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 .
Priority
Current application, US Application No. 18/189,906 is filed on 03/24/2023.
DETAILED ACTION
This office action is responsive to the application filed on 03/24/2023. Claims 1-20 are currently pending.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
As per claims 1, 11 and 16, the limitation “wherein the second extent is greater than the first extent” is ambiguous because the specification discloses that the second extent refers to extended regions from the first extent and the extended regions are not recited to include the first extent (see specification – extrapolating … extended seismic dataset [0019-0020, 00111, Fig. 11-12 1106 1110 Left Part 1116 1112 Right Part]), thus the second extent being greater than the first extent cannot be generalized. Furthermore, the first and the second extent are not clearly specified in the claims nor in the specification.
For the sake of examination, the limitation is interpreted as “the second extent comprises the extrapolated regions beyond boundaries of the first extent”.
As per claims 2-10, 12-15 and 17-20, claims are also rejected because base claims 1, 11 and 16 are rejected.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-4, 6-7, 9, and 11-18 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Baumstein (US 20210318458 A1), hereinafter ‘Baum’ best understood by the examiner.
As per claim 1, Baum discloses
A method, (A method [abs, 0002, 0042]) comprising:
obtaining, using a seismic processor (preprocessor [0078, Fig. 2C], multi-processor computer system, multi-processor server [0096, 0099], suitable processor [0099]), a training seismic dataset, comprising an input seismic dataset with a first extent and an output seismic dataset with a second extent, wherein the second extent is greater than the first extent; (subsurface, survey, seismic data, training dataset, input, output [0011-0012, 0042, 0074-0091, Fig 2A-5B, 12], extending bandwidth to a lower frequency range, dataset being split separately into a lower frequency range ‘e.g. , low - frequency patches’ and a higher frequency range ‘e.g. , high - frequency patches’, such as 200 traces by 500 time samples, time and spatial sampling, widening azimuth, narrow azimuth part, wide azimuth part [0074], the first portion and the second portion of the second dataset are used for training the neural network, batch size [0075]) and
training, using the seismic processor and the training seismic dataset, a machine-learning (ML) network to predict the output seismic dataset, at least in part, from the input seismic dataset. (training a neural network using … geophysical dataset [0013], processor [0078, 0096, 0099], training the neural network, after … trained, the desired features in the first method are predicted, e.g. with bandwidth extension, lower frequencies in towed streamer data are predicted [0087-0091, Fig, 5A-5B], The trained network was then used to predict low frequencies from their high - frequency counterparts for streamer data [0093, Figs. 8A-8B, 9A-9B]).
As per claim 11, Baum discloses
A non-transitory computer-readable medium storing computer-executable instructions stored thereon that, when executed by a computer processor, cause the computer processor to perform steps of: (non - transitory computer readable medium … software instructions … executed by a processor to perform the method [0126])
obtaining a training seismic dataset, comprising an input seismic dataset with a first extent and an output seismic dataset with a second extent, wherein the second extent is greater than the first extent; (subsurface, survey, seismic data, training dataset, input, output [0011-0012, 0042, 0074-0091, Fig 2A-5B, 12], extending bandwidth to a lower frequency range, dataset being split separately into a lower frequency range ‘e.g. , low - frequency patches’ and a higher frequency range ‘e.g. , high - frequency patches’, such as 200 traces by 500 time samples, time and spatial sampling, widening azimuth, narrow azimuth part, wide azimuth part [0074], the first portion and the second portion of the second dataset are used for training the neural network, batch size [0075]) and
training, using the training seismic dataset, a machine-learning (ML) network to predict the output seismic dataset, at least in part, from the input seismic dataset. (training a neural network using … geophysical dataset [0013], training the neural network, after … trained, the desired features in the first method are predicted, e.g. with bandwidth extension, lower frequencies in towed streamer data are predicted [0087-0091, Fig, 5A-5B], The trained network was then used to predict low frequencies from their high - frequency counterparts for streamer data [0093, Figs. 8A-8B, 9A-9B])
As per claim 16, Baum discloses
A system, (systems [0032, 0085]) comprising:
a seismic acquisition system configured to record an observed seismic dataset pertaining to a subsurface region of interest; (streamer acquisition, survey [abs, 0005], seismic survey … source and receivers [0004], acquisition [0008-0009, 0012]), and
a seismic processor, (multi-processor computer system, multi-processor server [0096, 0099], suitable processor [0099]) configured to:
obtain a training seismic dataset, comprising an input seismic dataset with a first extent and an output seismic dataset with a second extent, wherein the second extent is greater than the first extent; (subsurface, survey, seismic data, training dataset, input, output [0011-0012, 0042, 0074-0091, Fig 2A-5B, 12], extending bandwidth to a lower frequency range, dataset being split separately into a lower frequency range ‘e.g. , low - frequency patches’ and a higher frequency range ‘e.g. , high - frequency patches’, such as 200 traces by 500 time samples, time and spatial sampling, widening azimuth, narrow azimuth part, wide azimuth part [0074], the first portion and the second portion of the second dataset are used for training the neural network, batch size [0075])
train, using the training seismic dataset, a machine-learning (ML) network to predict the output seismic dataset, at least in part, from the input seismic dataset; (training a neural network using … geophysical dataset [0013], training the neural network, after … trained, the desired features in the first method are predicted, e.g. with bandwidth extension, lower frequencies in towed streamer data are predicted [0087-0091, Fig, 5A-5B], The trained network was then used to predict low frequencies from their high - frequency counterparts for streamer data [0093, Figs. 8A-8B, 9A-9B])
obtain an observed seismic dataset pertaining to a subsurface region of interest with a third extent; (reservoir surveillance data, observed/measured geophysical data [0051]) and
predict, using the trained ML network, an extended seismic dataset with a fourth extent, at least in part, from the observed seismic dataset, wherein the fourth extent is greater than the third extent. (Machine learning, predictions [0042], the desired features … are predicted using the trained neural network, with bandwidth extension [0090, Fig. 5A-5B], trained network was then used to predict low frequencies from their high-frequency counterparts [0093, 0095, Fig. 8A-8B, 9A-9B])
As per claims 2 and 12, Baum discloses claims 1 and 11 set forth above.
Baum further discloses
obtaining (or receiving) an observed seismic dataset pertaining to a subsurface region of interest with a third extent; (reservoir surveillance data, observed/measured geophysical data [0051])
and predicting, using the seismic processor and the trained ML network, an extended seismic dataset with a fourth extent, at least in part, from the observed seismic dataset, wherein the fourth extent is greater than the third extent. (Machine learning, predictions [0042], the desired features … are predicted using the trained neural network, with bandwidth extension [0090, Fig. 5A-5B], trained network was then used to predict low frequencies from their high-frequency counterparts [0093, 0095, Fig. 8A-8B, 9A-9B])
As per claims 3, 13 and 17, Baum discloses claims 1, 11 and 16, set forth above.
Baum further discloses the training seismic dataset comprises a synthetic seismic dataset. (training neural networks using synthetic datasets [0055]).
As per claims 4 and 14, Baum discloses claims 3 and 13 set forth above.
Baum further discloses
the synthetic seismic dataset comprises: a plurality of synthetic events of seismic reflectivity having a geometrical trajectory in space-time; (various type of preprocessing, side note: preprocessed dataset is equivalent to the synthetic dataset, … seismic trace, reflection event, …migration … seismic events are geometrically re-located in either space or time to the location the event occurred in the subsurface [0079, Fig. 6A-11B], side note: geometrical trajectory means the seismic curve shape in the spatial time domain according to the specification – see [0047 Fig 2])
and at least one seismic wavelet. (preprocessing comprises … wavelet shaping, e.g. changing wavelet phase and amplitude [0081])
As per claims 6, 15 and 18, Baum discloses claims 1, 11 and 16 set forth above.
Baum discloses the ML network is a convolutional neural network. (convolution neural networks ‘CNNs’ [0012, 0046], neural network … for supervised learning, parameters of the neural network ‘e.g., coefficients of the convolution filters’ [0089, Fig, 5B 570], convolution network [0130]).
As per claims 7, Baum discloses claim 1 set forth above.
Baum further discloses training the ML network comprises supervised learning. (neural network … supervised learning [0060, 0067, 0069, 0089, Fig. 5B 560]).
As per claims 9 and 19, Baum discloses claims 2 and 16 set forth above.
Baum further discloses
determining, using the seismic processor, a seismic image of the subsurface region of interest based, at least in part, on the extended seismic dataset; (preprocessor [0078, Fig. 2C], multi-processor computer system, multi-processor server [0096, 0099], suitable processor [0099] images of subsurface [abs, 0005, 0007, 0011], neural network, subsurface geology, extrapolate … transferring wide azimuth information from a collocated sparse … survey [0070], image patches outside of … range, within the … range[0074])
and determining, using a seismic interpretation workstation, a drilling target in the subsurface region of interest based, at least in part, on the seismic image. (computer workstations [0099], high performance computer [0100], above described techniques and/or systems implementing such techniques … include hydrocarbon management, seismic images, feature … maps, drilling a well, well to be drilled, a location determined, further prospecting for and/or producing hydrocarbons using the well [0101], framework …. Interpretation of … seismic images [0041], identifying potential hydrocarbon - bearing formations, characterizing, identifying well locations … reviewing, hydrocarbon management [0051])
Claim Rejections - 35 USC § 103
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 5 is rejected under 35 U.S.C. 103 as being unpatentable over Baum in view of Li (CN 108645994 B), hereinafter ‘Li’.
As per claim 5, Baum discloses claim set forth above.
Baum is silent regarding the synthetic seismic dataset further comprises random perturbations to at least one of the at least one seismic wavelet and the geometrical trajectory.
Li discloses multi point random perturbation including wavelet seismic record and reflection coefficient (random simulation algorithm … multi-point geological statistic,
perturbation according to the random sampling method, pseudo-reservoir physical parameter model, wavelet synthetic seismic record, R is the reflection coefficient [claim 15], side note: reflection coefficient will impact the geometrical data curve shape, i.e. trajectory)
Li is in the same geological and petroleum exploration technology field handling seismic data processing and interpretation as Baum.
Therefore, it would have been obvious to one of ordinary skill in the art at the time when invention is filed before the effective filing date of the current application to modify the teachings of Baum in view of Li to apply random perturbations to at least one of the at least one seismic wavelet and the geometrical trajectory for the generation of the synthetic seismic data for compensating deficiencies in a geophysical dataset by using extrapolation technique (See Baum – compensating deficiencies [abs, 0002], compensate for ghost [0006], extrapolate, compensate for desired feature [0070]).
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Baum in view of Lupin (US 20140156194 A1).
As per claim 8, Baum discloses claim 2 set forth above.
Baum discloses first, second, third and fourth extents in claims 1 and 2.
Baum also discloses widening seismic dataset based on the spatial rage (widening azimuth, narrow azimuth part, wide azimuth part [0074])
However, Baum fails to explicitly recited the special extent.
Lupin discloses spatial extent (extrapolating data items … with a corresponding spatial coordinate across the geological surface [abs, 0004]) and Lupin is in the same geophysical surveying, drilling, logging … production field processing seismic dataset like Baum.
Therefore, it would have been obvious to one of ordinary skill in the art at the time when invention is filed before the effective filing date of the current application to modify the teachings of Baum in view of Lupin to use spatial extend for the first, second, third and fourth extent in order to compensate deficiencies in a geophysical dataset by using extrapolation technique.
Claims 10 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Baum in view of Sun (US 20240319396 A1).
As per claims 10 and 20, Baum discloses claims 9 and 16 set forth above.
Baum is silent regarding planning, using a wellbore planning system, a planned wellbore trajectory to intersect the drilling target and drilling, using a drilling system, a wellbore guided by the planned wellbore trajectory.
Sun discloses planning, using a wellbore planning system, a planned wellbore trajectory to intersect the drilling target and drilling, using a drilling system, a wellbore guided by the planned wellbore trajectory. (drilling … wellbores [0029, Fig. 1], wellbore planning system, wellbore drilling plan … wellbore trajectories to reach the drilling targets, drilling system [0036, claims 3-4, 14, Fig. 1 118 120 122], drilling, using a drilling system, a wellbore guided by the planned well trajectory [0106]) and Sun is in the same oil and gas industry field performing seismic survey and seismic data processing like Baum.
Therefore, it would have been obvious to one of ordinary skill in the art at the time when invention is filed before the effective filing date of the current application to modify the teachings of Baum in view of Sun to plan, using a wellbore planning system, a planned wellbore trajectory to intersect the drilling target and drill, using a drilling system, a wellbore guided by the planned wellbore trajectory by compensating deficiencies in a geophysical dataset by using extrapolation technique and using automated and reliable interpretations (see Sun – automated, quickly, reliable and repeatably performing a wholistic interpretation [0004]).
Notes with regard to Prior Art
The prior arts made of record below are considered being pertinent to applicant's disclosure.
Xia (US 20220066059 A1) also discloses a plurality of synthetic events of seismic reflectivity having a geometrical trajectory in space-time (visual inspection of these seismic time sections can Intuitively suggest shapes and locations of subsurface reflecting formations [0002], relocates all of the recorded samples and builds an image with the events of the image displayed at their proper positions in time (or depth) and space. Imaging [0011], generate a high-resolution time-migrated image gathers for reservoir characterization, and interpretation, seismic data … graphic form [0076, Fig. 7], seismic data, common shot gathers with horizonal axis of offset and vertical axis of time, side note: representing space-time domain data, hyperbolic moveout analysis [0077, Fig. 7]).
Ramsay (US 20200341162 A1) also discloses spatial extents (extrapolate seismic data or measurements of formation properties in space and time [0049]).
Dukalski (US 20240255665 A1) also discloses planning, using a wellbore planning system, a planned wellbore trajectory to intersect the drilling target and drilling, using a drilling system, a wellbore guided by the planned wellbore trajectory (using a well planning system, a planned wellbore trajectory may be planned to reach the drilling target and a wellbore guided by the planned wellbore trajectory may be drilled, using a drilling system [0090, claims 6 and 17]).
Groover (US 20220049594 A1) also discloses planning, using a wellbore planning system, a planned wellbore trajectory to intersect the drilling target and drilling, using a drilling system, a wellbore guided by the planned wellbore trajectory (drilling plan, a target location, a drilling path [0001], drilling operation, drilling tools, well plan [0002]).
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DOUGLAS KAY whose telephone number is (408) 918-7569. The examiner can normally be reached on M, Th & F 8-5, T 2-7, and W 8-1.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Arleen M Vazquez can be reached on 571-272-2619. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DOUGLAS KAY/Primary Examiner, Art Unit 2857