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 .
Claim Objections
Claim 2 is objected to because of the following informalities: “implemented at a neural network” should read “implemented as a neural network”. Claims 3-7 are also objected to by virtue of their dependence from claim 2. Appropriate correction is required.
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.
Claim 1 recites the limitation "their… respective sample sediment supplies" in lines 6-7. There is insufficient antecedent basis for this limitation in the claim. The sediment supplies in the first element are not sample sediment supplies. Accordingly, claims 2-7 are also rejected under 112(b) by virtue of their dependence on claim 1.
Claim 8 recites the limitation "their… respective sample sediment supplies" in lines 7-9. There is insufficient antecedent basis for this limitation in the claim. The sediment supplies in the first element are not sample sediment supplies. Accordingly, claims 9-14 are also rejected under 112(b) by virtue of their dependence on claim 8.
Claim 15 recites the limitation "their… respective sample sediment supplies" in lines 10-11. There is insufficient antecedent basis for this limitation in the claim. The sediment supplies in the first element are not sample sediment supplies. Accordingly, claims 16-20 are also rejected under 112(b) by virtue of their dependence on claim 15.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 8-14 are rejected under 35 U.S.C. 101 because they are directed to non-statutory subject matter.
Claim 8
Claim 8 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claims do not fall within at least one of the four categories of patent eligible subject matter because the claims are directed to ineligible signals per se. The claim recites a “tangible computer-readable medium” and the original disclosure does not provide a clear, deliberate and sufficient definition to exclude signals per se. Instead, the disclosure describes [0045] — “Computer-readable media includes both computer storage media and communication media including any medium that may be enabled to transfer a computer program from one place to another. Storage media may be any available media that may be accessed by a computer. By way of example, and not limitation, such computer-readable media may include RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that may be used to store desired program code in the form of instructions or data structures and that may be accessed by a computer. Also, any connection may be properly termed a computer-readable medium.” and therefore, under the broadest reasonable interpretation, "tangible computer-readable medium" as recited in the claim covers transitory signals (see MPEP 2106.03(I): “Non-limiting examples of claims that are not directed to any of the statutory categories include: Transitory forms of signal transmission (often referred to as "signals per se"), such as a propagating electrical or electromagnetic signal or carrier wave”). Furthermore, dependent claims 9-14 are also rejected under 35 USC 101 by virtue of their dependence on claim 8. The examiner notes that these rejections may be withdrawn if the applicant amends the claim language to read “non-transitory computer-readable medium”.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 8, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Davies et al. (US 20220188338 A1, hereinafter Davies) in view of Le Guern et al. (US 20200158898 A1, hereinafter Le Guern).
Claims 1, 8 and 15
Davies teaches a method, a corresponding one or more tangible computer-readable mediums including instructions executable by one or more processors (Abstract — “The system may include a processor and a non-transitory computer-readable medium comprising instructions that are executable by the processor to cause the processor to perform various operations.”), and a corresponding one or more processors (Abstract — “The system may include a processor and a non-transitory computer-readable medium comprising instructions that are executable by the processor to cause the processor to perform various operations.”), comprising determining geological features (Abstract — “A system is described for determining an analogue geological feature.”) for wells ([0032] — “In some examples, the operator or supervisor of the operation may use the displayed information to model the geological feature of interest for making a recommendation for whether or how to drill a well into the geological feature of interest.”) based on rate of change of subsidence ([0012] — “Using quantitative information for a range of processes, such as subsidence rate, wave energy, and eustasy, for characterizing parameter spaces of geological features of interest and an analogue geological feature may allow direct comparisons using unsupervised machine learning to identify the analogue geological feature.”) and sediment supplies ([0010] — “The extracted parameters may include wave energy, tidal energy, transport distance, amount of sediment, and other suitable parameters for describing the geological feature of interest.”). However, Davies fails to teach generating a training dataset including a plurality of sample systems tracts each associated with a respective sample rate of change of subsidence and a respective sediment supply and training a learning machine to indicate predicted systems tracts for wells based on the plurality of sample system tracts and their respective sample rate of change of subsidence and respective sample sediment supplies.
Le Guern teaches predicting systems tracts ([0156] — “As an example, a GPM framework can allow for creation of stratigraphic models showing expected sediment geometries and for prediction of lithology distributions as well as insights into composition and deposition of sedimentary sequences.”; sedimentary sequences include systems tracts). It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to include indicating predicted systems tracts taught by Le Guern in the method for determining geological features for wells based on rate of change of subsidence and sediment supplies taught by Davies in order to better understand how sediment is deposited over time and how it could affect a geological system.
Le Guern further teaches generating a training dataset ([0139] — “As an example, a workflow may involve: automated and quantitative analysis of exposed analogues; automated geostatistical analysis of the reservoir properties; sedimentary forward modelling; synthetic seismic generation and matching to subsurface seismic data; and training data generation for interpretation constrained by geological rules derived from analogues.”). Le Guern also teaches training a learning machine ([0203] — “Such an approach may involve the use of training data for artificial intelligence/machine learning applications.”; [0231] — “As an example, a machine learning framework can include one or more trained neural networks that can classify features present in imagery and, for example, match such features with features in a subsurface region of the Earth (e.g., via a link established through seismic data, etc.).”; training data and a trained neural network implies training occurred). It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to include the step of training a learning machine using a training dataset taught by Le Guern including a plurality of sample systems tracts each associated with a respective sample rate of change of subsidence and a respective sediment supply as disclosed by Davies and Le Guern (Davies and Le Guern disclose indicating predicted systems tracts based on their respective rate of change of subsidence and respective sample sediment supplies; samples of such would be used for the training dataset), thereby training a learning machine to indicate predicted systems tracts for wells based on the plurality of sample systems tracts and their respective sample rate of change of subsidence and respective sample sediment supplies, in order to improve the flexibility of the method in indicating predicted systems tracts on new data by using machine learning.
Claims 2, 4, 9, 11, 16, and 18 are rejected under 35 USC 103 as being unpatentable over Davies and Le Guern in further view of Assunção et al. (A Structured Stochastic Model for Prediction of Geological Stratal Stacking Patterns, Elsevier, 16 August 2013, https://doi.org/10.1016/j.entcs.2013.07.003; hereinafter Assunção).
Claims 2, 9, and 16
The learning machine taught by Davies and Le Guern in claim 1 is a neural network (Le Guern, [0231] — “As an example, a machine learning framework can include one or more trained neural networks that can classify features present in imagery and, for example, match such features with features in a subsurface region of the Earth (e.g., via a link established through seismic data, etc.).”) wherein said neural network indicates predicted system tracts for wells based on the plurality of sample system tracts and their respective sample rate of change of subsidence and respective sample sediment supplies. However, Davies and Le Guern fail to teach determining an input sea level curve indicating rates of change of subsidence and an input sediment supply curve indicating sediment supplies; and determining, via the neural network, one or more predicted systems tracts through time for a well based on the input sea level curve and the input sediment supply curve.
Assunção teaches a sea level curve indicating rates of change of subsidence (Figure 3 — The plot shows an eustatic curve (an eustatic curve is a sea level curve) with a corresponding rate of subsidence) and a sediment supply curve indicating sediment supplies (Figure 4 — The plot shows a sediment supply curve, the area under which is shaded in green). It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to determine the sea level and sediment supply curves as taught by Assunção and provide them as inputs to the neural network taught by Davies and Le Guern in order to improve the predictive ability of the neural network by enabling it to predict system tracts over time by providing, as input, how the sea level and sediment supply change over time.
Claims 4, 11, and 18
Davies, Le Guern, and Assunção already teach wherein the input sea level curve includes an eustatic curve (Assunção, Figure 3 — The plot shows an eustatic curve (an eustatic curve is a sea level curve) with a corresponding rate of subsidence).
Claims 3, 10, and 17 are rejected under 35 USC 103 as being unpatentable over Davies, Le Guern, and Assunção in further view of Tiedemann et al. (US 11397278 B2, hereinafter Tiedemann).
Claims 3, 10, and 17
Davies, Le Guern, and Assunção fail to teach determining a location of the well and a geological time interval.
Tiedemann teaches determining a location of the well and a geological time interval (Col. 1, lines 49-54 — “The oil and gas industry predominantly uses [chrono-stratigraphy] to estimate a date for sedimentary rocks and identify areas of hydrocarbon reserves (i.e. drilling prospects). One approach allows geologic events or intervals to be related to intervals in a pre-existing scheme such as the geologic timescale.”). It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to determine the values taught by Tiedemann in the method taught by Davies, Le Guern, and Assunção in order to more accurately identify future well locations that have hydrocarbon reserves.
Claims 5-7, 12-14, and 19 are rejected under 35 USC 103 as being unpatentable over Davies, Le Guern, and Assunção in further view of Miall, Andrew D. (Springer Charm, Stratigraphy: A Modern Synthesis, pp. 261-265, 4 March 2022, https://doi.org/10.1007/978-3-030-87536-7; hereinafter Miall)
Claims 5, 12, and 19
Davies, Le Guern, and Assunção fail to teach determining a relationship between the geological time interval and depth of the well and determining a depth for each of the one or more predicted systems tracts based on the relationship.
Miall teaches a determining a relationship between the geological time interval and the depth of systems tracts (Page 263, Figure 5.29 — The plot shows a relationship between depth (leftmost vertical axis) of systems tracts (the color-coded layers) and geological time interval (rightmost vertical axis)) and determining a depth for each of the one or more systems tracts based on the relationship (Page 263, Figure 5.29 — The depth of a system tract may be determined using the leftmost vertical axis). It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to determine a relationship between the geological time interval and depth of the well and then compare it to the color-coded systems tracts on the plot to determine a depth as taught by Miall for each of the one or more predicted systems tracts taught by Davies, Le Guern, and Assunção in order to better understand the structure of the layers of rock beneath the well.
Claims 6 and 13
Davies, Le Guern, Assunção, and Miall already teach presenting the one or more predicted systems tracts on a depth scale (Miall, Page 263, Figure 5.29 — The plot shows systems tracts presented on a depth scale (the leftmost axis)), and wherein the predicted systems tracts are color coded based on a plurality of types (Miall, Page 263, Figure 5.29 — The plot shows systems tracts color-coded based on their type).
Claims 7 and 14
Davies, Le Guern, Assunção, and Miall fail to explicitly teach wherein the types of systems tracts include highstand systems tracts, lowstand systems tracts, transgressive systems tracts, and falling stage systems tracts.
Le Guern teaches highstand systems tracts (Le Guern, [0166] — “HST can constitute the upper systems tract of a stratigraphic sequence, and can lie directly on the maximum flooding surface (mfs) formed when marine sediments reached their most landward position.”; “HST” means highstand systems tract), lowstand systems tracts ([0167] — “A lowstand systems tract (LST) includes deposits that accumulate after the onset of relative a sea-level rise.”), transgressive systems tracts ([0167] — “This systems tract can lie directly on the upper surface of the falling stage systems tract and can be capped by the transgressive surface formed when the sediments onlap onto the shelf margin.”; a transgressive surface separates a lowstand systems tract from a transgressive systems tract), and falling stage systems tracts ([0167] — “This systems tract can lie directly on the upper surface of the falling stage systems tract and can be capped by the transgressive surface formed when the sediments onlap onto the shelf margin.”). It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to include these four types of systems tracts in order to improve the predictive accuracy of the method taught by Davies, Le Guern, Assunção, and Miall because together these four types of systems tracts constitute a full stratigraphic sequence.
Claim 20 is rejected under 35 USC 103 as being unpatentable over Davies and Le Guern in further view of Ku Shafie et al. (Bulletin of the Geological Society of Malaysia, A Review of Stratigraphic Simulation Techniques and their Applications in Sequence Stratigraphy and Basin Analysis, vol. 54, pp. 81-91, November 2008, https://doi.org/10.7186/bgsm54200814; hereinafter Ku Shafie).
Claim 20
Davies and Le Guern fail to teach analyzing forward stratigraphic modelling simulations to identify systems tracts for any rate of change of sea level, subsidence rate, sediment supply, sediment compaction, isostatic loading, and initial bathymetry.
Ku Shafie teaches forward stratigraphic modelling simulations (Page 83, SEDPAK Model — “SEDPAK is a 2D forward stratigraphic simulation program…”) for systems tracts (Page 84, SEDPAK Model — “The program is able to define the chronostratigraphic framework for the deposited sediments. In addition, it provides the illustration of the relationship between sequences and system tracts observed in cores, outcrop, well, and seismic data…”) for any rate of change of sea level, subsidence rate, sediment supply (Page 84, SEDPAK Model — “A few assumptions have to be made when applying SEDPAK program in stratigraphic simulation. Those assumptions include: (1) controlling factors (sea-level changes, sediment supply rates, and subsidence rates) will vary independently…”), sediment compaction (Page 84, SEDPAK Model — “…(2) subsidence events due to compaction and tectonic are handled by SEDPAK separately…”), and isostatic loading (Page 84, SEDPAK Model — “(6) tectonic movement is only modeled vertically in which it substitutes for the combined effects of crustal cooling and the isostatic response to sediment loading.”). It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to analyze the forward stratigraphic modelling simulation taught by Ku Shafie in the system taught by Davies and Le Guern in order to improve the generality of the system by better understanding how systems tracts change in simulations considering more variables that better approximate reality.
Davies, Le Guern, and Ku Shafie as considered thus far still fail to teach initial bathymetry.
Ku Shafie separately teaches a forward stratigraphic model simulation (Page 86, DIONISOS — “DIONISOS (Diffusion Oriented- Normal and Inverse – Simulation of Sedimentation) is a 3D numerical stratigraphic forward model…”) that considers initial bathymetry (Page 86, DIONISOS — “The model simulates the geometry that is similar to the architectures observed on seismic profiles. Moreover, the image (A) in Figure 11B illustrates the initial bathymetry showing coastal plains, shelves, and slopes on two margins and a basin floor at 600 – 1000m water depth with some local highs and lows. The image (B) in Figure 11b shows a final stratal architecture produced by the DIONISOS.”). It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to include initial bathymetry measurements in the model analyzed by the system taught by Davies, Le Guern, and Ku Shafie in order to improve the model’s adherence to real-world conditions by including water depth as a variable.
Prior Art
The prior art made of record and not relied upon is considered pertinent to the applicant’s disclosure:
Ross et al. (WO 2017155528 A1), Updating Models of Complex Geological Sequences
Klinger, Jimmy (WO 2017016895 A1), Assignment of Systems Tracts
Cross et al. (US 6754588 B2), Method of Predicting Three-Dimensional Stratigraphy Using Inverse Optimization Techniques
Cross et al. (US 6246963 B1), Method for Predicting Stratigraphy
Davinroy, Robert D. (US 5653592 A), Method and Apparatus for Micro Modeling the Sediment Transport Characteristics of a River
Hou et al. (US 20250342684 A1), Method and System for Identifying Grain Boundaries and Minerals in a Sample
Lou et al. (US 20240175352 A1), Predicting Reservoir Quality
Xu et al. (US 20230266494 A1), Stratigraphic Trap Recognition Using Orbital Cyclicity
Wang, Sophia (US 20210109248 A1), System, Method, and Device for Real-Time Sinkhole Detection
Harris et al. (US 20170175492 A1), Methodology for Building Realistic Numerical Forward Stratigraphic Models in Data Sparse Environment
Klinger et al. (US 20150066460 A1), Stratigraphic Function
Lin et al. (AU 2020101809 A4), A Simulation System for Depositional Sequence Formation and Evolution
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RYAN JAMES STEAR whose telephone number is (571)272-8334. The examiner can normally be reached 7:30-5:30 EST/EDT.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Arleen Vazquez can be reached at (571) 272-2619. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/RYAN JAMES STEAR/Examiner, Art Unit 2857
/ARLEEN M VAZQUEZ/Supervisory Patent Examiner, Art Unit 2857
Prosecution Insights