WO2013019546A2 - Procédés pour exécuter un déroulement de travail totalement automatique pour création et calibration de modèle de performances de puits - Google Patents

Procédés pour exécuter un déroulement de travail totalement automatique pour création et calibration de modèle de performances de puits Download PDF

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Publication number
WO2013019546A2
WO2013019546A2 PCT/US2012/048316 US2012048316W WO2013019546A2 WO 2013019546 A2 WO2013019546 A2 WO 2013019546A2 US 2012048316 W US2012048316 W US 2012048316W WO 2013019546 A2 WO2013019546 A2 WO 2013019546A2
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WIPO (PCT)
Prior art keywords
well
model
data
pressure
liquid rate
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PCT/US2012/048316
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English (en)
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WO2013019546A3 (fr
Inventor
Ahmad Tariq AL-SHAMMARI
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Saudi Arabian Oil Company
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Publication date
Priority claimed from US13/196,567 external-priority patent/US8688426B2/en
Priority claimed from US13/196,525 external-priority patent/US8731892B2/en
Application filed by Saudi Arabian Oil Company filed Critical Saudi Arabian Oil Company
Publication of WO2013019546A2 publication Critical patent/WO2013019546A2/fr
Publication of WO2013019546A3 publication Critical patent/WO2013019546A3/fr

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Classifications

    • E—FIXED CONSTRUCTIONS
    • E21—EARTH OR ROCK DRILLING; MINING
    • E21B—EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
    • E21B49/00—Testing the nature of borehole walls; Formation testing; Methods or apparatus for obtaining samples of soil or well fluids, specially adapted to earth drilling or wells
    • E21B49/008—Testing the nature of borehole walls; Formation testing; Methods or apparatus for obtaining samples of soil or well fluids, specially adapted to earth drilling or wells by injection test; by analysing pressure variations in an injection or production test, e.g. for estimating the skin factor

Definitions

  • This invention relates in general to oil and gas recovery, in particular to the optimization of production and injection rates, and more specifically to systems, program product, and methods that provide improved well performance modeling, building, and calibration.
  • An oil and gas reservoir is generally composed of porous and permeable rock which contains the oil and gas (and other hydrocarbons) in its pores.
  • the oil and gas stored in the reservoir is prevented from reaching the surface due to an impermeable rock.
  • the oil and gas within the reservoir can exert a substantial amount of vertical pressure on the impermeable rock.
  • Portions of an oil and gas well can be run through the non-permeable rock to access the oil and gas in the reservoir.
  • the typical oil and gas well can be thought of as a hole in the ground in which a steel pipe called a casing is placed.
  • the annular space between the casing and the formation rock is filled with cement, ideally resulting in a smooth steel lined hole in the ground passing through the reservoir.
  • Well models are heavily used for production optimization, designing well completions, and creating well performance tables for reservoir simulation studies.
  • Well production and injection modeling is a process practiced daily by many disciplines within the oil and gas industry. Petroleum engineers rely heavily on well modeling after analyzing and evaluating a wide range of data that influence well productivity to predict and optimize production and injection rates. Conventionally, many of the well modeling users do not follow a standard method in feeding the correct data into the simulator nor in the performance calibration step. The process is lengthy and subject to human input errors.
  • the data gathering and importing process involves dealing with several data components that need filtration, QC or validation before entering them into a well model, which is subject to human input error and inaccurate judgment.
  • the calibration step is also subject to wrong, inaccurate or inefficient practices. Further, such process can result in a relatively long software license utilization time because the engineers normally leave the software running for many hours, especially when the process is interrupted for any reason.
  • various embodiments of the present invention advantageously provide systems, program product, and methods of managing hydrocarbon production, for example, through the creation and calibration of production and injection well models.
  • Various embodiments of the present invention advantageously provide systems, program product, and methods of creating and calibrating the production and injection well models through comprehensive retrieval of all required data components and through the development and implementation of an optimal automated workflow.
  • the systems, program product, and methods can provide accurate, reliable and error-free well performance models that can be delivered in a timely manner.
  • the systems, program product, and methods can also serve to eliminate the manual process of browsing and searching for multiple data components scattered in several database repositories, and eliminate the tedious process of manually feeding them into well modeling software.
  • the systems, program product, and methods can apply scientific techniques to build the well model and history match it, and can provide an interactive interface for customized calibration, allowing users to override data used in model history matching and select the calibration parameters.
  • the systems, program product, and methods can capture the "best practices" and experience of the engineers, and provide a standardized scientific approach that can essentially guarantee creating accurate and calibrated well models within a fraction of the time required/allotted according to conventional processes.
  • an embodiment of a method for creating and calibrating production and injection well models for a reservoir includes, for example, the steps of providing a video screen or other input tool to a user to facilitate user selection of a well to be
  • -_>- modeled and performing a comprehensive retrieval of all required data components can include importing or otherwise gathering well data from at least one, but more typically, a plurality of entity databases.
  • the method can also include feeding the gathered data into well performance software to thereby develop a model of the well, performing an initial calibration of the well model, performing a total system calibration on the well model, and optionally, performing a recalibration to fine tune the well mode!.
  • the step of gathering well data can include gathering a plurality of rate test measurements from a well production or injection rate test recorded within, e.g., six months of each other.
  • This can include gathering a set of at least three wellhead pressure (WHP) measurements, gathering a set of at least three gas oil ratio (GOR) measurements, gathering a set of, e.g., at least three percent water cut (WC%) measurements, and gathering a set of at least three liquid rate measurements.
  • WTP wellhead pressure
  • GOR gas oil ratio
  • the steps can also or alternatively include determining an average wellhead pressure measurement value for the at least three wellhead pressure measurements, determining an average gas oil ratio measurement value for the at least three gas oil ratio measurements, determining an average percent water cut measurement value for the at least three percent water cut measurements, and/or determining an average liquid rate measurement value for the at least three liquid rate measurements.
  • the step of gathering well data can also or alternatively include analyzing a plurality of pressure surveys conducted periodically on a plurality of wells in a field associated with the well to be modeled, and determining an average static reservoir pressure responsive to the analysis of the plurality of pressure surveys.
  • average static reservoir pressure are determined from one or more pressure surveys having a pressure survey date as close as capable to an associated well production or injection rate test and having a surveyed well location as adjacent as capable to that of the well to be modeled.
  • the step of gathering well data can also or alternatively include providing a pressure-volume-temperature source selection criteria interface configured to receive a user selection of a source of pressure-volume- temperature test data used in generating the well model.
  • the pressure-volume-temperature source selection criteria can include a plurality of user selectable pressure-volume- temperature selection criteria fields including a pressure-volume-temperature latest report date and source location option (first option field), a pressure-volume-temperature source based on well location option (second option field), and an external pressure-volume- temperature data option (third option field).
  • the first option field can include an input field providing user selection of a number of pressure-volume-temperature sources desired to be accessed.
  • the method further includes receiving a user input identifying user selection of the first option field and a user input indicating the user desired number of pressure-volume- temperature sources, and retrieving report data for a number of latest reports matching the number of user desired sources.
  • the latest reports are the most recent reports retrieved for the user desired number of sources closest to the well to be modeled.
  • the steps can alternatively include modeling a plurality of wells each having a well area code, and retrieving latest report having a same well area code as the respective well for each of the plurality of wells responsive to user selection of the second option field.
  • the step of gathering well data can include the steps of retrieving or importing we I lbore description data including well profile, deviation survey, production tubing, and casing data, and the step of feeding the gathered data into well performance software can include feeding the wellbore description data into the well performance software.
  • the step of gathering well description data can further include the steps of retrieving a plurality of deviation survey point readings including a substantial number of measured depth versus true vertical depth readings, and filtering the plurality of deviation survey point readings to thereby select an optimal number of between approximately 6-8 survey readings based on deviation angle.
  • the step of filtering can include selecting an optimal number of between only approximately 2-3 survey readings.
  • the step of gathering well data can also or alternatively include importing inside diameter and length data for each of at least substantially all tubing segments inside the wellbore of the well to be modeled.
  • the imported tubing segments only include those having a minimum length of, e.g., at least approximately 10 feet to thereby reduce data importation requirements.
  • the step of gathering well data can also or alternatively include determining a minimum casing diameter and locating tubing packer depth to thereby identify at least substantially all casing sections being in contact with fluid, and importing data for only those casing sections determined to be in contact with fluid.
  • the imported casing sections data do not include casing section data for casing sections that are not in contact with fluid.
  • the step of gathering well data can also or alternatively include determining the tubing outside diameter and casing inside diameter throughout each wellbore section having fluid flowing in an annular space therebetween for the well being modeled.
  • the initial calibration of the well model can include performing a vertical flow correlation validation of a flow correlation used to model a pressure drop inside a well bore of the well to be modeled to thereby calibrate the flow coiTelation so that flowing bottom-hole pressure predicted using the flow correlation at the gauge depth matches a corresponding field measured value.
  • the total system calibration can include providing well performance data to a simulator, receiving a model-predicted liquid rate, and determining if a difference between the model-predicted liquid rate and corresponding field measured liquid rate is within a preselected value.
  • the step of providing well performance data to a simulator can include providing average rate test conditions to the simulator to calculate the model-predicted liquid rate.
  • the rate test conditions include wellhead pressure (WHP), gas oil ratio (GOR), and/or percent water cut (WC%) measurements. The average of each of the rate test conditions, rather than individual measurements, is provided to reduce an effect of measurement outliers when present.
  • the steps can include decreasing a well productivity index value when the model-predicted liquid rate is greater than the field measured liquid rate, or modifying flow correlation parameters to increase the model-predicted liquid rate when the model-predicted liquid rate is less than the field measured liquid rate.
  • the step of decreasing the well productivity index value can include incrementally reducing the productivity index and recalculating the model- predicted liquid rate until an absolute error therebetween is within a preselected value of, for example, approximately ⁇ 5% or as otherwise selected.
  • the steps can include determining a productivity index value that when applied to the well model, results in a model-predicted liquid rate that at least substantially matches the field measured liquid rate.
  • the steps can also includes providing a model recalibration interface configured to receive a user selection of a calibration parameter to be changed so that the model-predicted liquid rate better matches the field measured liquid rate.
  • a model recalibration interface configured to receive a user selection of a calibration parameter to be changed so that the model-predicted liquid rate better matches the field measured liquid rate.
  • this option allows a user to change one or more of the calibration reference measurements, such as, for example, wellhead pressure (WHP), gas oil ratio (GOR), mass flow (Ql), and static bottom hole pressure (SBHP), and repeat the calibration process.
  • HTP wellhead pressure
  • GOR gas oil ratio
  • Ql mass flow
  • SBHP static bottom hole pressure
  • the model recalibration interface includes a plurality of user selectable parameter fields to include a productivity index field and a correlation parameters field.
  • the steps can include calculating the well productivity index value that results in the model-predicted liquid rate at least substantially matching the field measured liquid rate in response to a user selecting the productivity index field.
  • the steps can include iteratively modifying a value of at least one of a plurality of calibration reference measurements until the model-predicted liquid rate at least substantially matches the field measured liquid rate in response to user selection of the correlation parameters field.
  • the step of iteratively modifying a value of at least one of a plurality of calibration reference measurements is performed while maintaining the well productivity index value during performance of the iterative modifications in response to user selection of both the productivity index field and the correlation parameters field.
  • the steps can also or alternatively include iteratively reperforming the total system calibration on the well model utilizing corresponding iteratively modified values of the at least one of the plurality of calibration reference measurements.
  • Various embodiments of the present invention also include systems for creating and calibrating production and injection well models for a reservoir.
  • An exemplary embodiment of the system can include a well performance modeling computer having a processor and memory in communication with the processor to store software therein, one or more database stored in memoiy accessible to the well performance modeling computer, and well performance modeling program product stored in the memoiy of the well performance modeling computer to create and calibrate production and injection well models for a reservoir.
  • the program product includes instructions that when executed by the well performance modeling computer, cause the computer to perform various operations including those described above with respect to the program product stored on the computer readable medium, and as will be described below.
  • Various embodiments of the present invention include well performance modeling program product for creating and calibrating production and injection well models for a reservoir.
  • the well performance modeling program product including a set of instructions, stored on a tangible computer readable medium, that when executed by a computer, cause the computer to perform various operations including gathering well data for a well or wells to be modeled, feeding the gathered data into well performance software/engine to thereby develop a model of the well, and performing a vertical flow correlation validation of a flow correlation used to model a pressure drop inside a well bore of the well to be modeled to thereby calibrate the flow correlation so that flowing bottom-hole pressure predicted using the flow correlation, for example, at the gauge depth matches a corresponding field measured value.
  • the operations can also include performing a total system calibration on the well model.
  • the total system calibration can include decreasing a well productivity index value when the well has a valid productivity index (PI) test associated therewith having a performed date later than any well work-over date for the well and when the model-predicted liquid rate is greater than the field measured liquid rate.
  • the total system calibration can include modifying flow correlation parameters to increase the model- predicted liquid rate when the well has a valid productivity index (PI) test having a performed date later than any well work-over date for the well but the model-predicted liquid rate is, instead, less than the field measured liquid rate.
  • the total system calibration can include determining a productivity index value that when applied to the well model results in a model-predicted liquid rate that at least substantially matches the field measured liquid rate.
  • the operations can also include providing a model recalibration interface configured to receive a user selection of a calibration parameter to be changed so that the model-predicted liquid rate better matches the field measured liquid rate.
  • the model recalibration interface can include a plurality of user selectable parameter fields, such as, for example, a productivity index field and a correlation parameters field.
  • the operation can also include calculating the well productivity index value that results in the model-predicted liquid rate at least substantially matching the field measured liquid rate in response to a user selecting the productivity index field.
  • the operations can also include iteratively modifying a value of at least one of a plurality of calibration reference measurements until the model- predicted liquid rate at least substantially matches the field measured liquid rate in response to user selection of the correlation parameters field.
  • the operations can further include iteratively modifying a value of at least one of a plurality of calibration reference measurements while maintaining the well productivity index value in response to user selection of both the productivity index field and the con-elation parameters field.
  • the operations can also or alternatively include iteratively reperforming the total system calibration on the well model utilizing corresponding iteratively modified values of the at least one of the plurality of calibration reference measurements.
  • the operations can also include, for example, comprehensive computer- implementable data gathering steps according to various embodiments of the methods described above, and as will be described below.
  • Various embodiments of the present invention advantageously establish a new era in the normal practices of well performance modeling.
  • Various embodiments of the present invention enable petroleum engineers to create and calibrate thousands of well models within a fraction of the time they would normally spend—completing a portion of a process that normally consumes an average of 4 hours of an engineer's time in less than as little as approximately 6-7 seconds per well model.
  • the required time to create, update, and/or calibrate 6500 well models is approximately 26,000 hours using conventional processes (based on an average of 4 hours per well)
  • the expected amount of time needed to perform the creation, update, and/or initial calibration steps utilizing one or more embodiments of the present invention is approximately 1 1 hours (based on an average of 6 seconds per well).
  • such improved performance is expected to yield an annual savings of 25,989 man-hours.
  • Various embodiments of the present invention gather state of the art techniques and expertise and combine them in an automated system that considerably improves the quality of well performance models.
  • Various embodiments of the present invention eliminate the manual process of browsing and searching for multiple data components scattered in several, e.g., Oracle, database repositories and manually feed them into well modeling software.
  • the present invention collect state-of-the-art human expertise in the field and incorporate it in a system that can generate the highest of quality well models, apply scientific techniques to build the well model and history match it, and provide an interactive interface for customized calibration, allowing users to ovemde data used in model history matching and select the calibration parameters.
  • Various embodiments of the present invention provide systems, software (program product) and methods designed to perform the following high-level operations/steps: providing user selection of a well to be modeled, gathering well data from a plurality of databases, feeding the gathered data into well performance software, performing a vertical flow correlation validation, comparing predicted well performance with actual measured well performance, and performing a calibration on parameters utilized to develop the model based on the comparison.
  • the present invention provide a system including program product and related methods which provide an automated workflow for creating production and injection well models by comprehensive retrieval of all data components stored in the corporate database. After the well models are created, the system runs a scientific calibration process on each well model to match their individual performances with field measurements. Eventually, the production conditions are displayed in an interactive portal through which the well performance can be evaluated using different conditions.
  • Various embodiments of the present invention provide systems, program product, and methods which incorporate a workflow including the steps of importing fluid properties data and fine-tuning the pressure volume time (PVT) Black-Oil correlation, importing productivity index (PI) well testing and average reservoir pressure data, importing wellbore description data (deviation survey and tubing/casing details), importing field measured production or injection conditions and flow rate data, feeding the input data into well performance modeling software, running a vertical flow correlation validation, running well performance modeling and capturing the predicted rate by the software, comparing the predicted rate and the measured rate and performing calibration on PI or flow con-elation parameters, and providing tools for a user to perform a recalibration and sensitivity analysis.
  • PVT pressure volume time
  • PI productivity index
  • PI productivity index
  • FIG. 1 is a schematic diagram of a general system architecture of a system for creating and calibrating production and injection well models according to an embodiment of the present invention
  • FIG. 2 is a schematic flow diagram illustrating steps for creating and calibrating production and injection well models according to an embodiment of the present invention
  • FIG. 3 is a schematic diagram of a graphical user interface for selecting the well bore wells to be modeled according to an embodiment of the present invention
  • FIG. 4 is a schematic data flow diagram illustrating data flow according to an embodiment of the present invention.
  • FIG. 5 is a schematic diagram illustrating comprehensive data gathering according to an embodiment of the present invention.
  • FIG. 6 is a schematic diagram of a graphical user interface for selecting a pressure-volume-temperature source criteria according to an embodiment of the present invention
  • FIG. 7 is a schematic diagram of a graphical user interface illustrating examples of data utilized according to an embodiment of the present invention.
  • FIG. 8 is a schematic diagram of a graphical user interface illustrating calibration parameter selection according to an embodiment of the present invention.
  • Various embodiments of the present invention can serve to eliminate the manual process of browsing and searching for multiple data components scattered in multiple database repositories and manually feeding them into well modeling software. Such embodiments can also serve to apply scientific techniques to build the well model and history match it, and to provide an interactive interface for customized calibration allowing users to override data used in model history matching and select the calibration parameters.
  • FIG. 1 provides an example of an embodiment of a system 30 for managing hydrocarbon production, for example, through the creation and calibration of production and injection well models.
  • the system 30 can include a well performance modeling computer 31 having a processor 33, memory 35 coupled to the processor 33 to store software and database records therein, and a user interface 37 which can include a graphical display 39 for displaying graphical images, and a user input device 41 as known to those skilled in the art, to provide a user access to manipulate the software and database records.
  • the computer 31 can be in the form of a personal computer or in the form of a server or server farm serving multiple user interfaces 37 and/or providing multiple disparate functions or other configurations known to those skilled in the art.
  • the user interface 37 can be either directly connected to the computer 31 or indirectly connected through a network as known to those skilled in the art, such as, for example, network 38.
  • the system 30 can also include a database 43 stored in the memory 35 (internal or externally assessable) of the well performance modeling computer 31.
  • the database 43 can include data indicating: general well data such as, for example, well location (X-Y coordinates), well reservoir, lifting mechanism (ESP or naturally flowing), and well configuration (single branch or multilateral), etc.
  • the database 43 can also include pressure volume time (PVT) test report and fluid properties data; and wellbore description data including deviation survey data, tubing details data, and casing details data.
  • PVT pressure volume time
  • the database 43 can also include average static reservoir pressure data for a selected number of wells; well productivity index (PI) testing reports data including the well formation PI, wellhead flowing conditions, and bottom hole flowing conditions; well work-over data; and well production and index rate test report data, along with others as recognized by those of ordinary skill in the art.
  • database 43 can comprise a plurality of databases stored on a plurality of geographically/positionally separate data storage devices (not shown).
  • the system 30 can also include well performance modeling program product 51 stored in memory 35 of the well performance modeling computer 31.
  • the well performance modeling program product 51 can be in the form of microcode, programs, routines, and symbolic languages that provide a specific set for sets of ordered operations that control the functioning of the hardware and direct its operation, as known and understood by those skilled in the art.
  • the well performance modeling program product 51 according to an embodiment of the present invention, need not reside in its entirety in volatile memory, but can be selectively loaded, as necerney, according to various methodologies as known and understood by those skilled in the art.
  • FIG. 2 provides a flow diagram illustrating steps for performing well performance model creation and calibration.
  • the high-level steps can include providing user selection of a well to be modeled (block 61), gathering/importing and processing well data from a plurality of databases (block 63), feeding the gathered data into well perfonnance software (block 65), performing a vertical flow correlation validation (block 67), comparing predicted well performance with actual measured well perfonnance (block 69), performing a calibration on parameters utilized to develop the model based on the comparison (block 71), and performing an assisted recalibration on the model (block 73).
  • FIG. 3 illustrates a well selection screen (graphical interface) 100, according to an embodiment of the system 30, that locates all active wells in the corporate database 43 for user selection.
  • the screen 100 includes a "well selection steps" information table 101 providing a well selection order to a user, a reservoir field name drop-down menu 103, and a reservoir field section code selection menu 105. After selecting the reservoir field code, several filtration options in a "well filter options" section 107 are provided to assist in locating the looked-for wells.
  • the workflow includes, for example, the following steps:
  • the process can include gathering data including "General Well data,” “Pressure-Volume-Temperature (PVT) Source Selection and Fluid Properties,” “Wellbore Description,” and “Average Static Reservoir Pressure,” among others, across multiple corporate databases.
  • a robot is provided to gather data as the data is updated, typically according to user settings.
  • the data is gathered on demand.
  • some portions of the data are gathered automatically, and other portions are gathered on demand in response to user selected settings.
  • the general well data includes, for example, the following items: well location (X-Y coordinates), current reservoir, electrical submersible pump (ESP) assisted or naturally flowing, single branch or multilateral, among others.
  • ESP data can include depth, number of stages, power, model, etc.
  • PVT reports are generated after collecting fluid samples from a selected number of wells in the field. According to an exemplary configuration, it is preferable to select a recent PVT sampling report from the same well or an adjacent one. However, due to the scarcity in PVT test reports, as shown in FIG. 6, according to the exemplary configuration, the user is provided a "PVT source selection criteria" interface/screen 120 to make a spatial- temporal reasoning by either selecting the latest report in the field regardless of the well location or the closest PVT report to the well under consideration regardless of the date.
  • the PVT source selection criteria screen 120 is designed to offer three PVT source selection options.
  • the first option shown at 121 provides the user the ability to consider both the PVT report date and the source location. If the user selects this option and sets the number of latest PVT source to, e.g., " 1 " as shown, the most recent PVT test report will be used for all generated wells regardless of the location. When there are abundance of the recent PVT sources, a larger weight can be put to the location by selecting the number of more recent reports (based on the test/report date) to be selected and allowing the system/program product to match wells with PVT sources based on location.
  • the 2 nd option shown at 123 provides the user a module interface which allows the user to consider feeding PVT data from PVT reports taken from the latest test/report date with the same well area code.
  • the 3 rd option shown at 125 provides the user a module interface which allows the user to feed the PVT data from an external source.
  • the application starts importing the PVT data according to the user-establish criteria.
  • the PVT data imported from, e.g., an entity Oracle database are: bubble point pressure (Pb), oil viscosity at at Pb, oil formation volume at Pb, solution GOR at Pb, gas specific gravity, oil API gravity, H2S, C02, N2, Rs, Water SG, reservoir temperature (T res ), and FVF ( 3 ⁇ 4p b .
  • Pb bubble point pressure
  • oil viscosity at at Pb oil formation volume at Pb
  • solution GOR at Pb
  • gas specific gravity oil API gravity
  • H2S oil API gravity
  • C02 H2S
  • N2S oil API gravity
  • Rs Water SG
  • T res reservoir temperature
  • FVF FVF
  • wellbore description data is gathered and processed.
  • the wellbore description includes well profile along with deviation survey, production tubing, and casing details.
  • Deviation survey is generally available in the database as a large number of measured depth (MD) vs. true vertical depth (TVD) readings. It has been determined by the inventor that in non-vertical wells, preferably between 6- 10, and more preferably 8 deviation survey readings based on the deviation angle are sufficient to describe the well profile. As such, according to the exemplary figuration, the system/program product automatically filters all the deviation survey points and selects the desired 8 MD/TVD readings. Note, it has been similarly found that if the well is instead vertical, then two readings have been found to be sufficient. Providing the automated filtering can beneficially reduce computer/software processing time.
  • Point 2 The next step is to define the first kick-off point. This point is defined once the deviation angle reaches 5° and is increasing.
  • Point 8 The process goes to the maximum depth survey and reaches the maximum deviation angle.
  • Points 3-7 are then selected based on the deviation angle increments, e.g.,
  • tubing details According to the exemplary configuration, the system/program product imports the inside diameters, lengths, and depths for all tubing segments inside the wellbore of the selected wells.
  • Tubing details tables available in the database contain the description of the main production tubing along with a large number of short tubing segments such as, for example, tubing accessories, fittings and connections. It has been found to be inefficient by the inventor to import all these devices, especially when they have negligible impact on flow performance.
  • the system/program product imports tubing segments with minimum length of approximately 10 ft.
  • tubing segments having smaller tubing lengths can have a negligible impact on pressure drop. Accordingly, their application would consume resources with a disproportionate or negligible benefit. Using a significantly higher minimum tubing length, however, can result in additional error.
  • the system/program product imports only the casing sections of the selected well bore wells that are in contact with fluid. The selection process requires identifying such casing sections. In the exemplary configuration, the identification of which of the casing sections are in contact with fluid is made by perfonning the steps of determining the minimum casing diameter and locating the tubing packer depth—which provides adequate criteria. If the well is flowing in the annular space or in both annulus and tubing, according to the exemplary configuration, the system/program product locates the tubing outside diameter and the casing inside diameter throughout the whole wellbore section to perform the identification. According to an exemplary configuration, the imported data can include casing inside diameters, lengths, and depths.
  • Static reservoir pressure is one of the basic data that has been found to have a major impact on well performance and to provide enhanced performance. As such, in order to provide enhanced performance, according to the exemplary configuration, its value must be entered/recorded accurately. Pressure surveys are usually conducted periodically on a selected number of wells in the field. The pressure survey date has also been found by the inventors to be as important factor in providing enhanced performance. Specifically, according to the exemplary configuration, the pressure survey date should be as close as possible to the date of the well rate test and the surveyed well location should be as adjacent as possible to the well under consideration. Accordingly, the system/program product identifies and stores the dates accordingly.
  • a "static reservoir pressure criteria" interface/screen similar to that of the "PVT source selection criteria” screen 120 allows the user to indicate the number of adjacent wells to thereby select the latest report based on well location.
  • PI testing reports data is also gathered.
  • PI testing reports usually include the well formation productivity index in addition to wellhead and bottom-hole flowing conditions.
  • the PI value if determined to be valid, is used in modeling the inflow performance relationship and the flowing data is used in the vertical flow correlation validation.
  • the PI test date is also important and should be compared with the well work-over date to determine its validity. Additionally, if a work-over job is performed on the well after the well PI test date, then the PI value from the respective test will not be considered for validating the vertical flow correlation as the well conditions may have changed. Further according to the exemplary configuration, if no valid PI value is available, a default value can be automatically prescribed.
  • the process also includes importing the latest rate test conditions for the well under consideration.
  • Field measurements sometimes can include errors or non-realistic measurements.
  • the production should increase if the wellhead pressure decreases.
  • both wellhead pressure and rate have increased compared to the previous test, then there must be an error.
  • Such measures are generally flagged with a "good" indicator in the database. Accordingly, substantial errors can be introduced if only the last reading of pressure and rate are feed it to the modeling software. This applies also to GOR and WC% values.
  • the program collects a preselected number, e.g., 3, of the latest rate test measurements, provided they are within a preselected time period, e.g. 6 months, and the calibration process is run against the averaged conditions.
  • the recent production data imported for calibration can include liquid rate, well head pressure, water cut and gas oil ratio (GOR).
  • Well testing flowing data historical data for VLP validation
  • GOR water cut and gas oil ratio
  • the process reduces the effect of the "suspicious" readings and adds robustness to the model. It has been found that two readings are generally not enough to remove the effect of the erroneous measurement. Accordingly, according to the exemplary configuration, the process uses the latest three points. Notably, three points have been found to be optimal as using more than three points (four or more) can result in the incorporation of older conditions that may disturb the model consistency. By limiting the data used to three points according to the exemplary configuration, it has been determined that it is unlikely that such latest conditions will reflect old readings to the extent that the averaged conditions will be significantly affected. Nevertheless, the exemplary configuration includes the, e.g., six, months time limitation condition.
  • the well performance modeling software/program product is driven and communicated automatically using an external program, which also allows for data input and extraction.
  • An example of such external program is named "Prosper,” which is a vendor application developed by Petroleum Experts vvww.petex.com.
  • Other engines capable of performing the same functions including, for example, an engine incorporated into program product 51 according to an alternative embodiment of the present invention, can be utilized.
  • the pressure drop inside the well bore can be calculated using multi-phase flow correlations.
  • flowing well test conditions are used in order to validate and fine-tune the performance of the selected flow correlation.
  • the rows displayed in FIG. 7 will be empty and will be filled one by one, for example, to indicate that the input data has been loaded into the model building software.
  • the process utilizes default values (determined through industry analysis) to provide con-elation selection criteria.
  • the vertical flow correlation validation step includes providing a user a graphical interface (not shown) to allow a user selection of a correlation from a dropdown list or other access means.
  • the con-elation performance can be modified by applying gravity and friction correction factors so that the flowing bottom-hole pressure predicted by the conelation at the gauge depth matches the measured value. Note, the corrected values would not be expected to match if the well had a work-over job after the well test date.
  • the flow correlation will be used without validation. Later on, the correlation parameters can be changed to match the production rate based on a criterion described later. After the flow correlation is fine-tuned, the vertical flow modeling can be considered reliable and the well model is ready for the total system calibration, described below.
  • Performing a well model calibration step is essential before relying on the model in any study and design analysis.
  • the calibration process is canied out by sending, for example, the latest average rate test conditions (WHP, GOR and wc%) to the simulator to calculate the liquid rate.
  • WTP latest average rate test conditions
  • GOR latest average rate test conditions
  • wc% the latest average rate test conditions
  • the well model will be considered valid if the difference between the predicted and measured liquid rate is within approximately 5%. Otherwise, the calibration process will start as follows:
  • Case 1 The well has a "Valid" PI test not followed by a work-over.
  • Case 1 .a The model-predicted liquid rate is greater than the measured liquid rate.
  • Case l .b The model-predicted liquid rate is less than the measured liquid rate.
  • the system/program product will not increase the PI. Instead, the vertical flow performance modeling is considered questionable. As such, the system/program product will modify the flow correlation parameters to increase the predicted rate until the absolute error is within plus or minus 5%. Further according to the exemplary configuration, if the new correlation coefficients reaches 0.5, however, then the calibration process stops and the well will be highlighted in, e.g., red, which indicates a problem in the input data.
  • Case 2 The well does not have a Valid PI test or the latest test was followed by a work-over.
  • the system/program product will focus on finding the PI value to match between the model and the field measurements.
  • This option can be considered a post calibration process.
  • the model recalibration allows the user to change one or more of the calibration reference measurements (WHP, GOR, WC, Ql, SBHP or PI) and repeat the calibration process.
  • the user is provided with the ability to select the calibration parameter that can be changed by the system/program product to meet the measured rate. For example, as illustrated in FIG. 8, the user can select "PI" at 131 which will calculate the PI required for matching.
  • the user can alternatively select "correlation parameters" at 133, which will honor the PI value and modify the correlation parameter until matching is reached. Additionally, the user can further alternatively select "both" at 135, which will consider/execute the same procedure as described with respect to the initial model calibration process,
  • Examples of the computer readable media include, but are not limited to: nonvolatile, hard- coded type media such as read only memories (ROMs), CD-ROMs, and DVD-ROMs, or erasable, electrically programmable read only memories (EEPROMs), recordable type media such as floppy disks, hard disk drives, CD-R/RWs, DVD-RAMs, DVD-R RWs, DVD+R/RWs, HD-DVDs, memory sticks, mini disks, laser disks, Blu-ray disks, flash drives, and other newer types of memories, and certain types of transmission type media such as, for example, digital and analog communication links capable of storing the set of instructions.
  • nonvolatile, hard- coded type media such as read only memories (ROMs), CD-ROMs, and DVD-ROMs, or erasable, electrically programmable read only memories (EEPROMs)
  • recordable type media such as floppy disks, hard disk drives, CD-R/RWs, DVD-RAMs, DVD
  • Such media can contain, for example, both operating instructions and the operations instructions described with respect to the program product 5 1, and the computer executable portions of the method steps according to the various embodiments of a method of creating and calibrating production and injection well models to include implementing a workflow to create and calibrate the production and injection well models for a reservoir, described above.
  • Various embodiments of the present invention provide several unique advantages. For example, conventionally well modeling users generally do not follow a standard method in feeding the correct data into a well simulator, nor follow standard procedures in a performance calibration step, making the process lengthy and subject to human input errors.
  • Various embodiments of the present invention have been shown to employ a unique standardized methodology which allows the system to complete a data gathering process across multiple databases, which normally consumes an average of 4 hours of an engineer's time, in less than approximately seven seconds.
  • an embodiment of the present invention was used to create a total of 284 well models with an average time required to complete the task being approximately 33 minutes. The well models were then used in building surface network models of four gas oil separation plants (GOSPs) and providing accurate total system flow rate.
  • GOSPs gas oil separation plants
  • Various embodiments of the present invention advantageously collect conventional and unconventional human expertise in the hydrocarbon production field and apply it in systems that generates the highest of quality well models.
  • Various embodiments of the present invention can automatically build and calibrate well models from a database and provide methodologies that solve issues related to the manual process of well performance model building and calibration.
  • Various embodiments of the present invention can advantageously eliminate the manual process of browsing and searching for multiple data components scattered in several, e.g., Oracle, database repositories and the process of manually feeding them into well modeling software.
  • Various embodiments of the present invention advantageously apply scientific techniques to build the well model and history match it, and provide an interactive interface for customized calibration allowing users to override data used in model history matching and to select the calibration parameters.
  • Various embodiments of the present invention advantageously provide new systems that streamline and automate an integrated workflow for well model building and calibration, which can capture experiences and "best practices" in the area of well performance modeling, and apply them in an automated system.
  • the workflow can, for example, import fluid properties and fine-tune PVT Black-Oil correlation, import PI well testing data and average reservoir pressure, import wellbore description (deviation survey and tubing/casing details), import field measured production or injection conditions and flow rate, feed input data into well performance modeling module or standalone software, run a vertical flow correlation validation, run well performance modeling and capture the predicted rate by the module/software, compare predicted rate and measured rate and perfonn calibration on PI or flow correlation parameters, and provide a user interface to allow a user to perform re-calibration and sensitivity analysis.
  • Various embodiments of the present invention provide enhanced quality based upon criteria including a determination that the subject well has: a recent PVT test report stored in a reference database, a recent valid well PI test stored in the database, a pressure survey having the same date as that of the surface rate test, three recent rate test conditions that are accurate and validated, a produced gas oil ratio (GOR) that is close to the solution gas oil ratio (Rs) measured in the laboratory, and if the well is equipped with an ESP, a pump model for the ESP is available in the well modeling software.
  • GOR produced gas oil ratio
  • Rs solution gas oil ratio

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Abstract

L'invention porte sur des procédés pour créer et calibrer des modèles de puits de production et d'injection pour un réservoir. Un exemple d'un procédé pour créer et calibrer des modèles de puits peut mettre en œuvre la réalisation d'une récupération ou d'une collecte globale de composants de données requis, la délivrance des données rassemblées à un logiciel de performances de puits afin de développer ainsi un modèle de puits, la réalisation d'une calibration initiale du modèle du puits, la réalisation d'une calibration de système totale sur le modèle de puits et la réalisation d'une recalibration afin d'effectuer un réglage fin du modèle de puits.
PCT/US2012/048316 2011-08-02 2012-07-26 Procédés pour exécuter un déroulement de travail totalement automatique pour création et calibration de modèle de performances de puits WO2013019546A2 (fr)

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US13/196,567 US8688426B2 (en) 2011-08-02 2011-08-02 Methods for performing a fully automated workflow for well performance model creation and calibration
US13/196,525 US8731892B2 (en) 2011-08-02 2011-08-02 Systems and program product for performing a fully automated workflow for well performance model creation and calibration

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WO2013019546A3 (fr) 2013-08-15
EP2739823A2 (fr) 2014-06-11
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