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 Non-Final, first Office Action responsive to Applicant’s communication of 12/30/22, in which applicant filed the application. Claims 1-5 are pending in the instant application and have been rejected below.
Reasons for Subject Matter Eligibility under 35 USC 101
The claims 1-20 are subject matter eligible because the independent claim 8 recites: a computer performing a pressure drawdown and build up test with inflow control valve (ICV) at downhole sensor; calibrating a simulation model, performing simulations of a multilateral completion of a well based on plurality of ICV setting, selecting target ICV settings and facilitating production operation of the well by applying the target ICV settings. When viewing the claim as a whole, this is viewed as “not directed to an abstract idea”, as well as a practical application under step 2a, prong 2, as the claim is improving another technology when viewing all the limitations listed above (See MPEP 2106.05a) and/or is viewed as a using a judicial exception in a meaningful way under MPEP 2106.05(e) (See Diamond v. Diehr - Court evaluated additional elements such as the steps of installing rubber in a press, closing the mold, constantly measuring the temperature in the mold, and automatically opening the press at the proper time, and found them to be meaningful because they sufficiently limited the use of the mathematical equation to the practical application of molding rubber products). Claims 1 and 8 are similarly eligible, for being “not directed to an abstract idea” and a practical application, for similar reasons as above, as these claims require method an apparatuses for “integrated station comprising at least one inflow control valve, downhole sensor, generating using station a drawdown pressure and flowrate measurements by performing a pressure drawdown and build up test of each compartment of the multilateral of the well, calibrating simulations and performing simulation models to then select target ICV settings, and then performing the target ICV settings to the multilateral well for production operation.
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 1-6, 8-13, and 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over Alanazi (US 2020/0362674), in view of Tonkin (US 2016/0369590).
Concerning claim 1, Alanazi discloses:
A method for performing a production operation of a well (Alanazi – see par 138 - FIG. 28 is a flowchart of an example method 2800 for recommending/suggesting changes to downhole valve settings to optimize production in a multilateral well, according to some implementations of the disclosure; see par 147-148 - At 2810, the one or more of the recommended optimizing changes selected by the user are implemented by the multilateral well optimizing system. In some implementations, the control commands include choke setting commands to set different choke settings on different ICVs. For example, the computer-implemented system can send commands to ICVs of particular laterals, such as to choke particular laterals to specific percentages. After 2810), the method comprising:
disposing, within each of a plurality of compartments of a multilateral completion of the well (Applicant’s specification [0026] as published gives example of compartments as upper compartment l1U 151 and lower compartment L1L 152 of lateral #1 in FIG. 2.
Alanazi ‘674 discloses the limitations based on broadest reasonable interpretation in light of the specification – see par 43 - On each lateral, the flow and pressure drop through the reservoir can be determined in the horizontal section. see par 48 - FIG. 1 is a diagram of an example of a multi-zonal smart well completion 100, according to some implementations of the present disclosure.. Each zone 102 can further contain a lateral well. see par 103 - On each lateral, the flow and pressure drop through the reservoir were determined in the horizontal section as well as the annular flow between the casing and the tubing.), an integrated station comprising at least one inflow control valve (ICV) and at least one downhole sensor (Applicant’s specification [0027] as published gives example for station of “an integrated station (e.g., integrated station (151c)) is placed within each compartment for monitoring and controlling the production of unwanted fluids (e.g., gas and/or water).”
Alanazi ‘674 discloses the limitations based on broadest reasonable interpretation in light of the specification see par 40 - recommended changes can be based on data from current production conditions of the well, including information from downhole sensors and valves, which enable real-time monitoring and control of multilateral completions. See par 41 - Production optimization techniques can use real-time data and nodal models for multilateral wells. Wells contain inflow control devices (ICVs) with various choke settings to restrict flow based on the orifice size of each valve position).
While Alanazi discloses sections (See par 43) and zones 102 (See par 48), it discloses a “downhole sensor” in the well (See par 40). To any extent it is not clear if it the downhole sensor is within a compartment of a multilateral, Tonkin discloses:
disposing, within each of a plurality of compartments of a multilateral completion of the well, “an integrated station” comprising at least one inflow control valve (ICV) “and at least one downhole sensor” (Applicant’s specification [0026] as published gives example of compartments as upper compartment l1U 151 and lower compartment L1L 152 of lateral #1 in FIG. 2; Applicant’s specification [0027] as published gives example for station of “an integrated station (e.g., integrated station (151c)) is placed within each compartment for monitoring and controlling the production of unwanted fluids (e.g., gas and/or water
Tonkin discloses the limitations based on broadest reasonable interpretation in light of the specification -see par 16 - The dynamic data may include, for example, pressures, fluid compositions (e.g. gas oil ratio, water cut, and/or other fluid compositional information), and choke positions of down hole flow control valves, and other information that may be monitored via downhole sensors. The downhole sensors may include sensors which are part of the down hole flow control valves and sensors, e.g. pressure and temperature sensors, which are located separately in the various well zones and/or other well locations. see par 19 The single lateral of the horizontal or vertical well may have multiple zones isolated by packers and down hole flow control valves. In a multilateral well, multiple laterals may exist. see par 21 - In particular, a well zone is a region along a lateral that is demarcated by two adjacent packers).
Alanazi and Tonkin disclose:
generating, using each integrated station of the plurality of compartments, drawdown pressure and flowrate measurements by performing a pressure drawdown (Alanazi – see par 41 - . The production optimization techniques can estimate flowing parameters of individual laterals, determine the optimum pressure drop across each downhole valve; See par 43 - On each lateral, the flow and pressure drop through the reservoir can be determined in the horizontal section. The annular flow between the casing and the tubing can also be determined. The concept of nodal analysis can be used that includes a combination of the pressure drop across each lateral to the production system for an individual well to estimate production rates and optimize the components of the production system.) and build up test of each compartment (Alanazi – see par 45 - During regular field optimization procedures, the multilateral well can be produced one lateral at a time. Surface and downhole pressures and production metrics can be recorded for these tests. See par 64 - At 416, a preliminary network model is constructed, for example, using well static data and reservoir and fluid properties. The preliminary network model, for example, can be part of an approach which starts first by building a preliminary model (not yet calibrated) using the well's previous production and pressure data. see par 65 - At 418, single lateral testing is commenced on the field for model calibration. At 420, each lateral in the field is set up and tested while the other laterals are disabled. For example, in a multilateral well 430, a lateral 432 is set up and tested while laterals 434 are disabled.);
calibrating a simulation model of the multilateral completion (Alanazi – see par 39 - optimization techniques for smart well completions (SWCs). For example, optimizing smart well completions can refer to achieving well performance values that indicate or result in a performance above a predefined threshold. see par 62 - The SWC model starts from permanent downhole measurement (PDHMs); See par 64 - At 414 (after it is confirmed that the ICVs and PDHMs are working properly), SWC data is reviewed and the laterals' PIs are estimated. At 416, a preliminary network model is constructed, for example, using well static data and reservoir and fluid properties. The preliminary network model, for example, can be part of an approach which starts first by building a preliminary model (not yet calibrated) using the well's previous production and pressure data. Then, the model is calibrated. After calibration, the model is capable of providing recommended downhole choke valve settings guided by user objectives.), wherein a simulation result of the pressure drawdown and build up test using the calibrated simulation model matches the drawdown pressure and flowrate measurements of each compartment (Alanazi –see par 65, FIG. 4A - At 418, single lateral testing is commenced on the field for model calibration. At 420, each lateral in the field is set up and tested while the other laterals are disabled. For example, in a multilateral well 430, a lateral 432 is set up and tested while laterals 434 are disabled. see par 66 - At 422, a same test is run in the model and compared to PDHMs, and the results are rated. At 424, a determination is made whether the results match within a threshold (for example, 5%). see par 68-69 - Using the process 400 (FIG. 4A) can provide a better understanding of flow contributions of each lateral in order to determine optimum ICV settings. The approach was successfully field-tested and validated. The generated models were used to predict well performance at various conditions. The approach started by collecting well rates and flowing bottom-hole pressure data at various chokes settings including commingled and individual lateral testing. The acquired data was used to calibrate the model, generate different production scenarios, and optimize the performance of each lateral..);
performing, using the calibrated simulation model, a plurality of production simulations of the multilateral completion to generate a plurality of production simulation results, wherein each of the plurality of production simulations is based on one set of a plurality sets of ICV settings for the multilateral completion (Alanazi – see par 54 - FIG. 3 is a flow diagram of an example of a genetic algorithm optimization procedure 300 for determining optimum valve settings as given by the well model, according to some implementations of the present disclosure. see par 55 - At 302, the control parameters are defined and their feasible limits are identified. For example, limits for possible wellhead pressure values are set between a flow-line pressure (minimum) and a reservoir minus hydrostatic pressure (maximum). see par 56 - At 304, a diverse pool of possible initial solutions is created. The diverse pool of possible initial solutions can include solutions that abide by parameter limits and cover the solution space. See par 66 - At 426 (if the determination indicates that the results do not match within the threshold), the PI (See par 44 – PI = “productivity index”) and reservoir pressure are re-assessed, and the same test is rerun at 422. At 428 (if the determination indicates that the results do match within the threshold), a determination is made whether additional laterals need to be calibrated. see par 67 - At 452, comingled production scenarios are generated using an optimization algorithm, for example, the genetic algorithm optimization procedure 300.);
selecting, by comparing the plurality of production simulation results to a maximum constraint, a set of target ICV settings from the plurality sets of ICV settings, wherein the set of target ICV settings comprises a target setting for each ICV of the multilateral completion (Alanazi – see par 57 - At 306, performance of the solutions is evaluated using the model. For example, an objective function is defined that is a function of at least a net present value and a total oil production. The objective function is evaluated for each of the solutions. The evaluation process can provide a reflection of a given solution's quality. see par 58- At 308, current solutions are ranked according to the value of the objective function. For example, a given solution can be ranked higher according to an estimated greater net present value and total oil production. see par 67, FIG. 4B - At 460 (if the determination indicates that the results do not match within the threshold), the PI and reservoir pressure are re-assessed, and the laterals are retested at 454. If the determination indicates that all comingled scenarios do not match the simulated ones, then the laterals are set up and tested under a comingled production scenario at 454.); and
performing, by at least applying the set of target ICV settings to the multilateral completion, the production operation of the well (Alanazi – see par 67 - At 464 (when it is determined that all comingled scenarios match the simulated ones), IVCs can be adjusted into their final settings;
see also Tonkin – see par 34 - the network modeling module (308) may be a repurposed tool that generally simulates flow through a surface network having multiple wells. In other words, the network modeling module (308) may be designed for a surface network simulation. In such a scenario, when input into the network modeling module (308), the down hole flow control valves may each be identified as individual wells to the network modeling module and the choke positions may be identified as choke positions for the well heads. see par 44, FIG. 4 - , the network modeling module uses the set of choke positions to simulate the movement of matter (e.g., hydrocarbons, water, and other matter) through the well in order to determine the simulated computed pressure at each down hole flow control valve. see par 47 - If the computed pressure matches the selected pressure in Block 415, the flow may proceed to Block 419. In Block 419, a field operation of the well is performed in accordance with one or more embodiments of the technology. In one or more embodiments of the technology, performing the field operation may include sending a control signal to the field equipment, such as one or more down hole flow control valves to change the choke positions on the down hole flow control valves).
Both Alanazi and Tonkin are analogous art as they are directed to simulating for optimizing well production (See Alanazi Abstract; Tonkin Abstract, par 34). Alanazi discloses sections (See par 43) and zones 102 (See par 48), it discloses a “downhole sensor” in the well (See par 40). Tonkin improves upon Alanazi by disclosing sensors and control valves in various well zones (See par 16, 19, 21). One of ordinary skill in the art would be motivated to further include sensors and control valves in various well zones to efficiently improve upon the monitoring of multilateral completions with optimization for ICVs in Alanazi.
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
monitoring of multilateral completions with optimization for ICVs in Alanazi to further include sensors and control valves in various well zones as disclosed in Tonkin, 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 independent claim 8, Alanazi and Tonkin disclose:
A non-transitory computer readable medium storing instructions for performing a production operation of a well, the instructions, when executed by a computer processor, comprising functionality (Alanazi – see par 188, 196, 203 - In a second implementation, a non-transitory, computer-readable medium stores one or more instructions executable by a computer system to perform operations providing automated production optimization for smart well completions using real-time nodal analysis including real-time modeling.) for
The remaining limitations are the same as claim 1 and claim 8 is rejected for the same reasons.
Concerning independent claim 15, Alanazi and Tonkin disclose:
A well system for performing a production operation of a well, the well system comprising (Alanazi – see par 138 - FIG. 28 is a flowchart of an example method 2800 for recommending/suggesting changes to downhole valve settings to optimize production in a multilateral well, according to some implementations of the disclosure; see par 147-148 - At 2810, the one or more of the recommended optimizing changes selected by the user are implemented by the multilateral well optimizing system.) for
The remaining limitations are the same as claim 1, 8 and claim 15 is rejected for the same reasons.
Concerning claims 2, 9, and 16, Alanazi and Tonkin disclose:
The method according to claim 1, further comprising:
disposing a plurality of swell packers in a plurality of laterals of the multilateral completion of the well to form the plurality of compartments (Applicant’s [0025] as published states “A swell packer, or swellable packer, is an isolation device that relies on elastomers to expand and form an annular seal when immersed in certain wellbore fluids
Alanazi discloses the limitations based on broadest reasonable interpretation in light of the specification – see par 48 - Each zone 102 (for example, a zone 102a) can be isolated with packers 104 and 106 and equipped with downhole pressure gauges 108 and a valve 110. Each zone 102 can further contain a lateral well. The packers 104 and 106 can include, for example, external swell packers, which are run with a screen, and internal swell packers, which run with completion. Downhole pressure gauges 108 can include, for example, a downhole pressure gauge with i-wire. To achieve optimal economic values of the multi-zonal smart well completion 100, the surface and subsurface choke valves settings can be frequently optimized using various techniques.)
Concerning claims 3, 10, and 17, Alanazi and Tonkin disclose:
The method according to claim 2, wherein the maximum constraint comprises a maximum allowable liquid production rate of the plurality of compartments and a maximum allowable reservoir pressure drawdown (Alanazi – see par 78 - optimization problems search for a set of variables that achieve a maximum objective function according to equation 3, where C.su.n. corresponds to problem constraints; For the well control optimization problem, for example, x.sub.opt contains the ICV setting(s) of all valves in the smart wells. Objective functions in smart multilateral well completions can include, for example, cumulative oil production, cumulative oil flow rate, and economic implications, which can consider a net present value (NPV) of a project (for example, the multilateral well); see par 107 - production constraints can be added to the network for maintaining certain production targets and system constraints such as gas production and pressure downstream of the choke..
see also Tonkin – see par 31 - FIG. 3, constraints (318) are a set of limitations on an optimization problem. The constraints may be defined as an inequality or an equality equation. For example, the optimization problem has at least one objective function (not shown), such as maximization of oil and/or minimization of water and gas production; constraints are limitations that should be satisfied in determining the value of the objective function. For example, the constraints may be limitations regarding draw down, bubble point, flow balance, and flow rate restriction).
It would have been obvious to combine Alanazi and Tonkin for the same reasons as claim 1 above.
Concerning claims 4, 11, and 18 Alanazi and Tonkin disclose:
The method according to claim 3, wherein the set of target ICV settings is selected to balance flowrate contributions from the plurality of laterals of the multilateral completion (Alanazi – see par 119 - FIGS. 20A and 20B are graphs showing examples of flow rates plots 2000 and 2050 for a well model using an equalized flow; For example, the flow rates plots 2000 and 2050 correspond to a well model ICVs equalized flow scenario. see par 120 - The second production scenario is the equalized flow that attempts to balance the production from the laterals by managing the drawdown of each. This production technique can be applied to create a better sweep from the area and an even flood-front advancement.
see also Tonkin – see par 31 - In particular, the balancing condition may be that a value of a property at the down hole flow control valves in a lateral are within a threshold difference from each other when the choke positions are set. The threshold difference may be zero, in which case the value of the property is equal for each of the down hole flow control valves within a lateral. For example, the property subject to the balance condition may be water and/or gas break through, pressure, oil and/or gas production rate, or another property.).
It would have been obvious to combine Alanazi and Tonkin for the same reasons as claim 1 above.
Concerning claims 5, 12, and 19, Alanazi and Tonkin disclose:
The method according to claim 4, wherein balancing flowrate contributions from the plurality of laterals of the multilateral completion is based on the maximum allowable liquid production rate of the plurality of compartments and the maximum allowable reservoir pressure drawdown… (Alanazi – see par 78 - optimization problems search for a set of variables that achieve a maximum objective function according to equation 3, where C.su.n. corresponds to problem constraints; For the well control optimization problem, for example, x.sub.opt contains the ICV setting(s) of all valves in the smart wells. Objective functions in smart multilateral well completions can include, for example, cumulative oil production, cumulative oil flow rate; see par 103 - On each lateral, the flow and pressure drop through the reservoir were determined in the horizontal section as well as the annular flow between the casing and the tubing. The model was used to determine the optimum pressure drop across each ICV and the appropriate ICV setting at different operational conditions; see par 120 - The second production scenario is the equalized flow that attempts to balance the production from the laterals by managing the drawdown of each)
Tonkin discloses:
The method according to claim 4, wherein balancing flowrate contributions from the plurality of laterals of the multilateral completion is based on the maximum allowable liquid production rate of the plurality of compartments and the maximum allowable reservoir pressure drawdown “to prevent early breakthrough of unwanted gas and/or water” (Tonkin –see par 21, FIG. 2 - The multilateral well may have well completion systems (20). Well completion systems may include sections of tubing (30) which extend between and/or through various completion components, including packers (32) which isolate corresponding well zones (34). In particular, a well zone is a region along a lateral that is demarcated by two adjacent packers. In one or more embodiments of the technology, the well completion system (20) may include down hole flow control valves (36) which control fluid flows and fluid flow rates from the various corresponding well zones (34) into multilateral well completion systems (20). See par 31 - FIG. 3, constraints (318) are a set of limitations on an optimization problem. For example, the optimization problem has at least one objective function (not shown), such as maximization of oil and/or minimization of water and gas production. The constraints are limitations that should be satisfied in determining the value of the objective function. For example, the constraints may be limitations regarding draw down, bubble point, flow balance; the property subject to the balance condition may be water and/or gas break through, pressure, oil and/or gas production rate, or another property; see par 48 - In performing the optimization over time, additional constraints may be applied, such as to delay water breakthrough or have other conditions be delayed; see par 56-57 – the onset of water breakthrough may be delayed by adding the following balancing constraint).
It would have been obvious to combine Alanazi and Tonkin for the same reasons as claim 1 above. In addition, Alanazi discloses having an objective function for multilateral well completions including cumulative oil production and cumulative oil flow rate along with pressure drop being utilized where equalized flow balances production (See par 78, 103, 120). Tonkin improves upon Alanazi where the constraints regarding draw down subject to a balance condition further includes draw down, pressure, water and/or gas break through, and gas production rate. One of ordinary skill in the art would be motivated to further include further considering break through to efficiently improve upon the monitoring of multilateral completions with optimization for objective functions having a variety of considerations for ICVs (See e.g. par 78) in Alanazi.
Concerning claims 6, 13, and 20, Alanazi and Tonkin disclose:
The method according to claim 1, wherein performing the pressure drawdown and build up test of each compartment comprises:
receiving, by the integrated station of said each compartment and from a smart downhole-to-surface communication and control system (Alanazi – see par 1 - present disclosure applies to optimization techniques for smart well completions. Many wells are instrumented with downhole sensors and valves enabling real-time monitoring and control of multilateral completions. see par 41 - multilateral wells are typically equipped with surface and subsurface downhole valves that can provide real-time pressure and temperature data along with a surface flow meter to measure multi-phase flow. Moreover, the wells contain inflow control devices (ICVs) with various choke settings to restrict flow based on the orifice size of each valve position. The production optimization techniques can use field data collected during optimization and well control parameter (for example, ICV settings) regression using a commercial steady-state model. The optimum flow scenario can be determined, for example, using a genetic optimization algorithm which can intelligently manipulate ICV valve settings in the calibrated model to provide the maximum oil production, the minimum water production, or both. see par 92 - For the well control optimization problem, for example, x.sub.opt contains the ICV setting(s) of all valves in the smart wells. see par 145 - recommended downhole ICV settings for the valves 110 and the ICV 116 can be provided in a user interface included with (or communicating with) the computer-implemented system at the surface of the multi-zonal smart well completion 100), a test setting of the at least one ICV of said each compartment (Alanazi – see par 46 - the model can be fine-tuned based on the actual well and lateral test results in order to make more accurate well performance calculations in the future; see par 52 - The optimization algorithm process 200 can use model results to generate new ICV settings to be tested by the model. The circular flow of elements 202-212 of the optimization algorithm process 200 shows an interaction between the model and the algorithm. see par 62, FIG. 4 - The model can be accurately calibrated and combined with field tests. The workflow combines modeling and streamlines field testing for the calibration process.
Tonkin – see par 16 - downhole sensors may include sensors which are part of the down hole flow control valves and sensors, e.g. pressure and temperature sensors, which are located separately in the various well zones and/or other well locations.);
controlling, by the integrated station using the test setting, the at least one ICV during the pressure drawdown and build up test of said each compartment (Alanazi – see par 45 - During regular field optimization procedures, the multilateral well can be produced one lateral at a time. Surface and downhole pressures and production metrics can be recorded for these tests. see par 64 - The preliminary network model, for example, can be part of an approach which starts first by building a preliminary model (not yet calibrated) using the well's previous production and pressure data. Then, the model is calibrated. After calibration, the model is capable of providing recommended downhole choke valve settings guided by user objectives. see par 65 - At 418, single lateral testing is commenced on the field for model calibration. At 420, each lateral in the field is set up and tested while the other laterals are disabled. For example, in a multilateral well 430, a lateral 432 is set up and tested while laterals 434 are disabled.); and
transmitting, by the integrated station of said each compartment and to the smart downhole-to-surface communication and control system, the drawdown pressure and flowrate measurements of said each compartment (Alanazi – see par 41 - The model can require entering individual reservoir pressure for each lateral. Other additional data can be deemed critical for calibration, such as the well production rates (including oil and water rates) and wellhead pressure, which affects the PI. Such changes can be incorporated in a periodic manner for proper calibration and modeling. see par 42 - The production optimization techniques started by collecting well rates and flowing bottom-hole pressure data at various choke settings for two flow conditions: commingled and individual lateral testing. see par 48 - Each zone 102 (for example, a zone 102a) can be isolated with packers 104 and 106 and equipped with downhole pressure gauges 108 and a valve 110; see par 42, 159 - a computer-implemented system at the surface of the multi-zonal smart well completion 100 can collect information from each of the zones 102, including the zone 102a. The information that is collected can include, for example, pressure information collected from the downhole pressure gauges 108 and information collected from equipment measuring the flow from each zone 102;
Tonkin – see par 21 - In particular, a well zone is a region along a lateral that is demarcated by two adjacent packers. In one or more embodiments of the technology, the well completion system (20) may include down hole flow control valves (36) which control fluid flows and fluid flow rates from the various corresponding well zones (34) into multilateral well completion systems (20). see par 24 - The multilateral well model may further model the flow of downhole fluids and gas into the laterals and through the borehole based on reservoir properties, pressures, fluid data, choke positions, and/or other inputs data to the model. Once choke positions of down hole flow control valves are implemented based on the validated optimization scenarios, the multilateral well model may be continually recalibrated, which effectively continues the optimization loop. See par 54 - The optimization problem formulation above may simply close the down hole inflow control valves connected to the zones with the highest values of the gas oil ratio. Thus, a potential exists for the producing zones in a lateral to see an increase in the gas oil ratio of the produced fluid between sampling periods which in turn can lead to the new control valves positions for these previously open valves being closed for the next period).
It would have been obvious to combine Alanazi and Tonkin for the same reasons as claim 1 above.
Claims 7 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Alanazi (US 2020/0362674), in view of Tonkin (US 2016/0369590, as applied to claims 1-6, 8-13, and 15-20 above, and further in view of Wheeler (US 2017/0356275).
Concerning claims 7 and 14, Alanazi discloses:
The method according to claim 1, wherein the at least one ICV comprises an electrical ICV (Alanazi – see par 147 - At 2810, the one or more of the recommended optimizing changes selected by the user are implemented by the multilateral well optimizing system. For example, the user selections made in the user interface can be applied automatically by the computer-implemented system to corresponding ICVs for which ICV settings are to be made.
see also Tonkin - see par 47 - In Block 419, a field operation of the well is performed in accordance with one or more embodiments of the technology. In one or more embodiments of the technology, performing the field operation may include sending a control signal to the field equipment, such as one or more down hole flow control valves to change the choke positions on the down hole flow control valves. For example, the field control module may send the control signal, directly or indirectly with or without human intervention and modification, to the down hole flow control valves.)
Alanazi discloses ICVs adjusted by computer-implemented settings (See par 147). Tonkin discloses control signal changes flow control valves (See par 47).
Alternatively, Wheeler discloses explicitly “electrically” the ICV:
The method according to claim 1, wherein the at least one ICV comprises an “electrical” ICV.
(Wheeler – see par 61 - An “inflow control valve,” also known as an “interval control valve” or “ICV” is a remote controlled active valve that allows user control over interval access and/or can be used to prevent steam breakthrough. At the high end of the scale are electrically controlled continuously variable ICVs with pressure and temperature measurements and valve position feedback at each valve.).
Alanazi, Tonkin, and Wheeler are analogous art as they are directed to simulating for optimizing well production (See Alanazi Abstract; Tonkin Abstract, par 34; Wheeler Abstract, par 104-106). Alanazi discloses ICVs adjusted by computer-implemented settings (See par 147). Tonkin discloses control signal changes flow control valves (See par 47). Wheeler improves upon Alanazi and Tonkin by disclosing the ICV (control valve) is “electrically” controlled (See par 61). One of ordinary skill in the art would be motivated to further include “electrically” variable valves to efficiently improve upon the monitoring of multilateral completions with optimization for ICVs and adjustment of ICVs by computers in Alanazi and control signals in Tonkin.
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
monitoring of multilateral completions with optimization for ICVs in Alanazi to further include sensors and control valves in various well zones as disclosed in Tonkin, to further include ICVS that are electrically controlled and continuously variables as disclosed in Wheeler, since 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.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Bukhamseen (US 2021/0148196) – directed to multi-segmented oil production with optimization for smart well completion (See Abstract)
Filippov (US 2016/0153265) – directed to determines optimal parameters for inflow control devices (ICD) along a horizontal portion of the wellbore (See abstract)
Shahkarami (US 2020/0242497) – directed to each well completion plan having a flow control device with location and associated flow setting or rating, and optionally a packer and location to provide output data for each well completion plan evaluation (See abstract) AlQahtani, et al., “High Definition Modeling for Complex Multilateral Well with Smart Completions,” 2020, In International Petroleum Technology Conference, IPTC-19977-MS, D011S010R001, pages 1-16 – directed to smart multilateral well completions, divided into segments or compartments using swell packers, and a simulator (See page 1, 2nd paragraph).
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