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 .
Information Disclosure Statement
The information disclosure statements (IDS) were submitted on 10/23/2024 and 12/17/2025. The submission 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 1-6, 8-14, and 16-20 are rejected under 35 U.S.C. 101. The claimed invention is directed to the abstract concept of performing abstract steps without significantly more. The claim(s) recite(s) the following abstract concepts in BOLD of
1. A method for geology-based facies trend modeling for extracting hydrocarbons from a subsurface region, the method comprising:
accessing a three-dimensional (3D) facies model including a set of layers, each layer including boundaries data representing initial facies boundaries for a set of facies represented in that layer;
accessing geological constraints data representing facies trends for the set of facies represented in the facies model;
for each layer:
for each facies type in the layer, determining, for a portion of the layer, a distance from the portion of the layer to a facies boundary for that facies type in the layer; and
based on the distance for each facies type and the facies trends represented in the geological constraints data, determining a facies probability value;
stacking the layers each including facies probability values for each facies type represented in that layer;
based on the facies probability values of the stacked layers, simulating facies trends for each facies type; and
generating a 3D facies trend model including the simulated facies trends.
9. A system for geology-based facies trend modeling for extracting hydrocarbons from a subsurface region, the system comprising:
at least one processor; and
a memory storing instructions that, when executed by the at least one processor, cause the at least one processor to perform operations comprising:
accessing a three-dimensional (3D) facies model including a set of layers, each layer including boundaries data representing initial facies boundaries for a set of facies represented in that layer;
accessing geological constraints data representing facies trends for the set of facies represented in the facies model;
for each layer:
for each facies type in the layer, determining, for a portion of the layer, a distance from the portion of the layer to a facies boundary for that facies type in the layer; and
based on the distance for each facies type and the facies trends represented in the geological constraints data, determining a facies probability value;
stacking the layers each including facies probability values for each facies type represented in that layer;
based on the facies probability values of the stacked layers, simulating facies trends for each facies type; and
generating a 3D facies trend model including the simulated facies trends.
17. One or more non-transitory computer readable media storing instructions for geology-based facies trend modeling for extracting hydrocarbons from a subsurface region, the instructions, when executed by at least one processor, configured to cause the at least one processor to perform operations comprising:
accessing a three-dimensional (3D) facies model including a set of layers, each layer including boundaries data representing initial facies boundaries for a set of facies represented in that layer;
accessing geological constraints data representing facies trends for the set of facies represented in the facies model;
for each layer:
for each facies type in the layer, determining, for a portion of the layer, a distance from the portion of the layer to a facies boundary for that facies type in the layer; and
based on the distance for each facies type and the facies trends represented in the geological constraints data, determining a facies probability value;
stacking the layers each including facies probability values for each facies type represented in that layer;
based on the facies probability values of the stacked layers, simulating facies trends for each facies type; and
generating a 3D facies trend model including the simulated facies trends.
Under step 1 of the eligibility analysis, we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: process, machine, manufacture, or composition of matter. The above claims are considered to be in a statutory category.
Under Step 2A, Prong One, we consider whether the claim recites a judicial exception (abstract idea). In the above claim, the highlighted portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitation the fall into/recite abstract idea exceptions. Specifically, under the 2019 Revised Patent Subject Matter Eligibility Guidance, it falls into the grouping of subject matter that, when recited as such in a claim limitation, covers performing mathematics or mental steps.
Next, under Step 2A, Prong Two, we consider whether the claim that recites a judicial exception is integrated into a practical application. In this step, we evaluate whether the claim recites additional elements that integrate the exception into a practical application of that exception.
This judicial exception is not integrated into a practical application because there is no improvement to another technology or technical field; improvements to the functioning of the computer itself; a particular machine; effecting a transformation or reduction of a particular article to a different state or thing. Examiner notes that since the claimed methods and system are not tied to a particular machine or apparatus, they do not represent an improvement to another technology or technical field. Similarly there are no other meaningful limitations linking the use to a particular technological environment. Finally, there is nothing in the claims that indicates an improvement to the functioning of the computer itself or transform a particular article to a new state.
Finally, under Step 2B, we consider whether the additional elements are sufficient to amount to significantly more than the abstract idea. Claims 1, 9, and 17 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because a step of accessing a model and data and accessing geological constraints data is considered necessary data gathering. As recited in MPEP section 2106.05(g), necessary data gathering (i.e. accessing data) is considered extra solution activity in light of Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015). The processor and memory of Claim 9 are interpreted under broadest reasonable interpretation to be a generic computer elements. Generic computer elements are not considered significantly more than the abstract idea and do not integrate the abstract idea into a practical application. As recited in the MPEP, 2106.05(b), merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 134 S. Ct. 2347, 2359-60, 110 USPQ2d 1976, 1984 (2014). See also OIP Techs. v. Amazon.com, 788 F.3d 1359, 1364, 115 USPQ2d 1090, 1093-94.
Claims 2, 10, and 18 detail the additional limitation of receiving well log data. A step of receiving data is considered necessary data gathering. As recited in MPEP section 2106.05(g), necessary data gathering (i.e. receiving data) is considered extra solution activity in light of Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015).
Claims 2-6, 8, 10-14, 16, and 18-20 further limit the abstract ideas without integrating the abstract concept into a practical application or including additional limitations that can be considered significantly more than the abstract idea.
Claims 7 and 15 are not rejected under 35 U.S.C. 101 because the additional limitation of “drilling, at the location in the subsurface region, a well” integrates the judicial exception into a practical application.
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.
Claims 1, 9, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Khattab (US20230274497) in view of Strebelle (US20110213600).
In regards to Claims 1, 9, and 17, Khattab teaches “at least one processor (computer 124, server 120– [0024]); and
a memory storing instructions that, when executed by the at least one processor (computer 124, server 120– [0024]), cause the at least one processor to perform operations comprising:
accessing a three-dimensional (3D) facies model including a set of layers, each layer including boundaries data representing initial facies boundaries for a set of facies represented in that layer (“In block 214 of the process 200 of FIG. 2 , each stratigraphic layer/interval may be scanned to define a maximum thickness, a minimum thickness, and volume fraction for each facies and to define corresponding wells and area. FIG. 8 illustrates chart 800 showing volume fractions generated for each facies 404 (i.e., 404 M, 4040 , 404 S, 404 C, 404 P, and 4040 G as shown) for the plurality of sequences 300 . The chart 800 includes columns including code 802 to set forth the codes for the plurality of facies 404 , name 804 to set forth the respective names for each of the plurality of facies 404 , a percentage volume fraction 806 , an additional metric value N (number of samples) 808 for each facies 404 , an Intervals value 810 for each facies 404 , a minimum thickness 812 as shown in feet for each facies 404 , a maximum thickness 814 as shown in feet for each facies 404 , and a standard deviation 816 for each facies 404 . In embodiments, a mean thickness may be shown as well. The 3D facies model processor 104 may be further operable to execute machine-readable instructions of the model generator component 112 to generate a minimum thickness, a maximum thickness, and volume fraction for each facies of the plurality of facies based on the sequence stratigraphy. The minimum thickness, the maximum thickness, and the volume fraction for each facies of the plurality of facies 404 may further be displayed such as shown in in chart 800 of FIG. 8 . The 3D facies model processor 104 may be further operable to execute machine-readable instructions of the model generator component 112 to (i) generate the 3D stratigraphic sequence framework 299 comprising a plurality of sequences 300 based on the one or more sequences of strata of the sequence stratigraphy, wherein each sequence of the plurality of sequences comprises at least one facies from the plurality of facies 404 for the subject reservoir, and (ii) generate a minimum and a maximum thickness and volume fraction for each facies of the plurality of facies 404 for each of the plurality of sequences 300” – [0042]);
accessing geological constraints data representing facies trends for the set of facies represented in the facies model (“each stratigraphic layer/interval may be scanned to define a maximum thickness, a minimum thickness, and volume fraction for each facies and to define corresponding wells and area” – [0042]; “3D trend model may be generated based on data analysis, paleo shoreline, and depositional azimuth” – [0044]);
for each layer:
for each facies type in the layer, determining, for a portion of the layer, a distance from the portion of the layer to a facies boundary for that facies type in the layer (“In block 204 , the imported data and zonation may be used to build a 3D stratigraphic sequence framework based on the sequence stratigraphy and/or the stratigraphic interval for the study reservoir” – [0035]; “In block 216 of the process 200 of FIG. 2 , a 3D trend model may be generated based on data analysis, paleo shoreline, and depositional azimuth. Probabilities as a log curve may be generated for each facies based on the facies input data at wells (e.g., based on the defined facies volume fractions for each stratigraphic interval)” – [0044]); and
based on the distance for each facies type and the facies trends represented in the geological constraints data, determining a facies probability value (“Probabilities as a log curve may be generated for each facies based on the facies input data at wells (e.g., based on the defined facies volume fractions for each stratigraphic interval [i.e. distance]). If the facies exist, then the probability would equal one, otherwise the probability will be zero if the facies does not exist. FIG. 13 illustrates a probability log 1300 for each facies 404M, 4040 , 404 S, 404 C, 404 P, and 4040 G for the plurality of sequences 300 including the first sequence 301 , the second sequence 302 , and the third sequence 303 for the 3D stratigraphic sequence framework 299 of FIG. 3 . The first overall log track is representative of the described facies 404 along a well trajectory, while each of facies 404 M, 4040 , 404 S, 404 C, 404 P, and 4040 G is shown with a respective log track as a respective probability log 1300 showing the facies probabilities as a log curve generated from the facies input data of the first overall log track. FIG. 14 illustrates a 3D probability trend 1400 shown as a North-South (N-S) section based on the probability log 1300 of FIG. 13 for a specific facies 404 C of Cladocoropsis with respect to the plurality of sequences 300 . The 3D facies model processor 104 may be operable to execute machine-readable instructions of the model generator component 112 to generate a plurality of 3D probability trends 1400 for the plurality of facies 404 , each 3D probability trend 1400 associated with respective facies of the plurality of facies 404 based on the facies volume fraction for the respective facies. In embodiments, the plurality of 3D probability trends 1400 may be generated for each sequence facies of the at least one facies 404 for each sequence of the plurality of sequences 300.” – [0044]; “The plurality of 3D probability trends 1400 for the plurality of facies 404 may be generated based on a plurality of probability logs 1300 , each facies of the plurality of facies 404 associated with a respective probability log of the plurality of probability logs 1300 , such as show in FIG. 13 . The 3D stratigraphic sequence framework 299 may be generated comprising a plurality of sequences 300 based on the one or more sequences of strata of the sequence stratigraphy, wherein each sequence of the plurality of sequences comprises at least one facies from the plurality of facies 404 for the subject reservoir, and each 3D probability trend 1400 may be generated for each facies 404 of each sequence 301 , 302 , 303 of the plurality of sequences 300 based on the respective probability log 1300 for the facies 404 for each sequence 301 , 302 , 303 of the plurality of sequences 300 . The probability log 1300 for each facies 404 may be modeled in 3D using kriging testing different connectivity ranges guided by variogram analysis for sensitivity. Vertical proportion curve and two-dimensional (2D) trend maps for each stratigraphic interval may be used as additional inputs to the 3D probability trend 1400” – [0045]);
stacking the layers each including facies probability values for each facies type represented in that layer (“Comparison of the 3D facies model 1500 with the depositional model 700 finds a success and sufficient close results and that the vertical stacking and shallowing/coursing upward cycles of the subject reservoir are captured such as the depositional events are successfully reflected in the 3D facies model 1500” – [0047]); and
generating a 3D facies trend model including the simulated facies trends (“In block 216 of the process 200 of FIG. 2 , a 3D trend model may be generated based on data analysis, paleo shoreline, and depositional azimuth. Probabilities as a log curve may be generated for each facies based on the facies input data at wells (e.g., based on the defined facies volume fractions for each stratigraphic interval). If the facies exist, then the probability would equal one, otherwise the probability will be zero if the facies does not exist…The 3D facies model processor 104 may be operable to execute machine-readable instructions of the model generator component 112 to generate a plurality of 3D probability trends 1400 for the plurality of facies 404 , each 3D probability trend 1400 associated with respective facies of the plurality of facies 404 based on the facies volume fraction for the respective facies. In embodiments, the plurality of 3D probability trends 1400 may be generated for each sequence facies of the at least one facies 404 for each sequence of the plurality of sequences 300” – [0044]).”
Khattab is silent with regards to the language of “based on the facies probability values of the stacked layers, simulating facies trends for each facies type”
Strebelle teaches “based on the facies probability values of the stacked layers, simulating facies trends for each facies type (“When it is determined at operation 14, in some embodiments, that the reservoir property is to be simulated within a specific facies, then MPS programs are modified to consider the additional category(ies) created at operation 16 as follows: 1. Prior to the MPS simulation, all the grid nodes that are outside the specific facies where the reservoir property is to be simulated, are assigned to one of the additional category(ies), and then used as conditioning data for the MPS simulation in addition to the categorical well data. Those additional conditioning data corresponding to the additional category(ies) provide information about facies boundaries for simulating the intra-facies petrophysical trend. 2. In some embodiments, the additional category(ies) are not simulated within the specific facies where the reservoir property is to be simulated and the initial categories determined at operation 12 can be simulated” – [0040]-[0042]; “At operation 28 of FIG. 1, a variogram-based simulation approach is applied in each sub-region to create a reservoir property model that reproduces the category histogram of each associated sub-region; wherein the reservoir property model models the reservoir property trends” – [0046]; “At operation 28 of FIG. 1, a variogram-based simulation approach is applied in each sub-region to create a reservoir property model that reproduces the category histogram of each associated sub-region; wherein the reservoir property model models the reservoir property trends” – [0047]).”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Khattab to incorporate the teaching of Strebelle to perform simulations with the facies data. By performing a simulation with the model, this is an improvement that yields predictable results in the operation of a model and determination of the model’s accuracy.
Claims 2-3, 10-11, and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Khattab in view of Strebelle as applied to claims 1, 9, and 17 above, and further in view of Algheryafi (US20220082729).
In regards to Claims 2, 10, and 18, Khattab in view of Strebelle disclose the claimed invention as detailed above. Khattab further teaches “receiving, from a plurality of wells of a reservoir, well log data in each of the wells of the plurality of wells, wherein at least a subset of the wells are associated with facies data representing facies at the subset of the wells (“The 3D facies model processor 104 may be further operable to execute machine-readable instructions of the model generator component 112 to upscale the facies data from well log data of one or more wells of the subject reservoir to assign to upscaled cells 408 to display in a grid model, and display the upscaled cells 408 in the grid model such as shown in FIG. 4 B against the well log data 410 for the one or more wells” – [0036]; “Thus, these facies 4040 , 404 M may be assigned to another facies 404 in the first sequence 301 by inspecting physical properties such as porosity and permeability and assigning the facies 4040 and/or 404 M to a facies 404 in the first sequence 301 with similar properties.” – [0040]);
determining, from the facies data, facies proportions associated with the subset of wells (“The facies data comprises a plurality of depositional inputs for the subject reservoir, and the sequence stratigraphy comprises the one or more sequences of strata for the total strata of the subject reservoir. The 3D facies model processor is communicatively coupled to the 3D facies model memory and operable to execute machine-readable instructions of the model generator component to receive, from the facies data repository, (i) the facies data comprising the plurality of depositional inputs for the subject reservoir and (ii) the sequence stratigraphy comprising the one or more sequences of strata for the subject reservoir, generate a 3D stratigraphic sequence framework comprising a plurality of facies for the subject reservoir based on the facies data and the sequence stratigraphy, and generate a facies volume fraction for each facies of the plurality of facies. The 3D facies model processor is further operable to execute machine-readable instructions of the model generator component to compare each facies volume fraction for each facies of the plurality of facies with the depositional model for the subject reservoir to determine whether a facies of the 3D stratigraphic sequence framework is correctly assigned based on a comparison with the depositional model, and reassign the facies volume fraction for the facies of the plurality of facies to another facies of the plurality of facies when the comparison determines that the facies of the 3D stratigraphic sequence framework is incorrectly assigned. The 3D facies model processor is further operable to execute machine-readable instructions of the model generator component to generate a plurality of 3D probability trends for the plurality of facies, each 3D probability trend associated with respective facies of the plurality of facies based on the facies volume fraction for the respective facies, and generate the 3D facies model based on the plurality of 3D probability trends for the plurality of facies” – [0049]); and
based on the determined facies proportions at the plurality of wells, generating the 3D facies trend model for the subsurface region at each of the wells of the plurality (“In block 216 of the process 200 of FIG. 2 , a 3D trend model may be generated based on data analysis, paleo shoreline, and depositional azimuth. Probabilities as a log curve may be generated for each facies based on the facies input data at wells (e.g., based on the defined facies volume fractions for each stratigraphic interval). If the facies exist, then the probability would equal one, otherwise the probability will be zero if the facies does not exist…The 3D facies model processor 104 may be operable to execute machine-readable instructions of the model generator component 112 to generate a plurality of 3D probability trends 1400 for the plurality of facies 404 , each 3D probability trend 1400 associated with respective facies of the plurality of facies 404 based on the facies volume fraction for the respective facies. In embodiments, the plurality of 3D probability trends 1400 may be generated for each sequence facies of the at least one facies 404 for each sequence of the plurality of sequences 300” – [0044]).”
Khattab in view of Strebelle are silent with regards to the language of “receiving, from a plurality of wells of a reservoir, well log data measured by sensors in each of the wells of the plurality of wells, the well log data representing porosity measurements of the subsurface region at each of the wells of the plurality”
Algheryafi teaches “receiving, from a plurality of wells of a reservoir, well log data measured by sensors in each of the wells of the plurality of wells, the well log data representing porosity measurements of the subsurface region at each of the wells of the plurality (“Wireline logging typically includes advancing a logging tool through a wellbore and acquiring measurements along the length of the wellbore to generate a wireline log (or “well log”) that identifies characteristics of the formation rock as a function of depth in the wellbore.” – [0004]; “In some embodiments, the control system 122 stores, or otherwise has access to, facies data 140 . The facies data 140 may include data that is indicative of various characteristics of the rock of the formation 104 surrounding the well 106 . In some embodiments, the facies data 140 includes vertical wellbore core data 150 and wireline log data 152 . In some embodiments, the vertical wellbore core data 150 includes core data obtained by way of core samples extracted from vertical portions of wellbores of wells that extend into the formation 104 . For example, the vertical wellbore core data 150 may include core data obtained by way of core samples extracted from vertical wellbore portions of 10 different wells in a region that is proximate the well 106 and that extend into the formation 104 (e.g., core data for core samples extracted from vertical wellbores within a 10 kilometer radius of the well 106 ). In some embodiments, the core data include characteristics of the core samples measured in a core laboratory. For example, the core data may include density, porosity, or other characteristics of the core samples measured in a core laboratory at the surface 108 , after extraction of the core sample from their respective wellbores. The core data may be assembled to generate core logs that indicate characteristics of the formation 104 as a function of depth in the respective wellbores.” – [0028]; “In some embodiments, the wireline log data 152 includes characteristics of the rock of the formation 104 measured in-situ by way of a downhole logging tool. For example, the core data may include density, porosity, or other characteristics of the core samples measured by way of a logging tool that is operated to acquire measurements of surrounding portions of the formation 104 as the tool is advanced along the wellbore of the well. This may include use of a density logging tool to acquire measurements indicative of density of the formation 104 , a porosity logging tool to acquire measurements indicative of porosity of the formation 104 , a gamma ray logging tool to acquire measurements indicative of radioactivity of the formation 104 , and a resistivity logging tool to acquire measurements indicative of electrical resistivity of the formation 104 . In some embodiments, the wireline log data 152 includes at least four common types of logs (e.g., density, porosity, gamma ray, and resistivity) for each of the wellbores, and the logged wellbore portions include a horizontal wellbore portion that remains within a given (or “target”) depth interval, such as the depth interval (I) associated with the reservoir 102 . In some embodiments, the gamma ray log (e.g., including a log indicative of measured radioactivity of the formation 104 as function of depth) is utilized to distinguish sand and shale intervals (e.g., in a siliciclastics reservoir), the density log (e.g., including a log indicative of measured density of the formation 104 as function of depth) is utilized for lithology and porosity identification, the porosity log (e.g., including a log indicative of measured porosity of the formation 104 as function of depth) is used to identify the pay zones, and the resistivity log (e.g., including a log indicative of measured resistivity of the formation 104 as function of depth) is used to identify the fluid type.” – [0030])”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Khattab in view of Strebelle to incorporate the teaching of Algheryafi to utilize sensors in the wellbore to measure the porosity. By utilizing sensors in the wellbore to measure the porosity, this is an improvement that yields predictable results in the measurement of well log data and the evaluation of facies..
In regards to Claims 3, 11, and 19, Khattab in view of Strebelle and Algheryafi discloses the claimed invention as detailed above. Khattab is silent with regards to the language of “wherein the subset of wells include cored wells from which facies data are directly measured.”
Algheryafi further teaches “wherein the subset of wells include cored wells from which facies data are directly measured (“In some embodiments, the wireline log data 152 includes characteristics of the rock of the formation 104 measured in-situ by way of a downhole logging tool. For example, the core data may include density, porosity, or other characteristics of the core samples measured by way of a logging tool that is operated to acquire measurements of surrounding portions of the formation 104 as the tool is advanced along the wellbore of the well. This may include use of a density logging tool to acquire measurements indicative of density of the formation 104 , a porosity logging tool to acquire measurements indicative of porosity of the formation 104 , a gamma ray logging tool to acquire measurements indicative of radioactivity of the formation 104 , and a resistivity logging tool to acquire measurements indicative of electrical resistivity of the formation 104 . In some embodiments, the wireline log data 152 includes at least four common types of logs (e.g., density, porosity, gamma ray, and resistivity) for each of the wellbores, and the logged wellbore portions include a horizontal wellbore portion that remains within a given (or “target”) depth interval, such as the depth interval (I) associated with the reservoir 102 . In some embodiments, the gamma ray log (e.g., including a log indicative of measured radioactivity of the formation 104 as function of depth) is utilized to distinguish sand and shale intervals (e.g., in a siliciclastics reservoir), the density log (e.g., including a log indicative of measured density of the formation 104 as function of depth) is utilized for lithology and porosity identification, the porosity log (e.g., including a log indicative of measured porosity of the formation 104 as function of depth) is used to identify the pay zones, and the resistivity log (e.g., including a log indicative of measured resistivity of the formation 104 as function of depth) is used to identify the fluid type.” – [0030]).”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Khattab in view of Strebelle to incorporate the teaching of Algheryafi to utilize sensors in the wellbore to measure the porosity. By utilizing sensors in the wellbore to measure the porosity, this is an improvement that yields predictable results in the measurement of well log data and the evaluation of facies.
Claims 4-5, 12-13, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Khattab in view of Strebelle and Algheryafi as applied to claims 2, 10, and 18 above, and further in view of Sung (US20150279093)
In regards to Claims 4, 12, and 20, Khattab in view of Strebelle and Algheryafi discloses the claimed invention as detailed above. Khattab is silent with regards to the language of “generating, based on the 3D facies trend model, extended facies data for one or more wells of the plurality of wells that are not in the subset of wells that are associated with facies data.”
Sung teaches “generating, based on the 3D facies trend model, extended facies data for one or more wells of the plurality of wells that are not in the subset of wells that are associated with facies data (“Turning to FIG. 7A, FIG. 7A illustrates a 360-degree formation image log interpretation and 3D lithofacies model 7 A of an example well field based upon one or more 360-degrees formation image log interpretations according to an implementation. For example, 3D lithofacies modeling of wellbores 702 a , 704 a , and 706 a from 360-degree formation image log interpretations is illustrated in FIG. 7A. Using the 360-degree formation image log interpretations, additional lithofacies associated with other wells can be predicted even without 360-degree well core sample photo images, borehole image data, etc.” – [0045]).”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Khattab in view of Strebelle and Algheryafi to incorporate the teaching of Sung to utilize the model to predict facies data. By utilizing the model to determine facies data this is an improvement to the evaluation of reservoirs for hydrocarbons by improving the accuracy of the lithofacies interpretations associated with the reservoirs.
In regards to Claims 5 and 13, Khattab in view of Strebelle, Algheryafi, and Sung disclose the claimed invention as detailed above. Khattab is silent with regards to the language of “wherein the extended facies data represent facies that are present at each of the plurality of wells.”
Sung further teaches “wherein the extended facies data represent facies that are present at each of the plurality of wells (“Turning to FIG. 7A, FIG. 7A illustrates a 360-degree formation image log interpretation and 3D lithofacies model 7 A of an example well field based upon one or more 360-degrees formation image log interpretations according to an implementation. For example, 3D lithofacies modeling of wellbores 702 a , 704 a , and 706 a from 360-degree formation image log interpretations is illustrated in FIG. 7A. Using the 360-degree formation image log interpretations, additional lithofacies associated with other wells can be predicted even without 360-degree well core sample photo images, borehole image data, etc.” – [0045]).”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Khattab in view of Strebelle and Algheryafi to incorporate the teaching of Sung to utilize the model to predict facies data. By utilizing the model to determine facies data this is an improvement to the evaluation of reservoirs for hydrocarbons by improving the accuracy of the lithofacies interpretations associated with the reservoirs.
Claims 6 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Khattab in view of Strebelle as applied to claims 1 and 9 above, and further in view of Pyrcz (US20160103245).
In regards to Claims 6 and 14, Khattab in view of Strebelle discloses the claimed invention as detailed above. Khattab is silent with regards to the language of “determining, based on the 3D facies trend model, reservoir volumetrics data for the subsurface region; and
determining, based on the reservoir volumetrics data, a location in the subsurface region that includes hydrocarbon deposits.”
Pyrcz teaches “determining, based on the 3D facies trend model, reservoir volumetrics data for the subsurface region; and determining, based on the reservoir volumetrics data, a location in the subsurface region that includes hydrocarbon deposits (“The objective of reservoir modeling is to build 3D models of petrophysical properties (typically porosity, and permeability, and sometimes water saturation) that reservoir engineers can use to run flow simulations, forecast future hydrocarbon production and ultimate recovery, and design well development plans. In most geological environments, especially in clastic environments, porosity and permeability heterogeneity is primarily driven by facies depositional events. As such, porosity and permeability spatial distributions can be mainly characterized through the geometry and location of facies geobodies, for example sinuous sand channels. Therefore geomodelers very often first build 3D facies models (depositional facies, and sometimes lithofacies), and then populate porosity and permeability values within those models.” – [0002]; “Spatial trends, such as downwards decreasing porosity and permeability due to rock compaction or diagenesis or decreasing upwards porosity and permeability within a facies body due to waning energy in deposition, may be present in the reservoir. To account for such trends in reservoir models, petrophysical or facies input trend models need to be generated and imposed during the modeling process. 1D vertical trend curves and 2D horizontal trends maps are the most common trend models used to constrain reservoir models. Vertical trend curves provide a target petrophysical property average value or target facies proportion values in each layer of a grid of columns and layers in which a model is to be built. This may be further restricted by specific regions of the grid that are modeled separately. In each grid layer, the target property average value or the target facies proportion values can be based on a mean value of well data for that property in the layer, and edited by the modeler to address limited well data, data bias and analogue information. Furthermore, areal trend maps provide a target petrophysical average value or target facies proportion values along each column of a grid in which a model is to be built. In each column, such target values can be initialized as a mean value of well data in the column, or, if such well data is not present in the column, can be based on an interpolated average value based on previously computed columns, such as those columns including well data. This interpolation can be based, for example, on inverse distance or a kriging computation. A user, typically a geomodeler, can then edit the property areal trend map, particularly in areas away from well data. In some cases, 3D trend models can also be generated by calibrating secondary data available at each model cell, typically seismic attributes, to known well data, or by quantifying a reservoir stratigraphy interpretation. Such 3D models provide a prior local property average value, or prior local probabilities of facies occurrences in each cell of the grid in which a model is to be built. This trend model could vary from weakly informative (e.g. local proportions close to global proportions) to strongly informative and when the inter-well information supports, indicating a degree of certainty. This component of model conditioning is called an “input trend model” for the remainder of this document” – [0004]; “Referring to FIG. 1, generally, an example flowchart in which a method 100 of modeling of subsurface properties is disclosed, according to an example embodiment of the present disclosure. The method 100 generally can be performed by a computing system, such as the system described below in connection with FIG. 2, to model reservoir characteristics. In some embodiments, the method 100 can be used to model subsurface reservoir characteristics, such as facies or other characteristics (e.g., porosity, permeability, etc.) of underground volumes of interest, such as for hydrocarbon exploration, modeling, and forecasting.” – [0024]).”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Khattab in view of Strebelle to incorporate the teaching of Pyrcz to utilize the model to determine the location of hydrocarbons. By utilizing the model to determine facies data this is an improvement that yields predictable results in the evaluation and production of wellbores for hydrocarbons.
Claims 7 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Khattab in view of Strebelle and Pyrcz as applied to claims 6 and 14 above, and further in view of Algheryafi (US20220082729).
In regards to Claims 7 and 15, Khattab in view of Strebelle and Pyrcz discloses the claimed invention as detailed above. Khattab is silent with regards to the language of “drilling, at the location in the subsurface region, a well.”
Algheryafi teaches “drilling, at the location in the subsurface region, a well (“A hydrocarbon reservoir is a pool of hydrocarbons (e.g., oil or gas) trapped in a subsurface rock formation. Hydrocarbon wells are often drilled into hydrocarbon reservoirs to extract (or “produce”) the trapped hydrocarbons. These types of wells are typically drilled and operated in a manner to optimize production of hydrocarbons from a reservoir. For example, a reservoir is typically assessed to identify characteristics of the reservoir, the locations and trajectories (or “paths”) of wells to be drilled into the reservoir are determined based on the characteristics, the wells are drilled into the reservoir in accordance with the locations and trajectories, and the wells are operated to efficiently extract hydrocarbon production from the reservoir.” – [0002]; “In some embodiments, the modified facies model 164 ′ is used as a basis for determining parameters 170 for the well 106 . For example, a well parameter 170 of the horizontal wellbore of the well 106 may be determined based on the modified facies model 164 ′, and the well 106 may be developed in accordance with the parameter. The well parameter 170 may include, for example, a drilling parameter (e.g., a well trajectory) and the well 106 may be drilled in accordance with the drilling parameter (e.g., the well 106 may be drilled to follow the well trajectory).” – [0037]).”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Khattab in view of Strebelle and Pyrcz to incorporate the teaching of Algheryafi to drill a wellbore based on the location given by the model. By drilling a wellbore at the location the model gives, this is an improvement that yields predictable results in the optimization of hydrocarbon production.
Claims 8 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Khattab in view of Strebelle as applied to claims 1 and 9 above, and further in view of Thorne (US20060052938).
In regards to Claims 8 and 16, Khattab in view of Strebelle discloses the claimed invention as detailed above. Khattab further teaches “generating, based on the vertical proportion curves, the 3D facies model (“The plurality of 3D probability trends 1400 for the plurality of facies 404 may be generated based on a plurality of probability logs 1300 , each facies of the plurality of facies 404 associated with a respective probability log of the plurality of probability logs 1300 , such as show in FIG. 13 . The 3D stratigraphic sequence framework 299 may be generated comprising a plurality of sequences 300 based on the one or more sequences of strata of the sequence stratigraphy, wherein each sequence of the plurality of sequences comprises at least one facies from the plurality of facies 404 for the subject reservoir, and each 3D probability trend 1400 may be generated for each facies 404 of each sequence 301 , 302 , 303 of the plurality of sequences 300 based on the respective probability log 1300 for the facies 404 for each sequence 301 , 302 , 303 of the plurality of sequences 300 . The probability log 1300 for each facies 404 may be modeled in 3D using kriging testing different connectivity ranges guided by variogram analysis for sensitivity. Vertical proportion curve and two-dimensional (2D) trend maps for each stratigraphic interval may be used as additional inputs to the 3D probability trend 1400” – [0045]).”
Khattab is silent with regards to the language of “detecting, in the facies data of well log data, vertical trends in facies in the subsurface region; generating vertical proportion curves from the vertical trends in the facies data;”
Thorne teaches “detecting, in the facies data of well log data, vertical trends in facies in the subsurface region; generating vertical proportion curves from the vertical trends in the facies data (“An overall or composite vertical proportion graph/data is then created by combining the vertical proportion graphs computed using respectively the facies well log data, the digitized geological cross-sections, and the vertical proportion section. Each vertical proportion graph can be weighted in accordance with the certainty that that particular graph accurately represents the vertical facies trends or distributions of facies.” – [0064])”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Khattab in view of Strebelle to incorporate the teaching of Thorne to detect and evaluate the vertical trends in facies with vertical proportion graphs. By detecting and utilizing the vertical trends with vertical proportion graphs, this is an improvement that yields predictable results in the evaluation of the facies in reservoirs.
Examiner’s Note
The following prior art are considered to be of interest to the instant application.
Xie (YuLong Xie, et. al., “Surface-Geometry and Trend Modeling for Integration of Stratigraphic Data in Reservoir Models”, SPE 68817, March 2001, https://onepetro.org/SPEWRM/proceedings/01WRM/01WRM/SPE-68817-MS/133322) teaches a method of accurate prediction of reservoir performance that depends on the estimation of the subsurface structure including lithofacies.
Gkortsas (US20220146705) teaches a classification method of facies with a model.
Strebelle’933 (US20160048933) teaches a method of trend modeling of subsurface properties.
Strebelle’942 (US20250298942) teaches a method of quantile trained machine learning with model of a formation property across subsurface formations of a reservoir.
Conclusion
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/YOSSEF KORANG-BEHESHTI/ Primary Examiner, Art Unit 2857
