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 .
DETAILED ACTION
The Office Action is in response to the application filed on 08/24/2022. Claims 1-22 are pending in the application. Claims 1 and 12 are independent claims.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 8/24/2022 and 12/13/2022 in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Specification
The abstract dated 08/24/2022 has been reviewed. It has 130 words and 9 lines and no legal
phraseology. It is accepted.
Claim Objections
Claims 1, 11-12 and 19 are objected to because of the following informalities:
Claim 1 recites “determine one or more near-range earth models of the subterranean earth formation at each of a plurality of depths along the wellbore derived …” in line 14, should read “determine one or more near-range earth models of the subterranean earth formation at each of the plurality of depths along the wellbore derived …”
Claim 12 recites similar limitations and are objected to for the same reasons discussed above.
Claim 12 recites “A method drilling a wellbore into a subterranean earth formation comprising:” in line 1, should read “A method for drilling a wellbore into a subterranean earth formation comprising:”
Claim 11 recites “… further cause the processor to direct one or more of a depth or an orientation of the drill bit into the subterranean earth formation” should read “… further cause the processor to direct one or more of a depth or an orientation of the drill bit into the subterranean earth formation. ”
Claim 19 recites “… to determine one or more layer density inhomogeneities within one or more layers of the at least one of the mid-range formation model or the far-range formation model..” should read “… to determine one or more layer density inhomogeneities within one or more layers of the at least one of the mid-range formation model or the far-range formation model.[[.]]”
Appropriate correction is required.
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.
The claims 1-22 are rejected under 35 USC § 101 because the claimed invention is directed to
judicial exception, an abstract idea, it has not been integrated into practical application and the claims further do not recite significantly more than the judicial exception. Examiner has evaluated the claims under the framework provided in the 2019 Patent Eligibility Guidance published in the Federal Register 01/07/2019 and has provided such analysis below.
Step 1: Are the claims to a process, machine, manufacture or composition of matter?"
Yes, Claims 1-11 are directed to system and fall within the statutory category of machine;
Yes, Claims 12-22 are directed to method and fall within the statutory category of process.
In order to evaluate the Step 2A inquiry "Is the claim directed to a law of nature, a natural phenomenon or an abstract idea?" we must determine, at Step 2A Prong 1, whether the claim recites a law of nature, a natural phenomenon or an abstract idea and further whether the claim recites additional elements that integrate the judicial exception into a practical application.
Step 2A Prong 1:
Claim 1: The limitations of “determine one or more near-range earth models of the subterranean earth formation at each of a plurality of depths along the wellbore derived from an inversion algorithm of the subterranean earth formation based on the near-range wellbore measurement data at each of the plurality of depths along the wellbore as constrained by the reference data, wherein each of the one or more near-range earth models comprises a density model of a layer of the subterranean earth formation,” as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation (BRI) in light of specification, covers performance of the limitation in the mind. For example a person is capable of observing a set of measurement values obtained along the wellbore, compare the measured values with obtained reference data, and mentally infer or estimate relationships representing how density varies with depth. The person could further conceptualize an approximate model of the earth formation by imagining or sketching layers having different densities corresponding to the inferred relationships (The courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011)) – MPEP 2106.04(a)(2)(III).
Claim 1: The limitations of “determine at least one of a mid-range formation model or a far-range formation model at each of the plurality of depths along the wellbore based on the one or more near-range earth models and the plurality of surface gravitational data,” as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation (BRI) in light of specification, covers performance of the limitation in the mind. For example a person is capable of observing and evaluating the approximate models together with known or obtained surface gravitational data, compare or correlate data points, then mentally infer an extended model of the earth formation at multiple depths along the wellbore (The courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011)) – MPEP 2106.04(a)(2)(III).
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea under step 2A Prong I.
In MPEP 2106.04(II)(B): A claim may recite multiple judicial exceptions. For example, claim 4 at issue in Bilski v. Kappos, 561 U.S. 593, 95 USPQ2d 1001 (2010) recited two abstract ideas, and the claims at issue in Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 101 USPQ2d 1961 (2012) recited two laws of nature. However, these claims were analyzed by the Supreme Court in the same manner as claims reciting a single judicial exception, such as those in Alice Corp., 573 U.S. 208, 110 USPQ2d 1976.
Claim 1, The limitation recites “… derived from an inversion algorithm … ” as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation (BRI) in light of specification, can be reasonably considered to represent mathematical concept, specifically:
MPEP 2106.4(a)(2)(I): “The mathematical concepts grouping is defined as mathematical
relationships, mathematical formulas or equations, and mathematical calculations”.
MPEP 2106.04(a)(2)(I)(A), “A mathematical relationship is a relationship between variables or numbers. A mathematical relationship may be expressed in words or using mathematical symbols.”
Further, MPEP recites: “For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.
Claim 1: The limitations of “determine one or more near-range earth models of the subterranean earth formation at each of a plurality of depths along the wellbore derived from an inversion algorithm …” The limitation with broadest reasonable interpretation (BRI) can be considered to represent mathematical concepts as described in the instance specification, for example, [0024] “In some non-limiting aspects, the inversion algorithm may, for example, successively modify the initial model based on a gradient search technique, such as a Gauss-Newton search method. Typically, an inversion algorithm solution may be a one dimensional solution curve of wellbore measurement data versus measurement depth. In some non-limiting aspects, the inversion algorithm may also generate a two dimensional solution of wellbore measurement data versus measurement depth and angular position about the wellbore. The model produced by the inversion algorithm may include, for example, modeled wellbore measurement data over distance or distance and angular position. The modeled wellbore measurement data may be produced by a specific model of the formation defined over a measurement depth. In some aspects, the modeled wellbore measurement data may be compared to the measured wellbore measurement data obtained from measured wellbore data using a least-squares algorithm.” Therefore, determination of near-range earth models derived from an inversion algorithm discloses mathematical relationships between numbers or variables, such as correlation, conversion, or inversion relationships. – MPEP 2106.04(a)(2)(I).
The elements of claim 12 is substantially the same as those of claim 1. Therefore, the elements of claim 12 is rejected due to the same reasons as outlined above for claim 1.
Therefore, claims 1 and 12 recite judicial exceptions. The claims have been identified to recite judicial exceptions, Step 2A Prong 2 will evaluate whether the claim as a whole integrates the exception into a practical application of that exception.
Step 2A Prong 2: Claims 1 and 12: The judicial exception is not integrated into a practical application.
In particular, the claims recite the following additional elements – “A system for drilling a wellbore into a subterranean earth formation comprising: a logging tool operable to measure formation data and locatable in the wellbore, wherein the logging tool comprises at least one near-range measurement sensor; and a processor and a non-transitory memory device in data communication with the logging tool, wherein the non-transitory memory device comprises instructions that, when executed by the processor, cause the processor to:" and “by a processor” which merely recitations of instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to implement the judicial exception (see MPEP § 2106.05(f)) with the broad reasonable interpretation, which does not integrate judicial exception into a practical application.
Further, the following additional element – “receive, from the at least one near-range measurement sensor, near-range wellbore measurement data at each of a plurality of depths along the wellbore; receive reference data related to a density measurement of the subterranean earth formation at each of the plurality of depths along the wellbore” and “receive a plurality of surface gravitational data, wherein each of the plurality of surface gravitational data is obtained at each of a plurality of surface locations proximate to the wellbore” which are merely a recitation of insignificant extra-solution data gathering (i.e., receiving data) activity (see MPEP § 2106.05(g)) which does not integrate a judicial exception into practical application.
Further, the following additional element – “provide the at least one of the mid-range formation model or the far-range formation model to a well driller, wherein the well driller uses the at least one of the mid-range formation model or the far-range formation model for geosteering a drill bit into the subterranean earth formation,” which is merely a recitation of insignificant extra-solution activity: Insignificant application (i.e., well driller geosteers a drill bit using determined/obtained range formation model) which does not integrate a judicial exception into practical application (see MPEP § 2106.05(g)). See also In re Brown, 645 Fed. App'x 1014, 1016-1017 (Fed. Cir. 2016) (non-precedential).
Therefore, "Do the claims recite additional elements that integrate the judicial exception into a practical application? No, these additional elements do not integrate the abstract idea into a practical application and they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
After having evaluated the inquires set forth in Steps 2A Prong 1 and 2, it has been concluded that claims 1 and 12 not only recite a judicial exception but that the claims are directed to the judicial exception as the judicial exception has not been integrated into practical application.
Step 2B: Claims 1 and 12: The claim does not include additional elements, alone or in combination, that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than generic computing components which do not amount to significantly more than the abstract idea. Limitations that the courts have found not to be enough to qualify as "significantly more" when recited in a claim with a judicial exception include: i. Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 573 U.S. at 225-26, 110 USPQ2d at 1984 (see MPEP § 2106.05(f)); ii. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 (see MPEP § 2106.05(d)); iii. Adding insignificant extra-solution activity to the judicial exception, e.g., mere data gathering in conjunction with a law of nature or abstract idea such as a step of obtaining information about credit card transactions so that the information can be analyzed by an abstract mental process, as discussed in CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011) (see MPEP § 2106.05(g)); …
The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, …; ii. Performing repetitive calculations, … iii. Electronic recordkeeping, … (updating an activity log). iv. Storing and retrieving information in memory,…
Therefore, "Do the claims recite additional elements that amount to significantly more than the judicial exception? No, these additional elements, alone or in combination, do not amount to significantly more than the judicial exception. Having concluded analysis within the provided framework, claims 1 and 12 do not recite patent eligible subject matter under 35 U.S.C. § 101.
Dependent claims 2-11 and 13-22 are also similar rejected under same rationale as cited above wherein these claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. These claims are merely further elaborate the mental process and/or mathematical concepts, or providing additional definition of process which does not impose any meaningful limits on practicing the abstract idea. Claims 2-11 and 13-22 are also rejected for incorporating the deficiency of their independent claims 1 and 12.
Claim 2 recites “The system of claim 1, wherein the at least one near-range measurement sensor comprises one or more of a wellbore acoustic sensor, a wellbore NMR sensor, a wellbore resistivity sensor, a wellbore gravimetric sensor, a pulse neutron sensor, a gamma ray source/gamma ray sensor, and a passive gamma detection sensor.”
The limitation further defines near-range measurement sensor recited in claim 1 by specifying it includes one or more types of wellbore sensors; therefore, it merely adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to implement the judicial exception (see MPEP § 2106.05(f)). Therefore, the office finds that the claim 2 is ineligible under 35 USC 101.
Claim 3 recites “The system of claim 1, wherein the near-range wellbore measurement data comprise one or more of wellbore acoustic data, wellbore NMR data, wellbore resistivity data, neutron data, and gamma ray data.”
The limitation further defines near-range wellbore measurement data is received from the near-range measurement sensor recited in claim 1 by specifying it includes one or more particular types of wellbore data; therefore, it merely an insignificant extra-solution data gathering (i.e., receiving data) activity to the judicial exception (MPEP § 2106.05(g)). Therefore, the office finds that the claim 3 is ineligible under 35 USC 101.
Claim 4 recites “The system of claim 1, wherein the reference data comprise data physically or directly indicative of the density of the subterranean earth formation at each of the plurality of depths along the wellbore.”
The limitation further defines reference data is received recited in claim 1 by specifying it physically or directly indicative of the density of the subterranean earth formation at each of the plurality of depths along the wellbore; therefore, it merely an insignificant extra-solution data gathering (i.e., receiving data) activity to the judicial exception (MPEP § 2106.05(g)). Therefore, the office finds that the claim 4 is ineligible under 35 USC 101.
Claim 5 recites “The system of claim 4, wherein the reference data comprise one or more of a bulk density measurement of the subterranean earth formation, gamma ray source/gamma ray data, neutron density data, acoustic density data, photometric data, core sample data, cutting sample data, a formation fluid data, and down well composition data.”
The limitation further defines reference data is received recited in claims 1 and 4 by specifying it includes one or more particular types of data; therefore, it merely an insignificant extra-solution data gathering (i.e., receiving data) activity to the judicial exception (MPEP § 2106.05(g)). Therefore, the office finds that the claim 5 is ineligible under 35 USC 101.
Claim 6 recites “The system of claim 1, wherein the plurality of surface gravitational data are obtained from a plurality of surface gravity sensors, wherein each of the plurality of surface gravity sensors is located at each of the plurality of surface locations proximate to the wellbore.”
The limitation specifies surface gravitational data is received from gravity sensors, and the surface gravity sensors is located at each of the plurality of surface locations proximate to the wellbore; therefore, it merely adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to implement the judicial exception (see MPEP § 2106.05(f)) and merely an insignificant extra-solution data gathering (i.e., receiving data) activity to the judicial exception (MPEP § 2106.05(g)). Therefore, the office finds that the claim 6 is ineligible under 35 USC 101.
Claim 7 recites “The system of claim 1, wherein the plurality of surface gravitational data are obtained from at least one surface gravity sensor located sequentially at each of the plurality of surface locations proximate to the wellbore.”
The limitation specifies surface gravitational data is received from gravity sensors, and the surface gravity sensors is located sequentially at each of the plurality of surface locations proximate to the wellbore; therefore, it merely adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to implement the judicial exception (see MPEP § 2106.05(f)) and merely an insignificant extra-solution data gathering (i.e., receiving data) activity to the judicial exception (MPEP § 2106.05(g)). Therefore, the office finds that the claim 7 is ineligible under 35 USC 101.
Claim 8 recites “The system of claim 1, wherein the plurality of surface gravitational data are obtained from one or more quantum gravity sensors.”
The limitation specifies reference data are obtained from one or more quantum gravity sensors; therefore, it merely an insignificant extra-solution data gathering (i.e., receiving data) activity to the judicial exception (MPEP § 2106.05(g)). Therefore, the office finds that the claim 8 is ineligible under 35 USC 101.
Claim 9 recites “The system of claim 1, wherein the non-transitory memory device comprises instructions that, when executed by the processor, further cause the processor to correlate the at least one of the mid-range formation model or the far-range formation model at a first of the plurality of depths along the wellbore with the at least one of the mid-range formation model or the far-range formation model at a second of the plurality of depths along the wellbore, to determine one or more layer density inhomogeneities within one or more layers of the at least one of the mid-range formation model or the far-range formation model.”
The limitation specifies range formation model at different depths along the wellbore, and layer density inhomogeneities is determined or estimated; therefore, it merely an extension of mental process (e.g., observing formation data from two depths, compare differences in density values, and identify areas showing uneven density distribution between layers) and merely recitations of instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to implement the judicial exception (see MPEP § 2106.05(f)), which does not integrate judicial exception into a practical application. Therefore, the office finds that the claim 9 is ineligible under 35 USC 101.
Claim 10 recites “The system of claim 1, wherein the non-transitory memory device comprises instructions that, when executed by the processor, further cause the processor to constrain, the at least one of the mid-range formation model or the far-range formation model at each of the plurality of depths along the wellbore based on survey data.”
The limitation specifies range formation model is determined/constrained based on survey data; therefore, it merely an extension of mental process (e.g., evaluating or adjusting the previously determined model using additional data inputs) and merely recitations of instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to implement the judicial exception (see MPEP § 2106.05(f)) with the broad reasonable interpretation, which does not integrate judicial exception into a practical application. Therefore, the office finds that the claim 10 is ineligible under 35 USC 101.
Claim 11 recites “The system of claim 1, wherein the non-transitory memory device comprises instructions that, when executed by the processor, further cause the processor to direct one or more of a depth or an orientation of the drill bit into the subterranean earth formation”
The limitation specifies that the processor executes instructions to direct one or more of a depth or an orientation of the drill bit into the subterranean earth formation; therefore, it merely recitations of instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to implement the judicial exception (see MPEP § 2106.05(f)), which does not integrate judicial exception into a practical application. Therefore, the office finds that the claim 11 is ineligible under 35 USC 101.
Claims 13-20 and 22 recite the similar elements as claims 3-11, and are rejected for the same reasons under 35 U.S.C. 101.
Claim 21 recites “The method of claim 20, wherein constraining the at least one of the mid-range formation model or the far-range formation model based on survey data comprises constraining the at least one of the mid-range formation model or the far-range formation model based on one or more of geological survey data, acoustic survey data, or magnetic survey data.”
The limitation further defines range formation model is determined/constrained based on survey data recited in claim 20, and the survey data includes one or more different type of survey data; therefore, it merely an extension of mental process. Therefore, the office finds that the claim 21 is ineligible under 35 USC 101.
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) 1-6, 10-16 and 20-22 are rejected under 35 U.S.C. 103 as being unpatentable over
Bartetzko US20200333495A1 in view of Miles US20170160425A1 and Wang US20200371268A1 and Priezzhev US20120232871A1.
Claim 1, Bartetzko teaches A system for drilling a wellbore into a subterranean earth formation (Abstract; [0022], “Referring to FIG. 1, an exemplary embodiment of a drilling and monitoring system 10 is shown. A drill string 14 is disposed in a borehole 12, which penetrates at least one earth formation 16, which may include one or more stratigraphic layers 18.”) comprising:
a logging tool operable to measure formation data and locatable in the wellbore, wherein the logging tool comprises at least one near-range measurement sensor ([0025], “Data and information regarding the formation 16 and/or stratigraphic layers 18 can be acquired by various measurement devices that may be included in the drilling assembly 20 and/or drill string 14, such as a downhole measurement tool 27 (e.g., a LWD tool). Exemplary devices include pulsed neutron tools, gamma ray measurement tools, neutron tools, resistivity tools, acoustic tools, nuclear magnetic resonance tools, density measurement tools, seismic data acquisition tools, acoustic impedance tools, formation pressure testing tools, fluid sampling and analysis tools, coring tools and/or any other type of sensor or device capable of providing information that can be used to identify or estimate formation features.” ); and
a processor and a non-transitory memory device in data communication with the logging tool, wherein the non-transitory memory device comprises instructions that, when executed by the processor ([0028], “The surface processing unit 28 is configured to receive, store and/or transmit data and signals, and includes processing components configured to analyze data and/or control operational parameters. In one embodiment, the surface processing unit 28 is configured to control the drilling assembly 20 and receive data from the measurement tool 27 and any other downhole and/or surface sensors. Operational parameters may be controlled or adjusted automatically by the surface processing unit 28 in response to sensor data, or controlled by a human driller or remote processing device. The surface processing unit 28 includes any number of suitable components, such as processors, memory, communication devices and power sources. For example, the surface processing unit 28 may include a processor 30 (e.g., a microprocessor), and a memory 32 storing software 34. In addition or as an alternative to surface processors, processing capability may be located downhole, for example, as downhole electronics 36, which may perform all or some of the functions described in conjunction with the surface processing unit 28.”), cause the processor to:
receive, from the at least one near-range measurement sensor, near-range wellbore measurement data at each of a plurality of depths along the wellbore ([0025], “… a downhole measurement tool 27 (e.g., a LWD tool). Exemplary devices include pulsed neutron tools, gamma ray measurement tools, neutron tools, resistivity tools, acoustic tools, nuclear magnetic resonance tools, density measurement tools, seismic data acquisition tools, acoustic impedance tools, formation pressure testing tools, fluid sampling and analysis tools, coring tools and/or any other type of sensor …” [0028], “… the surface processing unit 28 is configured to control the drilling assembly 20 and receive data from the measurement tool 27 and any other downhole and/or surface sensors …” Fig.10; [0074], “During drilling, a target well log 130 is monitored and a relative change log 132 and absolute change log 134 are calculated as measurement data is received. Similar changes or fingerprints and their corresponding depths are noted as A′, B′ and C′. The fingerprints from the reference and target logs are compared and depth shifts are calculated.”);
receive reference data related to a density measurement of the subterranean earth formation at each of the plurality of depths along the wellbore (Fig.9 and Fig.10 show different depth of reference data; [0041], “Reference data includes various types of data and information acquired from the one or more reference boreholes. Exemplary reference data discussed herein includes well logs such as gamma ray logs taken from a reference borehole. However, any suitable data or information that reflects changes in lithology or stratigraphy may be used. Examples of suitable data include well logs, such as resistivity, acoustic compressional or shear slowness, formation density, magnetic resonance, pulsed neutron, gamma ray, spontaneous potential, and neutron porosity logs, or a combination thereof.”);
determine one or more near-range earth models of the subterranean earth formation at each of a plurality of depths along the wellbore ([0061], “FIGS. 5 and 6 illustrate depth shift functions for two examples of a lithology model 90. FIG. 5 shows the lithology model 90 as thinning as a formation extends from a reference borehole 92 (“Well 1”) towards a target borehole 94 (“Well 2”).” – Examiner note: determining an earth model (lithology model 90) of a subterranean formation and comparing reference and target boreholes that derived from measured data constrained by reference data. [0076], “… The measured impedance log 148 is monitored for patterns that are similar to the reference pattern 146 … The comparison between the predicted and the monitored acoustic impedance can be used to update a seismic model during the drilling operation …” – Examiner note: the model determination based on measurement data (impedance log 148) constrained by reference data (reference pattern 146) as “based on the measurement data as constrained by the reference data.” [0031], “This data may include resistivity, dielectric constant, water saturation, porosity, density,…” – Examiner note: the data used in constructing the model includes density among other physical parameters is interpreted as “each of the one or more near-range earth models comprises a density model of a layer.”);
provide the at least one of the ([0078], “A user or operator may thus utilize embodiments described herein to make proactive decisions during an operation, e.g., decisions regarding drilling parameters, completion equipment, downhole components, mud properties, and/or steering a well.” [0076], “… The comparison between the predicted and the monitored acoustic impedance can be used to update a seismic model during the drilling operation. This is extremely useful, for example, for landing a borehole during a reservoir navigation operation.” [0052], “the processor transmits and/or displays the predicted features and their corresponding depths or times (e.g., as an alert or alarm, or as a report) to allow an operator or user to adjust operational parameters …, weight on bit and/or rotational speed. Other changes may include changes in equipment, such as changing the drill bit or drill string components. In another embodiment, actions include making changes to reservoir navigation operations, e.g., by steering a drilling assembly to change the path of the borehole and/or updating the planning for landing of the well.”).
However, Bartetzko fails to teach model is derived from inversion algorithm.
Miles teaches model is derived from inversion algorithm ([0073], “General methods for inversion are known in the art. In blocks 107-113, an inversion process is carried out by the data processing system using the stored forward models for the number of radiation detector measurements … The mathematical process of tuning the parameters of the formation and wellbore may employ the method of Gauss-Newton minimization or other variants that are known in the art …… If not, the inversion process of blocks 107-113 is iteratively repeated using the tuned forward models until convergence is reached.” - Examiner note: the system repeatedly adjusts formation parameters using Gauss-Newton minimization (i.e., inversion algorithm) until modeled responses the measured data).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Bartetzko to incorporate the teachings of Miles, and apply model from an inversion algorithm in order to iteratively refine the near-range earth model for predicting formation responses converge with measured data to improve modeling accuracy and reliability. In this case, Bartetzko teaches determining near-range earth models of the subterranean formation based on wellbore measurement data constrained by reference data. Miles teaches a formation model is derived through an inversion process with Gauss-Newton minimization. The combination of teachings provide benefit of producing a more accurate and physically consistent formation model with iterative correction of formation parameters.
However, Bartetzko and Miles fail to teach receive a plurality of surface gravitational data, wherein each of the plurality of surface gravitational data is obtained at each of a plurality of surface locations proximate to the wellbore.
Wang teaches receive a plurality of surface gravitational data, wherein each of the plurality of surface gravitational data is obtained at each of a plurality of surface locations proximate to the wellbore ([0008], “obtaining a Bouguer gravity anomaly in a target region, wherein the Bouguer gravity anomaly comprises coordinates and field values of a plurality of sampling points in the target region; [0009], “determining a first pre-set range corresponding to each sampling point in the target region;” [0010], “obtaining a first regional field value of sampling points within the first pre-set range corresponding to each sampling point using a surface fitting method based on coordinates and field values of the sampling points;” [0096], “The Bouguer gravity anomaly comprises coordinates and field values of a plurality of sampling points in the target region.” – Examiner note: obtaining Bouguer gravity anomalies data that include field values of a plurality of sampling pints in the targe region, and each sampling point corresponds to a distinct surface location, and a first regional field value is obtained for each point based on coordinates and field values are collected at a plurality of surface sampling points within the target region. A POSITA would understand that “sampling points” are physical surface observation stations distributed around a borehole or target region for subsurface modeling and anomaly prediction).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Bartetzko and Miles to incorporate the teachings of Wang, and apply surface gravitational data in order to improve the subsurface modeling accuracy and regional geological prediction by integrating surface and downhole measurements. In this case, Bartetzko teaches determining near-range earth models of the subterranean formation based on wellbore measurement data constrained by reference data. Miles teaches a formation model is derived through an inversion process with Gauss-Newton minimization. Wang teaches obtaining Bouguer gravity anomaly data (i.e., surface gravitational data) from multiple surface sampling locations near the target region. The combination of teachings provide benefit of constructing a more comprehensive and spatially constrained subsurface model by jointly utilizing surface gravity measurements with near wellbore models and improving the resolution and accuracy of formation characterization and well placement prediction.
However, Bartetzko and Miles and Wang fail to teach determine at least one of a mid-range formation model or a far-range formation model at each of the plurality of depths along the wellbore based on the one or more near-range earth models and the plurality of surface gravitational data, and provide the at least one of the mid-range formation model or the far-range formation model for further operation.
Priezzhev teaches determine at least one of a mid-range formation model or a far-range formation model at each of the plurality of depths along the wellbore based on the one or more near-range earth models and the plurality of surface gravitational data and provide the at least one of the mid-range formation model or the far-range formation model for further operation ([0065], “…, shown at 12 is generating a three-dimensional (3-D) forward model in the wavenumber domain of a physical parameter having a potential field from a model of spatial distribution of the physical property. During this step, a volume (e.g., a cube) with the modeled physical parameter having a potential field will be created … If wellbores are specified within the modeled volume, then the potential field (e.g., gravity potential field) will be interpolated to include the data from any such wellbore …” – Examiner note: generating 3D volumetric model (i.e., a formation model) that extends through a subsurface region and interpolates along the wellbore depths using gravity potential field data. [0069], “… shown at 20 is generating by inversion in the wavenumber domain a revised model of spatial distribution of the physical property (e.g., density) 32 from the revised model of spatial distribution of the potential field (e.g., gravity) 28.” – Examiner note: the inversion process refines the forward model into a more accurate revised formation model. Although the reference does not using term mid or far formation model, but it describes a volumetric model refined through inversion that extends beyond the borehole and incorporates both near-wellbore and surface gravity data. A POSITA would understand that the resulting integrated model as representing a broader-scale (mid to far-range) formation model, because it spatially combines local and surface gravitational information into subsurface representation used for subsequent operation or decision-making. [0072], “…, measured gravity data may be used in the present method. Gravity data may be obtained, for example, using a gravity sensor similar to the LaCoste and Romberg sensor shown schematically in FIG. 2. Such a sensor may be deployed proximate the Earth's surface at selected locations …” – Examiner note: the system obtains gravitational measurements both at the surface and within the wellbore. [0095], “… interpretation of borehole gravity measurements in conjunction with surface gravity measurements. The fast Fourier transform permits applying this method to models with very large dimensions (tens of millions of cells). Sparse borehole gravity data can be interpolated to the entire volume of the subsurface being modeled, while taking into account surface gravity data. The result of this interpolation will determine the solution of the inversion problem, and it is this interpolation step along with the constraints provided by the initial density distribution that ensures a unique solution. A manageable solution to the inverse problem requires well controlled and powerful software such as PETREL software to interpolate the gravitational field from the borehole and surface data over the entire area of investigation.” - Examiner note: the model is determined based on a combination of near wellbore (borehole) and surface gravitational data, thereby generating an integrated volumetric formation model extending across the broader subsurface region, which provides an interpretable representation of the subsurface suitable for further operational decision making for well placement or drilling trajectory adjustment. [0096], “When using gravity field data for geological interpretation one may use as much a priori information as is available to build the initial density model, such as borehole log data, the results of seismic data inversion, and geological and structural models. By finding a forward modeling solution maximum close to the initial model one can have high confidence that the solution is unique.” Examiner note: the borehole data corresponds to the near-range earth models, and the gravity field data provides surface gravitational information, and by integrating multi-scale data sources, the model expands its representational scope to the mid to far-range formation model used for subsequent operational analysis or decision-making).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Bartetzko and Miles and Wang to incorporate the teachings of Priezzhev, and apply generating a three-dimensional (3-D) forward model and inversion refinement in order to determine a mid-range or far-range formation model based on near range earth models and surface gravitational data for improved subsurface representation. In this case, Bartetzko teaches determining near-range earth models of the subterranean formation based on wellbore measurement data constrained by reference data. Miles teaches a formation model is derived through an inversion process with Gauss-Newton minimization. Wang teaches obtaining Bouguer gravity anomaly data (i.e., surface gravitational data) from multiple surface sampling locations near the target wellbore. Priezzhev teaches integrating both near borehole and surface gravity data through forward and inversion modeling to generate a volumetric formation model that extends through the subsurface and provides a refined representation suitable for operational decision-making. The combination of teachings provide benefit of generating a unified, multi-scale formation model that bridges near-range and regional data, provides more accurate geosteering, drilling control, and reservoir characterization based on both borehole and surface measurements.
Claim 2, Bartetzko further teaches The system of claim 1, wherein the at least one near-range measurement sensor comprises one or more of a wellbore acoustic sensor, a wellbore NMR sensor, a wellbore resistivity sensor, a wellbore gravimetric sensor, a pulse neutron sensor, a gamma ray source/gamma ray sensor, and a passive gamma detection sensor ([0025], “Data and information regarding the formation 16 and/or stratigraphic layers 18 can be acquired by various measurement devices that may be included in the drilling assembly 20 and/or drill string 14, such as a downhole measurement tool 27 (e.g., a LWD tool). Exemplary devices include pulsed neutron tools, gamma ray measurement tools, neutron tools, resistivity tools, acoustic tools, nuclear magnetic resonance tools, density measurement tools, seismic data acquisition tools, acoustic impedance tools, formation pressure testing tools, fluid sampling and analysis tools, coring tools and/or any other type of sensor or device capable of providing information that can be used to identify or estimate formation features.”).
Claim 3, Bartetzko further teaches The system of claim 1, wherein the near-range wellbore measurement data comprise one or more of wellbore acoustic data, wellbore NMR data, wellbore resistivity data, neutron data, and gamma ray data ([0025], “Data and information regarding the formation 16 and/or stratigraphic layers 18 can be acquired by various measurement devices that may be included in the drilling assembly 20 and/or drill string 14, such as a downhole measurement tool 27 (e.g., a LWD tool). Exemplary devices include pulsed neutron tools, gamma ray measurement tools, neutron tools, resistivity tools, acoustic tools, nuclear magnetic resonance tools, density measurement tools, seismic data acquisition tools, acoustic impedance tools, formation pressure testing tools, fluid sampling and analysis tools, coring tools and/or any other type of sensor or device capable of providing information that can be used to identify or estimate formation features.”).
Claim 4, Bartetzko further teaches The system of claim 1, wherein the reference data comprise data physically or directly indicative of the density of the subterranean earth formation at each of the plurality of depths along the wellbore ([0041], “Reference data includes various types of data and information acquired from the one or more reference boreholes. Exemplary reference data discussed herein includes well logs such as gamma ray logs taken from a reference borehole. However, any suitable data or information that reflects changes in lithology or stratigraphy may be used. Examples of suitable data include well logs, such as resistivity, acoustic compressional or shear slowness, formation density, magnetic resonance, pulsed neutron, gamma ray, spontaneous potential, and neutron porosity logs, or a combination thereof.”).
Claim 5, Bartetzko further teaches The system of claim 4, wherein the reference data comprise one or more of a bulk density measurement of the subterranean earth formation, gamma ray source/gamma ray data, neutron density data, acoustic density data, photometric data, core sample data, cutting sample data, a formation fluid data, and down well composition data ([0041], “Reference data includes various types of data and information acquired from the one or more reference boreholes. Exemplary reference data discussed herein includes well logs such as gamma ray logs taken from a reference borehole. However, any suitable data or information that reflects changes in lithology or stratigraphy may be used. Examples of suitable data include well logs, such as resistivity, acoustic compressional or shear slowness, formation density, magnetic resonance, pulsed neutron, gamma ray, spontaneous potential, and neutron porosity logs, or a combination thereof.”).
Claim 6, Bartetzko and Miles fail to teach, but Wang teaches The system of claim 1, wherein the plurality of surface gravitational data are obtained from a plurality of surface gravity sensors, wherein each of the plurality of surface gravity sensors is located at each of the plurality of surface locations proximate to the wellbore ([0008], “obtaining a Bouguer gravity anomaly in a target region, wherein the Bouguer gravity anomaly comprises coordinates and field values of a plurality of sampling points in the target region;” [0010], “obtaining a first regional field value of sampling points within the first pre-set range corresponding to each sampling point using a surface fitting method based on coordinates and field values of the sampling points.” Examiner note: A POSITA would understand that the Bouguer gravity measurements are taken from a plurality of surface gravity sensors positioned at a plurality of surface locations proximate to the target wellbore).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Bartetzko and Miles to incorporate the teachings of Wang, and apply obtaining a first regional field value of sampling points within the first pre-set range corresponding to each sampling point using a surface fitting method based on coordinates and field values of the sampling points in order to determine and calibrate surface gravitation data collected from multiple surface gravity sensors positioned at different sampling locations proximate to the wellbore.
However, Bartetzko and Miles and Wang fail to teaches obtaining gravitation data from gravity sensor.
Priezzhev teaches obtaining gravitation data from gravity sensor ([0072], “…, measured gravity data may be used in the present method. Gravity data may be obtained, for example, using a gravity sensor similar to the LaCoste and Romberg sensor shown schematically in FIG. 2. Such a sensor may be deployed proximate the Earth's surface at selected locations …”).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Bartetzko and Miles and Wang to incorporate the teachings of Priezzhev, and apply gravity sensor to measure gravity data and deploy proximate Earth's surface at selected locations in order to improve the accuracy and density of surface gravitational measurements collected for subsurface modeling and anomaly detection.
Claim 10, Bartetzko and Miles and Wang fail to teach, but Priezzhev teaches The system of claim 1, wherein the non-transitory memory device comprises instructions that, when executed by the processor, further cause the processor to constrain, the at least one of the mid-range formation model or the far-range formation model at each of the plurality of depths along the wellbore based on survey data ([0069], “…, shown at 20 is generating by inversion in the wavenumber domain a revised model of spatial distribution of the physical property (e.g., density) 32 from the revised model of spatial distribution of the potential field (e.g., gravity) 28.” – Examiner note: the inversion process constrains the formation model (density distribution) using survey-derived potential field (gravity) data, as the model is refined based on measured gravitational field information. [0072], “…, measured gravity data may be used in the present method. Gravity data may be obtained, for example, using a gravity sensor similar to the LaCoste and Romberg sensor shown schematically in FIG. 2. Such a sensor may be deployed proximate the Earth's surface at selected locations …” – Examiner note: the reference shows that gravity survey data acquired at surface locations are used to constrain and update the modeled gravitational field distribution. [0095], “… interpretation of borehole gravity measurements in conjunction with surface gravity measurements. The fast Fourier transform permits applying this method to models with very large dimensions (tens of millions of cells). Sparse borehole gravity data can be interpolated to the entire volume of the subsurface being modeled, while taking into account surface gravity data.” – Examiner note: the reference shows that survey-based gravity data from both borehole and surface measurements are integrated and interpolated to constrain the model solution during inversion, updating or limiting the mid-range or far-range formation model based on survey-derived gravity field data).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Bartetzko and Miles and Wang to incorporate the teachings of Priezzhev, and apply constrain, the at least one of the mid-range formation model or the far-range formation model at each of the plurality of depths along the wellbore based on survey data in order to refine and calibrate the predicted subsurface model using real world gravitational measurements obtained from both surface and borehole surveys.
Claim 11, Bartetzko further teaches The system of claim 1, wherein the non-transitory memory device comprises instructions that, when executed by the processor, further cause the processor to direct one or more of a depth or an orientation of the drill bit into the subterranean earth formation ([0053], “the processor automatically changes operational parameters at the appropriate time to react to the feature of interest. The alert and automatic adjustments may be performed in the alternative or together.” [0026], “Operational parameters may be controlled or adjusted automatically by the surface processing unit 28 in response to sensor data,…” [0035], “Knowing in time that this particular formation is being approached allows for adjustment and optimization of the completion scheme, mud type, and/or the navigation path of a drilling assembly.” [0069], “… the processor may send updates or alerts to a user regarding predicted features of interest, and/or perform automatic adjustments to the drilling operation is response to the predictions.”).
Claim 21, Bartetzko and Miles and Wang fail to teach, but Priezzhev teaches The method of claim 20, wherein constraining the at least one of the mid-range formation model or the far-range formation model based on survey data comprises constraining the at least one of the mid-range formation model or the far-range formation model based on one or more of geological survey data, acoustic survey data, or magnetic survey data ([0096], “When using gravity field data for geological interpretation one may use as much a priori information as is available to build the initial density model, such as borehole log data, the results of seismic data inversion, and geological and structural models.” [0036], “One of the most widely used correlation curves used in the joint interpretation of seismic and gravity data was established by Gardner et al. … The investigators conducted a series of studies to determine an empirical relationship between the rock density and compressional wave velocity, …” – Examiner note: the model is constrained using seismic (acoustic) survey data within joint inversion).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Bartetzko and Miles and Wang to incorporate the teachings of Priezzhev, and apply constrain, the at least one of the mid-range formation model or the far-range formation model at each of the plurality of depths along the wellbore based on survey data in order to refine and calibrate the predicted subsurface model using real world gravitational measurements obtained from both surface and borehole surveys.
The elements of claims 12-16, 20 and 22 are substantially the same as those of claims 1, 3-6 and 10-11. Therefore, the elements of claims 12-16, 20 and 22 are rejected due to the same reasons as outlined above for claims 1, 3-6 and 10-11.
Claim(s) 7-8 and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over
Bartetzko and Miles and Wang and Priezzhev as applied to claims 1 and 12 above, and further in view of Wu (“Gravity surveys using a mobile atom interferometer,” published in 2019).
Claim 7, Bartetzko and Miles and Wang and Priezzhev fail to teach, but Wu teaches The system of claim 1, wherein the plurality of surface gravitational data are obtained from at least one surface gravity sensor located sequentially at each of the plurality of surface locations proximate to the wellbore (page.4, Gravity survey in Berkeley Hills, “… We operated the atomic gravimeter inside a vehicle using passive vibration isolation and measured gravity at six locations. At each location, it took about 15 min to set up the gravimeter, including powering up the instrument and aligning the interferometer beam to the gravity axis ...” A POSITA would understand that the atomic gravimeter (i.e., gravity sensor) can be located sequentially at each of the plurality of surface locations proximate to the wellbore”).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Bartetzko and Miles and Wang and Priezzhev to incorporate the teachings of Wu, and apply atomic gravimeter inside a vehicle using passive vibration isolation and measured gravity at six locations sequentially in order to improve the spatial resolution and calibration accuracy of surface gravitational data used for detect subsurface formation.
Claim 8, Bartetzko and Miles and Wang and Priezzhev fail to teach, but Wu teaches The system of claim 1, wherein the plurality of surface gravitational data are obtained from one or more quantum gravity sensors (Page.4, Gravity survey in Berkeley Hills, “To demonstrate the use of the atomic gravimeter (examiner note: i.e., one type of quantum gravity sensors) in the field, we measured absolute gravity in the Berkeley Hills.”).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Bartetzko and Miles and Wang and Priezzhev to incorporate the teachings of Wu, and apply a mobile atomic gravimeter in order to measure absolute gravity using light-pulse atom interferometry, which exploits quantum interference of atomic matter wave to achieve more accurate and reliable surface gravitational data for detecting subsurface formation.
The elements of claims 17-18 are substantially the same as those of claims 7-8 . Therefore, the elements of claims 17-18 are rejected due to the same reasons as outlined above for claims 7-8.
Claim(s) 9 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over
Bartetzko and Miles and Wang and Priezzhev as applied to claims 1 and 12 above, and further in view of Xu (“Multilayer densities using a wavelet-based gravity method and their tectonic implications beneath the Tibetan Plateau,” published in 2018).
Claim 9, Bartetzko and Miles and Wang fail to teach, but Priezzhev teaches The system of claim 1, wherein the non-transitory memory device comprises instructions that, when executed by the processor, further cause the processor to correlate the at least one of the mid-range formation model or the far-range formation model at a first of the plurality of depths along the wellbore with the at least one of the mid-range formation model or the far-range formation model at a second of the plurality of depths along the wellbore, to determine one or more layer density inhomogeneities within one or more layers of the at least one of the mid-range formation model or the far-range formation model ([0065], “…, shown at 12 is generating a three-dimensional (3-D) forward model in the wavenumber domain of a physical parameter having a potential field from a model of spatial distribution of the physical property …” [0069], “… shown at 20 is generating by inversion in the wavenumber domain a revised model of spatial distribution of the physical property (e.g., density) 32 from the revised model of spatial distribution of the potential field (e.g., gravity) 28 …” [0095], “The model calculations according to the present invention demonstrate the use of the proposed method for interpretation of borehole gravity measurements … Sparse borehole gravity data can be interpolated to the entire volume of the subsurface being modeled, while taking into account surface gravity data. The result of this interpolation will determine the solution of the inversion problem, and it is this interpolation step along with the constraints provided by the initial density distribution that ensures a unique solution.”).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Bartetzko and Miles and Wang to incorporate the teachings of Priezzhev, and apply generating a three-dimensional (3-D) forward model and inversion refinement in order to determine a mid-range or far-range formation model based on near range earth models and surface gravitational data for improved subsurface representation.
However, Bartetzko and Miles and Wang and Priezzhev fail to teach correlation between models at two different depths and determining layer density inhomogeneities within a layer of the model.
Xu teaches correlation between models at two different depths (Page.2087, above 3 Method, “To reveal refined subsurface density structure at different depths, the Bouguer gravity anomalies should be decomposed further.” Page.2087 -2088, 3 Method, “In this study, the complete Bouguer gravity anomalies Δg (ϕ, λ) on the geoid (spherical approximation is assumed and the undulation of the geoid is neglected in this paper) are decomposed into wavelet approximation AS(ϕ, λ) and wavelet details Ds(ϕ, λ) using wavelet multiscale analysis … According to the solution to 2-D Laplace’s equation … can be obtained.” Page.2090, 4.2 Multilayer densities from decomposed gravity anomalies, “Based on the decomposed gravity anomalies … the crust and upper mantle of the TP are divided into S = 6 layers (see Table 2). The thicknesses rs (s = 1,2,··· ,6) of the six layers … Subsequently, each layer is … Lastly, multilayer densities ρs (s = 1,2,··· ,6) … are determined by eq. (17). Page.2093, 6 CONCLUSIONS, “… There are strong correlations between the decomposed gravity anomalies and the tectonic features at different depths in the crust and upper mantle.”) and determining layer density inhomogeneities within a layer of the model (Page. 2090, last paragraph, “… Densities of Layer 1 and Layer 2 (see Figs 5a and b) primarily reflect material distributions of 0–6 km depth and 6–10 km depth in the upper crust, respectively. Densities of these two layers range from 2.52 to 2.67 g cm−3, indicating strong lateral density inhomogeneity existing in the upper crust (Yang et al. 2015) ...” Page 2093, last paragraph, “There are strong correlations between the decomposed gravity anomalies and the tectonic features at different depths in the crust and upper mantle … The fifth- and sixth-order wavelet details reflect the attenuating lateral density inhomogeneity in the lower lithosphere. Moreover, six-layer densities of the TP with the lateral spatial resolution of 0.5◦ × 0.5◦ are inverted based on the decomposed gravity anomalies. The inverted multilayer densities provide a clear 3-D model to further insight into the tectonic structure and development. Densities of Layer 1 and Layer 2 imply strong lateral density inhomogeneity existing in the upper crust. From Layer 3 to Layer 5, fold structure and possible channel flows can be identified. Layer 6 gives smooth density distribution at the bottom of lithosphere.”).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Bartetzko and Miles and Wang and Priezzhev to incorporate the teachings of Xu, and apply the multi-depth gravity correlation and heterogeneity analysis methods is in order to determine lateral density inhomogeneities at different subsurface layers (e.g., upper crust to lithosphere) to improve the accuracy of formation modeling by using the processor to identify and quantify layer density inhomogeneities within the mid-range and far-range formation models.
The elements of claim 19 is substantially the same as those of claim 9. Therefore, the elements of claims 19 is rejected due to the same reasons as outlined above for claim 9.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Habashy US 20090164187 A1, discloses building a predictive or forward model adapted for
predicting the future evolution of a reservoir, comprising: integrating together a plurality of measurements thereby generating an integrated set of deep reading measurements, the integrated set of deep reading measurements being sufficiently deep to be able to probe the reservoir and being self-sufficient in order to enable the building of a reservoir model and its associated parameters; generating a reservoir model and associated parameters in response to the set of deep reading measurements; and receiving, by a reservoir simulator, the reservoir model and, responsive thereto, generating, by the reservoir simulator, the predictive or forward model.
Ander US 20040250614 A1, discloses Techniques for using gravity in applications such as drilling and
logging. Techniques are present for (1) gravity well logging using gravity sensors arrays; (2) creating density pseudosections using gravity measurements; (3) performing Gravity Measurement While Drilling (GMWD) using single or multiple gravity sensors; and (4) geosteering using GMWD.
Vasilevskiy US 20100286967 A1, discloses estimating a property of an earth formation includes: a plurality of sensors configured to estimate at least one property, each of the plurality of sensors located at a known position relative to one another; and a processor in operable communication with the plurality of sensors and configured to estimate uncertainties of the location of the plurality of sensors over a period of time. A method and computer program product for estimating a property of an earth formation is also disclosed.
Green US 20110166840 A1, discloses electromagnetic subsurface mapping to derive information with respect to subsurface features whose sizes are near to or below the resolution of electromagnetic data characterizing the subsurface are shown. Embodiments operate to identify a region of interest (203) in a resistivity image generated (202) using electromagnetic data (201). One or more scenarios may be identified for the areas of interest, wherein the various scenarios comprise representations of features whose sizes are near to or below the resolution of the electromagnetic data (204). According to embodiments, the scenarios are evaluated (205), such as using forward or inverse modeling, to determine each scenarios' fit to the available data and further to determine their geologic reasonableness (206). Resulting scenarios may be utilized in a number of ways, such as to be substituted in a resistivity image for a corresponding region of anomalous resistivity for enhancing the resistivity image (207).
Any inquiry concerning this communication or earlier communications from the examiner should be
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/YI . HAO/
Examiner, Art Unit 2187
/EMERSON C PUENTE/Supervisory Patent Examiner, Art Unit 2187
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