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 .
Response to Arguments
103 Rejection
Applicant’s arguments with respect to claim(s) 1 and 11 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Singer et al. (2021/0373106) in view Wildenschild et al. (X-ray imaging and analysis techniques for quantifying pore-scale structure and processes in subsurface porous medium systems) further in view of Alsinan et al. (2023/0305186).
With respect to claim 11, Singer et al. teaches a system, comprising: a system obtains a three-dimensional image of a pore space (as Singer et al. teaches obtaining 3D-based measurements presenting an image via a scanning methodology; [0067]); construct, using parametric functions, a reference surface and a constructed surface from the scanned surface, wherein the reference surface preserves a smoother shape of the pore space than the constructed surface, evaluate, using the reference surface and the constructed surface, a plurality of surface distances, wherein the plurality comprises a surface distance for each mesh point (as Singer et al. teaches using a Fourier transforms to perform a deconvolution method, a subtraction process, block 808, where the image processor removes the pore size contribution via a pore feature map, from the original surface height map, e.g., via convolution or frequency analysis, to create a "pore corrected surface height map" and the perform a refinement and edge correction, block 810, to ensure that the pore-corrected surface height map is accurate; [0067] and [0070-0071]) and determine, using the plurality of surface distances, a roughness coefficient (as Singer et al. teaches calculating, from distance data, a dimensionless surface roughness; [0072-0073]); a nuclear magnetic resonance (“NMR”) well logging tool configured to obtain an observed T2 time for at least one sample depth in a well (as Singer et al. teaches in [0032] using NMR measurements to obtain an observed T2); and a well log analysis system [0031-0032] configured to: determine, using the roughness coefficient, a corrected T2 time from the observed T2 time, and determine, using the corrected T2 time, an average pore size in a rock formation (as Singer et al. teaches determining a pore size distribution from the NMR T2 distribution that directly relies on a dimensionless surface roughness; also see [0082]).
Singer et al. remains silent regarding an image analysis system configured to: discretize the 3D image to generate a meshed surface, wherein the meshed surface comprises a description of the surface of at least one pore space embedded within a reservoir rock and a micro-computed tomography system.
Wildenschild et al. teaches a similar system that includes an image analysis system configured to: discretize the 3D image to generate a meshed surface (as Wildenschild et al. teaches reconstruction of 3D scanned images via a discrete Radon transformation; Section 3.4 and generating a meshed surface via marching cubes algorithms; Section 5.4.1), wherein the meshed surface comprises a description of the surface of at least one pore space embedded within a reservoir rock (as Wildenschild et al. teaches curvature flow functions applied to gain roughness descriptions of the pore space of a rock via the calculated curvatures functions; Section 5.4.2).
It would have been obvious to one of ordinary skill in the art before the effective filing of the instant invention to modify Singer et al. to include the algorithmic process taught in Wildenschild et al. because Wildenschild et al. teaches such a modification enhances computational efficiency, enabling faster processing of large geological datasets.
Singer et al. as modified remains silent regarding a micro-computed tomography system.
Alsinan et al. teaches a similar system that includes a micro-computed tomography system [0032].
It would have been obvious to one of ordinary skill in the art before the effective filing of the instant invention to modify the scanning system of Singer et al. to use a micro-computed tomography system taught in Alsinan et al. because the system improves the accuracy of the calculations; [0024].
The method steps of claim 1 are performed during the operation of the rejected structure of claim 11.
With respect to claims 2 and 12, Singer et al. as modified teaches the method and system using the determined average pore size (taught in [0030]) to perform mapping however, remains silent regarding simulate, using a reservoir simulator, a reservoir simulation; select a drilling target based, at least in part, on the reservoir simulation; and plan, using a well planning system, a planned well trajectory to penetrate the drilling target.
Alsinan et al. teaches a similar system that takes calculated roughness data to simulate, using a reservoir simulator [0029], a reservoir simulation (i.e. the simulator’s respective software); select a drilling target (i.e. a target zone of the drilling site; [0026]) based, at least in part, on the reservoir simulation (as the simulation uses data from the drilling site to simulate the drilling target); and plan [0047], using a well planning system (i.e. a computing based system employed by Alsinan et al. to develop the plan relative to the calculated and simulated data; [0047-0048]), a planned well trajectory to penetrate the drilling target (as Alsinan et al. teaches using the plan to develop a well drilling trajectory to according to the updated models; [0048-0049]).
It would have been obvious to one of ordinary skill in the art before the effective filing of the instant invention to modify the system of Singer et al. such to include a simulation tool for planning a well drilling trajectory based on the collected and calculated data as taught by Alsinan et al. because such a modification improves the accuracy of a drilling operation.
With respect to claims 3 and 13, Singer et al. as modified by Alsinan teaches the method and system further comprising using the determined average pore size to drill (as taught in Singer et al. [0029]), using a drilling system (as taught in Fig. 1 of Alsinan), a borehole guided by the planned well trajectory (as the combination as a whole teaches calculating and simulated data used to plan a drilling trajectory at a well site guided by the determined plan).
With respect to claim 4 and 14, Singer et al. as modified by Alsinan teaches the method and system wherein discretizing the 3D image comprises converting the 3D image into a binary image (as Fig. 4-6 depicts 3D image data and 2D image data of Singer et al.).
With respect to claims 5 and 15, Singer et al. as modified by Alsinan teaches the system wherein discretizing the 3D image comprises a marker-controlled watershed segmentation (as Singer et al. teaches in Fig. 4, para. [0045] [0047] [0052] and block 802 includes topographies, which would include watersheds, calculated from the collected data).
With respect to claims 6 and 16, Singer et al. as modified by Alsinan teaches the method and system wherein discretizing the 3D image further comprises fixing a topology of the binary image by filling holes and removing pixels (as the images include a topography of the binary image by filling holes and removing pixels, [0068], as Singer et al. teaches the images have been filtered such that holes and pixels are moved if they represent surface height variations attributed to local surface roughness).
With respect to claims 7 and 17, Singer et al. as modified by Alsinan teaches the method and system wherein evaluating the plurality of surface distances comprises determining a plurality of height differences between the constructed surface and the reference surface.
With respect to claims 8 and 18, Singer et al. as modified by Alsinan teaches the method and system wherein the parametric functions are spherical harmonic functions (as Singer et al. teaches using spherical functions using equations 1-2, 13, when constructing the reference surface).
With respect to claims 9 and 19, Singer et al. as modified by Alsinan teaches the method and system wherein the image analysis system is further configured to construct the constructed surface using a control of area and length distortion algorithm (as Singer et al. teaches in the abstract an algorithmic process that includes a CALD that effectively corrects for overestimation of surface area and thus length/surface distortion caused by including deep pores and large sample defects in surface measurements).
With respect to claims 10 and 20, Singer et al. as modified by Alsinan teaches the method and system further comprising a second-level pore separation algorithm configured to simplify a pore space (as Singer et al. teaches in the abstract a second-level pore separation algorithm configured to simplify a pore space by distinguishing between porosity and surface roughness; as the abstract and overall disclosure teaches a method and system used to refine surface measurements by filtering out non-porosity contributions, like saw marks or surface irregularities, to improve the calculation of pore distribution and surface relaxivity).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Seperhrnoori et al. (10,914,140) which teaches a system and method for simulating subterranean region.
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 MATTHEW G MARINI whose telephone number is (571)272-2676. The examiner can normally be reached Monday-Friday 8am-5pm.
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/MATTHEW G MARINI/ Primary Examiner, Art Unit 2853
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