SYSTEM AND METHOD FOR MONITORING SUBSURFACE STEAM CHAMBER DEVELOPMENT USING FIBER OPTIC CABLES

Abstract

Methods and systems are provided for monitoring subsurface steam chamber development in a thermal hydrocarbon recovery operation, which employ a fiber optic cable in a horizontal wellbore beneath the steam chamber as part of a distributed acoustic sensing technique in which the cable receives attenuated sound waves passing through the steam chamber and transmits a signal corresponding to the attenuated sound waves and unattenuated sound waves for subsequent signal processing.

Claims

1. A system for monitoring steam chamber development in a thermal hydrocarbon recovery operation, the system comprising: at least one horizontal wellbore extending at least partially beneath the steam chamber; a seismic wave source configured to generate sound waves, the sound waves becoming attenuated sound waves after passing through the steam chamber; a fiber optic cable situated within a portion of the at least one horizontal wellbore, the fiber optic cable configured to receive the attenuated sound waves and generate a signal; and a receiver configured to receive the signal.

2. The system of claim 1, wherein the at least one horizontal wellbore is a plurality of horizontal wellbores, each provided with the fiber optic cable for receiving the attenuated sound waves.

3. The system of claim 1 wherein the at least one horizontal wellbore is used in the thermal hydrocarbon recovery operation.

4. The system of claim 3 wherein the at least one horizontal wellbore is used in a steam-assisted gravity drainage operation that generates the steam chamber, such that the at least one horizontal wellbore aids in mobilizing hydrocarbon in the thermal hydrocarbon recovery operation and houses the fiber optic cable is used in monitoring the steam chamber development.

5. The system of claim 1 further comprising a second fiber optic cable positioned on surface above the steam chamber.

6. The system of claim 1 wherein the seismic wave source is a sound wave generator located in a vertical wellbore.

7. The system of claim 6 wherein the vertical wellbore is drilled for purposes of the steam chamber monitoring.

8. The system of claim 6 wherein the vertical wellbore is an existing observation well that is part of the thermal hydrocarbon recovery operation.

9. The system of claim 1 wherein the seismic wave source is in a subsurface location to reduce surface disruption.

10. The system of claim 1 wherein the seismic wave source comprises naturally occurring passive subsurface seismic waves.

11. The system of claim 1 wherein the receiver is part of or in communication with a processor, the processor configured to process the signal.

12. The system of claim 11 wherein the processing generates an image of the steam chamber based on the signal.

13. A method for monitoring steam chamber development in a thermal hydrocarbon recovery operation, the method comprising the steps of: a. drilling at least one horizontal wellbore extending at least partially beneath the steam chamber; b. deploying a fiber optic cable in the at least one horizontal wellbore; c. receiving a seismic wave at the fiber optic cable, the seismic wave attenuated when passing through the steam chamber, and generating a signal; d. transmitting the signal along the fiber optic cable to a receiver; and e. receiving the signal at the receiver.

14. The method of claim 13 wherein the seismic wave is naturally occurring due to subsurface movement.

15. The method of claim 13 wherein the seismic wave source is a piezoelectric source.

16. The method of claim 13 wherein the seismic wave is generated by a seismic wave source located in a vertical well.

17. The method of claim 16 wherein the vertical well is an existing observation well that is part of the thermal hydrocarbon recovery operation.

18. The method of claim 13 further comprising the further step after step e. of processing the signal to generate data regarding the steam chamber.

19. The method of claim 18 wherein the processing of the signal involves full-waveform inversion.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In the accompanying drawings, which illustrate exemplary embodiments of the present disclosure:

[0028] FIG. 1 is a perspective schematic view of an exemplary system according to the present disclosure.

[0029] FIG. 2 is a sectional schematic view showing inline wave paths, along the length of one of the subsurface fiber optic cables.

[0030] FIG. 3 is a sectional schematic view showing cross-line wave paths, perpendicular to the parallel subsurface fiber optic cables.

[0031] FIG. 4 is the view of FIG. 3 but including shadow zones of reduced direct wave passage through the steam chambers between the seismic source and the subsurface fiber optic cable.

[0032] FIG. 5 is a schematic diagram of an exemplary workflow of full wave inversion (FWI) of three-dimensional (3D) distributed acoustic sensing (DAS) survey data.

[0033] FIG. 6 illustrates an exemplary subsurface velocity model that can be used for full wave inversion (FWI) of two-dimensional (2D) distributed acoustic sensing (DAS) survey data according to the present disclosure.

[0034] FIG. 7 illustrates an exemplary subsurface velocity model that results from full wave inversion (FWI) of two-dimensional (2D) distributed acoustic sensing (DAS) survey data according to the present disclosure.

[0035] FIG. 8 illustrates an exemplary subsurface velocity model that results from full wave inversion (FWI) of three-dimensional (2D) distributed acoustic sensing (DAS) survey data according to the present disclosure.

[0036] Exemplary embodiments will now be described with reference to the accompanying drawings.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

[0037] Throughout the following description, specific details are set forth in order to provide a more thorough understanding to persons skilled in the art. However, well known elements may not have been shown or described in detail to avoid unnecessarily obscuring the disclosure. The following description of examples of the invention is not intended to be exhaustive or to limit the invention to the precise form of any exemplary embodiment. Accordingly, the description and drawings are to be regarded in an illustrative, rather than a restrictive, sense.

[0038] The present disclosure is directed to systems and methods for using DAS with fiber optic cables to monitor steam chamber development in a thermal hydrocarbon recovery operations such as a SAGD heavy hydrocarbon production operation. Other possible applications would be clear to those skilled in the art based on the within disclosure, such as for one non-limiting example monitoring subsurface CO2 sequestration structures.

[0039] Exemplary embodiments of the present disclosure use fiber optic cables as data receiving points and downhole acoustic signal generators as seismic source points. The fiber optic cables can be installed in subsurface horizontal wells, and optionally also laid out on the earth's surface, to record seismic signals from an acoustic source. As complementary receivers, surface fiber optic cables can be deployed at approximately 20-30 cm using a micro-trenching machine that cuts a shallow, narrow slit to reduce surface disturbance, and place cable without opening a deep trench.

[0040] Existing surface-based seismic programs require surface disturbance such as line clearing in forested areas, hole-digging for placement of seismic signal receivers (geophones), and hole-drilling for programs requiring dynamite as the signal source. In exemplary embodiments of the present disclosure, in the context of SAGD projects, the horizontal producer wells already have fiber optic lines installed therein, and vertical observation wells can be used to deploy a signal source. After acquiring the DAS data, data processing techniques such as full-waveform inversion are preferably applied to the raw field data to obtain the steam chamber image.

[0041] DAS using fiber optic cables is known in the oil and gas industry for monitoring fluid inflow in wellbores—which is the reason that cables are already present in some existing horizontal wellbores in SAGD operations—and on surface to monitor ground movement. Both are passive applications of the technology rather than an active surveying/monitoring technique. In the present disclosure, DAS can be employed to actively monitor the development of steam chambers, and by using existing cables in some cases the surface impact is significantly reduced.

[0042] Turning now to FIG. 1, there is illustrated an exemplary system according to the present disclosure. The system 10 comprises a series of parallel horizontal wellbores 12, each of which contains a length of fiber optic cable 14. Additional fiber optic cabling 16 is laid out on the surface in a back-and-forth pattern to cover the target area, although persons skilled in the art will know of other arrangements that would be useful in different contexts or in light of surface geography complications impacting deployment.

[0043] As noted above, vertical wells 18 can be used to deploy source generators 20 in the subsurface, to again reduce surface disruption. Although only a single source generator 20 is labelled in FIG. 1, numerous generators 20 can be deployed in various wells 18 and at varying depths, as determined by the operator.

[0044] FIG. 1 further points out an in-line axis 22, which is generally parallel to the horizontal extent of the wellbores 12 and cables 14, and a cross-axis or x-axis 24 which is perpendicular to the horizontal extent of the wellbores 12 and cables 14, as are further discussed below.

[0045] As the steam chamber develops, it has an impact on the velocity of the acoustic signal that passes through it, resulting in an attenuated signal that can provide useful information to the skilled data analyst regarding steam chamber development.

[0046] Using the fiber optic cables 14 installed in the subsurface horizontal wells 12 may provide a very high resolution of the steam chamber image along the horizontal length (inline direction 22) because the data are over-sampling along the inline direction 22. See for example FIG. 2, which illustrates the system 10 along the in-line axis 22. As can be seen, the acoustic wave source 20 generates direct waves 28 which may or may not pass through the steam chamber 26 and be attenuated, and reflected waves 30 and multiples 32 are also generated within the subsurface environment, all of which can be detected at the subsurface and surface fiber optic cables 14, 16.

[0047] However, there will be an under-sampling issue along the cross-line direction 24 due to the creation of “shadow zones” as illustrated in FIG. 3 and FIG. 4. FIG. 3 shows how the direct waves 28, reflected waves 30 and multiples 32 travel through the subsurface, including passing through the steam chambers 26 forming adjacent the horizontal wellbores 12. Due to the spacing of the horizontal wellbores 12, seismic waves penetrating through the shadow zones 34 cannot be received as direct waves 28 by the downhole fiber optic cables 14. To address this issue, surface-deployed fiber optic cables 16 can be used to mitigate the shadow issue by providing additional receivers for the waves.

[0048] A further aspect of some exemplary embodiments of the present disclosure is the use of newer data processing techniques that are being considered in some seismic data contexts, but have not yet been used in the present context, and may be more cost-effective and entail a reduced surface disturbance compared to conventional 3D seismic acquisition programs. Specifically, some exemplary embodiments of the present disclosure employ full-waveform inversion techniques given the potential complexity of the data sets given the use of downhole and optional surface fiber optic cables as receivers.

[0049] Full-waveform inversion (FWI), also known as full-wavefield inversion, is a data processing technique that has been employed in the oil and gas industry in seismic surveying, primarily in offshore 3D surveying of subsurface structures. In seismic surveying, sound waves are generated and reflect back from numerous subsurface layers and structures, being received at microphones such as hydrophones (in the offshore context) and geophones (in some onshore settings). This results in a very large amount of data, and for many years computer processing capabilities could only utilize a fraction of that data—one could see rock layers, but the rock properties were generally too difficult to ascertain. Historically, 3D seismic methods such as travel time tomography (which is focused only on wave travel time) were limited as to both the waves that could be used and the depth of penetration. FWI is known to be useful for addressing the inherent complexity of seismic data, as it can use all of the wave; with the use of supercomputers and more advanced algorithms the full wavefield can be processed. In FWI, field data is used to generate simulated models of the subsurface, and then the models are compared against the raw field data in an iterative process to improve the model (the “inversion” aspect). The model is compared to the raw data, the differences being a residual which is determined and minimized through the iterative inversion process.

[0050] However, FWI has not previously been used with distributed acoustic sensing, nor for monitoring subsurface changes over time such as steam chamber development. Some embodiments of the present disclosure preferably employ FWI to enhance the data received from the fiber optic cables. FWI is of particular utility due to the complexity involved in acquiring data from both subsurface- and surface-deployed fiber optic cables.

[0051] FWI initially emerged as an advanced tool for complex velocity model building. The FWI-derived velocity model coupled with advanced imaging algorithms such as pre-stack depth migration (RTM) can dramatically improve the subsurface imaging from extremely complicated structures that exhibit abrupt vertical and lateral velocity changes. The oil and gas industry has seen very successful applications of FWI using the surface seismic data in different geologic settings such as the complex subsalt targets in the Gulf of Mexico.

[0052] The application of FWI to 3D DAS is an innovative way to image the steam chamber. The proposed 3D DAS geometry is very different from conventional seismic programs in that conventional seismic surveys use the source points and receivers on the surface and only the reflected wave is utilized to image the subsurface structure and rock properties. The conventional seismic workflow using reflection only cannot provide a clear subsurface imaging because the seismic wavefield gets shattered and complicated when the seismic wave travels from the low-velocity zone to the high-velocity zone or vice versa. 3D DAS FWI preferably uses supercomputers and an advanced algorithm of FWI, processing the full wavefield including all the seismic wave types (refraction, diffraction, multiples, or even elastic wave) through computer simulation to get a subsurface earth model in rich details in depth domain. The input of 3D DAS FWI workflow is the large quantities of the shot gathers recorded from the 3D DAS survey with a minor precondition of the data, and the output of the workflow are the subsurface rock properties mainly P-wave velocity and other anisotropy parameters. The inverted P-wave velocity can be used as a direct indicator of the steam chamber.

[0053] 3D DAS FWI is driven by minimization of the data residual between the real raw shot gathers and the simulated shot gathers by an iterative process that results in a high-resolution velocity model (see FIG. 5). Two key requirements in the 3D DAS FWI workflow are the efficient forward modeling and the local differential computation, which are two major computational costs in the FWI process.

[0054] The 3D DAS FWI objective function can be formulated as:

ϕ.sub.FWI=∥d.sub.s.sup.obs−d.sub.s.sup.pre∥.sup.2+τ.sub.s∥Lm.sub.s∥.sup.2  (1)

[0055] where, d.sub.s.sup.obs and d.sub.s.sup.pre represent the observed seismic waveform data and synthetic data. τs is the smoothing parameters to balance the data misfit term and a regularization term. m.sub.s is the velocity model. The gradient of the FWI objective function is defined as the partial derivative of the cost function with respect to the model slowness:

[00001]$\begin{array}{cc}\frac{\partial {\varphi }_{\mathrm{FWI}}}{\partial m_s}={\stackrel{.Math.}{P}}_F{P}_B+{\tau }_s{L}^T{\mathrm{Lm}}_s& \left(2\right)\end{array}$

[0056] where {umlaut over (P)}.sub.F and P.sub.B are the forward and backward propagation wavefield for imaging that provides sensitivity impacts and directs waveform inversion.

[0057] For time-lapse 3D DAS FWI processing, the double-difference workflow can be used to invert the subsurface difference between baseline and monitor. The starting model of the double-difference FWI is the final model of the baseline 3D DAS FWI, and the input data of this workflow is the waveform difference between the monitor 3D DAS and baseline 3D DAS surveys. The waveform difference generated by the elastic property changes between the time-lapse surveys can be regarded as the scatter waves. Even though the starting model, which is the baseline model in this workflow, may have its own error, the scatter waves can be imaged/migrated to the isolated areas around the reservoirs, rather than to distribute the energy to the whole area. The theory of the double-difference FWI is described below.

[0058] Consider the following cost function in a joint baseline/monitor FWI:

[00002]$\begin{array}{cc}E\left(m_{\mathrm{baseline}},m_{\mathrm{monitor}}\right)={.Math.\left[d_{\mathrm{monitor}}-u_{\mathrm{monitor}}\left(m_{\mathrm{monitor}}\right)\right]-\left[d_{\mathrm{baseline}}-u_{\mathrm{baseline}}\left(m_{\mathrm{baseline}}\right)\right].Math.}^2={.Math.\left[d_{\mathrm{monitor}}-d_{\mathrm{baseline}}\right]-\left[u_{\mathrm{monitor}}\left(m_{\mathrm{monitor}}\right)-u_{\mathrm{baseline}}\left(m_{\mathrm{baseline}}\right)\right].Math.}^2& \left(3\right)\end{array}$

[0059] where d.sub.baseline and d.sub.monitor are the baseline and monitor seismic waveforms, m.sub.baseline and m.sub.monitor are the baseline and monitor models u.sub.baseline and u.sub.monitor are the synthetic waveforms using the exiting baseline and monitor models respectively. The time-lapse 3D DAS FWI objective is to find a solution for m.sub.baseline and m.sub.monitor that can minimize the double differences in the cost function of equation (3).

[0060] Assuming a reasonable baseline model can be obtained from a standard baseline 3D DAS FWI, equation (3) can be written as follows:

E(m.sub.monitor)=∥[d.sub.monitor−d.sub.baseline+u.sub.baseline(m.sub.baseline)]−u.sub.monitor(m.sub.monitor)∥.sup.2  (4)

This cost function can be minimized by performing a monitor 3D DAS FWI but replacing the monitor seismic waveform with the input waveform that is the difference between the monitor and baseline waveforms plus the baseline synthetic waveform. The advantage of this method compared with a sequential 3D DAS FWI is that it should guarantee to converge to the baseline model if there is no waveform difference between the time-lapse 3D DAS surveys, so we can safely mask the areas if we believe there shouldn't be any change from the baseline model.

[0061] A multi-stage and multi-scale strategy can also be used in this 3D DAS FWI processing, from the travel time tomography which provides enough close initial velocity model to the double-difference FWI and from the low to the high-frequency components through the following workflow steps:

Workflow for the baseline 3D DAS FWI:

[0062] 1. Geometry validation and trace editing

[0063] 2. Theoretical transverse sensibility compensation

[0064] 3. Surface-consistent amplitude balancing

[0065] 4. Picking first breaks

[0066] 5. Model building and travel time tomography

[0067] 6. Surface-consistent residual statics

[0068] 7. Test of bandwidth and Ricker frequency

[0069] 8. Test of time-shifts of the Ricker wavelet so the synthetics of the starting model can match the first arrival seismic waveform

[0070] 9. 3D DAS FWI and the final baseline velocity model

Workflow for the monitor 4D time-lapse DAS double-difference FWI:

[0071] 1. Geometry validation and trace editing of the monitor

[0072] 2. Theoretical transverse sensibility compensation

[0073] 3. Amplitude scaling of the monitor survey to match the baseline survey

[0074] 4. Phase matching of the monitor and baseline surveys

[0075] 5. Surface-consistent residual statics

[0076] 6. 4D time-lapse DAS double-difference FWI

[0077] 7. Final velocity difference and monitor velocity model

2D DAS FWI on the Synthetic 2D Data

[0078] 2D DAS synthetic shot gathers were generated using the finite difference method. The source line is parallel to the subsurface horizontal. A total of 100 synthetic shot gathers were generated with 100 meters spacing, and each shot gather has 100 traces with 10-meter gauge length along the fiber optic. The subsurface velocity model (see FIG. 6) was the input of the finite difference simulation using the 2D acoustic wave equation to take into the variation of compressional velocity and density including the multiple simulation. The 2D DAS FWI algorithm was applied to the synthetic data and the results are shown in FIG. 7. The inverted reliable and high resolution velocity model is very close to the input velocity model.

3D DAS FWI on the Real Field 3D DAS Data

[0079] A substantial amount of 3D DAS shot gathers were recorded simultaneously when the 4D time-lapse surface seismic was shot. The 3D DAS FWI methodology was adapted for an innovative application on the 3D DAS shot gathers. Compared with the traditional 4D time-lapse surface seismic, the new 3D DAS FWI technology demonstrated some advantages and limitations. 2D DAS FWI has demonstrated FWI algorithm can produce a reliable and high-resolution velocity model using 2D DAS synthetic data generated from the finite difference method. However, the inadequate illumination from the shadow zone in the 3D DAS due to the sparse fiber-optic receiver lines imposes challenges on 3D DAS FWI. The inverted velocity along the horizontals shows a higher resolution image in the inline direction compared with the crossline direction which has an inadequate illumination. The preliminary analysis of the inverted 3D DAS FWI velocity shows a high-resolution velocity result where the illumination is highest. On the other hand, the 3D DAS FWI velocity at the edge of the survey shows some artifacts and poor results due to the inadequate illumination. The inverted velocity from the 3D DAS is shown in FIG. 8. 3D DAS FWI, as an innovative and cost-effective alternative to the conventional 4D time-lapse surface seismic, can deliver the velocity volume in the depth domain directly from the raw shot gathers with minor pre-processing of the 3D DAS shot gathers, which can result in a significantly reduced turnaround time to implement timely production decisions. To mitigate the illumination issue in the 3D DAS technology, the fiber on the surface can be used to resolve the illumination issue in the 3D DAS.

[0080] The foregoing is considered as illustrative only of the principles of the present invention. The scope of the claims should not be limited by the exemplary embodiments set forth in the foregoing, but should be given the broadest interpretation consistent with the specification as a whole.