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 .
Detailed Action
The following NON-FINAL office action is in response to application 18/584650 filed on
2/22/24. This communication is the first action on the merits.
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 .
Status of Claims
Claims 1-20 are currently pending and have been rejected as follows.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 2/22/2024 complies with the provisions of 37 CFR 1.97 and is being considered.
Specification
The disclosure is objected to because of the following informalities:
Paragraph [0074] reads “Determining the normalized cumulative fluid flow rate may be include determining a first combination of the measured fluid flow rate.” The word “be” should be omitted.
Appropriate correction is required.
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 1-9 and 11-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more. A subject matter eligibility analysis is set forth below. See MPEP 2106.
Specifically, representative Claim 1 recites:
A method comprising: measuring, using a flowmeter, a measured fluid flow rate at a plurality of depths in a well penetrating a hydrocarbon reservoir;
using a reservoir simulator: determining a normalized cumulative fluid flow rate for the well based on the measured fluid flow rate at the plurality of depths;
receiving a reservoir simulation model for the hydrocarbon reservoir;
determining a normalized fluid flow capacity for the well based on the reservoir simulation model; and
updating the reservoir simulation model based, at least in part, on the normalized cumulative fluid flow rate and the normalized fluid flow capacity.
The claim limitations in the abstract idea have been underlined above; the remaining limitations are “additional elements.” Similar limitations comprise the abstract idea of Claim 11.
Step 1:
Under Step 1 of the analysis, Claim 1 belongs to a statutory category, namely it is a method claim. Likewise, Claim 11 is a system claim.
Step 2A – Prong I:
This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim.
In the instant case, Claim 1 is found to recite at least one judicial exception (i.e. abstract idea), that being a Mental Process and mathematical Calculation. This can be seen in the following claim limitations: “using a reservoir simulator: determining a normalized cumulative fluid flow rate for the well based on the measured fluid flow rate at the plurality of depths,” “determining a normalized fluid flow capacity for the well based on the reservoir simulation model,” and “updating the reservoir simulation model based, at least in part, on the normalized cumulative fluid flow rate and the normalized fluid flow capacity.” Determining the normalized cumulative flow rate involves integration and mathematical steps. See Specification Paragraph [0074] and Eq. [12]. Similarly, determining the normalized fluid flow rate involves division and integration. See Specification Paragraph [0076] and Eq. [04]. Updating the model is based on a comparison between production data and modeled fluid flow and as such, is both a Mental Process and Mathematical Calculation. See Specification Paragraphs [0050] and [0077]. Similar limitations comprise the abstract ideas of Claim 11.
Step 2A – Prong II:
Step 2A, prong 2 of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception(s) into a practical application of the exception. This evaluation is performed by (a) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (b) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application.
Claims 1 and 11 do not amount to the recitation of a particular practical application as they do not recite any specific steps that would improve upon the underlying hydrocarbon extraction process.
Thus, under Step 2A, prong 2 of the analysis, even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application and the claim is directed to the judicial exception.
Step 2B:
In addition to the abstract ideas recited in Claims 1 and 11, the claimed method and system recite the following additional elements: “A method comprising: measuring, using a flowmeter, a measured fluid flow rate at a plurality of depths in a well penetrating a hydrocarbon reservoir,” “receiving a reservoir simulation model for the hydrocarbon reservoir,” and ”a reservoir simulator configured to receive the measured fluid flow rate at a plurality of depths.” Measuring flow rate at a plurality of depths using a flowmeter and receiving a reservoir simulation model (i.e., obtaining the model to be updated) are found to be insignificant extra-solution activity. See MPEP 2106.05(g). Measuring flow rate at a plurality of depths and receiving a reservoir simulation model are both mere data gathering steps necessary to implement the judicial exception and, when re-evaluated under Step 2B is further found to be well-understood, routine, and conventional as evidenced by MPEP 2106.05(d)(II) (describing conventional activities that include transmitting and receiving data over a network, electronic recordkeeping, storing and retrieving information from memory, and electronically scanning or extracting data from a physical document).
Under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, as described above with respect to Step 2A Prong 2, merely amount to a general purpose computer system that attempts to apply the abstract idea in a technological environment, limiting the abstract idea to a particular field of use, and/or merely performs insignificant extra-solution activities.
Therefore, similarly the combination and arrangement of the above identified additional elements when analyzed under Step 2B also fails to necessitate a conclusion that Claims 1 and 11, amount to significantly more than the abstract idea.
With regards to the dependent claims, Claims 2-10 and 12-20 merely further expand upon the algorithm/abstract idea and do not set forth further additional elements that integrate the recited abstract idea into a practical application or amount to significantly more. Therefore, these claims are found ineligible for the reasons described for parent claims 1 and 11. Specifically:
Claims 2 and 12 recite the reservoir simulation model comprising a permeability matrix, while Claims 3 and 13 recite the model comprising fracture permeability. These are both numerical values used in the mathematical calculations (See Specification Paragraph [0045] and Eqs. [3] and [8]) of the model and thus part of the abstract idea.
Claims 4 and 5 recite determining a first combination of the measured fluid flow rate at the plurality of depths, the measured fluid flow rate at the plurality of depths comprising a measured fluid flow rate at the surface of the well, and determining a quotient of the first combination and the measured fluid flow rate at the surface.
Claims 6 and 7 recite the reservoir simulation model comprising a fluid flow capacity of the well, a plurality of layers, and a corresponding plurality of layer fluid flow capacities, determining a quotient of a second combination of the plurality of layer fluid flow capacities and the fluid flow capacity of the well, and performing integration with respect to length. The fluid flow capacities of the well and plurality of layers are merely parameters with which to perform the abstract idea and are thus part of the mental processes/calculations. The quotient and integration are mathematical calculations. The well itself and plurality of layers merely serves as the environment to which the mathematical calculations are generally linked.
Claims 14, 15, 16, and 17, recite similar elements to Claims 4-7 and thus do not contain additional elements that integrate the claims into practical application or amount to significantly more than the claimed system.
Claims 8 and 18 recite updating the reservoir simulation model based on a difference between the normalized cumulative flow rate and normalized fluid flow capacity. The normalized fluid flow rate and capacity are both parameters used in the mathematical calculations, while basing an update on the difference between them is both a mathematical calculation (i.e. subtraction) and mental process (i.e. comparing).
Claims 9 and 19 recite receiving a fluid pressure distribution and simulating the fluid pressure distribution at a second time. Receiving the fluid pressure distribution amounts to mere data gathering step necessary to predict the fluid pressure distribution at a later time using the model. The fluid pressure distribution itself is a distance (depth)-varying mathematical function, while the simulation with the model amounts to mathematical calculations and the generic computer/instructions to execute the calculations.
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, 4-9, 11, and 14-19 are rejected under 35 U.S.C. 103 as being unpatentable under Ehlig-Economides et. al. (US 4803873 A) in view of Bello et. al. (US 20160356125 A1), in further view of Anisur Rahman, et. al. (US 10392922 B2).
Regarding Claim 1, Ehlig-Economides discloses a method comprising: measuring, using a flowmeter, a measured fluid flow rate at a plurality of depths in a well penetrating a hydrocarbon reservoir [Col. 3 Ln. 9-13 – “The flow measurements are made using a flowmeter 13 (for example, as described in French Pat. No. 74/22 391) lowered into the well at the end of a cable 14, then moved vertically several times in a sweeping movement throughout the total depth of the formation.”];
Ehlig-Economides does not disclose using a reservoir simulator.
However, Bello discloses using a reservoir simulator [Paragraph [0032] – “The systems and methods described herein provide features for performing and facilitating production monitoring, data analysis and production prediction and optimization. Various computational algorithms, referred to collectively as virtual flow metering (VFM) algorithms, perform processes including estimation of multi-phase flow rates and production allocation from single and multi-zonal wells using a coupled thermal reservoir-borehole model, and automatic and/or online production prediction and model calibration.” – collection of VFM algorithms comprise the reservoir simulator; Paragraph [0042] – “Generally, the system 60 and/or components thereof, perform a variety of functions related to production analysis and prediction. The system 60 executes an analysis and modeling application stored in a memory of a processing device, e.g., a client 68, that utilizes a set of models collectively referred to as a virtual flow metering (VFM) model.”].
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to use the reservoir simulator of Bello in the method of Ehlig-Economides in order to better represent the conditions of the reservoir.
The combination of Ehlig-Economides and Bello discloses determining a cumulative fluid flow rate for the well based on the measured fluid flow rate at the plurality of depths [Ehlig-Economides, Col. 2, Ln. 64-Col. 3 Ln. 4 – “When the well is placed in production, it delivers a total oil flow to the surface through its production string 11. The annular space between the casing 10 and the production string 11 is sealed off by the packer 9. The partial flowrates q1 to q5 of layers 1 through 5 make up the total flowrate. The total flowrate Q measured above the formation is the sum of flowrates q1 to q5. It should be noted that the flowrate measured on the surface may differ due to the wellbore storage effect. These flowrates are identical if the effect is zero.”; Col. 3 Ln. 9-21 – “The flow measurements are made using a flowmeter 13 (for example, as described in French Pat. No. 74/22 391) lowered into the well at the end of a cable 14, then moved vertically several times in a sweeping movement throughout the total depth of the formation. When it is immediately above a layer, the flowmeter measures the cumulative flowrate of that layer and those below it. With each passage, a curve such as the one shown in FIG. 2 is recorded, indicating the flowrate measured as a function of depth, from which the cumulative flowrates of the various layers 1 through 5, i.e., q5, q5+q4, q5+q4+q3, etc., recorded in front of intermediate layers 45, 34, 23, etc., can be deduced.”]
receiving a reservoir simulation model for the hydrocarbon reservoir [Bello, Paragraph [0044] – “Selected formatted and filtered data is input to a thermal reservoir modeling engine or module that employs a two or three-dimensional (2-D or 3-D) thermal reservoir model capable of coupling multiphase flow and transport processes in single and multi-layered heterogeneous reservoirs completed with and without intelligent completions systems.“ – See also Fig. [2], client, 68 receives set of models comprising VFM, which includes thermal reservoir model from production data hub 62];
determining a fluid flow capacity for the well based on the reservoir simulation model [Bello, Paragraph [0076] – “At block 92, simulation input is prepared, which includes the initial parameters, and forward simulations are run using the forward model at block 93. At block 94, forward simulation output is received, such as fluid flow rates, multi-phase flow rates and production allocation. At block 95, additional measurements are taken during production (at a next time step immediately following the current time step), such as downhole pressure, temperature and flow rates. At block 96, an inversion model is run to calculate values of expected measurements, e.g., pressure, temperature and/or flow rates, compare the expected measurements to the additional measurements and iteratively adjust parameters of the forward model. This process is performed for one or more time steps, and the results are output for comparison…” – expected flow rate calculated by model is fluid flow capacity, refer to Fig. [4]].
The combination does not disclose that the cumulative fluid flow rate and fluid flow capacities are normalized.
Anisur Rahman, however discloses that the cumulative fluid flow rate and fluid flow capacities are normalized [Col. 1, Ln. 24-26 – “During transient tests, both the production rates of fluids at surface and the pressures at downhole conditions are measured with time.” – production rate at surface is fluid flow rate at surface; Col. 2 Ln. 35-38 – “Briefly, the present invention provides a new and improved computer implemented method of determining a measure of inter-reservoir crossflow rate between adjacent formation layers of a subsurface reservoir” – crossflow rates are fluid flow rates in reservoir layers; Col. 14 Ln. 51-56 – “The data plotted in FIG. 6 shows changes in well flowing pressures, pressure derivatives, crossflow rates and their relative crossflow rate to the total rate of production (as the ratio of crossflow rate to production rate expressed in percentage)” – See Fig. [6], “crossflow rate to production rate ratio”].
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to normalize layer flow rates to surface flow rates, as disclosed by Anisur Rahman using the layer and surface cumulative fluid flow rate and fluid flow capacities of the combination of Ehlig-Economides and Bello in order to improve ease of interpretation of the fluid flow through the well layers.
The combination of Ehlig-Economides, Bello, and Anisur Rahman discloses updating the reservoir simulation model based, at least in part, on the normalized cumulative fluid flow rate and the normalized fluid flow capacity [Bello, Paragraph [0076] – “At block 95, additional measurements are taken during production (at a next time step immediately following the current time step), such as downhole pressure, temperature and flow rates. At block 96, an inversion model is run to calculate values of expected measurements, e.g., pressure, temperature and/or flow rates, compare the expected measurements to the additional measurements and iteratively adjust parameters of the forward model. This process is performed for one or more time steps, and the results are output for comparison…” – refer to Fig. [4], expected flow rate calculated by model is fluid flow capacity and cumulative fluid flow rate is from additional measurements of flow rates, both normalized according to Anisur Rahman].
Regarding Claim 11, Ehlig-Economides discloses a system comprising: a flowmeter configured to measure a measured fluid flow rate at a plurality of depths in a well penetrating a hydrocarbon reservoir [Col. 3 Ln. 9-13 – “The flow measurements are made using a flowmeter 13 (for example, as described in French Pat. No. 74/22 391) lowered into the well at the end of a cable 14, then moved vertically several times in a sweeping movement throughout the total depth of the formation.”].
Ehlig-Economides does not disclose a reservoir simulator.
However, Bello discloses a reservoir simulator configured to receive the measured fluid flow rate at a plurality of depths [Paragraph [0032] – “The systems and methods described herein provide features for performing and facilitating production monitoring, data analysis and production prediction and optimization. Various computational algorithms, referred to collectively as virtual flow metering (VFM) algorithms, perform processes including estimation of multi-phase flow rates and production allocation from single and multi-zonal wells using a coupled thermal reservoir-borehole model, and automatic and/or online production prediction and model calibration.” – collection of VFM algorithms comprise the reservoir simulator; Paragraph [0042] – “Generally, the system 60 and/or components thereof, perform a variety of functions related to production analysis and prediction. The system 60 executes an analysis and modeling application stored in a memory of a processing device, e.g., a client 68, that utilizes a set of models collectively referred to as a virtual flow metering (VFM) model.”].
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to use the reservoir simulator of Bello in the flowrate measuring system of Ehlig-Economides in order to process the measured flow rates.
The combination discloses the model configured to: determine a cumulative fluid flow rate for the well based on the measured fluid flow rate at the plurality of depths [Ehlig-Economides, Col. 2, Ln. 64-Col. 3 Ln. 4 – “When the well is placed in production, it delivers a total oil flow to the surface through its production string 11. The annular space between the casing 10 and the production string 11 is sealed off by the packer 9. The partial flowrates q1 to q5 of layers 1 through 5 make up the total flowrate. The total flowrate Q measured above the formation is the sum of flowrates q1 to q5. It should be noted that the flowrate measured on the surface may differ due to the wellbore storage effect. These flowrates are identical if the effect is zero.”; Col. 3 Ln. 9-21 – “The flow measurements are made using a flowmeter 13 (for example, as described in French Pat. No. 74/22 391) lowered into the well at the end of a cable 14, then moved vertically several times in a sweeping movement throughout the total depth of the formation. When it is immediately above a layer, the flowmeter measures the cumulative flowrate of that layer and those below it. With each passage, a curve such as the one shown in FIG. 2 is recorded, indicating the flowrate measured as a function of depth, from which the cumulative flowrates of the various layers 1 through 5, i.e., q5, q5+q4, q5+q4+q3, etc., recorded in front of intermediate layers 45, 34, 23, etc., can be deduced.”],
receive a reservoir simulation model for the hydrocarbon reservoir [Bello, Paragraph [0044] – “Selected formatted and filtered data is input to a thermal reservoir modeling engine or module that employs a two or three-dimensional (2-D or 3-D) thermal reservoir model capable of coupling multiphase flow and transport processes in single and multi-layered heterogeneous reservoirs completed with and without intelligent completions systems.“ – See also Fig. [2], client, 68 receives set of models comprising VFM, which includes thermal reservoir model from production data hub 62],
determine a fluid flow capacity for the well based on the reservoir simulation model [Bello, Paragraph [0076] – “At block 96, an inversion model is run to calculate values of expected measurements, e.g., pressure, temperature and/or flow rates, compare the expected measurements to the additional measurements and iteratively adjust parameters of the forward model. This process is performed for one or more time steps, and the results are output for comparison…” – Refer to Fig. [4], expected flow rate calculated by model is fluid flow capacity].
The combination does not disclose that the cumulative fluid flow rate and fluid flow capacities are normalized.
Anisur Rahman, however, discloses that the cumulative fluid flow rate and fluid flow capacities are normalized [Col. 1, Ln. 24-26 – “During transient tests, both the production rates of fluids at surface and the pressures at downhole conditions are measured with time.” – production rate at surface is fluid flow rate at surface; Col. 2 Ln. 35-38 – “Briefly, the present invention provides a new and improved computer implemented method of determining a measure of inter-reservoir crossflow rate between adjacent formation layers of a subsurface reservoir” – crossflow rates are fluid flow rates in reservoir layers; Col. 14 Ln. 51-56 – “The data plotted in FIG. 6 shows changes in well flowing pressures, pressure derivatives, crossflow rates and their relative crossflow rate to the total rate of production (as the ratio of crossflow rate to production rate expressed in percentage)” – See Fig. [6], “crossflow rate to production rate ratio”].
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to normalize layer flow rates to surface flow rates, as disclosed by Anisur Rahman using the layer and surface cumulative fluid flow rate and fluid flow capacities of the combination of Ehlig-Economides and Bello in order to improve ease of interpretation of the fluid flow through the well layers.
The combination of Ehlig-Economides, Bello, and Anisur Rahman discloses updating the reservoir simulation model based, at least in part, on the normalized cumulative fluid flow rate and the normalized fluid flow capacity [Bello, Paragraph [0076] – “At block 95, additional measurements are taken during production (at a next time step immediately following the current time step), such as downhole pressure, temperature and flow rates. At block 96, an inversion model is run to calculate values of expected measurements, e.g., pressure, temperature and/or flow rates, compare the expected measurements to the additional measurements and iteratively adjust parameters of the forward model. This process is performed for one or more time steps, and the results are output for comparison…” – Refer to Fig. [4], expected flow rate calculated by model is fluid flow capacity and cumulative fluid flow rate is from additional measurements of flow rates, both normalized according to Anisur Rahman].
Regarding Claim 4, the combination of Ehlig-Economides, Bello, and Anisur Rahman discloses the method of claim 1, wherein determining the normalized cumulative fluid flow rate comprises determining a first combination of the measured fluid flow rate at the plurality of depths [Ehlig-Economides, Col. 3 Ln. 9-21 – “The flow measurements are made using a flowmeter 13 (for example, as described in French Pat. No. 74/22 391) lowered into the well at the end of a cable 14, then moved vertically several times in a sweeping movement throughout the total depth of the formation. When it is immediately above a layer, the flowmeter measures the cumulative flowrate of that layer and those below it. With each passage, a curve such as the one shown in FIG. 2 is recorded, indicating the flowrate measured as a function of depth, from which the cumulative flowrates of the various layers 1 through 5, i.e., q5, q5+q4, q5+q4+q3, etc., recorded in front of intermediate layers 45, 34, 23, etc., can be deduced.” – q3 + q4 + q5 layer can be first combination; cumulative fluid flow rate normalized as per Anisur Rahman].
Regarding Claim 5, the combination of Ehlig-Economides, Bello, and Anisur Rahman discloses the method of claim 4, wherein the measured fluid flow rate at the plurality of depths comprises a measured fluid flow rate at a surface of the well [Ehlig-Economides , Col. 2, Ln. 64-Col. 3 Ln. 2 – “When the well is placed in production, it delivers a total oil flow to the surface through its production string 11. The annular space between the casing 10 and the production string 11 is sealed off by the packer 9. The partial flowrates q1 to q5 of layers 1 through 5 make up the total flowrate. The total flowrate Q measured above the formation is the sum of flowrates q1 to q5.];
and determining the normalized cumulative fluid flow rate further comprises determining a quotient of the first combination and the measured fluid flow rate at the surface of the well [Anisur Rahman, Col. 1, Ln. 24-26 – “During transient tests, both the production rates of fluids at surface and the pressures at downhole conditions are measured with time.” – production rate at surface is fluid flow rate at surface; Col. 2 Ln. 35-38 – “Briefly, the present invention provides a new and improved computer implemented method of determining a measure of inter-reservoir crossflow rate between adjacent formation layers of a subsurface reservoir” – crossflow rates are fluid flow rates in reservoir layers; Col. 14 Ln. 51-56 – “The data plotted in FIG. 6 shows changes in well flowing pressures, pressure derivatives, crossflow rates and their relative crossflow rate to the total rate of production (as the ratio of crossflow rate to production rate expressed in percentage)” – See Fig. [6], “crossflow rate to production rate ratio”, the ratio is the quotient].
Regarding Claim 6, the combination of Ehlig-Economides, Bello, and Anisur Rahman discloses the method of claim 1, wherein: the reservoir simulation model [Bello, Paragraph [0044] – “Selected formatted and filtered data is input to a thermal reservoir modeling engine or module that employs a two or three-dimensional (2-D or 3-D) thermal reservoir model capable of coupling multiphase flow and transport processes in single and multi-layered heterogeneous reservoirs completed with and without intelligent completions systems.“ – See also Fig. [2], client, 68 receives set of models comprising VFM, which includes thermal reservoir model from production data hub 62] comprises a fluid flow capacity of the well [Bello, Paragraph [0076] – “At block 96, an inversion model is run to calculate values of expected measurements, e.g., pressure, temperature and/or flow rates, compare the expected measurements to the additional measurements and iteratively adjust parameters of the forward model. This process is performed for one or more time steps, and the results are output for comparison…” – expected flow rate calculated by model is fluid flow capacity, refer to Fig. [4]], a plurality of layers [See Ehlig-Economides Fig. [2], plurality of layers], and a corresponding plurality of layer fluid flow capacities [Ehlig-Economides, Col. 2, Ln. 68-Col. 3 Ln. 4 – “The partial flowrates q1 to q5 of layers 1 through 5 make up the total flowrate. The total flowrate Q measured above the formation is the sum of flowrates q1 to q5.” – see also Fig. [2]”];
and determining the normalized fluid flow capacity comprises determining a quotient of a second combination of the plurality of layer fluid flow capacities [Bello, Paragraph [0044] – “Selected formatted and filtered data is input to a thermal reservoir modeling engine or module that employs a two or three-dimensional (2-D or 3-D) thermal reservoir model capable of coupling multiphase flow and transport processes in single and multi-layered heterogeneous reservoirs…” – applying the reservoir layers in Ehlig-Economides Fig. [2], where the q4+q5 layer is identified as the second combination, to this model deployed on a multilayered reservoir] and the fluid flow capacity of the well [Paragraph [0076] – “At block 96, an inversion model is run to calculate values of expected measurements, e.g., pressure, temperature and/or flow rates, compare the expected measurements to the additional measurements and iteratively adjust parameters of the forward model. This process is performed for one or more time steps, and the results are output for comparison…” – expected flow rate calculated by model is fluid flow capacity, refer to Fig. [4]; Anisur Rahman, Col. 1, Ln. 24-26 – “During transient tests, both the production rates of fluids at surface and the pressures at downhole conditions are measured with time.” – production rate at surface is fluid flow rate at surface; Col. 2 Ln. 35-38 – “Briefly, the present invention provides a new and improved computer implemented method of determining a measure of inter-reservoir crossflow rate between adjacent formation layers of a subsurface reservoir” – crossflow rates are fluid flow rates in reservoir layers; Col. 14 Ln. 51-56 – “The data plotted in FIG. 6 shows changes in well flowing pressures, pressure derivatives, crossflow rates and their relative crossflow rate to the total rate of production (as the ratio of crossflow rate to production rate expressed in percentage)” – See Fig. [6], “crossflow rate to production rate ratio”, the ratio is the quotient].
Regarding Claim 7, the combination of Ehlig-Economides, Bello, and Anisur Rahman discloses the method of claim 5, wherein determining the first combination comprises performing integration with respect to depth [Ehlig-Economides, Col. 3 Ln. 9-21 – “The flow measurements are made using a flowmeter 13 (for example, as described in French Pat. No. 74/22 391) lowered into the well at the end of a cable 14, then moved vertically several times in a sweeping movement throughout the total depth of the formation. When it is immediately above a layer, the flowmeter measures the cumulative flowrate of that layer and those below it. With each passage, a curve such as the one shown in FIG. 2 is recorded, indicating the flowrate measured as a function of depth, from which the cumulative flowrates of the various layers 1 through 5, i.e., q5, q5+q4, q5+q4+q3, etc., recorded in front of intermediate layers 45, 34, 23, etc., can be deduced.” – sweeping through a given layer and measuring the cumulative flow rates is integrating over depth].
Regarding Claim 8, the combination of Ehlig-Economides, Bello, and Anisur Rahman discloses the method of claim 1, wherein updating the reservoir simulation model is based, at least in part, on a difference between the normalized cumulative fluid flow rate and the normalized fluid flow capacity [Bello, Paragraph [0076] – “At block 95, additional measurements are taken during production (at a next time step immediately following the current time step), such as downhole pressure, temperature and flow rates. At block 96, an inversion model is run to calculate values of expected measurements, e.g., pressure, temperature and/or flow rates, compare the expected measurements to the additional measurements and iteratively adjust parameters of the forward model. This process is performed for one or more time steps, and the results are output for comparison…” – expected flow rate calculated by model is fluid flow capacity, refer to Fig. [4]; Paragraph [0081] – “If a deviation between the simulated data and received measurement data beyond a selected magnitude is detected, certain model parameters are adjusted to compensate for the model drift. The selected model parameters, in one embodiment, are chosen from a model constraining group, which includes parameters such as pressure, temperature and/or fluid flow rate.” - cumulative fluid flow rate and fluid flow capacity normalized as per Anisur Rahman].
Regarding Claim 9, the combination of Ehlig-Economides, Bello, and Anisur Rahman discloses the method of claim 1, further comprising, receiving, using the reservoir simulator [Bello, Paragraph [0032] – “The systems and methods described herein provide features for performing and facilitating production monitoring, data analysis and production prediction and optimization. Various computational algorithms, referred to collectively as virtual flow metering (VFM) algorithms, perform processes including estimation of multi-phase flow rates and production allocation from single and multi-zonal wells using a coupled thermal reservoir-borehole model, and automatic and/or online production prediction and model calibration.” – collection of VFM algorithms comprise the reservoir simulator; Paragraph [0042] – “Generally, the system 60 and/or components thereof, perform a variety of functions related to production analysis and prediction. The system 60 executes an analysis and modeling application stored in a memory of a processing device, e.g., a client 68, that utilizes a set of models collectively referred to as a virtual flow metering (VFM) model.”], a fluid pressure distribution at a first time [Bello, Paragraph [0018] – “Under the assumption of the subsurface multiphase flow model, an inversion algorithm is configured to estimate formation properties and/or production properties (e.g., pressure, temperature, distribution of reservoir temperature and pressure, distributed reservoir parameters, near-wellbore features and single or multi-phase flow rates) specific to a given measurement domain by numerically reproducing available measurement data.”; Paragraph [0027] – “The temperature, pressure and flow sensors provide measurements for the pressure, temperature and flow rate of the fluid.”; Paragraph [0045] – “In addition, the thermal reservoir model employed by the application may be used to generate forecasts of production output for one or more production wells. This forecasting may be standard forecasting based on received data representing selected time intervals”], wherein the fluid pressure distribution represents fluid pressure throughout at least a first portion of the hydrocarbon reservoir [Bello, Paragraph [0060] – “Initial conditions used to run the forward model include, e.g., pressure (P), temperature (T), water-oil ratio (WOR), gas-oil ratio (GOR), and concentrations of any chemical solutes as a function of borehole radius (r) and depth (z) at an initial time “t”. Permeability and porosity are also specified as functions of position. Random and stochastic distributions of properties are possible, as well as uniform distributions in each producing layer.” – one of the producing layers is a first portion of the hydrocarbon reservoir];
and simulating, using the reservoir simulator [Bello, Paragraph [0032] – “The systems and methods described herein provide features for performing and facilitating production monitoring, data analysis and production prediction and optimization. Various computational algorithms, referred to collectively as virtual flow metering (VFM) algorithms, perform processes including estimation of multi-phase flow rates and production allocation from single and multi-zonal wells using a coupled thermal reservoir-borehole model, and automatic and/or online production prediction and model calibration.” – collection of VFM algorithms comprise the reservoir simulator; Paragraph [0042] – “Generally, the system 60 and/or components thereof, perform a variety of functions related to production analysis and prediction. The system 60 executes an analysis and modeling application stored in a memory of a processing device, e.g., a client 68, that utilizes a set of models collectively referred to as a virtual flow metering (VFM) model.”] and the updated reservoir simulation model, the fluid pressure distribution at a second time, wherein the second time is later than the first time [Bello, Paragraph [0045] – “In addition, the thermal reservoir model employed by the application may be used to generate forecasts of production output for one or more production wells. This forecasting may be standard forecasting based on received data representing selected time intervals (e.g., four week forecasting using 30-minute data intervals), and/or “what-if” forecasting that predicts production output in response to multiple scenarios. Examples of forecasts include forecasts of short-term reservoir parameters, long-term reservoir parameters, near-wellbore reservoir features and production performance properties. Production performance properties include, e.g., distributed reservoir parameters, multiphase flow rates, cumulative multiphase volumes, near-wellbore features, and distribution of reservoir temperature and pressure.”; second time is after the updates of Bello, Paragraphs [0081] and [0085], Figs. [6] and [7] have occurred].
Regarding Claim 14, the combination of Ehlig-Economides, Bello, and Anisur Rahman discloses the system of claim 11, wherein: the reservoir simulation model [Bello, Paragraph [0044] – “Selected formatted and filtered data is input to a thermal reservoir modeling engine or module that employs a two or three-dimensional (2-D or 3-D) thermal reservoir model capable of coupling multiphase flow and transport processes in single and multi-layered heterogeneous reservoirs completed with and without intelligent completions systems.“ – See also Fig. [2], client, 68 receives set of models comprising VFM, which includes thermal reservoir model from production data hub 62] comprises a fluid flow capacity of the well [Bello, Paragraph [0076] – “At block 96, an inversion model is run to calculate values of expected measurements, e.g., pressure, temperature and/or flow rates, compare the expected measurements to the additional measurements and iteratively adjust parameters of the forward model. This process is performed for one or more time steps, and the results are output for comparison…” – expected flow rate calculated by model is fluid flow capacity, refer to Fig. [4]], a plurality of layers [Ehlig-Economides , Col. 2, Ln. 64-Col. 3 Ln. 2 – “When the well is placed in production, it delivers a total oil flow to the surface through its production string 11. The annular space between the casing 10 and the production string 11 is sealed off by the packer 9. The partial flowrates q1 to q5 of layers 1 through 5 make up the total flowrate. The total flowrate Q measured above the formation is the sum of flowrates q1 to q5.”], and a corresponding plurality of layer fluid flow capacities, and the reservoir simulator [Bello, Paragraph [0034] – “The algorithms use a modeling framework that includes partial differential equation models for subsurface thermal multiphase flow through porous media and a borehole. A fast and accurate forward model incorporates measurement response functions for numerical simulation using measurement data.”; Paragraph [0047] – “The thermal reservoir model is a forward model that includes a partial differential equation model for forward simulation of two or three-dimensional (2-D or 3-D) subsurface transient thermal multiphase flow through porous media and the borehole.”; Paragraph [0050] – “The fluid property simulation includes inputting PVT (pressure, volume and temperature) properties into a compositional simulator to predict thermodynamic and transport properties of fluids…”] is further configured to determine the normalized fluid flow capacity based on a quotient of a first combination of the plurality of layer fluid flow capacities and the fluid flow capacity of the well [Bello, Paragraph [0044] – “Selected formatted and filtered data is input to a thermal reservoir modeling engine or module that employs a two or three-dimensional (2-D or 3-D) thermal reservoir model capable of coupling multiphase flow and transport processes in single and multi-layered heterogeneous reservoirs…” – applying the reservoir layers in Ehlig-Economides Fig. [2], where the q3 + q4 + q5 layer is identified as the first combination, to this model deployed on a multilayered reservoir; Paragraph [0076] – “At block 95, additional measurements are taken during production (at a next time step immediately following the current time step), such as downhole pressure, temperature and flow rates. At block 96, an inversion model is run to calculate values of expected measurements, e.g., pressure, temperature and/or flow rates, compare the expected measurements to the additional measurements and iteratively adjust parameters of the forward model. This process is performed for one or more time steps, and the results are output for comparison…” – expected flow rate calculated by model is fluid flow capacity, refer to Fig. [4]; Anisur Rahman, Col. 1, Ln. 24-26 – “During transient tests, both the production rates of fluids at surface and the pressures at downhole conditions are measured with time.” – production rate at surface is fluid flow rate at surface; Col. 2 Ln. 35-38 – “Briefly, the present invention provides a new and improved computer implemented method of determining a measure of inter-reservoir crossflow rate between adjacent formation layers of a subsurface reservoir” – crossflow rates are fluid flow rates in reservoir layers; Col. 14 Ln. 51-56 – “The data plotted in FIG. 6 shows changes in well flowing pressures, pressure derivatives, crossflow rates and their relative crossflow rate to the total rate of production (as the ratio of crossflow rate to production rate expressed in percentage)” – See Fig. [6], “crossflow rate to production rate ratio”, the ratio is the quotient].
Regarding Claim 15, the combination of Ehlig-Economides, Bello, and Anisur Rahman discloses the system of claim 11, wherein the reservoir simulator [Bello, Paragraph [0032] – “The systems and methods described herein provide features for performing and facilitating production monitoring, data analysis and production prediction and optimization. Various computational algorithms, referred to collectively as virtual flow metering (VFM) algorithms, perform processes including estimation of multi-phase flow rates and production allocation from single and multi-zonal wells using a coupled thermal reservoir-borehole model, and automatic and/or online production prediction and model calibration.” – collection of VFM algorithms comprise the reservoir simulator; Paragraph [0042] – “Generally, the system 60 and/or components thereof, perform a variety of functions related to production analysis and prediction. The system 60 executes an analysis and modeling application stored in a memory of a processing device, e.g., a client 68, that utilizes a set of models collectively referred to as a virtual flow metering (VFM) model.”] is further configured to determine the normalized cumulative fluid flow rate based [Ehlig-Economides, Col. 2, Ln. 64-Col. 3 Ln. 4 – “When the well is placed in production, it delivers a total oil flow to the surface through its production string 11. The annular space between the casing 10 and the production string 11 is sealed off by the packer 9. The partial flowrates q1 to q5 of layers 1 through 5 make up the total flowrate. The total flowrate Q measured above the formation is the sum of flowrates q1 to q5. It should be noted that the flowrate measured on the surface may differ due to the wellbore storage effect. These flowrates are identical if the effect is zero.”; Col. 3 Ln. 9-21 – “The flow measurements are made using a flowmeter 13 (for example, as described in French Pat. No. 74/22 391) lowered into the well at the end of a cable 14, then moved vertically several times in a sweeping movement throughout the total depth of the formation. When it is immediately above a layer, the flowmeter measures the cumulative flowrate of that layer and those below it. With each passage, a curve such as the one shown in FIG. 2 is recorded, indicating the flowrate measured as a function of depth, from which the cumulative flowrates of the various layers 1 through 5, i.e., q5, q5+q4, q5+q4+q3, etc., recorded in front of intermediate layers 45, 34, 23, etc., can be deduced.”; cumulative fluid flow rate normalized as per Anisur Rahman], at least in part, on a second combination of the measured fluid flow rate at the plurality of depths [Ehlig-Economides, Col. 2, Ln. 64-Col. 3 Ln. 4 – “When the well is placed in production, it delivers a total oil flow to the surface through its production string 11. The annular space between the casing 10 and the production string 11 is sealed off by the packer 9. The partial flowrates q1 to q5 of layers 1 through 5 make up the total flowrate. The total flowrate Q measured above the formation is the sum of flowrates q1 to q5.”; Col. 3, Ln. 13-15 – “When it is immediately above a layer, the flowmeter measures the cumulative flowrate of that layer and those below it.” – q4 + q5 layer is second combination, partial flowrate means flowrate of single layer is divided by total flowrate, taken at the surface].
Regarding Claim 16, the combination of Ehlig-Economides, Bello, and Anisur Rahman discloses the system of claim 15, wherein the reservoir simulator [Bello, Paragraph [0032] – “The systems and methods described herein provide features for performing and facilitating production monitoring, data analysis and production prediction and optimization. Various computational algorithms, referred to collectively as virtual flow metering (VFM) algorithms, perform processes including estimation of multi-phase flow rates and production allocation from single and multi-zonal wells using a coupled thermal reservoir-borehole model, and automatic and/or online production prediction and model calibration.” – collection of VFM algorithms comprise the reservoir simulator; Paragraph [0042] – “Generally, the system 60 and/or components thereof, perform a variety of functions related to production analysis and prediction. The system 60 executes an analysis and modeling application stored in a memory of a processing device, e.g., a client 68, that utilizes a set of models collectively referred to as a virtual flow metering (VFM) model.”] is further configured to determine the second combination [Ehlig-Economides, Col. 2, Ln. 64-Col. 3 Ln. 4 – “When the well is placed in production, it delivers a total oil flow to the surface through its production string 11. The annular space between the casing 10 and the production string 11 is sealed off by the packer 9. The partial flowrates q1 to q5 of layers 1 through 5 make up the total flowrate. The total flowrate Q measured above the formation is the sum of flowrates q1 to q5.”; Col. 3, Ln. 13-15 – “When it is immediately above a layer, the flowmeter measures the cumulative flowrate of that layer and those below it.” – q4 + q5 layer is second combination] by performing integration with respect to depth [Ehlig-Economides, Col. 3 Ln. 9-21 – “The flow measurements are made using a flowmeter 13 (for example, as described in French Pat. No. 74/22 391) lowered into the well at the end of a cable 14, then moved vertically several times in a sweeping movement throughout the total depth of the formation. When it is immediately above a layer, the flowmeter measures the cumulative flowrate of that layer and those below it. With each passage, a curve such as the one shown in FIG. 2 is recorded, indicating the flowrate measured as a function of depth, from which the cumulative flowrates of the various layers 1 through 5, i.e., q5, q5+q4, q5+q4+q3, etc., recorded in front of intermediate layers 45, 34, 23, etc., can be deduced.” – sweeping through a given layer and measuring the cumulative flow rates is integrating over depth].
Regarding Claim 17, the combination of Ehlig-Economides, Bello, and Anisur Rahman discloses the system of claim 15, wherein the measured fluid flow rate at the plurality of depths comprises a measured fluid flow rate at a surface of the well [Ehlig-Economides, Col. 2, Ln. 64-Col. 3 Ln. 2 – “When the well is placed in production, it delivers a total oil flow to the surface through its production string 11. The annular space between the casing 10 and the production string 11 is sealed off by the packer 9. The partial flowrates q1 to q5 of layers 1 through 5 make up the total flowrate. The total flowrate Q measured above the formation is the sum of flowrates q1 to q5.];
and the reservoir simulator [Bello, Paragraph [0032] – “The systems and methods described herein provide features for performing and facilitating production monitoring, data analysis and production prediction and optimization. Various computational algorithms, referred to collectively as virtual flow metering (VFM) algorithms, perform processes including estimation of multi-phase flow rates and production allocation from single and multi-zonal wells using a coupled thermal reservoir-borehole model, and automatic and/or online production prediction and model calibration.” – collection of VFM algorithms comprise the reservoir simulator; Paragraph [0042] – “Generally, the system 60 and/or components thereof, perform a variety of functions related to production analysis and prediction. The system 60 executes an analysis and modeling application stored in a memory of a processing device, e.g., a client 68, that utilizes a set of models collectively referred to as a virtual flow metering (VFM) model.”] is further configured to determine the normalized cumulative fluid flow rate based on a quotient between the second combination and the measured fluid flow rate at the surface of the well [Ehlig-Economides , Col. 2, Ln. 64-Col. 3 Ln. 2 – “When the well is placed in production, it delivers a total oil flow to the surface through its production string 11. The annular space between the casing 10 and the production string 11 is sealed off by the packer 9. The partial flowrates q1 to q5 of layers 1 through 5 make up the total flowrate. The total flowrate Q measured above the formation is the sum of flowrates q1 to q5.” – layer comprising q5 + q4 is second combination; cumulative fluid flow rate normalized as per Anisur Rahman].
Regarding Claim 18, the combination of Ehlig-Economides, Bello, and Anisur Rahman discloses the system of claim 11, wherein the reservoir simulator [Bello, Paragraph [0032] – “The systems and methods described herein provide features for performing and facilitating production monitoring, data analysis and production prediction and optimization. Various computational algorithms, referred to collectively as virtual flow metering (VFM) algorithms, perform processes including estimation of multi-phase flow rates and production allocation from single and multi-zonal wells using a coupled thermal reservoir-borehole model, and automatic and/or online production prediction and model calibration.” – collection of VFM algorithms comprise the reservoir simulator; Paragraph [0042] – “Generally, the system 60 and/or components thereof, perform a variety of functions related to production analysis and prediction. The system 60 executes an analysis and modeling application stored in a memory of a processing device, e.g., a client 68, that utilizes a set of models collectively referred to as a virtual flow metering (VFM) model.”] is further configured to update the reservoir simulation model based, at least in part, on a difference between the normalized cumulative fluid flow rate and the normalized fluid flow capacity [Bello, Paragraph [0076] – “At block 95, additional measurements are taken during production (at a next time step immediately following the current time step), such as downhole pressure, temperature and flow rates. At block 96, an inversion model is run to calculate values of expected measurements, e.g., pressure, temperature and/or flow rates, compare the expected measurements to the additional measurements and iteratively adjust parameters of the forward model. This process is performed for one or more time steps, and the results are output for comparison…” – expected flow rate calculated by model is fluid flow capacity, refer to Fig. [4]; Paragraph [0081] – “If a deviation between the simulated data and received measurement data beyond a selected magnitude is detected, certain model parameters are adjusted to compensate for the model drift. The selected model parameters, in one embodiment, are chosen from a model constraining group, which includes parameters such as pressure, temperature and/or fluid flow rate.” – cumulative fluid flow rate and fluid flow capacity normalized as per Anisur Rahman].
Regarding Claim 19, the combination of Ehlig-Economides, Bello, and Anisur Rahman discloses the system of claim 11, wherein the reservoir simulator is further configured to: receive [Bello, Paragraph [0032] – “The systems and methods described herein provide features for performing and facilitating production monitoring, data analysis and production prediction and optimization. Various computational algorithms, referred to collectively as virtual flow metering (VFM) algorithms, perform processes including estimation of multi-phase flow rates and production allocation from single and multi-zonal wells using a coupled thermal reservoir-borehole model, and automatic and/or online production prediction and model calibration.” – collection of VFM algorithms comprise the reservoir simulator; Paragraph [0042] – “Generally, the system 60 and/or components thereof, perform a variety of functions related to production analysis and prediction. The system 60 executes an analysis and modeling application stored in a memory of a processing device, e.g., a client 68, that utilizes a set of models collectively referred to as a virtual flow metering (VFM) model.”] a fluid pressure distribution at a first time [Bello, Paragraph [0018] – “Under the assumption of the subsurface multiphase flow model, an inversion algorithm is configured to estimate formation properties and/or production properties (e.g., pressure, temperature, distribution of reservoir temperature and pressure, distributed reservoir parameters, near-wellbore features and single or multi-phase flow rates) specific to a given measurement domain by numerically reproducing available measurement data.”; Paragraph [0027] – “The temperature, pressure and flow sensors provide measurements for the pressure, temperature and flow rate of the fluid.”; Paragraph [0045] – “In addition, the thermal reservoir model employed by the application may be used to generate forecasts of production output for one or more production wells. This forecasting may be standard forecasting based on received data representing selected time intervals”], wherein the fluid pressure distribution represents fluid pressure throughout at least a first portion of the hydrocarbon reservoir [Bello, Paragraph [0060] – “Initial conditions used to run the forward model include, e.g., pressure (P), temperature (T), water-oil ratio (WOR), gas-oil ratio (GOR), and concentrations of any chemical solutes as a function of borehole radius (r) and depth (z) at an initial time “t”. Permeability and porosity are also specified as functions of position. Random and stochastic distributions of properties are possible, as well as uniform distributions in each producing layer.” – one of the producing layers is a first portion of the hydrocarbon reservoir];
and simulate, using the updated reservoir simulation model [Bello, Paragraph [0034] – “The algorithms use a modeling framework that includes partial differential equation models for subsurface thermal multiphase flow through porous media and a borehole. A fast and accurate forward model incorporates measurement response functions for numerical simulation using measurement data.”; Paragraph [0047] – “The thermal reservoir model is a forward model that includes a partial differential equation model for forward simulation of two or three-dimensional (2-D or 3-D) subsurface transient thermal multiphase flow through porous media and the borehole.”; Paragraph [0050] – “The fluid property simulation includes inputting PVT (pressure, volume and temperature) properties into a compositional simulator to predict thermodynamic and transport properties of fluids…”], the fluid pressure distribution at a second time [Bello, Paragraph [0045] – “In addition, the thermal reservoir model employed by the application may be used to generate forecasts of production output for one or more production wells. This forecasting may be standard forecasting based on received data representing selected time intervals (e.g., four week forecasting using 30-minute data intervals), and/or “what-if” forecasting that predicts production output in response to multiple scenarios. Examples of forecasts include forecasts of short-term reservoir parameters, long-term reservoir parameters, near-wellbore reservoir features and production performance properties. Production performance properties include, e.g., distributed reservoir parameters, multiphase flow rates, cumulative multiphase volumes, near-wellbore features, and distribution of reservoir temperature and pressure.” ; second time is after the updates of Bello, Paragraphs [0081] and [0085], Figs. [6] and [7] have occurred].
Claims 2, 3, 12, and 13 are rejected under 35 U.S.C. 103 as being unpatentable under Ehlig-Economides et. al., in view of Bello et. al., in view of Anisur Rahman et. al., in further view of Liu et. al. (US 20210222543 A1).
Regarding Claim 2, the combination of Ehlig-Economides, Bello, and Anisur Rahman discloses the method of claim 1, wherein the reservoir simulation model comprises a model input [Bello, Paragraph [0044] – “Selected formatted and filtered data is input to a thermal reservoir modeling engine or module that employs a two or three-dimensional (2-D or 3-D) thermal reservoir model capable of coupling multiphase flow and transport processes in single and multi-layered heterogeneous reservoirs completed with and without intelligent completions systems.“], but fails to disclose that the input is a permeability matrix.
However, Liu discloses wherein the reservoir simulation model comprises a permeability matrix [Paragraph [0026]-[0027] – “Certain methods for determination of properties of natural fractures in hydrocarbon reservoirs depend on idealized dual-porosity models…This disclosure describes implementing the dual-porosity, dual-permeability model that accounts for both matrix and fracture flows to analyze wellbore gas potential response during production from a hydraulically fractured well in a naturally fractured formation….Matrix and fracture permeability together with inter-porosity flow coefficient are computationally determined from analytical equations representing the multiple flow regimes.”].
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to use the matrix permeability of Liu in the reservoir simulation model of Bello and Ehlig-Economides in order to better characterize fluid flow.
Regarding Claim 3, the combination of Ehlig-Economides, Bello, and Anisur Rahman discloses the method of claim 1, wherein the reservoir simulation model comprises a model input [Bello, Paragraph [0044] – “Selected formatted and filtered data is input to a thermal reservoir modeling engine or module that employs a two or three-dimensional (2-D or 3-D) thermal reservoir model capable of coupling multiphase flow and transport processes in single and multi-layered heterogeneous reservoirs completed with and without intelligent completions systems.“], but fails to disclose hat the input is fracture permeability.
However, Liu discloses wherein the reservoir simulation model comprises a fracture permeability [Paragraph [0026]-[0027] – “Certain methods for determination of properties of natural fractures in hydrocarbon reservoirs depend on idealized dual-porosity models…This disclosure describes implementing the dual-porosity, dual-permeability model that accounts for both matrix and fracture flows to analyze wellbore gas potential response during production from a hydraulically fractured well in a naturally fractured formation….Matrix and fracture permeability together with inter-porosity flow coefficient are computationally determined from analytical equations representing the multiple flow regimes].
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to use the fracture permeability of Liu in the reservoir simulation model of Bello and Ehlig-Economides in order to better characterize fluid flow.
Regarding Claim 12, the combination of Ehlig-Economides, Bello, and Anisur Rahman discloses the system of claim 11, wherein the reservoir simulation model comprises a model input [Bello, Paragraph [0044] – “Selected formatted and filtered data is input to a thermal reservoir modeling engine or module that employs a two or three-dimensional (2-D or 3-D) thermal reservoir model capable of coupling multiphase flow and transport processes in single and multi-layered heterogeneous reservoirs completed with and without intelligent completions systems.“], but fails to disclose that the input is a permeability matrix.
However, Liu discloses wherein the reservoir simulation model comprises a permeability matrix [Paragraph [0026]-[0027] – “Certain methods for determination of properties of natural fractures in hydrocarbon reservoirs depend on idealized dual-porosity models…This disclosure describes implementing the dual-porosity, dual-permeability model that accounts for both matrix and fracture flows to analyze wellbore gas potential response during production from a hydraulically fractured well in a naturally fractured formation….Matrix and fracture permeability together with inter-porosity flow coefficient are computationally determined from analytical equations representing the multiple flow regimes.”].
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to use the matrix permeability of Liu in the reservoir simulation model of Ehlig-Economides, Bello, and Anisur Rahman in order to better characterize fluid flow.
Regarding Claim 13, the combination of Ehlig-Economides, Bello, and Anisur Rahman discloses the system of claim 11, wherein the reservoir simulation model comprises a model input [Bello, Paragraph [0044] – “Selected formatted and filtered data is input to a thermal reservoir modeling engine or module that employs a two or three-dimensional (2-D or 3-D) thermal reservoir model capable of coupling multiphase flow and transport processes in single and multi-layered heterogeneous reservoirs completed with and without intelligent completions systems.“], but fails to disclose that the model input is fracture permeability.
However, Liu discloses wherein the reservoir simulation model comprises a fracture permeability [Paragraph [0026]-[0027] – “Certain methods for determination of properties of natural fractures in hydrocarbon reservoirs depend on idealized dual-porosity models…This disclosure describes implementing the dual-porosity, dual-permeability model that accounts for both matrix and fracture flows to analyze wellbore gas potential response during production from a hydraulically fractured well in a naturally fractured formation….Matrix and fracture permeability together with inter-porosity flow coefficient are computationally determined from analytical equations representing the multiple flow regimes].
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to use the fracture permeability of Liu in the reservoir simulation model of Bello and Ehlig-Economides in order to better characterize fluid flow.
Claims 10 and 20 are rejected under 35 U.S.C. 103 as being unpatentable under Ehlig-Economides et. al., in view of Bello et. al., in view of Anisur Rahman et. al., in further view of Al-Nasser et. al. (US 20210225070 A1), in further view of Ba et. al. (US 20240247578 A1).
Regarding Claim 10, the combination of Ehlig-Economides, Bello, and Anisur Rahman discloses the method of claim 9.
The combination does not disclose the method further comprising: identifying, using a reservoir interpretation system, a location for drilling an infill well based, at least in part, on the fluid pressure distribution at the second time; planning, using a wellbore planning system, a wellbore trajectory to intersect the location; and drilling, using a drilling system, the infill well guided by the planned wellbore trajectory.
Al-Nasser, however, discloses the method further comprising: identifying, using a reservoir interpretation system [Paragraph [0027] – “…The computer system determines locations…”], a location for drilling an infill well based, at least in part, on the fluid pressure distribution at the second time [Paragraph [0026]-[0027] – “A display device (for example, the display device 424 illustrated in FIG. 4) of the computer system generates a graphical representation of the variations in the reservoir permeability 138, the variations in the reservoir pressure 114, and the variations in the reservoir saturation 136 in accordance with time... In some implementations, the computer system determines locations of infill drillings and a logging frequency 118 based on the virtual 3D model 112. Based on a magnitude of a difference between the predicted dynamic reservoir saturation 136 and observed data, the logging frequency 118 and an annual surveillance program for the oilfield is improved compared to traditional methods. The computer system determines locations of infill drillings by the generated graphical maps of the predicted remaining saturation and pressure at a future time to target areas characterized by increased pressure and a slow change in saturation relative to other areas.”];
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to use the reservoir interpretation system of Al-Nasser with the reservoir simulator of the combination of Ehlig-Economides, Bello, and Anisur Rahman in order to identify locations for drilling.
The combination of Bello, Ehlig-Economides, and Al-Nasser does not disclose planning, using a wellbore planning system, a wellbore trajectory to intersect the location or drilling, using a drilling system, the infill well guided by the planned wellbore trajectory.
However, Ba discloses planning, using a wellbore planning system [Paragraph [0005] – “According to another aspect, a system that includes a processor, memory accessible by the processor, processor-executable instructions stored in the memory and executable to instruct the system to receive a well plan and determine a plurality of sections of the well plan.”], a wellbore trajectory to intersect the location [Paragraph [0005] – “The instructions also cause the system to receive data from surface and downhole to determine a current location of a drill bit and to analyze the well plan to automatically derive trajectory constraints that are associated with each of the plurality of sections of the well plan. The instructions additionally cause the system to determine a plurality of trajectory candidates that pertain to respective paths from the current location of the drill bit to respective targets included within each of the plurality of sections of the well plan based on a consideration of the trajectory constraints.”];
and drilling, using a drilling system, the infill well guided by the planned wellbore trajectory [Paragraph [0005] – “The instructions further cause the processor to determine a working plan that includes an optimal path from the current location of the drill bit to reach a final target based on the plurality of trajectory candidates. The working plan is executed to provide steering commands to steer the drill bit to the final target.”].
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to use the wellbore planning system and trajectory of Ba to drill at the locations identified by the combination of Bello, Ehlig-Economides, and Al-Nasser in order to identify and access optimal locations for drilling.
Regarding Claim 20, the combination of Ehlig-Economides, Bello, and Anisur Rahman discloses the system of claim 19.
The combination does not disclose the method further comprising: a reservoir interpretation system configured to identify a location for drilling an infill well based, at least in part, on the fluid pressure distribution at the second time; a wellbore planning system configured to plan a wellbore trajectory to intersect the location; and a drilling system configured to drill the infill well guided by the planned wellbore trajectory.
However, Al-Nasser discloses the method further comprising: a reservoir interpretation system configured to identify [Paragraph [0027] – “…The computer system determines locations…”] a location for drilling an infill well based, at least in part, on the fluid pressure distribution at the second time [Paragraph [0026]-[0027] – “A display device (for example, the display device 424 illustrated in FIG. 4) of the computer system generates a graphical representation of the variations in the reservoir permeability 138, the variations in the reservoir pressure 114, and the variations in the reservoir saturation 136 in accordance with time... In some implementations, the computer system determines locations of infill drillings and a logging frequency 118 based on the virtual 3D model 112. Based on a magnitude of a difference between the predicted dynamic reservoir saturation 136 and observed data, the logging frequency 118 and an annual surveillance program for the oilfield is improved compared to traditional methods. The computer system determines locations of infill drillings by the generated graphical maps of the predicted remaining saturation and pressure at a future time to target areas characterized by increased pressure and a slow change in saturation relative to other areas.”].
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to use the reservoir interpretation system of Al-Nasser with the reservoir simulator of the combination of Ehlig-Economides, Bello, and Anisur Rahman in order to identify locations for drilling.
The combination of Ehlig-Economides, Bello, Anisur Rahman, and Al-Nasser does not disclose a wellbore planning system configured to plan a wellbore trajectory to intersect the location or a drilling system configured to drill the infill well guided by the planned wellbore trajectory.
However, Ba discloses a wellbore planning system [Paragraph [0005] – “According to another aspect, a system that includes a processor, memory accessible by the processor, processor-executable instructions stored in the memory and executable to instruct the system to receive a well plan and determine a plurality of sections of the well plan.”] configured to plan a wellbore trajectory to intersect the location [Paragraph [0005] – “The instructions also cause the system to receive data from surface and downhole to determine a current location of a drill bit and to analyze the well plan to automatically derive trajectory constraints that are associated with each of the plurality of sections of the well plan. The instructions additionally cause the system to determine a plurality of trajectory candidates that pertain to respective paths from the current location of the drill bit to respective targets included within each of the plurality of sections of the well plan based on a consideration of the trajectory constraints.”];
and a drilling system configured to drill the infill well guided by the planned wellbore trajectory [Paragraph [0005] – “The instructions further cause the processor to determine a working plan that includes an optimal path from the current location of the drill bit to reach a final target based on the plurality of trajectory candidates. The working plan is executed to provide steering commands to steer the drill bit to the final target.”].
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to use the wellbore planning system and trajectory of Ba to drill at the locations identified by the combination of Bello, Ehlig-Economides, and Al-Nasser in order to identify and access optimal locations for drilling.
Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US-20030066651-A1, Apparatus And Methods For Flow Control Gravel Pack
US-7369979-B1, Method For Characterizing And Forecasting Performance Of Wells In Multilayer Reservoirs Having Commingled Production
US-20150233239-A1, Methods For Interpretation Of Downhole Flow Measurement During Wellbore Treatments
US-20160169856-A1, Physical Reservoir Rock Interpretation In A 3D Petrophysical Modeling Environment
US-20190093469-A1, Dynamic Reservoir Characterization
US-20200095858-A1, MODELING RESERVOIR PERMEABILITY THROUGH ESTIMATING NATURAL FRACTURE DISTRIBUTION AND PROPERTIES
US-20200284945-A1, METHOD AND ALARMING SYSTEM FOR CO2 SEQUESTRATION
US-5305209-A, Method For Characterizing Subterranean Reservoirs
Conclusion
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/J.A.H./Examiner, Art Unit 2857
/ARLEEN M VAZQUEZ/Supervisory Patent Examiner, Art Unit 2857
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