservoir containing a fluid is proposed. The proposed apparatus for monitoring a well or a reservoir containing a fluid comprises:
an optical fiber extending along the well or the reservoir; a light pulse generator connected to the optical fiber and adapted for sending light pulses down the optical fiber; an acoustic wave generator adapted for generating acoustic waves that propagate in the fluid and exert pressure changes onto the optical fiber; a sensor connected to the optical fiber and adapted for detecting propagation of acoustic waves through measuring of modulation of light backscattered in the optical fiber generated by the pressure changes exerted onto the optical fiber; and a processing module adapted for determining a limit of acoustic wave propagation in the fluid based on acoustic wave propagation data generated by the sensor.
The proposed apparatus advantageously uses acoustic wave propagation data generated through DAS data acquisition to process such data in order to determine a limit of acoustic wave propagation. The processing may include identifying specific acoustic wave propagation profiles, and determining a limit of acoustic wave propagation based on such profiles.
In some embodiments, the processing module is further adapted for generating, based on acoustic wave propagation data generated by the sensor, image data representing acoustic wave propagation over a predetermined period of time, and for processing said image data using pattern recognition for determining the limit of acoustic wave propagation.
In some embodiments, the processing module is further adapted for determining an estimate of the annulus fluid level based on the determined limit of acoustic wave propagation in the fluid.
Therefore, the proposed scheme for determining a limit of acoustic wave propagation based on DAS acquired data may be advantageously exploited to obtain an estimate of the annulus fluid level so as to monitor the well or reservoir, as the case may be.
In some embodiments, the processing module is further adapted for determining a plurality of limits of acoustic wave propagation in the fluid over a period of time based on acoustic wave propagation data generated by the sensor, and dynamically monitoring the annulus fluid level based on the determined plurality of limits of acoustic wave propagation in the fluid.
In some embodiments, the acoustic wave generator is included in a pump immersed in the well and the generated acoustic waves correspond to noise

3 generated by the pump during operation.
In some embodiments, the processing module is further adapted for monitoring the annulus fluid level above a pump immersed in the well.
Embodiments may include a pump of the electrical submersible pump (ESP) type.
In some embodiments, the processing module comprises an interface for receiving data captured by the sensor, a processor, and a memory operatively connected to the processor and storing a computer program that, when executed, causes the processor to determine a limit of acoustic wave propagation in the fluid based on acoustic wave propagation data received from the sensor through the interface.
According to another aspect of the present subject disclosure, a method for monitoring the level of fluid in the annulus of a well is proposed. The proposed method comprises: processing image data generated using distributed acoustic sensing on an optical fiber extending along the well to determine at least one acoustic wave propagation limit in the annulus; determining an estimate of the annulus fluid level based on the determined at least one acoustic wave propagation limit.
In some embodiments, the processing image data includes pattern recognition image processing for determining the at least one acoustic wave propagation limit.
In some embodiments, distributed acoustic sensing is used to determine a plurality of acoustic wave propagation limits in the annulus over a period of time, and further comprising dynamic monitoring of the annulus fluid level over the period of time based on the determined plurality of acoustic wave propagation limits in the annulus.
According to yet another aspect of the present subject disclosure, a method for monitoring a well or a reservoir containing a fluid, wherein an optical fiber extends along the well or the reservoir, is proposed. The method comprises:
sending light pulses down the optical fiber; generating acoustic waves that propagate in the fluid and exert pressure changes onto the optical fiber;
generating image data representing acoustic wave propagation over a predetermined period of time; and determining a limit of acoustic wave propagation in the fluid based on a processing of the generated image data.
In some embodiments, the proposed method further comprises:

4 determining an estimate of the annulus fluid level based on the determined limit of acoustic wave propagation in the fluid.
In some embodiments, the proposed method further comprises:
determining a plurality of limits of acoustic wave propagation in the fluid over a period of time, and dynamically monitoring the annulus fluid level based on the determined plurality of limits of acoustic wave propagation in the fluid.
In some embodiments, the proposed method further comprises: monitoring the annulus fluid level above a pump immersed in the well or reservoir.
According to other aspects, disclosed is a computer-readable storage medium storing computer-executable instructions for monitoring a well or a reservoir, the computer executable instructions comprising instructions for implementing any of the methods disclosed herein for monitoring a well or a reservoir.
According to yet other aspects, disclosed is a computer program product comprising computer program code tangibly embodied in a computer readable medium, said computer program code comprising instruction to, when provided to a computer system and executed, cause said computer to perform any of the methods disclosed herein for monitoring a well or a reservoir.
According to further aspects of the present disclosure, disclosed is a non-transitory computer-readable storage medium. The computer-readable storage medium can store a computer program that, when executed, causes an apparatus comprising a processor operatively connected with a memory, to perform any of the methods disclosed herein for monitoring a well or a reservoir.
It should be appreciated that the present invention can be implemented and utilized in numerous ways, including without limitation as a process, an apparatus, a system, a device, and as a method for applications now known and later developed.
The present subject disclosure will be better understood and its numerous objects and advantages will become more apparent to those skilled in the art by reference to the following drawings, in conjunction with the accompanying specification, in which:
Fig. 1 illustrates an example DAS system according to an embodiment;
Fig. 2 illustrates the configuration of a test well setup used for performing DTS and DAS data acquisition according to an embodiment;
Fig. 3 illustrates DAS and DTS acquisition data acquired on an example test well setup;
Fig. 4 illustrates an example DAS spectrogram generated using a raw FFT
analysis from pump shut-in to rate 1 operation;
Fig. 5 illustrates a zoom of the spectrogram of Fig. 4 on the 0-50 Hz frequency band;
Fig. 6 illustrates another example DAS spectrogram generated using a fine-tuned FFT analysis from pump shut-in to rate 1 operation;
Fig. 7 illustrates another example DAS spectrogram generated using a fine-tuned FFT analysis from rate 1 to rate 2 operations;
Fig. 8 illustrates an example DAS acoustic signal propagation during well shut-in;
Fig. 9 illustrates an example DAS acoustic signal propagation during stable rate 1 operations;
Fig. 10 illustrates an example DAS acoustic signal propagation during stable rate 2 operations;
Fig. 11a and 11b illustrate example DAS acoustic signal propagation images during various stages of operation of a pump in a well on which the invention implemented for annulus fluid level monitoring according to an embodiment;
Fig. 12 is an example graph that may be used for annulus fluid level monitoring with BHP pump according to an embodiment;
Fig. 13 is an example DTS image data usable for annulus liquid level interface determination according to an embodiment;
Fig. 14 illustrates an example reservoir/well monitoring system configured to use distributed acoustic sensor technology according to an embodiment;
Fig. 15 illustrates an example architecture of the DAS module shown on Fig. 14. according to an embodiment;
Fig. 16 shows an example architecture of a well/reservoir monitoring system according to an embodiment;
Fig. 17a, 17b, 170, and 17d illustrate example DAS acquisition images with corresponding frequency analysis and classification processing according to an embodiment.
Figures 4, 5, 6, 7, 8, 9, 10, 11a and 11b show frequency spectrum amplitude values represented by different grey shades. Those exemplary amplitude values are assumed to be normalized around a zero value.

On Figure 13 measured temperature values in Celsius are also represented by different grey shades.
The advantages, and other features of the components disclosed herein, will become more readily apparent to those having ordinary skill in the art form.
The following detailed description of certain preferred embodiments, taken in conjunction with the drawings, sets forth representative embodiments of the subject technology, wherein like reference numerals identify similar structural elements.
In addition, it should be apparent that the teaching herein can be embodied in a wide variety of forms and that any specific structure and/or function disclosed herein is merely representative. In particular, one skilled in the art will appreciate that an aspect disclosed herein can be implemented independently of any other aspects and that several aspects can be combined in various ways.
The present disclosure is described below with reference to functions, engines, block diagrams and flowchart illustrations of the methods, systems, and computer program according to one or more embodiments. Each described function, engine, block of the block diagrams and flowchart illustrations can be implemented in hardware, software, firmware, middleware, microcode, or any suitable combination thereof. If implemented in software, the functions, engines, blocks of the block diagrams and/or flowchart illustrations can be implemented by computer program instructions or software code, which may be stored or transmitted over a computer-readable medium, or loaded onto a general purpose computer, special purpose computer or other programmable data processing apparatus to produce a machine, such that the computer program instructions or software code which execute on the computer or other programmable data processing apparatus, create the means for implementing the functions described herein.
Embodiments of computer-readable media includes, but are not limited to, both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. As used herein, a "computer storage media" may be any physical media that can be accessed by a computer. Examples of computer storage media include, but are not limited to, a flash drive or other flash memory devices (e.g. memory keys, memory sticks, key drive), CD-ROM or other optical storage, DVD, magnetic disk storage or other magnetic storage devices, memory chip, RAM, ROM, EEPROM, smart cards, or any other suitable medium from that can be used to carry or store program code in the form of instructions or data structures which can be read by a computer processor. Also, various forms of computer-readable media may transmit or carry instructions to a computer, including a router, gateway, server, or other transmission device, wired (coaxial cable, fiber, twisted pair, DSL cable) or wireless (infrared, radio, cellular, microwave). The instructions may comprise code from any computer-programming language, including, but not limited to, assembly, C, C++, Visual Basic, HTML, PHP, Java, Javascript, Python, and bash scripting.
Additionally, the word "example" as used herein means serving as an example, instance, or illustration. Any aspect or design described herein as "example" is not necessarily to be construed as preferred or advantageous over other aspects or designs.
The inventive concepts and features disclosed herein related to the monitoring of well or reservoir are described hereinafter in the non-limiting context of embodiments in wells, including wells with a complex configuration such as the well configuration illustrated in Figure 2. However, such specific context is not meant to limit the various features described herein, which are applicable to other types of wells or reservoirs.
The operating principle of the Distributed Acoustic Sensing (DAS) technology is illustrated in Figure 1. Figure 1 shows a DAS device (10) connected to an optical fiber (11) through an optical interface (13). The DAS device (10) includes a light pulse generator (12) connected to the fiber (11) through the interface (13) and adapted for generating a pulse of light (16) (for example, a laser pulse) which travel down the optical fiber (11). A small amount of the light is naturally backscattered (15) in the fiber (11) and collected by a sensor unit (14) of the DAS device (10) through the interface (13). The DAS device (10) utilizes an optoelectronics architecture that measures the modulation of the backscattered light (15).
Also shown on Figure 1 is an acoustic wave generator (17) adapted for generating acoustic waves (18) that propagate towards the optical fiber (11) and exert pressure and/or strain changes onto the fiber (11), resulting in vibrations thereof. The DAS sensor unit (14) measures these pressure changes at a rate of up to several kilohertz, so can be used to measure the acoustic field generated by the generator (17).
The deployment and use of the so-called Distributed Temperature Sensing (DTS) technique has proven advantageous for well and reservoir monitoring applications. However, for multiphasic fluids cases, there are still limitations on achieving reliable quantitative temperature interpretations in horizontal wells. In such wells, these limitations are mainly due to the close Joule Thomson effects for oil and water, and the lack of temperature gradient. Most of the time, a standalone temperature acquisition is not enough to get a reliable and quantitative rate interpretation.
Additional acquisitions such as the acquisition of Distributed Acoustic Sensing (DAS) data can help to better constrain the temperature interpretation.
The DAS can be used in water injectors to precisely localize the injection zones, and acoustic/noise data can play an important role in the reduction of uncertainties on temperature interpretation.
The present disclosure describes a DAS data acquisition in an example complex well configuration illustrated on Figure 2: a horizontal, multi lateral producer equipped with a pump, for example an ESP (Electrical Submersible Pump). In this example well, DAS acquisitions and interpretations, which were initially considered as a complement to DTS acquisitions in order to achieve more reliable DTS interpretation from the reservoir zones, can be performed so as to monitor the well according to the subject disclosure.
However the invention is not limited to specific well configuration or reservoir configuration, and may be implemented for the monitoring of various wells or reservoirs containing a fluid.
Example Well Configuration Figure 2 shows the configuration of a test well setup (50) in which tests were performed, and provides an example well configuration in which the present invention can be implemented. The pilot was performed in an offshore dry tree well in the Gulf of Guinea. The well is made of two branches: (1) an open hole lateral branch (51a) and (2) an horizontal branch (51b), for example of approximately 1000 meters in length and producing a viscous oil (28 API) carbonate reservoir, through 4 reservoirs zones completed with sand screens (52a, 52b, 52c, 52d) isolated with blank pipes and swell packers (53a, 53b, 53c, 53d).
The well (50) is equipped with an ESP (54) to ensure an average oil production rate around 800 bbl/d. In addition, to monitor the whole main branch, a DTS (55) may be deployed on a tail pipe attached below the ESP and running to the toe inside the sand screens, together with four Bragg grating based optical pressure/temperature (P/T) gauges (56a, 56b, 56c, 56d) which may be deployed at each sand screen zone. In the illustrated well, DTS data may be acquired from a double ended multimode fiber (not shown on the figure), while DAS data may be acquired from one or several DAS units, for example two DAS units (such as the ones described above with respect to Fig. 1) respectively connected to two single mode fibers linking the optical P/T gauges. Two fibers (for example single mode fibers) may indeed be used and respectively connected to DAS devices (or units) so as to perform DAS data acquisition through two acquisition channels.
The optical fiber(s) used for the DAS data acquisition may be any optical fiber suitable for DAS data acquisition in view of the application considered, and the invention is not limited to any specific type of optical fiber or else any specific equipment or scheme for the DAS data acquisition.
Well Monitoring History The example test well illustrated in Fig. 2 was first equipped with an early DTS and optical P/T gauge system at the end of 2005. A temperature interpretation was performed in 2006 from a DTS acquisition during well clean-up operations in November 2005, with some difficulties due to bad data quality. The interpretation showed that 80% of the production was coming from the toe of the drain and 20%

from the heel.
A second DTS acquisition was performed in January 2008. A new quantitative interpretation concluded on opposite results: 20% of the production was coming from the toe and 80% from the heel.
The 2005 and 2008 DTS data acquisitions were performed on demand.
Due to the lack of continuous DTS monitoring, no preference could be given to 2005 or 2008 interpretations, as the well behaviour could have changed within this period.
In 2010, following a work-over to replace the ESP, the well was equipped with a new DTS and optical P/T acquisition system. One DTS trace per day was recorded and stored on site. The latest DTS interpretations showed more or less the same production split as the 2008 one.
Therefore, in order to obtain a more reliable production distribution diagnosis, simultaneous DTS & DAS acquisition was performed in December 2012.

Design and reality of DTS and DAS Acquisition Program In oil & gas applications, quantitative DTS interpretations are typically performed to analyze the temperature profile changes of a well and link these changes to fluid production or injection. Two main phenomena impact the temperature profile of a well: (1) introduction of a fluid of a different temperature into the wellbore; and (2) temperature variation due to the Joule-Thomson effects directly associated with the pressure drawdown experienced by fluids as they pass from the reservoir to the wellbore.
The main difficulty for interpretations in horizontal wells is the lack of geothermal gradient inside the well leading to a constant reservoir temperature along the drain. Temperature variations inside the drain during production are then mainly controlled by Joule-Thomson effect.
In the test well setup a DTS acquisition has been designed in order to emphasize this Joule-Thomson effect with an initial shut in period to establish a good geothermal baseline temperature, followed by two different rates, leading to two different temperature disturbances. This was achieved by varying the ESP
pump frequency.
During startup and the ESP frequency changes, high resolution and simultaneous data sampling from DTS and DAS was designed as illustrated in Figure 3, which shows an example DTS and DAS acquisition scheme with ESP
frequency changes. Time was allocated between rate changes to allow the well to reach steady state production, within practical time limitations imposed by operational requirements. Another factor that had to be considered was that the platform is normally unmanned and crew would have to return, by boat, to another offshore accommodation facility every evening.
The design of the test acquisition was split into 6 stages Stage 1: Well is shut-in with a continuous DTS acquisition at a 1 trace/min acquisition frequency Stage 2: Simultaneous DTS & DAS acquisitions at the end of the shut-in period (3hr5) and after the start up of the ESP (2hr5) Stage 3: DTS acquisition at 1 trace/min for rate 1 Stage 4: Simultaneous DTS & DAS acquisitions at the end of rate 1 (3hr5) and after the ESP frequency change (2hr5) Stage 5: DTS acquisition at 1 trace/min for rate 2 Stage 6: Simultaneous DTS & DAS acquisitions once well stabilization occurred for rate 2 The acquisition was performed as planned until the end of stage 4, but five unexpected ESP shutdowns occurred during stage 5. Stage 6 was then been performed without a fully stabilized well. Figure 3 illustrates the 3 DTS and DAS
acquisition periods (periods 2, 4, and 6 on the Figure) performed during ESP
frequency changes. Bottom hole flowing pressure variations are shown by the dotted-line curve.
Results from DAS acquisition Figure 3 shows DAS data acquired in the test well during stages 2, 4 and 6 described above with two DAS units (also interchangeably referred to herein as "DAS devices") respectively connected to two single mode optical fibers connected to Pressure/Temperature gauges.
The measurements may be investigated, that is, acquired data (herein referred to as "DAS data set") may be processed, in order to determine: (1) the optical performance; (2) the ESP acoustic signature: frequency vs. time ¨
spectrograms; (3) the acoustic wave propagation at different ESP frequencies i.e.
rates; and/or (4) a flow analysis above and below the ESP pump.
(1) DAS spectrograms:
Frequencies generated around the ESP pump during various phases of its operation (for example, during the start-up, and/or during rates changes) may be determined through a Fast Fourier Transform algorithm (FFT analysis), to perform acoustical and vibrational diagnostics. This frequency analysis may be performed on the entire DAS data set so as to generate image data representing ESP
frequency response changes during operation.
Different tonal frequency components of an example ESP frequency response along with harmonics can be observed in Figures 4, 5, and in Figure 6.
Figures 4, 5, and 6 show examples of DAS spectrogram corresponding to the initial 2-hour-period of the ESP pump operation at a first rate (Rate1) including the pump turn-on. Figure 4 shows an example spectrogram for a time period which includes the ESP start-up time, in the [0-500Hz] frequency band, therefore corresponding to a raw FFT analysis, while Figure 5 is a zoom on the graph shown on Figure 4 for the [0-50Hz] frequency band. Figure 6 shows another example spectrogram for a time period of approximately 2 hours in the [0-100Hz]
frequency band, therefore corresponding to a fine tuned FFT analysis, These graphs capture the initial start-up of the pump which can be seen from the sharply increasing tonal frequency components of the ESP acoustic signature. Once the ESP reaches its operating frequency these detected frequency components stabilize and it can be seen that the ESP pump exhibits a fundamental pump frequency of 22Hz. A fundamental frequency for a centrifugal pump is defined as the "self-excited vibration" and corresponds to rotor instability.
This "self-excited vibration" is most commonly associated with radial journal bearings, annular seals, and hydraulic impeller-casing interaction. In case of vertical pumps a typical "self-excited vibration" lies with 0.5x running speed. DAS analysis is able to detect this "self-excited vibration" around 22Hz along with a surface frequency of 46 Hz.
Irregularities in the operating frequencies can indicate defects or problems with the ESP pump.
Figure 7 shows a different example DAS spectrogram, covering three periods of DAS data acquisition, among which two periods of stable operation of the ESP pump at a first rate (referred to as "Rate1") followed by the transition period between Rate1 and a second rate (referred to as "Rate2"). It can be seen that during the transition between rates that the surface frequency increases from 46 to 50Hz, but also an increase of the fundamental frequency. This change in the fundamental frequency between rates shows that it is directly linked to the ESP
operating frequency. The tonal component is not recorded by the ESP log at surface but DAS shows that it is relatively high amplitude suggesting the source of this signal is generating a large amount of energy. This confirms that investigations are required to localize the source of this fundamental frequency and see if this can be detrimental for the ESP pump life duration.
(2) Acoustic wave propagation at different ESP frequencies i.e. rates:
In an embodiment, the DAS data set may be processed for performing an acoustic signal analysis in order to detect waves' propagation directions.
This analysis may be performed for various time periods covering different stages of operating the well. For example, the following periods may be considered:
(a) Well shut-in (b) Pump operating at a first rate (Rate 1) (c) Pump operating at a second rate, different from the first rate (Rate 2) 2.a Acoustic wave propagation during well shut-in Figure 8 shows two images which respectively represent acoustic wave propagation profiles along each of the two optical fibers (Fiber 1 and Fiber 2) at well depth spanning from approximately 750 meters to 1600 meters. Shown on the left hand side of Figure 8 is a schematic view of the well architecture, more specifically of the pipe 100 inside diameter 101 (ID) and pipe outside diameter 102 (OD) thereof. The two horizontal stripes103 indicate the location of the pump (in this embodiment, an ESP) along the pipe. The horizontal dashed line 104 indicates the location of a pressure/temperature gauge (P/T gauge), which as shown on the figure is located under the ESP.
Fig. 8 shows that no acoustic wave propagation (although one could have expected some due to cross flow for example) occurs along the fiber during the shut-in period (vertical stripes on the fiber 2 are an artefact of the image pixel decimation and not the data).
2.b Rate1 wave propagation Figure 9 shows the detected acoustic wave propagation profiles along each of the two optical fibers (Fiber 1 and Fiber 2) at well depth spanning from approximately 750 meters to 1600 meters, once the pump is turned on and operates at a substantially stable first rate ("Rate 1") during a first period of time ("Rate 1 period"). As on Fig. 8, two images respectively corresponding to an acoustic wave propagation profile based on DAS data captured with respect to Fiber 1 and Fiber 2 are shown on Figure 9.
According to an embodiment of the proposed process the two images shown on Fig. 9 may be analyzed and interpreted as follows, with respect to the stable Rate 1 period:
There are strong propagating acoustic waves travelling up and down the well, which may be identified through recognition of the presence of diagonal lines indicating pressure disturbances moving up and down the well.
All detected propagating acoustic energy is originating from the location of the ESP. This suggests the detected signal is the noise generated by the ESP

propagating up and down the well.
The acoustic signal propagates to a much greater distance above the ESP
than below it.
A limit of acoustic wave propagation 105a, 105b, located on the images of Fig. 9 above the ESP (at approximately 1120 meters), can be determined where the acoustic waves reflect and travel back down towards the well 107a, 107b.
This is thought to correspond to a coupling effect i.e. a fluid interface within the annulus.
A background noise profile 106a, 106b, similar to the ones observed on the images of Fig. 8 corresponding to a well shut-in case, can be identified above the limit of acoustic wave propagation 105a, 105b.
In another embodiment, a limit of acoustic wave propagation 105a, 105b can be determined as corresponding to the interface between the acoustic waves' propagation profile 107a, 170b identified based on recognition of the presence of diagonal lines indicating pressure disturbances moving up and down the well, and a background noise profile 106a, 106b where such diagonal lines cannot be detected.
As indicated above, although Figures 7 and 8 show example image data representing acoustic wave propagation profile changes over a predetermined period of time for two fibers (Fiber 1 and Fiber 2), corresponding to the example and non-limiting case of two DAS acquisition channels, the present disclosure is not limited to any specific number of DAS acquisition channels or any specific DAS
acquisition scheme.
Some waves propagate below the ESP ¨ this propagation likely results from the noise from the ESP or from some fluids dropping down below the pump during the transient period to reach a stable rate. However, the first interpretation related to the ESP noise is the most probable.
DTS data interpretation can show that, for the example well configuration in which tests were performed, most of the fluid is produced from the heel of the well (from 1370 meter measured depth (mMD) to 1470 mMD). Due to the proximity of the producing zone to the ESP, the noise generated by the ESP may possibly be hiding the noise due to the production.
2.c Rate2 wave propagation Figure 10 shows the detected acoustic wave propagation profiles along each of the two optical fibers once the pump is turned on and operates at a substantially stable second rate ("Rate 2") during a second period of time ("Rate 2 period").
The stable Rate2 period allows the same interpretations as those made above for the Rate1 period but with a depth limit for wave propagation at a depth of 1150m, a little deeper as compared to Rate1.
Wave propagation for Annulus fluid level monitoring:
In an embodiment of the proposed process, a plurality of acoustic signal analyses are performed at different times in order to investigate possible changes of coupling above the ESP during transient periods, for instance from shut-in to the stable Rate1.
The result of such plurality of acoustic signal analyses is that acoustic signal reflections can be detected and interpreted as fluid interfaces.
Reference is now made to Figures 11a and 11b, which each shows 3 images representing respective detected acoustic wave propagation profiles along an optical fiber, for the following 6 example time periods: (1) well shut in;
(2) transitory between pump turned on and pump operating at a first rate ("Rate 1"); (3) pump operating at Rate 1, 2 hours, 1 min, 53s after pump turn on; (4) pump operating at Rate 1, 15 hours, 19 min, 55s after pump turn on; (5) pump operating at Rate 1, 17 hours, 21 min, 54s after pump turn on; and (6) pump operating at Rate 1, 20 hours, 54 min, 33s after pump turn on.
Also shown on the 6 images of Figures lla and llb is a determined ESP
pump depth level, as well as, except for the first one (well shut in), the annulus liquid level (that is, a limit of acoustic wave propagation in the fluid) determined as a result of digital image processing of the image.
The respective acoustic wave propagation limits determined for each of the

5 images representing acoustic wave propagation data for an ESP pump operating at a first rate Rate 1 at different times after being turned-on are as follows: (2) approximate liquid level = 884 m; (3) approximate liquid level = 955 m; (4) approximate liquid level = 1100 m; (5) approximate liquid level = 1115 m; and

(6) approximate liquid level = 1135 m. Therefore, determined acoustic wave propagation limits are linked to different fluid levels within the annulus. In other words, the determined annulus liquid level evolves with time. Such an analysis over different time periods allows dynamic monitoring of the annulus fluid level with time and is consistent with the Bottom Hole Pressure (BHP) history as illustrated in Figure 12. On Figure 12, the DAS-detected annulus level (referred to as "DAS
level" on the figure) is plotted in crosses, against the BHP history and the ESP
pump surface log frequency.
As illustrated above the proposed process provides for both direct and dynamic annulus fluid level monitoring in a well, for example to monitor that such fluid level stays above an ESP pump. This dynamic measurement is very important in early diagnosis of any gas locking event within the well. This can extend the life of the ESP pump by ensuring enough submergence i.e. to optimize the ESP
frequency to generate an optimal drawdown.
A correlation between the bottomhole pressure and DTS can also be observed, as shown in Figure 13, where BHP is overlaid on DTS as a black line.

The DTS interface detection is, however, much weaker and harder to detect in real time, making the DAS technique proposed herein more robust. In addition, the fact that the interface is observed at the same level as identified by DAS does provide confidence that the interpreted acoustic effect is indeed related to the annular liquid level.
Referring to the figures, Fig. 14 illustrates an example reservoir/well monitoring system 200 configured to use distributed acoustic sensor technology in accordance with the present disclosure. The reservoir/well monitoring system includes a distributed acoustic sensor (DAS) module 201 connected to an optical fiber 202 extending along the well 203 (for example an oil & gas well, and more specifically a deep seawater oil & gas well), preferably through one of the packers 207a, 207b which link the tubing of the well 203 to the casing thereof. In the example shown on the Figure 14, the packer 207a may be designed to include a feed-through for the optical fiber 202 used for DAS acquisition. The DAS
module 201 includes a light pulse generator (not shown on the figure) connected to the optical fiber 202 and adapted for sending light pulses down the optical fiber 202. In this embodiment, a pump 204, preferably an electric submerged pump (ESP), is inserted in the well 203 and contributes to the extraction of the oil or gas from the well. The pump is preferably located in a region of the well where it is immersed in a fluid 206 contained in the well. In this embodiment, the fluid 206 is a liquid composed of a mixture of water, oil, and gas. An acoustic wave generator 205 can be inserted in the well, which can be in some embodiments connected to the fiber 202. The acoustic wave generator 205 is adapted for generating acoustic waves that propagate in the fluid and exert pressure changes onto the optical fiber.
In some embodiments, the system 200 is provided with a plurality of acoustic wave generators such as the generator 205, for example to be located along the optical fiber 202 inserted in the well 203. In other embodiments, the acoustic wave generator 205 is provided by the pump 204 itself, with the acoustic waves corresponding to the noise generated by the pump as described above. In other embodiments, one or several acoustic wave generators 205 are provided without the pump 204 being placed in the well.
The acoustic wave generator 205 may also be or include a speaker, and in particular a hydro speaker adapted for immersion in a fluid, or a piezoelectric device, and a combination of those may be used in embodiments where a plurality of acoustic wave generators 205 are used. Such devices may also be used on top of the pump 204 in order to ensure a noise level which is high enough for operating distributed acoustic sensing, including when the pump 204 is not running (or, said otherwise, not in operation). This may help for instance ensuring that the pump is indeed submersed in liquid before starting operation thereof. In such instances, the pump serves as acoustic wave generator and is complemented by an additional acoustic wave generator device.
The DAS module 201 also include a sensor (not shown on the figure) connected to the optical fiber 202, and adapted for detecting propagation of acoustic waves through measuring of modulation of light backscattered in the optical fiber 202 generated by the pressure changes exerted onto it, as explained above. The sensor generates data which is processed by a DAS engine (not shown on the figure) of the DAS module 201. The DAS engine comprises a processing module adapted for determining a limit of acoustic wave propagation in the fluid based on acoustic wave propagation data generated by the sensor. In an embodiment, the processing module includes a processor, which may be any suitable microprocessor, ASIC, and/or state machine. The processing module may also comprise, or may be in communication with, computer storage media, such as, without limitation, data memory, capable of storing computer program instructions or software code that, when executed by the processor, cause the processor to perform the elements described herein. Data generated by the sensor may be stored in a DAS database memory, operatively connected to the processor, which may be any computer storage medium connected to the DAS
engine and operable with one or more associated database management systems to facilitate management of data stored in respective databases and associated hardware.
It will be appreciated that the reservoir/well monitoring system 200 shown and described with reference to Fig. 14 is provided by way of example only.
Numerous other architectures, operating environments, and configurations are possible. Other embodiments of the system may include fewer or greater number of components, and may incorporate some or all of the functionality described with respect to the system components shown in Fig. 14.
Fig. 15 shows an example architecture of the DAS module 201. The DAS
module 201 is a computer system which includes a DAS database memory 210, an acoustic wave propagation image database memory 211, a DAS data acquisition engine 212, a DAS data processing engine 213. The DAS module 201 also includes as described above a sensor 214 connected in operation to an optical fiber through an interface 215. In the architecture illustrated on Fig. 15, all of the DAS database memory 210, acoustic wave propagation image database memory 211, DAS data acquisition engine 212, sensor 214, interface 215, and DAS data processing engine 213 are operatively connected with one another through a control engine 216.
In an embodiment, the DAS data acquisition engine 212 manages the DAS
data acquisition through the sensor 214, which is adapted for detecting propagation of acoustic waves according to DAS technology. As described above, the acquired DAS data may include acoustic wave propagation data. Acquired DAS data may be stored in the DAS database memory 210. The DAS data processing engine 213 processes acquired DAS data stored in the DAS database memory 210 and generates, based thereon, image data representing acoustic wave propagation over a predetermined period of time. The predetermined period of time may in an embodiment be chosen equal to a value in an time interval spanning from lOs to 100s, and preferably equal to 50s. The image data generated by the DAS data processing engine 213 will typically generate image data that include images representing acoustic wave propagation corresponding to a predetermined location or section of the well/reservoir to be monitored.
Preferably, the generated image data may include images representing acoustic wave propagation at the vicinity of an acoustic source (e.g. a pump in an oil & gas well) used for DAS data acquisition.
In some embodiments corresponding to DAS data acquisition in an oil &
gas well in which a pump is immersed, the generated image data includes images representing acoustic wave propagation at the vicinity of the pump. The image data generated by the DAS data processing engine 213 may be stored in the acoustic wave propagation image database memory 211.
In an embodiment, the control engine 216 includes a processor, which may be any suitable microprocessor, ASIC, FPGA, and/or state machine. According to various embodiments, one or more of the computers can be configured as a multi-processor computer having multiple processors for providing parallel computing.
The control engine 216 may also comprise, or may be in communication with, computer storage media, capable of storing computer program instructions or software code that, when executed by the processor, cause the processor to perform the elements described herein. The DAS database memory 210 and acoustic wave propagation image database memory 211 may be any computer storage medium connected to the control engine 216 and operable with one or more associated database management systems to facilitate management of data stored in respective databases and associated hardware.
It will be appreciated that the DAS module 201 shown and described with reference to Fig. 15 is provided by way of example only. Numerous other architectures, operating environments, and configurations are possible. Other embodiments of the system may include fewer or greater number of components, and may incorporate some or all of the functionality described with respect to the system components shown in Fig. 15. Accordingly, although the DAS database memory 210, acoustic wave propagation image database memory 211, DAS data acquisition engine 212, DAS data processing engine 213, sensor 214, interface 215, and control engine 216 are illustrated as part of the DAS module 201, no restrictions are placed on the location and control of components 210 - 216.
In particular, in other embodiments, components 210 - 216 may be part of different entities or computing systems.
Fig. 16 shows an example architecture of a well/reservoir monitoring system 220 according to an embodiment of the present subject disclosure. The well/reservoir monitoring system 220 is also computer system which includes a DAS image data database memory 221, a DAS image data processing engine 222, a data memory 223 and a control engine 224. In the architecture illustrated on Fig.
16, all of the DAS image data database memory 221, DAS image data processing engine 222, and data memory 223 are operatively connected with one another through the control engine 216. In addition, Fig. 16 shows an example DAS
image data processing engine 222 which comprises an image processing pattern recognition engine 225, an acoustic wave limit determination engine 226, and a fluid level monitoring engine 227.
In an embodiment, the DAS image data processing engine 222 retrieves from the DAS image data database memory 221 image data representing acoustic wave propagation over a period of time (referred to hereinafter as "data analysis window") generated based on data acquired using DAS technology. The data analysis window may be chosen equal to a few seconds, for example 5 seconds.
Such image data may correspond in an embodiment to DAS image data generated by the example DAS module 201 shown on Figure 15. The DAS image data processing engine 222 processes such image data to determine a limit of acoustic wave propagation in the fluid contained in the monitored well/reservoir. Such processing is managed by the acoustic wave limit determination engine 226, and includes in an embodiment an image pattern recognition processing which may be provided in the example architecture shown on Fig. 16 by the image processing pattern recognition engine 225.
In an embodiment, the DAS image data processing engine 222 is configured to use a determined limit of acoustic wave propagation in the fluid for determining an estimate of the annulus fluid level in the well/reservoir. Such processing is managed by the fluid level monitoring engine 227, and also includes in an embodiment an image pattern recognition processing which may be provided in the example architecture shown on Fig. 16 by the image processing pattern recognition engine 225.
In another embodiment, the DAS image data processing engine 222 is configured to determine a plurality of limits of acoustic wave propagation in the fluid contained in the well/reservoir over a period of time, and to dynamically monitor the annulus fluid level based on the determined plurality of limits of acoustic wave propagation in the fluid contained in the well/reservoir. The determination of each or a plurality of the limits of acoustic wave propagation may use image pattern recognition processing as described above.
Data generated by the processing performed by the DAS image data processing engine 222 may be stored in the data memory 223.
In an embodiment, the control engine 224 includes a processor, which may be any suitable microprocessor, ASIC, FPGA, and/or state machine. According to various embodiments, one or more of the computers can be configured as a multi-processor computer having multiple processors for providing parallel computing.
The control engine 224 may also comprise, or may be in communication with, computer storage media, capable of storing computer program instructions or software code that, when executed by the processor, cause the processor to perform the elements described herein. The DAS image data database memory 221 and data memory 223 may be any computer storage medium connected to the control engine 224 and operable with one or more associated database management systems to facilitate management of data stored in respective databases and associated hardware.
It will be appreciated that the well/reservoir monitoring system 220 shown and described with reference to Fig. 16 is provided by way of example only.
Numerous other architectures, operating environments, and configurations are possible. Other embodiments of the system may include fewer or greater number of components, and may incorporate some or all of the functionality described with respect to the system components shown in Fig. 16. Accordingly, although the DAS image data database memory 221, DAS image data processing engine 222, data memory 223, image processing pattern recognition engine 225, acoustic wave limit determination engine 226, fluid level monitoring engine 227, and control engine 224 are illustrated as part of the well/reservoir monitoring system 220, no restrictions are placed on the location and control of components 221 - 227.
In particular, in other embodiments, components 221 - 227 may be part of different entities or computing systems.
Figures 17a, 17b, 17c and 17d show image data with image processing data according to an exemplary embodiment. Shown on the left hand-side of figures 17a, 17b, 17c, and 17d is image data representing acoustic wave propagation over a data analysis window spanning approximately 5 seconds generated based on data acquired using DAS technology. The vertical axis corresponds in those figures to the depth (in meters).
For each horizontal line of the images of figures 17a, 17b, 17c, and 17d, a Fourier analysis is performed so as to generate frequency spectrum data for each depth acquisition in the well/reservoir over the data analysis time window.
For example, assuming that the time domain data was sampled with a frequency sampling of 1 KHz, a 500 point Fast Fourier Transform may be performed over the time domain data so as to generate a frequency spectrum spanning a bandwidth of 0 to 500 Hz.

Preferably, the data analysis time window will be chosen sufficiently short so that the acquired image data are of a size such that the data processing time can be maintained at a predetermined level. Also, the time window may be chosen short enough so as to obtain a snapshot type view of the fluid level, for instance in case a dynamic monitoring of a well/reservoir is performed. At the same time, a sufficient amount of data in the time domain is required for a meaningful frequency analysis. The selected data analysis time window may therefore be the result of a compromise, depending on the type of monitoring performed on the well/reservoir.
Shown on the right hand-side of figures 17a, 17b, 17c, and 17d is the spectrum data 300a, 300b, 300c, 300d generated from respective time data 301a, 301b, 301c, 301d shown on the left hand-side of the figures. In this example, the frequency axis spans a 0 - 500 Hz bandwidth, and dark portions of the spectra correspond to low frequency values, while light portions correspond to high frequency values.
Taking the depth - frequency image of figure 17a as an example, the low frequency peaks 303a, 303b, 303c, 303d seen on the left hand-side bottom part of the image have been interpreted as the frequency image of a pump 304a, 304b, 304c, 304d in the well/reservoir. On the other hand the depth areas 305a, 305b, 305c, 305d that may be interpreted as corresponding to acoustic wave propagation in the fluid in the time data 301a, 301b, 301c, 301d show a different frequency profile 306a, 306b, 306c, 306d with peaks in the medium low part of the spectrum.
The depth areas 307a, 307b, 307c, 307d that may be interpreted as corresponding to noise in the fluid in the time data 301a, 301b, 301c, 301d show a frequency profile 308a, 308b, 308c, 308d with low peaks disseminated on almost the entire spectrum bandwidth.
The depth - frequency image generated through the Fourier analysis of each line of the DAS image data is then processed using a classification algorithm, for example a Kohonen classification algorithm or a nearest neighbor classification algorithm, in order to distinguish those different frequency spectrum profiles from each other so as to distinguish the depth areas corresponding to the fluid level from areas corresponding to noise or a pump, as the case may be.
The classification algorithm is performed on the depth - frequency image data, with an initial learning stage, for which a first depth - frequency image is use (for example, the depth - frequency image of Fig. 17 a).
The outcome of this learning stage is a predetermined number C of classes corresponding to the C most represented frequency spectrum profiles among the frequency spectrum profiles that can be found in the learning depth ¨
frequency image. In the example shown on figure 17a, the number C of classes has been chosen equal to 5, so that the classification algorithm learning stage has determined 5 frequency spectrum profiles to which the frequency profiles found in the spectra of Fig. 17a are the closest. Those 5 classes are represented on the figure by different hatchings or fillings on the narrow areas 302a located in-between the time data 301a and the frequency data 300a.
The learning stage is performed at least once, so that the C classes used for the classification stages be defined. In an embodiment, the image used for the class learning may be checked, e.g. by an operator, so as to ensure that the areas of interest are indeed present in the image. Once the C classes are defined, the classification image processing is reduced to a classification stage, and the learning stage is no longer necessary to complete the classification processing on a new image.
Fig. 17b, Fig. 17c, and Fig. 17d are examples of images processed through only a classification stage, based on the 5 classes defined by the learning stage performed on the first image shown on Fig. 17a. Preferably, the acquisition and processing parameters of all four images are identical or substantially identical. For example, the images are generated based on a single well/reservoir configuration (in the example shown in the figures a well with an ESP pump), for a predetermined data analysis time window and a given FFT processing.
Shown on Fig. 17a ¨ Fig. 17d is an indication of the class (among the 5 predetermined classes) to which each frequency spectrum (corresponding to a line in the time data area of each figure) has been associated. For example, the class with horizontal stripe corresponds to the depth area 307a, 307b, 307c, 307d interpreted as representing noise, the two classes with oblique hatching (oriented left and right) correspond to the depth area 305a, 305b, 305c, 305d interpreted as representing acoustic wave propagation in the liquid, the class with small dots filling corresponds to the depth area 304a, 304b, 304c, 304d interpreted as representing the pump.
In an embodiment, if several classes are shown to correspond to a single area of interest (e.g. noise, liquid over the pump, pump, liquid under the pump), such may be merged into one class, so as to better distinguish the areas of interest from one another. For example, the two classes with oblique hatching (oriented left and right) corresponding to the depth area 305a, 305b, 305c, 305d interpreted as representing acoustic wave propagation in the liquid, may be merged into a single class, as illustrated on Fig. 17c. The annulus fluid level may then be determined by calculating the depth interval corresponding to such merged class, or by determining the boundary between such merged class and the class corresponding to a noise area.
While the invention has been described with respect to preferred embodiments, those skilled in the art will readily appreciate that various changes and/or modifications can be made to the invention without departing from the spirit or scope of the invention. In particular, the invention is not limited to specific embodiments regarding the apparatus for monitoring a well or a reservoir and may be implemented using various architecture or components thereof without departing from its spirit or scope.
In addition, it should be understood that, while the invention has been described with respect to preferred embodiments, the invention may be used for the monitoring of devices inserted in a well, such as, for example and as described herein, pumps, such as electrical submersible pumps (ESP), the monitoring of vibrations, of fluid level in a well or a reservoir (for instance the amount in which a pump, or another liquid pulling/lifting device, such as a gas lift device, is submerged), of a gas rate at a pump level, of gas split in the annular, of gas lock and/or temperature level of devices inserted in a well, such as pumps... Such monitoring may be used for dynamic or non-dynamic tuning and optimization of devices inserted in a well, for example and without limitations with respect to their lifespan, for the maximization of drawdown, the optimization of maintenance of such devices, and/or the maximization of the HSE (Hygiene Securite Environnement, french for Hygiene Safety and Environmental issues) level when operating such devices. Such tuning, optimization or shut in shut off operations may be performed automatically or manually according to the well fluid level monitored.
Although this invention has been disclosed in the context of certain preferred embodiments, it should be understood that certain advantages, features and aspects of the systems, devices, and methods may be realized in a variety of other embodiments. Additionally, it is contemplated that various aspects and features described herein can be practiced separately, combined together, or substituted for one another, and that a variety of combination and subcombinations of the features and aspects can be made and still fall within the scope of the invention. Furthermore, the systems and devices described above need not include all of the modules and functions described in the preferred embodiments.
Information and signals described herein can be represented using any of a variety of different technologies and techniques. For example, data, instructions, commands, information, signals, bits, symbols, and chips can be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields or particles, or any combination thereof.
Depending on the embodiment, certain acts, events, or functions of any of the methods described herein can be performed in a different sequence, may be added, merged, or left out altogether (e.g., not all described acts or events are necessary for the practice of the method). Moreover, in certain embodiments, acts or events may be performed concurrently rather than sequentially.

Claims (16)

26

1. An apparatus for monitoring a well or a reservoir containing a fluid, comprising:
- an optical fiber extending along the well or the reservoir;
- a light pulse generator connected to the optical fiber and adapted for sending light pulses down the optical fiber;
- an acoustic wave generator adapted for generating acoustic waves that propagate in the fluid and exert pressure changes onto the optical fiber;
- a sensor connected to the optical fiber and adapted for detecting propagation of acoustic waves through measuring of modulation of light backscattered in the optical fiber generated by the pressure changes exerted onto the optical fiber;
- a processing module adapted for determining a limit of acoustic wave propagation in the fluid based on acoustic wave propagation data generated by the sensor wherein the processing module is further adapted for generating, based on acoustic wave propagation data generated by the sensor, image data representing acoustic wave propagation over a predetermined period of time, and for processing said image data using pattern recognition for determining the limit of acoustic wave propagation.

2. An apparatus according to claim 1, wherein the processing module is further adapted for determining an estimate of the annulus fluid level based on the determined limit of acoustic wave propagation in the fluid.

3. An apparatus according to claim 2, wherein the processing module is further adapted for determining a plurality of limits of acoustic wave propagation in the fluid over a period of time based on acoustic wave propagation data generated by the sensor, and dynamically monitoring the annulus fluid level based on the determined plurality of limits of acoustic wave propagation in the fluid.

4. An apparatus for monitoring a well according to any one of claims 1 to 3, wherein the acoustic wave generator is included in a pump immersed in the well and the generated acoustic waves correspond to noise generated by the pump during operation.

5. An apparatus for monitoring a well according to any one of claims 1 to 4, wherein the processing module is further adapted for monitoring the annulus fluid level above a pump immersed in the well.

6. An apparatus for monitoring a well according to any of claims 4 and 5, wherein the pump is an electrical submersible pump (ESP).

7. An apparatus according to any one of claims 1 to 6, wherein the processing module comprises an interface for receiving data captured by the sensor, a processor, and a memory operatively connected to the processor and storing a computer program that, when executed, causes the processor to determine a limit of acoustic wave propagation in the fluid based on acoustic wave propagation data received from the sensor through the interface.

8. A method for monitoring the level of fluid in the annulus of a well, comprising:
- processing image data generated using distributed acoustic sensing on an optical fiber extending along the well to determine at least one acoustic wave propagation limit in the annulus;
- determining an estimate of the annulus fluid level based on the determined at least one acoustic wave propagation limit, and wherein the processing image data includes pattern recognition image processing for determining the at least one acoustic wave propagation limit.

9. A method according to claim 8, wherein distributed acoustic sensing is used to determine a plurality of acoustic wave propagation limits in the annulus over a period of time, and further comprising dynamic monitoring of the annulus fluid level over the period of time based on the determined plurality of acoustic wave propagation limits in the annulus.

10. A method for monitoring a well or a reservoir containing a fluid, wherein an optical fiber extends along the well or the reservoir, the method comprising:
- sending light pulses down the optical fiber;
- generating acoustic waves that propagate in the fluid and exert pressure changes onto the optical fiber;
- generating image data representing acoustic wave propagation over a predetermined period of time; and - determining a limit of acoustic wave propagation in the fluid based on a processing of the generated image data;
wherein the processing image data includes pattern recognition image processing for determining the at least one acoustic wave propagation limit.

11. The method according to claim 10, further comprising: determining an estimate of the annulus fluid level based on the determined limit of acoustic wave propagation in the fluid.

12. The method according to claim 11 further comprising: determining a plurality of limits of acoustic wave propagation in the fluid over a period of time, and dynamically monitoring the annulus fluid level based on the determined plurality of limits of acoustic wave propagation in the fluid.

13. The method according to claim 11 or 12, further comprising: monitoring the annulus fluid level above a pump immersed in the well or reservoir.

14. A computer-readable storage medium storing computer-executable instructions for detecting activation of a virtual sensor in a scene, the computer executable instructions comprising instructions for implementing any of the methods according to claims 8 to 13.

15. A computer program product comprising computer program code tangibly embodied in a computer readable medium, said computer program code comprising instructions to, when provided to a computer system and executed, cause said computer to perform any of the methods according to claims 8 to 13.

16. A non-transitory computer-readable storage medium storing a computer program that, when executed, causes a system comprising a processor operatively connected with a memory, to perform any of the methods according to claims 8 to 13.