DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-20 are presented for examination based on the amended claims in the application filed on February 23, 2026.
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 1-8 and 10-20 are rejected under 35 U.S.C. § 103 as being unpatentable over US 2012/0215364 A1 Rossi, David John [herein “Rossi”] in view of Alarcón, Gabriel A., Carlos F. Torres-Monzón, Nellyana Gonzalo, and Luis E. Gómez. “Global Optimization of Gas Allocation to a Group of Wells in Artificial Lift Using MATLAB” In Engineering Technology Conference on Energy, vol. 80197, pp. 515-520. American Society of Mechanical Engineers, 2001 [herein “Alarcón”].
Claim 9 is rejected under 35 U.S.C. § 103 as being unpatentable over Rossi and Alarcón as applied to claim 8 above, and in further view of Rashid, Kashif “Optimal allocation procedure for gas-lift optimization.” Industrial & Engineering Chemistry Research 49, no. 5 (2010): 2286-2294 [herein “Rashid”].
This action is made Non-Final.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on February 23, 2026 has been entered.
Response to Amendment
The amendment filed February 23, 2026 has been entered. Claims 1-20 remain pending in the application. Applicant’s amendments to the Drawings and Claims have overcome each and every objection previously set forth in the Final Office Action mailed January 27, 2026.
Claim Objections
Claims 19-20 are objected to because of the following informalities:
Claim 19, which recites “generate a first gas lift profile for first the well” in Ln. 15, should be “generate a first gas lift profile for first well”.
Claim 19, which recites “a second will included in the the the plurality of wells” in Ln. 10-20, should be “a second well included in the the ”.
Claim 19, which recites “by an electronic gas lift value” in Ln. 20, should be “by an electronic gas lift valve”.
Claim 20, having similar limitations of claim 19, is also objected
Appropriate correction is required.
Claim Rejections - 35 U.S.C. § 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.
Claim 1 recites the phrase “for production of fluid by the well” in Ln. 17. This phrase renders the claim indefinite, because it is unclear what “the well” in the phrase is referring to. The instance of “the well” could be referring to either the instance of a “first well” or the instance of a “second well”. Therefore, it is unclear which is being referred to and the scope of the claim is unclear (See MPEP § 2173.05(h)). For examination purposes, the examiner has interpreted that “the well” in this phrase to be the instance of a “first well”. The examiner recommends that applicant amend the claim language from “the well” in this phrase to “the first well” or similar, as supported by the specification, when referring to an “the well”. Claims 2-18, which are dependent on claim 1, are similarly rejected. Claim 17 and 19-20, having similar limitations of claim 11, is also rejected under the similar rationale.
Claim 16 recites the phrase “the generating the gas lift profile” in Ln. 1-2. This phrase renders the claim indefinite, because it is unclear what “the gas lift profile” in the phrase is referring to. The instance of “the gas lift profile” could be referring to either the instance of a “first gas lift profile” or the instance of a “second gas lift profile”. Therefore, it is unclear which is being referred to and the scope of the claim is unclear (See MPEP § 2173.05(h)). For examination purposes, the examiner has interpreted that “the gas lift profile” in this phrase to be the instance of a “first gas lift profile”. The examiner recommends that applicant amend the claim language from “the gas lift profile” in this phrase to “the first gas lift profile” or similar, as supported by the specification, when referring to an “the gas lift profile”.
Claim Rejections - 35 U.S.C. § 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. § 102(b)(2)(C) for any potential 35 U.S.C. § 102(a)(2) prior art against the later invention.
Claims 1-8 and 10-20 are rejected under 35 U.S.C. § 103 as being unpatentable over US 2012/0215364 A1 Rossi, David John [herein “Rossi”] in view of Alarcón, Gabriel A., Carlos F. Torres-Monzón, Nellyana Gonzalo, and Luis E. Gómez. “Global Optimization of Gas Allocation to a Group of Wells in Artificial Lift Using MATLAB” In Engineering Technology Conference on Energy, vol. 80197, pp. 515-520. American Society of Mechanical Engineers, 2001 [herein “Alarcón”].
As per claim 1, Rossi teaches “A method comprising: receiving production fluid flow rate data from a first well in a field that comprises a plurality of wells”. (Para. 0066-0068, “During certain field operations, several measurements are made for gas lifted wells, and may be repeated at predetermined intervals: 1. Injected lift gas pressure and flow rate (which, in some embodiments, is measured daily) 2. Well production liquid flow rate, gas-oil ratio (GOR) and water cut (i.e., ratio of water flow rate to liquid flow rate, which is typically taken during occasional well tests, e.g., every few weeks)” [receiving production fluid flow rate data from a first well]. Para. 0062, “FIG. 3 illustrates an oilfield 300 for performing production operations in accordance with implementations of various technologies and techniques described herein. As shown, the oilfield has a plurality of wellsites 302 operatively connected to central processing facility 354” [in a field that comprises a plurality of wells]. Further see Para. 0062-0072. The examiner has interpreted that making measurements including well production liquid flow rate, gas-oil ratio (GOR) and water cut for a plurality of wellsites in an oilfield as a method comprising receiving production fluid flow rate data from a first well in a field that comprises a plurality of wells.)
Rossi teaches “defining an operating point for the first well”. (Para. 0064, “For each well, the gas lift well model that was created during the initial step of designing the gas lift completion may used to compute gas lift well performance curves, as illustrated conceptually in FIG. 4 at 400. Each gas lift well performance curve indicates the output wellbore production liquid flow rate versus the input injected lift gas flow rate; a family of performance curves will be computed for a set of wellhead flowing pressures (i.e., the surface network back-pressure against which the well produces). For a given value of injected lift gas flow rate, a higher value of wellhead flowing pressure (higher back-pressure) results in a smaller wellbore production liquid flow rate. More particularly, the gas lift well performance curves include a first performance curve 402 illustrating the output wellbore production liquid flow rate with a wellhead flowing pressure at 50 psig, a second performance curve 404 illustrating the output wellbore production liquid flow rate with a wellhead flowing pressure at 100 psig, a third performance curve 406 illustrating the output wellbore production liquid flow rate with a wellhead flowing pressure at 150 psig, and a fourth performance curve 408 illustrating the output wellbore production liquid flow rate with a wellhead flowing pressure at 200 psig” [defining an operating point for first well]. Para. 0072, “the gas lift well performance curves 402-408 for a well (FIG. 4) is used to compute the optimum operating point for that well”. Further see Para. 0064-0072. The examiner has interpreted having an initial gas lift well model to computer gas lift well performance curves that show the production liquid flow rate versus the input injected lift gas flow rate for a set of pressures then used to generate to compute the optimum operating point for that well as defining an operating point for the first well.)
Rossi teaches “generating a first gas lift profile for the well using [the first polynomial and] the production fluid flow rate data”. (Para. 0064, “Each gas-lifted well can be thought of a having one input (lift gas) and one output (produced liquid). For each well, the gas lift well model that was created during the initial step of designing the gas lift completion may used to compute gas lift well performance curves, as illustrated conceptually in FIG. 4 at 400. Each gas lift well performance curve indicates the output wellbore production liquid flow rate versus the input injected lift gas flow rate; a family of performance curves will be computed for a set of wellhead flowing pressures (i.e., the surface network back-pressure against which the well produces). For a given value of injected lift gas flow rate, a higher value of wellhead flowing pressure (higher back-pressure) results in a smaller wellbore production liquid flow rate”. Further see Para. 0064. The examiner has interpreted that computing gas lift performance curves that indicate the output wellbore production liquid flow rate versus the input injected lift gas flow rate as a result in a change of pressure as generating a first gas lift profile for the first well using the production fluid flow rate data.)
Rossi teaches “solving a system of equations [that includes the first polynomial] representing the first gas lift profile and [a second polynomial] representing a second gas lift profile associated with a second well included in the plurality of wells to generate first results”. (Para. 0064, “For each well, the gas lift well model that was created during the initial step of designing the gas lift completion may used to compute gas lift well performance curves, as illustrated conceptually in FIG. 4 at 400. Each gas lift well performance curve indicates the output wellbore production liquid flow rate versus the input injected lift gas flow rate; a family of performance curves will be computed for a set of wellhead flowing pressures (i.e., the surface network back-pressure against which the well produces). For a given value of injected lift gas flow rate, a higher value of wellhead flowing pressure (higher back-pressure) results in a smaller wellbore production liquid flow rate” [equations representing the first gas lift profile and a second gas lift profile associated with a second well included in the plurality of wells]. Para. 0074, “ln denotes the gas lift rate into well n=1, 2, . . . , N” [equations representing the first gas lift profile and a second gas lift profile associated with a second well included in the plurality of wells]. Para. 0078, “the field-level optimization problem in Equations 1a-b is solved (block 606) with respect to well, surface network, and facility equipment constraints (block 608), resulting in a candidate set of recommended lift gas flow rates low rates In for the wells” [solving a system of equations representing equations representing the first gas lift profile and a second gas lift profile associated with a second well included in the plurality of wells to generate first results]. Further see Para. 0064 and 0074-0078. The examiner has interpreted that computing gas lift well performance curves for each well based on the lift gas flow rate to solve a set of recommended lift gas flow rates low rates for the wells as solving a system of equations representing the first gas lift profile and representing a second gas lift profile associated with a second well included in the plurality of wells to generate first results.)
Rossi teaches “issuing a first instruction based at least in part on the first results to control gas lift for production of fluid by the well.” (Para. 0072, “the gas lift well performance curves 402-408 for a well (FIG. 4) is used to compute the optimum operating point for that well. When gas supply is unlimited, an optimum operating point for a well is typically at the maximum value of the curve for the current tubing head pressure (which itself depends on the production from neighboring wells due to network back-pressure effects). In the more general case where lift gas supply is limited, an optimization problem may be solved that computes the amount of lift gas to inject into each gas lifted well in order to maximize the overall oil production from the field” [e.g., instruction based at least in part on the first results to control gas lift for production of fluid by the first well]. Para. 0085, “These optimized gas lift flow rates are transmitted to well controllers 804a-g for each respective well and are used by the respective well controllers 804a-g with a closed-loop set point controller to set and maintain the gas lift rate for each respective well at its optimized value” [e.g., issuing a first instruction based at least in part on the results to control gas lift]. Further see Para. 0072 and 0085. The examiner has interpreted that using the gas lift performance curve to compute the optimum operating point for that well and computing the amount of lift gas to injected into each well to be transmitted to well controllers to maximize the overall oil production from the field as issuing a first instruction based at least in part on the first results to control gas lift for production of fluid by the well.)
Rossi teaches “automatically adjusting, by an electronic gas lift valve, the flow rate of gas into the first well based on the first instruction”. (Para. 0084, “Individual wells may also be equipped with a Distributed Control System (DCS), such as the one illustrated by the section 710 (marked by the dashed lines) of the lift gas flow control line 700 of FIG. 7. For control of the lift gas rate at each well, a measurement is made of the lift gas temperature, pressure and pressure drop across an orifice, ΔP, which may be used in an AGA industry-standard computation to determine the rate of lift gas flow for the well. Via an electrical actuator or a motorized automated choke or valve, the system may continuously regulate the gas lift flow rate to maintain the rate at the desired well rate set points given in Equation 2 (this may be referred to as closed-loop set point control technology”) [automatically adjusting, by an electronic gas lift valve, the flow rate of gas into the first well]. Para. 0085, “These optimized gas lift flow rates are transmitted to well controllers 804a-g for each respective well and are used by the respective well controllers 804a-g with a closed-loop set point controller to set and maintain the gas lift rate for each respective well at its optimized value” [e.g., based on the first instruction]. Further see Para. 0084-0085. The examiner has interpreted that equipping wells with a Distributed Control System and well controllers to regulate, set, and maintain the gas lift flow rate for each respective well at its optimized value and to maintain the rate at the desired well rate set points as automatically adjusting, by an electronic gas lift valve, the flow rate of gas into the first well based on the first instruction.)
Rossi teaches “detecting, using one or more composition sensors, a change in composition of gas within the first well; updating a parameter of the first gas lift profile based in part on the change in composition of gas within the first well” (Para. 0107-0111, “FIG. 15 indicates a set of information (S*, ln, qn, Pwf) that flows between the Central Controller and the Well Controller on a recurring basis, e.g., every few minutes. Less frequently, other information (not illustrated in FIG. 15) may be communicated, including: 1. Well level constraints such as maximum production rates (liquid, oil, water or gas), minimum bottom hole flowing pressure, and/or maximum well head temperature; 2. Produced fluid attributes such as gas-oil ratio and water cut; 3. The well gas lift performance curves (q versus l) for different values of P.sub.wf. This information typically varies much more slowly than ln, qn, Pwf and S*, and thus may be communicated between the Central and Well Controllers on a much less frequent basis, for example only from time to time when changes occur” [e.g., detecting a change in composition of gas within the first well]. Para. 0105, “The field-wide lift gas usage L is obtained by summing the well lift gas rates ln, and the field-wide oil production rate Q is obtained by summing the well oil rates qn (block 1514). If L is below Lmax (block 1516), there is spare unused lift gas capacity, so the Central Controller will decrease the single-variable slope control to a new value S* and transmit that to each well (block 1518). Once the summed value L is very close to the limiting value Lmax, the field oil production has been optimized with respect to the constraint on available lift gas. The process is repeated to maintain the field system at an optimized condition” [updating a parameter of the first gas lift profile based in part on the change in composition of gas within the first well]. Para. 0040, “Sensors (S), such as gauges, may be positioned about oilfield 100 to collect data relating to various oilfield operations as described previously. As shown, sensor (S) is positioned in one or more locations in the drilling tools and/or at rig 128 to measure drilling parameters, such as weight on bit, torque on bit, pressures, temperatures, flow rates, compositions, rotary speed, and/or other parameters of the field operation. Sensors (S) may also be positioned in one or more locations in the circulating system” [detecting, using one or more composition sensors]. Further see Para. 0040, 0105, and 0107-0111. The examiner has interpreted that when change occurs in produced fluid attributes such as gas-oil ratio and composition of the gas to decrease the performance curve slope and the single-variable slope control to a new value as detecting, using one or more composition sensors, a change in composition of gas within the first well and updating a parameter of the first gas lift profile based in part on the change in composition of gas within the first well.)
Rossi teaches “after updating the parameter, resolving the system of equations to generate additional results; issuing a second instruction based at least in part on the additional results to control the gas lift for production of fluid by the first well; and automatically adjusting, by the electronic gas lift valve, the flow rate of gas into the first well based on the second instruction”. (Para. 0104, “The well head flowing pressure Pwf is monitored; it may vary due to the fact that all of the wells on the network are simultaneously adjusting their own lift gas flow rates, and the well pressures interact through the network. Because all of the wells in the field are adjusting their lift gas injection rates at the same time, to avoid the risk of system-wide instability, it may be necessary to introduce limits on how large a change each well controller can make at one time, or how quickly successive changes can be made by each well head controller. Once the well head flowing pressure Pwf stabilizes, the value is determined (block 1414) and then compared to the value of Pwf at the start of the cycle (block 1416). If they are significantly different, the procedure may be repeated until Pwf does not change significantly from one cycle to the next, by returning control to block 1406” [repeating the solving, issuing, and adjusting, e.g., after updating the parameter, resolving the system of equations to generate additional results; issuing a second instruction based at least in part on the additional results to control the gas lift for production of fluid by the first well; and automatically adjusting, by the electronic gas lift valve, the flow rate of gas into the first well based on the second instruction]. Further see Para. 0104-0105 and 0107-0111. The examiner has interpreted limiting how quickly successive changes by each well head controller are made so the value of the pressure is stable and does not differ significantly after repeated changes as after updating the parameter, resolving the system of equations to generate additional results; issuing a second instruction based at least in part on the additional results to control the gas lift for production of fluid by the first well; and automatically adjusting, by the electronic gas lift valve, the flow rate of gas into the first well based on the second instruction.)
Rossi does not specifically teach “evaluating a Jacobian as a first function of the operating point to return a first result”, “evaluating a Hessian as a second function of the operating point to return a second result”, “constructing a first polynomial based in part on the first result and the second result, wherein the first result is a first coefficient for a linear term in the first polynomial and the second result is a second coefficient for a quadratic term in the first polynomial”, “generating a first gas lift profile for the well using the first polynomial and the production fluid flow rate data”, and “solving a system of equations that includes the first polynomial representing the first gas lift profile and a second polynomial representing a second gas lift profile associated with a second well included in the plurality of wells to generate first results”.
However, in the same field of endeavor namely optimization of gas lift models, Alarcón teaches “evaluating a Jacobian as a first function of the operating point to return a first result”. (Pg. 3, “the objective function, f(x), to be minimized or maximized may be subject to constraints which may be in the form of equality constraints, gj(x) = 0 (j = 1, 2,..., me), inequality constraints, gj(x) ≤ 0 (j =me+1,..., m), and or parameter bounds… g is the vector of equality and inequality constraints”. Eq. 13 shows the derivative of g, given as ∇gj (x*)= 0, at the solution point. This is obtaining a first order derivative of the constraints at the solution point, e.g., evaluating a Jacobian as a first function of the operating point to return a first result. Further see Pg. 3-4. The examiner has interpreted that determining the derivative of vectors of equality constraints and inequality constraints at a solution point as evaluating a Jacobian as a first function of the operating point to return a first result.)
Alarcón teaches “evaluating a Hessian as a second function of the operating point to return a second result; and constructing a first polynomial based in part on the first result and the second result, wherein the first result is a first coefficient for a linear term in the first polynomial and the second result is a second coefficient for a quadratic term in the first polynomial.” (Pg. 2, “the GLPR curve can be generated with field data by measuring the rate of injection gas and the rate of oil production. This work uses the GLPR curves of a group of wells as the basis of the optimization technique for the gas lift allocation to each one of them. It is necessary to fit the field data with a mathematical expression to help the computer handle the GLPR curves. The second-degree polynomial has been traditionally used for this purpose. However, the Eq. (1) is proposed in this work to fit the field data.” [different second-degree polynomial, e.g., constructing a polynomial]. Pg. 4, “The GP is simplified by assuming that bound constraints have been expressed as inequality constraints. The QP sub-problem is obtained by linearizing the nonlinear constraint and is written as” [see Eq. 16-18] “where, d is an n-dimensional vector indicating a search direction, Hk is a positive definite approximation of the Hessian matrix of the Lagrangian function, Eq. (15), and k is the number of the actual iteration. This sub-problem can be solved using any QP algorithm and the solution used to form a new iterate” [e.g., evaluating a Hessian as a second function of the operating point to return a second result]. Pg. 4, “The nonlinear programming algorithms based on KT equations attempt to compute directly the LM. Constrained Quasi-Newton methods guarantee super linear convergence by accumulating second order information regarding the KT equations using a Q-N updating procedure. These methods are commonly referred to as Sequential Quadratic Programming (SQP) since a QP sub-problem is solved at each major iteration. SQP methods represent the state-of-the-art in nonlinear programming. Given the GP of Eq. (8), (9), (10) and (11), the principal idea is the formulation of a QP sub-problem based on a quadratic approximation of the Lagrangian function” [e.g., Jacobian function, using the first result]. Further equations 16-18 show using the optimizing of the gas injection rates to obtain the maximum oil production rate using the derivative of the constraint and the Hessian matrix, e.g., constructing a first polynomial based in part on the first result and the second result. Jacobian functions represent a linear approximation of a first order derivative, and Hessian matrix represent a second-order derivatives of coefficients of quadratics approximations of the function as defined in NPL document1, e.g. the first result is a first coefficient for a linear term in the first polynomial and the second result is a second coefficient for a quadratic term in the first polynomial. Further see Pg. 2-4. The examiner has interpreted that fitting field data of rate of oil production using a second-degree polynomial with an optimized gas injection rate to meet the constraint vector and include a quadratic approximation of the Lagrangian function and an approximation of the Hessian matrix of the Lagrangian function as evaluating a Hessian as a second function of the operating point to return a second result; and constructing a first polynomial based in
1Nocedal, Jorge, and Stephen J. Wright. Numerical optimization. New York, NY: Springer New York, 2006.
part on the first result and the second result, wherein the first result is a first coefficient for a linear term in the first polynomial and the second result is a second coefficient for a quadratic term in the first polynomial.)
Alarcón teaches “generating a first gas lift profile for the well using the first polynomial and the production fluid flow rate data”. (Pg. 2, “the GLPR curve can be generated with field data by measuring the rate of injection gas and the rate of oil production. This work uses the GLPR curves of a group of wells as the basis of the optimization technique for the gas lift allocation to each one of them. It is necessary to fit the field data with a mathematical expression to help the computer handle the GLPR curves. The second-degree polynomial has been traditionally used for this purpose. However, the Eq. (1) is proposed in this work to fit the field data.” [generating a gas lift profile for the well using the production fluid flow rate data]. Pg. 4, “The nonlinear programming algorithms based on KT equations attempt to compute directly the LM. Constrained Quasi-Newton methods guarantee super linear convergence by accumulating second order information regarding the KT equations using a Q-N updating procedure. These methods are commonly referred to as Sequential Quadratic Programming (SQP) since a QP sub-problem is solved at each major iteration. SQP methods represent the state-of-the-art in nonlinear programming. Given the GP of Eq. (8), (9), (10) and (11), the principal idea is the formulation of a QP sub-problem based on a quadratic approximation of the Lagrangian function”. Further equations 17 & 18 show using the optimizing of the gas injection rates to obtain the maximum oil production rate using the derivatives of the constraint, a quadratic approximation of the Lagrangian function and an approximation of the Hessian matrix of the Lagrangian function, e.g., generating a first gas lift profile for the well using the first polynomial. Further see Pg. 2-4. The examiner has interpreted that fitting field data of rate of oil production with an optimized gas injection rate to meet the constraint vector using a quadratic approximation of the Lagrangian function and an approximation of the Hessian matrix of the Lagrangian function as generating a first gas lift profile for the well using the first polynomial and the production fluid flow rate data.)
Alarcón teaches “solving a system of equations that includes the first polynomial representing the first gas lift profile and a second polynomial representing a second gas lift profile associated with a second well included in the plurality of wells to generate first results”. (Pg. 2, “It is necessary to fit the field data with a mathematical expression to help the computer handle the GLPR curves. The second-degree polynomial has been traditionally used for this purpose. However, the Eq. (1) is proposed in this work to fit the field data.” [different second-degree polynomial, e.g., the first polynomial]. Pg. 3, “Therefore, the problem of finding the optimum gas injection rates to maximize total oil production is expressed as:” [Equ. 4] “Subject to the following constraints” [Equ. 4-7] “The constraint defined by Eq. (5) indicates that the sum of the individual gas injection rates should be less than or equal to the total injection gas rate available for the system, Qg_Available. The constraint represented by Eq. (6) indicates that each gas injection rate must not be negative. Finally, Eq (7) indicates that each gas injection rate can not be greater than the gas injection rate originating the maximum individual oil production rate, Qgi max. Therefore, the gas injection rates should always satisfy the set of constraints defined by Eq. (5), (6) and (7) during the optimization computation” [e.g., solving a system of equations that includes the first polynomial representing the first gas lift profile and a second polynomial representing a second gas lift profile associated with a second well included in the plurality of wells to generate first]. Pg. 4, “The nonlinear programming algorithms based on KT equations attempt to compute directly the LM. Constrained Quasi-Newton methods guarantee super linear convergence by accumulating second order information regarding the KT equations using a Q-N updating procedure. These methods are commonly referred to as Sequential Quadratic Programming (SQP) since a QP sub-problem is solved at each major iteration. SQP methods represent the state-of-the-art in nonlinear programming. Given the GP of Eq. (8), (9), (10) and (11), the principal idea is the formulation of a QP sub-problem based on a quadratic approximation of the Lagrangian function” [wherein the solving is subject to the polynomial]. Further equations 17 & 18 show using the optimizing of the gas injection rates to obtain the maximum oil production rate using the derivatives of the constraint, a quadratic approximation of the Lagrangian function and an approximation of the Hessian matrix of the Lagrangian function, e.g., generating a first gas lift profile for the well using the first polynomial Further see Pg. 2-4. The examiner has interpreted that fitting field data of rate of oil production using a second-degree polynomial with an optimized gas injection rate to meet the constraint vector and using a quadratic approximation of the Lagrangian function and an approximation of the Hessian matrix of the Lagrangian function in addition to specified constrains for individual production of a number of wells as solving a system of equations that includes the first polynomial representing the first gas lift profile and a second polynomial representing a second gas lift profile associated with a second well included in the plurality of wells to generate first results.)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to add “evaluating a Jacobian as a first function of the operating point to return a first result”, “evaluating a Hessian as a second function of the operating point to return a second result”, “constructing a first polynomial based in part on the first result and the second result, wherein the first result is a first coefficient for a linear term in the first polynomial and the second result is a second coefficient for a quadratic term in the first polynomial”, “generating a first gas lift profile for the well using the first polynomial and the production fluid flow rate data”, and “solving a system of equations that includes the first polynomial representing the first gas lift profile and a second polynomial representing a second gas lift profile associated with a second well included in the plurality of wells to generate first results” as conceptually seen from the teaching of Alarcón, into that of Rossi because this modification of using both a Jacobian and Hessian function to represent the wells for the advantageous purpose of representing the necessary constraints in guaranteeing the convergence and optimization of the gas production as well as linearizing the nonlinear constraints for the wells in a field (Alarcón, Pg. 3-4). Further motivation to combine be that Rossi and Alarcón are analogous art to the current claim are directed to optimization of gas lift models.
As per claim 2, Rossi teaches “wherein the solving the system of equations includes subjecting [the first polynomial] to a first gas limit and subjecting [the second polynomial] to a second gas limit.” (Para. 0091, “What follows is a description of a system that solves the optimization problem in Equations 1a-b, that is, it maximizes the field oil production rate subject to a constraint on available lift gas” [wherein the solving the system of equations includes subjecting gas limits]. Para. 0104, “The well head flowing pressure Pwf is monitored; it may vary due to the fact that all of the wells on the network are simultaneously adjusting their own lift gas flow rates, and the well pressures interact through the network. Because all of the wells in the field are adjusting their lift gas injection rates at the same time, to avoid the risk of system-wide instability, it may be necessary to introduce limits on how large a change each well controller can make at one time, or how quickly successive changes can be made by each well head controller. Once the well head flowing pressure Pwf stabilizes, the value is determined (block 1414) and then compared to the value of Pwf at the start of the cycle (block 1416). If they are significantly different, the procedure may be repeated until Pwf does not change significantly from one cycle to the next, by returning control to block 1406” [multiple gas limits, e.g., a first gas limit and a second gas limit]. Further see Para. 0091 and 0104-0105. The examiner has interpreted that solving the optimization problem that is subject to the optimization problem provided a constraint on available lift gas used to maximum the field oil production and how large a change each well controller can make at one time as wherein the solving the system of equations includes subjecting a first gas limit and a second gas limit.)
Rossi does not teach “wherein the solving the system of equations includes subjecting the first polynomial to a first gas limit and subjecting the second polynomial to a second gas limit.”
However, Alarcón teaches “wherein the solving the system of equations includes subjecting the first polynomial to a first gas limit and subjecting the second polynomial to a second gas limit.” (Pg. 2, “It is necessary to fit the field data with a mathematical expression to help the computer handle the GLPR curves. The second-degree polynomial has been traditionally used for this purpose. However, the Eq. (1) is proposed in this work to fit the field data.” [different second-degree polynomial, e.g., the first polynomial]. Pg. 3, “Therefore, the problem of finding the optimum gas injection rates to maximize total oil production is expressed as:” [Equ. 4] “Subject to the following constraints” [Equ. 4-7] “The constraint defined by Eq. (5) indicates that the sum of the individual gas injection rates should be less than or equal to the total injection gas rate available for the system, Qg_Available. The constraint represented by Eq. (6) indicates that each gas injection rate must not be negative. Finally, Eq (7) indicates that each gas injection rate can not be greater than the gas injection rate originating the maximum individual oil production rate, Qgi max. Therefore, the gas injection rates should always satisfy the set of constraints defined by Eq. (5), (6) and (7) during the optimization computation” [e.g., wherein the solving the system of equations includes subjecting the first polynomial to a first gas limit and subjecting the second polynomial to a second gas limit]. Further see Pg. 2-4. The examiner has interpreted that fitting field data of rate of oil production using a second-degree polynomial with an optimized gas injection rate to meet the constraint vector and using a quadratic approximation of the Lagrangian function and an approximation of the Hessian matrix of the Lagrangian function in addition to specified constrains for individual production of a number of wells as wherein the solving the system of equations includes subjecting the first polynomial to a first gas limit and subjecting the second polynomial to a second gas limit.)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to add “wherein the solving the system of equations includes subjecting the first polynomial to a first gas limit and subjecting the second polynomial to a second gas limit” as conceptually seen from the teaching of Alarcón, into that of Rossi because this modification of using both a Jacobian and Hessian function to represent the well for the advantageous purpose of representing the necessary constraints in guaranteeing the convergence and optimization of the gas production as well as linearizing the nonlinear constraints for available gas (Alarcón, Pg. 3-4). Further motivation to combine be that Rossi and Alarcón are analogous art to the current claim are directed to optimization of gas lift models.
As per claim 3, Rossi teaches, “wherein the resolving the system of equations includes subjecting [the first polynomial] to the first gas limit and subjecting [the second polynomial] to a third gas limit.” (Para. 0091, “What follows is a description of a system that solves the optimization problem in Equations 1a-b, that is, it maximizes the field oil production rate subject to a constraint on available lift gas” [wherein the solving the system of equations includes subjecting gas limits]. Para. 0104, “The well head flowing pressure Pwf is monitored; it may vary due to the fact that all of the wells on the network are simultaneously adjusting their own lift gas flow rates, and the well pressures interact through the network. Because all of the wells in the field are adjusting their lift gas injection rates at the same time, to avoid the risk of system-wide instability, it may be necessary to introduce limits on how large a change each well controller can make at one time, or how quickly successive changes can be made by each well head controller. Once the well head flowing pressure Pwf stabilizes, the value is determined (block 1414) and then compared to the value of Pwf at the start of the cycle (block 1416). If they are significantly different, the procedure may be repeated until Pwf does not change significantly from one cycle to the next, by returning control to block 1406” [multiple gas limits, e.g., a first gas limit, a second gas limit, and third gas limit]. Further see Para. 0091 and 0104-0105. The examiner has interpreted that solving the optimization problem that is subject to the optimization problem provided a constraint on available lift gas used to maximum the field oil production and how large a change each well controller can make at one time and repeating from until condition and change specification are met as wherein the resolving the system of equations includes subjecting to the first gas limit and subjecting to a third gas limit.)
Rossi does not specifically teach “wherein the resolving the system of equations includes subjecting the first polynomial to the first gas limit and subjecting the second polynomial to a third gas limit”.
However, Alarcón teaches “wherein the resolving the system of equations includes subjecting the first polynomial to the first gas limit and subjecting the second polynomial to a third gas limit”. (Pg. 2, “It is necessary to fit the field data with a mathematical expression to help the computer handle the GLPR curves. The second-degree polynomial has been traditionally used for this purpose. However, the Eq. (1) is proposed in this work to fit the field data.” [different second-degree polynomial, e.g., the first polynomial]. Pg. 3, “Therefore, the problem of finding the optimum gas injection rates to maximize total oil production is expressed as:” [Equ. 4] “Subject to the following constraints” [Equ. 4-7] “The constraint defined by Eq. (5) indicates that the sum of the individual gas injection rates should be less than or equal to the total injection gas rate available for the system, Qg_Available. The constraint represented by Eq. (6) indicates that each gas injection rate must not be negative. Finally, Eq (7) indicates that each gas injection rate can not be greater than the gas injection rate originating the maximum individual oil production rate, Qgi max. Therefore, the gas injection rates should always satisfy the set of constraints defined by Eq. (5), (6) and (7) during the optimization computation” [e.g., wherein the solving the system of equations includes subjecting the first polynomial to a first gas limit and subjecting the second polynomial to a gas limit]. Further see Pg. 2-4. The examiner has interpreted that fitting field data of rate of oil production using a second-degree polynomial with an optimized gas injection rate to meet the constraint vector and using a quadratic approximation of the Lagrangian function and an approximation of the Hessian matrix of the Lagrangian function in addition to specified constrains for individual production of a number of wells as wherein the solving the system of equations includes subjecting the first polynomial to a first gas limit and subjecting the second polynomial to a gas limit.)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to add “wherein the resolving the system of equations includes subjecting the first polynomial to the first gas limit and subjecting the second polynomial to a third gas limit” as conceptually seen from the teaching of Alarcón, into that of Rossi because this modification of using both a Jacobian and Hessian function to represent the well for the advantageous purpose of representing the necessary constraints in guaranteeing the convergence and optimization of the gas production as well as linearizing the nonlinear constraints for available gas (Alarcón, Pg. 3-4). Further motivation to combine be that Rossi and Alarcón are analogous art to the current claim are directed to optimization of gas lift models.
As per claim 4, Rossi teaches “wherein the generating the first gas lift profile comprises utilizing local perturbation around an incumbent, constraint-feasible operating point, wherein the local perturbation does not have a substantial effect on one or more other operating points of one or more other wells of the plurality of wells.” (Para. 0072, “the gas lift well performance curves 402-408 for a well (FIG. 4) is used to compute the optimum operating point for that well” [incumbent operating point]. Para. 0129, “In the event that any well is actively limited by a local well-level constraint, that well may still be included in the computations in Equations 4 through 11, using the current (limited) values of lift gas lj* and oil rate qj*” [constraint-feasible operating point]. Para. 0121, “the middle term in the right-hand side corresponds to the perturbation in the lift gas rates due to the change in the field-wide slope control by ΔSj = Sj–S* when well j is shut-in. The last term corresponds to the perturbation in the lift gas rates due to the change in the nth well Pwf due to well j being shut-in and its lift gas being optimally redistributed to the remaining N-1 wells across the field” [utilizing local perturbation around an incumbent, constraint-feasible operating point]. Equation 5 shows Δpnj as the last term. Para. 0122, “By also allowing the lift gas from well j to be redistributed across the N-1 wells using single-variable slope control, the lift rates in the N-1 wells will generally increase, production rates will increase, the network pressure will rise and Pwf values will increase. After all of these effects have occurred and stabilized, the net change in well head pressure in well n is reflected in the term Δpnj. In the case where lift gas redistribution results in the network re-pressurizing to roughly the same well head pressures as the base case, Δpnj may have small magnitude” [wherein the local perturbation does not have a substantial effect on one or more other operating points of one or more other wells of the plurality of wells]. Further see Para. 0072 and 0121-0122. The examiner has interpreted that using gas lift performance curves to compute optimum operation points for wells that are actively limited by a local well-level constraint using the lift gas rate that as a term that corresponds to the perturbation in the lift gas rates due to the change between wells and seeks results to equalize the well pressure in minimizing the magnitude of the perturbation as wherein the generating the first gas lift profile comprises utilizing local perturbation around an incumbent, constraint-feasible operating point, wherein the local perturbation does not have a substantial effect on one or more other operating points of one or more other wells of the plurality of wells.)
As per claim 5, Rossi teaches “wherein the generating the first gas lift profile comprises utilizing local sensitivity information.” (Para. 0121, “the middle term in the right-hand side corresponds to the perturbation in the lift gas rates due to the change in the field-wide slope control by ΔSj = Sj–S* when well j is shut-in. The last term corresponds to the perturbation in the lift gas rates due to the change in the nth well Pwf due to well j being shut-in and its lift gas being optimally redistributed to the remaining N-1 wells across the field” [wherein the generating the first gas lift profile comprises utilizing local information]. Equation 5 shows Δpnj as the last term. Para. 0122, “After all of these effects have occurred and stabilized, the net change in well head pressure in well n is reflected in the term Δpnj. In the case where lift gas redistribution results in the network re-pressurizing to roughly the same well head pressures as the base case, Δpnj may have small magnitude” [wherein the generating the first gas lift profile comprises utilizing local sensitivity information]. Further see Para. 0072 and 0121-0122. The examiner has interpreted that using gas lift performance curves to compute optimum operation points for wells comprising a term that corresponds to the perturbation in the lift gas rates due to the change between wells and seeks results to equalize the well pressure in minimizing the magnitude of the perturbation as wherein the generating the first gas lift profile comprises utilizing local sensitivity information.)
As per claim 6, Rossi teaches “wherein the local sensitivity information is based on perturbation in a defined neighborhood of an existing operating point.” (Para. 0072, “the gas lift well performance curves 402-408 for a well (FIG. 4) is used to compute the optimum operating point for that well” [existing operating point]. Para. 0121, “the middle term in the right-hand side corresponds to the perturbation in the lift gas rates due to the change in the field-wide slope control by ΔSj = Sj–S* when well j is shut-in. The last term corresponds to the perturbation in the lift gas rates due to the change in the nth well Pwf due to well j being shut-in and its lift gas being optimally redistributed to the remaining N-1 wells across the field” [wherein the local sensitivity information is based on perturbation in a defined neighborhood]. Equation 5 shows Δpnj as the last term. Para. 0122, “After all of these effects have occurred and stabilized, the net change in well head pressure in well n is reflected in the term Δpnj. In the case where lift gas redistribution results in the network re-pressurizing to roughly the same well head pressures as the base case, Δpnj may have small magnitude” [local sensitivity information]. Further see Para. 0072 and 0121-0122. The examiner has interpreted that using gas lift performance curves to compute optimum operation points for wells comprising a term that corresponds to the perturbation in the lift gas rates due to the change between wells and seeks results to equalize the well pressure in minimizing the magnitude of the perturbation that is distributed across the well of the field as wherein the local sensitivity information is based on perturbation in a defined neighborhood of an existing operating point.)
As per claim 7, Rossi teaches “wherein the generating the first gas lift profile comprises constructing a representative local approximating model in a trust region.” (Para. 0086-0088, “one optimization approach may use centralized modeling of the well behavior and may depend on the computation of a mathematical model for the surface network in order to estimate the pressure interactions among the wells in the network. This can present certain operational challenges: 1) The mathematical network model is an approximation to reality, so the computed optimized lift gas rates are an approximation to the true optimum rates the mathematical network model may need to be continually re-calibrated so that it remains an accurate representation of the real network”. Further see Para. 0086-0088. The examiner has interpreted that computing optimized lift gas rates that are an approximation to the true optimum rates using a centralized model of the well behavior as wherein the generating the first gas lift profile comprises constructing a representative local approximating model in a trust region.)
As per claim 8, Rossi does not specifically teach “wherein the first polynomial is a quadratic.”
However, Alarcón teaches “wherein the first polynomial is a quadratic”. (Pg. 4, “The nonlinear programming algorithms based on KT equations attempt to compute directly the LM. Constrained Quasi-Newton methods guarantee super linear convergence by accumulating second order information regarding the KT equations using a Q-N updating procedure. These methods are commonly referred to as Sequential Quadratic Programming (SQP) since a QP sub-problem is solved at each major iteration. SQP methods represent the state-of-the-art in nonlinear programming. Given the GP of Eq. (8), (9), (10) and (11), the principal idea is the formulation of a QP sub-problem based on a quadratic approximation of the Lagrangian function” [wherein the first polynomial is a quadratic]. Further see Pg. 2-4. The examiner has interpreted that fitting field data of rate of oil production with an optimized gas injection rate to meet the constraint vector using a quadratic approximation of the Lagrangian function and an approximation of the Hessian matrix of the Lagrangian function as wherein the first polynomial is a quadratic. )
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to add “wherein the first polynomial is a quadratic” as conceptually seen from the teaching of Alarcón, into that of Rossi because this modification of using both a Jacobian and Hessian function to represent the wells for the advantageous purpose of representing the necessary constraints in guaranteeing the convergence and optimization of the gas production as well as linearizing the nonlinear constraints for the wells in a field (Alarcón, Pg. 3-4). Further motivation to combine be that Rossi and Alarcón are analogous art to the current claim are directed to optimization of gas lift models.
As per claim 10, Rossi does not specifically teach “wherein the system of equations comprises a nonlinear system of equations.”
However, Alarcón teaches “wherein the system of equations comprises a nonlinear system of equations.” (Pg. 3, “MATLABTM nonlinear constrained optimization is based on the solution of the Kuhn-Tucker (KT) equations, which are necessary conditions for optimality for a constrained optimization problem. If the problem is a so-called convex programming problem, that is, f(x) and gi(x) are convex functions, then the KT equations are both necessary and sufficient for a global solution point” [wherein the system of equations comprises a nonlinear system of equations]. Further see Pg. 3-4. The examiner has interpreted that determining a solution to a nonlinear constrained optimization problem for a set of vector constraints using KT equations as wherein the system of equations comprises a nonlinear system of equations.)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to add “wherein the system of equations comprises a nonlinear system of equations” as conceptually seen from the teaching of Alarcón, into that of Rossi because this modification of using nonlinear equations for the advantageous purpose of modeling an optimal distribution of lift gas for each well in a field for the backpressure effects imposed by interconnected wells and at each major iteration of the optimization problem (Alarcón, Pg. 2 and 4). Further motivation to combine be that Rossi and Alarcón are analogous art to the current claim are directed to optimization of gas lift models.
As per claim 11, Rossi teaches “comprising waiting a period of time and repeating at least the receiving, the solving and the issuing.” (Para. 0104, “Because all of the wells in the field are adjusting their lift gas injection rates at the same time, to avoid the risk of system-wide instability, it may be necessary to introduce limits on how large a change each well controller can make at one time, or how quickly successive changes can be made by each well head controller” [comprising waiting a period of time and repeating at least the receiving, the solving and the issuing]. Further see Para. 0104. The examiner has interpreted limiting how quickly successive changes by each well head controller are made as comprising waiting a period of time and repeating at least the receiving, the solving and the issuing.)
As per claim 12, Rossi teaches “comprising regulating the period of time based at least in part on production fluid flow rate data from one or more of the plurality of wells.” (Para. 0104, “Because all of the wells in the field are adjusting their lift gas injection rates at the same time, to avoid the risk of system-wide instability, it may be necessary to introduce limits on how large a change each well controller can make at one time, or how quickly successive changes can be made by each well head controller” [waiting a period of time to make changes to lift gas injection rates, e.g., regulating the period of time]. Para. 0107-0111, “FIG. 15 indicates a set of information (S*, ln, qn, Pwf) that flows between the Central Controller and the Well Controller on a recurring basis, e.g., every few minutes. Less frequently, other information (not illustrated in FIG. 15) may be communicated, including: 1. Well level constraints such as maximum production rates (liquid, oil, water or gas), minimum bottom hole flowing pressure, and/or maximum well head temperature; 2. Produced fluid attributes such as gas-oil ratio and water cut; 3. The well gas lift performance curves (q versus l) for different values of P.sub.wf. This information typically varies much more slowly than ln, qn, Pwf and S*, and thus may be communicated between the Central and Well Controllers on a much less frequent basis, for example only from time to time when changes occur” [regulating the period of time based at least in part on production fluid flow rate data from one or more of the plurality of wells]. Further see Para. 0107-0111. The examiner has interpreted that limiting how quickly successive changes by each well head controller are made due to maximum production rates being communicated less frequently as comprising regulating the period of time based at least in part on production fluid flow rate data from one or more of the plurality of wells.)
As per claim 13, Rossi teaches “wherein the solving comprises applying at least one field constraint and at least one well constraint for the first well.” (Para. 0091, “What follows is a description of a system that solves the optimization problem in Equations 1a-b, that is, it maximizes the field oil production rate subject to a constraint on available lift gas” [wherein the solving comprises applying at least one field constraint]. “As is the case with conventional centralized optimization procedures using a mathematical network model, the method disclosed herein can be extended to handle additional constraints at the well level” [and at least one well constraint for the first well]. Further see Para. 0091. The examiner has interpreted that solving the optimization problem that is subject to a constraint on available lift gas and additional constraints at the well level as wherein the solving comprises applying at least one field constraint and at least one well constraint for the first well.)
As per claim 14, Rossi teaches “comprising receiving data characterizing lift gas and wherein the solving comprises utilizing at least a portion of the data characterizing the lift gas.” (Para. 0066-0067, “During certain field operations, several measurements are made for gas lifted wells, and may be repeated at predetermined intervals: 1. Injected lift gas pressure and flow rate (which, in some embodiments, is measured daily)” [receiving data]. Para. 0064, “Each gas-lifted well can be thought of a having one input (lift gas) and one output (produced liquid). For each well, the gas lift well model that was created during the initial step of designing the gas lift completion may used to compute gas lift well performance curves, as illustrated conceptually in FIG. 4 at 400. Each gas lift well performance curve indicates the output wellbore production liquid flow rate versus the input injected lift gas flow rate; a family of performance curves will be computed for a set of wellhead flowing pressures (i.e., the surface network back-pressure against which the well produces). For a given value of injected lift gas flow rate, a higher value of wellhead flowing pressure (higher back-pressure) results in a smaller wellbore production liquid flow rate” [e.g., data characterizing lift gas]. Para. 0078, “the field-level optimization problem in Equations 1a-b is solved (block 606) with respect to well, surface network, and facility equipment constraints (block 608), resulting in a candidate set of recommended lift gas flow rates low rates In for the wells” [wherein the solving comprises utilizing at least a portion of the data characterizing the lift gas]. Further see Para. 0064, 0066-0067, and 0078. The examiner has interpreted that computing gas lift well performance curves for each well based on the lift gas flow rate to solve a set of recommended lift gas flow rates as a result in a change of pressure for the wells as comprising receiving data characterizing lift gas and wherein the solving comprises utilizing at least a portion of the data characterizing the lift gas.)
As per claim 15, Rossi teaches “comprising, responsive to a change in one or more conditions, repeating the solving wherein the solving comprises utilizing a prior operating point for the first well and wherein the results comprise a new operating point for the first well.” (Para. 0107-0111, “FIG. 15 indicates a set of information (S*, ln, qn, Pwf) that flows between the Central Controller and the Well Controller on a recurring basis, e.g., every few minutes. Less frequently, other information (not illustrated in FIG. 15) may be communicated, including: 1. Well level constraints such as maximum production rates (liquid, oil, water or gas), minimum bottom hole flowing pressure, and/or maximum well head temperature; 2. Produced fluid attributes such as gas-oil ratio and water cut; 3. The well gas lift performance curves (q versus l) for different values of P.sub.wf. This information typically varies much more slowly than ln, qn, Pwf and S*, and thus may be communicated between the Central and Well Controllers on a much less frequent basis, for example only from time to time when changes occur” [e.g., comprising, responsive to a change in one or more conditions, repeating the solving]. Para. 0105, “The field-wide lift gas usage L is obtained by summing the well lift gas rates ln, and the field-wide oil production rate Q is obtained by summing the well oil rates qn (block 1514). If L is below Lmax (block 1516), there is spare unused lift gas capacity, so the Central Controller will decrease the single-variable slope control to a new value S* and transmit that to each well (block 1518). Once the summed value L is very close to the limiting value Lmax, the field oil production has been optimized with respect to the constraint on available lift gas. The process is repeated to maintain the field system at an optimized condition” [wherein the solving comprises utilizing a prior operating point for the first well and wherein the results comprise a new operating point for the first well]. Further see Para. 0105 and 0107-0111. The examiner has interpreted that when changes occur to decrease the performance curve slope to a new value and transmitting that to each well as comprising, responsive to a change in one or more conditions, repeating the solving wherein the solving comprises utilizing a prior operating point for the first well and wherein the results comprise a new operating point for the first well.)
As per claim 16, Rossi teaches “comprising repeating the generating the gas lift profile for the first well based at least in part on the change to generate a new gas lift profile wherein the repeating the solving comprises utilizing the new gas lift profile.” (Para. 0107-0111, “FIG. 15 indicates a set of information (S*, ln, qn, Pwf) that flows between the Central Controller and the Well Controller on a recurring basis, e.g., every few minutes. Less frequently, other information (not illustrated in FIG. 15) may be communicated, including: 1. Well level constraints such as maximum production rates (liquid, oil, water or gas), minimum bottom hole flowing pressure, and/or maximum well head temperature; 2. Produced fluid attributes such as gas-oil ratio and water cut; 3. The well gas lift performance curves (q versus l) for different values of P.sub.wf. This information typically varies much more slowly than ln, qn, Pwf and S*, and thus may be communicated between the Central and Well Controllers on a much less frequent basis, for example only from time to time when changes occur” [e.g., based at least in part on the change]. Para. 0105, “The field-wide lift gas usage L is obtained by summing the well lift gas rates ln, and the field-wide oil production rate Q is obtained by summing the well oil rates qn (block 1514). If L is below Lmax (block 1516), there is spare unused lift gas capacity, so the Central Controller will decrease the single-variable slope control to a new value S* and transmit that to each well (block 1518). Once the summed value L is very close to the limiting value Lmax, the field oil production has been optimized with respect to the constraint on available lift gas. The process is repeated to maintain the field system at an optimized condition” [comprising repeating the generating the gas lift profile for the first well to generate a new gas lift profile wherein the repeating the solving comprises utilizing the new gas lift profile]. Further see Para. 0105 and 0107-0111. The examiner has interpreted that when changes occur to decrease the performance curve slope to a new value and transmitting that to each well as comprising repeating the generating the gas lift profile for the first well based at least in part on the change to generate a new gas lift profile wherein the repeating the solving comprises utilizing the new gas lift profile.)
As per claim 17, Rossi teaches “wherein the first instruction comprises an operating point instruction for the well.” (Para. 0072, “the gas lift well performance curves 402-408 for a well (FIG. 4) is used to compute the optimum operating point for that well. When gas supply is unlimited, an optimum operating point for a well is typically at the maximum value of the curve for the current tubing head pressure (which itself depends on the production from neighboring wells due to network back-pressure effects). In the more general case where lift gas supply is limited, an optimization problem may be solved that computes the amount of lift gas to inject into each gas lifted well in order to maximize the overall oil production from the field” [e.g., wherein the first instruction comprises an operating point instruction for the well]. Para. 0085, “These optimized gas lift flow rates are transmitted to well controllers 804a-g for each respective well and are used by the respective well controllers 804a-g with a closed-loop set point controller to set and maintain the gas lift rate for each respective well at its optimized value” [e.g., operating point instruction for the well]. Further see Para. 0072 and 0085. The examiner has interpreted that using the gas lift performance curve to compute the optimum operating point for that well and computing the amount of lift gas injected into each well to be transmitted to well controllers to maximize the overall oil production from the field as wherein the first instruction comprises an operating point instruction for the well.)
As per claim 18, Rossi teaches “comprising receiving production fluid flow rate data for operation of the first well at the operating point, waiting an equilibration increment, and repeating at least the solving and the issuing.” (Para. 0066-0068, “During certain field operations, several measurements are made for gas lifted wells, and may be repeated at predetermined intervals: 1. Injected lift gas pressure and flow rate (which, in some embodiments, is measured daily) 2. Well production liquid flow rate, gas-oil ratio (GOR) and water cut (i.e., ratio of water flow rate to liquid flow rate, which is typically taken during occasional well tests, e.g., every few weeks)” [comprising receiving production fluid flow rate data for operation of the first well]. Para. 0104, “Because all of the wells in the field are adjusting their lift gas injection rates at the same time, to avoid the risk of system-wide instability, it may be necessary to introduce limits on how large a change each well controller can make at one time, or how quickly successive changes can be made by each well head controller” [waiting a period of time to make changes to lift gas injection rates when lift gas rate injection is adjusted, e.g., receiving production fluid flow rate data for operation of the first well at the operating point waiting an equilibration increment, and repeating at least the solving and the issuing]. Further see Para. 0062-0068 and 0104. The examiner has interpreted that making measurements including well production liquid flow rate, gas-oil ratio (GOR) and water cut for wells and limiting how quickly successive changes by each well head controller are made as comprising receiving production fluid flow rate data for operation of the first well at the operating point, waiting an equilibration increment, and repeating at least the solving and the issuing.)
Re Claim 19, it is a system claim, having similar limitations of claim 1. Thus, claim 19 is also rejected under the similar rationale as cited in the rejection of claim 1.
Furthermore, regarding claim 19, Rossi teaches “A system comprising: one or more processors; memory accessible to at least one of the one or more processors; processor-executable instructions stored in the memory and executable to instruct the system”. (Para. 0149, “It should be understood that the various technologies described herein may be implemented in connection with hardware, software or a combination of both. Thus, various technologies, or certain aspects or portions thereof, may take the form of program code (i.e., instructions) embodied in tangible media, such as floppy diskettes, CD-ROMs, hard drives, or any other machine-readable storage medium wherein, when the program code is loaded into and executed by a machine, such as a computer, the machine becomes an apparatus for practicing the various technologies” [memory and processor-executable instructions stored in the memory and executable to instruct the system]. “In the case of program code execution on programmable computers, the computing device may include a processor, a storage medium readable by the processor (including volatile and non-volatile memory and/or storage elements), at least one input device and at least one output device” [A system comprising: one or more processors; memory accessible to at least one of the one or more processors]. Further see Para. 0149. The examiner has interpreted that implementing instructions in a media executed by a computer having a processor as a system comprising: one or more processors; memory accessible to at least one of the one or more processors; processor-executable instructions stored in the memory and executable to instruct the system.)
Re Claim 20, it is an articles of manufacture claim, having similar limitations of claim 1. Thus, claim 20 is also rejected under the similar rationale as cited in the rejection of claim 1.
Furthermore, regarding claim 20, Rossi teaches “One or more non-transitory computer-readable storage media comprising computer-executable instructions executable to instruct a computing system”. (Para. 0149, “It should be understood that the various technologies described herein may be implemented in connection with hardware, software or a combination of both. Thus, various technologies, or certain aspects or portions thereof, may take the form of program code (i.e., instructions) embodied in tangible media, such as floppy diskettes, CD-ROMs, hard drives, or any other machine-readable storage medium wherein, when the program code is loaded into and executed by a machine, such as a computer, the machine becomes an apparatus for practicing the various technologies” [computer-executable instructions executable to instruct a computing system]. “In the case of program code execution on programmable computers, the computing device may include a processor, a storage medium readable by the processor (including volatile and non-volatile memory and/or storage elements), at least one input device and at least one output device” [non-transitory computer-readable storage media]. Further see Para. 0149. The examiner has interpreted that implementing instructions in a media executed by a computer having storage medium readable by the processor (including volatile and non-volatile memory and/or storage elements as one or more non-transitory computer-readable storage media comprising computer-executable instructions executable to instruct a computing system.)
Claim 9 is rejected under 35 U.S.C. § 103 as being unpatentable over Rossi and Alarcón as applied to claim 8 above, and in further view of Rashid, Kashif “Optimal allocation procedure for gas-lift optimization.” Industrial & Engineering Chemistry Research 49, no. 5 (2010): 2286-2294 [herein “Rashid”].
As per claim 9, Rossi and Alarcón does not specifically teach “wherein the generating the first gas lift profile comprises utilizing a Taylor series expansion to generate the first polynomial”.
However, Rashid teaches “wherein the generating the first gas lift profile comprises utilizing a Taylor series expansion to generate the first polynomial.” (Pg. 2287 Col. 1, “Here, the flow rate versus gas injection profile, known as the gas-lift performance curve (GLPC), is defined for each well (see Figure 2) and the objective function is given as the sum of all well flow rates”. Pg. 2287 Col. 2 - Pg. 2288 Col. 1, “The optimal allocation method presented in this paper, the Newton reduction method (NRM), also returns an equal-slope solution. With second-order polynomials used to fit the GLPC, a quasi-Newton method23 and the Lagrange multiplier method with a standard NLP solver were demonstrated” [wherein the generating the first gas lift profile comprises to generate the first polynomial]. “SLP techniques were proposed27,28 assuming piecewise linear lift performance curves and linearization of constraints using first-order Taylor series expansion” [utilizing a Taylor series expansion to generate the first polynomial]. Further see Pg. 2287-2288. The examiner has interpreted that fitting second-order polynomials to the gas-lift performance curve using sequential linear programming techniques with a first-order Taylor series expansion as wherein the generating the first gas lift profile comprises utilizing a Taylor series expansion to generate the first polynomial.)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to add “wherein the generating the first gas lift profile comprises utilizing a Taylor series expansion to generate the first polynomial” as conceptually seen from the teaching of Rashid, into that of Rossi and Alarcón because this modification of generating a polynomial with a Taylor series expansion for the advantageous purpose of increasing the accuracy of the model for a better representation of the wells and creating a fast solution for the model (Rashid, Pg. 2287 Col. 2 - Pg. 2288 Col. 1 & Pg. 2288 Col. 1). Further motivation to combine be that Rossi, Alarcón, and Rashid are analogous art to the current claim are directed to optimization of gas lift models.
Response to Arguments
Applicant's arguments filed on February 23, 2026 have been fully considered but they are not persuasive.
Applicant argues that the combination of references does not teach each and every limitation in the amend claims 1, 19, and 20 because cited references fail to teach “wherein the first result is a first coefficient for a linear term in the first polynomial and the second result is a second coefficient for a quadratic term in the first polynomial” (See Applicant’s response, Pg. 10-14).
MPEP § 2143.03 states that “All words in a claim must be considered in judging the patentability of that claim against the prior art” and “Examiners must consider all claim limitations when determining patentability of an invention over the prior art.”
As mapped in the rejection claim 1 above, Alarcón discloses “wherein the first result is a first coefficient for a linear term in the first polynomial and the second result is a second coefficient for a quadratic term in the first polynomial” by include a quadratic approximation of the Lagrangian function and an approximation of the Hessian matrix of the Lagrangian function. Additional emphasis has been added to this mapping in the rejection above to the amended limitation. The fundamental nature of the Jacobian functions represent a linear approximation of a first order derivative, and also the fundamental nature of the Hessian matrix represent a second-order derivatives of coefficients of quadratics approximations of the function as defined in NPL document (see footnote in the rejection of claim 1). Therefore, by including both the quadratic approximation of the Lagrangian function and an approximation of the Hessian matrix of the Lagrangian function into the second-degree polynomial, the claimed limitation is taught.
Therefore, all of the limitations of the amended claims 1, 19, and 20 are disclosed in Rossi or Alarcón, and the combination of these references renders the claimed invention obvious. Therefore, applicant’s arguments are not persuasive and the rejection of claim 1, 19, and 20 as obvious over Rossi in view of Alarcón is maintained.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Dehdari, Vahid, Dean S. Oliver, and Clayton V. Deutsch. "Comparison of optimization algorithms for reservoir management with constraints—a case study." Journal of Petroleum Science and Engineering 100 (2012): 41-49 explores different methos of optimizing reservoir production rates by artificial lift optimized by Hessian and Jacobian constraints.
Examiner’s Note: The examiner has cited particular columns and line numbers in the reference that applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the art and are applied to specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant, to fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner. In the case of amending the claimed invention, the applicant is respectfully requested to indicate the portion(s) of the specification which dictate(s) the structure relied on for the proper interpretation and also to verify and ascertain the metes and bound of the claimed invention.
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/SIMEON P DRAPEAU/ Examiner, Art Unit 2188
/RYAN F PITARO/ Supervisory Patent Examiner, Art Unit 2188
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