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 .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Specifically, representative Claim 1 recites: “A computer-implemented method comprising: receiving, by one or more processors and from probes, petrophysical data indicative of reservoir conditions within a subterranean region; executing, by the one or more processors, a water injectivity test within the subterranean region using test constants based on the petrophysical data; generating, by the one or more processors and using an output of the water injectivity test, a water-related variable; determining, by the one or more processors and using a nodal analysis and the output of the water injectivity test, a well production potential; and predicting, by the one or more processors and using a carbon dioxide estimation model, carbon dioxide injection rates, the carbon dioxide estimation model processing the water-related variables, the test constants, and a ratio of carbon dioxide density at reservoir condition to carbon dioxide density at standard conditions”.
The claim limitations in the abstract idea have been highlighted in bold above; the remaining limitations are “additional element”.
Under the Step 1 of the eligibility analysis, we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: Process, machine, manufacture, or composition of matter. The above claim is considered to be in a statutory category (process).
Under the Step 2A, Prong One, we consider whether the claim recites a judicial exception (abstract idea). In the above claim, the highlighted portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitations that fall into/recite an abstract idea exceptions. Specifically, under the 2019 Revised Patent Subject matter Eligibility Guidance, it falls into the groupings of subject matter when recited as such in a claim limitation that falls into the grouping of subject matter when recited as such in a claim limitation, that covers mathematical concepts - mathematical relationships, mathematical formulas or equations, mathematical calculations and mental processes – concepts performed in the human mind including an observation, evaluation, judgement, and/or opinion.
The steps of “generating, by the one or more processors and using an output of the water injectivity test, a water-related variable; determining, and using a nodal analysis and the output of the water injectivity test, a well production potential” are treated as belonging to the mathematical calculations grouping and the steps of “predicting, and using a carbon dioxide estimation model, carbon dioxide injection rates, the carbon dioxide estimation model processing the water-related variables, the test constants, and a ratio of carbon dioxide density at reservoir condition to carbon dioxide density at standard conditions” are treated as belonging to mental process grouping.
This mental step represents a process that, under its broadest reasonable
interpretation, covers performance of the limitation in the mind. That is, nothing in
the claim element precludes the step from practically being performed in the
mind. In the context of this claim, it encompasses predicting carbon dioxide injection rates using the carbon dioxide estimation model processing the water-related variables, the test constants, and a ratio of carbon dioxide density at reservoir condition to carbon dioxide density at standard conditions.
Next, under the Step 2A, Prong Two, we consider whether the claim that recites a judicial exception is integrated into a practical application.
In this step, we evaluate whether the claim recites additional elements that
integrate the exception into a practical application of that exception.
The above claims comprise the following additional elements:
Claim 1: A computer-implemented method comprising: receiving, by one or more processors and from probes, petrophysical data indicative of reservoir conditions within a subterranean region; executing, by the one or more processors, a water injectivity test within the subterranean region using test constants based on the petrophysical data
Claim 11: A computer-implemented system comprising: one or more processors; and a non-transitory computer-readable storage medium coupled to the one or more processors and storing programming instructions for execution by the one or more processors, the programming instructions instructing the one or more processors to perform operations comprising: receiving, from probes, petrophysical data indicative of reservoir conditions within a subterranean region; executing a water injectivity test within the subterranean region using test constants based on the petrophysical data
Claim 20: A non-transitory computer-readable media encoded with a computer program, the computer program comprising instructions that when executed by one or more computers cause the one or more computers to perform operations comprising: receiving, from probes, petrophysical data indicative of reservoir conditions within a subterranean region; executing a water injectivity test within the subterranean region using test constants based on the petrophysical data
The above additional element of a computer-implemented method comprising: are generically recited, not meaningful, do not represent a particular machine and/or eligible transformation, they do not indicate a practical application, receiving, by one or more processors and from probes, petrophysical data indicative of reservoir conditions within a subterranean region; executing, by the one or more processors, a water injectivity test within the subterranean region using test constants based on the petrophysical data are generically recited and represent mere data gathering steps necessary to execute the abstract idea. Lastly, the additional elements in Claims 1, 11, and 20 such as a processor and a non-transitory computer-readable storage medium coupled to the one or more processors is an example of generic computer equipment (components) that is generally recited and, therefore, is not qualified as a particular machine.
Therefore, the claims are directed to a judicial exception and require further analysis under the Step 2B.
However, the above claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception (Step 2B analysis) because these additional elements/steps are well-understood and conventional in the relevant art based on the prior art of record including references (Alhuraifi and Mullins).
The independent claims, therefore, are not patent eligible.
With regards to the dependent claims, claims 2-10 and 12-19 provide additional features/steps which are either part of an expanded abstract idea of the independent claims or adding additional elements/steps that are not meaningful as they are recited in generality and/or not qualified as particular machine/ and/or eligible transformation and, therefore, do not reflect a practical application as well as not qualified for “significantly more” based on prior art of record.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Alhuraifi et al. (US 20210109251), hereinafter referred to as ‘Alhuraifi’ and in further view of Mullins et al. (US8061444), hereinafter referred to as ‘Mullins’.
Regarding Claim 1, Alhuraifi discloses a computer-implemented method comprising: receiving, by one or more processors and from probes, petrophysical data indicative of reservoir conditions within a subterranean region (The injection well 100 is used to flow injection fluid 124, such as water, wastewater, brine (salt water), or water mixed with chemicals, into a subterranean zone that includes a formation, a portion of a formation, or multiple formations, for example, sandstone, limestone or other formations [0013]; The pressure sensor 136 is located at a location 128 on the surface 112 upstream from the surface leak 140 on the single-well flowline 144. In practice, the injection well 100 will contain several pressure sensors and gauges located at different locations at the surface 112. [0016]); executing, by the one or more processors, a water injectivity test within the subterranean region using test constants based on the petrophysical data (For example, the surface leak 140 at location 116 can be on the wellhead 108 or a flowline 144 of the injection well 100. In practice, the surface leak 140 can be located at any part of the wellhead 108, the valves of the wellhead 108, or along a segment of the flowline 144. The surface leak 140 causes fluids to leak at the surface 112 where pressure is less. Such a leak affects the integrity of the injection well 100 and poses challenges to hydrocarbon recovery from the hydrocarbon reservoir 104 [0014]); generating, by the one or more processors and using an output of the water injectivity test, a water-related variable (The pressure sensor 136 is located at a location 128 on the surface 112 upstream from the surface leak 140 on the single-well flowline 144. In practice, the injection well 100 will contain several pressure sensors and gauges located at different locations at the surface 112. The pressure is measured at the different locations on the wellhead 108 and the flowline 144 [0016]); determining, by the one or more processors and using a nodal analysis and the output of the water injectivity test, a well production potential (The production well model is a steady-state multiphase simulation software model of the injection well that uses nodal analysis, based on single-phase and multiphase flow equations, to determine the well injection behavior in terms of outputs such as a flow rate (sometimes referred to as an “injection rate”) and a pressure profile [0011]; Once the presence of the surface leak 140 is determined, the computer system 600 generates an injection well performance model of the injection well 100. An example injection well performance model 200 is illustrated and described in more detail with reference to FIG. 2 [0018]); and predicting, by the one or more processors and using a carbon dioxide estimation model, carbon dioxide injection rates (The injection well 100 can be used for hydrocarbon recovery from the hydrocarbon reservoir 104 shown in FIG. 1. For example, fluid 124 such as steam, carbon dioxide, or water can be injected into the hydrocarbon reservoir 104 to maintain reservoir pressure, or heat the hydrocarbons in the reservoir 104, [0013]), the carbon dioxide estimation model processing the water-related variables, the test constants (The injection well performance model 200 is a software model of the injection well 100 based on measured parameters of the injection well, such as a reservoir pressure, a fluid injection pressure, injection fluid properties, and the well dimensions, i.e. test constants [0018]), and a ratio of carbon dioxide density at reservoir condition to carbon dioxide density at standard conditions (In some implementations, properties of the injection fluids 124 are used to generate the injection well performance model 200. The properties of the injection fluids 124 refer, among others, to the type of the injection fluids 124, the density of the injection fluids 124, the formation volume factor, the viscosity of the injection fluids 124, and the amount of impurities in the injection fluids 124. The formation volume factor refers to the ratio of the volume of fluids 124 at reservoir (in-situ) conditions to that at stock tank (surface) conditions [0021]).
However, Alhuraifi does not explicitly disclose predicting, by the one or more processors and using a carbon dioxide estimation model, carbon dioxide injection rates, the carbon dioxide estimation model processing the water-related variables, and a ratio of carbon dioxide density at reservoir condition to carbon dioxide density at standard conditions.
Nevertheless, Mullins discloses a predicting, by the one or more processors and using a carbon dioxide estimation model (The monitored concentrations or other values derived therefrom are compared to corresponding log data predicted from the fluid map by the tool response simulator 320. In some cases, a discrepancy between measured data and predicted data greater than the measurement uncertainty may be indicative of compartmentalization that was not accounted for in the reservoir fluid model. In other cases, a discrepancy between measured data and predicted data greater than the measurement uncertainty may be indicative of the source of the methane, or carbon dioxide that was not accounted for in the reservoir fluid model. In yet other cases, a discrepancy between measured data and predicted data greater than the measurement uncertainty may be indicative of inaccurate composition gradients or inaccurate location of flood fronts in the fluid model, Col. 20, Lines 27-41), the carbon dioxide estimation model processing the water-related variables and carbon dioxide density at reservoir condition to carbon dioxide density at standard conditions (The monitored concentrations or other values derived therefrom are compared to corresponding log data predicted from the fluid map by the tool response simulator 320. In some cases, a discrepancy between measured data and predicted data greater than the measurement uncertainty may be indicative of compartmentalization that was not accounted for in the reservoir fluid model. In other cases, a discrepancy between measured data and predicted data greater than the measurement uncertainty may be indicative of the source of the methane, or carbon dioxide that was not accounted for in the reservoir fluid model. In yet other cases, a discrepancy between measured data and predicted data greater than the measurement uncertainty may be indicative of inaccurate composition gradients or inaccurate location of flood fronts in the fluid model, Col. 20, Lines 27-41).
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 invention of Alhuraifi with the teachings of Mullins to monitor concentrations or other values derived from and comparing to corresponding log data predicted from the fluid map and improve accuracy of the estimation model.
Regarding Claim 2, Alhuraifi and Mullins disclose the claimed invention discussed in claim 1.
Alhuraifi discloses the petrophysical data (as discussed above).
However, Alhuraifi does not explicitly disclose the petrophysical data (as discussed above) comprises neutron-density porosity logs, resistivity logs, image logs, gamma ray logs, pulse neutron capture logs, and nuclear magnetic resonance logs.
Nevertheless, Mullins discloses the petrophysical data comprises neutron-density porosity logs, resistivity logs, image logs, gamma ray logs, pulse neutron capture logs, and nuclear magnetic resonance logs (Preferably, but not necessarily, the reservoir geological model database 302 also stores information relating to depositional sequences and reservoir structural information obtained from well image data such as, for example, gamma ray image data, density image data, and/or resistivity image data, Col. 12, Lines 22-27; The data stored in the formation evaluation log database is preferably, but not necessarily, collected using tools which have at least the capabilities of tools referred to as "triple combo" tools that include, for example, a resistivity tool, a neutron porosity tool, and a nuclear density tool, Col. 12, Lines 38-43).
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 invention of Alhuraifi with the teachings of Mullins to store information relating to depositional sequences and reservoir structural information obtained from well image data and improve accuracy of data collection.
Regarding Claim 3, Alhuraifi and Mullins disclose the claimed invention discussed in claim 1.
Alhuraifi discloses the test constants comprise a water injection rate (FIG. 1 illustrates a schematic of an injection well 100, in accordance with one or more implementations. The injection well 100 is used to flow injection fluid 124, such as water, wastewater, brine (salt water), or water mixed with chemicals, into a subterranean zone that includes a formation, a portion of a formation, or multiple formations, for example, sandstone, limestone or other formations [0013]; The injection data includes continuous measurements of the fluid injection rate of the injection fluids 124 and fluid injection pressures of the injection fluids 124 [0017]).
Regarding Claim 4, Alhuraifi and Mullins disclose the claimed invention discussed in claim 1.
Alhuraifi discloses the output of the water injectivity test comprises a wellhead pressure (The pressure sensor 136 is located at a location 128 on the surface 112 upstream from the surface leak 140 on the single-well flowline 144. In practice, the injection well 100 will contain several pressure sensors and gauges located at different locations at the surface 112. The pressure is measured at the different locations on the wellhead 108 and the flowline 144 [0016]) and a bottom hole flowing pressure (During normal operation of the injection well 100, prior to determining the presence of the surface leak 140, a downhole pressure gauge is lowered into the injection well 100 to repeatedly measure the shut-in bottomhole pressure [0017]).
Regarding Claim 5, Alhuraifi and Mullins disclose the claimed invention discussed in claim 1.
Alhuraifi discloses the water-related variable comprises an injectivity index (The computer system 600 determines an injectivity index of the injection well 100 based on the PTA to provide the injection well performance model 200 [0024]).
Regarding Claim 6, Alhuraifi and Mullins disclose the claimed invention discussed in claim 1.
However, Alhuraifi does not explicitly disclose controlling, by the one or more processors, probe data collection using a probe data collection schedule defining a frequency of probe data collection for each device of one or more devices.
Nevertheless, Mullins discloses probe data collection using a probe data collection schedule defining a frequency of probe data collection for each device of one or more devices (Thus, a sidewall fluid sampling operation may be performed if the mud gas logging data indicates that a significant change in fluid composition has occurred. Alternatively, a sidewall sampling operation may be scheduled at predetermined intervals or check points along the well trajectory such as, for example, close to expected gas-oil or oil-water contacts or other fluid transitions. In some example implementations, the operation of block 414 could be performed by an operator (e.g., an operator-performed decision) and the operator could provide user input based on the measurements made by the mud gas logging tool 138, Col. 21, Lines 20-30).
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 invention of Alhuraifi with the teachings of Mullins to provide user input based on the measurements made by the logging tool while minimizing drilling risks and control costs.
Regarding Claim 7, Alhuraifi and Mullins disclose the claimed invention discussed in claim 1.
Alhuraifi discloses the subterranean region comprises a sink or a reservoir (The injection well 100 is used to flow injection fluid 124, such as water, wastewater, brine (salt water), or water mixed with chemicals, into a subterranean zone that includes a formation, a portion of a formation, or multiple formations, for example, sandstone, limestone or other formations. The injection well 100 can be used for hydrocarbon recovery from the hydrocarbon reservoir 104 shown in FIG. 1. [0013]).
Regarding Claim 8, Alhuraifi and Mullins disclose the claimed invention discussed in claim 1.
Alhuraifi discloses executing, by the one or more processors, subterranean region modeling (Once the presence of the surface leak 140 is determined, the computer system 600 generates an injection well performance model of the injection well 100 [0018]).
Regarding Claim 9, Alhuraifi and Mullins disclose the claimed invention discussed in claim 1.
Alhuraifi discloses selecting, by the one or more processors, a surface equipment (as discussed above).
However, Alhuraifi does not explicitly disclose selecting, by the one or more processors, an action plan comprising a well count and a surface equipment.
Nevertheless, Mullins discloses selecting, by the one or more processors, an action plan comprising a well count (Formation fluid test data can be used to design completion equipment, or to plan trajectories of successive wells in the same reservoir or to monitor the reservoir over time in order to manage production and recovery, etc., Col. 1, Lines 40-44; The drill string 104 further includes a bottom hole assembly (BHA) 116 coupled to the drill bit 106. The BHA 116 includes a directional drilling subassembly 118 to adjust the drilling direction of the drill bit 106 based on control signals received from, for example, a surface logging and control system 120. The BHA 116 includes capabilities for measuring, processing, and storing information, as well as communicating with surface equipment, Col. 6, Lines 52-59)
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 invention of Alhuraifi with the teachings of Mullins to plan trajectories of successive wells in the same reservoir or to monitor the reservoir over time in order to manage production and recovery.
Regarding Claim 10, Alhuraifi and Mullins disclose the claimed invention discussed in claim 1.
Alhuraifi discloses the probes comprise any of a temperature probe, a pressure probe, a porosity probe, a gamma ray detector, a camera, and a nuclear magnetic resonance detector (The pressure sensor 136 is located at a location 128 on the surface 112 upstream from the surface leak 140 on the single-well flowline 144. In practice, the injection well 100 will contain several pressure sensors and gauges located at different locations at the surface 112. The pressure is measured at the different locations on the wellhead 108 and the flowline 144 [0016]).
Regarding Claim 11, Alhuraifi discloses a computer-implemented system comprising: one or more processors; and a non-transitory computer-readable storage medium coupled to the one or more processors and storing programming instructions for execution by the one or more processors, the programing instructions instructing the one or more processors to perform operations comprising: receiving, from probes, petrophysical data indicative of reservoir conditions within a subterranean region (The injection well 100 is used to flow injection fluid 124, such as water, wastewater, brine (salt water), or water mixed with chemicals, into a subterranean zone that includes a formation, a portion of a formation, or multiple formations, for example, sandstone, limestone or other formations [0013]; The pressure sensor 136 is located at a location 128 on the surface 112 upstream from the surface leak 140 on the single-well flowline 144. In practice, the injection well 100 will contain several pressure sensors and gauges located at different locations at the surface 112. [0016]); executing a water injectivity test within the subterranean region using test constants based on the petrophysical data (For example, the surface leak 140 at location 116 can be on the wellhead 108 or a flowline 144 of the injection well 100. In practice, the surface leak 140 can be located at any part of the wellhead 108, the valves of the wellhead 108, or along a segment of the flowline 144. The surface leak 140 causes fluids to leak at the surface 112 where pressure is less. Such a leak affects the integrity of the injection well 100 and poses challenges to hydrocarbon recovery from the hydrocarbon reservoir 104 [0014]); generating, by using an output of the water injectivity test, a water-related variable (The pressure sensor 136 is located at a location 128 on the surface 112 upstream from the surface leak 140 on the single-well flowline 144. In practice, the injection well 100 will contain several pressure sensors and gauges located at different locations at the surface 112. The pressure is measured at the different locations on the wellhead 108 and the flowline 144 [0016]); determining, by using a nodal analysis and the output of the water injectivity test, a well production potential (The production well model is a steady-state multiphase simulation software model of the injection well that uses nodal analysis, based on single-phase and multiphase flow equations, to determine the well injection behavior in terms of outputs such as a flow rate (sometimes referred to as an “injection rate”) and a pressure profile [0011]; Once the presence of the surface leak 140 is determined, the computer system 600 generates an injection well performance model of the injection well 100. An example injection well performance model 200 is illustrated and described in more detail with reference to FIG. 2 [0018]); and carbon dioxide injection rates (The injection well 100 can be used for hydrocarbon recovery from the hydrocarbon reservoir 104 shown in FIG. 1. For example, fluid 124 such as steam, carbon dioxide, or water can be injected into the hydrocarbon reservoir 104 to maintain reservoir pressure, or heat the hydrocarbons in the reservoir 104, [0013]), the test constants (The injection well performance model 200 is a software model of the injection well 100 based on measured parameters of the injection well, such as a reservoir pressure, a fluid injection pressure, injection fluid properties, and the well dimensions, i.e. test constants [0018]), and a ratio of carbon dioxide (In some implementations, properties of the injection fluids 124 are used to generate the injection well performance model 200. The properties of the injection fluids 124 refer, among others, to the type of the injection fluids 124, the density of the injection fluids 124, the formation volume factor, the viscosity of the injection fluids 124, and the amount of impurities in the injection fluids 124. The formation volume factor refers to the ratio of the volume of fluids 124 at reservoir (in-situ) conditions to that at stock tank (surface) conditions [0021]).
However, Alhuraifi does not explicitly disclose predicting, by the one or more processors and using a carbon dioxide estimation model, carbon dioxide injection rates, the carbon dioxide estimation model processing the water-related variables, and a ratio of carbon dioxide density at reservoir condition to carbon dioxide density at standard conditions.
Nevertheless, Mullins discloses a predicting, by the one or more processors and using a carbon dioxide estimation model (The monitored concentrations or other values derived therefrom are compared to corresponding log data predicted from the fluid map by the tool response simulator 320. In some cases, a discrepancy between measured data and predicted data greater than the measurement uncertainty may be indicative of compartmentalization that was not accounted for in the reservoir fluid model. In other cases, a discrepancy between measured data and predicted data greater than the measurement uncertainty may be indicative of the source of the methane, or carbon dioxide that was not accounted for in the reservoir fluid model. In yet other cases, a discrepancy between measured data and predicted data greater than the measurement uncertainty may be indicative of inaccurate composition gradients or inaccurate location of flood fronts in the fluid model, Col. 20, Lines 27-41), the carbon dioxide estimation model processing the water-related variables and carbon dioxide density at reservoir condition to carbon dioxide density at standard conditions (The monitored concentrations or other values derived therefrom are compared to corresponding log data predicted from the fluid map by the tool response simulator 320. In some cases, a discrepancy between measured data and predicted data greater than the measurement uncertainty may be indicative of compartmentalization that was not accounted for in the reservoir fluid model. In other cases, a discrepancy between measured data and predicted data greater than the measurement uncertainty may be indicative of the source of the methane, or carbon dioxide that was not accounted for in the reservoir fluid model. In yet other cases, a discrepancy between measured data and predicted data greater than the measurement uncertainty may be indicative of inaccurate composition gradients or inaccurate location of flood fronts in the fluid model, Col. 20, Lines 27-41).
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 invention of Alhuraifi with the teachings of Mullins to store information relating to depositional sequences and reservoir structural information obtained from well image data and improve accuracy of data collection.
Regarding Claim 12, Alhuraifi and Mullins disclose the claimed invention discussed in claim 11.
Alhuraifi discloses the petrophysical data (as discussed above).
However, Alhuraifi does not explicitly disclose the petrophysical data (as discussed above) comprises neutron-density porosity logs, resistivity logs, image logs, gamma ray logs, pulse neutron capture logs, and nuclear magnetic resonance logs.
Nevertheless, Mullins discloses the petrophysical data comprises neutron-density porosity logs, resistivity logs, image logs, gamma ray logs, pulse neutron capture logs, and nuclear magnetic resonance logs (Preferably, but not necessarily, the reservoir geological model database 302 also stores information relating to depositional sequences and reservoir structural information obtained from well image data such as, for example, gamma ray image data, density image data, and/or resistivity image data, Col. 12, Lines 22-27; The data stored in the formation evaluation log database is preferably, but not necessarily, collected using tools which have at least the capabilities of tools referred to as "triple combo" tools that include, for example, a resistivity tool, a neutron porosity tool, and a nuclear density tool, Col. 12, Lines 38-43).
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 invention of Alhuraifi with the teachings of Mullins to store information relating to depositional sequences and reservoir structural information obtained from well image data and improve accuracy of data collection.
Regarding Claim 13, Alhuraifi and Mullins disclose the claimed invention discussed in claim 11.
Alhuraifi discloses the test constants comprise a water injection rate (FIG. 1 illustrates a schematic of an injection well 100, in accordance with one or more implementations. The injection well 100 is used to flow injection fluid 124, such as water, wastewater, brine (salt water), or water mixed with chemicals, into a subterranean zone that includes a formation, a portion of a formation, or multiple formations, for example, sandstone, limestone or other formations [0013]; The injection data includes continuous measurements of the fluid injection rate of the injection fluids 124 and fluid injection pressures of the injection fluids 124 [0017]).
Regarding Claim 14, Alhuraifi and Mullins disclose the claimed invention discussed in claim 11.
Alhuraifi discloses the output of the water injectivity test comprises a wellhead pressure (The pressure sensor 136 is located at a location 128 on the surface 112 upstream from the surface leak 140 on the single-well flowline 144. In practice, the injection well 100 will contain several pressure sensors and gauges located at different locations at the surface 112. The pressure is measured at the different locations on the wellhead 108 and the flowline 144 [0016]) and a bottom hole flowing pressure (During normal operation of the injection well 100, prior to determining the presence of the surface leak 140, a downhole pressure gauge is lowered into the injection well 100 to repeatedly measure the shut-in bottomhole pressure [0017]).
Regarding Claim 15, Alhuraifi and Mullins disclose the claimed invention discussed in claim 11.
Alhuraifi discloses the water-related variable comprises an injectivity index (The computer system 600 determines an injectivity index of the injection well 100 based on the PTA to provide the injection well performance model 200 [0024]).
Regarding Claim 16, Alhuraifi and Mullins disclose the claimed invention discussed in claim 11.
However, Alhuraifi does not explicitly disclose controlling, by the one or more processors, probe data collection using a probe data collection schedule defining a frequency of probe data collection for each device of one or more devices.
Nevertheless, Mullins discloses probe data collection using a probe data collection schedule defining a frequency of probe data collection for each device of one or more devices (Thus, a sidewall fluid sampling operation may be performed if the mud gas logging data indicates that a significant change in fluid composition has occurred. Alternatively, a sidewall sampling operation may be scheduled at predetermined intervals or check points along the well trajectory such as, for example, close to expected gas-oil or oil-water contacts or other fluid transitions. In some example implementations, the operation of block 414 could be performed by an operator (e.g., an operator-performed decision) and the operator could provide user input based on the measurements made by the mud gas logging tool 138, Col. 21, Lines 20-30).
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 invention of Alhuraifi with the teachings of Mullins to provide user input based on the measurements made by the logging tool while minimizing drilling risks and control costs.
Regarding Claim 17, Alhuraifi and Mullins disclose the claimed invention discussed in claim 11.
Alhuraifi discloses the subterranean region comprises a sink or a reservoir (The injection well 100 is used to flow injection fluid 124, such as water, wastewater, brine (salt water), or water mixed with chemicals, into a subterranean zone that includes a formation, a portion of a formation, or multiple formations, for example, sandstone, limestone or other formations. The injection well 100 can be used for hydrocarbon recovery from the hydrocarbon reservoir 104 shown in FIG. 1. [0013]).
Regarding Claim 18, Alhuraifi and Mullins disclose the claimed invention discussed in claim 11.
Alhuraifi discloses selecting, by the one or more processors, surface equipment (as discussed above).
However, Alhuraifi does not explicitly disclose selecting, by the one or more processors, an action plan comprising a well count and a surface equipment.
Nevertheless, Mullins discloses an action plan comprising a well count (Formation fluid test data can be used to design completion equipment, or to plan trajectories of successive wells in the same reservoir or to monitor the reservoir over time in order to manage production and recovery, etc., Col. 1, Lines 40-44; The drill string 104 further includes a bottom hole assembly (BHA) 116 coupled to the drill bit 106. The BHA 116 includes a directional drilling subassembly 118 to adjust the drilling direction of the drill bit 106 based on control signals received from, for example, a surface logging and control system 120. The BHA 116 includes capabilities for measuring, processing, and storing information, as well as communicating with surface equipment, Col. 6, Lines 52-59)
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 invention of Alhuraifi with the teachings of Mullins to plan trajectories of successive wells in the same reservoir or to monitor the reservoir over time in order to manage production and recovery.
Regarding Claim 19, Alhuraifi and Mullins disclose the claimed invention discussed in claim 11.
Alhuraifi discloses the probes comprise any of a temperature probe, a pressure probe, a porosity probe, a gamma ray detector, a camera, and a nuclear magnetic resonance detector (The pressure sensor 136 is located at a location 128 on the surface 112 upstream from the surface leak 140 on the single-well flowline 144. In practice, the injection well 100 will contain several pressure sensors and gauges located at different locations at the surface 112. The pressure is measured at the different locations on the wellhead 108 and the flowline 144 [0016]).
Regarding Claim 20, Alhuraifi discloses a non-transitory computer-readable media encoded with a computer program, the computer program comprising instructions that when executed by one or more computers cause the one or more computers to perform operations comprising: receiving, from probes, petrophysical data indicative of reservoir conditions within a subterranean region (The injection well 100 is used to flow injection fluid 124, such as water, wastewater, brine (salt water), or water mixed with chemicals, into a subterranean zone that includes a formation, a portion of a formation, or multiple formations, for example, sandstone, limestone or other formations [0013]; The pressure sensor 136 is located at a location 128 on the surface 112 upstream from the surface leak 140 on the single-well flowline 144. In practice, the injection well 100 will contain several pressure sensors and gauges located at different locations at the surface 112. [0016]); executing a water injectivity test within the subterranean region using test constants based on the petrophysical data (For example, the surface leak 140 at location 116 can be on the wellhead 108 or a flowline 144 of the injection well 100. In practice, the surface leak 140 can be located at any part of the wellhead 108, the valves of the wellhead 108, or along a segment of the flowline 144. The surface leak 140 causes fluids to leak at the surface 112 where pressure is less. Such a leak affects the integrity of the injection well 100 and poses challenges to hydrocarbon recovery from the hydrocarbon reservoir 104 [0014]); generating, by using an output of the water injectivity test, a water-related variable (The pressure sensor 136 is located at a location 128 on the surface 112 upstream from the surface leak 140 on the single-well flowline 144. In practice, the injection well 100 will contain several pressure sensors and gauges located at different locations at the surface 112. The pressure is measured at the different locations on the wellhead 108 and the flowline 144 [0016]); determining, by using a nodal analysis and the output of the water injectivity test, a well production potential (The production well model is a steady-state multiphase simulation software model of the injection well that uses nodal analysis, based on single-phase and multiphase flow equations, to determine the well injection behavior in terms of outputs such as a flow rate (sometimes referred to as an “injection rate”) and a pressure profile [0011]; Once the presence of the surface leak 140 is determined, the computer system 600 generates an injection well performance model of the injection well 100. An example injection well performance model 200 is illustrated and described in more detail with reference to FIG. 2 [0018]); ; and carbon dioxide injection rates (The injection well 100 can be used for hydrocarbon recovery from the hydrocarbon reservoir 104 shown in FIG. 1. For example, fluid 124 such as steam, carbon dioxide, or water can be injected into the hydrocarbon reservoir 104 to maintain reservoir pressure, or heat the hydrocarbons in the reservoir 104, [0013]), the test constants (The injection well performance model 200 is a software model of the injection well 100 based on measured parameters of the injection well, such as a reservoir pressure, a fluid injection pressure, injection fluid properties, and the well dimensions, i.e. test constants [0018]), and a ratio of carbon dioxide density (In some implementations, properties of the injection fluids 124 are used to generate the injection well performance model 200. The properties of the injection fluids 124 refer, among others, to the type of the injection fluids 124, the density of the injection fluids 124, the formation volume factor, the viscosity of the injection fluids 124, and the amount of impurities in the injection fluids 124. The formation volume factor refers to the ratio of the volume of fluids 124 at reservoir (in-situ) conditions to that at stock tank (surface) conditions [0021]).
However, Alhuraifi does not explicitly disclose predicting, by the one or more processors and using a carbon dioxide estimation model, carbon dioxide injection rates, the carbon dioxide estimation model processing the water-related variables, and a ratio of carbon dioxide density at reservoir condition to carbon dioxide density at standard conditions.
Nevertheless, Mullins discloses a predicting, by the one or more processors and using a carbon dioxide estimation model (The monitored concentrations or other values derived therefrom are compared to corresponding log data predicted from the fluid map by the tool response simulator 320. In some cases, a discrepancy between measured data and predicted data greater than the measurement uncertainty may be indicative of compartmentalization that was not accounted for in the reservoir fluid model. In other cases, a discrepancy between measured data and predicted data greater than the measurement uncertainty may be indicative of the source of the methane, or carbon dioxide that was not accounted for in the reservoir fluid model. In yet other cases, a discrepancy between measured data and predicted data greater than the measurement uncertainty may be indicative of inaccurate composition gradients or inaccurate location of flood fronts in the fluid model, Col. 20, Lines 27-41), the carbon dioxide estimation model processing the water-related variables and carbon dioxide density at reservoir condition to carbon dioxide density at standard conditions (The monitored concentrations or other values derived therefrom are compared to corresponding log data predicted from the fluid map by the tool response simulator 320. In some cases, a discrepancy between measured data and predicted data greater than the measurement uncertainty may be indicative of compartmentalization that was not accounted for in the reservoir fluid model. In other cases, a discrepancy between measured data and predicted data greater than the measurement uncertainty may be indicative of the source of the methane, or carbon dioxide that was not accounted for in the reservoir fluid model. In yet other cases, a discrepancy between measured data and predicted data greater than the measurement uncertainty may be indicative of inaccurate composition gradients or inaccurate location of flood fronts in the fluid model, Col. 20, Lines 27-41).
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 invention of Alhuraifi with the teachings of Mullins to store information relating to depositional sequences and reservoir structural information obtained from well image data and improve accuracy of data collection.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Nasser Al-Haijri (US20170321522) discloses determining wellbore leak crossflow rate between formations in an injection well are described. During normal operation of an injection well, multiple bottomhole pressures are determined based on multiple surface injection pressures.
Eric Holderby (US20160266278) discloses systems and methods for modeling a fracturing operation in a subsurface formation.
Nikita Chugunov (US20120101730) discloses Percolation theory is applied to establish a connection between magnetization decay of nuclear magnetic resonance (NMR) measurements and residual carbon dioxide saturation.
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/SHARAH ZAAB/Examiner, Art Unit 2857
/ALEXANDER SATANOVSKY/Primary Examiner, Art Unit 2857
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