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 .
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 (i.e., changing from AIA to pre-AIA ) 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.
Response to Arguments
The amendment filed 8/25/2025 has been entered. Claims 1-20 remain pending in the application.
Regarding the 35 U.S.C. 112 rejections, Applicant argues:
Applicant has amended the claims as discussed during the interview to address the matters raised in the 112 rejection. Accordingly, Applicant respectfully requests that the rejection of Claims 1-20 under Section 112 be withdrawn.
(Response filed 8/25/2025 [page 11 paragraph 7]).
Examiner agrees the amendments overcome the rejection.
Regarding the 35 U.S.C. 101 rejection, under step 2A prong two, Applicant argues:
Like the above case, Applicant's claimed solution employs any alleged mathematical concept or mental process to achieve an improved result when combined with the specific technical components recited in the claims. For example, Applicant's claimed technique involves a specific process for using a trained machine learning algorithm to synthesize pore pressure at or ahead of the bit position for precise optimization of parameters affecting drilling such as mud weight. The well is then drilled using the optimized mud weight. Thus, Applicant's claim features incorporate any alleged abstract idea into a practical application to improve the technical field of reservoir drilling optimization, and not to merely perform a mental process of mathematical concept. The claims thus reflect the improvement described in Applicant's Specification. See e.g., Specification as Filed [0033].
(Response filed 8/25/2025 [page 19 paragraph 2])(Emphasis added).
Examiner agrees. Based on Applicant’s argument and amendment, the 35 U.S.C. 101 rejection is overcome.
Regarding the prior art rejections, Applicant argues:
Taylor generally describes training a machine learning algorithm using historical data to predict properties such as pore pressure. Taylor, pp. 3-5. In Taylor, the algorithm is "trained on seismic attributes as inputs and logged attributes as targets." Id., at p. 3. However, there is no teaching in Taylor of such training data including labels indicating historical determined pore pressure values "that are derived from one or more of: an increasing or decreasing size or shape of drill cuttings; a detection of gas in drilling fluids; a kick; a loss; a connection gas; or an adjusted pore pressure estimation based on an interpretation of a D-exponent value or an Eaton's method value," as recited in Applicant's claims.
(Response filed 8/25/2025 [page 23 paragraph 5]).
Examiner agrees. Taylor teaches “The model may be populated with any property that is
logged or measured in the wellbore”, (Taylor [page 1 col 2 paragraph 2]), and then goes on to mention pore pressure without describing how pore pressure is derived. Instead, Taylor cites other references to teach how these properties are derived, (Taylor [page 1 col 1 paragraph 1 lines 1-10). Taylor’s purpose is to improve the models used in these references with the use of a machine learning algorithm. Examiner performed an updated search. One of the references cited by Taylor teaches the amended claim limitations. Updated rejections are provided below.
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) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over “Seeing-ahead-of-the-bit: A game changer enabled by Machine Learning” (2020-Taylor) in view of “Ahead of Bit Pore Pressure Prediction using VSP - A Case Study in South China Sea” (2010-Xie)
With respect to claim 1, Taylor teaches A method to synthesize one or more properties for optimizing one or more operations in a well, comprising (see section 1. introduction, [pages 1-2], and section 3.1. methodology, [pages 3-5]): receiving measurements or qualitative indicators of one or more parameters in association with performing operations in the well (The model may be populated with any property that is logged or measured in the wellbore, [page 1 col 2 paragraph 2 lines 1-2], with a focus on "logged or "measured"; where use of Logging While Drilling (LWD) data that is passed via WITSML to a Cloud-based computation environment, [page 1 col 2 paragraph 3 lines 1-3]); providing, based on the measurements, one or more inputs to a machine learning algorithm (MLA) (locally calibrated machine learning model is updated, [page 1 col 2 paragraph 3 lines 3-4], here, “updated” means real-time data is input/ingested in order to update the pre-drill earth model, [page 2 col 1 paragraph 1 line 1]) that has been trained using historical or training well data comprising (The model may be populated with any property that is logged or measured in the wellbore, [page 1 col 2 paragraph 2 lines 1-2], with a focus on "populated"; the model may also be "locally calibrated", [page 2 col 2 paragraph 3 lines 3-4], which is a form of training; more details given in the methodology section: "In this study a shallow feed-forward neural network (FFNN) is used. The FFNN predicts rock properties at the reservoir scale from a network trained on seismic attributes as inputs and logged attributes as targets, [page 3 col 1 paragraph 3 lines 1-4]) historical measured values or historical qualitative indicators corresponding to the one or more parameters (these are the inputs, which may include any of the listed properties, [page 1 col 2 paragraph 2]; however, the methodology section states, which properties are inputs more precisely: "are presented to the neural network, both above and below a given point of analysis for any given seismic sample. The conditioned inputs to the FFNN are seismic amplitudes and, optionally, derived attributes (e.g. Hilbert Transform, AVO, inversion and seismic velocities, etc.). For each point in space, several seismic amplitude values, [page 3 col 1 paragraph 5 line 2]-[page 3 col 2 paragraph 1 line 2]); and labels indicating historical determined pore pressure values (these are the targets, which may include any of the listed properties, [page 1 col 2 paragraph 2]; however, the methodology section states, which properties are targets more precisely: Well log data that have been previously tied to the seismic data are input as training targets, [page 3 col 2 paragraph 1 lines 3-4]; pore pressure is specifically provided as training data, [page 1 col 2 paragraph 2 line 6]) determining, based on one or more outputs from the MLA in response to the one or more inputs, one or more synthesized properties relating to the well, wherein the one or more synthesized properties comprise a synthesized pore pressure at or ahead of a bit position (used to rebuild the entire 3D seismic volume, explicitly in the depth domain, for any of the properties listed above, [page 1 col 2 paragraph 3 lines 4-6]; where the listed above properties include pore pressure, [page 1 col 2 paragraph 2 line 6]; the machine learning algorithm autonomously updates the elastic, petrophysical geomechanical or drilling properties for the entire seismic volume, thus delivering ahead-of-the-bit predictions, [page 2 col 1 paragraph 1 lines 2-5]; pore pressure is also listed as one of the geomechanical properties specifically simulated in table 2, [page 5]); and determining, based on the one or more synthesized properties, one or more optimized parameters relating to drilling the well (parameters relating to drilling are: optimize well delivery with the latest earth model information, [page 2 col 1 paragraph 1 lines 9-10], and parameters for steering are “well delivery teams can more precisely position the well in the desired landing location, [page 6 col 2 paragraph 4 lines 1-4]).
Taylor does not teach that are derived from one or more of: an increasing or decreasing size or shape of drill cuttings; a detection of gas in drilling fluids; a kick; a loss; a connection gas; or an adjusted pore pressure estimation based on an interpretation of a D-exponent value or an Eaton's method value; and wherein the one or more optimized parameters comprises an optimized mud weight that is determined based on the synthesized pore pressure such that the optimized mud weight is not less than or equal to the synthesized pore pressure, and wherein the well is drilled using the optimized mud weight.
However, Xie teaches that are derived from one or more of: an increasing or decreasing size or shape of drill cuttings; a detection of gas in drilling fluids; a kick; a loss; a connection gas; or an adjusted pore pressure estimation based on an interpretation of a D-exponent value or an Eaton's method value (eq. (3) shows how to derive pore pressure using Eaton's model, [page 4]); and wherein the one or more optimized parameters comprises an optimized mud weight that is determined based on the synthesized pore pressure such that the optimized mud weight is not less than or equal to the synthesized pore pressure, and wherein the well is drilled using the optimized mud weight (how and why to optimize mud weight based on pore pressure, [page 1 paragraph 1]; The fourth track shows pore pressure gradient profiles: the red curve is the pore pressure gradient calculated from the wireline sonic data above the current TD, the blue curve is the pore pressure gradient calculated from VSP inverted data, including both above and below the current TD, the brown line is for the mud weight used, [page 5 paragraph 4 lines 1-3]; in FIG. 5, the red line stops, and the brown line/mud weight is increased to match the blue line, which is pore pressure at the bit as calculated, [page 6]; they also discuss that the mud weight was increased during drilling, but not going further than a max to avoid fracture, [page 6 paragraph 2 lines 1-6]).
It would have been obvious to one skilled in the art before the effective filing date to combine Taylor with Xie because a teaching, suggestion, or motivation in the prior art would have led one skilled in the art to combine prior art teaching to arrive at the claimed invention. Taylor discloses a system and method that teaches all of the claimed features except for how to derive pore pressure for purposes of training data and how to use the synthesized/simulated pore pressure ahead of the bit to optimize mud weight. Taylor cites other references to teach how these properties are derived, and why they are used (Taylor [page 1 col 1 paragraph 1 lines 1-10]). In fact, Taylor specifically references Xie (see Reference [2] of Taylor, [page 7]). Xie provides a specific motivation for why optimizing mud weight is important:
Knowledge of formation pore pressure is critical in drilling and completing a well safely and economically. Unknowingly drilling an abnormally overpressured permeable formation can be extremely dangerous. A mud weight lower than the formation pore pressure could result in kicks and blowouts, whilst an overly high mud weight not only slows down rate of penetration but also could fracture the formation and result in mud losses. An accurate assessment and prediction of fluid pressure in the formations ahead of the bit is therefore essential for decisions on the optimal mud weight and casing point for next openhole section.
(Xie [page 1 paragraph 1]).
Xie then goes into why the Eaton model is used to achieve this goal:
Eaton model was adopted to calculate pore pressure profile both above and below current well TD for its simplicity and flexibility to calibrate the model based on the available data above the current TD.
(Xie [page 3 paragraph 6 lines 1-2]).
A person having skill in the art would have a reasonable expectation of successfully calculating the pore pressure ahead of the bit using the teachings of Xie, and then increasing the speed or accuracy of the calculations by incorporating these calculations into a machine learning model as is taught by Taylor. Therefore, it would have been obvious to combine Taylor with Xie to a person having ordinary skill in the art, and this claim is rejected under 35 U.S.C. 103.
With respect to claim 2, Taylor in view of Xie teaches all of the limitations of claim 1, as noted above. Taylor further teaches wherein the measurements or qualitative indicators comprise one or more of: drilling data; a resistivity value; a gamma value; or a sonic velocity value (The model may be populated with any property that is logged or measured in the wellbore ... including... Elastic Properties: e.g. DT, DTS, Vp/Vs, etc., Petrophysical Properties: e.g. Density, Gamma Ray, Neutron, etc., Geomechanical Properties: e.g. Pore Pressure, Principal Stresses, Young’s Modulus, Poisson’s Ratio, etc. and Drilling Properties: e.g. Wellbore Stability, UCS, [page 1 col 2 paragraph 2]).
With respect to claim 3, Taylor in view of Xie teaches all of the limitations of claim 1, as noted above. Taylor further teaches comprising deriving, based on the synthesized pore pressure at or ahead of the bit position, one or more of: a confidence interval; or a fracture gradient (performance analysis were conducted next; involving error analysis, [page 5 col 1 paragraph 5 lines 10-11]; where confidence metrics were produced, [page 5 col 2 paragraph 2 line 4]; in the example, applied to UCS and acoustic impedance, but extendable to all of the properties listed in table 2 including pore pressure in column 3 row 1, see Table 2 [page 5]; which is at or ahead because the simulation is for the entire geometric volume, [page 2 col 1 paragraph 1 lines 2-5]; fracture gradient is one of the drilling properties for which the simulation may be extended in Table 2, column 4 row 4, [page 5]).
With respect to claim 4, Taylor teaches all of the limitations of claim 1, as noted above. Taylor does not teach wherein determining, based on the one or more synthesized properties, the one or more optimized parameters comprises determining one or more of: a rate of penetration (ROP); a weight on bit (WOB); or a depth for setting casing.
However, Xie teaches wherein determining, based on the one or more synthesized properties, the one or more optimized parameters comprises determining one or more of: a rate of penetration (ROP); a weight on bit (WOB); or a depth for setting casing (high mud weight slows down rate of penetration, [page 1 paragraph 1 lines 3-4]; so by increasing mud weight in response to higher pore pressure, reference is also optimizing rate of penetration; see the citations to optimization of mud weight: how and why to optimize mud weight based on pore pressure, [page 1 paragraph 1]; The fourth track shows pore pressure gradient profiles: the red curve is the pore pressure gradient calculated from the wireline sonic data above the current TD, the blue curve is the pore pressure gradient calculated from VSP inverted data, including both above and below the current TD, the brown line is for the mud weight used, [page 5 paragraph 4 lines 1-3]; in FIG. 5, the red line stops, and the brown line/mud weight is increased to match the blue line, which is pore pressure at the bit as calculated, [page 6]; they also discuss that the mud weight was increased during drilling, but not going further than a max to avoid fracture, [page 6 paragraph 2 lines 1-6]).
It would have been obvious to one skilled in the art before the effective filing date to combine Taylor with Xie because a teaching, suggestion, or motivation in the prior art would have led one skilled in the art to combine prior art teaching to arrive at the claimed invention. Taylor discloses a system and method that teaches all of the claimed features except for how to derive pore pressure for purposes of training data and how to use the synthesized/simulated pore pressure ahead of the bit to optimize mud weight. Taylor cites other references to teach how these properties are derived, and why they are used (Taylor [page 1 col 1 paragraph 1 lines 1-10]). In fact, Taylor specifically references Xie (see Reference [2] of Taylor, [page 7]). Xie provides a specific motivation for why optimizing mud weight is important:
Knowledge of formation pore pressure is critical in drilling and completing a well safely and economically. Unknowingly drilling an abnormally overpressured permeable formation can be extremely dangerous. A mud weight lower than the formation pore pressure could result in kicks and blowouts, whilst an overly high mud weight not only slows down rate of penetration but also could fracture the formation and result in mud losses. An accurate assessment and prediction of fluid pressure in the formations ahead of the bit is therefore essential for decisions on the optimal mud weight and casing point for next openhole section.
(Xie [page 1 paragraph 1]).
Xie then goes into why the Eaton model is used to achieve this goal:
Eaton model was adopted to calculate pore pressure profile both above and below current well TD for its simplicity and flexibility to calibrate the model based on the available data above the current TD.
(Xie [page 3 paragraph 6 lines 1-2]).
A person having skill in the art would have a reasonable expectation of successfully calculating the pore pressure ahead of the bit using the teachings of Xie, and then increasing the speed or accuracy of the calculations by incorporating these calculations into a machine learning model as is taught by Taylor. Therefore, it would have been obvious to combine Taylor with Xie to a person having ordinary skill in the art, and this claim is rejected under 35 U.S.C. 103.
With respect to claim 5, Taylor in view of Xie teaches all of the limitations of claim 1, as noted above. Taylor further teaches wherein: the labels in the historical or training well data are further based on one or more of: historical compressive strength values; historical wellbore stability values; historical rock stress values; historical formation temperature values; historical Young's modulus value; or historical Poisson's ratio values (The model may be populated with any property that is logged or measured in the wellbore ... including... Elastic Properties: e.g. DT, DTS, Vp/Vs, etc., Petrophysical Properties: e.g. Density, Gamma Ray, Neutron, etc., Geomechanical Properties: e.g. Pore Pressure, Principal Stresses, Young’s Modulus, Poisson’s Ratio, etc. and Drilling Properties: e.g. Wellbore Stability, UCS, [page 1 col 2 paragraph 2]); and the one or more synthesized properties further comprise one or more of: a synthesized compressive strength at or ahead of the bit position; a synthesized wellbore stability at or ahead of the bit position; a synthesized rock stress at or ahead of the bit position; a synthesized formation temperature at or ahead of the bit position a synthesized Young's modulus at or ahead of the bit position; or a synthesized Poisson's ratio at or ahead of the bit position (the properties can be simulated using the QEarth methodology and are shown in Table 2, [page 5], compressive strength is abbreviated “UCS” under drilling properties, wellbore stability is WBS, stress and Poisson’s ratio are under geomechanical properties; the properties are at or ahead because the simulation is for the entire geometric volume, [page 2 col 1 paragraph 1 lines 2-5]).
With respect to claim 6, Taylor in view of Xie teaches all of the limitations of claim 1, as noted above. Taylor further teaches determining, based on the one or more outputs from the MLA, an alert related to a potentially problematic condition related to the operations in the well (the alert is a snapshot, shown in FIG. 7a-7c, [page 6]; and this alert allows drilling engineer to mitigate potential drilling hazards such as shallow water flow, shallow gas, anomalous high pressured pockets, lost circulation, wellbore instability, stuck pipe, top/base target formation and salt/sediment proximity to delineate salt boundaries, [page 6 col 2 paragraph 4 lines 7-11]).
With respect to claim 7, Taylor in view of Xie teaches all of the limitations of claim 1, as noted above. Taylor further teaches wherein the alert indicates one or more of: a risk of a blowout; a risk of a stuck pipe; a risk of damaging a drill bit; a risk of a drilling abnormality or a drilling inefficiency; a recommended change in mud weight; a recommended change in rate of penetration (ROP); a recommended change in weight on bit (WOB); or a recommended depth for setting casing(the alert is a snapshot, shown in FIG. 7a-7c, [page 6]; and this alert allows drilling engineer to mitigate potential drilling hazards such as shallow water flow, shallow gas, anomalous high pressured pockets, lost circulation, wellbore instability, stuck pipe, top/base target formation and salt/sediment proximity to delineate salt boundaries, [page 6 col 2 paragraph 4 lines 7-11]; these are all risks of drilling abnormality and inefficiency because the improvement is to reduce operational risk and improve drilling efficiency, see solution at [Abstract] paragraph 2, solving the problem of operational risk and drilling inefficiency, [Abstract] paragraph 1).
With respect to claim 8, Taylor in view of Xie teaches all of the limitations of claim 1, as noted above. Taylor further teaches providing one or more additional inputs to the MLA based on the one or more optimized parameters; and determining, based on one or more additional outputs received from the MLA in response to the one or more additional inputs, one or more updated synthesized properties relating to the well (optimize well delivery with the latest earth model information, [page 2 col 1 paragraph 1 lines 9-10], where "latest" refers to the fact that updates occur in real-time, see [page 1 col 2 paragraph 1], so each action taken to optimize well delivery effectively changes the model while the well is being drilled; example given where the acoustic impedance model is revised autonomously and in near real-time. Important updates can be discerned from the retrained and resimulated earth model after each 30-minute sequence. Properties ahead of the drill bit are also updated as a by-product of revising the entire seismic volume, [page 6 col 2 paragraph 2 lines 5-10]).
With respect to claim 9, Taylor teaches A system, comprising: one or more processors; and a memory comprising instructions that, when executed by the one or more processors, cause the system to: (cloud-based computation environment, [page 1 col 2 paragraph 3 lines 2-3], running QEarth, [page 1 col 2 paragraph 1 line 1]; where a cloud-based computation environment has memory with the instructions for QEarth executed by the processors of the cloud based computation environment).
With regard to the rest of claim 9, incorporating the rejection of claim 1, claim 9 is rejected for a substantially similar rationale.
With respect to claim 10, incorporating the rejections of claim 9 and claim 2, claim 10 is rejected for a substantially similar rationale.
With respect to claim 11, incorporating the rejections of claim 9 and claim 3, claim 11 is rejected for a substantially similar rationale.
With respect to claim 12, incorporating the rejections of claim 9 and claim 4, claim 12 is rejected for a substantially similar rationale.
With respect to claim 13, incorporating the rejections of claim 9 and claim 5, claim 13 is rejected for a substantially similar rationale.
With respect to claim 14, incorporating the rejections of claim 9 and claim 6, claim 14 is rejected for a substantially similar rationale.
With respect to claim 15, incorporating the rejections of claim 9 and claim 7, claim 15 is rejected for a substantially similar rationale.
With respect to claim 16, incorporating the rejections of claim 9 and claim 8, claim 16 is rejected for a substantially similar rationale.
With respect to claim 17, Taylor teaches A non-transitory computer-readable medium comprising instructions that, when executed by one or more processors of a computing system, cause the computing system to: (memory of the cloud-based computation environment, [page 1 col 2 paragraph 3 lines 2-3], running QEarth, [page 1 col 2 paragraph 1 line 1]; where a cloud-based computation environment has memory with the instructions for QEarth executed by the processors of the cloud based computation environment).
With regard to the rest of claim 17, incorporating the rejection of claim 1, claim 17 is rejected for a substantially similar rationale.
With respect to claim 18, incorporating the rejections of claim 17 and claim 2, claim 18 is rejected for a substantially similar rationale.
With respect to claim 19, incorporating the rejections of claim 17 and claim 3, claim 19 is rejected for a substantially similar rationale.
With respect to claim 20, incorporating the rejections of claim 17 and claim 4, claim 20 is rejected for a substantially similar rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
U.S. 2020/0233113 A1 (Luo) – Claim 6. The computer-implemented method of claim 2, wherein updating the seismic logging information during the drilling operation comprises: applying a moveout correction to traces in the seismic logging information by applying a source-receiver distance dependent time shift to each trace of the seismic logging information; combining the traces into a single trace by summing amplitude values of all traces at each time step and normalizing the traces to create a supertrace; applying a time-frequency analysis method to the supertrace, decomposing the time series within each short-time window into different frequency components; predicting, using the time-frequency analysis and by applying machine learning techniques, rock properties around and ahead of the drill bit, the rock properties associated with geological formations, including rock hardness, pore pressure, and fractures; and using the predicted rock properties around and ahead of the drill bit to adjust a drilling program in real time, [see claim 6].
U.S. 11,982,174 B2 (Anifowose) – Calculating the D-Exponent, [col 3 ln 48-62]; where the D-Exponent is a surface drilling parameter: “During the drilling, the surface sensors measure and monitor the surface drilling parameters. In one or more embodiments, the surface drilling parameters are rate of penetration (ROP), weight of the drill bit (WOB), torque, revolutions per minute (RPM) of the drill bit, hook load, mud flow rate, D-Exponent, mud density, standpipe pressure, and mud temperature. The well sensors measure the LWD data representing the formation. In one or more embodiments, the LWD data comprise various physical parameters, such as sound, gamma, and neutron ray emissions which comprise gamma ray, sonic, resistivity, and neutron porosity”, [col 3 ln 36-47]; followed by inputting the parameter into the ML engine: “The chemical composition of the gases (mud gas data) together with the LWD data and the surface drilling parameters is then inputted into a machine learning (ML) engine to determine an estimated pore pressure log of a well in the reservoir. The ML engine outputs the real-time pore pressure log of the well in the reservoir on a monitor 128. The ML engine for performing the method for determining a real-time pore pressure log of a well in a reservoir is illustrated in FIG. 2.”, [col 4 ln 34-42].
U.S. 10,242,312 B2 (Storm, Jr.) - As the drilling fluid 37 is being pumped into the production wellbore 10 by the mud pump 26 and the returns 39 are being received from the return line 30, the mud logging tool 34 may analyze the cuttings. The mud logging tool 34 may include an extractor for separating gas entrained in the cuttings, a gas analyzer, and a carrier system for delivering the gas sample to the analyzer. The gas analyzer may be a chromatograph or optical analyzer. The mud logging tool 34 may further include a source rock analyzer (SRA) for elemental analysis and/or mineral composition of the cuttings. The SRA may include a pyrolyzer, such as an oven or laser, an infrared cell, and a flame ionization detector. The measurements by the mud logging tool 34 may be recorded in a MEM 40g for later use. Parameters of the drilling fluid 37, such as density (aka mud weight) and resistivity may be measured by the mud logging tool and/or input by the mud engineer and stored in the MEM 40g for later use. An equivalent circulation density (ECD) of the drilling fluid 37 may be greater than a pore pressure gradient of the reservoir 6 such that an overbalanced condition is maintained during drilling thereof. Alternatively, the ECD of the drilling fluid 37 may be less than or equal to the pore pressure gradient such that a balanced or underbalanced condition is maintained during drilling of the reservoir 6. For balanced or underbalanced drilling, the drilling system may further include a rotating control device connected to the wellhead 14 (above the flow cross 35) and having a rotating seal engaged with the drill stem 12s. In this alternative, the drilling system 11 may further include a variable choke valve assembled as part of the return line 30 and in communication with the PLC 36 for operation thereby and a mud-gas separator (MGS) assembled as part of the return line 30. In this alternative, the gas analyzer may then be in communication with a gas outlet of the MGS, thereby obviating the need for an extractor, [col 6 ln 26-60]
“Looking Ahead Of The Bit While Drilling: From Vision To Reality” (2016-Constable) - A vision in the oil industry for decades is becoming a reality - we can now finally drill and react pro-actively to formation resistivity properties identified several meters ahead of the drill-bit, instead of drilling reactively on resistivity measurements at or behind the bit. Through a technology collaboration with Schlumberger, Statoil supported a targeted technology development for measuring resistivity contrasts ahead of the bit in real-time to reduce cost and risk during drilling operations.
“Seismic-While-Drilling Operation and Applications” (2007-Dethloff) - While many SWD projects include well path-on seismic mapping objectives, it is becoming increasingly desirable to perform waveform processing of SWD data in order to image and detect certain targets ahead of the projected well path. Applications in this arena include, for example, sub-seismic fault imaging and overpressure detection. In order to support these design objectives, it is necessary to model wavefields and amplitude distributions in 3D using wave-front ray tracing and finite difference modelling tools. These tools have been specifically designed for the borehole to include all aspects of borehole and source geometries while accounting for diffractions, anisotropy, and converted waves, [page 3 col 1 paragraph 3].
“Application of Drilling Performance Data to Overpressure Detection” (1966-Jorden) – See eq. (1) for the D-exponent method, [page 1391].
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DANIEL MILLER whose telephone number is (408) 918-7548. The examiner can normally be reached on Monday-Friday from 11am to 5pm (PT).
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Emerson Puente, can be reached at telephone number (571) 272-3652. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/D.M./Examiner, Art Unit 2187
/EMERSON C PUENTE/ Supervisory Patent Examiner, Art Unit 2187