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    • 专利摘要:
    method for monitoring reservoir voltage in an underground formation. The present invention relates to an apparatus and method for recovering hydrocarbons from an underground formation including collecting baseline and subsequent sonic data, calculating open or coated pit cross dipole and stoneley dispersions using the baseline and later sonic data, estimate the magnitude of minimum and maximum horizontal stress using the calculation of dispersions, calculation of a pressure, and hydrocarbon recovery.
    公开号:BR112014007583B1
    申请号:R112014007583-2
    申请日:2012-09-11
    公开日:2021-07-13
    发明作者:Bikash K. Sinha；Ergun Simsek
    申请人:Prad Research And Development Limited；
    IPC主号:
    专利说明:

    FIELD
    [0001] The modalities of this application refer to the estimation of changes in stresses in the underground formation caused by depletion or injection of the reservoir using time-lapse exploration well sonic data. FUNDAMENTALS
    [0002] Mechanical disturbances can be used to generate elastic waves in terrestrial formations around an exploration well, and the properties of these waves can be measured to obtain important information about the formations through which the waves propagated. Stoneley compression, shear and wave parameters, such as their velocity (or their reciprocal slowness) in the formation and in the exploration well, are indicators of formation characteristics that help in assessing the location and/or producibility of hydrocarbon resources. Recent wave propagation studies on pre-strained materials indicate that it can reverse measured shear slowness and compression data to estimate the formation stress parameters.
    [0003] One device that has been used to obtain and analyze sonic record measurements of formations around an earth exploration well is a SCANNER SONIC” such as a general type described in Pistre et al., “A modular wireline sonic tool for measurements of 3D (azimuthal, radial, and axial) formation acoustic properties, by Pistre, V., Kinoshita, T., Endo, T., Schilling, K., Pabon, J., Sinha, B., Plona, T. , Ikegami, T., and Johnson, D. "Proceedings of the 46'" Annual Logging Symposium, Society of Professional Well Log Analysts, Paper P, 2005. shear, Ai, and Stoneley slowness, Atst, each as a function of depth, z. [Slowness is reciprocal of velocity and corresponds to the transit time interval normally measured by sonic recording tools.]
    [0004] An acoustic source in a fluid filled exploration well generates frontal waves as well as relatively stronger exploration well guided modes. A standard sonic measurement system consists of placing a piezoelectric source and an array of hydrophone receivers within a fluid-filled exploration well. The piezoelectric source is configured in the form of a monopole or dipole source. The source bandwidth typically ranges from 0.5 to 20 kHz. A monopole source primarily generates the lowest order axisymmetric mode, also known as Stoneley mode, along with the frontal waves of compression and shear. In contrast, a dipole source primarily excites the lower order flexural exploration well mode along with the frontal compression and shear waves. Frontal waves are caused by the coupling of transmitted acoustic energy with waves in the formation that propagate along the axis of the exploration well. A compression wave incident on the exploration well fluid produces compression waves critically refracted in the formation. Those refracted along the surface of exploration well 20 are known as frontal compression waves. The critical incidence angle θi = sin’1 (Vf/Vc) , where Vf is the velocity of the compression wave in the exploration well fluid; and Vc is the velocity of the compression wave in the formation. When the front compression wave travels along the interface, it radiates energy back into the fluid which can be detected by hydrophone receivers placed in the fluid-filled exploration well. In fast formations, the shear wavefront can be similarly excited by a compression wave for the critical incidence angle θi = sin’1 (V£/Vs), where Vs is the velocity of the shear wave in the formation. It is also interesting to note that the frontal waves are excited only when the wavelength of the incident wave is smaller than the exploration well diameter so that the boundary can be effectively treated as a planar interface. In a homogeneous and isotropic rapid formation model, as mentioned above, frontal compression and shear waves can be generated by a monopole source placed in a fluid-filled exploration well to determine the formation's compression and shear wave velocities. It is known that refracted frontal shear waves cannot be detected in slow formations (where the shear wave velocity is less than the compression velocity of the exploration well fluid) with receivers placed in the exploration well fluid . In slow formations, formation shear velocities are obtained from the flexural dispersion low frequency asymptote. There are processing techniques for estimating formation shear velocities in fast or slow formations of an array of recorded dipole waveforms.
    [0005] The sonic data from the exploration well after depletion 15 or reservoir injection or injection is usually acquired in a coated well. However, processing sonic data in a lined well to estimate the three modulus of shear is more challenging. Among other things, the quality of the bond between the coating and the cement is an important factor in the processing and interpretation of sonic data. SUMMARY
    [0006] Modalities refer to apparatus and a method for recovering hydrocarbons from an underground formation, including collecting baseline and later sonic data, 25 calculating cross-dipole and Stoneley dispersions of the coated well using the posterior sonic data and from baseline, estimate the magnitude of minimum and maximum horizontal stress using dispersion calculation, pressure calculation, and hydrocarbon recovery. FIGURES
    [0007] Figure 1 is a schematic diagram of an underground formation during or after the injection of carbon dioxide and/or water in a tertiary recovery project.
    [0008] Figure 2 is a schematic diagram of the sonic tool 35 placed concentrically in an exploration well filled with coated fluid.
    [0009] Figure 3 is a series of graphical representations of the normalized amplitude as a function of the radial variation of 3 and 5 kHz.
    [0010] Figure 4 is a graphical representation of the amplitude normalized as a function of the radial variation when the Stoneley mode is varied.
    [0011] Figure 5 is a graphical representation of the slowness as a function of the frequency of the Stoneley mode of field and simulation data.
    [0012] Figure 6 is a series of graphical representations of the amplitude normalized as a function of the radial variation with a dipole mode at 3 and 5kHZ.
    [0013] Figure 7 is a schematic diagram of an exploration well in an underground formation when two horizontal stresses are equivalent.
    [0014] Figure 8 is a schematic diagram of an exploration well in an underground formation exposed to multiple stresses and varying pressure.
    [0015] Figure 9 is a graphical representation of the slowness as a function of frequency for an exploration well in an initial state.
    [0016] Figure 10 is a graphical representation of sluggishness as a function of frequency for an exploration well in an exploration well study after injection for the simulation and observed results.
    [0017] Figure 11 is a graphical representation of the shear rate as a function of before and after injection.
    [0018] Figure 12 is a graphical representation of the shear modulus as a function of before and after injection.
    [0019] Figure 13 is a graphical representation of estimated voltage changes as a function of before and after injection.
    [0020] Figure 14 is a flowchart for estimating the formation shear modulus using Stoneley data in a lined well.
    [0021] Figure 15 is a flowchart for estimating reservoir voltages using time-lapse sonic data acquired before and after reservoir depletion or injection. DETAILED DESCRIPTION
    [0022] Reservoir depletion and subsequent fluid injection (water and carbon dioxide) for improved oil recovery cause changes in reservoir pressure and formation stresses. Large voltage changes can lead to activation of pre-existing defects and cap rock fractures that can lead to unwanted CO2 leakage. It is important to monitor reservoir stresses as a function of changes in reservoir pressure to avoid reactivating an existing fault or introducing unwanted fractures into the cap rock that would result in the release of C02. Time-lapse seismic surveys can detect impedance changes in the order of 3 to 9 percent in C02 saturated rocks and are indicators of qualitative changes in reservoir pressure and saturation. A technique for detecting small sonic velocity changes that may be related to changes in in situ stresses and fluid mobility caused by reservoir depletion or injection is discussed in this document. Reliable estimates of these changes help reservoir managers maintain sink integrity and reservoir adaptation of injected carbon dioxide.
    [0023] Sonic velocities in changing formations as a function of lithology/mineralogy, porosity, clay content, fluid saturation, stresses, and rock temperature. To estimate changes in stress magnitudes from the formation of measured changes in sonic velocities, it is necessary to select a depth range with a reasonably uniform lithology, clay content, saturation, and temperature so that the measured changes in velocities can be largely related to corresponding changes in formation stress magnitudes. Any change in porosity caused by normal compaction in the chosen depth range is accounted for in the inversion model, by a corresponding change in the effective elastic modulus and formation density. Assuming that measured changes in sonic velocities are largely caused by changes in stress magnitudes, it is possible to invert exploration well sonic velocities to estimate changes in formation stress magnitudes.
    [0024] It has been shown that the differences in the shear modulus are related to differences in principal stresses in a homogeneously stressed rock. There are two independent difference equations for three shear moduli C44, C55, and Cee, θ three unknowns: the minimum and maximum horizontal stresses, and an acoustoelastic coefficient. Consequently, there are two independent equations for three unknowns. However, one can resolve the magnitude of the maximum horizontal stress and an acoustoelastic coefficient when a Mechanical Earth Model provides the overload stress, pore pressure, and minimum horizontal stress as a function of depth. Minimum horizontal stress can be estimated from mini-fracture or leakage tests.
    [0025] This Smnax estimation algorithm using the three shear modules assumes that the differences in the three shear modules are mainly caused by differences in the three main stresses - overload, minimum and maximum horizontal stresses. Although this assumption is largely valid in a sand reservoir with moderate fluid permeability, it is possible to correct for the fluid permeability or mobility-induced slope in the Stoneley Ceβ shear modulus in the cross-sectional plane for the exploration well. The presence of fluid mobility in the absence of any stress effects increases Stoneley's sluggishness in the low and intermediate frequency band of 1 to 3 kHz. This is associated with a decrease in the Stoneley Cee shear modulus that can be estimated from an advanced model based on a low frequency approximation of the Biot model. Generally, a fluid mobility of 100 to 1000 md/cp can cause a reduction in the Cgε shear modulus by about 5 to 10%. Therefore, it is necessary to increase the measured value of C66 by this amount before inputting into the voltage magnitude estimation algorithm. It is suggested that the effects induced by fluid mobility in Cβ6 (in the absence of stresses) be calculated using an independent estimate of the fluid permeability/mobility of a pre-test of MDT, NMR or core permeability. When the fluid mobility of an independent source is not known, it is recommended that the voltage magnitude estimation algorithm should run for at least two additional values of Cee1 which could describe an upper and lower limit on Cββ' in view of the possible slope in the data caused by fluid permeability.
    [0026] Similarly, one can compensate for the slope of the C66' shear modulus caused by intrinsic TI-anisotropy (shale) in estimating the formation stress magnitudes since this structural anisotropy of core samples was estimated in the presence of limit pressure at the depth of interest. Generally, Cββ θ greater than C44 or C55 in TI- horizontally layered formation. Consequently, the measured Cββ needs to be reduced by an amount that was introduced because of structural effects. In the absence of any real core data, one can run the stress magnitude algorithm using an upper bound and a lower bound for the Cgg modulus that would cover any structural or intrinsic anisotropy effects. This procedure would allow placing a reasonable limit on the estimated stress magnitudes that account for the slope of structural anisotropy in the measured shear modulus.
    [0027] Figure 1 shows a schematic diagram of CO2 and water injection to increase hydrocarbon production in a tertiary recovery project. It is important to monitor changes in reservoir pressure and stresses to reduce the chances of CO2 leakage through fracturing or reactivating the cap rock of any pre-existing fault. Sonic data on lined wells acquired before and after reservoir depletion or injection show changes on the order of 2 to 6 percent in compression velocity along with flexural dipole and Stoneley dispersions from the exploration well. This procedure described in this document estimates time lapse changes in the three shear modules using sonic data. This procedure is based on the sensitivity analysis of measured exploration well dispersions for various exploration well and formation fluid parameters. The 10 shear modules are then estimated from the measured exploration well dispersions. These far-field shear modules can be transformed into corresponding changes in the effective forming stresses. However, an independent estimate of any increase in fluid mobility possibly caused by CO2-induced oil swelling is desirable. In the absence of any such estimate of fluid mobility change, an upper bound for the difference between overload and horizontal stresses can be estimated as a parameter in controlling fracture pressure. Reservoir depletion or fluid injection for enhanced recovery can lead to two types of structural failure. First, if the reservoir stresses present together with the pore pressure cause the collapse pressure PB to exceed a threshold, cap rock fractures will be initiated and the integrity of the reservoir will be damaged. The collapse pressure is given by the following equation
    where Sh and SH are the minimum and maximum horizontal stresses, α is the Biot parameter, Pp is the pore pressure, and Ts 30 is the rock tensile strength.
    [0028] Second, if the Pp pore pressure exceeds a limit provided by the Coulomb criterion for slip initiation along a pre-existing fault, a reactivation of such faults would also cause reservoir damage. To mitigate the risk of such slippage, the reservoir pressure must be kept below a limit provided by
    where n and T are shear and normal stresses 5 acting on a fault forming an angle θ with the horizontal; Sv and SH are the horizontal and overload stresses of the formation; C is the cohesive force and μf is the coefficient of internal friction.
    [0029] The procedure for estimating changes in formation rms stresses includes the following. 1. Prior to production (or injection), estimate the minimum horizontal and overload stresses along with pore pressure. 2. Measure the Stoneley borehole and far-field compression slowness and cross-dipole and Stoneley dispersions of the exploration well using sonic data in a cased well or open well within a reservoir range. This constitutes a baseline study. 3. Estimate far-field shear moduli using exploration well dispersions. 4. Calculate an acoustoelastic coefficient based on a nonlinear continuous mechanics model. 5. After production (or injection), measure the far-field compression slowness and cross-dipole and Stoneley flexural dispersions of the exploration well into the coated well. This is referred to as a monitor study. 6. Estimate the three far-field shear moduli using the sonic data acquired in step 5. 7. Estimate changes in far-field rms modulus using differences in shear modulus and acoustoelastic coefficient calculated before production (or injection) in the step 4.
    [0030] The inversion of exploration well sonic data for 10 formation stress parameters assumes that the measured changes in plane wave velocities in the far field are mainly caused by corresponding changes in stresses. Note that changes in elastic modulus are determined from the plane wave velocities or inversion of exploration well dispersions.
    [0031] Generally, the modalities of this application refer to a technique for estimating changes in formation stresses caused by reservoir depletion or injection using time-lapse exploration well sonic data. A baseline study consists of sonic data acquired from an open or lined well along with estimates of reservoir pressure, overhead, and minimum horizontal stresses. Subsequently, a monitor study would comprise sonic data acquired from an observation well. Sonic data acquired before and after depletion or injection is processed to obtain cross-dipole and Stoneley dispersions from the exploration well. An inversion algorithm inverts the measured Stoneley dispersion to estimate the far-field shear modulus C66 in the cross-cut plane of the exploration well. The two cross-line flexural dispersions produce the two shear modules C44 and C55 in two orthogonal planes containing the axis of the exploration well. These two shear modules are directly obtained from the low frequency asymptotes of the two flexural dispersions. The differences in the three modulus of shear from the baseline study yield the magnitude of the maximum horizontal stress and an acoustoelastic coefficient using estimates of pore pressure, overload and minimum horizontal stresses from standard techniques known in the prior art. The three far-field shear modules in three orthogonal planes are obtained from the monitor study after depletion or injection. Using the acoustoelastic coefficient obtained from the baseline study and the three shear moduli after depletion or injection, the new algorithm provides estimates of the minimum and maximum horizontal stress magnitudes caused by a change in reservoir pressure. The estimated minimum and maximum horizontal stresses after depletion or injection along with the estimated reservoir pressure can then be used to calculate a safe injection pressure below a threshold to avoid unwanted fractures. They can also be used to provide a secure reservoir pressure window that will reduce changes from any shear slip on any existing fault. Cee estimate using Stoneley scattering of the exploration well into a lined well.
    [0032] Figure 2 shows a schematic diagram of a sonic tool (solid yellow cylinder) placed concentrically in a fluid-filled lined exploration well. The coating is represented by a hollow brown cylinder. Waveforms recorded on a matrix of hydrophone receivers can be processed by a modified matrix pencil algorithm to isolate dispersive and wave train arrivals. The lowest-order axisymmetric Stoneley mode is a dispersive mode whose velocity changes as a function of frequency.
    [0033] The Stoneley dispersion in a fluid-filled exploration well in the presence of a casing can also be calculated from the solution of a classical limit value problem. Stoneley dispersion for an exploration well surrounded by an effectively isotropic formation can be calculated in the presence of an equivalent tool structure placed concentrically with the exploration well axis to account for tool effects on the measured sonic data. To calculate the Stoneley dispersion in a lined well surrounded by an effectively isotropic formation, it is necessary to enter the following geometric parameters and 5 materials of the equivalent tool structure, exploration well fluid, casing material, and formation: 1. Condition of surface impedance at the boundary between tool and exploration well fluid; 2. Exploration well fluid compression velocity and mass density; 3. Bulk density of coating material, compression and shear speeds; 4. Inner and outer diameters of the cladding; and 5. Bulk density of formation, assumed shear and compression velocities.
    [0034] All these parameters can be estimated using information that is available from various sources, except the formation shear rate which continues to be estimated to calculate the far field 20 shear modulus Ce6> Note that the modulus of estimated shear from Stoneley Cee data = P Vs2, where Vs is the formation shear rate for an effective isotropic formation.
    [0035] To analyze sonic data in a lined well, investigation of the radial depth of the investigation as a function of the Stoneley wave frequency can be performed. Figures 3a and 3b, respectively, show the radial variation of displacement and voltage amplitudes associated with the Stoneley mode at 3 and 5 kHz. It is evident from these results that Stoneley waves at these frequencies exhibit radial depth of investigation that extends to about 2xOD of sheath (OD: Outer Diameter) in the presence of a well-bonded sheath. Figure 4 illustrates the radial variations of radial stress Trr as a function of frequency plotted on a logarithmic scale. These results help in selecting a frequency range that probes deeper into the formation. The results in Figure 4 suggest that the far-field shear modulus can be estimated by minimizing the differences between the model's predicted Stoneley dispersions and measured over a frequency range of 1 to 3 kHz. Estimating Ceε using this frequency range guarantees a reliable estimate of shear modulus 5 out of any possible change near the well.
    [0036] A technique for estimating Cg6 consists of an algorithm that minimizes the difference between model Stoneley dispersions and measurements over a chosen bandwidth where the Stoneley data are largely sensitive to far field formation properties. .
    [0037] The cost function to be minimized can be expressed as:
    where Si (data) and Si (model) denote the predicted Stoneley wave slownesses of the model and measured at different frequencies, and the index i= 1,2,3,....N, denotes chosen slownesses (or velocities) on the i-th frequency.
    [0038] The value of Cβ6 that minimizes the cost function ε, as defined in equation (5) is the estimated far field formation shear modulus. Figure 5 shows the comparison between the measured Stoneley dispersion (blue circles) and the model-based prediction (solid red curve) obtained with the estimated value of Cg6 Estimation of C44 and C55 using cross-dipole dispersions in a lined well.
    [0039] The other two shear modules C44 and C55 are obtained from the dipole data acquired in a lined well. A dipole source placed in a fluid-filled exploration well 30 generates refracted frontal waves and higher amplitude exploration well flexural modes. Processing a matrix of recorded waveforms by a modified matrix pencil algorithm produces two flexural dispersions corresponding to fast and slow shear waves in orthogonal planes containing the exploration well axis. The low-frequency asymptotes of the exploration well's flexural dispersions coincide with the shear delays of far-field formation. The radial depth investigation of flexural dipole data as a function of frequency helps confirm that the estimated shear moduli 10 C44 and C55 are far-field parameters out of any near-well alteration caused by the cement's annular space. Figures 6a and 6b, respectively, illustrate radial variations of displacement components and radial stress components as a function of the radial distance from the axis of the exploration well normalized by the outer radius of casing a. Fast shear and slow shear speeds can be easily converted to shear moduli as described by the following equations:
    where p is the formation mass density; Vss and VFS are the fast and slow shear speeds obtained from processing cross-dipole data. Note that low frequency asymptotes of flexural dispersions are independent of the presence of coating and any possible sonic tool effects on dipole data.
    [0040] Thus, it can be estimated that the three shear modules using the cross-dipole and Stoneley dispersions measured obtained from sonic waveforms generated by a monopole and two orthogonal dipole transmitters 30 placed in a well filled with fluid.
    [0041] Since it is known that the C66 horizontal shear modulus is somewhat reduced in the presence of horizontal fluid mobility in a porous reservoir, this fact can help in placing appropriate limits on the estimated vertical to horizontal stress ratios. For example, equations (11a) and (12b) are likely to produce an upper bound of the estimated vertical to horizontal stress ratio over a reservoir interval where mobility-induced effects in the C66 shear modulus are not accounted for. Generally, the tube wave slowness decreases by only about 2 to 3% in the presence of fluid mobility, and this leads to a decrease in Cg6 by about 4 to 6%. 10
    [0042] Similarly, it is known that the horizontal shear modulus Cgg θ significantly higher in the presence of high clay content in an interval of shale. Consequently, it can be challenging to estimate the ratio of vertical to horizontal stress ratios in shale intervals if it is not possible to compensate for the increase induced by structural anisotropy in C66 from other sources. One way to compensate for the structural anisotropy-induced increase in Cg6 is to measure the difference between the shear modulus C66 in the bed plane and C44 in a plane perpendicular to the treat from core samples 20 subjected to a confinement pressure at depth of interest. Theory
    [0043] Consider an exploration well parallel to the X3 axis and its transverse plane parallel to the X1-X2 plane as shown in Figure 7. The overload voltage Sv is parallel to the X3 axis, and the horizontal voltage SH is in the X2-plane X3. The processing of dipole data acquired by a transmitter aligned with the X3 axis produces the C55 shear modulus, whereas the other orthogonal transmitter aligned with the X2 axis produces the C44 shear modulus. Stoneley data is used to obtain the C&6 shear modulus in the cross-sectional plane of the exploration well (Xi~X2). Sonic velocities and corresponding elastic moduli are functions of effective stresses in the propagation medium. Figure 8 shows the schematic diagram of an exploration well in a triaxially stressed formation, where the rms stresses are defined in terms of the total formation stress and pore pressure along with the Biot a coefficient. Note that sonic velocities or slowness are sensitive to effective voltages in the propagation medium. The rms voltage Oij = Tij - δij α Pp, where Tij is the applied voltage, δij is the Kronecker delta, and α is the Biot parameter.
    [0044] With reference to an isotropically charged reference state, the shear modulus of formation in three orthogonal planes are the same (C44 = C55 = C66 = p) • When this rock is subjected to anisotropic incremental stresses, the changes in the three shear modules can be expressed as
    where ΔC55 is obtained from the fast dipole shear slowness and formation mass density, C55, Y [= 2p(l+v)], and v is the shear modulus, Young modulus, and Poisson ratio, respectively; C144 and C155 are non-linear constants that refer to the chosen reference state; and Δ033, Δon and ΔO22Z respectively, indicate changes in the effective overload, and maximum horizontal and minimum horizontal voltages of an effectively isotropic reference state.
    where ΔC44 is obtained from the slow dipole shear slowness and bulk density of formation at a given depth, and C44 (=C55) is the shear modulus at the chosen reference state.
    where ΔC66 is obtained from the Stoneley shear slow dispersion and formation mass density at a given depth, and C66 (=C44) is the shear modulus 5 in the chosen reference state. Difference Equations Using Far-Field Shear Modules
    [0045] A sand reservoir in the absence of formation stresses and fluid mobility behaves as an isotropic material, characterized by shear modulus and bulk. However, a complex schist sand reservoir is characterized by anisotropic elastic stiffness. Anisotropic elastic stiffness and the three modulus of shear are affected by (a) structural anisotropy; (b) stress-induced anisotropy; and (c) training mobility. The structural anisotropy caused by clay microstratification in shales is described as transversely isotropic anisotropy (TI) which exhibits the horizontal shear modulus Cg6 to be greater than the vertical shear modulus C44=Cs5 in the absence of any stress-induced effects. Shales are impermeable and do not form part of a production reservoir. Since the effect of formation stresses on the effective shear modulus over a sand and shale interval are substantially different, it is necessary to apply appropriate corrections to the measured shear modulus in estimating the formation stress magnitudes.
    [0046] The acoustoelastic theory refers to changes in the effective shear modulus for incremental changes in stresses and tilt forces from a reference state of the material. The three shear modules can be estimated from sonic data from the exploration well. With the recent introduction of algorithms for radial Stoneley logging of horizontal shear slowness (Cgβ) and radial dipole logging of vertical shear slowness (C44 and C55), one can unequivocally estimate the pure formation shear moduli. These algorithms account for sonic tool bias and possible near-well alteration effects on measured sonic data'
    [0047] As described above, the differences between the effective shear moduli are related to differences in the main stress magnitudes through an acoustoelastic coefficient 10 defined in terms of non-linear formation constants that refer to a chosen reference state and to a given formation lithology. Next, it is assumed that the axes Xi, X2, and X3, respectively, are parallel to the maximum horizontal (oH), minimum horizontal (oh), and 15 vertical (ov) stresses. Under these circumstances, the equations (10) yield difference equations in the modulus of shear effective in terms of differences in the main stress magnitudes through an acoustoelastic coefficient defined in terms of predicted non-linear formation constants for a chosen reference state and in a given formation lithology. The following three equations refer to changes in the modulus of shear corresponding to changes in the main rms stresses:
    where 033, or, and 022 denote effective overload, maximum horizontal stresses, and minimum horizontal stresses, respectively; and
    is the acoustoelastic coefficient, C55 and C44 denote the shear modulus for the fast and slow shear waves, respectively; C456 = (Ci55-Ci44)/2 is a non-linear formation parameter that defines the acoustoelastic coefficient; and 5 µ represents the shear modulus at a chosen reference state. However, only two of the three difference equations in (10) are independent.
    [0048] The presence of unbalanced stress in the cross-sectional plane of the exploration well causes dipole shear wave separation and the observed slow shear anisotropy can be used to calculate the acoustoelastic coefficient AE of equation (10c), once that estimates of the two main stresses (or e 022) as a function of depth are presented. Note that dipole shear waves are little affected by fluid mobility. One can then estimate the stress-induced change in the Stoneley C66 shear modulus using equations (10a) and (10b), and the effective stress magnitudes ov, oH, θ °h at a given depth.
    [0049] When presenting estimates of the minimum horizontal stress magnitudes (022) θ of overload (033) as a function of depth, one can determine the acoustoelastic parameter AE in terms of the far-field shear modulus C55 and Cββ using the ratio
    where it is assumed that the permeability effects on these shear modules are essentially similar and insignificant.
    [0050] Once the acoustoelastic parameter 30 is determined for a given lithology interval, it is possible to determine the magnitude ΔSH of maximum horizontal stress as a function of depth from the following equation
    where C55 and C44 denote the fast and slow dipole shear modules, respectively. Similarly, the magnitude Sh of the minimum horizontal stress as a function of depth from the following equation

    [0051] Therefore, one can estimate magnitudes of horizontal formation stress as a function of depth in terms of the three shear modules C44, C55 and Cçg, θ of the acoustoelastic coefficient AE. Estimate of Maximum Horizontal Voltage Magnitude
    [0052] The differences between the three shear modules outside the annular stress concentration space are related to the differences between the three principal stresses in terms of an acoustoelastic coefficient that refer to a local reference state. There are two independent difference equations that refer to overload voltage magnitudes, horizontal minimum and maximum effective, and the acoustoelastic coefficient. These two equations can be solved for the magnitude of the maximum horizontal stress and acoustoelastic coefficient since the magnitudes of the minimum horizontal stress and overload are known from other sources. The overload voltage is reliably known from the bulk density of the formation. Minimum horizontal stress can be reliably estimated from a mini-fracture or leak test and interpolated along a reasonably uniform lithology. Therefore, one can use equation (12a) to calculate the maximum horizontal stress magnitude at a given depth that exhibits dipole crossing as an indicator of stress-induced shear slow anisotropy dominating the data. Estimate of Stress Magnitudes in Shale Tl
    [0053] To estimate the stress magnitudes in TI shale using the three modulus of shear, it is necessary to compensate for the effects of structural anisotropy in the difference between the Stoneley C66 shear modulus and the C44 or C55 dipole shear modulus. Generally, the shear modulus Cδ6 in the isotropic plane of a TI shale is greater than the shear modulus C44 or C55 in the sagittal planes (X2-X4 or X3-Xi planes). When an independent estimate of the Tl-anisotropy of core data under confinement pressure is presented, one can express the increase in C66 induced by structural anisotropy in terms of the Thomsen parameter y. The ratio of C66 15 to C44 can be expressed as

    [0054] If Y = 0.2, the ratio C6β/C44 = 1.4. Under this situation, one needs to reduce the measured C66 by 40 percent, before introducing the shear modulus Cg6 together with the shear modulus C44 and C55 for the voltage magnitude estimation algorithm using the three-module algorithm. shear. Here, it is assumed that any remaining differences between C44, C55, and Cg6 are exclusively caused by differences between the three principal stresses.
    [0055] When the Thomsen parameter y is not known, it is suggested to run the voltage magnitude estimation algorithm for a range of C66 that covers the possible influence of TI-anisotropy effects. One can then graphically represent stress magnitudes, as a function of parameter 30 C66'/C66, where Cgβ is the Stoneley shear modulus measured at a chosen depth, and Cee1 is the modified shear modulus in a shale interval where Cgβ1 < Cβ6- Reservoir Voltages after Depletion or Injection
    [0056] Consider an exploration well filled with vertical fluid parallel to the X3 direction, and minimum and maximum horizontal stresses parallel to the Xi and X2 directions, respectively.
    [0057] The sonic data acquired in a fluid filled open or coated well can be inverted to obtain the three far-field formation shear modules. These three shear moduli together with the effective minimum and overload horizontal stresses in a baseline study provide an estimate of the magnitude of maximum horizontal horizontal stress with an acoustoelastic coefficient as provided by the following equations:
    quantity before that supplied by
    where Sij is the total stress, δij is the Kronecker delta, a is the Biot coefficient, and BP is the pore or reservoir pressure. The total overload stress is estimated by integrating the surface mass density to the depth of interest and the minimum horizontal stress is estimated by minifracture or extended leakage tests.
    [0058] The difference between the effective minimum and overload horizontal voltages after depletion and injection can be described by
    where the superscript "A" denotes the amount after depletion or injection, and the overload voltage is assumed to be essentially the same as before depletion and injection. When the reservoir depletion is quite extensive, where there is no bridging effect, the total vertical stress will be driven by the formation and the total vertical stress will be essentially the same as before.
    [0059] The total minimum and maximum horizontal stresses after depletion can then be provided by the following equations
    where and denote the total minimum and maximum horizontal stresses in the reservoir after depletion or injection. Voltage Magnitude Estimate in Permeable Reservoirs
    [0060] Stress magnitude estimation algorithms assume that the observed differences in the three shear moduli are caused by differences in the three main formation stresses.
    [0061] However, when fluid permeability and stress-induced effects are simultaneously present in the effective shear modules measured in a permeable reservoir, it is necessary to remove the mobility-induced slope of the workflow from the stress magnitude estimate. The presence of fluid mobility causes a decrease in Stoneley Cee's shear modulus which can be estimated when fluid mobility/permeability is known from an independent source such as core or NMR data. Under this situation, one can modify the Stoneley shear modulus measured Cβε θ 3 modified Cee' input (>Cee) in the stress magnitude estimation algorithm.
    [0062] Otherwise, the ratio of formation overload to horizontal stress can be obtained as a function of parameter y = C' 66/066 (>1) . Generally, this y parameter can range from 1 to 1.15. Tension Magnitude Estimate in Tl Shales
    [0063] Recall stress magnitude estimation algorithms assume that the observed differences in the three shear modules are exclusively caused by differences in the three main stresses.
    [0064] However, transversely isotropicS shales in the absence of stresses present higher shear modulus C66 in the isotropic plane than the shear modulus C44 and C55 in two orthogonal sagittal planes.
    [0065] Therefore, it is necessary to remove any differences between C66 θ C44 caused by structural anisotropy and reverse any remaining differences in the shear moduli to formation stress magnitudes. Illustrative Example I
    [0066] Consider a baseline study at a given depth A in an impoverished reservoir consisting of crossed dipole and Stoneley scatters (blue circles) as shown in Figure 9. The cyan and green circles denote the dipole scatters fast and slow. The dashed lines 20 are theoretical dispersions based on the model that represent the presence of a tool structure and casing attached to the well. The three theoretical curves produce the estimated modulus of shear Cg6, C55, and C44 at the chosen depth prior to any fluid injection. Figure 10 shows the cross-dipole and Stoneley dispersions measured at the same depth after the field has been injected with fluid for about a year. The observation is the same as in Figure 9. The differences between the exploration well dispersions in Figures 9 and 10 are caused by changes in * 30 reservoir pressure and effective horizontal stresses.
    [0067] Figure 11 illustrates measured changes in Stoneley shear, fast dipole shear, and slow dipole shear velocities before and after fluid injection. The corresponding changes in the C66, C55 and 35 C44 shear module are shown in Figure 12.
    [0068] One can reverse these changes in the measured shear modulus caused by fluid injection to estimate the corresponding changes in reservoir stresses. These changes in minimum and 5 maximum horizontal stresses are illustrated in Figure 13.
    [0069] Figure 14 shows a flowchart that highlights several steps in the estimation of CÔ6 minimizing the differences between the Stoneley dispersions predicted in the model and measures over a chosen bandwidth. Calculated Stoneley Scatter 10 accounts for sonic tool effects in the search mode routine. The differences between the predicted dispersions in the model and measures (ε) are minimized to an acceptable tolerance "Tol" in the iteration process.
    [0070] Figure 15 illustrates the various steps required for this procedure. Figure 15 is a flowchart for estimating reservoir voltages using time-lapse sonic data acquired before and after reservoir depletion or injection. The algorithm assumes that the minimum pressure, overload, and horizontal stresses of the reservoir are known 20 in a baseline study prior to any depletion or injection.
    [0071] Although only a few example embodiments have been described in detail above, those skilled in the art will readily appreciate that many modifications are possible in the 25 example embodiments without materially departing from this invention. Accordingly, all such modifications are intended to be included within the scope of this disclosure as defined in the following claims. In the claims, means-plus-function clauses are intended to cover the structures described in this document as performing the recited function and not only as structural equivalents, but also equivalent structures. Thus, although a nail and a screw are not structural equivalents in that a nail employs a cylindrical surface to secure the pieces of wood 35 together, where a screw employs a helical surface, in the fastening environment of wood pieces, a nail and a screw can be equivalent structures. It is the applicant's express intent not to invoke 35 U.S.C §112, para 6 for any limitations of any of the claims herein, except for those where the claim expressly uses the 5 words "means to" in conjunction with an associated function.
    权利要求:
    Claims (22)
    [0001]
    1. Method for monitoring reservoir stresses in an underground formation characterized by comprising: collecting sonic data from the baseline for the formation using a sonic profiling tool before a project in the formation is executed; collect sonic data later for the formation using a sonic profiling tool after collecting baseline sonic data and after the project in the formation has been executed, where the project comprises at least one of (i) recovering hydrocarbons from the formation and (ii) inject fluid into the formation; calculate cross-dipole and Stoneley dispersions using baseline sonic data and later sonic data; estimate changes in magnitudes of minimum and maximum horizontal stresses in the formation for the design using the calculated dispersions; and determine a pressure to maintain formation integrity using changes in minimum and maximum horizontal stress magnitudes.
    [0002]
    2. Method according to claim 1, characterized in that the subsequent sonic data is collected in an open exploration well.
    [0003]
    3. Method according to claim 1, characterized in that the subsequent sonic data is collected in a coated exploration well.
    [0004]
    4. Method according to claim 1, characterized in that the baseline sonic data comprise estimates of reservoir pressure, overload and minimum horizontal stresses.
    [0005]
    5. Method according to claim 1, characterized in that the baseline sonic data and the subsequent sonic data are collected in an exploration well.
    [0006]
    The method of claim 5, further comprising inverting the Stoneley scatter to estimate the far-field shear modulus C66 in the cross-sectional plane of the exploration well.
    [0007]
    The method of claim 6, further comprising using low frequency asymptotes of the two flexural dispersions to calculate two shear dispersions C44 and C55.
    [0008]
    8. Method according to claim 1, characterized in that the project comprises recovering hydrocarbons.
    [0009]
    9. Method according to claim 1, characterized in that the design comprises injecting a fluid into the formation.
    [0010]
    10. Method according to claim 9, characterized in that the design comprises injecting a gas into the formation.
    [0011]
    The method of claim 1, characterized in that the pressure for maintaining the integrity of the formation comprises an injection pressure for injecting fluid into the formation.
    [0012]
    The method of claim 1, characterized in that the pressure to maintain formation integrity comprises a reservoir pressure.
    [0013]
    The method of claim 12, characterized in that the reservoir pressure comprises a reservoir pressure port through which the formation is maintained.
    [0014]
    The method of claim 13, characterized in that the reservoir pressure window comprises a reservoir pressure window through which the formation is maintained during formation depletion.
    [0015]
    15. Method for monitoring reservoir stresses in an underground formation characterized by comprising: collecting sonic baseline data from an exploration well traversing the formation using a sonic logging tool; collect sonic data later in the exploration well using a sonic logging tool after collecting baseline sonic data and after recovering hydrocarbons from the formation; calculate cross-dipole and Stoneley dispersions using baseline sonic data and later sonic data; estimate the (i) minimum and maximum horizontal stresses in the formation before hydrocarbon recovery and (ii) minimum and maximum horizontal stresses in the formation after hydrocarbon recovery using the calculated dispersions; and determine a pressure to maintain formation integrity using (i) the minimum and maximum horizontal stresses in the formation before hydrocarbon recovery and (ii) the minimum and maximum horizontal stresses in the formation after hydrocarbon recovery.
    [0016]
    16. Method according to claim 15, characterized in that the baseline sonic data comprise estimates of reservoir pressure, overload and minimum horizontal stresses.
    [0017]
    The method of claim 15, further comprising inverting the Stoneley scatter to estimate the far-field shear modulus C66 in a cross-sectional plane of the exploration well.
    [0018]
    The method of claim 17, further comprising using low frequency asymptotes of two flexural dispersions to calculate two shear dispersions C44 and C55.
    [0019]
    The method of claim 15, characterized in that the pressure to maintain the integrity of the formation comprises an injection pressure to inject fluid into the formation.
    [0020]
    The method of claim 15, characterized in that the pressure to maintain formation integrity comprises a reservoir pressure.
    [0021]
    The method of claim 20, characterized in that the reservoir pressure comprises a reservoir pressure port through which the formation is maintained.
    [0022]
    The method of claim 21, characterized in that the reservoir pressure window comprises a reservoir pressure window through which the formation is maintained during formation depletion.
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    同族专利:
    公开号 | 公开日
    AU2012316571A1|2014-04-10|
    NO20140341A1|2014-04-01|
    US20130081804A1|2013-04-04|
    GB201404803D0|2014-04-30|
    GB2508766A|2014-06-11|
    US9176250B2|2015-11-03|
    CA2849639A1|2013-04-04|
    WO2013048719A1|2013-04-04|
    GB2508766B|2018-02-21|
    AU2012316571B2|2015-07-16|
    BR112014007583A2|2017-04-04|
    CA2849639C|2021-01-26|
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    法律状态:
    2018-12-11| B06F| Objections, documents and/or translations needed after an examination request according [chapter 6.6 patent gazette]|
    2020-07-28| B06U| Preliminary requirement: requests with searches performed by other patent offices: procedure suspended [chapter 6.21 patent gazette]|
    2021-05-04| B09A| Decision: intention to grant [chapter 9.1 patent gazette]|
    2021-07-13| B16A| Patent or certificate of addition of invention granted [chapter 16.1 patent gazette]|Free format text: PRAZO DE VALIDADE: 20 (VINTE) ANOS CONTADOS A PARTIR DE 11/09/2012, OBSERVADAS AS CONDICOES LEGAIS. |
    优先权:
    申请号 | 申请日 | 专利标题
    US13/248,971|2011-09-29|
    US13/248,891|US9176250B2|2011-09-29|2011-09-29|Estimation of depletion or injection induced reservoir stresses using time-lapse sonic data in cased holes|
    PCT/US2012/054597|WO2013048719A1|2011-09-29|2012-09-11|Estimation of depletion or injection induced reservoir stresses using time-lapse sonic data in cased holes|
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