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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 12/22/2025 has been entered.
Response to Arguments
Claims 1-15 are pending in the application, independent claim 1 has been amended, and claims 2, 4, 7, 9, 11-13, and 15 have been cancelled.
Applicant’s arguments on pages 6 and 7, filed on 12/22/2025, with respect to U.S.C 101 rejection of claims 1-15 have been fully considered and are persuasive. The claim rejections with respect to 1-15 have been withdrawn.
Applicant’s arguments on page 7-10, filed on 12/22/2025, with respect to U.S.C 103 rejection of claims 1-15 have been fully considered and are not persuasive. The rejection based on the amended claims are below.
Applicant argues that Romero fails to disclose calculating a Pearson's correlation coefficient (r) between two variables for the identified well intervals, wherein the two variables are photoelectric factor and effective porosity by nuclear magnetic resonance. Applicant argues that Romero does not disclose calculating a Pearson's correlation coefficient (r) between two variables for the identified well intervals, and Romero does not disclose two variables are photoelectric factor and effective porosity by nuclear magnetic resonance.
Examiner respectfully disagrees. Romero is show to calculate Pearson’s correlation coefficient for multiple different variables and states in [0040]- [0041] “Exemplary functions of the software include performing a search for similarities (i.e., a correlation) between at least one of T.sub.2 bins and D bins with other data of various types (e.g. depth logs providing data associated with SP, GR, measurements for at least one of acoustic, seismic, neutron activation, natural radioactivity, induced gamma radioactivity (e.g., for density tool data), resistivity, mud logging, formation lithology and saturation (of water, hydrocarbon, and/or other such materials) and other quantities). Correlation may also be performed against spot samples (such as grab samples of fluids which are subsequently analyzed on the surface). In some embodiments applying one-dimensional (1D) analyses, the software calculates correlations and the probability values (p-Values) of the correlation based on the Pearson Correlation equation (provided herein as Eq. (2) below).” For the variable of photoelectric factor, Romero discusses in [0040] (read above), that induced gamma radioactivity is one of the variables that can be used in Pearson’s coefficients. One of ordinary skill in the art would know and appreciate that photoelectric factor is a measured value from gamma radioactivity, specifically photoelectric factor is how well a rock absorbs low-energy gamma rays, and the absorption of low-energy gamma rays would be measured by the induced gamma radioactivity. For the variable of effective porosity by nuclear magnetic resonance, Romero in the abstract mentions that they are correlation information collected by nuclear magnetic resonance. Furthermore, in [0040] Romero states one of the variables for Pearson’s coefficient to be saturation, saturation is calculated from archies equation which includes effective porosity, see [0032-0039]. Although Romero does not explicitly state that effective porosity is one of the variables to be used. The examiner believes one of ordinary skill in the art, following Romero’s teachings, would be fully capable of using effective porosity data provided by the data set as a variable of Pearson’s coefficient. For at least these reasons listed above, Applicant’s argument is unpersuasive.
Applicant argues that Romero fails to disclose or suggest defining Pearson's correlation coefficient (r) limit values for classifying the identified well intervals with diagenetic patterns based on the Pearson's correlation coefficient (r). Specifically, because the range provided in Pearson’s correlation is considered by the applicant to be too broad, and second because the applicant argues that Romero does not discuss the diagenetic pattern of rocks.
Examiner respectfully disagrees. Romero discloses defining Pearson correlation coefficient limit values, between -1 to 1 in [0049]. These coefficient limit values although broad are not specified in the limitations to need to be smaller at -1 to 1 is a limiting range that provides valuable information of the variables of the rock measurements being compared. Romero states in [0047] “Note that as discussed herein, the Pearson coefficient (sometimes known as the Pearson product-moment correlation coefficient, or PMCC) is a measure of the correlation of two variables (e.g., X and Y), where the variables are measured with relation to the same object or organism. The Pearson coefficient is generally denoted as (p), and provides a measure of a tendency of the variables to increase or decrease together (i.e., correlate with each other), where a degree of the tendency is generally defined by a user.” There is no limitation that indicating that the correlation limit values must fall within a certain range, simply that they must identify diagenetic rocks. Limitations that are not claimed are not addressed by the rejection. For at least these reasons, Applicant’s argument is unpersuasive. Furthermore, Romero is looking at the material makeup of the rocks and fluids in logging data. With regards to discussing diagenetic patterns of rocks based on the Pearson’s coefficient; diagenetic patterns in rocks are physical, chemical, and biological alterations occurring after deposition, converting sediments into rock (lithification) through compaction, cementation, and recrystallization. Therefore, as long as the variables used in Pearson’s correlation are relevant to understanding the material makeup of the rocks, the diagenetic patterns of the rocks can be identified. Romero provides a list of variables that identify information of a rocks material makeup, i.e., diagenetic pattern in [0040] “Exemplary functions of the software include performing a search for similarities (i.e., a correlation) between at least one of T.sub.2 bins and D bins with other data of various types (e.g. depth logs providing data associated with SP, GR, measurements for at least one of acoustic, seismic, neutron activation, natural radioactivity, induced gamma radioactivity (e.g., for density tool data), resistivity, mud logging, formation lithology and saturation (of water, hydrocarbon, and/or other such materials) and other quantities). Correlation may also be performed against spot samples (such as grab samples of fluids which are subsequently analyzed on the surface).” For at least these reasons, Applicant’s argument is unpersuasive.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 3, 6, 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over of Ali (WO 2021/221697 A1) in view of Romero (US 2009/0037109 A1) and Liu (WO 2021/177982 A1) and further in view of Al-Najm (Al-Najm, F.M., Determination of Horizontal Stress Orientations from Borehole Breakouts in Zubair Oilfield, Southern Iraq, 2019, International Journal of Mining Science, Volume 5, Issue 2, pg. 14-17).
Regarding Claim 1, Ali teaches, a) selecting the electrical profiles measured from well intervals of carbonate rocks ([0019], “a logging tool may be lowered into the wellbore (104) to acquire measurements as the tool traverses a depth interval (130) (e.g., targeted reservoir section) of the wellbore (104).” Fig. 1, where the measurements acquired are the NMR electrical profiles. Then, “the resulting logging measurements may be stored and/or processed, for example, by the control system (114), to generate corresponding well logs (140) for the well (102).” [0019], where to process or store a profile would be to use the electrical profile and thus the electric profile would need to be selected, where [0024] “As such, the output of the reservoir simulator (160) may be a near real-time reservoir/non-reservoir distribution map of the formation (106) in dolomitized carbonates (i.e., intervals of carbonate rocks.”); b) assessing the well intervals ([0019], “a logging tool may be lowered into the wellbore (104) to acquire measurements (i.e. assessing) as the tool traverses a depth interval (130) (i.e. the well interval) (e.g., targeted reservoir section) of the wellbore (104).”; Fig. 1, Depth interval 130, where, “the plot of the logging measurements versus depth may be referred to as a "log" or "well log",”[0019], and where multiple of these well logs are taken per well “well logs of a new well X (531).” Thus, implying that the depth interval can change per well log implying there is a plurality of depth intervals per well.)
Ali does not teach wherein assessing the well intervals comprises identifying well intervals with stable, no indication of caliper breakouts, and/or no infiltration of drilling fluid; c) calculating the Pearson's correlation coefficient between two variables for the identified well intervals, wherein the two variables are the photoelectric factor and effective porosity by nuclear magnetic resonance, d) defining Pearson’s correlation coefficient (r) limit values for classifying the identified well intervals with diagenetic patterns based on the Pearson’s correlation coefficient (r), e) determining the diagenetic patterns for the identified well intervals based on the calculated Pearson's correlation coefficient (r) for the identified well intervals and the defined Pearson's correlation coefficient (r) limit values, f) generating based on the calculated Pearson’s correlation coefficient (r), an analytical process and/or a computational solution configured for anticipating the determined diagenetic pattern for at least one identified well interval.
Romero teaches, c) calculating the Pearson's correlation coefficient between two variables for the identified well intervals ([0047] “the Pearson coefficient (sometimes known as the Pearson product-moment correlation coefficient, or PMCC) is a measure of the correlation of two variables”) wherein the two variables are the photoelectric factor ([0040] “measurements for at least one of acoustic, seismic, neutron activation, natural radioactivity, induced gamma radioactivity” where photoelectric factor is a measured value from gamma radioactivity, specifically photoelectric factor is how well a rock absorbs low-energy gamma rays.) and effective porosity by nuclear magnetic resonance ([0034] “
ϕ
represents porosity” where it is used in archies equation [0032] “An exemplary source of information for correlation to at least one of logs of the bins of the T.sub.2 distribution and logs of the bins of the D distribution includes the Archie Equation, presented as Eq. (1):”), where porosity is collected via nuclear magnetic resonance. Where the two measurements are used in the discussed invention designed [0008] “for correlating nuclear magnetic resonance(NMR) well logging data with other well logging data”, where [0040] “Exemplary functions of the software include performing a search for similarities (i.e., a correlation) between at least one of T.sub.2 bins and D bins with other data of various types (e.g. depth logs providing data associated with SP, GR, measurements for at least one of acoustic, seismic, neutron activation, natural radioactivity, induced gamma radioactivity (e.g., for density tool data), resistivity, mud logging, formation lithology and saturation (of water, hydrocarbon, and/or other such materials) and other quantities). Correlation may also be performed against spot samples (such as grab samples of fluids which are subsequently analyzed on the surface).” And [0041] “In some embodiments applying one-dimensional (1D) analyses, the software calculates correlations and the probability values (p-Values) of the correlation based on the Pearson Correlation equation (provided herein as Eq. (2) below). Reference may be had to FIGS. 2 and 3.”) d) defining Pearson’s correlation coefficient (r) limit values for diagenetic patterns based on the Pearson’s correlation coefficient (r) ([0049] “The Pearson coefficient ranges from -1 to 1. A value of 1 shows that a linear equation describes the relationship perfectly and positively, with all data points lying on the same line and with Y increasing with X. A score of -1 shows that all data points lie on a single line but that Y increases as X decreases. A value of 0 shows that a linear model is inappropriate-that there is no linear relationship between the variables. (i.e., determining limit value of Pearson’s correlation coefficient)” where the diagenetic pattern is used to determine [0032-0039] An exemplary source of information for correlation (i.e., would be able to classify the correlation of the diagenetic pattern) to at least one of logs of the bins of the T 2 distribution and logs of the bins of the D distribution includes the Archie Equation, presented as Eq. (1):
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where: Sw represents water saturation; cp represents porosity; Rw represents formation water resistivity; R, represents observed bulk resistivity; a represents a constant ( often taken to be 1 ); m represents a cementation factor (varies about 2); and, n represents a saturation exponent (generally about 2) (i.e., cementation and saturation are types diagenetic processes that create) e) calculated Pearson's correlation coefficient (r) for the identified well intervals and the defined Pearson's correlation coefficient (r) limit values ([0018] “FIG. 1 shows a well logging apparatus disposed in a wellbore 22 penetrating earth formations 23, 24, 26, 28 for making measurements of properties of the earth formations 23, 24, 26, 28 downhole (i.e., each measurement at a new depth is a well interval well log).” and [0049] “The Pearson coefficient ranges from -1 to 1. A value of 1 shows that a linear equation describes the relationship perfectly and positively, with all data points lying on the same line and with Y increasing with X. A score of -1 shows that all data points lie on a single line but that Y increases as X decreases. A value of 0 shows that a linear model is inappropriate-that there is no linear relationship between the variables. (i.e., determining limit value of Pearson’s correlation coefficient)” where the diagenetic pattern is used to determine [0032-0039] An exemplary source of information for correlation (i.e., would be able to classify the correlation of the diagenetic pattern) to at least one of logs of the bins of the T 2 distribution and logs of the bins of the D distribution includes the Archie Equation, presented as Eq. (1):
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where: Sw represents water saturation; cp represents porosity; Rw represents formation water resistivity; R, represents observed bulk resistivity; a represents a constant ( often taken to be 1 ); m represents a cementation factor (varies about 2); and, n represents a saturation exponent (generally about 2) (i.e., cementation and saturation are types diagenetic processes that create).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to “calculate the Pearson’s correlation coefficient between the two variables, in this case, the photoelectric factor and the effective porosity by nuclear magnetic resonance” using the Pearson's coefficient and measuring techniques described in Romero to the well intervals and diagenetic patterns discussed in Ali for the purpose of having a statistic which estimates the correlation of two variables that might on an initial investigation appear to be random and without interaction but actually have a significant correlation between the variables. It is advantageous because “in the case of heavy oil, the overlap of the oil distribution with at least one of clay bound water and capillary bound water is very common. When there is no significant contrast in the diffusivity between the fluids, the fluid typing represents a difficult problem to solve, because in presence of a magnetic field gradient, the distribution of the transverse relaxation time, T2 , for the various fluids present in the wellbore overlaps and cannot easily be cancelled.” [0006, Romero].
Ali and Romero do not teach d) classifying the identified well intervals with diagenetic patterns; e) determining the diagenetic patterns for the identified well intervals, and wherein assessing the well intervals comprises identifying well intervals with stable, no indication of caliper breakouts, and/or no infiltration of drilling fluid, f) generating based on the calculated Pearson’s correlation coefficient (r), an analytical process and/or a computational solution configured for anticipating the determined diagenetic pattern for at least one identified well interval.
Liu teaches, d) classifying the identified well intervals with diagenetic patterns ([0034] “a predetermined permeability value may correspond to a cut-off value for different types of rock, such as different diagenetic rock types”; [0022] “a depth interval (130) (e.g., targeted reservoir section) of the wellbore (104).”) e) determining the diagenetic patterns for the identified well intervals ([0034], “For examples, a predetermined permeability value may correspond to a cut-off value for different types of rock, such as different diagenetic rock types.” Where the “cut-off value” is the “interval thickness filter”; [0022] “a depth interval (130) (e.g., targeted reservoir section) of the wellbore (104).”), f) generating based on the calculated Pearson’s correlation coefficient (r), an analytical process and/or a computational solution configured for anticipating the determined diagenetic pattern for at least one identified well interval. ([0036] “Here, two lines within the diagram correspond to two separate permeability thresholds as a function of variable porosity values. The permeability thresholds (302, 303) divide data points of carbonate rocks into three subsets (e.g., a non-reservoir region, fair and good reservoirs regions).” Where [0034] “For examples, a predetermined permeability value may correspond to a cut-off value for different types of rock, such as different diagenetic rock types.” Where a graphical solution is both an analytical and computational solutions; [0022] “a depth interval (130) (e.g., targeted reservoir section) of the wellbore (104).”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the classification and determination of the identified well intervals with diagenetic patterns as found in Liu to the measurements discussed in Ali and Romero for the purpose of classifying the type of rock and fluid in the formation. It is advantageous because “Where many rock classification schemes focus primarily on classifying depositional lithofacies based on a porosity-versus-permeability analysis, the effects of diagenesis (e.g., dolomitization) on reservoir quality are often ignored.” [0018, Liu] and using the information provided in steps d and e “a hydrocarbon trap (i.e., a geological formation underground where oil and natural gas are trapped) may be identified within a geological region of interest.” [0018, Liu].
Ali, Romero and Liu fail to teach wherein assessing the well intervals comprises identifying well intervals with stable, no indication of caliper breakouts, and/or no infiltration of drilling fluid.
Al-Najm teaches, wherein assessing the well intervals comprises identifying well intervals with stable, no indication of caliper breakouts, and/or no infiltration of drilling fluid
(pg. 14 “Borehole breakouts and enlargement are one of the crucial issues during drilling, it can reduce the drill bit life, stuck pipe, logging problems, bad cementing and often the need to sidetrack (Last, 2001). Failure of the borehole walls creates intervals with noncircular cross sections, which has long axes at the same orientation.” where it is discussed that caliper breakouts are a negative thing to have occur in the well, thus indicating that one of ordinary skill in the art would want to use a wellhole in which there is no breakout occurring to the well walls.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the knowledge of borehole breakouts that occur during drill as mentioned in Al-Najm to the diagenetic rock testing in wells discussed in Liu in view of Ali in order to avoid a failure in the borehole walls. This is advantageous because “borehole breakouts and enlargement are one of the crucial issues during drilling, it can reduce the drill bit life, stuck pipe, logging problems, bad cementing and often the need to sidetrack” (introduction pg. 14, Al-Najm).
Regarding Claim 3, Ali, Romero, Liu and Al-Najm teach the limitations of Claim 1.
Ali fails to teach, wherein the well intervals are identifiable using borehole profile and anomalies with values of porosity and photoelectric factor.
Romero teaches the photoelectric factor, ([0040] “measurements for at least one of acoustic, seismic, neutron activation, natural radioactivity, induced gamma radioactivity” where photoelectric factor is a measured value from gamma radioactivity, specifically photoelectric factor is how well a rock absorbs low-energy gamma rays.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to “calculate the Pearson’s correlation coefficient between the two variables, in this case, the photoelectric factor and the effective porosity by nuclear magnetic resonance” using the Pearson's coefficient and measuring techniques described in Romero to the well intervals and diagenetic patterns discussed in Ali for the purpose of having a statistic which estimates the correlation of two variables that might on an initial investigation appear to be random and without interaction but actually have a significant correlation between the variables. It is advantageous because “in the case of heavy oil, the overlap of the oil distribution with at least one of clay bound water and capillary bound water is very common. When there is no significant contrast in the diffusivity between the fluids, the fluid typing represents a difficult problem to solve, because in presence of a magnetic field gradient, the distribution of the transverse relaxation time, T2 , for the various fluids present in the wellbore overlaps and cannot easily be cancelled.” [0006, Romero].
Ali and Romero do not teach, wherein the well intervals are identifiable using borehole profile and anomalies with values of porosity.
Liu teaches, wherein the well intervals are identifiable using borehole profile and anomalies with values of porosity. ([0038] “data points from well logs and core samples (i.e., borehole profiles) that fall on a permeability threshold may be extracted, except where the data points include a dolomite fraction of zero (i.e., corresponding to pure limestone) (i.e., anomalies). These extracted data points may be projected into a porosity-versus-dolomite-volume fraction diagram, similar to the ones shown in FIGs. 5.1 and 5.2 below. For example, data points with a dolomite volume fraction value of zero (e.g., pure limestone) may have large variations in porosity values.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the classification and determination of the identified well intervals with diagenetic patterns as found in Liu to the measurements discussed in Ali and Romero for the purpose of classifying the type of rock and fluid in the formation. It is advantageous because “Where many rock classification schemes focus primarily on classifying depositional lithofacies based on a porosity-versus-permeability analysis, the effects of diagenesis (e.g., dolomitization) on reservoir quality are often ignored.” [0018, Liu] and using the information provided in steps d and e “a hydrocarbon trap (i.e., a geological formation underground where oil and natural gas are trapped) may be identified within a geological region of interest.” [0018, Liu].
Regarding Claim 6, Ali, Romero, Liu and Al-Najm teach the limitation of Claim 1.
Ali does not teach, further comprising defining Pearson's correlation coefficient (r) limit values for classifying the identified well intervals as uncertainty zones, rather than with diagenetic patterns based on the Pearsons’ correlation coefficient (r).
Romero teaches, further comprising defining Pearson’s correlation coefficient (r) limit values for classifying the identified well intervals based on the Pearsons’ correlation coefficient (r) ([0049] “The Pearson coefficient ranges from -1 to 1. A value of 1 shows that a linear equation describes the relationship perfectly and positively, with all data points lying on the same line and with Y increasing with X. A score of -1 shows that all data points lie on a single line but that Y increases as X decreases. A value of 0 shows that a linear model is inappropriate-that there is no linear relationship between the variables. (i.e., determining limit value of Pearson’s correlation coefficient)” where diagenetic patterns are compared [0032-0039] An exemplary source of information for correlation (i.e., would be able to classify the correlation of the diagenetic pattern) to at least one of logs of the bins of the T 2 distribution and logs of the bins of the D distribution includes the Archie Equation, presented as Eq. (1):
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where: Sw represents water saturation; cp represents porosity; Rw represents formation water resistivity; R, represents observed bulk resistivity; a represents a constant ( often taken to be 1 ); m represents a cementation factor (varies about 2); and, n represents a saturation exponent (generally about 2) (i.e., cementation and saturation are types diagenetic processes that create)”, and [0050] “The Pearson coefficient is a statistic which estimates the correlation of the two given random variables,”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to “calculate the Pearson’s correlation coefficient between the two variables, in this case, the photoelectric factor and the effective porosity by nuclear magnetic resonance” using the Pearson's coefficient and measuring techniques described in Romero to the well intervals and diagenetic patterns discussed in Ali for the purpose of having a statistic which estimates the correlation of two variables that might on an initial investigation appear to be random and without interaction but actually have a significant correlation between the variables. It is advantageous because “in the case of heavy oil, the overlap of the oil distribution with at least one of clay bound water and capillary bound water is very common. When there is no significant contrast in the diffusivity between the fluids, the fluid typing represents a difficult problem to solve, because in presence of a magnetic field gradient, the distribution of the transverse relaxation time, T2 , for the various fluids present in the wellbore overlaps and cannot easily be cancelled.” [0006, Romero].
Ali and Romero do not teach classifying the identified well intervals as uncertainty zones, rather than with diagenetic patterns.
Liu further teaches, classifying the identified well intervals as uncertainty zones, rather than with diagenetic patterns, ([0038], “For example, data points with a dolomite volume fraction value of zero (e.g., pure limestone) may have large variations in porosity values. Linear regressions of the extracted data points can thus generate a boundary line for a reservoir region.” Where the use of linear regression gives values as uncertainties instead of exact patterns because linear regression incorporates uncertainties, which are typically represented by confidence intervals around the regression line, reflecting the range within which the true population values of the predicted variable are likely to fall based on the data.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the classification and determination of the identified well intervals with diagenetic patterns as found in Liu to the measurements discussed in Ali and Romero for the purpose of classifying the type of rock and fluid in the formation. It is advantageous because “Where many rock classification schemes focus primarily on classifying depositional lithofacies based on a porosity-versus-permeability analysis, the effects of diagenesis (e.g., dolomitization) on reservoir quality are often ignored.” [0018, Liu] and using the information provided in steps d and e “a hydrocarbon trap (i.e., a geological formation underground where oil and natural gas are trapped) may be identified within a geological region of interest.” [0018, Liu].
Regarding Claim 8, Ali, Romero, Liu and Al-Najm teach the limitation of Claim 3.
Ali does not teach, further comprising defining Pearson's correlation coefficient (r) limit values for classifying the identified well intervals as uncertainty zones, rather than with diagenetic patterns based on the Pearsons’ correlation coefficient (r).
Romero teaches, further comprising defining Pearson’s correlation coefficient (r) limit values for classifying the identified well intervals based on the Pearsons’ correlation coefficient (r) ([0049] “The Pearson coefficient ranges from -1 to 1. A value of 1 shows that a linear equation describes the relationship perfectly and positively, with all data points lying on the same line and with Y increasing with X. A score of -1 shows that all data points lie on a single line but that Y increases as X decreases. A value of 0 shows that a linear model is inappropriate-that there is no linear relationship between the variables. (i.e., determining limit value of Pearson’s correlation coefficient)” where diagenetic patterns are compared [0032-0039] An exemplary source of information for correlation (i.e., would be able to classify the correlation of the diagenetic pattern) to at least one of logs of the bins of the T 2 distribution and logs of the bins of the D distribution includes the Archie Equation, presented as Eq. (1):
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where: Sw represents water saturation; cp represents porosity; Rw represents formation water resistivity; R, represents observed bulk resistivity; a represents a constant ( often taken to be 1 ); m represents a cementation factor (varies about 2); and, n represents a saturation exponent (generally about 2) (i.e., cementation and saturation are types diagenetic processes that create)”, and [0050] “The Pearson coefficient is a statistic which estimates the correlation of the two given random variables,”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to “calculate the Pearson’s correlation coefficient between the two variables, in this case, the photoelectric factor and the effective porosity by nuclear magnetic resonance” using the Pearson's coefficient and measuring techniques described in Romero to the well intervals and diagenetic patterns discussed in Ali for the purpose of having a statistic which estimates the correlation of two variables that might on an initial investigation appear to be random and without interaction but actually have a significant correlation between the variables. It is advantageous because “in the case of heavy oil, the overlap of the oil distribution with at least one of clay bound water and capillary bound water is very common. When there is no significant contrast in the diffusivity between the fluids, the fluid typing represents a difficult problem to solve, because in presence of a magnetic field gradient, the distribution of the transverse relaxation time, T2 , for the various fluids present in the wellbore overlaps and cannot easily be cancelled.” [0006, Romero].
Ali and Romero do not teach classifying the identified well intervals as uncertainty zones, rather than with diagenetic patterns.
Liu further teaches, classifying the identified well intervals as uncertainty zones, rather than with diagenetic patterns, ([0038], “For example, data points with a dolomite volume fraction value of zero (e.g., pure limestone) may have large variations in porosity values. Linear regressions of the extracted data points can thus generate a boundary line for a reservoir region.” Where the use of linear regression gives values as uncertainties instead of exact patterns because linear regression incorporates uncertainties, which are typically represented by confidence intervals around the regression line, reflecting the range within which the true population values of the predicted variable are likely to fall based on the data.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the classification and determination of the identified well intervals with diagenetic patterns as found in Liu to the measurements discussed in Ali and Romero for the purpose of classifying the type of rock and fluid in the formation. It is advantageous because “Where many rock classification schemes focus primarily on classifying depositional lithofacies based on a porosity-versus-permeability analysis, the effects of diagenesis (e.g., dolomitization) on reservoir quality are often ignored.” [0018, Liu] and using the information provided in steps d and e “a hydrocarbon trap (i.e., a geological formation underground where oil and natural gas are trapped) may be identified within a geological region of interest.” [0018, Liu].
Claim(s) 5, 10, and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over of Ali, Romero, Liu, Al-Najm, and further in view of Alkhaldi (US 2022/0003891 A1).
Regarding Claim 5, Ali, Romero, Liu and Al-Najm teach the limitation of Claim 1.
Ali does not teach, wherein the intervals with Pearson’s correlation coefficients (r) greater than zero are classified in positive correlation pattern representing a normal correlation pattern with cementation or dissolution of pore space and the identified well intervals with Pearson’s correlation coefficients (r) lower than zero are classified in negative correlation pattern representing, an inverse diagenetic pattern with cementation of the pore space and selective dissolution of scaffold grains.
Romero teaches wherein the intervals with Pearson’s correlation coefficients (r) greater than zero are classified in positive correlation pattern representing a normal correlation pattern and the identified well intervals with Pearson’s correlation coefficients (r) lower than zero are classified in negative correlation pattern representing, an inverse diagenetic pattern ([0049], “The Pearson coefficient ranges from −1 to 1. A value of 1 shows that a linear equation describes the relationship perfectly and positively (i.e., often reffered to as normal or positive correlation patterns, with all data points lying on the same line and with Y increasing with X. A score of −1 shows that all data points lie on a single line but that Y increases as X decreases. A value of 0 shows that a linear model is inappropriate—that there is no linear relationship between the variables.”, and in [0046] “In FIG. 10, the distribution of the diffusivity, D, versus the distribution of the transverse relaxation time, T 2, is provided. The depiction includes an overlay of the Pearson correlation for the T 2 bins.” Where the transverse relaxation time T2 is related to fluid mobility due to pore size in a scaffold, and diffusivity indicates the fluid mobility due to type of fluid. Therefore the +1 Pearson’s coefficient, or the positive, normal, correlation regions depicted in Fig. 10 are regions of the formation which that D and T2 trend the same direction, the regions of -1 Pearson’s coefficients, or the negative, inverse correlation are regions of the formation of which D and T trend in opposing directions.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the relationship (positive correlation and negative correlations) of Pearson’s coefficient ranges found in Romero to the method for determining diagenetic patterns in carbonate rocks by resonance and photoelectric factor profiles discussed in Ali for the purpose of having a statistic which estimates the positive or negative correlation of two variables. It is advantageous because “Fluid typing analyses of distributions of relaxation time, T, ( e.g., the transverse relaxation time, T2 , as well as the longitudinal relaxation time, T1) faces the problem of discriminating individual contributions of water and hydrocarbons, especially when signals from the water and hydrocarbons may not be easily discriminated from each other,” [0006, Romero], therefore “techniques for correlating logging information for at least the transverse relaxation time, T2 , the longitudinal relaxation time, Ti, and the diffusivity, D, with other types of logging data,” [0007, Romero] are needed.
Ali, Romero, Liu and Al-Najm do not teach, cementation or dissolution of pore space, and cementation of the pore space and selective dissolution of scaffold grains.
Alkhaldi teaches , cementation or dissolution of pore space, and cementation of the pore space and selective dissolution of scaffold grains ([0053] “The rocks are investigated in terms of pore types and cementation (step 156). Step 156 represents a diagenetic processes that has occurred in the meteoric and shallow burial realms. In step 156, rocks are separated based on whether or not they show dissolution feature (i.e., whether or not the scaffold grains, grains making the structure of the formation rock, are being selectively dissolved) and whether or not they have been subjected to full cementation and have no pore spaces. For example, some dissolution features include moldic porosity (MO), Vugy porosity, and Intraparticle particles porosity (WP). Rocks are sorted based on pore space in terms of whether or not the rocks show preservation of primary sedimentary structure (step 158). For example, in step 158, rocks preserving sedimentary structure, such as cross bedding and parallel lamination, are separated from rocks that show more bioturbation. Bioturbation is considered a diagenetic feature since it occurs after the precipitation of the sediments. Rocks are sorted based on whether or not they show cross stratification or lamination (step 160).”; Fig. 3A Meteoric to shallow Dissolution and Cementation).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to classifying rocks based on the diagenetic patterns via dissolution and cementation taught in Alkhaldi the method for determining and classifying diagenetic patterns in carbonate rocks by resonance and photoelectric factor profiles discussed in Ali, Romero, Liu and Al-Najm for the purpose of being able to classify cementation and dissolution classifications and compare them using Pearson’s Correlation Coefficient. This is advantageous because “classification and grouping of carbonate rock is important for assessing and modeling petrophysical properties of a reservoir,” [0003].
Regarding Claim 10, Ali, Liu and Romero in view of Alkhaldi teach the limitations of Claim 5.
Ali does not teach, further comprising defining Pearson's correlation coefficient (r) limit values for classifying the identified well intervals as uncertainty zones, rather than with diagenetic patterns based on the Pearsons’ correlation coefficient (r).
Romero teaches, further comprising defining Pearson’s correlation coefficient (r) limit values for classifying the identified well intervals based on the Pearsons’ correlation coefficient (r) ([0049] “The Pearson coefficient ranges from -1 to 1. A value of 1 shows that a linear equation describes the relationship perfectly and positively, with all data points lying on the same line and with Y increasing with X. A score of -1 shows that all data points lie on a single line but that Y increases as X decreases. A value of 0 shows that a linear model is inappropriate-that there is no linear relationship between the variables. (i.e., determining limit value of Pearson’s correlation coefficient)” where diagenetic patterns are compared [0032-0039] An exemplary source of information for correlation (i.e., would be able to classify the correlation of the diagenetic pattern) to at least one of logs of the bins of the T 2 distribution and logs of the bins of the D distribution includes the Archie Equation, presented as Eq. (1):
PNG
media_image1.png
33
4
media_image1.png
Greyscale
where: Sw represents water saturation; cp represents porosity; Rw represents formation water resistivity; R, represents observed bulk resistivity; a represents a constant ( often taken to be 1 ); m represents a cementation factor (varies about 2); and, n represents a saturation exponent (generally about 2) (i.e., cementation and saturation are types diagenetic processes that create)”, and [0050] “The Pearson coefficient is a statistic which estimates the correlation of the two given random variables,”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to “calculate the Pearson’s correlation coefficient between the two variables, in this case, the photoelectric factor and the effective porosity by nuclear magnetic resonance” using the Pearson's coefficient and measuring techniques described in Romero to the well intervals and diagenetic patterns discussed in Ali for the purpose of having a statistic which estimates the correlation of two variables that might on an initial investigation appear to be random and without interaction but actually have a significant correlation between the variables. It is advantageous because “in the case of heavy oil, the overlap of the oil distribution with at least one of clay bound water and capillary bound water is very common. When there is no significant contrast in the diffusivity between the fluids, the fluid typing represents a difficult problem to solve, because in presence of a magnetic field gradient, the distribution of the transverse relaxation time, T2 , for the various fluids present in the wellbore overlaps and cannot easily be cancelled.” [0006, Romero].
Ali and Romero do not teach classifying the identified well intervals as uncertainty zones, rather than with diagenetic patterns.
Liu further teaches, classifying the identified well intervals as uncertainty zones, rather than with diagenetic patterns, ([0038], “For example, data points with a dolomite volume fraction value of zero (e.g., pure limestone) may have large variations in porosity values. Linear regressions of the extracted data points can thus generate a boundary line for a reservoir region.” Where the use of linear regression gives values as uncertainties instead of exact patterns because linear regression incorporates uncertainties, which are typically represented by confidence intervals around the regression line, reflecting the range within which the true population values of the predicted variable are likely to fall based on the data.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the classification and determination of the identified well intervals with diagenetic patterns as found in Liu to the measurements discussed in Ali and Romero for the purpose of classifying the type of rock and fluid in the formation. It is advantageous because “Where many rock classification schemes focus primarily on classifying depositional lithofacies based on a porosity-versus-permeability analysis, the effects of diagenesis (e.g., dolomitization) on reservoir quality are often ignored.” [0018, Liu] and using the information provided in steps d and e “a hydrocarbon trap (i.e., a geological formation underground where oil and natural gas are trapped) may be identified within a geological region of interest.” [0018, Liu].
Regarding Claim 14, Ali, Romero, Liu and Al-Najm teach the limitation of Claim 3.
Ali does not teach, wherein the intervals with Pearson’s correlation coefficients (r) greater than zero are classified in positive correlation pattern representing a normal correlation pattern with cementation or dissolution of pore space and the identified well intervals with Pearson’s correlation coefficients (r) lower than zero are classified in negative correlation pattern representing, an inverse diagenetic pattern with cementation of the pore space and selective dissolution of scaffold grains.
Romero teaches wherein the intervals with Pearson’s correlation coefficients (r) greater than zero are classified in positive correlation pattern representing a normal correlation pattern and the identified well intervals with Pearson’s correlation coefficients (r) lower than zero are classified in negative correlation pattern representing, an inverse diagenetic pattern ([0049], “The Pearson coefficient ranges from −1 to 1. A value of 1 shows that a linear equation describes the relationship perfectly and positively (i.e., often reffered to as normal or positive correlation patterns, with all data points lying on the same line and with Y increasing with X. A score of −1 shows that all data points lie on a single line but that Y increases as X decreases. A value of 0 shows that a linear model is inappropriate—that there is no linear relationship between the variables.”, and in [0046] “In FIG. 10, the distribution of the diffusivity, D,
versus the distribution of the transverse relaxation time, T 2, is provided. The depiction includes an overlay of the Pearson correlation for the T 2 bins.” Where the transverse relaxation time T2 is related to fluid mobility due to pore size in a scaffold, and diffusivity indicates the fluid mobility due to type of fluid. Therefore the +1 Pearson’s coefficient, or the positive, normal, correlation regions depicted in Fig. 10 are regions of the formation which that D and T2 trend the same direction, the regions of -1 Pearson’s coefficients, or the negative, inverse correlation are regions of the formation of which D and T trend in opposing directions.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the relationship (positive correlation and negative correlations) of Pearson’s coefficient ranges found in Romero to the method for determining diagenetic patterns in carbonate rocks by resonance and photoelectric factor profiles discussed in Ali for the purpose of having a statistic which estimates the positive or negative correlation of two variables. It is advantageous because “Fluid typing analyses of distributions of relaxation time, T, ( e.g., the transverse relaxation time, T2 , as well as the longitudinal relaxation time, T1) faces the problem of discriminating individual contributions of water and hydrocarbons, especially when signals from the water and hydrocarbons may not be easily discriminated from each other,” [0006, Romero], therefore “techniques for correlating logging information for at least the transverse relaxation time, T2 , the longitudinal relaxation time, Ti, and the diffusivity, D, with other types of logging data,” [0007, Romero] are needed.
Ali, Romero, Liu and Al-Najm do not teach, cementation or dissolution of pore space, and cementation of the pore space and selective dissolution of scaffold grains.
Alkhaldi teaches , cementation or dissolution of pore space, and cementation of the pore space and selective dissolution of scaffold grains ([0053] “The rocks are investigated in terms of pore types and cementation (step 156). Step 156 represents a diagenetic processes that has occurred in the meteoric and shallow burial realms. In step 156, rocks are separated based on whether or not they show dissolution feature (i.e., whether or not the scaffold grains, grains making the structure of the formation rock, are being selectively dissolved) and whether or not they have been subjected to full cementation and have no pore spaces. For example, some dissolution features include moldic porosity (MO), Vugy porosity, and Intraparticle particles porosity (WP). Rocks are sorted based on pore space in terms of whether or not the rocks show preservation of primary sedimentary structure (step 158). For example, in step 158, rocks preserving sedimentary structure, such as cross bedding and parallel lamination, are separated from rocks that show more bioturbation. Bioturbation is considered a diagenetic feature since it occurs after the precipitation of the sediments. Rocks are sorted based on whether or not they show cross stratification or lamination (step 160).”; Fig. 3A Meteoric to shallow Dissolution and Cementation).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to classifying rocks based on the diagenetic patterns via dissolution and cementation taught in Alkhaldi the method for determining and classifying diagenetic patterns in carbonate rocks by resonance and photoelectric factor profiles discussed in Ali, Romero, Liu and Al-Najm for the purpose of being able to classify cementation and dissolution classifications and compare them using Pearson’s Correlation Coefficient. This is advantageous because “classification and grouping of carbonate rock is important for assessing and modeling petrophysical properties of a reservoir,” [0003].
Conclusion
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/EMMA ALEXANDER/Patent Examiner, Art Unit 2863
/Catherine T. Rastovski/Supervisory Primary Examiner, Art Unit 2863