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 .
This communication is responsive to application filed on 05/26/2023.
Claims 1-20 are presented for examination.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 05/26/2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 1-3, and 5-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 (Does this claim fall within at least one statutory category?):
Claims 1-11 are directed to a method.
Claims 12-18 are directed to a product.
Claims 19-20 are directed to a system.
Therefore, claims 1-20 fall into at least one of the four statutory categories.
Step 2A, Prong 1: ((a) identify the specific limitation(s) in the claim that recites an abstract idea: and (b) determine whether the identified limitation(s) falls within at least one of the groups of abstract ideas enumerates in MPEP 2106.04(a)(2)):
Claim 1:
A method comprising:
obtaining a measured value of a geological parameter from each of a plurality of positions within a subterranean region of interest [insignificant extra solution, e.g. mere data-gathering];
for each position within the plurality of positions in turn, using a computer processor [e.g. a generic computer element for performing a generic computer function]:
assigning each position as a current position [“mental process i.e. concepts performed with pen and paper (including an observation, evaluation judgement, opinion)], and
determining a weight for the current position, based, at least in part, on a distance from the current position to each of the plurality of positions [mathematical concepts];
determining, using the computer processor, a normalization factor comprising a sum of a plurality of the determined weights [mathematical concepts];
determining, using the computer processor and by applying the normalization factor, a normalized weight for each of the plurality of the determined weights [mathematical concepts]; and
determining, using the computer processor, a geological model, that represents the subterranean region of interest, based, at least in part, on a plurality of the obtained measured values and a plurality of the determined normalized weights [mathematical concepts].
Step 2A, Prong 2 (1. Identifying whether there are any additional elements recited in the claim beyond the judicial exception; and 2. Evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application): The claim is directed to the judicial exception.
Claim 1 recites additional element of “obtaining”, and “computer processor”. The additional element of “obtaining” is insignificant pre-solution (i.e. data gathering). The additional element of “processor” recited at a high level of generality (e.g. a generic computer element for performing a generic computer functions) such that it amounts to no more than mere application of the judicial exception using generic computer component(s). Accordingly, the additional element(s) of each of this claim does not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Step 2B: (Does the claim recite additional elements that amount to significantly more than the judicial exception? No): As discussed above with respect to the integration of the abstract into a practical application, the additional element of “obtaining” is insignificant pre-solutions (i.e. data gathering). At most the additional element is not found to including anything more than data gathering or mere data output. See MPEP 2106.04(d) referencing MPEP 2106.05(g), example (iv) - Obtaining information about transactions. Further, as discussed above with respect to the integration of the abstract into a practical application, the additional element of “processor” amount to no more than mere instructions to apply the judicial exception using generic computer component(s). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
As per claim 2, the claim falls into [mathematical concepts].
As per claim 3, the claim falls into [mathematical concepts].
As per claim 5, the claim falls into [mathematical concepts, formulas].
As per claim 6, the claim falls into [mathematical concepts, formulas].
As per claim 7, the claim falls into [mathematical concepts].
As per claim 8, the claim falls into [mathematical concepts].
As per claim 9, the claim falls into [insignificant extra solution, e.g. mere data-gathering] and/or [mathematical concepts].
As per claim 10, the claim falls into [mathematical concepts].
As per claim 11, the claim falls into [mathematical concepts].
As per Claims 12-18, claims 12-18 recite limitations analogous in scope to those of claims 1-11, and as such are similar rejected.
As per Claim 19, independent claim 19 recites limitations analogous in scope to those of independent claim 1, and as such are similar rejected. Further, claim 19 recites additional elements of “analysis tool” and “computer system”. The “analysis tool” recited at a high level of generality (e.g. a logging tool for performing explore the subsurface based on magnetic interactions) such that it amounts to no more than mere application of the judicial exception using generic logging tool components. The “computer processor” recited at a high level of generality (e.g. a generic computer element for performing a generic computer functions) such that it amounts to no more than mere application of the judicial exception using generic computer component(s). Accordingly, the additional element(s) of each of these claims do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Further, as discussed above with respect to the integration of the abstract into a practical application, the additional elements of “analysis tool” amounts to no more than mere instructions to apply the judicial exception using conventional analysis tool. Mere instructions to apply an exception using a generic logging tool cannot provide an inventive concept. The component is considered well-known, routing or conventional (See: Applicant Specification, par [0028] “An analysis tool may be configured to determine a measured value of a geological parameter from one or more positions (104i-r). The analysis tool may be, but is not limited to, a core plug analysis tool (e.g., permeability system), well logging tool, or seismic survey analysis tool (i.e., a seismic acquisition system). However, a person of ordinary skill in the art will appreciate that the analysis tool used to characterize one position (104i-r) need not be the analysis tool used to characterize another position (104i-r)”). In addition, as discussed above with respect to the integration of the abstract into a practical application, the additional elements of “computer system” amount to no more than mere instructions to apply the judicial exception using generic computer component(s). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
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 1-4, 6, 7, 11-14, 16-17, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Kristoffersen et al (B. S. Kristofferesen, M. C. Bellout, T. L. Silva, C. F. Berg, “An Automatic Well Planner for Complex Well Trajectories”, pgs. 1881-1905, 2020) in view of US Publication No. 2008/0077371 A1 issued to Yeten et al.
Claim 1. Kristofferson et al discloses a method comprising:
obtaining a measured value of a geological parameter from each of a plurality of positions within a subterranean region of interest (See: pg. 1884, The ANN driving the AWP procedure gathers local information around the current well head position and outputs the direction of the next section of well trajectory….while AWP generates trajectories that closely adapt to realistic geological heterogeneities, the procedure enables a low-level representation of these complex trajectories. Specifically, the data-driven approach enabling this well parameterization takes as input the heel and toe values of a well and yields, according to the specified fitness target; Fig. 4 Flowchart outlining the series of operations and constraint checks performed by the AWP procedure to obtain the well path trajectory); for each position within the plurality of positions in turn (See: pg. 1892, If γm = π/2, then the set of coordinates {s} spans the half sphere of radius dm in front of the current position. When xi is the current position and γm = π/2, then the sample points {xi + s} are as illustrated in Fig. 5a. At each sampling point we collect the value of the property field f , so that a set of values { f (xi + s)} is passed as input to the network. In our implementation, we use a constant step length l determined by the approximate length of one drill pipe. The only decision parameter out of the neural network is the continuous elevation angle θi, as indicated in Fig. 5. The cyan bars at the current location in Fig. 5 indicate the bound constraint with regard to the DLS, as given by Eq. (9)), using a computer processor:
assigning each position as a current position (See: pg. 1882, the drilling operation adapts in a reactive manner to the surroundings based on continuous processing and incorporation of measurements related to the current position of the drill bit; pg. 1885, Well parameterization refers to how wells are specified as variables within an encom passing routine. In this work, wells are specified by the location of their heel xh = (xh,yh,zh) and toe xt = (xt,yt,zt). These are the start and end points of the perforated part of the well, i.e., the part of the well that interacts with the reservoir. Thus, a well is defined by a vector of the two positions, (xh,xt), where x ∈ R3, similar to the well parameterization found in (Sayyafzadeh and Alrashdi 2019); pg. 1889, The input into the ANN is the set of local properties around the current location xi, the current position and angles, in addition to the toe coordinate xh; pg. 1893, by the AWP procedure. The set of sampling points for the next decision are indicated by orange circles, distributed with in the grey information horizon. Dogleg severity bounds for the next elevationθi are indicated by cyan-colored bars from the current location. The change in elevation at the previous decision is shown as the angle between the brown and dotted cyan line), and
determining a weight for the current position, based, at least in part, on a distance from the current position to each of the plurality of positions (See: pg. 1882, Decisions during the drilling phase, on the other hand, rely on information acquired during drilling; from readily available drilling parameters such as torque and weight on bit, to less available traditional logging while-drilling measurements, and sophisticated logging tools embedded in the drill string that are able to acquire information from deeper in the formation; pg. 1887, This evolutionary approach allows modification of the topology of the network by adding or removing nodes and connections. The algorithm is furthermore allowed to change weights, biases, activation function, nodes, and connections; pg. 1890, The first condition in Eq. (5) applies to the case when the distance to the toe is less than tg, which yields a positive contribution of Rg to the fitness. Rg is a constant value equal to many times the expected value of Eq. (4). Accordingly, Rg serves as a discriminator between those networks that do succeed in reaching close to the toe and those that do not. If the first condition is not met, the contribution to the fitness is set equal to the square of the distance to the toe (here α is a vector that provides weights for the different directions); pg. 1902, By including other penalties and parameters into the fitness function, we expect to be able to train an AWP that is more adapted to increasing the NPV instead of productivity. Parameters might include porosity and saturation, while penalties could depend on distance to oil-water contact and distance to other wells. The set and weighting of parameters and penalties would need to be tailored to the objectives for the wells, such as different fitness functions for injectors and producers);
determining, a normalization factor comprising a sum of a plurality of the determined weights (See: pg. 1889, Given a property field f (x), a simple fitness function gf can be defined by the sum…; pg. 1893, To maintain generality in the neural networks, each member of the genetic algorithm was evaluated through all the training data and given an aggregate fitness. For each member of the NEAT algorithm, the aggregate fitness for the member is given by summing the fitness function in Eq. (6) for all the training data; pg. 1896, Fig. 7 Location of cross sections for the five example cases, illustrated on the permeability field of the reservoir; all values are normalized from 0 to 1; pg. 1900, Fig. 10 Cumulative histogram of total liquid production (solid) and NPV (dashed) distribution, for the SL (blue) and AWP trajectories (orange). The x-axis is normalized to the highest liquid production or NPV value);
determining, by applying the normalization factor, a normalized weight for each of the plurality of the determined weights (pg. 1896, Fig. 7 Location of cross sections for the five example cases, illustrated on the permeability field of the reservoir; all values are normalized from 0 to 1; pg. 1900, Fig. 10 Cumulative histogram of total liquid production (solid) and NPV (dashed) distribution, for the SL (blue) and AWP trajectories (orange). The x-axis is normalized to the highest liquid production or NPV value); and
determining, a geological model, that represents the subterranean region of interest, based, at least in part, on a plurality of the obtained measured values and a plurality of the determined normalized weights (See: Abstract, develops well trajectories based on a selected geology-based fitness measure using an artificial neural network as the decision maker in a virtual sequential drilling process within a reservoir model; pg. 1884, Fitness functions and required constraints are imposed during the training. These performance measures ensure both that the AWP trajectories adapt to the surrounding geology according to the specified fitness and drilling criteria).
Kristofferson et al does not specify but Yeten et al discloses computer processor (See: [0005] Initial efforts towards computer assisted history matching focused on the calculation of sensitivity of flow responses with respect to reservoir properties; [0082] With a small-size computer cluster of 20 nodes, it took a few weeks to evaluate 27 realizations for a total of approximately 10,000 simulations. It would be completed in a matter of days with a current medium-size computer cluster of 256 nodes).
It would have been obvious before the effective filing date to combine method for history matching and uncertainty quantification assisted by global optimization techniques as taught by Yeten et al to an automatic well planner for complex well trajectories of Kristofferson et al would be to collect all acceptable models and apply clustering techniques to select representative models prior to forecasting the production of the reservoir (Yeten et al, [0008]).
Claim 2. Kristofferson et al discloses the method of claim 1, further comprising determining a field management plan based, at least in part, on the geological model (See: Abstract, A data-driven automatic well planner procedure is implemented to develop complex well trajectories by efficiently adapting to near-well reservoir properties and geometry…... the proposed procedure develops well trajectories based on a selected geology-based fitness measure using an artificial neural network as the decision maker in a virtual sequential drilling process within a reservoir model…..Well trajectories generated in a realistic reservoir by the automatic well planner are qualitatively and quantitatively compared to trajectories generated by a differential evolution algorithm; pg. 1883, The AWP procedure proposed in this work couples virtual geosteering with reservoir simulation models to provide for complex well trajectory design with low-order parameterization.).
Claim 3. Yeten et al discloses the method of claim 2, further comprising taking one or more field management actions based, at least in part, on the field management plan (See: [0078] Good continuous downhole pressure data exists for the R Field based on permanent downhole pressure gauges installed in each completion string. These gauges have provided valuable pressure data for the management of the reservoir; [0079] The five producing gas wells have been on production since 2001. Permanent downhole gauges were installed in each completion string, and have been providing invaluable pressure data for the management of the reservoir).
Claim 4. Kristofferson et al discloses the method of claim 2, further comprising: planning a wellbore trajectory within the subterranean region of interest based, at least in part, on the field management plan (Abstract, A data-driven automatic well planner procedure is implemented to develop complex well trajectories by efficiently adapting to near-well reservoir properties and geometry…... the proposed procedure develops well trajectories based on a selected geology-based fitness measure using an artificial neural network as the decision maker in a virtual sequential drilling process within a reservoir model…..Well trajectories generated in a realistic reservoir by the automatic well planner are qualitatively and quantitatively compared to trajectories generated by a differential evolution algorithm; pg. 1883, The AWP procedure proposed in this work couples virtual geosteering with reservoir simulation models to provide for complex well trajectory design with low-order parameterization); and drilling a wellbore based, at least in part, on the wellbore trajectory (See: Abstract, develops well trajectories based on a selected geology-based fitness measure using an artificial neural network as the decision maker in a virtual sequential drilling process within a reservoir model).
Claim 6. Kristofferson et al discloses the method of claim 1, wherein the weight comprises a sum of the distance from the current position to each of the plurality of positions (See: pg. 1890, When using the fitness function given in Eq. (4), the trained network yields trajec tories traversing parts of the reservoir with high values for the property field f (x).To ensure that the resulting trajectory lies between the given heel and toe coordinates, we introduce a second fitness term bt(xn). This additional term is proportional to the distance between the last point xn of the developing trajectory N(xh,xt),andthetarget toe xt coordinate. As such, this term serves as a “reward” measure for the developing trajectory; Fig. 5 Description of the AWP procedure. The well trajectory is built in steps of equal length l, generated by the AWP procedure. The set of sampling points for the next decision are indicated by orange circles, distributed with in the grey information horizon. Dogleg severity bounds for the next elevationθi are indicated by cyan-colored bars from the current location).
Claim 7. Kristofferson et al discloses the method of claim 1, wherein the distance comprises a horizontal distance (See: pg. 1885, Well parameterization refers to how wells are specified as variables within an encompassing routine. In this work, wells are specified by the location of their heel xh = (xh,yh,zh) and toe xt = (xt,yt,zt). These are the start and end points of the perforated part of the well, i.e., the part of the well that interacts with the reservoir. Thus, a well is defined by a vector of the two positions, (xh,xt), where x ∈ R3, similar to the well parameterization found in (Sayyafzadeh and Alrashdi 2019)).
Claim 11. Kristofferson et al discloses the method of claim 1, wherein determining the geological model comprises a gridding algorithm (See: Abstract, Well trajectories generated in a realistic reservoir by the automatic well planner are qualitatively and quantitatively compared to trajectories generated by a differential evolution algorithm).
As per Claims 12-14, 16-17, and 19, claims 12-14, 16-17, and 19 recite limitations analogous in scope to those of claims 1-4, 6, and 7, and as such are similar rejected.
20. Kristofferson et al discloses the system of claim 19, further comprising: field management planning software configured to determine a field management plan; wellbore planning software configured to plan a wellbore trajectory within the subterranean region of interest based, at least in part, on the field management plan; and a drilling system configured to drill a wellbore based, at least in part, on the wellbore trajectory (See: Abstract, The procedure draws inspiration from geosteering drilling operations, where modern logging-while-drilling tools enable the adjustment of well trajectories during drilling….pg. 1888, Modern logging tools integrated in the drill string enable the real-time gathering of information regarding local geology and fluid content around the drill bit. Based on this information, drillers can make informed decisions of whether to alter the current trajectory of the well).
Claims 5, 8-10, 15 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Kristofferson et al & Yeten et al as applied to claims 1, and 12 above, and further in view of Wu et al (W. Wu, D. Grana, “Integrated petrophysics and rock physics modeling for well log interpretation of elastic, electrical, and petrophysical properties”, pgs. 54-66, 2017).
Claim 5. Neither Kristofferson et al nor Yeten et al but Wu et al discloses wherein the geological parameter comprises a seismic velocity (See: pg. 54 right side column, On the other hand, rock physics models are usually applied in reservoir characterization to estimate rock properties from elastic attributes such as borehole sonic or seismic velocities).
It would have been obvious before the effective filing date to combine integrated petrophysics and rock physics modeling as taught by Wu et al to an automatic well planner for complex well trajectories of Kristofferson et al would be to improve by integrating petrophysics and rock physics modeling of elastic, electrical, and petrophysical properties (Wu et al, 4. Conclusion).
Claim 8. Wu et al discloses the method of claim 1, wherein the normalized weight comprises a ratio of the weight and the normalization factor (See: pg. 56, right side column, The cementation exponent generally varies between approximately 1.3 and 2.5 for most sedimentary rocks and is close to 2 for sandstones. The saturation exponent is generally assumed to be 2, but can vary as well. The ratio of the resistivity of a fully water saturated rock to water resistivity is defined as the formation factor (R/Rw); pg. 58 right side column, We then define the normalized resistivity as the ratio of measured resistivity to water resistivity(Rt/Rw).The relation between normalized resistivity and porosity is modeled by Archie's equation at different water saturations and it is shown in Fig.1,right).
Claim 9. Wu et al discloses the method of claim 1, wherein obtaining the measured value comprises: obtaining a rock core (See: pg. 55 right side column, Constitutive equations link rock properties with well-log measurements. Various rock physics models have been presented in the literature based on the lithology and fluid type in the porous rocks); and determining the measured value from the rock core (See: Abstract, Rock and fluid volumetric properties, such as porosity, saturation, and mineral volumes, are generally estimated from petrophysical measurements such as density, resistivity, neutron porosity and gamma ray, through petrophysical equations).
Claim 10. Wu et al discloses the method of claim 1, wherein the geological model comprises a seismic velocity model (See: pg. 54 right side column, On the other hand, rock physics models are usually applied in reservoir characterization to estimate rock properties from elastic attributes such as borehole sonic or seismic velocities).
As per Claims 15 and 18, claims 15 and 18 recite limitations analogous in scope to those of claims 5, and 8, and as such are similar rejected.
Conclusion
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KIBROM K. GEBRESILASSIE
Primary Examiner
Art Unit 2189
/KIBROM K GEBRESILASSIE/Primary Examiner, Art Unit 2189 08/14/2026
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