DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Notice to Applicant
The following is a Final Office action. In response to Examiner’s Non-Final Rejection of 2/3/26, Applicant, on 3/5/26, amended claims. Claims 1-5 are pending in this application and have been rejected below.
Response to Amendment
Applicant’s amendments are acknowledged.
The 112b rejections are withdrawn in light of the amendments.
The 101 rejections are withdrawn in light of the amendments, where the claim ends in a method comprising steps for injecting gas into a reservoir and producing oil according to the determined optimal combination.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-2 and 4-5 are rejected under 35 U.S.C. 103 as being unpatentable over Olusola, et al., “Optimization of Recovery by Huff and Puff Gas Injection in Shale Oil Reservoirs Using the Climbing Swarm Derivative Free Algorithm,” 2020, Society of Petroleum Engineers (SPE) Latin America and Caribbean Petroleum Engineering Conference, pages 1-22, in view of Kanfar, et al. "A modeling study of EOR potential for CO2 Huff-n-Puff in tight oil reservoirs-Example from the Bakken Formation," 2017, Society of Petroleum Engineers (SPE) Canada Unconventional Resources Conference, pages 1-17.
Concerning claim 1, Olusola discloses:
A collaborative optimization method for gas injection huff-n-puff parameters in tight oil reservoirs (Olusola – see Abstract, page 1 - Recent improved and enhanced oil recovery (IOR and EOR) methods in shale reservoirs use huff and puff gas injection (H&P); See page 7, 2nd paragraph - the higher GIR is sufficient to allow penetration of gas into the tight matrix of the rock, such that it interacts with oil mobilizing it to the wellbore.
See also Kanfar – page 1, Abstract, 3rd paragraph - In this study, the success and profitability of huff-n-puff is evaluated for the Bakken tight oil reservoir) is characterized by the following steps:
Step 1, establishing a numerical simulation model that describes the actual reservoirs (Olusola – see page 4, 2nd paragraph - Investigating the technical and economic impact of H&P for one well using reservoir simulators is challenging and time consuming. Even more so when the petroleum company is planning H&P and RF (refracturing) jobs in multiple wells. Thus, in this paper we present a methodology to learn how to perform these tasks faster and at lower cost to improve oil recovery using derivative free algorithms; see page 7, last paragraph - Using numerical reservoir simulation for several horizontal wells in shale reservoirs for generating production forecasts and NPV calculations is challenging because multiple numerical reservoir simulation runs for use in field optimization calculations are computationally expensive, making the whole exercise prohibitive in practice. To solve this problem, we use for the first-time a combination of tank MatBal calculations, derivative free algorithms and NPV calculations for optimizing H&P gas injection;
see also Kanfar - starting point for model development in this work is the study by Yu et al. (2014) in which a Bakken well was history-matched and the resulting reservoir and fluid data published. Some of the fluid, rock, and fracture properties, and relative permeability curves used in the Yu et al. (2014) were adapted herein.);
Step 2, determining the optimization parameters of gas injection huff-n-puff, giving the optimization range of gas injection huff-n-puff parameters and variable constraint of oil recovery, and establishing the optimization objective function (Olusola –See page 4, last section – H&P (huff-n-puff) gas injection data from a pilot horizontal well; generate production forecasts required for NPV calculations using a MatBal forecast; In MatBal calculations… oil rates are used for calculating incremental cumulative oil recovery and cumulative oil production; see page 5, last paragraph - Using key reservoir and wellbore parameters stated in Table 1, we history matched with MatBal calculations the real data from the Eagle Ford shale Pilot well. The production history includes 30 months of primary production and 30 months of actual huff and puff gas injection. see page 8, last section “Objective Function Formulation” - To solve the field development optimization problem for conducting H&P and refracturing (RF) operations in the case at hand, 10 objective functions, each representing a group must be considered; see page 10, 2nd paragraph – lower and upper bound constraints for constraining generation of optimization parameters- e.g. gas injection rate, gas injection duration, PTI (average reservoir pressure to initiate RF (re-fractured) and H&P);
see also Kanfar page 5, “Genetic Algorithm” - The ability to incorporate integer variables is paramount because the number of huff-n-puff cycles—an important tuning parameter—is an integer variable. In this study, the GA is setup to run 25 generations. Each generation has a population of 50 individuals (i.e. 50 simulation scenarios). Moreover, the mutation rate is set to 15%, and the selection is based on Tournament Selection; see page 9, 2nd paragraph and Table 2 – upper and lower bounds of tuning parameters used in the optimization – includes huff-n-puff start-up day; injection period; soaking period; production period; max number of cycles);
Step 3, based on the collaborative optimization model of gas injection huff-n-puff parameters, the particle swarm optimization algorithm is used to solve the objective function to obtain the optimal gas injection rate, gas injection time, …, and production time (Olusola –see page 4, 2nd paragraph – in this paper, methodology to learn how to improve tasks faster and at lower cost to improve oil recovery; to achieve that goal, we combine global stochastic particle swarm optimization (PSO) and hill climber optimization and call this combination “Climbing Swarm” (CS); see page 11 – Optimization Results – include GIR (gas injection rate), GID (gas injection duration), column 6 is “cumulative oil production” and column 7 is “oil recovery factor”; see page 13, FIG. 5 - compares performance of stand-alone industry accepted PSO and CS algorithms. The two algorithms were applied to solve a well control (WC) optimization problem for a well undergoing H&P gas injection in the Eagle Ford shale. see page 14 last paragraph, #2 – CS and stand-alone and particle swarm optimization (PSO) recommend “gas injection rate”… gas injection duration)
Olusola mentions “soaking time” but only with regards to discussing another article (See page 3, 5th paragraph).
Kanfar discloses:
Step 3, based on the collaborative optimization model of gas injection huff-n-puff parameters, the particle swarm optimization algorithm is used to solve the objective function to obtain the optimal gas injection rate, gas injection time, “soaking time” and production time (Kanfar – see page 2, 1st paragraph - A huff-n-puff cycle consists of
the following: gas (typically CO2 or separator gas) is injected through a (production) well, gas is allowed to "soak" in the in-situ fluid, and finally the well is opened for production. Huff-n-puff improves recovery; see page 9, 2nd paragraph - tuning parameters used in the optimization consist of: the maximum number of possible huff-n-puff cycles; the duration of injection, soaking and production periods; and huff-n-puff start-up time).
Olusola and Kanfar disclose:
outputting, for a domain of the numerical simulation model (Olusola – see page 9, 2nd paragraph – bound and linear constraints, e.g. reservoir boundary constraints included in the set … e.g. simulation-based constraints;
see also Kanfar page 3, Table 1 Simulation Model setup and input includes “quarter dimensions, xyz, Ft (170 x 350 x 40); number of grid blocks, xyz (518 x 1 x 1); see page 7 – grid blocks changed; uses a gridding scheme for simulation), top ten combinations of the gas injection huff-n-puff parameters in terms of net present value for mining ten years (Olusola – see page 9, 3rd paragraph - In Equation 1 Qo and Po are the cumulative volumes of oil produced (STB) and price of oil ($/STB),respectively; R is the royalty rate, C is the total expenditure which includes capital (original hydraulic fracturing cost and refracturing cost) and operating costs, Cp(x) is the cost used to penalize the result if the average reservoir pressure after gas injection goes to ninety percent of the initial reservoir pressure (this assumption ensures that this function evaluation does not make it to become the best solution and that the final solution selects optimization parameters below the formation fracturing pressure), and T is the tax rate. The maximum allowable time is 10 years; See page 10, Table – time period of evaluation is “10 years”), and each of the top ten combinations of the gas injection huff-n-puff parameters comprising one obtained gas injection rate, one obtained gas injection time, one obtained soaking time, and one obtained production time (Olusola – page 11, Optimization Results – 300 simulations per iterations were conducted; in Table 6 – shows “Top 10 Groups” with Column 3 GIR (Gasi injection rate), Column 4 GID (gas injection duration), Column 6 (oil production); column 7 (oil recovery factor); and resulting column 9 (NPV); See page 13, 1st-2nd paragraphs – Table 6 – optimize well control variables for economic recovery of oil during production operations; grouping the wells by analogy and combining MatBal calculations with derivative-free optimization algorithms help to generate all possible field development options using H&P and RF technology on multiple wells within a short time-frame. Based on economic ranking of these groups of wells, the operator can make a reasonable decision about how to prioritize development of assets;
see also Kanfar – page 10 – uses NPV and tuned parameters for top 5 ranked solutions (Eliges #1-5) in Table 3; table includes “soaking period” in combination with injection and production and resulting NPV);
selecting an optimal combination of the gas injection huff-n-puff parameters from the top ten combinations of the gas injection huff-n-puff parameters according to the net present value (Olusola – see page 13, 1st-2nd paragraphs - Based on economic ranking of these groups of wells, the operator can make a reasonable decision about how to prioritize development of assets;
see also Kanfar – see page 10, “Discussion” _ optimization algorithm (GA) was successful in finding an optimum huff-n-puff scheme which improves
NPV by $400,000 over the primary recovery scenario (see Fig. 8). Although the oil price assumed in NPV calculation is high relative to today's prices, this work illustrates the usefulness of combining NPV and GA for optimizing huff-n-puff design.); and
using the optimal combination of the gas injection huff-n-puff parameters for mining tight oil reservoirs, comprising: injecting gas into a tight oil reservoir according to the obtained optimal gas injection rate and gas injection time; maintaining the reservoir in a soaking state according to the obtained optimal soaking time; and producing oil from the tight oil reservoir according to the obtained optimal production time (Olusola – see page 1, 2nd paragraph - procedure is explained with the use of an actual H&P gas injection pilot horizontal well in the EagleFord shale whose performance is matched using the methodology developed in this paper; see page 16, Conclusions - The proposed methodology improves oil recovery and NPV from a single horizontal well or from multiple horizontal wells operating under H&P gas injection; see page 13, 1st-2nd paragraphs - Based on economic ranking of these groups of wells, the operator can make a reasonable decision about how to prioritize development of assets; see page 17 - Optimization of H&P gas injection and RF for single and multiple wells in various stages of field development can be assessed using this methodology. A priority list of the most feasible solutions for multiple wells can be quickly generated by grouping like-wells. Oil recovery and NPV may be improved significantly by a strategic combination of H&P gas injection and RF.
see also Kanfar – page 11 - paper utilizes the recent findings of Kanfar and Clarkson (2017) and an optimization algorithm (GA) to determine if huff-n-puff could be successful in the Bakken tight oil reservoir. The results indicate that
huff-n-puff can improve oil recovery (and NPV))
Both Olusola and Kanfar are analogous art as they are directed to optimization in reservoirs (See Olusola Abstract; Kanfar Abstract). Olusola mentions “soaking time” but only with regards to discussing another article (See page 3, 5th paragraph) and having a variety of optimization results including gas injection rate, gas injection duration, cumulative oil production, oil recovery factor and more (See page 11, Table 6) and showing 10 groupings of different NPV calculations with different amounts of wells (column 2). Kanfar improves upon Olusola by disclosing optimizing and considering soaking in the oil recovery process, as well as having a “top 5” ranking of combined Optimized Parameters for different resulting NPVs (See page 10, Table 3). One of ordinary skill in the art would be motivated to further include soaking in the optimization along with a “top 5” ranking of combined parameters to efficiently improve upon the optimization results in Olusola. See also MPEP 2144.05 “where the claimed ranges "overlap or lie inside ranges disclosed by the prior art" a prima facie case of obviousness exists.” Here, the prior art disclose presenting a top 5 ranking, from 25 generations of Maximum NPV (See Kanfar page 9, last paragraph). There is no “criticality” disclosed here (See MPEP 2144.05) to having “top 10” scenarios listed, as opposed to top 5, especially considering primary reference Olusola is already showing 10 “groupings” with different well amounts and different NPVs.
Accordingly, it would have been obvious to one of ordinary skill in the art before
the effective filing date of the claimed invention to modify the system and method of
using particle swarm algorithm for optimizing oil recovery in Olusola to further optimize while considering soaking and presenting a listing of some top ranking combination of parameters leading to different NPVs as disclosed in Kanfar, as the claimed invention is merely a combination of old elements, and in combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable and there is a reasonable expectation of success.
Concerning claim 2, Olusola discloses:
According to a collaborative optimization method for gas injection huff-n-puff parameters in tight oil reservoirs as described in claim 1, its characteristics are as follows: The numerical simulation model in Step 1 that conforms to the actual oil reservoirs is a numerical simulation model after fracturing flowback fitting and production history matching (Olusola – see page 4, “History Match” section, 1st paragraph - Actual performance is matched, and predictions are made about future performance of the well, and anticipated Net Present Value (NPV) results; “History Match” section, 3rd paragraph - Reservoir, fluid and wellbore properties in Table 1 (page 5 – includes fracture water saturation; fracture oil saturation; fracture porosity; fracture spacing; permeability of natural fractures, etc); correspond to real Eagle Ford data provided by a petroleum company; the data were published by Orozco et al. (2019); see page 5, last paragraph - Using key reservoir and wellbore parameters stated in Table 1, we history matched with MatBal calculations the real data from the Eagle Ford shale Pilot well ; see page 8, 1st paragraph - One of the benefits of MatBal calculations used in this work is that contribution of rock properties such as strength and brittleness can be incorporated into the hydraulic fracture properties (porosity and permeability)for calculating productivity index and subsequently oil production rates).
Concerning claim 4, Olusola discloses:
According to a collaborative optimization method for gas injection huff-n-puff parameters in tight oil reservoirs as described in claim 1, its characteristics are as follows: The particle swarm optimization algorithm is used to solve the objective function to obtain the optimal gas injection rate, gas injection time (Olusola see page 22, 1st paragraph - we present a methodology for optimizing recovery by Huff and Puff gas injection in shale oil reservoirs using an original Climbing Swarm (CS) derivative free Algorithm), soaking time (Kanfar – see page 2, 1st paragraph - A huff-n-puff cycle consists of the following: gas (typically CO2 or separator gas) is injected through a (production) well, gas is allowed to "soak" in the in-situ fluid, and finally the well is opened for production. Huff-n-puff improves recovery; see page 9, 2nd paragraph - tuning parameters used in the optimization consist of: the maximum number of possible huff-n-puff cycles; the duration of injection, soaking and production periods; and huff-n-puff start-up time), and production time in Step 3; The specific steps include:
(1) The particle swarm optimization method is used to perform random initialization of each particle, including initial velocity and initial position (Olusola see page 10, 1st paragraph - derivative-free algorithm is now applied to solve the well control (WC) optimization problem for H&P and RF operations in multi-wells. The size of the swarm is 30 and each particle (xi) in the derivative-free algorithm contains a selected set of well control (WC) variables such as gas injection rates (GIR), gas injection duration (GID), and average reservoir pressure. This is represented for one group by Equation 2; see page 13 – “Comparison of Particle Swarm Optimization (PSO) and Climbing Swarm (CS)Optimization” - Fig. 5 shows a flowchart of the optimization procedure with CS or PSO; Fig. 5 includes “random initialization of WC (well control) parameters); see page 22, Appendix B, #1 – Choose swarm size (N), termination tolerances, step-length, maximum number of iterations, and number of variables. Climbing Swarm (CS) Algorithm - Randomly generate the initial population of X in the range xlj to xuj, where xlj and xuj indicate the lower and upper bounds on X for particle j, respectively. This can be determined using : [equation]
where is particle j in i iteration, and r1 is a uniformly distributed random number in the range 0 and 1 (disclosing “initial position”). Initialize the velocity of each particle to zero (disclosing “initial velocity”););
(2) The reservoir numerical simulator is automatically called based on parameter sequence to run simulation schemes (Olusola page 13, FIG. 5 and last paragraph – “run simulation” using material balance calculations and determine NPV);
(3) The simulation results are automatically read to calculate the optimization objective function, evaluate each particle and obtain the current individual extremum and group global optimal solution (Olusola page 4, 2nd paragraph - in this paper we present a methodology to learn how to perform these tasks faster and at lower cost to improve oil recovery using derivative free algorithms. To achieve that goal, we combine in a systematic manner the global stochastic particle swarm optimization (PSO) by Kennedy and Eberhart (1995a and 1995b) and the hill climber (HC)optimization (local search) method (Hooke and Jeeves, 1960). We call this combination Climbing Swarm(CS); page 14, 3rd paragraph - Fig. 6 shows comparisons between stand-alone PSO and CS based on individual optimization runs. In general, CS outperforms the stand-alone industry accepted PSO both in effectiveness (higher NPV) and robustness; see page 22, 2nd paragraph - CS, which combines the benefit of global and local search algorithms, accelerates convergence to high quality solution and saves computational time and cost);
(4) The velocity and position of each particle are updated according to the particle velocity and position update formulas, and the objective function value of the updated particle is calculated (Olusola page 22, Optimization Cycle #7 – Find the velocity of particle j in iteration i+1 using [equation]; Optimization Cycle, #8 – “find the new position of particle j in iteration i+ 1…);
(5) The historical optimal position of each particle and the global optimal solution of the swarm are updated (Olusola page 22, Optimization Cycle #8 - Find the new position of particle j in iteration i +1 assuming time increment, Δt = 1 using:
Evaluate the objective function values corresponding to the particles. If there is an improvement in objective function values, repeat step 5 to 9, otherwise proceed to Step 11; step 2 - Update a selected parameter in local search with historical best value of X, xg from global search);
(6) Judge whether the optimization has reached the termination condition; If it does, the global optimal solution of the gas injection huff-n-puff parameter is obtained; If not, continue to update the particle velocity and position, generate a new parameter sequence, and return to Step (2) (Olusola see page 11, 2nd paragraph - The optimization results are summarized in Table 6 and Fig. 4 A total of 300 simulations (tank MatBal calculations) per iteration were conducted to generate the results; See page 22, Initiation #2 – Choose “termination tolerances”; see page 23, steps 15-18 - If there is an improvement in objective function values, repeat Step 14 otherwise reduce step-length and continue; 16. If step-length < tolerance value or no improvement in objective function values, proceed to Step 17; 17. Check for convergence and termination criteria. If not satisfied, update the historical best value of X, xg from global search with best value from HC; 18. Repeat the process from Step 5 until the convergence or termination criteria is satisfied).
Concerning claim 5, Olusola discloses:
According to a collaborative optimization method for gas injection huff-n-puff parameters in tight oil reservoirs as described in claim 4, its characteristics are as follows: The termination condition described in Step (6) is that the maximum number of iterations has been reached or that the difference in calculation results between two adjacent generations is less than 0.1% (Olusola – see page 11, 2nd paragraph - The optimization results are summarized in Table 6 and Fig. 4 A total of 300 simulations (tank MatBal calculations) per iteration were conducted to generate the results; See page 22, Initiation #2 – Choose “termination tolerances”; see page 23, steps 17-18- 17. Check for convergence and termination criteria. If not satisfied, update the historical best value of X, xg from global search with best value from HC; 18. Repeat the process from Step 5 until the convergence or termination criteria is satisfied).
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Olusola, et al., “Optimization of Recovery by Huff and Puff Gas Injection in Shale Oil Reservoirs Using the Climbing Swarm Derivative Free Algorithm,” 2020, SPE Latin America and Caribbean Petroleum Engineering Conference, pages 1-22, in view of Kanfar, et al. "A modeling study of EOR potential for CO2 Huff-n-Puff in tight oil reservoirs-Example from the Bakken Formation," 2017, Society of Petroleum Engineers (SPE) Canada Unconventional Resources Conference, pages 1-17, as applied to claims 1-2 and 4-5 above, and further in view of Wang, et al., “A Novel Bayesian Optimization Framework for Computationally Expensive Optimization Problem in Tight Oil Reservoirs,” 2017, Society of Petroleum Engineers (SPE) Annual Technical Conference and Exhibition, pages 1-17.
Concerning claim 3, Olusola discloses:
According to a collaborative optimization method for gas injection huff-n-puff parameters in tight oil reservoirs as described in claim 1, Step 2 comprises: (1) Optimization range of gas injection huff-n-puff parameters is represented as:
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Where x.sub.i is the value of the i.sup.th gas injection huff-n-puff parameter, x.sub.i,min is the lower limit of the i.sup.th gas injection huff-n-puff parameter, and x.sub.i,max is the upper limit of the i.sup.th gas injection huff-n-puff parameter (Olusola – see page 10, 3rd paragraph - Lower and upper bound constraints shown in Table 5 were used to constrain the generation of optimization parameters in such a way that resulting solutions did not lead to infeasible solutions e.g. low gas injection rate ; page 10, Table 5 – Optimization boundary parameters for re-fracturing and H&P (huff and puff) gas injection – includes “lower bound” and “upper bound”; see page 14, Table 7 – boundary parameters; see page 22 – Initialization, #2 - generate the initial population of X in the range xlj to xuj, where xlj and xuj indicate the lower and upper bounds on X for particle j, respectively;
see also Kanfar – page 9, 2nd -3rd paragraph and table 2 – upper and lower bounds of tuned parameters including “huff-n-puff start-up day”);
(2) The variable constraint of oil recovery is: RF≥RF.sub.min; Where RF.sub.min is lower limit of oil recovery for optimizing numerical simulation schemes;
Olusola - See page 6, FIG. 3, y-axis - “oil recovery factor; page 7 - optimization in Table 2 includes column 6 for oil recovery factor (ORF) and column 5 for cumulative oil production, where “optimizing GIR and GID leads to higher oil recovery” (See page 7, 4th paragraph) and optimization results include considering column 7 “oil recovery factor”( See page 11, Table 6);
see also Wang – see page 15, “Oil Recovery” - 12 illustrates the total oil recovery of the well-pad for all evaluations. As can be seen, the total oil recovery factors are significantly low (i.e., 3.4% ~ 10.8%) for all kinds of well placement and fracture design scenarios in tight oil formation. This is consistent with the observation from Sandrea (2012), who estimates that the primary oil recovery factor is approximately in the range of 1.0% ~ 5.6% of the original oil in place (OOIP) for top six major tight oil plays in the US even after long horizontal wells have been drilled and multi-stage fractured);
(3) The net present value or oil recovery is selected as the objective function of gas injection huff-n-puff parameter optimization;
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Olasula page 9, 2nd paragraph – objective is to “minimize negative NPV” (J(x)),
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Olasula equation has same summation; “N is the total number of group NPVs considered for determining the overall NPV”; page 9, 2nd paragraph Jo(x) is overall objective function be optimized (NPV); page 10 , 1st paragraph - The three well control variables considered for optimization are (1) GIR (from page 6 – “gas injection rate”), (2) GID (from page 6 – “gas injection duration”) and (3) the average reservoir pressure (PTI) at which RF and H&P operation should be initiated; Olasula has price of oil as Po
Kanfar discloses aspects of NPV equation not in Olusola:
Kanfar – see page 6
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is evaluation time, year; Np is the number of evaluation wells; b is the discount rate, %
Kanfar – see page 6 – each variable – claimed Rop is sales price of oil which in Kanfar is Co; claimed Rgp is sales of gas which in Kanfar is Cg; claimed Qop is oil production disclosed by Kanfar’s Qo is oil production; claimed qqgi is disclosed by Kanfar’s Qinj “incremental cumulative gas injection”; claimed b is discount rate and Kanfar’s R is discount rate
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Wang discloses equation in entirety on page 4:
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Olusola in combination with Wang discloses the limitations in equation (2):
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q.sub.op.sup.i,t is the oil production of the i.sup.th well in the i.sup.th year (in equation (2) );
N is original oil in place (this N is only in “Oil Recovery equation”).
Olusola discloses:
(Olusola – page 4, last paragraph - original oil in place is calculated volumetrically using reservoir properties of that particular area”; See page 6, FIG. 3, y-axis - “oil recovery factor; page 7 - optimization in Table 2 includes column 6 for oil recovery factor (ORF) and column 5 for cumulative oil production, where “optimizing GIR and GID leads to higher oil recovery” (See page 7, 4th paragraph) and optimization results include considering column 7 “oil recovery factor”( See page 11, Table 6); See page 9, 2nd paragraph - This leads to a multiple objective optimization problem. Rao (2009) stated that when solving multiple objective optimization problems, there is usually a conflict between objective functions, as one design or control variable that worsens one objective, may improve another objective. One simple way of handling this problem is to construct an overall objective function as a linear combination of the conflicting multiple objective functions; see page 17, #4 - Oil recovery and NPV may be improved significantly by a strategic combination of H&P gas injection and RF).
Wang – see page 12 “It is found that the best NPV optimized by the Bayesian optimization technique achieve a maximum value of USD 8.22 million, which is increased by 55.7% in comparison to the reference case. The NPV increase is mainly attributed to a good balance of the increment of oil production with the
well drilling and completion costs.”)
Olusola, Kanfar, and Wang are analogous art as they are directed to optimization in reservoirs (See Olusola Abstract; Kanfar Abstract; Wang Abstract). Olusola discloses having an NPV (see page 9, equation 1) and “oil recovery factor” (column 7 on page 11, Table 6) in optimization. Kanfar improves upon Olusola by disclosing an NPV equation that considers sales prices, oil production, discount rates (See page 6). Wang improves upon Olusola and Kanfar by disclosing NPV equation in its entirety with the same variables as claimed, and further disclosing oil recovery factors along with increase in NPV optimization based on balancing oil production with costs (See page 4, 12, 15). One of ordinary skill in the art would be motivated to further include using a commonly used objective function for net present value, while also considering oil production relative to costs, to improve upon the NPV equations in Olusola and Kanfar.
Accordingly, it would have been obvious to one of ordinary skill in the art before
the effective filing date of the claimed invention to modify the system and method of
using particle swarm algorithm for optimizing oil recovery in Olusola to further optimize while considering soaking as disclosed in Kanfar, to further use a commonly used objection function for net present value as disclosed in Wang, as the claimed invention is merely a combination of old elements, and in combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable and there is a reasonable expectation of success.
Response to Arguments
Applicant's arguments filed 3/5/26 have been fully considered but they are not persuasive and/or are moot in view of the new rejections.
Applicant argues that Olusola and Kanfar do not disclose the new limitations - “a domain of he numerical simulation model”; “outputting top ten combinations”; “mining ten years”. The arguments are moot in view of the revised rejections necessitated by the amendments.
Conclusion
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 IVAN R GOLDBERG whose telephone number is (571)270-7949. The examiner can normally be reached 830AM - 430PM.
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/IVAN R GOLDBERG/Primary Examiner, Art Unit 3619
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