DETAILED ACTIONS
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) submitted on 01/06/2025 and 10/09/2024. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-6, are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Jain Praveen. (US 2016/0327680 A1, hereinafter Jain, IDS reference).
Regarding Claim 1, Jain teaches
A method of determining measured depth in a borehole (Jain, Figure 1, [0023] a downhole drilling progress monitoring unit configured to calculate incremental depth of a drilling assembly in the borehole based on detection of the nuclear radiation and the axial distance”) comprising the steps of:
moving a first sensor and a second sensor through a borehole while continuously sending data to a processor (Jain, Figure 2-3, Control unit 20) wherein the first sensor and the second sensor are spaced longitudinally apart in the borehole by a known distance (Jain, Figure 4a-4b, Sensors S1, S2, [0069], [0069] Referring to FIGS. 4A and 4B, the concept of incremental depth estimation using geological markers involves making continuous measurements along the borehole with two or more sensors (Sl, S2), arranged for instance in the BHA and separated by a known distance L”.);
continuously recording data from first sensor and the second sensor, using the processor, to a memory, wherein the processor and the memory are in the borehole forming data trends based on the first sensor data and the second sensor data; (Jain, Figure 2-3, control unit 20, [0058] “The control unit 20 will take input from all possible measurements and processes it”; [0080] The first sensor S1 has collected a dataset Y, comprising N measurement values. The second sensor S2 collects a second dataset X, or pattern X, comprising M values. Herein, M<N. The control unit 20 (not shown in FIG. 5; See for instance FIG. 3) tries to match the dataset Mat a specific location within the dataset N, using a predetermined algorithm. The dataset N is sufficiently large to include all possible matches corresponding to dataset M.
identifying a feature of interest in each of the first sensor data trend and the second sensor data trend using time-varying pattern matching between the two trends
(Jain, Figure 2-3, control unit 20,] [0080] The control unit 20 (not shown in FIG. 5; See for instance FIG. 3) tries to match the dataset Mat a specific location within the dataset N, using a predetermined algorithm. The dataset N is sufficiently large to include all possible matches corresponding to dataset M.”); and
continuously estimating a measured depth using the feature of interest identification in the first sensor data trend and the second sensor data trend and the known distance between the first sensor and the second sensor (Jain, [0081] FIG. 6 shows an example of an algorithm for depth calculation using a two-sensor arrangement.
wherein the first sensor and the second sensor are moved through the borehole at a variable speed (Jain, [0002] “Boreholes for the production of crude oil and/or
natural gas from a subsurface formation are generally drilled using a rotatable drill string. A downhole end of the drill string may typically be provided
with a Bottom Hole Assembly (BHA). A drilling rig at surface for holding the drill string is provided with a drive system for rotating the drill string, typically including a top drive or other rotary table”.it is known in the art that drilling string moves at a variable speed and it depends on and the sensors attached to it also moves at a variable speed due to drilling condition water pressure and other conditions).
and wherein the first sensor data trend and the second sensor data trend are formed independent of variations in speed of the first sensor and the second sensor. Jain, Figure 3-5, [0011] “While drilling, the two or more sensors pass the
same location in the formation at a different time, depending on their separation and the rate of penetration. An algorithm can be used to compare the outputs from these sensors in time to correlate the character of the signals which come
from the same subsurface formation. As these signals correspond to the same subsurface location, the progress along the borehole as well as the rate of penetration can be calculated, using the known distance L between the sensors.
I.e., the drill string has progressed a distance L along the borehole during the time difference .M=(t2-tl). The rate of penetration ROP=L/(t2-tl). By integrating the ROP, incremental depth can be calculated. Also see [0070]”. since the rate of penetration is measured based on the time difference the sensors progress through a known distance can give data without any influence of the variational speed of the drill string. See (Jain [0102], the components in correlation of geological markers is matching of responses from multiple sensors, to handle any drift in the sensors. Any offset caused by sensor drift will not affect the cross-correlation coefficient as the offset will also reflect in the mean which is subtracted from both the dataset Y and the dataset X in the numerator and denominator, leaving the cross-correlation coefficient unchanged).
Regarding Claim 3, Jain teaches the method of claim 1,
Jain further teaches further comprising: moving an orientation sensor through the borehole with the first sensor and the second sensor; continuously recording orientation data from the orientation sensor, using the processor, to the memory; and continuously estimating a true vertical depth using the measured depth and the orientation data. (Jain, Figure 3, [0024] “The downhole drilling progress monitoring unit
may be arrm1ged in a Bottom Hole Assembly (BHA) of a drilling assembly and be configured to provide real time data regarding the incremental depth, an associated Rate Of Penetration (ROP), azimuth and/or inclination of the BHA to
an automated drill bit navigation system in the BHA and the drilling progress monitoring unit may be configured to transmit the real time data to the automated drill bit navigation system via a signal transmission assembly in the BHA without requiring transmission of the real time data to data processing equipment located at the earth surface. [0217] Measured values of borehole characteristics are
provided in step 182. During drilling of the borehole, survey instruments, such as a LWD tool, provide measured valuesof, for instance, borehole inclination, azimuth, and depth.”)
Regarding Claim 4, Jain teaches the method of claim 1,
Jain further teaches comprising: moving a clock through the borehole with the processor; recording time information associated with the first sensor data trend and the second sensor data trend (Jain, Figure 3, control unit 20 [0060] Another option is to arrange the 'Brain' 20, i.e. the control unit, nearer to the bit 13 as illustrated in FIG. 3. Downhole, measurements relating to formation evaluation (such as gamma ray, resistivity, density, sonic velocity logs, etc.) may be readily available, and may be used as a basis for geo-steering decisions. Locating the control unit 20 donwhole
will greatly reduce the bandwidth requirement for data transmission to and from surface. Located downhole, the control unit may utilize high resolution datasets in real time, substantially eliminating time lag issues and data compression requirements, which will enhance accuracy and quality of the decisions” the downhole control unit 20 collects data with time stamp. It is known in the art that the control unit has a built in clock to time stamp data collection. It is not an inventive step)..;
continuously estimating a time interval by comparing the time in each of the first sensor data trend and the second sensor data trend when the feature of interest is encountered by its respective sensor; and estimating a rate of penetration based on the known distance between the first sensor and the second sensor and the time interval. (Jain, Figure 4A-4B, [0070], [0070] A schematic of the arrangement is shown in FIG.4A, wherein S1 and S2 are identical sensors located in the bottom hole assembly and spaced apart a distance L. In FIG. 4A, the first sensor S1 passes an area having a high gamma ray signature at time tl. The second sensor S2 passes the same high gamma ray signature t1 at time t2. As the sensors are a distance L apart, the drill string has progressed a distance of L along the borehole in time (t2-tl). [0071] Hence average rate of penetration (ROP) is L/ (t1-t2) see equation 1”).
Regarding Claim 5, Jain teaches the method of claim 1,
Jain further teaches wherein the first sensor and the second sensor are each one of: an acoustic sensor, a gamma-ray sensor, a neutron sensor and an ultrasonic sensor. (Jain, [233], 0233] 7. For a depth measurement system including two gamma ray sensors S1 and S2 (FIG. 4A), the error in incremental depth may increase in a horizontal section of the borehole, which typically shows less variation in gamma ray radiation as the borehole extends within the same formation layer”);
Regarding Claim 6, Jain teaches the method of claim 1,
Jain further teaches further comprising at least one of: filtering, scaling, normalizing, and transforming the sensor data into a suitable format for the pattern matching techniques before forming the data trends. (Jain, [0098] I. Normalized Cross-Correlation Algorithm [0099] Cross correlation is a method used in signal processing
to recognize time-lag between two signals. The same has been used in determining average rate of penetration at the end of drilling”).
Regarding Claim 11, Jain teaches,
A non-transitory computer-readable medium product, the medium containing instructions thereon that, when executed by a processor, executes a method, the method comprising the steps (Jain, Figure 2-3, control unit 20, [0194] “In automated drilling, said storage medium (not shown) and the control unit 20 are included in the BHA (See FIG. 3)”:
continuously estimating a measured depth using the feature of interest identification in the first sensor data trend and the second sensor data trend and the known distance between the first sensor and the second sensor (Jain, [0081] FIG. 6 shows an example of an algorithm for depth calculation using a two sensor arrangement.
wherein the first sensor and the second sensor are moved through the borehole at a variable speed (Jain, [0002] “Boreholes for the production of crude oil and/or
natural gas from a subsurface formation are generally drilled using a rotatable drill string. A downhole end of the drill string may typically be provided
with a Bottom Hole Assembly (BHA). A drilling rig at surface for holding the drill string is provided with a drive system for rotating the drill string, typically including a top drive or other rotary table”.it is known in the art that drilling string moves at a variable speed and it depends on and the sensors attached to it also moves at a variable speed due to drilling condition water pressure and other conditions ).
and wherein the first sensor data trend and the second sensor data trend are formed independent of variations in speed of the first sensor and the second sensor. Jain, Figure 3-5, [0011] “While drilling, the two or more sensors pass the
same location in the formation at a different time, depending on their separation and the rate of penetration. An algorithm can be used to compare the outputs from these sensors in time to correlate the character of the signals which come
from the same subsurface formation. As these signals correspond to the same subsurface location, the progress along the borehole as well as the rate of penetration can be calculated, using the known distance L between the sensors.
I.e., the drill string has progressed a distance L along the borehole during the time difference .M=(t2-tl). The rate of penetration ROP=L/(t2-tl). By integrating the ROP, incremental depth can be calculated. Also see [0070]”. since the rate of penetration is measured based on the time difference the sensors progress through a known distance can give data without any influence of the variational speed of the drill string. See (Jain [0102], the components in correlation of geological markers is matching of responses from multiple sensors, to handle any drift in the sensors. Any offset caused by sensor drift will not affect the cross-correlation coefficient as the offset will also reflect in the mean which is subtracted from both the dataset Y and the dataset X in the numerator and denominator, leaving the cross correlation coefficient unchanged).
Regarding Claim 13, Jain teaches the non-transitory computer-readable medium product of claim 11,
Jain further teaches wherein the medium further contains instructions thereon that, when executed, by the processor (Jain, Figure 2-3, control unit 20, [0023] a downhole drilling progress monitoring unit configured to calculate incremental depth of a drilling assembly in the borehole based on detection of the nuclear radiation and the axial distance, [0194] “In automated drilling, said storage medium (not shown) and the control unit 20 are included in the BHA (See FIG. 3)”):
, executes the steps of: continuously recording orientation data from an orientation sensor moving through the borehole with the first sensor and the second sensor; and estimating a true vertical depth using the measured depth and the orientation data. Jain, Figure 3, [0024] “The downhole drilling progress monitoring unit
may be arrm1ged in a Bottom Hole Assembly (BHA) of a drilling assembly and be configured to provide real time data regarding the incremental depth, an associated Rate Of Penetration (ROP), azimuth and/or inclination of the BHA to
an automated drill bit navigation system in the BHA and the drilling progress monitoring unit may be configured to transmit the real time data to the automated drill bit navigation system via a signal transmission assembly in the BHA without requiring transmission of the real time data to data processing equipment located at the earth surface. [0217] Measured values of borehole characteristics are
provided in step 182. During drilling of the borehole, survey instruments, such as a LWD tool, provide measured valuesof, for instance, borehole inclination, azimuth, and depth.”)
Regarding Claim 14, Jain teaches the non-transitory computer-readable medium product of claim 11,
Jain further teaches wherein the medium further contains instructions thereon that, when executed, by the processor (Jain, Figure 3, [0194] The synthetic logs are pre-calculated at surface before the drilling commences, and stored in a storage medium coupled to the control unit 20. In automated drilling, said storage medium (not shown) and the control unit 20 are included in the BHA (See FIG. 3)”), executes the steps of:
recording time information associated with the first sensor data trend and the second sensor data trend; and estimating a time interval by comparing the time in each of the first sensor data trend and the second sensor data trend when the feature of interest is encountered by its respective sensor; and continuously estimating a rate of penetration based the known distance between the first sensor and the second sensor and the time interval. (Jain, Figure 4A-4B, [0070], [0070] A schematic of the arrangement is shown in FIG.4A, wherein S1 and S2 are identical sensors located in the bottom hole assembly and spaced apart a distance L. In FIG. 4A, the first sensor S1 passes an area having a high gamma ray signature at time tl. The second sensor S2 passes the same high gamma ray signature t1 at time t2. As the sensors are a distance L apart, the drill string has progressed a distance of L along the borehole in time (t2-tl). [0071] Hence average rate of penetration (ROP) is L/ (t1-t2) see equation 1”).
Regarding Claim 15, Jain teaches the non-transitory computer-readable medium product of claim 11,
Jain further teaches wherein the medium comprises at least one of: i) a ROM, ii) an EPROM, iii) an EEPROM, iv) a flash memory, v) an optical disk, vi) a solid state drive, and vii) a hard drive. (Jain, Figure 2-3, Control unit 20, (Jain, Figure 3, [0194] The synthetic logs are pre-calculated at surface before the drilling commences, and stored in a storage medium coupled to the control unit 20. In automated drilling, said storage medium (not shown) and the control unit 20 are included in the BHA (See FIG. 3)”),
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 7-10 are rejected under 35 U.S.C. 103 as being unpatentable over Jain Praveen. (US 2016/0327680 A1, hereinafter Jain) and in view of Madasu et al. (US 2021/0148213 A1, hereinafter Madasu).
Regarding Claim 7, Jain teaches the method of claim 1,
Jain teaches using of algorithm to match patterns (see Jain, figure 6, [0081] FIG. 6 shows an example of an algorithm for depth calculation using a two sensor arrangement “)
Jain is silent on wherein the pattern matching comprises using at least one of: Advanced Sequential Pattern Matching Algorithms and Deep Neural-Network Based Algorithms.
However, Madasu teaches wherein the pattern matching comprises using at least one of: Advanced Sequential Pattern Matching Algorithms and Deep Neural-Network Based Algorithms. (Madasu, Figure 12A-12B, [0078] FIGS. 12A and 12B are plot graphs showing a comparison between predicted ROP values and normalized actual ROP values along a well path over depth (e.g., in feet), where the predicted values are based on a recurrent DNN with and without a noise filter, respectively. As shown by the plot graph in each of FIGS. 12A and 12B”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Jain’s method of pattern matching algorithm to incorporate a machine learning network algorithm as taught by Madasu and obtain an accurate correlation of data and calculate depth (Madasu, [0078]). It would have been obvious to a person of ordinary skill to include the well-known Deep leaning algorithm along with the other machine learning network, in order to yield the predicted results of calculating accurate depth, yet with higher accuracy (KSR).
Regarding Claim 8, combination of Jain and Madasu teaches the method of claim 7,
Jain further teaches wherein the Advanced Sequential Pattern Matching Algorithms comprise one or more of: Dynamic Time Warping (DTW), Time Warp Edit Distance (TWED), Longest Common Subsequence (LCSS), Correlation Filtering, Cross- Correlation, Convolution, Edit Distance with Real Penalty (ERP), FastDTW, and Subsequence Dynamic Time Warping (SDTW). (Jain, [0098]” Normalized Cross-Correlation Algorithm “).
Regarding Claim 9, combination of Jain and Madasu teaches the method of claim 7,
The method of claim 7,
wherein the Deep Neural-Network Based Algorithms comprise one or more of: Hidden Markov Models (HMM), Recurrent Neural Networks (RNN), Convolutional Neural Networks (CNN), Graph Neural Networks (GNN), Transformers, Sequence (Seq2Seq) Neural Network-based models, and Reinforcement Learning Algorithms. (Madasu, 0067] In some implementations, DNN 700 may incorporate a root-mean-square error loss and back propagation through time (BPTT) architecture. An example of such a recurrent DNN architecture is shown in FIG. 9. In FIG. 9, input data for the recurrent DNN is passed to a Convolution Neural Network (CNN) to filter noise”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Jain’s method of pattern matching algorithm to incorporate a machine learning network algorithm as taught by Madasu and obtain an accurate correlation of data and calculate depth (Madasu, [0078]). It would have been obvious to a person of ordinary skill to include the well-known Deep leaning algorithm along with the other machine learning network, in order to yield the predicted results of calculating accurate depth, yet with higher accuracy (KSR).
Regarding Claim 10, combination of Jain and Madasu teaches the method of
claim 1, Jain teaches signal processing using [0098] I. Normalized Cross-Correlation Algorithm [0099] Cross correlation is a method used in signal processing”)
Jain is silent on wherein the time-varying pattern matching is implemented on one of: a Digital Signal Processor (DSP), a machine learning device, a tensor processing unit, and an artificial intelligence accelerator.
However, Madasu teaches wherein the time-varying pattern matching is implemented on one of: a Digital Signal Processor (DSP), a machine learning device, a tensor processing unit, and an artificial intelligence accelerator (Madasu, Figure 7, DNN [0061] FIG. 7 is a schematic of a recurrent deep neural network (DNN) 700 with one or more Gated Recurrent Unit (GRU) cells for predicting values of one or more operating variables of a drilling operation along a well path”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Jain’s method of pattern matching algorithm to incorporate a machine learning network algorithm as taught by Madasu and obtain an accurate correlation of data and calculate depth (Madasu, [0078]). It would have been obvious to a person of ordinary skill to include the well-known Deep leaning algorithm along with the other machine learning network, in order to yield the predicted results of calculating accurate depth, yet with higher accuracy (KSR).
Conclusion
Citation of Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Sugiura; Junichi. (US 9970285 B2) describes “ A method for managing a drilling operation includes generating, by a first sensor and a second sensor of a bottom hole assembly (BHA), a first time based data log and a second time based data log representing a borehole parameter along a drilling trajectory. A time shift is determined by comparing the first time based data log and the second time based data log. The time shift is processed along with an axial distance between the sensors to compute a rate of penetration (Abstract)
Auchere; et al. (US 20190345816 A1) The invention provides “Methods, apparatus, systems, and articles of manufacture are disclosed to measure a formation feature. An example apparatus includes a pre-processor to compare a first measurement obtained from a first sensor included in a logging tool at a first depth at a first time and a second measurement obtained from a second sensor included in the logging tool at the first depth at a second time. The example apparatus also include a semblance calculator to: calculate a correction factor based on a difference between the first measurement and the second measurement; and calculate a third measurement based on the correction factor and a fourth measurement obtained from the first sensor at a second depth at the second time. The example apparatus also includes a report generator to generate a report including the third measurement. (abstract.)
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DILARA SULTANA whose telephone number is (571)272-3861. The examiner can normally be reached Mon-Fri, 9 AM-5:30 PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s
supervisor, EMAN ALKAFAWI can be reached on (571) 272-4448. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/DILARA SULTANA/Examiner, Art Unit 2858
07/25/2026
/EMAN A ALKAFAWI/Supervisory Patent Examiner, Art Unit 2858 7/27/2026
Prosecution Insights