DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . The rejections from the Office Action of 11/19/2025 are hereby withdrawn. New grounds for rejection are presented below.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 4-8, 10-15, 17, and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bakulin et al., Evaluating strategies for estimation of local kinematic parameters in noisy land data: quality versus performance trade-offs, Journal of Geophysics and Engineering, 2021 [hereinafter “Bakulin”] and Abma (US 20090292476 A1).
Regarding Claims 1, 10, and 18, Bakulin discloses a method, comprising:
obtaining a seismic data set from a seismic survey conducted over a subterranean region of interest (with a corresponding “seismic acquisition system” as recited in Claim 18); discretizing the seismic data set into a plurality of sub-regions [See Fig. 1 – “Figure 1. Distribution of the traces for a single ensemble in a cross-spread gather, shown for a field dataset used in this study. Each dot denotes a location of seismic traces with relative source X and receiver Y coordinates (origin (0,0) corresponds to the geometric center of the cross-spread).The red square encloses all traces of the single ensemble used for nonlinear beamforming. All these traces contribute during summation to produce enhanced output trace in the middle (black dot). In the 5D brute force strategy, all ensemble traces are also used for parameters estimation. In contrast, the «2+2+1» strategy only uses traces bounded by blue rectangles oriented along the x or y axes during the first and second estimation steps, respectively.” The seismic data set corresponding to Fig. 1 being discretized into many sub-regions through use of the red square and blue rectangles.] each with a local move-out function comprising a parameter [Inherent, see Page 896 second column – “The ‘operator’ defines a traveltime surface used for the local moveout correction.”];
dividing the plurality of sub-regions into a first subset and a second subset [See Fig. 1 – “In contrast, the «2+2+1» strategy only uses traces bounded by blue rectangles oriented along the x or y axes during the first and second estimation steps, respectively.”];
calculating a value of the parameter for each sub-region of the first subset using a non-linear solver [Page 892 first column – “Considering only two variable spatial coordinates and simplifying equation (1), we obtain the following second-order traveltime approximation in a 2D plane with respect to some reference point (x0, y0):
t (x, y) = t (x0, y0) + A ⋅ (x − x0) + B ⋅ (y − y0)
+C ⋅ (x − x0) (y − y0) + D ⋅ (x − x0)2
+E ⋅ (y − y0)2, (2)
where x and y represent two selected coordinates in the data domain. A, B, C, D and E are five unknown kinematic parameters to be estimated.”Page 892 second column – “(i) First, we fixed the value y0 on a so-called estimation grid, where the parameters are to be estimated. Taylor series, in this case, is defined by two parameters only:
t (x, y0) = t (x0, y0) + A ⋅ (x − x0) + D ⋅ (x − x0)2.
(4)
So, in the direction of x-coordinate, we have to solve the optimisation problem of defining only two parameters A and D. Only a subset of data ensemble is used in this step, which may influence estimation robustness, as we show later. Since the number of parameters is decreased by three, and the data dimension is reduced by one, the total computation cost for this new problem is roughly four orders of magnitude less than the full 5D problem.”];
determining a value of the parameter for each sub-region of the second subset using a method [Page 892 second column – “(ii) Second, we fix another direction, i.e. value x0 and use a similarly sparse estimation grid. Taylor series, in this case, is defined by two other parameters:
t (x0, y) = t (x0, y0) + B ⋅ (y − y0) + E ⋅ (y − y0)2.
(5)
So in the direction of y-coordinate, we have to solve the optimisation problem of searching for E and D parameters. The computational cost of this step is similar to the first one.”] based, at least in part, on the calculated value of the parameter of each sub-region of the first subset [Page 896 – “After choosing an appropriate strategy, one needs to select a grid in the data domain to estimate these parameters. Irrespective of the strategy, using every trace and time sample is not computationally feasible for massive datasets. A faster method is a so-called operator-oriented approach (Hoecht et al. 2009). The ‘operator’ defines a traveltime surface used for the local moveout correction. Kinematic parameters are estimated and stored as samples of so-called parameter traces at the decimated uniform grid in data space. These parameters define traveltime trajectories for summation. They are assumed valid for the neighborhoods between the estimated grid points. The coarser the spatial grid of parameter traces, the faster algorithm performs. However, a too-coarse grid may not accurately capture complex moveouts, leading to incoherent summation and loss of data quality for enhanced traces located far from the operator traces. To mitigate these effects in this study, we also invoke parameter interpolation: estimating parameters on coarser regular or random grids, then interpolating them to a dense original grid. While advanced interpolation and inpainting techniques can be considered (Gadylshin et al. 2020), we find that the simplest linear interpolation method is suitable when coarse and dense grids are regular.”], wherein the method is applied to a parameter data set comprising each parameter of the plurality of sub-regions [See Fig. 1 – “In contrast, the «2+2+1» strategy only uses traces bounded by blue rectangles oriented along the x or y axes during the first and second estimation steps, respectively.”], and the parameter data set is structured according to a temporal and spatial relationship of the plurality of sub-regions [Abstract – “Nonlinear beamforming had proved very powerful for 3D land data. However, it requires computationally intensive estimations of local coherency on dense spatial/temporal grids in 3D prestack data cubes. We present an analysis of various estimation methods focusing on a trade-off between computational efficiency and enhanced data quality. We demonstrate that the popular sequential «2 + 2 + 1» scheme is highly efficient[.]”]; and
determining an enhanced seismic dataset by performing non-linear beamforming using the local move-out function for each sub-region of the plurality of sub-regions [Page 905 first column – “We apply nonlinear beamforming to this data using the «2 + 2 + 1» strategy both with and without parameters interpolation”See Fig. 1 – “the «2 + 2 + 1» strategy only uses traces bounded by blue rectangles oriented along the x or y axes during the first and second estimation steps, respectively.”Page 891 second column – “The main idea of the nonlinear beamforming method is to describe traveltime moveout locally as a second-order surface, estimate its parameters and perform local summation along this local moveout to improve a signal-to-noise ratio.”].
Bakulin fails to disclose determining the local traveltime operator for each sub-region of the second subset using a convergent POCS method based, at least in part, on the calculated value of the parameter of each sub-region of the first subset, wherein the convergent POCS method is applied to a parameter data set comprising each parameter of the plurality of sub-regions, and the parameter data set is structured according to a temporal and spatial relationship of the plurality of sub-regions; and determining a location of a hydrocarbon reservoir in the subterranean region of interest using the enhanced seismic data set.
However, Abma discloses using POCS as an interpolation method [Abstract – “According to a preferred aspect of the instant invention, there is provided herein a system and method for Interpolation of seismic data with a POCS (projection onto convex sets) algorithm that can produce high quality interpolation results, at a reduced computational cost.”] and performing hydrocarbon reservoir determination using seismic data [Paragraph [0007]]. The use of POCS would have been obvious because it could be used to perform interpolation and Bakulin considers the use of advanced interpolation techniques [Page 896 second column – “advanced interpolation and inpainting techniques can be considered”]. Determining the locations of hydrocarbon reservoirs using the seismic data sets would have been obvious in order to improve the results of subsurface hydrocarbon exploration.
One having ordinary skill in the art would have understood that Bakulin discloses computer-readable memory, instructions, and processor [See Page 902 of Bakulin – “As a next step, we examine a computation cost for all the strategies. Table 2 shows a comparison of running times required to estimate kinematic parameters for one cross-spread gather. Although the full 5D brute force approach provides the best results in terms of quality, it is unacceptably slow for practical applications at this time. The «2 + 2 + 1» strategy provides the best result in performance and shows a 40× faster computational time compared to the «dips + curvatures » strategy.”].
Regarding Claims 4 and 11, Bakulin discloses that a selected percentage of sub-regions in the plurality of sub-regions are divided into the first subset [See Fig. 1 – “In contrast, the «2+2+1» strategy only uses traces bounded by blue rectangles oriented along the x or y axes during the first and second estimation steps, respectively.”Only a percentage of subregions correspond to each rectangle.].
Regarding Claims 5 and 12, the combination would fail to disclose that the non-liner solver is a 5D brute force method. However, Bakulin contemplates that a 5D brute force method can be used for traveltime approximation [See the description of equations 2 and 3 and section “2.1.1. 5D brute force strategy”]. One having ordinary skill in the art would have considered the use of 5D brute force strategy obvious because its use is taught as being viable.
Regarding Claims 6 and 13, Bakulin discloses receiving a selected move-out function [Page 896 – “After choosing an appropriate strategy, one needs to select a grid in the data domain to estimate these parameters. Irrespective of the strategy, using every trace and time sample is not computationally feasible for massive datasets. A faster method is a so-called operator-oriented approach (Hoecht et al. 2009). The ‘operator’ defines a traveltime surface used for the local moveout correction. Kinematic parameters are estimated and stored as samples of so-called parameter traces at the decimated uniform grid in data space. These parameters define traveltime trajectories for summation. They are assumed valid for the neighborhoods between the estimated grid points. The coarser the spatial grid of parameter traces, the faster algorithm performs. However, a too-coarse grid may not accurately capture complex moveouts, leading to incoherent summation and loss of data quality for enhanced traces located far from the operator traces. To mitigate these effects in this study, we also invoke parameter interpolation: estimating parameters on coarser regular or random grids, then interpolating them to a dense original grid. While advanced interpolation and inpainting techniques can be considered (Gadylshin et al. 2020), we find that the simplest linear interpolation method is suitable when coarse and dense grids are regular.”].
Regarding Claims 7 and 14, Bakulin discloses that the local move-out function of each sub-region in the plurality of sub-regions is independent and shares a common mathematical form as the selected move-out function [Page 896 – “After choosing an appropriate strategy, one needs to select a grid in the data domain to estimate these parameters. Irrespective of the strategy, using every trace and time sample is not computationally feasible for massive datasets. A faster method is a so-called operator-oriented approach (Hoecht et al. 2009). The ‘operator’ defines a traveltime surface used for the local moveout correction. Kinematic parameters are estimated and stored as samples of so-called parameter traces at the decimated uniform grid in data space. These parameters define traveltime trajectories for summation. They are assumed valid for the neighborhoods between the estimated grid points. The coarser the spatial grid of parameter traces, the faster algorithm performs. However, a too-coarse grid may not accurately capture complex moveouts, leading to incoherent summation and loss of data quality for enhanced traces located far from the operator traces. To mitigate these effects in this study, we also invoke parameter interpolation: estimating parameters on coarser regular or random grids, then interpolating them to a dense original grid. While advanced interpolation and inpainting techniques can be considered (Gadylshin et al. 2020), we find that the simplest linear interpolation method is suitable when coarse and dense grids are regular.”].
Regarding Claims 8 and 15, Bakulin discloses that the local move-out function of each sub-region of the plurality of sub-regions further comprises another parameter [Page 896 – “After choosing an appropriate strategy, one needs to select a grid in the data domain to estimate these parameters. Irrespective of the strategy, using every trace and time sample is not computationally feasible for massive datasets. A faster method is a so-called operator-oriented approach (Hoecht et al. 2009). The ‘operator’ defines a traveltime surface used for the local moveout correction. Kinematic parameters are estimated and stored as samples of so-called parameter traces at the decimated uniform grid in data space. These parameters define traveltime trajectories for summation. They are assumed valid for the neighborhoods between the estimated grid points. The coarser the spatial grid of parameter traces, the faster algorithm performs. However, a too-coarse grid may not accurately capture complex moveouts, leading to incoherent summation and loss of data quality for enhanced traces located far from the operator traces. To mitigate these effects in this study, we also invoke parameter interpolation: estimating parameters on coarser regular or random grids, then interpolating them to a dense original grid. While advanced interpolation and inpainting techniques can be considered (Gadylshin et al. 2020), we find that the simplest linear interpolation method is suitable when coarse and dense grids are regular.”].
Regarding Claim 17, Adma discloses that the convergent POCS method comprises a convergence criterion [Paragraph [0052] – “The cost of interpolation via POCS depends directly on the number of iterations that must be performed before convergence to a final answer is reached.”].
Claim(s) 2, 3, 19, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bakulin et al., Evaluating strategies for estimation of local kinematic parameters in noisy land data: quality versus performance trade-offs, Journal of Geophysics and Engineering, 2021 [hereinafter “Bakulin”]; Abma (US 20090292476 A1); and Alhukail et al. (US 20170176614 A1)[hereinafter “Alhukail”].
Regarding Claims 2 and 19, the combination fails to disclose planning a wellbore to penetrate the hydrocarbon reservoir based on the location, wherein the planned wellbore comprises a planned wellbore path. However, Alhukail discloses such wellbore path planning [See Paragraph [0005]]. It would have been obvious to use the survey results in such planning in order to effectively recover hydrocarbons from the reservoir.
Regarding Claims 3 and 20, the combination fails to disclose drilling the wellbore guided by the planned wellbore path. However, Alhukail discloses such wellbore path planning and drilling [See Paragraph [0005]]. It would have been obvious to use the survey results in such planning/drilling in order to effectively recover hydrocarbons from the reservoir.
Allowable Subject Matter
Claims 9 and 16 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Response to Arguments
Applicant argues:
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130
890
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Examiner’s Response:
The corresponding rejections under 35 USC 101 are hereby withdrawn and Applicant’s arguments are moot and will not be addressed at this time.
Applicant argues:
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732
885
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Examiner’s Response:
The Examiner respectfully disagrees. The referred-to claim language merely requires calculating “a value of the parameter for each sub-region of the first subset.” The determination of the traveltime operator for each of the blue rectangle sub-regions reads on this limitation because the same parameter type is being determined for each.
Applicant argues:
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832
891
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Examiner’s Response:
The Examiner respectfully disagrees. There are no claim limitations specifying that the POCS method is not applied directly to seismic data. Bakulin discloses that the parameter data set is structured according to a temporal and spatial relationship of the plurality of sub-regions [Abstract – “Nonlinear beamforming had proved very powerful for 3D land data. However, it requires computationally intensive estimations of local coherency on dense spatial/temporal grids in 3D prestack data cubes. We present an analysis of various estimation methods focusing on a trade-off between computational efficiency and enhanced data quality. We demonstrate that the popular sequential «2 + 2 + 1» scheme is highly efficient[.]”].
Applicant argues:
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233
889
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284
889
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Examiner’s Response:
The Examiner respectfully disagrees. The use of POCS would have been obvious because it could be used to perform interpolation and Bakulin considers the use of advanced interpolation techniques [Page 896 second column – “advanced interpolation and inpainting techniques can be considered”].
Applicant argues:
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285
889
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Examiner’s Response:
The Examiner agrees. However, Alhukail is not relied on as disclosing such.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US 20220268957 A1 – ENHANCEMENT OF SEISMIC DATA
US 20220260742 A1 – ENHANCEMENT OF SEISMIC DATA
US 20200400847 A1 – SYSTEMS AND METHODS TO ENHANCE 3-D PRESTACK SEISMIC DATA BASED ON NON-LINEAR BEAMFORMING IN THE CROSS-SPREAD DOMAIN
US 20220260740 A1 – ENHANCEMENT OF SEISMIC DATA
US 20210389485 A1 – COMPUTER-IMPLEMENTED METHOD AND SYSTEM EMPLOYING COMPRESS-SENSING MODEL FOR MIGRATING SEISMIC-OVER-LAND CROSS-SPREADS
US 8203907 B2 – Updating Velocity Models Using Migration Velocity Scans
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KYLE ROBERT QUIGLEY whose telephone number is (313)446-4879. The examiner can normally be reached 9AM-5PM EST.
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/KYLE R QUIGLEY/Primary Examiner, Art Unit 2857