Notice of Pre-AIA or AIA Status
Claims 1-24 are currently presented for Examination.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
The amendment filed on 08/21/2025 has been entered and considered by the examiner. By the amendment, claims 1, 5, 12, 16 and 23 are amended, claim 24 is newly added. In view of amendment, the prior art rejection of the claims is modified in view of amendments made. The 112(b) rejection is added for the new claim 24. See office action for detail.
Response to prior art Arguments
Applicant's arguments
Claims 1, 12, and 23 recite "determining a radius of investigation as an output based on: simulated production data for the oilfield including at least the existing well, the measured production data from the oilfield including at least the existing well, the reservoir properties, model simulation results, and the reservoir being under hydraulic control or volumetric control." Teletzke teaches, at most, that a "radius of investigation" generally exists, but does not discuss how or on what basis the radius is determined. As such, Applicant respectfully submits that Teletzke does not disclose, teach, or render obvious at least a "determining a radius of investigation as an output based on: simulated production data for the oilfield including at least the existing well, the measured production data from the oilfield including at least the existing well, the reservoir properties, model simulation results, and the reservoir being under hydraulic control or volumetric control," as recited in amended claims 1, 12, and 23.
Claims 2-4, 6-11, 13-15, and 17-22 (and new claim 24) depend, respectively, from amended independent claims 1 and 12, and are patentable at least for the reasons mentioned above and on their own merits. Therefore, Applicant respectfully submits that dependent claims 2-4, 6-11, 13-15, and 17-22 (and new claim 24) are also patentable over the applied art for at least the same reasons as discussed above with respect to claims 1 and 12, and requests withdrawal of the foregoing rejection.
Examiner response
Applicant arguments regarding the newly added limitation “determining a radius of investigation as an output based on: simulated production data for the oilfield including at least the existing well, the measured production data from the oilfield including at least the existing well, the reservoir properties, model simulation results”. See new reference Jalali et al. (PUB NO: US 20110191029 A1). For new claim 24 see Valdez et al. ("Gas EOR Methodology and Integrated Basin Forecast of Offshore Sarawak Fields, Baram Delta Malaysia." SPE Asia Pacific Enhanced Oil Recovery Conference. SPE, 2011.) in combination with Teletzke.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 24 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. The term “good” and “poor” in claim 24 are relative and subjective terms which renders the claim indefinite. The terms “good” and “poor” not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Terms like "good" and "poor" are problematic because they lack objective boundaries and rely on personal judgment, making it impossible to determine the precise scope of the invention. Such terms fail the "reasonable certainty" test, which requires that a skilled artisan understand the claim's scope in light of the specification.
Claim Rejections - 35 USC § 103
4. In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
5. The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
6. This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
7. Claims 1-4, 6-15 and 17-23 are rejected under 35 U.S.C. 103 as being unpatentable over Teletzke et al. ("Enhanced oil recovery pilot testing best practices." SPE Reservoir Evaluation & Engineering 13.01 (2010): 143-154.) in view of AlQahtani et al. (PUB NO: US20210350052A1) and further in view of Jalali et al. (PUB NO: US 20110191029 A1).
Regarding claim 1, 12 and 23
Teletzke teaches a method for implementing enhanced oil recovery, (see summary and page 143-This paper outlines a staged approach to EOR evaluation and focuses specifically on pilot testing best practices. These best practices were derived from ExxonMobil’s extensive piloting experience, which includes more than 50 field pilot tests covering the full range of EOR processes). comprising:
Claim 12. A computing system, comprising: one or more processors; and a memory system including one or more non-transitory computer-readable media storing instructions that, when executed by at least one of the one or more processors, cause the computing system to perform operations, (see Fig. 1 outlines a staged workflow that ExxonMobil has used for evaluation and design of EOR projects. See fig 1-reservoir simulation) the operations comprising;
Claim 23. A non-transitory computer-readable medium storing instructions that, when executed by at least one processor of a computing system, cause the computing system to perform operations, (see Fig. 1 outlines a staged workflow that ExxonMobil has used for evaluation and design of EOR projects. See fig 1-reservoir simulation) the operations comprising;
receiving a model of a subterranean volume of at least a portion of an oilfield and measurements collected for the subterranean volume, the measurements including at least production data and reservoir properties;; (see summary- Also included are aspects of instrumentation and measurements in pilot injection, production, and monitoring wells. see fig 1 reservoir simulation and page 143-144-Obtain data to calibrate reservoir-simulation models for full-field predictions. Lab data (fluid and rock property data) The pilot should be designed and operated to meet the objectives, aided by a predictive reservoir-simulation model. See also page 146 col 2- Methods for data acquisition from observation wells typically include logging, sampling, and pressure measurements. And table 1-data needed for interpretation)
determining a model confidence ; (see page 144-145-Pattern configuration, well design, the chosen injectant, and process operations should allow for confidence in scale up to the field-wide implementation of the process. Finally, the pilot location should be chosen to ensure as much as possible that it can be well characterized and is representative of the broader EOR target. Available reservoir characterization information should be reviewed to define key geologic factors that may affect injectivity and sweep efficiency and to identify a pilot site having representative geology. Additional geologic studies may be required in advance of the pilot to define the reservoir description to a sufficient level of accuracy. Fig. 2 illustrates factors that should be considered when selecting pilot type and scale. Two extreme cases are shown. In the first case, the recovery process is well understood because it has been proved commercially in other fields, the reservoir is well understood because there is a nearby analog or existing application in the same field, and there is low economic and injectant supply risk. In this case, commercial application without pilot testing may be considered, with some additional data gathering or phased implementation to manage risk, as discussed earlier in this section. In the second case, the recovery process is untested, the reservoir is complex or not understood, and there is significant economic and injectant supply risk. In this case, small- scale pilots, followed by a larger commercial demonstration pilot, are frequently used to manage risk before commercial application. Clearly, a range of alternatives between the two extreme cases is possible. see page 150-The original pilot operating and monitoring plan should be continued until sufficient data are acquired to validate simulation models; do not attempt to optimize on the basis of early results.) In cases in which the waterflood is less mature, the baseline waterflood recovery can be estimated by using a reservoir simulation model to history match the pilot area and extrapolate the prepilot waterflood production trend. This requires an adequate prepilot waterflood period to reduce uncertainty in the history match and extrapolation.)
Examiner note: Validation/Accuracy of simulation models provides the model confidence for the different cases based on data collected. Validation helps build confidence in the model's accuracy and reliability.
displaying a model confidence map ; (see fig 1 -reservoir simulation map)
Examiner note: Figure 1 represents the reservoir model map showing its reservoir structure and properties such as fluid and rock property data.
selecting one or more physical parameters for candidate pilot tests based at least in part on the model, the measurements, the model confidence index, the displayed model confidence map and the determined radius of investigation; (see page 144-145-By their nature, pilots are a scaled down version of the full commercial implementation of an EOR process. This scaling down is brought about to reduce key uncertainties for decision making in a manner that is as timely and cost-effective as possible. When designing a pilot, care should be taken to both understand and minimize the impact of the scaled down nature of the pilot. Reduced well spacing, judicious placement of observation wells, and elevated injection rates are techniques that have been used to provide information on process-recovery performance in a reason- able time frame. However, it is important that the pilot be designed to be scalable to the conditions for full-field application. Pattern configuration, well design, the chosen injectant, and process operations should allow for confidence in scale up to the field-wide implementation of the process. Finally, the pilot location should be chosen to ensure as much as possible that it can be well characterized and is representative of the broader EOR target. While each pilot configuration has its place and purpose, it is generally true that a more complex, and therefore, more costly, configuration will yield more data and be easier to scale up to commercial conditions. Therefore, a balance must be struck between the risks of a commercial project and the cost of ensurance provided by data from a pilot. Fig. 2 illustrates factors that should be considered when selecting pilot type and scale. see page 150 col 2-Therefore, an injectivity test was performed to determine injectivity before, during, and after CO2 injection and to estimate fi eld-scale injectivity to assist prediction of miscible process performance. The test consisted of 3 months of baseline water injection followed by 2 months of CO2 injection before returning the well to water injection. The radius of investigation of the test was approximately 100 ft.)
designing respective pilot tests for each of the candidate pilot tests based at least in part on one or more pilot test objectives, the model, the model confidence index, the displayed model confidence map and the determined radius of investigation; (see page 143 col 2 and fig 1- Defining clear pilot objectives is the first step in designing and executing a successful pilot. Pilots are conducted to address key technical and business uncertainties and risks associated with applying an EOR technology in a specific field. Conducting a pilot is one of several options for reducing risk that might include additional data gathering/appraisal or phased development. If there are better alternatives to address uncertainty and risk, then a pilot may not be required. Clearly stating the key uncertainties and pilot objectives early in the evaluation process helps determine if a pilot is the best approach for addressing these risks and helps guide pilot design and execution. With these comments in mind, specific piloting objectives may include the following Obtain data to calibrate reservoir-simulation models for full field predictions. See page 145 col1-With these definitions in mind, the types of pilots can be grouped into four configurations: 1. Nonproducing pilot. 2. Small-scale unconfined pilot. 3. Small-scale confined pilot. 4. Multipattern producing pilot. see page 150 col 2-Therefore, an injectivity test was performed to determine injectivity before, during, and after CO2 injection and to estimate fi eld-scale injectivity to assist prediction of miscible process performance. The test consisted of 3 months of baseline water injection followed by 2 months of CO2 injection before returning the well to water injection. The radius of investigation of the test was approximately 100 ft. See page 151-Successful pilot interpretation requires advance planning. It is essential that a detailed reservoir simulation model of the pilot area (with appropriate boundary conditions) be built in advance to optimize the pilot design and monitoring program, anticipate data needed for history matching the pilot, enable timely interpretation of pilot, and assess the need for selective use of additional observation wells and post-flood coring. The original pilot operating and monitoring plan should be continued until sufficient data are acquired to validate simulation models)
selecting one or more pilot tests from among the designed pilot tests; (see page 150-151 and table 2-The best practices described in the preceding text were derived from ExxonMobil’s extensive piloting experience, which includes more than 50 field pilot tests covering the full range of EOR processes. Table 2 is a list of representative ExxonMobil pilot tests that have been described previously in the open literature. Four ExxonMobil pilot tests are used below to illustrate (1) definition of pilot objectives, (2) design of pilots to meet the objectives, (3) tools and techniques for assessment of key reservoir mechanisms, and (4) integrated interpretation of pilot data aided by reservoir simulation) and
generating a pilot test implementation plan for the selected one or more pilot tests. (see page 143-144 and fig 1-Once pilot objectives have been defined clearly, sufficient time and effort need to be expended in designing a pilot to ensure that the pilot objectives can be achieved. Time spent up front in pilot design and optimization usually leads to earlier full-field implementation. By their nature, pilots are a scaled down version of the full commercial implementation of an EOR process. This scaling down is brought about to reduce key uncertainties for decision making in a manner that is as timely and cost-effective as possible. When designing a pilot, care should be taken to both understand and minimize the impact of the scaled down nature of the pilot. Reduced well spacing, judicious placement of observation wells, and elevated injection rates are techniques that have been used to provide information on process-recovery performance in a reasonable time frame. However, it is important that the pilot be designed to be scalable to the conditions for full-field application. Pattern configuration, well design, the chosen injectant, and process operations should allow for confidence in scale up to the field-wide implementation of the process. Finally, the pilot location should be chosen to ensure as much as possible that it can be well characterized and is representative of the broader EOR target. See also section pilot examples)
implementing an enhanced oil recovery (EOR) pilot test in accordance with the generated pilot test implementation plan, the implementing comprising at least one of: drilling one or more injection wells within the determined radius of investigation or drilling one or more production wells within the determined radius of investigation. (see page 150 col 2-Therefore, an injectivity test was performed to determine injectivity before, during, and after CO2 injection and to estimate fi eld-scale injectivity to assist prediction of miscible process performance. The test consisted of 3 months of baseline water injection followed by 2 months of CO2 injection before returning the well to water injection. The radius of investigation of the test was approximately 100 ft. see section pilot examples and page 152, fig 1, fig 12-The Celtic SSE pilot was designed as an isolated five-spot pattern with four corner injection wells, a central producing well, and three observation wells (see Fig. 12). Use of a full, isolated pattern minimized interference with existing operations and ensured that oil recovery during the pilot came from within the pilot pattern. Initial characterization of the pilot included logging, coring, extensive coreflood analysis, a new method to measure steady-state relative permeabilities for heavy oil systems, fluid characterization, geologic modeling, and reservoir simulation. Initial reservoir modeling studies were conducted before the pilot to confirm that the chosen well spacing and 3-year piloting period would be sufficient to gather necessary injection, production, and observation-well data to meet pilot objectives. Falloff tests were conducted periodically to characterize the pilot area further and to evaluate changes in well injectivity. The reservoir surveillance program included: close monitoring of injection and production rates, continuous measurement of bottomhole pressures and temperatures, producer sampling and analysis, tracers, and observation well logging. Fiber-optic sensors were placed in each of the observation wells to measure pressure response. Temperature logs were run in the observation wells on a routine basis to help detect the arrival of the slightly heated injected fluid. Carbon/oxygen and induction logs were run less frequently to detect changes in fluid saturation. Water-phase and injector-specific oil-phase tracers were added to the injected fluid to help track the movement of the injected fluids and to aid in the determination of in situ stability. Regular sampling and an in-line viscometer were used to control the quality of the injectant. These quality controls were helpful in identifying and correcting initial startup problems with injectant preparation. At the end of the 3-year pilot, a post-flood well was drilled to take core from the swept region of the flood.)
the reservoir being under hydraulic control or volumetric control; (see page 150 col 2-Therefore, an injectivity test was performed to determine injectivity before, during, and after CO2 injection and to estimate fi eld-scale injectivity to assist prediction of miscible process performance. The test consisted of 3 months of baseline water injection followed by 2 months of CO2 injection before returning the well to water injection. The radius of investigation of the test was approximately 100 ft. Bottomhole injection pressures and surface injection rates were monitored continuously during the test to determine injectivity index changes during injection of water and CO2. Pressure fall-off tests were conducted and injection flow profiles were measured during both the baseline water injection and CO2 injection to characterize the permeability distribution and changes in fluid mobilities in the near-well region.)
Teletzke does not teach upscaling a portion of the model corresponding to a location near an existing well in the oilfield; comparing the upscaled portion of the model to a well log to determine a well point upscalingquality; determining a model confidence index comprising confidence index values based at least in part on the model, the determined well point upscaling quality, and the measurements; displaying a model confidence map comprising a map of the oilfield overlaid with a respective indication of well confidence for a plurality of areas on the map of the oilfield, the indication of well confidence corresponding to the model confidence index values; determining a radius of investigation as an output based on: simulated production data for the oilfield including at least the existing well, the measured production data from the oilfield including at least the existing well, the reservoir properties, model simulation results.
In the related field of invention, AlQahtani teaches upscaling a portion of the model corresponding to a location near an existing well in the oilfield; (see para 108-126- The model from the resulting planning-to-production workflows as described, can be integrated into step 202 to provide for integration of the 3D geomechanical properties. Thus, in some aspects, step 202 includes integration work executed to combine MFSD 2D maps and 3D geomechanical completion quality parameters to provide a 3D dynamic simulation history matched model in this step of method 200. History matching was attained by introducing permeability multipliers around the wells that have been hydraulic fractured in the field. Geomechanical properties were mapped to the final dynamic grid in a similar process. For example, such properties are upscaled to the final grid using the upscaling process in the pre-processing application as shown in GUI 1800 in FIG. 18. This upscaling may map properties from the baseline grid to the target grid (the final one that will be used in dynamic modeling. The properties may then be exported using a pre-processing application. For each property, for instance, a GigaPOWERS™ (for example, a reservoir simulator by Saudi Arabian Oil Company) array was created. The arrays were then imported into the history matched 3D dynamic model. In this example, a total of 30 attributes were exported with 14 geomechanical and 16 mono frequency related attributes to arrive at the history matched 3D simulation model of step 202. In some aspects, therefore, step 202 includes importing several types of attributes (geomechanical and mono frequency) with different resolutions (both horizontal and vertical) into a single 3D geological model for use as a history matched 3D simulation model. This may be done by mapping these attributes into the final 3D grid by averaging the values by volume from the 3D representation of any model to the 3D representation to the destination grid.)
comparing the upscaled portion of the model to a well log to determine a well point upscaling quality; (see para 128-Method 200 may continue at step 206, which includes applying one or more geomechanical restraints to the 3D simulation model. For example, once the history matched 3D simulation model is generated in step 202, this model may be modified according to, for example, user-defined cut-offs for one or more of the integrated geomechanical data. For example, step 206 may include excluding one or more cells of the array created for each property according to the cut-off values shown in FIG. 21 for the geomechanical attributes 2105. Thus, cells in the array for each of the properties 2105 may be excluded if the value in the particular cell does not meet the cut-off relation as shown in Table 2100. See para 144-In some aspects, another feature that improves an overall performance of the workflow of step 210 may be filtering capability. Wells are placed in high-potential zones only. Depending on the nature of the reservoir and the objective of the study, cutoffs can be applied on oil saturation, permeability, or any reservoir static or dynamic property. Assessment wells are placed to penetrate only potential zones after filtering out unnecessary grid cells. As a result, this step reduces the size of the investigated solution space, and hence speeds up well calculations in the simulation runs. See para 158- In the example experimentation, to validate and evaluate the results obtained from applying geomechanical properties, mono frequency attributes, and gas reservoir simulation pressure related parameters, ten wells have been placed considering each of these three elements. The wells were placed according to the best spots obtained with TDPI values that factor in each of the geomechanical, mono frequency, and gas pressure related simulation parameters.)
determining a model confidence index comprising confidence index values based at least in part on the model, the determined well point upscaling quality, and the measurements;(see para 95-101- As described, the generated 3D simulation model may utilize geomechanical properties. Geomechanical properties may be obtained from planning-to-production workflows that use 1D/3D/4D geomechanical modeling approaches to cluster reservoir properties, stimulation, and drilling qualities. Such workflows may lead to landing lateral wellbores in a better reservoir quality, provide strong guidelines for wellbore drillability, and provide effective solutions for formation fracturability and injectivity related issues that is facing completion and stimulation operations. Constructing 3D models for reservoir, drilling, and stimulation quality using effective geostatistical technology. An example of heat maps that illustrate reservoir and completion quality parameters are heat maps 900 shown in FIG. 9. (5) Developing a technology to perform 3D clustering and providing the final confidence index of the geomechanical properties. An example of an index is shown in a heat map 1000 that shows 3D automated clustering FIG. 10. (6) Successfully applied 1D and 3D workflow modeling to drilling and stimulation operations. For example, heat maps 1100 shown in FIG. 11 illustrate a stimulation prediction for two wells (Well-I and Well-II). A heat map 1200 shown in FIG. 12 illustrates Fs a well placement, drilling, and stimulation prediction. See also para 138- Method 200 may continue at step 210, which includes applying a total dynamic productivity index (TDPI) process to the 3D simulation model to generate at least one 3D index that includes one or more hydrocarbon production sites. For example, the TDPI process may include a computational approach that computes unique index values for every simulation grid cell in a reservoir model based on a Productivity Index (PI) versus time relationship.)
displaying a model confidence map comprising a map of the oilfield overlaid with a respective indication of well confidence for a plurality of areas on the map of the oilfield, the indication of well confidence corresponding to the model confidence index values; (see para 138- Method 200 may continue at step 210, which includes applying a total dynamic productivity index (TDPI) process to the 3D simulation model to generate at least one 3D index that includes one or more hydrocarbon production sites. For example, the TDPI process may include a computational approach that computes unique index values for every simulation grid cell in a reservoir model based on a Productivity Index (PI) versus time relationship. see para 157-159- Method 200 may continue at step 212, which includes generating a graphical representation of the generated 3D index for presentation on a graphical user interface to a user. For example, after the completion of all simulation runs, FIGS. 35A-35C show 2D heat maps 3500 a-3500 c (respectively) that are output from the TDPI process of step 210 based on the geomechanical specified parameters (for example, from step 206). FIGS. 36A-36C show 2D maps 3600 a-3600 c (respectively) that are output from the TDPI process of step 210 based on specifying mono frequency attributes (for example, from step 204). After activating gas reservoir simulation parameters such as non-Darcy and pseudo pressure functions (for example, from step 208), FIG. 37 shows a 2D heat map 3700 that is the result which allows for energy plus gas rich cells and drillable wells to be revealed. In heat map 3700, lighter areas in the average map represent nonproductive areas whereas darker areas indicate areas with the highest values of TDPI. The wells were placed according to the best spots obtained with TDPI values that factor in each of the geomechanical, mono frequency, and gas pressure related simulation parameters. FIGS. 38-42 show TDPI computed values based on geomechanical properties, mono frequency attributes, a conventional spacing method, reservoir opportunity index, and gas pressure related simulation parameters, respectively. More specifically, FIG. 38 shows heat map 3800, which shows the ten gas wells placed based on TDPI values computed considering geomechanical properties. FIG. 39 shows heat map 3800, which shows the ten gas wells placed based on TDPI values computed considering mono frequency attributes. FIG. 40 shows heat map 4000, which shows the ten gas wells placed based on conventional well placement methods (in other words, not based on method 200). FIG. 41 shows heat map 4100, which shows the ten gas wells placed based on a Reservoir Opportunity Index (ROI), which includes gas saturation, porosity, and permeability. FIG. 42 shows heat map 4200, which shows the ten gas wells placed based on TDPI values computed considering gas pressure related simulation parameters. In these heat maps, some wells are close to each other, but at different layers to honor the spacing constraints.)
Therefore, 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 method of enhanced oil recovery as disclosed by Teletzke to include upscaling a portion of the model corresponding to a location near an existing well in the oilfield; comparing the upscaled portion of the model to a well log to determine a well point upscaling quality; determining a model confidence index comprising confidence index values based at least in part on the model, the determined well point upscaling quality, and the measurements; displaying a model confidence map comprising a map of the oilfield overlaid with a respective indication of well confidence for a plurality of areas on the map of the oilfield, the indication of well confidence corresponding to the model confidence index values as taught by AlQahtani in the system of Teletzke for determining one or more hydrocarbon sweet spots by generating the history matched 3D simulation model that includes the grid model that includes the plurality of grid cells without the excluded portion of the plurality of uniformly-sized grid cells. (See Abstract and para 002, AlQahtani)
The combination of Teletzke and AlQahtani does not teach determining a radius of investigation as an output based on: simulated production data for the oilfield including at least the existing well, the measured production data from the oilfield including at least the existing well, the reservoir properties, model simulation results.
In the related field of invention, Jalali teaches determining a radius of investigation as an output based on: simulated production data for the oilfield including at least the existing well, the measured production data from the oilfield including at least the existing well, the reservoir properties, model simulation results. (see para [0049-0050] and fig 1- As the well test starts, the measured data are gathered through a data acquisition and reporting tool (DART) 33. The radius of investigation r.+-..DELTA., 35, is calculated using the elapsed test time 37 (e.g., a shut-in time), and the formation diffusivity 31 is derived from the preliminary reservoir and downhole fluid data 2. The calculated radius of investigation 35 is then compared at 39 to the expected range of variation derived from the preliminary reservoir and downhole fluid data 2. This objective is not met unless the calculated radius of investigation 35 lies within the expected range of variation (e.g., the reservoir boundary is found at a distance within the expected range). If the test objective is met, the measurement and acquisition of data of this well test parameter (e.g., radius of investigation) can be terminated at 41. If the test objective is not met, measurement and acquisition of data are continued at 43 until the test objective is met. Thus, well test sequences and duration can be optimized. See para 87 and fig 13- Starting from the existing reservoir model, which is built integrating all suitably available data (e.g., geology, geophysics, drilling, well logs, etc.), the effect of production is simulated using reservoir simulators at step 1306 and the model parameters are accordingly adjusted to match the real-time reservoir behavior at steps 1302-1304 and 1308-1312. The benefit of real-time history matching of the reservoir behavior is reduction of the uncertainty range in reservoir parameters. As the test continues and the uncertainties are narrowed down, the test can be designed and the test program can be revised corresponding to the dynamic data acquired. The test is then continued until the achievement of the objectives.)
Therefore, 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 method of enhanced oil recovery as disclosed by Teletzke to include determining a radius of investigation as an output based on: simulated production data for the oilfield including at least the existing well, the measured production data from the oilfield including at least the existing well, the reservoir properties, model simulation results as taught by Jalali in the system of Teletzke and AlQahtani for well test design and interpretation that generates a test plan and an initial reservoir model and data from real/near-real-time, surface/downhole/manual data, aggregated data based on quality control/assurance, and optimization data based thereon and simulated downhole data; models/interprets the optimization data to meet test objectives for terminating/continuing the test plan; modifies the optimization; and/or generates reports from the modeling/interpretation when terminating the test plan. (See Abstract, Jalali)
Regarding claim 2 and 13
Teletzke further teaches wherein the determining the model confidence index comprises: conducting a well completion analysis; (see page 150 col 1-The following pilot design and operational best practices help to minimize uncertainties in test interpretation and facilitate history matching of pilot results: • Production facilities, well completions, tubulars, and artificial lift should be representative of the anticipated commercial-scale development.)
splitting well production as a portion of cumulative flow capacity; (see fig 11 and page 151- The observation well was placed within the expected WAG commingled zone on the basis of prepilot reser- voir simulation modeling. The location was chosen to confirm the expected size and shape of the WAG commingled zone (Fig. 11).• Production and injection profile logs for monitoring changes in fluid production rates and fluid entry horizons. These consisted of a suite of spinner, density, capacitance, and temperature tools.• Water and solvent tracer for defining the areal distribution of injected water and gas. A gas-phase tracer (sulfur hexafluoride) and liquid phase tracer (tritiated toluene) were used to monitor fluid movement. page 152 col 2- Initial reservoir modeling studies were conducted before the pilot to confirm that the chosen well spacing and 3-year piloting period would be sufficient to gather necessary injection, production, and observation-well data to meet pilot objectives.)
well logs (See also page 146 col 2- Methods for data acquisition from observation wells typically include logging, sampling, and pressure measurements.)
evaluating history match modification in the model. (See page 150-Successful pilot interpretation requires advance planning. It is essential that a detailed reservoir simulation model of the pilot area (with appropriate boundary conditions) be built in advance to optimize the pilot design and monitoring program, anticipate data needed for history matching the pilot, enable timely interpretation of pilot, and assess the need for selective use of additional observation wells and post-flood coring. The geology of the pilot area and a good understanding of the target oil distribution are critical inputs to the simulation model. Pilot wells should be cored and logged, if at all possible. Core, log, and pressure transient data should be integrated into a consistent reservoir description. In cases in which the waterflood is less mature, the baseline waterflood recovery can be estimated by using a reservoir simulation model to history match the pilot area and extrapolate the pre pilot waterflood production trend. This requires an adequate pre pilot waterflood period to reduce uncertainty in the history match and extrapolation.)
Teletzke does not teach evaluating an upscaled model versus well logs.
However, AlQahtani further teaches evaluating an upscaled model versus well logs. (see para 128-Method 200 may continue at step 206, which includes applying one or more geomechanical restraints to the 3D simulation model. For example, once the history matched 3D simulation model is generated in step 202, this model may be modified according to, for example, user-defined cut-offs for one or more of the integrated geomechanical data. For example, step 206 may include excluding one or more cells of the array created for each property according to the cut-off values shown in FIG. 21 for the geomechanical attributes 2105. Thus, cells in the array for each of the properties 2105 may be excluded if the value in the particular cell does not meet the cut-off relation as shown in Table 2100. See para 144-In some aspects, another feature that improves an overall performance of the workflow of step 210 may be filtering capability. Wells are placed in high-potential zones only. Depending on the nature of the reservoir and the objective of the study, cutoffs can be applied on oil saturation, permeability, or any reservoir static or dynamic property. Assessment wells are placed to penetrate only potential zones after filtering out unnecessary grid cells. As a result, this step reduces the size of the investigated solution space, and hence speeds up well calculations in the simulation runs. See para 158- In the example experimentation, to validate and evaluate the results obtained from applying geomechanical properties, mono frequency attributes, and gas reservoir simulation pressure related parameters, ten wells have been placed considering each of these three elements. The wells were placed according to the best spots obtained with TDPI values that factor in each of the geomechanical, mono frequency, and gas pressure related simulation parameters.)
Therefore, 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 method of enhanced oil recovery as disclosed by Teletzke to include evaluating an upscaled model versus well logs as taught by AlQahtani in the system of Teletzke for determining one or more hydrocarbon sweet spots by generating the history matched 3D simulation model that includes the grid model that includes the plurality of grid cells without the excluded portion of the plurality of uniformly-sized grid cells. (See Abstract and para 002, AlQahtani)
Regarding claim 3 and 14
Teletzke further teaches wherein the determining the model confidence index further comprises: denoising well production data received as input; (see page 143 and fig 1- EOR evaluation starts with screening-level data collection, candidate process selection, injectant source identification, and screening economics. If these are favorable, design and implementation of an EOR project then requires in-depth analysis of the most promising processes. In addition to standard laboratory tests, specialized fluid characterization and reservoir-conditions coreflood tests using in-situ fluids and a range of injectants are performed to customize a process for each reservoir. Reservoir characterization studies are conducted concurrently to identify the key geologic controls on field-scale sweep efficiency. The laboratory experiments and reservoir characterization studies are then used as input to geologic and dynamic reservoir-simulation modeling of the process at various scales to evaluate options, define a preferred process design, and provide input to screening-level development and facilities planning. See page 152 col 2- Initial reservoir modeling studies were conducted before the pilot to confirm that the chosen well spacing and 3-year piloting period would be sufficient to gather necessary injection, production, and observation-well data to meet pilot objectives. Falloff tests were conducted periodically to characterize the pilot area further and to evaluate changes in well injectivity.)
determining a well history match quality; (see page 144 col 2- A surveillance and monitoring plan should be developed that ensures that data are of high quality and that all needed data are obtained on a timely schedule. Data should be gathered on operational factors such as downtime and backpressure. See page 150-Successful pilot interpretation requires advance planning. It is essential that a detailed reservoir simulation model of the pilot area (with appropriate boundary conditions) be built in advance to optimize the pilot design and monitoring program, anticipate data needed for history matching the pilot, enable timely interpretation of pilot, and assess the need for selective use of additional observation wells and post-flood coring. The geology of the pilot area and a good understanding of the target oil distribution are critical inputs to the simulation model. Pilot wells should be cored and logged, if at all possible. Core, log, and pressure transient data should be integrated into a consistent reservoir description.)
Teletzke does not teach determining a historical behavior of one or more wells in the oilfield based at least in part on the well completion analysis, the denoised well production data, and the well history match quality.
In the related field of invention, AlQahtani teaches determining a historical behavior of one or more wells in the oilfield based at least in part on the well completion analysis, the denoised well production data, and the well history match quality. (See para 82- For example, a module or sub-process may include preparing 3D history matched simulation model that incorporates dynamic and static reservoir properties, wells performance data, scaled up 3D seismic Mono frequency maps, and 3D geomechanical reservoir properties. See para 108- The model from the resulting planning-to-production workflows as described, can be integrated into step 202 to provide for integration of the 3D geomechanical properties. Thus, in some aspects, step 202 includes integration work executed to combine MFSD 2D maps and 3D geomechanical completion quality parameters to provide a 3D dynamic simulation history matched model in this step of method 200.)
Therefore, 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 method of enhanced oil recovery as disclosed by Teletzke to include determining a historical behavior of one or more wells in the oilfield based at least in part on the well completion analysis, the denoised well production data, and the well history match quality as taught by AlQahtani in the system of Teletzke for determining one or more hydrocarbon sweet spots by generating the history matched 3D simulation model that includes the grid model that includes the plurality of grid cells without the excluded portion of the plurality of uniformly-sized grid cells. (See Abstract and para 002, AlQahtani)
Regarding claim 4 and 15
Teletzke does not teach generating a formation confidence map based at least in part on the historical behavior of the one or more wells, the well history match quality, the upscaled model versus well logs, and the history match modifications.
However, AlQahtani further teaches generating a formation confidence map based at least in part on the historical behavior of the one or more wells, the well history match quality, the upscaled model versus well logs, and the history match modifications. (See para 157-159- Method 200 may continue at step 212, which includes generating a graphical representation of the generated 3D index for presentation on a graphical user interface to a user. For example, after the completion of all simulation runs, FIGS. 35A-35C show 2D heat maps 3500 a-3500 c (respectively) that are output from the TDPI process of step 210 based on the geomechanical specified parameters (for example, from step 206). FIGS. 36A-36C show 2D maps 3600 a-3600 c (respectively) that are output from the TDPI process of step 210 based on specifying mono frequency attributes (for example, from step 204). After activating gas reservoir simulation parameters such as non-Darcy and pseudo pressure functions (for example, from step 208), FIG. 37 shows a 2D heat map 3700 that is the result which allows for energy plus gas rich cells and drillable wells to be revealed. In heat map 3700, lighter areas in the average map represent nonproductive areas whereas darker areas indicate areas with the highest values of TDPI. The wells were placed according to the best spots obtained with TDPI values that factor in each of the geomechanical, mono frequency, and gas pressure related simulation parameters. FIGS. 38-42 show TDPI computed values based on geomechanical properties, mono frequency attributes, a conventional spacing method, reservoir opportunity index, and gas pressure related simulation parameters, respectively. More specifically, FIG. 38 shows heat map 3800, which shows the ten gas wells placed based on TDPI values computed considering geomechanical properties. FIG. 39 shows heat map 3800, which shows the ten gas wells placed based on TDPI values computed considering mono frequency attributes. FIG. 40 shows heat map 4000, which shows the ten gas wells placed based on conventional well placement methods (in other words, not based on method 200). FIG. 41 shows heat map 4100, which shows the ten gas wells placed based on a Reservoir Opportunity Index (ROI), which includes gas saturation, porosity, and permeability. FIG. 42 shows heat map 4200, which shows the ten gas wells placed based on TDPI values computed considering gas pressure related simulation parameters. In these heat maps, some wells are close to each other, but at different layers to honor the spacing constraints.)
Therefore, 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 method of enhanced oil recovery as disclosed by Teletzke to include generating a formation confidence map based at least in part on the historical behavior of the one or more wells, the well history match quality, the upscaled model versus well logs, and the history match modifications as taught by AlQahtani in the system of Teletzke for determining one or more hydrocarbon sweet spots by generating the history matched 3D simulation model that includes the grid model that includes the plurality of grid cells without the excluded portion of the plurality of uniformly-sized grid cells. (See Abstract and para 002, AlQahtani)
Regarding claim 6 and 17
Teletzke further teaches wherein selecting one or more physical parameters for candidate pilot tests comprises: validating or more EOR methods for use based at least in part on the model and one or more EOR objectives; (see page 144 and fig 1- With these comments in mind, the following are the requirements for a successful pilot test: Pilot objectives should be clearly defined in advance. Pilot: The primary purpose is to validate the performance of a particular EOR process in the field. Example: Laboratory tests and simulation studies indicate that a CO₂ WAG project is likely to yield the highest recovery and best overall economic value among recovery processes considered. See page 150-The original pilot operating and monitoring plan should be continued until sufficient data are acquired to validate simulation models;
identifying one or more go/no go areas based on one or more surface constraints, one or more EOR objectives, and the model; (see page 143-144 and fig 1- Defining clear pilot objectives is the first step in designing and executing a successful pilot. Pilots are conducted to address key technical and business uncertainties and risks associated with applying an EOR technology in a specific field. The benefits of piloting, however, need to be weighed against the time and expense of piloting and against other available alternatives. Conducting a pilot is one of several options for reducing risk that might include additional data gathering/appraisal or phased development. If there are better alternatives to address uncertainty and risk, then a pilot may not be required. Clearly stating the key uncertainties and pilot objectives early in the evaluation process helps determine if a pilot is the best approach for addressing these risks and helps guide pilot design and execution. Reservoir simulation and geologic modeling, which incorporate the best available reservoir description and are history matched to pilot performance, are the most effective tools for designing and interpreting pilot performance and translating that performance to field-scale predictions. A properly designed pilot should ensure that the pilot area is sufficiently characterized and sufficient pilot data are collected to underpin reservoir modeling. Without proper pilot design, however, reliable data for history matching field performance will not be gathered, and, therefore, confident assessment of field-scale performance will be at risk. Ans see fig 9)
Teletzke does not teach teach classifying one or more formations in the subterranean volume for pilot selection.
In the related field of invention, AlQahtani teaches classifying one or more formations in the subterranean volume for pilot selection. (See para 157- Method 200 may continue at step 212, which includes generating a graphical representation of the generated 3D index for presentation on a graphical user interface to a user. For example, after the completion of all simulation runs, FIGS. 35A-35C show 2D heat maps 3500 a-3500 c (respectively) that are output from the TDPI process of step 210 based on the geomechanical specified parameters (for example, from step 206). FIGS. 36A-36C show 2D maps 3600 a-3600 c (respectively) that are output from the TDPI process of step 210 based on specifying mono frequency attributes (for example, from step 204). After activating gas reservoir simulation parameters such as non-Darcy and pseudo pressure functions (for example, from step 208), FIG. 37 shows a 2D heat map 3700 that is the result which allows for energy plus gas rich cells and drillable wells to be revealed. In heat map 3700, lighter areas in the average map represent nonproductive areas whereas darker areas indicate areas with the highest values of TDPI.)
Therefore, 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 method of enhanced oil recovery as disclosed by Teletzke to include classifying one or more formations in the subterranean volume for pilot selection as taught by AlQahtani in the system of Teletzke for determining one or more hydrocarbon sweet spots by generating the history matched 3D simulation model that includes the grid model that includes the plurality of grid cells without the excluded portion of the plurality of uniformly-sized grid cells. (See Abstract and para 002, AlQahtani)
Regarding claim 7 and 18
Teletzke further teaches wherein selecting the one or more physical parameters for candidate pilot tests further comprises: identifying pilot area candidates; (See page 150- Successful pilot interpretation requires advance planning. It is essential that a detailed reservoir simulation model of the pilot area (with appropriate boundary conditions) be built in advance to optimize the pilot design and monitoring program, anticipate data needed for history matching the pilot, enable timely interpretation of pilot, and assess the need for selective use of additional observation wells and post-flood coring. The geology of the pilot area and a good understanding of the target oil distribution are critical inputs to the simulation model. Pilot wells should be cored and logged, if at all possible. Core, log, and pressure transient data should be integrated into a consistent reservoir description.)
evaluating pilot sizes; (see page 151 col 2-The observation well was placed within the expected WAG commingled zone on the basis of prepilot reservoir simulation modeling. The location was chosen to confirm the expected size and shape of the WAG commingled zone (Fig. 11) see also fig 2-factors to consider when selecting pilot size and type)
evaluating shape and orientation of one or more wells for inclusion in the candidate pilot tests; (see page 143 col 1-Care should be taken when developing pilot objectives to ensure that the pilot is appropriately used as a component of an overall long-term field-development strategy. Pilots should not be a “trial- and-error” test of various field recovery processes; rather they are selectively applied to field test recovery processes that have been technically and economically evaluated beforehand. Additionally, the recovery process to be field tested should be optimized through both laboratory and reservoir-simulation studies in order to maximize oil recovery at the lowest possible cost. Before field testing, the most appropriate well spacing, pattern configuration, length and orientation of wells, injectant, and injection strategy [e.g, continuous gas injection, water-alternating gas (WAG), simultaneous water and gas (SWAG)] should be defined. See page 144 col 1-Pattern configuration, well design, the chosen injectant, and process operations should allow for confidence in scale up to the field-wide implementation of the process.)
selecting the one or more physical parameters of the pilot wells based at least in part on the identified pilot area candidates, the evaluated pilot sizes, and the evaluated shape and orientation. (see page 143 col 1 and fig 1-Pattern configuration, well design, the chosen injectant, and process operations should allow for confidence in scale up to the field-wide implementation of the process. Finally, the pilot location should be chosen to ensure as much as possible that it can be well characterized and is representative of the broader EOR target. See page 150 and fig 1-2- Successful pilot interpretation requires advance planning. It is essential that a detailed reservoir simulation model of the pilot area (with appropriate boundary conditions) be built in advance to optimize the pilot design and monitoring program, anticipate data needed for history matching the pilot, enable timely interpretation of pilot, and assess the need for selective use of additional observation wells and post-flood coring. The geology of the pilot area and a good understanding of the target oil distribution are critical inputs to the simulation model. Pilot wells should be cored and logged, if at all possible. Core, log, and pressure transient data should be integrated into a consistent reservoir description.)
Regarding claim 8 and 19
Teletzke further teaches wherein the designing the pilot tests comprises: designing a first case; (see page 150 and fig 1-Successful pilot interpretation requires advance planning. It is essential that a detailed reservoir simulation model of the pilot area (with appropriate boundary conditions) be built in advance to optimize the pilot design and monitoring program, anticipate data needed for history matching the pilot, enable timely interpretation of pilot, and assess the need for selective use of additional observation wells and post-flood coring. The geology of the pilot area and a good understanding of the target oil distribution are critical inputs to the simulation model. Pilot wells should be cored and logged, if at all possible. Core, log, and pressure transient data should be integrated into a consistent reservoir description
comparing the first case to a base case of no EOR activity; determining an uncertainty in the design of the first case; (see page 150 col 2-In cases in which the waterflood is less mature, the baseline waterflood recovery can be estimated by using a reservoir simulation model to history match the pilot area and extrapolate the prepilot waterflood production trend. This requires an adequate prepilot waterflood period to reduce uncertainty in the history match and extrapolation.
adjusting one or more design parameters of the first case; (see page 143 and fig 1-Conducting a pilot is one of several options for reducing risk that might include additional data gathering/appraisal or phased development. If there are better alternatives to address uncertainty and risk, then a pilot may not be required. Clearly stating the key uncertainties and pilot objectives early in the evaluation process helps determine if a pilot is the best approach for addressing these risks and helps guide pilot design and execution. see page 145-The primary purpose is to manage uncertainty by implementing a project in phases, with appropriate adjustments in scope and optimization of design between phases. Example: A new reservoir development with limited injectant supply planned as phased development, with the scope of the second phase (i.e., wells, facilities, recovery process) adjusted to incorporate learnings from the first phase.)and
interpreting results of the adjusting. (See page 150 and fig 1-Successful pilot interpretation requires advance planning. It is essential that a detailed reservoir simulation model of the pilot area (with appropriate boundary conditions) be built in advance to optimize the pilot design and monitoring program, anticipate data needed for history matching the pilot, enable timely interpretation of pilot, and assess the need for selective use of additional observation wells and post-flood coring. The geology of the pilot area and a good understanding of the target oil distribution are critical inputs to the simulation model. Pilot wells should be cored and logged, if at all possible. Core, log, and pressure transient data should be integrated into a consistent reservoir description. The following pilot design and operational best practices help to minimize uncertainties in test interpretation and facilitate history matching of pilot results)
Regarding claim 9 and 20
Teletzke further teaches wherein the interpreting the results comprises predicting a chance of pilot success. (See page 143-144 and fig 1-Additional laboratory, reservoir characterization, and simulation work may be undertaken after pilot testing to resolve uncertainties further, as indicated by the feedback loop in Fig. 1. With these comments in mind, the following are the requirements for a successful pilot test: • Pilot objectives should be clearly defined in advance. The key questions to be answered before doing a pilot are: (1) What results are needed to facilitate full-field investment and operating decisions and (2) when are results needed? • The pilot should be designed and operated to meet the objectives, aided by a predictive reservoir-simulation model. See page 150- Successful pilot interpretation requires advance planning. It is essential that a detailed reservoir simulation model of the pilot area (with appropriate boundary conditions) be built in advance to optimize the pilot design and monitoring program, anticipate data needed for history matching the pilot, enable timely interpretation of pilot, and assess the need for selective use of additional observation wells and post-flood coring. The geology of the pilot area and a good understanding of the target oil distribution are critical inputs to the simulation model. Pilot wells should be cored and logged, if at all possible. Core, log, and pressure transient data should be integrated into a consistent reservoir description.)
Regarding claim 10 and 21
Teletzke further teaches wherein the designing the first case comprises reusing a production rate of the base case with a corresponding injection rate. (See page 143-144 and fig 1-Additional laboratory, reservoir characterization, and simulation work may be undertaken after pilot testing to resolve uncertainties further, as indicated by the feedback loop in Fig. 1. see page 145 col 2- Pilots that incorporate production wells, other- wise known as “oil-in-the-tank” pilots, provide the most direct data on oil recovery, fluid transport through the reservoir, and pressure drop between injectors and producers. Important factors to con- sider when designing and interpreting producing pilots include: • Drift: Is the pattern acting as a truly confined flow system? • Balance: Are the relative rates of injectors and producers allocated to maximize areal sweep efficiency in the pilot area?)
Regarding claim 11 and 22
Teletzke further teaches wherein the pilot test implementation plan includes a monitoring plan that includes parameters to be measured, frequency for taking measurements, location for taking measurements, or a combination thereof. (see page 144 and fig 1-A surveillance and monitoring plan should be developed that ensures that data are of high quality and that all needed data are obtained on a timely schedule. Data should be gathered on operational factors such as downtime and backpressure. See page 150 col 2-The original pilot operating and monitoring plan should be continued until sufficient data are acquired to validate simulation models; of miscible process performance. The test consisted of 3 months of baseline water injection followed by 2 months of CO₂ injection before returning the well to water injection. The radius of investigation of the test was approximately 100 ft. Bottomhole injection pressures and surface injection rates were monitored continuously during the test to determine injectivity index changes during injection of water and CO₂. see page 152 col 2- The reservoir surveillance program included: close monitoring of injection and production rates, continuous measurement of bottomhole pressures and temperatures, producer sampling and analysis, tracers, and observation well logging.)
8. Claims 5 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Teletzke et al. ("Enhanced oil recovery pilot testing best practices." SPE Reservoir Evaluation & Engineering 13.01 (2010): 143-154.) in view of AlQahtani et al. (PUB NO: US20210350052A1), further in view of Jalali et al. (PUB NO: US 20110191029 A1) and further in view of Stone et al. (PUB NO: US20170336811A1)
Regarding claim 5 and 16
Teletzke further teaches wherein determining the historical behavior of the one or more wells comprises: conducting a well radius investigation within the determined radius of investigation; (see page 150 col 2-This example is a low permeability sandstone reservoir located in Wyoming, USA. Average reservoir permeability is 6.6 md, average formation thickness is 50 ft, and the reservoir is being waterflooded on a vertical well spacing of 80 acres. The concern was that injectivity would be low during miscible CO₂ WAG injection. Therefore, an injectivity test was performed to determine injectivity before, during, and after CO₂ injection and to estimate field-scale injectivity to assist prediction of miscible process performance. The test consisted of 3 months of baseline water injection followed by 2 months of CO₂ injection before returning the well to water injection. The radius of investigation of the test was approximately 100 ft.) and
determining a formation pattern performance. (see page 143 col 2-Before field testing, the most appropriate well spacing, pattern configuration, length and orientation of wells, injectant, and injection strategy [e.g, continuous gas injection, water-alternating gas (WAG), simultaneous water and gas (SWAG)] should be defined. See page 146 col 2- Fig. 5 summarizes some representative producing pilot configurations. Producing pilots provide not only an understanding of the injectivity of fluids into the formation, but more importantly, some quantitative data on the production potential of the recovery process, and subsequently a rough estimate of oil recovery. Single, inverted five-spot patterns are often used to provide such information. Observation wells are often included to evaluate the vertical sweep and displacement efficiency at the observers, vertical and areal sweep at a distance, fluid mobilities within the formation, and to estimate oil recovery.)
The combination of Teletzke, Jalali and AlQahtani does not teach determining the historical behavior of the one or more wells comprises: conducting a decline curve analysis using a machine learning algorithm; determining one or more well production behavior trends using a machine learning algorithm;
In the related field of invention, Stone teaches wherein determining the historical behavior of the one or more wells comprises: conducting a decline curve analysis using a machine learning algorithm; determining one or more well production behavior trends using a machine learning algorithm; (see para 49-A production decline curve or graph 208.4 is a dynamic data plot of the fluid flow rate over time. The production decline curve generally provides the production rate as a function of time. As the fluid flows through the wellbore, measurements are taken of fluid properties, such as flow rates, pressures, composition, etc. see para 93- Next, in block 474, optimization is performed of the selected subset of optimization parameters to maximize an objective function, e.g., an economic output metric such as improved NPV or some other objective function based upon various economic and/or oilfield development factors (e.g., production rates or amounts, capital expenditure costs, maximize oil recovery, promoting oil production while penalizing non-revenue-generating fluids, such as water, maximizing some conformance metric that is used within the reservoir simulator to maintain an even inflow profile along the well, or sections of the well, maintain conformance of a steam chamber if the flow control devices are used in SAGD developments and so on), along with the tunable parameters, as will be apparent to those of ordinary skill in the art. The optimization, as noted above, may incorporate running a plurality of reservoir simulations using different combinations of values for the selected subset of optimization parameters to attempt to determine an optimum set of values for the subset of optimization parameters, and may be controlled by an optimizer that operates on various optimization algorithms, including random (e.g., Monte-Carlo methods), pseudo-random (e.g., Sobol-type methods), derivative-free schemes such as downhill simplex, proxy methods, such as neural networks and radial basis functions, evolutionary algorithms such as genetic algorithm, and also derivative-based approaches such as the conjugate gradient method or others or other optimization approaches.)
Therefore, 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 method of enhanced oil recovery as disclosed by Teletzke to include determining the historical behavior of the one or more wells comprises: conducting a decline curve analysis using a machine learning algorithm; determining one or more well production behavior trends using a machine learning algorithm as taught by Stone in the system of Teletzke, Jalali and AlQahtani in order to output a design that is used to perform an oilfield operation such as installing and/or configuring one or more physical flow control devices corresponding to the design, maximizing oil and gas recovery from the region around the well, maximizing net present value. (See para 72, Stone)
9. Claim 24 is rejected under 35 U.S.C. 103 as being unpatentable over Teletzke et al. ("Enhanced oil recovery pilot testing best practices." SPE Reservoir Evaluation & Engineering 13.01 (2010): 143-154.) in view of AlQahtani et al. (PUB NO: US20210350052A1), further in view of Jalali et al. (PUB NO: US 20110191029 A1) and further in view of Valdez et al. ("Gas EOR Methodology and Integrated Basin Forecast of Offshore Sarawak Fields, Baram Delta Malaysia." SPE Asia Pacific Enhanced Oil Recovery Conference. SPE, 2011.)
Regarding claim 24
Teletzke further teaches further comprising determining formation pattern performance, comprising: wherein accessing well completions data and the measured production data; (see page 152 col 2-The reservoir surveillance program included: close monitoring of injection and production rates, continuous measurement of bottomhole pressures and temperatures, producer sampling and analysis, tracers, and observation well logging. Fiber-optic sensors were placed in each of the observation wells to measure pressure response.)
recognizing one or more current well patterns in the reservoir related to an areal completion layout, each of the one or more current well patterns having a unique identifier; (see page 152- The Celtic SSE pilot was designed as an isolated five-spot pattern with four corner injection wells, a central producing well, and three observation wells (see Fig. 12). Use of a full, isolated pattern minimized interference with existing operations and ensured that oil recovery during the pilot came from within the pilot pattern. Initial characterization of the pilot included logging, coring, extensive coreflood analysis, a new method to measure steady-state relative permeabilities for heavy oil systems, fluid characterization, geologic modeling, and reservoir simulation. Initial reservoir modeling studies were conducted before the pilot to confirm that the chosen well spacing and 3-year piloting period would be sufficient to gather necessary
injection, production, and observation-well data to meet pilot objectives. Falloff tests were conducted periodically to characterize the pilot area further and to evaluate changes in well injectivity. See also fig 10)
identifying good patterns and poor patterns in the reservoir, (see page 144-145-Pattern configuration, well design, the chosen injectant, and process operations should allow for confidence in scale up to the field-wide implementation of the process. Finally, the pilot location should be chosen to ensure as much as possible that it can be well characterized and is representative of the broader EOR target. With these definitions in mind, the types of pilots can be grouped into four configurations: 1. Nonproducing pilot. 2. Small-scale unconfined pilot. 3. Small-scale confined pilot. 4. Multipattern producing pilot.)
The combination of Teletzke, Jalali and AlQahtani does not teach calculating total injection fraction to hydrocarbon pore volume (HCPV) versus total production fraction to HCPV; wherein the reservoir is determined to be under hydraulic control responsive to there being no apparent decline in reservoir pressure because of at least one of water influx or water drive, and wherein the reservoir is determined to be under volumetric control responsive to there being no water influx to replace displaced oil and the oil being replaced by gas.
In the related field of invention, Valdez teaches calculating total injection fraction to hydrocarbon pore volume (HCPV) versus total production fraction to HCPV; (see page 5-6-Once the different models were history matched, different gas injection forecasts were conducted to predict incremental EOR (or not, in some cases, losses to the gas cap were too severe or immiscible injection resulted in poorer sweep). The performance difference between continuing aquifer support or waterflood versus gas injection was compared on a dimensionless basis whereby the cumulative fluids (injected and produced) were divided by the hydrocarbon pore volume (HCPV). This is a useful method for comparing incremental reserve potential for cases where reservoir processing rate (injection volume injected per annum) can vary. A schematic is shown in Figure 7.)
wherein the reservoir is determined to be under hydraulic control responsive to there being no apparent decline in reservoir pressure because of at least one of water influx or water drive, (see page 8 and table 1- Critical to the forward prediction is the assumed process rate of the reservoir. The initial forward prediction assumes the current production rate, which is equal to the aquifer influx for the reservoirs with stable reservoir pressure.)
and
wherein the reservoir is determined to be under volumetric control responsive to there being no water influx to replace displaced oil and the oil being replaced by gas. (see fig 7 and page 7- Drive mechanism, e.g. waterflood, aquifer, solution drive)
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Examiner note: The absence of a strong aquifer drive (as indicated by the "Aquifer Drive" curve being below the "Gas Injection" curve and showing a lower recovery factor compared to gas injection) supports the notion of a volumetric system without substantial water influx to replace produced oil. The "Gas Injection" curve clearly shows an increase in recovery factor due to the injection of gas, which is replacing the displaced oil. Solution-gas drive is a volumetric control.
Therefore, 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 method of enhanced oil recovery as disclosed by Teletzke to include calculating total injection fraction to hydrocarbon pore volume (HCPV) versus total production fraction to HCPV; wherein the reservoir is determined to be under hydraulic control responsive to there being no apparent decline in reservoir pressure because of at least one of water influx or water drive, and wherein the reservoir is determined to be under volumetric control responsive to there being no water influx to replace displaced oil and the oil being replaced by gas as taught by Valdez in the system of Teletzke, Jalali and AlQahtani in order to build robust full field forecasts capturing key physics for the Baram Delta gas injection study, thus enhancing of incremental oil recovery, gas breakthrough times, gas utilization and gas recycling. (See abstract, Valdez)
Conclusion
10. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Liu et al. US20140067347A1
Discussing a method to generate an estimation of an incremental recovery for an Enhanced Oil Recovery (EOR) process performed on a naturally-fractured reservoir by classifying the naturally-fractured reservoir based upon a set of reservoir properties associated with the naturally-fractured reservoir, and generating an estimation of the incremental recovery for at least one EOR process based on the classification of the naturally-fractured reservoir.
Gurpinar et al. WO2020047451A1
Discussing a methods and systems for comparative evaluation and optimization of enhanced oil recovery (EOR) and improved oil recovery (IOR) development schemes in case of heterogeneous formation which combines digital rock approach with density functional modeling of processes at pore scale.
11. All claims 1-24 are rejected.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
11. Any inquiry concerning this communication or earlier communications from the examiner should be directed to PURSOTTAM GIRI whose telephone number is (469)295-9101. The examiner can normally be reached 7:30-5:30 PM, Monday to Friday.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ryan Pitaro can be reached on 571-272-4071. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/PURSOTTAM GIRI/
Examiner, Art Unit 2188
/RYAN F PITARO/Supervisory Patent Examiner, Art Unit 2188
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