DETAILED ACTION
Claims 1-20 are presented for examination.
Petition for color drawings is GRANTED.
This office action is in response to submission of application on 11-JUL-2022.
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 statement (IDS) submitted on 08/17/2022 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 § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1, 9, and 17 recites the two limitations using the term “bound fluid volume”.
The first usage is “indicative of a bound fluid volume at the subsurface region”.
The second usage is “a bound fluid volume for the subsurface region”.
The term “subsurface region” is introduced in the preamble and has sufficient antecedent basis. In the body of the claim, “subsurface region” is consistent.
The use of the introduction of two instances of “bound fluid volume” are “at” and “for” the “subsurface region”. It is unclear how “at” and “for” different in their interpretation other than “for” could be interpreted as intended use.
The first usage appears to be directed towards the “correlation” or training phase of the invention where the second usage appears to be directed towards the implementation of a “prediction”. It does appear the intention is two different outcomes for different limitations; however, the claim seems to recite the “bound fluid volumes” are the same.
Examiner does not have a simple recommendation because it is unclear how these two different uses should be interpreted and if the elements following “for” are interpreted as intended use. For purposes of examination, the “bound fluid volumes” will be interpreted as the same “bound fluid volumes” in both instances.
There is insufficient antecedent basis for this limitation in the claim.
All dependent claims have inherited the same deficiency and are rejected for the same reasons as above.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 (Statutory Category – Process)
Step 2A – Prong 1: Judicial Exception Recited?
Yes, the claim recites a mental process, specifically:
MPEP 2106.04(a)(2)(Ill) “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, Judgments, and opinions.”
Further, the MPEP recites “The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation.”
2106.04(a)(2)(I)(A) “Mathematical Relationships A mathematical relationship is a relationship between variables or numbers. A mathematical relationship may be expressed in words or using mathematical symbols. For example, pressure (p) can be described as the ratio between the magnitude of the normal force (F) and area of the surface on contact (A), or it can be set forth in the form of an equation such as p = F/A.”
2106.04(a)(2)(I)(B) “Mathematical Formulas or Equations A claim that recites a numerical formula or equation will be considered as falling within the "mathematical concepts" grouping. In addition, there are instances where a formula or equation is written in text format that should also be considered as falling within this grouping. For example, the phrase "determining a ratio of A to B" is merely using a textual replacement for the particular equation (ratio = A/B). Additionally, the phrase "calculating the force of the object by multiplying its mass by its acceleration" is using a textual replacement for the particular equation (F= ma).”
2106.04(a)(2)(I)(C) “Mathematical Calculations A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.”
processing, at the predictive model, each of the plurality of inputs based on one or more algorithms used to train the predictive model;
Processing the input of the one or more algorithms is using known methods for the training process of the algorithm which amounts to a series of mathematical calculations.
based on the processing of the inputs, computing, at the predictive model, correlations between data points in the log data and reference parameters that are indicative of a bound fluid volume at the subsurface region;
Performing correlations between data points is a mathematical relationship. The reference parameters are used when determining the mathematical relationship.
based on the computed correlation, generating, by the predictive model, a prediction comprising a bound fluid volume for the subsurface region; and
The prediction is the output of the mathematical relationship.
determining, based on the bound fluid volume, a characteristic of a non-hydrocarbon fluid at a first zone of the subsurface region.
The characteristic can amount to a value from the mathematical relationship. The characterization could merely be an output of the water location based on the interpretation of the well log values.
Therefore, the claim recites mathematical concepts.
Step 2A – Prong 2: Integrated into a Practical Solution?
MPEP 2106.05(g) Insignificant Extra-Solution Activity has found mere data gathering and post solution activity to be insignificant extra-solution activity.
The following step is merely gathering the data on elements to be used in the calculation:
deriving a plurality of inputs from log data generated for one or more wells;
MPEP 2106.05(f) Mere Instructions To Apply An Exception has found simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more.
The additional elements have been considered both individually and as an ordered combination in to determine whether they integrate the exception into a practical application.
Therefore, no meaningful limits are imposed on practicing the abstract idea.
The claim is directed to the abstract idea.
Step 2B: Claim provides an Inventive Concept?
No, as discussed with respect to Step 2A, the additional limitation is mere data gathering/post solution activity (Insignificant Extra-Solution Activity) and a general purpose computer do not impose any meaningful limits on practicing the abstract idea and therefore the claim does not provide an inventive concept in Step 2B.
Further, in regards to step 2B and as cited above in step 2A, MPEP 2106.05(g) “Obtaining information about transactions using the Internet to verify credit card transactions, CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir.2011)” is merely data gathering.
The additional elements have been considered both individually and as an ordered combination in the significantly more consideration.
The claim is ineligible.
2. “The method of claim 1, further comprising: controlling well drilling operations based on the prediction comprising the bound fluid volume and the characteristic of the non-hydrocarbon fluid.”
The well drilling component amounts to post solution activity. MPEP 2106.05(g).
3. “The method of claim 2, further comprising: computing, using the predictive model, characterizations of the reservoir of the subsurface region and the first zone of the subsurface region based on the computed correlations and the bound fluid volume.”
Further characterizations using the correlations amount to using the mathematical relationships. Step 2A prong 1.
4. “The method of claim 1, wherein the log data comprises one or more of: gamma ray logs; density logs; resistivity logs; and neutron logs.”
The limitation amounts to mere data gathering. MPEP 2106.05(g).
5. “The method of claim 4, wherein computing the correlation comprises computing the correlation using:
first input features derived from electrical values of the resistivity logs;
second input features derived from radiation values of the gamma ray logs; and
third input features derived from porosity values of the neutron logs or the density logs.”
Assigning the input features of the gathered data is pre solution activity. MPEP 2106.05(g).
6. “The method of claim 5, wherein computing the correlation comprises computing the correlation based on a regression algorithm that processes the first, second, and third input features as independent variables.”
A regression algorithm is a known mathematical expression. Step 2A Prong 1.
7. “The method of claim 6, wherein the regression algorithm processes the reference parameters that are indicative of a fluid volume at the subsurface region as dependent variables.”
Using the known mathematical expression, regression algorithm, with defined parameters is still only implementing a mathematical concept. Step 2A Prong 1.
8. “The method of claim 1, wherein the log data:
i) is generated for a plurality of development wells; and
ii) comprises gamma ray logs, density logs, resistivity logs, and neutron logs.”
Collecting logs from a plurality of wells is mere data gathering. MPEP 2106.05(g).
Claims 9 and 11-16 are system claims, containing substantially the same elements as method Claims 1 and 3-8, respectively, and are rejected on the same grounds under 35 U.S.C. 101 as Claims 1 and 3-8, respectively, Mutatis mutandis. The additional components of “a hardware integrated circuit of the system, the system comprising a processor and a non-transitory machine-readable storage device storing instructions that are executable by the processor to perform operations” are interpreted as a general purpose computer and mere instructions to apply. MPEP 2106.05(f).
10. “The system of claim 9, wherein the operations further comprise:
controlling well drilling operations based on the prediction comprising the bound fluid volume and the characteristic of the non-hydrocarbon fluid, wherein the well drilling operations are for stimulating hydrocarbon production at a reservoir of the subsurface region.”
The controlling of the well drilling operations is recited at a high level of generality and amounts to post solution activity. MPEP 2106.05(g).
Claims 17 and 19-20 are medium claims, containing substantially the same elements as method Claims 1 and 3-4, respectively, and are rejected on the same grounds under 35 U.S.C. 101 as Claims 1-6, respectively, Mutatis mutandis. The additional components of “A non-transitory machine-readable storage device storing instructions for managing operations at a subsurface region using a predictive model implemented on a hardware integrated circuit, the instructions being executable by a processor to perform operations” are interpreted as a general purpose computer and mere instructions to apply. MPEP 2106.05(f).
18. “The machine-readable storage device of claim 17, wherein the operations further comprise:
controlling well drilling operations based on the prediction comprising the bound fluid volume and the characteristic of the non-hydrocarbon fluid, wherein the well drilling operations are for stimulating hydrocarbon production at a reservoir of the subsurface region.”
The controlling of the well drilling operations is recited at a high level of generality and amounts to post solution activity. MPEP 2106.05(g).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 4-9, 12-17, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over
Baouche et al., “Intelligent methods for predicting nuclear magnetic resonance of porosity and permeability by conventional well-logs: a case study of Saharan field” (hereinafter ‘Baouche’) [2017] in view of
Tooski, “Developing Synthetic Magnetic Resonance Logs (CMR logs) from Conventional Well Logs” (hereinafter ‘Tooski’) [2011].
Regarding Claim 1: A method for managing operations involving a well in a subsurface region using a predictive model implemented on a hardware integrated circuit, the method comprising:
Baouche teaches deriving a plurality of inputs from log data generated for one or more wells; (Pg. 5 right col last paragraph Baouche “…Five classical logs and effective porosity obtained from the Petrolog software (Petrolog 2010) including DTC (corrected rope), PE, GR, RHOB, and NPHI were considered as inputs and MPHIE as output of the fuzzy model…”)
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Baouche teaches processing, at the predictive model, each of the plurality of inputs based on one or more algorithms used to train the predictive model; (Pg. 15 right col 5th paragraph Baouche “…In the learning technique, the approach to functions that can depend on a large number of inputs is generally unknown (Dominic et al. 1991). These are interconnected neural systems that exchange messages. It is then possible to form a three layered BP-NN with four neurons in its hidden layer. The best results were achieved by “Levenberg Marquardt” algorithm for permeability model and the secant conjugate gradient (SCG) algorithm (Narushima and Hiroshi 2012) for porosity model. The results are presented in Table 3 and Fig. 11…”)
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Baouche teaches based on the processing of the inputs, computing, at the predictive model, correlations between data points in the log data and reference parameters … (Pg. 8 Fig. 6 Baouche “…Fig. 5 The relationship between log data and permeability (MPRN [mD]) for well GS-37. Same notations as in Fig. 4a–e, except PHIE (f)…”)
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Baouche teaches based on the computed correlation, generating, by the predictive model, a prediction … (Pg. 8 Fig. 6 Baouche “…Fig. 6 Predictions for well GS- 37. Cambrian: in situ formation; GR [API]: gamma ray; Depth [m]: depth; MPHIE [%]: NMR porosity; MPHIE_NN [%]: NN porosity; CPor [%]: core porosity; MPRN [mD]: NMR permeability; MPRN_NN [mD]: NN permeability; CPerm [mD]: core permeability; subscript NN relates to predicted values from neural network…”)
Baouche does not appear to explicitly disclose
that are indicative of a bound fluid volume at the subsurface region;
comprising a bound fluid volume for the subsurface region; and
determining, based on the bound fluid volume, a characteristic of a non-hydrocarbon fluid at a first zone of the subsurface region.
However, Tooski teaches that are indicative of a bound fluid volume at the subsurface region; and comprising a bound fluid volume for the subsurface region; and (Abstract Tooski “…Neural network model is developed using CMR logs and conventional logs such as gamma ray, neutron, resistivity and etc. obtained from three wells of South Pars Field, Iran. Then the model is applied to generate synthetic MRI logs such as Free Fluid porosity (CMFF), Bound Fluid porosity (BFV) and permeability (KTIM) by using just the conventional logs in another well. The Synthetic logs are generated through two different methods of Backpropagation and General regression…”)
Tooski teaches determining, based on the bound fluid volume, a characteristic of a non-hydrocarbon fluid at a first zone of the subsurface region. (Pg. 2 2nd paragraph Tooski “…The MR log measures effective porosity – total porosity minus the clay bound porosity – as well as irreducible water saturation. The irreducible water saturation is the combinations of clay bound water and water held due to the surface tension of the matrix material. The difference between total porosity (TCMR) and the irreducible water saturation (BFV) is called the free fluid index (CMFF). This is the producible fluid in the reservoir. This demonstrates how valuable MR log is to low resistivity reservoirs. In many low resistivity reservoirs, matrix irreducible water rather than producible water may cause a drop in resistivity. While producible water can seriously hamper production and make the pay quite unattractive, the same cannot be said for the irreducible water…”)
Baouche and Tooski are analogous art because they are from the same field of endeavor, reservoir interpretation.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the based on the processing of the inputs, computing, at the predictive model, correlations between data points in the log data and reference parameters as disclosed by Baouche by that are indicative of a bound fluid volume at the subsurface region and determining, based on the bound fluid volume, a characteristic of a non-hydrocarbon fluid at a first zone of the subsurface region as disclosed by Tooski.
One of ordinary skill in the art would have been motivated to make this modification in order to generate synthetic NMR logs when data is absent or incomplete as discussed on pg. 2 4th paragraph of Tooski “…However, for economical reasons, companies do not always posses all the logs that are required to determine reservoir characteristics. This study presents a methodology that can help to solve the aforementioned problems by generating synthetic logs for those locations where the set of logs that are necessary to analyze the reservoir properties, are absent or are not complete…”
Regarding Claim 4: Baouche and Tooski teach The method of claim 1, wherein the log data comprises one or more of:
Baouche teaches gamma ray logs; density logs; resistivity logs; and neutron logs. (Pg. 5 right col last paragraph Baouche “…Five classical logs and effective porosity obtained from the Petrolog software (Petrolog 2010) including DTC (corrected rope), PE, GR, RHOB, and NPHI were considered as inputs and MPHIE as output of the fuzzy model…”)
Regarding Claim 5: Baouche and Tooski teach The method of claim 4, wherein computing the correlation comprises computing the correlation using:
Baouche teaches first input features derived from electrical values of the resistivity logs; (Fig. 4 and Fig. 11 Baouche selected portion shown, however, entire figure should be considered)
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Baouche teaches second input features derived from radiation values of the gamma ray logs; and (Fig. 4 and Fig. 11 Baouche selected portion shown, however, entire figure should be considered)
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Baouche teaches third input features derived from porosity values of the neutron logs or the density logs. (Fig. 4 and Fig. 11 Baouche selected portion shown, however, entire figure should be considered)
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Regarding Claim 6: Baouche and Tooski teach The method of claim 5, wherein computing the correlation comprises
Baouche teaches computing the correlation based on a regression algorithm that processes the first, second, and third input features as independent variables. (Pg. 6 Baouche “…Fig. 4 The relationship between log data including NPHI (a), DTC (b), GR (c), PE (d), RHOB (e), and RT (f) and NMR porosity (MPHIE [%]) for well GS-37…”
Abstract Baouche “…First, the support vector regression method was applied to a sandy clay reservoir with a model based on the prediction of porosity and permeability. NMR parameters estimated using intelligent systems, i.e., fuzzy logic (FL) model, back propagation neural network (BP-NN), and support vector machine, with conventional well-log data are combined with those of NMR, resulting in a good estimation of porosity and permeability. The results obtained during the processing are then compared to the FL and NN regression models performed by the regression method during the validation stage…”)
Regarding Claim 7: Baouche and Tooski teach The method of claim 6,
Tooski teaches wherein the regression algorithm processes the reference parameters that are indicative of a fluid volume at the subsurface region as dependent variables. (Pg. 4 5th-6th paragraph Tooski “…Although the correlation coefficients for all the virtual logs modeled by general regression method are quite satisfactory, it should be noted that once these logs are used to calculate estimated recoverable reserves, the results are even more promising. This is due to the fact that many times the effective porosity and saturation is averaged. After all, MR logs are used in two different ways. One-way is to locate and complete portions of the pay zone that have been missed due to the conventional log analysis. This is more a qualitative analysis than a quantitative one since the engineer will look for an increase in the difference between BFV and (TCMR) i.e. free fluid volume (CMFF) that corresponds to a high permeability interval. The second use of these logs is to estimate the recoverable reserves more realistically. The reserve estimates calculated using virtual MRI logs when compared to estimates calculated using actual MRI logs are quite accurate…”)
Regarding Claim 8: Baouche and Tooski teach The method of claim 1, wherein the log data:
Baouche teaches i) is generated for a plurality of development wells; and (Abstract Baouche “…The well-log data of five wells were completed during the construction of intelligent models in the Saharan oil field Oued Mya Basin in order to assess the reliability of the developed models…”)
Baouche teaches ii) comprises gamma ray logs, density logs, resistivity logs, and neutron logs. (Pg. 6 Baouche “…Fig. 4 The relationship between log data including NPHI (a), DTC (b), GR (c), PE (d), RHOB (e), and RT (f) and NMR porosity (MPHIE [%]) for well GS-37…”)
Claims 9 and 12-16 are system claims, containing substantially the same elements as method Claims 1 and 4-8, respectively, and are rejected on the same grounds under 35 U.S.C. 103 as Claims 1 and 4-8, respectively, Mutatis mutandis.
Claims 17 and 20 are medium claims, containing substantially the same elements as method Claims 1 and 4, respectively, and are rejected on the same grounds under 35 U.S.C. 103 as Claims 1 and 4, respectively, Mutatis mutandis.
Claims 2-3, 10-11, and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over
Baouche et al., “Intelligent methods for predicting nuclear magnetic resonance of porosity and permeability by conventional well-logs: a case study of Saharan field” (hereinafter ‘Baouche’) [2017] in view of
Tooski, “Developing Synthetic Magnetic Resonance Logs (CMR logs) from Conventional Well Logs” (hereinafter ‘Tooski’) [2011] further in view of
Kruspe et al., U.S. Patent Application Publication 2005/0088176 A1 (hereinafter ‘Kruspe’).
Regarding Claim 2: Baouche and Tooski teach The method of claim 1, further comprising:
Baouche and Tooski do not appear to explicitly disclose
controlling well drilling operations based on the prediction comprising the bound fluid volume and the characteristic of the non-hydrocarbon fluid.
However, Kruspe teaches controlling well drilling operations based on the prediction comprising the bound fluid volume and the characteristic of the non-hydrocarbon fluid. ([0045] Kruspe “…The surface control unit or processor 40 also receives signals from other downhole sensors and devices and signals from sensors S.sub.1-S.sub.3 and other sensors used in the system 10 and processes such signals according to programmed instructions provided to the surface control unit 40. The surface control unit 40 displays desired drilling parameters and other information on a display/monitor 42 utilized by an operator to control the drilling operations. The surface control unit 40 preferably includes a computer or a microprocessor-based processing system, memory for storing programs or models and data, a recorder for recording data, and other peripherals. The control unit 40 is preferably adapted to activate alarms 44 when certain unsafe or undesirable operating conditions occur…”
[0026] Kruspe “…Once the signals have been corrected, prior art methods can be used to determine parameters of interest of the earth formation and fluids therein. These include clay-bound water (CBW), bound water moveable (BVM), bound water irreducible (BVI), and porosity. Such techniques are well known and are not discussed further herein. It is known in the art that these parameters are estimated from NMR measurements and are not precisely determinable to infinite accuracy. The term "determine" is to be interpreted as being equivalent to "estimate."…”)
Baouche, Tooski, and Kruspe are analogous art because they are from the same field of endeavor, reservoir interpretation.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the determining, based on the bound fluid volume, a characteristic of a non-hydrocarbon fluid at a first zone of the subsurface region as disclosed by Baouche and Tooski by controlling well drilling operations based on the prediction comprising the bound fluid volume and the characteristic of the non-hydrocarbon fluid as disclosed by Kruspe.
One of ordinary skill in the art would have been motivated to make this modification in order to use NMR in the extraction of the hydrocarbons as discussed in [0006] by Kruspe “…One commonly used technique involves utilizing Nuclear Magnetic Resonance (NMR) logging tools and methods for determining, among other things, porosity, hydrocarbon saturation and permeability of the rock formations. The NMR logging tools are utilized to excite the nuclei of the liquids in the geological formations surrounding the wellbore so that certain parameters such as nuclear spin density, longitudinal relaxation time (generally referred to in the art as T 1) and transverse relaxation time (generally referred to as T 2) of the geological formations can be measured. From such measurements, porosity, permeability and hydrocarbon saturation are determined, which provides valuable information about the make-up of the geological formations and the amount of extractable hydrocarbons…”
Regarding Claim 3: Baouche, Tooski, and Kruspe teach The method of claim 2, further comprising:
Tooski teaches computing, using the predictive model, characterizations of the reservoir of the subsurface region and the first zone of the subsurface region based on the computed correlations and the bound fluid volume. (Pg. 5 4th paragraph Tooski “…This method generates synthetic magnetic resonance logs by using conventional logs. For developing these virtual logs just some wells with magnetic resonance logs are required. After that both conventional well logs data and magnetic logs are used to develop a neural predictive model. Then this model will be applied for new wells in the field that only contain conventional logs. By using this method the synthetic MR logs can be predict for all the wells in the field. So this process reduces the cost of reservoir characterization from well logs data significantly…”)
Regarding Claim 10: Baouche, Tooski, and Kruspe teach The system of claim 9, wherein the operations further comprise:
Baouche and Tooski do not appear to explicitly disclose
controlling well drilling operations based on the prediction comprising the bound fluid volume and the characteristic of the non-hydrocarbon fluid, wherein the well drilling operations are for stimulating hydrocarbon production at a reservoir of the subsurface region
However, Kruspe teaches controlling well drilling operations based on the prediction comprising the bound fluid volume and the characteristic of the non-hydrocarbon fluid, wherein the well drilling operations are for stimulating hydrocarbon production at a reservoir of the subsurface region. ([0045] Kruspe “…The surface control unit or processor 40 also receives signals from other downhole sensors and devices and signals from sensors S.sub.1-S.sub.3 and other sensors used in the system 10 and processes such signals according to programmed instructions provided to the surface control unit 40. The surface control unit 40 displays desired drilling parameters and other information on a display/monitor 42 utilized by an operator to control the drilling operations. The surface control unit 40 preferably includes a computer or a microprocessor-based processing system, memory for storing programs or models and data, a recorder for recording data, and other peripherals. The control unit 40 is preferably adapted to activate alarms 44 when certain unsafe or undesirable operating conditions occur…”
[0026] Kruspe “…Once the signals have been corrected, prior art methods can be used to determine parameters of interest of the earth formation and fluids therein. These include clay-bound water (CBW), bound water moveable (BVM), bound water irreducible (BVI), and porosity. Such techniques are well known and are not discussed further herein. It is known in the art that these parameters are estimated from NMR measurements and are not precisely determinable to infinite accuracy. The term "determine" is to be interpreted as being equivalent to "estimate."…”)
Baouche, Tooski, and Kruspe are analogous art because they are from the same field of endeavor, reservoir interpretation.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the determining, based on the bound fluid volume, a characteristic of a non- hydrocarbon fluid at a first zone of the subsurface region as disclosed by Baouche and Tooski by controlling well drilling operations based on the prediction comprising the bound fluid volume and the characteristic of the non-hydrocarbon fluid, wherein the well drilling operations are for stimulating hydrocarbon production at a reservoir of the subsurface region as disclosed by Kruspe.
One of ordinary skill in the art would have been motivated to make this modification in order to use NMR in the extraction of the hydrocarbons as discussed in [0006] by Kruspe “…One commonly used technique involves utilizing Nuclear Magnetic Resonance (NMR) logging tools and methods for determining, among other things, porosity, hydrocarbon saturation and permeability of the rock formations. The NMR logging tools are utilized to excite the nuclei of the liquids in the geological formations surrounding the wellbore so that certain parameters such as nuclear spin density, longitudinal relaxation time (generally referred to in the art as T 1) and transverse relaxation time (generally referred to as T 2) of the geological formations can be measured. From such measurements, porosity, permeability and hydrocarbon saturation are determined, which provides valuable information about the make-up of the geological formations and the amount of extractable hydrocarbons…”
Regarding Claim 11: Baouche, Tooski, and Kruspe teach The system of claim 10, wherein the operations further comprise:
Tooski teaches computing, using the predictive model, characterizations of the reservoir of the subsurface region and the first zone of the subsurface region based on the computed correlations and the bound fluid volume. (Pg. 5 4th paragraph Tooski “…This method generates synthetic magnetic resonance logs by using conventional logs. For developing these virtual logs just some wells with magnetic resonance logs are required. After that both conventional well logs data and magnetic logs are used to develop a neural predictive model. Then this model will be applied for new wells in the field that only contain conventional logs. By using this method the synthetic MR logs can be predict for all the wells in the field. So this process reduces the cost of reservoir characterization from well logs data significantly…”)
Regarding Claim 18: Baouche, Tooski, and Kruspe teach The machine-readable storage device of claim 17, wherein the operations further comprise:
Baouche and Tooski do not appear to explicitly disclose
controlling well drilling operations based on the prediction comprising the bound fluid volume and the characteristic of the non-hydrocarbon fluid, wherein the well drilling operations are for stimulating hydrocarbon production at a reservoir of the subsurface region.
However, Kruspe teaches controlling well drilling operations based on the prediction comprising the bound fluid volume and the characteristic of the non-hydrocarbon fluid, wherein the well drilling operations are for stimulating hydrocarbon production at a reservoir of the subsurface region. ([0045] Kruspe “…The surface control unit or processor 40 also receives signals from other downhole sensors and devices and signals from sensors S.sub.1-S.sub.3 and other sensors used in the system 10 and processes such signals according to programmed instructions provided to the surface control unit 40. The surface control unit 40 displays desired drilling parameters and other information on a display/monitor 42 utilized by an operator to control the drilling operations. The surface control unit 40 preferably includes a computer or a microprocessor-based processing system, memory for storing programs or models and data, a recorder for recording data, and other peripherals. The control unit 40 is preferably adapted to activate alarms 44 when certain unsafe or undesirable operating conditions occur…”
[0026] Kruspe “…Once the signals have been corrected, prior art methods can be used to determine parameters of interest of the earth formation and fluids therein. These include clay-bound water (CBW), bound water moveable (BVM), bound water irreducible (BVI), and porosity. Such techniques are well known and are not discussed further herein. It is known in the art that these parameters are estimated from NMR measurements and are not precisely determinable to infinite accuracy. The term "determine" is to be interpreted as being equivalent to "estimate."…”)
Baouche, Tooski, and Kruspe are analogous art because they are from the same field of endeavor, reservoir interpretation.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the determining, based on the bound fluid volume, a characteristic of a non-hydrocarbon fluid at a first zone of the subsurface region as disclosed by Baouche and Tooski by controlling well drilling operations based on the prediction comprising the bound fluid volume and the characteristic of the non-hydrocarbon fluid, wherein the well drilling operations are for stimulating hydrocarbon production at a reservoir of the subsurface region as disclosed by Kruspe.
One of ordinary skill in the art would have been motivated to make this modification in order to use NMR in the extraction of the hydrocarbons as discussed in [0006] by Kruspe “…One commonly used technique involves utilizing Nuclear Magnetic Resonance (NMR) logging tools and methods for determining, among other things, porosity, hydrocarbon saturation and permeability of the rock formations. The NMR logging tools are utilized to excite the nuclei of the liquids in the geological formations surrounding the wellbore so that certain parameters such as nuclear spin density, longitudinal relaxation time (generally referred to in the art as T 1) and transverse relaxation time (generally referred to as T 2) of the geological formations can be measured. From such measurements, porosity, permeability and hydrocarbon saturation are determined, which provides valuable information about the make-up of the geological formations and the amount of extractable hydrocarbons…”
Regarding Claim 19: The machine-readable storage device of claim 18, wherein the operations further comprise:
Tooski teaches computing, using the predictive model, characterizations of the reservoir of the subsurface region and the first zone of the subsurface region based on the computed correlations and the bound fluid volume. (Pg. 5 4th paragraph Tooski “…This method generates synthetic magnetic resonance logs by using conventional logs. For developing these virtual logs just some wells with magnetic resonance logs are required. After that both conventional well logs data and magnetic logs are used to develop a neural predictive model. Then this model will be applied for new wells in the field that only contain conventional logs. By using this method the synthetic MR logs can be predict for all the wells in the field. So this process reduces the cost of reservoir characterization from well logs data significantly…”)
Conclusion
Claims 1-20 are rejected.
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/JOHN E JOHANSEN/Examiner, Art Unit 2187