WO2021247378A1 - Quantification de la productivité de puits et de conditions de flux de puits de forage proches dans des réservoirs de gaz - Google Patents

Quantification de la productivité de puits et de conditions de flux de puits de forage proches dans des réservoirs de gaz Download PDF

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Publication number
WO2021247378A1
WO2021247378A1 PCT/US2021/034594 US2021034594W WO2021247378A1 WO 2021247378 A1 WO2021247378 A1 WO 2021247378A1 US 2021034594 W US2021034594 W US 2021034594W WO 2021247378 A1 WO2021247378 A1 WO 2021247378A1
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Prior art keywords
well
reservoir
determining
gas condensate
productivity index
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PCT/US2021/034594
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English (en)
Inventor
Ghazi Dhafer ALQAHTANI
Shahid Manzoor
Faisal Alnaseef
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Saudi Arabian Oil Company
Aramco Services Company
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Priority claimed from US16/890,225 external-priority patent/US11414975B2/en
Application filed by Saudi Arabian Oil Company, Aramco Services Company filed Critical Saudi Arabian Oil Company
Publication of WO2021247378A1 publication Critical patent/WO2021247378A1/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
    • E21B43/00—Methods or apparatus for obtaining oil, gas, water, soluble or meltable materials or a slurry of minerals from wells
    • E21B43/30—Specific pattern of wells, e.g. optimising the spacing of wells
    • 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
    • E21B2200/00—Special features related to earth drilling for obtaining oil, gas or water
    • E21B2200/20—Computer models or simulations, e.g. for reservoirs under production, drill bits

Definitions

  • This invention relates to optimizing the placement of wells in gas reservoirs in a gas field. More specifically, the present invention relates to quantifying Well productivity and near wellbore flow conditions in gas reservoirs.
  • Reservoir simulation is an important processing methodology for evaluating development of a gas fields. This is, of course, providing that the simulation results accurately account for the rock and fluid physics taking place.
  • reservoir dynamic conditions are based on an interdependence between several conditions and forces within the reservoir. For instance, high velocity gas flow in low quality rocks results from a higher pressure drop than indicated for normal fluid flow through the permeable formation rock. The normal fluid flow relationship is expressed by what is known as the Darcy flow equation. However, high gas velocity flow due to pressure drop causes a condition known as non-Darcy flow.
  • PI Productivity Index
  • the present invention provides a new and improved wells placement analysis system for a reservoir having gas condensate production from layers of a subsurface hydrocarbon reservoir.
  • the system includes one or more processors, and one or more input/output units adapted to be in communication with the one or more processors.
  • the system also includes one or more databases adapted to store and associate a plurality of reservoir metrics with a plurality of coordinates and reservoir metrics to thereby define one or more reservoir databases.
  • the one or more reservoir databases are in communication with the one or more processors.
  • the system further includes one or more non-transitory computer-readable mediums positioned in communication with the one or more processors and having one or more computer programs stored thereon including a set of instructions.
  • the stored instructions when executed by the one or more processors cause the one or more processors to perform a sequence of processing operations.
  • the processing operations include generating a plurality of assessment gas condensate wells having specified production constraints at specified locations through at least one layer of the reservoir layers, the specified locations having X n xY n xZ n coordinates.
  • the operations also include performing a numerical simulation of productivity in tire formation layers of a selected well set.
  • the well set takes the form of one or more of the plurality of assessment gas condensate wells.
  • the processing operations also include determining pseudo-pressure conditions in grid cells adjacent the selected gas condensate well set at the X n x Y n x Z n coordinates of the at least one of the layers.
  • Non-Darcy flow conditions in the grid cells adjacent the selected gas condensate well set at the X n x Y n x Z n coordinates of the at least one of the layers are then determined, based on the determined pseudo-pressure conditions in the grid cells.
  • the processing continues by determining a productivity index for the selected gas condensate well based on the determined pseudo-pressure conditions and non-Darcy flow conditions at the X n x Y n x Z n coordinates of the at least one of the layers.
  • the processing operations continue with determining a total dynamic productivity index for the at least one layer for the selected gas condensate well set over a time interval of interest, with subsequent determining of placement of the assessment gas condensate well set for the reservoir based on the determined the total dynamic productivity index for the assessment gas condensate well set.
  • the processing operations next are generating a production analysis report for the reservoir, tire production analysis report including a proposed well placement for the assessment gas condensate well set.
  • the present invention also provides a new and improved computer-implemented method of wells placement analysis for a reservoir having gas condensate production from layers of a subsurface hydrocarbon reservoir,
  • the method of wells placement analysis optimizes well placement of a reservoir, and is performed in a sequence of processing steps which include generating a plurality of assessment gas condensate wells having specified production constraints at specified locations through at least one layer of the reservoir layers, with the specified locations having X n x Y n x Z n coordinates.
  • the processing steps also include performing a numerical simulation of productivity in the formation layers of a selected well set comprising one or mote of the plurality of assessment gas condensate wells, and determining pseudo-pressure conditions in grid cells adjacent the selected gas condensate well set at the X n x Y n x Z n coordinates of the at least one of the layers.
  • Non-Darcy flow conditions in the grid cells adjacent the selected gas condensate well set at the X n x Y n x Z n coordinates of the at least one of the layers are then determined, based on the determined pseudo-pressure conditions in the grid cells.
  • the processing steps further include determining a productivity index for the selected gas condensate well based on the determined pseudo-pressure conditions and non-
  • the processing steps determine placement of the assessment gas condensate well set for the reservoir based on the determined the total dynamic productivity index for the assessment gas condensate well set, and generate a production analysis report for the reservoir, the production analysis report including an proposed well placement for the assessment gas condensate well set.
  • Figure 1 is a diagram illustrating pressure conditions in a wellbore of a gas condensate well adjacent subsurface formation rock.
  • Figure 2 is a workflow of processing by a wells placement analysis system according to tire present invention.
  • Figure 3 is a more detailed workflow of a portion of the processing of Figure 2.
  • Figure 4 is an example display of simulation results obtained according to the present invention and illustrating the effect of condensate banking on well pressure in a gas condensate well.
  • Figure 5 is an example display of simulation results obtained according to the present invention and illustrating the effect of condensate banking on condensate saturation near a gas condensate well.
  • Figure 6 is an example display of simulation results obtained according to the present invention and illustrating the effect of condensate banking on gas rate in a gas condensate well.
  • Figures 7, 8, 9 and 10 are example displays from reservoir simulation processing according to the present invention illustrating the effect of near wellbore formation gas condensate well flow on pressure conditions and well productivity.
  • Figures 11 and 12 are example displays of gas rate flow from a gas condensate well illustrating the effect of non-Darcy flow conditions on from the well.
  • Figure 13 is a schematic block diagram illustrating an example computer for the wells placement analysis system according to the present invention.
  • Figure 1 illustrates schematically a wellbore 20 in a gas condensate well 22 producing both hydrocarbon and gas condensate (oil) liquid, as indicated by an arrow
  • the well 22 is typically formed extending through several layers of a subsurface reservoir to receive gas condensate production. Pressure conditions in the layer in the formation layer adjacent the wellbore 20 are indicated by a pressure curve 26 indicating formation pressure as a function of radial distance r into the formation from a longitudinal axis of the wellbore 22.
  • the liquid or gas condensate phase of the formation production fluid accumulates in a near wellbore region 30, forming an annular region in the formation adjacent the wellbore.
  • the near wellbore region 30 is surrounded by what is known as a buildup region 32, which is in turn within an outer region 34 known as a dry gas region.
  • a buildup region 32 which is in turn within an outer region 34 known as a dry gas region.
  • fluid pressures in the regions 30, 32 and 34 vary according to radial distance r from the wellbore 32, according both pressure and formation conditions.
  • the velocity of gas is at least an order of magnitude greater than that of oil, due to the low density and viscosity of the gas.
  • Darcy component should be included in determining the flow of gases through a porous formation medium in gas condensate wells.
  • the non-Darcy component is significant only in the restricted region of high pressure drawdown and high velocity in near well region 30, close to the wellbore.
  • the gas flow condition is further adversely affected when liquid drop out in the near wellbore region occupies formation rock pore spaces and reduces effective flow area. Consequently, it is important to consider the additional pressure drops caused by non-Darcy flows in the near well bore region for gas- condensate reservoirs.
  • Reservoir boundary conditions come in the form of well controls that are used to match historical data and/or define operational limits for reservoir forecasting.
  • discrete grid cell size used in reservoir simulation is much larger than that of the wellbore and would introduce singularities if the well was discretized similar to that of grid cell size.
  • a measure known as a well productivity index (WI ⁇ ) has been utilized to couple well production measures to the reservoir simulation results, by relating wellbore pressure and flow to the grid cell parameters.
  • WI ⁇ well productivity index
  • the well inflow performance relationship for a compositional case for a given phase p through layer/completion / connected to grid block i is given by: where is the upstream hydrocarbon component molar mobility, Pi is grid cell pressure, is the wellbore pressure incorporating gravity and friction effects for the layer /.
  • the upstream hydrocarbon component molar mobility is the defined as: where represents relative permeability, P p corresponds to molar density, and ⁇ p is viscosity with subscript p relating to hydrocarbon (oil/gas) phase.
  • x c and y c represent oil and gas component-mole-fraction, respectively.
  • the reservoir parameters saturation/moles and pressure are the primary independent variables for which reservoir simulation is carried out.
  • the other reservoir simulation variables are considered secondary/dependent variables.
  • Mobility is a non-linear function of saturation/moles and pressure, and the mobility term is a non-linear function of gas condensate saturation and of pressure.
  • molar mobility A is determined in computerized matrix equation format in the computer 400 using upstream grid block quantities (i.e., grid block pressure and saturations).
  • upstream grid block quantities i.e., grid block pressure and saturations.
  • a pseudo-pressure method is commonly used.
  • the pseudo- pressure method computes the relation between molar or volumetric flow rate flora a well grid block and the local wellbore pressure.
  • the pseudo-pressure methodology replaces the traditional single point upstream well mobility (Equation (2)) with an integrated form that more accurately predicts condensate banking in the high drawdown regimes around the wellbore 22 independent of the geometry of well 20. Pseudo-pressure calculations are based on dividing tire area around the well into three flow regions or regimes 30, 32 and 34 as shown in Figure
  • the gas flow is also reduced due to presence of immobile oil of the gas condensate.
  • the reservoir pressure is such that no oil phase is present, since
  • Equation (3) is geometric factor defined as s, with r w and r 0 being wellbore and pressure equivalent block radius and s corresponds to user defined skin.
  • the non-Darcy D-factor in wells is usually determined by analysis of multi-rate pressure results, but such data is not available in many cases.
  • correlations obtained from literature can be employed to obtain connections D-factors. Such correlation are calculated based on the permeability and porosity of the connected grid block, together with the fluid properties of the wellbore gas.
  • Calculation of non-Darcy flow blockage factor does not require additional vapor liquid equilibration (VLE) processes, instead can be co-computed with pseudo-pressure integral.
  • VLE vapor liquid equilibration
  • FIG. 2 is a high level schematic diagram of a workflow W of the methodology of the wells placement analysis system according to the present invention.
  • the wells placement system operating to the workflow W quantifies well productivity and near wellbore flow conditions in gas condensate wells of subsurface hydrocarbon reservoirs.
  • processing begins with generating a plurality of potential or simulated assessment gas condensate wells having specified production constraints at specified locations through at least one layer of the reservoir layers, the specified locations having X n x Y n x Z n coordinates.
  • the assessment wells are generated during step 100 to the user specified point constraints to evaluate the reservoir productivity and energy of gas condensate at those particular locations.
  • step 110 numerical simulation of productivity per layer for assessment wells is then performed in a computer or data processing system 400 shown in Figure 13.
  • the numerical simulation during step 110 determines productivity in the formation layers of a selected well set comprising one or more of the plurality of assessment gas condensate wells, such as wellbore 20.
  • a reservoir simulator of Saudi Arabian Oil In tire disclosed embodiment, a reservoir simulator of Saudi Arabian Oil
  • GigaPOWERSTM is a suitable reservoir simulator used to perform the assessment case examples which are described. It should be understood that other reservoir simulation methodologies may also be used for such numerical simulation.
  • Step 120 is then performed in the computer 400 to determine pseudo-pressure and non-Darcy flow conditions in the manner described.
  • the determination of pseudo-pressure conditions and non-Darcy flow conditions during step 120 is performed for grid cells adjacent the selected gas condensate well set at the X n x Y n x Z n coordinates of at least one of the layers of the reservoir.
  • FIG. 3 illustrates in more detail the processing during step 120 which begins with step 122 which is performed to resolve the pseudo-pressure integral to model condensate banking according to the present invention.
  • step 122 the pseudo-pressure integral is determined in the manner described with numerical modelling according to the constant- volume-depletion (CVD) method as has been described.
  • CVD constant- volume-depletion
  • Step 122 is performed by to determine or calculate a pseudo-pressure flow blockage factor FBi.
  • the pseudo-pressure flow blockage factor FBi is expressed as: where ⁇ ⁇ ⁇ is the hydrocarbon phase molar mobility, is grid cell pressure, and is the wellbore pressure condition incorporating gravity and friction effects for the layer /.
  • the pseudo-pressure flow blockage factor FBi resulting from step 122 is then used as a set value as indicated at step 124 for subsequent processing during step determination 126.
  • the present invention provides a sequential method, whereby first during step 122, the pseudo-pressure integral is resolved during step 124 for determination of FB 1 , according to Equation (4). This is followed by non-Darcy flow modelling during step 126, fallowed by determination of the non-Darcy flow blockage factor during step 128.
  • Equation (3) the determined pseudo-pressure blocking factor FB 1 is embedded inside Equation (3) as has been set forth.
  • Equation (3) the term represents the free gas flow rate, and is given by: [0049] It is also to be noted that the present invention also resolves circular dependency between pseudo-pressure and non-Darcy flow modelling options. This is accomplished because the determined pseudo-pressure blockage factor FBi is included in the expression for determining the non-Darcy flow blockage factor FB2. The total blockage factor FB applied to gaseous phase is then given by:
  • the pseudo-pressure flow blockage factor FBi determined during step 124 is then provided to resolve non-linearity as indicated schematically in Figure 3 for non-Darcy flow computations during step 126 in the manner already described.
  • Step 128 is then performed to determine the completion blocking factor FB2 according to Equation (3) in the manner described.
  • Figures 4, 5 and 6 are example displays of simulation results obtained according to the present invention and illustrating the effect of condensate banking on simulations results of the type. Figures 4, 5 and 6 are obtained for well placement according to the present invention.
  • Figure 4 is a display of well pressure showing at 180 results of well pressure simulation with prior art techniques.
  • Well pressure simulation based on determination of pseudo-pressure integral, with mobility non-linearities resolved according to the present invention is shown at
  • Figure 5 is a display of condensate saturation as a fractional value of produced gas, showing at 184 results of condensate saturation simulation in a near wellbore region with prior art techniques. Condensate saturation simulation based on determination of pseudo-pressure integral, with mobility non-linearities resolved according to tire present invention is shown at
  • Figure 6 is a display of gas rate or well productivity from a well, showing at 188 results of well gas rate simulation in comparison with prior art techniques. Gas rate saturation simulation based on determination of the pseudo-pressure flow blockage factor, with mobility non-linearities resolved according to the present invention is shown at 190.
  • a productivity index is determined for the selected gas condensate well or wells of the set based on the determined pseudo-pressure conditions and non-Darcy flow conditions with the non- linearity resolved.
  • the productivity index is determined for the selected assessment gas condensate well or wells of the set during step 130 at the X n x Y n x Z n coordinates of the layers indicated.
  • matrix equations and wellbore equations are solved by reservoir simulation.
  • Step 140 is then performed to determine total dynamic productivity for determining a total dynamic productivity index for the selected assessment gas condensate well or wells of the set over a time interval of interest.
  • step 150 represents the amount of hydrocarbon volume that may be drained from a well drilled at a certain spot when the reservoir pressure is decreased by one unit with no time restriction.
  • a total dynamic productivity index determination is performed for each of the selected sets of assessment wells in the reservoir at their respective X n x Yi x Z n coordinates in the reservoir.
  • the total dynamic productivity index values determined during step 150 represents the amount of hydrocarbon volume that may be drained from each of the selected sets of assessment wells drilled at their respective coordinates when the reservoir pressure is decreased by one unit with no time restriction.
  • Step 160 is then performed to determine placement of wells responsive to total dynamic productivity index determined for the assessment wells designated during step 100.
  • a wells placement optimization algorithm is performed during step using the total dynamic productivity index to determine optimum placement of wells for a reservoir.
  • the wells placement optimization algorithm during step 160 preferably uses the formulas for determining optimum wells placements as described in previously referenced co-pending U. S. Patent
  • step 160 The wells placement analysis results of step 160 are then the basis for generating a production analysis report during step 170 for determining placement of the assessment gas condensate well set for the reservoir.
  • the production analysis report for the reservoir generated during step 170 provides a proposed well placement for the assessment gas condensate wells of the reservoir.
  • Figure 7 is a plot of simulated values of pressures in absolute pounds per square inch
  • Figure 8 is a plot of simulated values of pressures of well bottom hole pressure
  • Figure 9 is a plot of simulated values of pressures of well bottom hole pressure
  • (wpig) 226 at selected times from a computerized reservoir simulation when the influence of non-Darcy flow is taken into account during the simulation. As indicated at 226, gas productivity is substantially constant from the initial simulation time throughout the simulation.
  • Figure 9 illustrates how non-Darcy flow in the gas condensate being simulated adversely chokes gas condensate well productivity when a large value of D factor is used to compute non-Darcy flow component.
  • Figure 10 is a plot of is a plot of simulated values of pressures of well bottom hole pressure (wbhp) as indicated at 232; static well pressure (wswp) 234; and well gas productivity index (wpig) 236 at selected times from a computerized reservoir simulation when the influence of both a pseudo-pressure function and non-Darcy flow are each separately taken into account during the simulation.
  • Figure 10 thus shows the effect of pseudo-pressure function and non-Darcy flow contribution administered independently on both the productivity' and deliverability of the gas well.
  • Figure 11 is a plot of gas rates at selected times from a computerized reservoir simulation for selected values of non-Darcy flow D-factor is taken into account. Indicated at
  • gas rate 302 is a gas rate based on a pseudo-pressure function with conventional Darcy flow computations.
  • the other gas rates display are for selected values of a non-Darcy flow D-factor taken into account. These gas rates were obtained by considering the effects of non-Darcy' flow computations and pseudo-pressure function as separate and unrelated factors.
  • the gas rate plotted at 306 is for a case involving non-Darcy flow computation with a D factor value of 0.005 and with no pseudo-pressure computation.
  • the gas rate plotted at 308 indicates results for the case involving non-Darcy flow computation with a D factor value of
  • the gas rate plotted at 310 indicates results for a case involving non-Darcy flow computation with a D factor value of 0.1, with no pseudo-pressure function.
  • the gas rate plotted at 312 indicates results for a case involving non-Darcy flow computation with D factor value of 1.0 with no pseudo-pressure function.
  • the gas rate plotted at 314 indicates results for a case involving non-Darcy flow computation with D factor value of 32, and with no pseudo-pressure function.
  • the gas rates plotted in Figure 11 demonstrate that as the non-Darcy flow D factor value increases the impact on productivity becomes more severe.
  • Figure 12 is another plot of gas rates at selected times from a computerized reservoir simulation for selected values of non-Darcy flow D-factor is taken into account, both with and without a pseudo-pressure function.
  • the gas rates for selected values of non-Darcy flow D-factor with a pseudo-pressure function taken into account are obtained with the non-linearity of condensate saturation and pressure being taken into account according to the present invention.
  • Gas rate results 354 are for a case involving non-Darcy flow computation with a D factor value of 0.005, and with pseudo-pressure computation.
  • Gas rate results 356 are for a case involving non-Darcy flow computation with D factor value of 0.05 with pseudo-pressure function taken into account.
  • Gas rate results 358 were obtained for a case involving non-Darcy flow computation with a D factor value of 0.1, and with pseudo-pressure function considered.
  • Gas rate results 360 are for a case involving non-
  • Figure 12 is an example of the impact of the non-Darcy flow and the pseudo-pressure function, while taking into account the non- linearity of condensate saturation and pressure according to the present invention, further restricts simulated or estimated measures of gas condensate well productivity.
  • the methods of determining an optimum placement of wells in a reservoir can be driven by the computer 400 that can include, according to various exemplary embodiments of the present invention, at least a memory 405, a processor 410, and an input /output (I/O) device 415.
  • the processor 410 can include, for example, one or more microprocessors, microcontrollers, and other analog or digital circuit components configured to perform the functions described herein.
  • the processor is the ‘"brain” of the respective computer, and as such, can execute one or more computer program product or products.
  • the processor in the reservoir analysis system can execute a computer program product or instructions 420 stored in memory 405 of the computer
  • Such a product can include a set of instructions to display with an electronic interface 430 of computer 400 or at a remote computer that allows a user to input reservoir metrics of a selected reservoir.
  • Such a product can also include instructions 420 to calculate productivity indexes during step 130 for a selected assessment well or well set responsive to the reservoir metrics: to calculate during steps 140 and 150 the total dynamic productivity indexes; to determine during step 160 an optimal wells placement for a reservoir.
  • the instructions 420 also cause the computer 400 to generate reservoir analysis reports during step 170.
  • the processor 400 can be any suitable commercially available terminal processor, or plurality of terminal processors, adapted for use in or with the computer 400.
  • a processor may be any suitable processor capable of executing/performing instructions.
  • a processor may include a central processing unit (CPU) that carries out program instructions to perform the basic arithmetical, logical, and input/output operations of the computer 400.
  • CPU central processing unit
  • the processor 400 also includes code (e.g., processor firmware, a protocol stack, a database management system, an operating system, or a combination thereof) that creates an execution environment for program instructions.
  • code e.g., processor firmware, a protocol stack, a database management system, an operating system, or a combination thereof
  • the processor 400 may include general and/or special purpose microprocessors
  • the processor can be, for example, the Intel® Xeon® multicore terminal processors, Intel® micro-architecture Nehalem, and AMD OpteronTM multicore terminal processors, Intel® Core® multicore processors, Intel® Core iSeries® multicore processors, and other processors with single or multiple cores as is known and understood by those skilled in the art.
  • the processor 400 can be operated by operating system software installed on memory, such as Windows Vista, Windows NT, Windows XP, UNIX or UNIX-like family of systems, including BSD and GNU/Linux, and Mac OS X.
  • the processor can also be, for example the TI OMAP 3430, Arm Cortex A8, Samsung S5PC 100, or Apple A4.
  • the operating system for the processor can further be, for example, the Symbian OS, Apple iOS, Blackberry
  • Computer system 400 may be a processor system including one processor (e.g., processor 410a), or a multi-processor system including any number of suitable processors (e.g., 410a - 410n). Multiple processors of this type may be employed to provide for parallel and/or sequential execution of one or more portions of the techniques described herein. Processes and logic flows described herein may be performed by one or more programmable processors executing one or more computer programs to perform functions by operating on input data and generating corresponding output.
  • Computer system 400 may include a computer system employing a plurality of computer systems (e.g., distributed computer systems) to implement various processing functions.
  • the computer 400 as illustrated in the example described in Figure 13 can further include a non-transitory memory or more than one non-transitory memories (referred to as memory 405 herein).
  • Memory 405 can be configured, for example, to store data, including computer program product or products, which include instructions 420 for execution on tire processor 410.
  • Memory 405 can include, for example, both non-volatile memory, e.g., hard disks, flash memory, optical disks, and the like, and volatile memory, e.g., SRAM, DRAM, and SDRAM as required to support embodiments of the instant invention.
  • the memory 405 is depicted on, e.g., a motherboard, of the computer 400, the memory 405 can also be a separate component or device, e.g., flash memory. connected to the computer 400 through an input/output unit or a transceiver.
  • the program product or products, along with one or more databases, data libraries, data tables, data fields, or other data records can be stored either in memory 405 or in separate memory (also non-transitory), for example, associated with a storage medium such as a database (not pictured) locally accessible to the computer 400, positioned in communication with the computer 400 through the I/O device 415.
  • non-transitory memory can include a server-side markup language processor (e.g., a PHP processor) to interpret server-side markup language and generate dynamic web content (e.g., a web page document) to serve to client devices over a communications network.
  • server-side markup language processor e.g., a PHP processor
  • Exemplary embodiments of the present invention include a reservoir analysis interface.
  • a reservoir analysis interface is, for example, a graphical user interface facilitating the acquisition of data from the user to determine the impact of reservoir metrics on wells placement optimization.
  • This electronic interface can also display the reservoir analysis report.
  • the graphical user interface device can include, for example, a CRT monitor, a LCD monitor, a LED monitor, a plasma monitor, an OLED screen, a television, a DLP monitor, a video projection, a three-dimensional projection, a holograph, a touch screen, or any other type of user interface which allows a user to interact with one of the plurality of remote computers using images as is known and understood by those skilled in the art.
  • the reservoir analysis report computer can be a server and can include, for example, any type of mainframe, physical appliance, or personal computing device such as rack server, mainframe, desktop computer, or laptop computer, dedicated in whole or in part to running one or more services to serve the needs or requests of client programs which may or may not be running on the same computer.
  • the reservoir analysis computer can be, for example, a dedicated software/hardware system (i.e., a software service running on a dedicated computer) such as an application server, web server, database server, file server, home server, or standalone server.
  • the reservoir analysis computer can interface with a separate web server, application server, or network server to access the functionality of the reservoir analysis computer, for example, through a communications network or other network options, and such a configuration may be preferred for certain large-scale implementations.
  • Non-transitory- memory can further include drivers, modules, libraries, or engines allowing the reservoir analysis computer to function as a dedicated software/hardware system (i.e., a software service running on a dedicated computer) such as an application server, web server, database server, file server, home server, standalone server.
  • a dedicated software/hardware system i.e., a software service running on a dedicated computer
  • both memory- and the processor can also include, for example, components (e.g., drivers, libraries, and supporting hardware connections) that allow the computers to be connected to a display peripheral device and an input peripheral device that allow a user direct access to the processor and the memory.
  • the display peripheral device can be, for example, a computer monitor, which may also be known in the art as a display or a visual display unit.
  • the input peripheral device 415 can be, for example, a computer keyboard, computer mouse, a touch screen (such as a touch screen device comprising display peripheral device), a pen device, character recognition device, voice recognition device, or a similar input device that will be known to those having skill in the art that allows the user at the remote computer to send discrete or continuous signals to the processor.
  • a status or other output associated with input peripheral device can be displayed at the display peripheral device, such as, for example, mouse pointer or a keyboard prompt.
  • the computer readable program product can be in the form of microcode, programs, routines, and symbolic languages that provide a specific set or 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.
  • Examples of 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 electrically erasable programmable read-only memory
  • recordable type media such as floppy disks, hard disk drives, CD-R/RWs, DVD-
  • RAMs DVD-R/RWs, DVD+R/RWs, flash drives, memory sticks, HD-DVDs, mini disks, laser disks, Blu-ray disks, and other newer types of memories, and transmission type media such as digital and analog communication links.
  • Drilling inefficient hydrocarbon wells can cost the company millions of dollars every year. For gas reservoirs, the cost is more to drill and place gas wells when compared to oil wells. Therefore, it is imperative that a robust system is required and necessary to make sure that every drilled well is justified with robust sweet spots system that provide energetic view and understanding about every layer in the reservoir. This will help reservoir engineers to prioritize locations for new wells and sidetracks. It will help them as well to contrast and rank different fields performance and establish compare and tank list for fields to be developed economically. It will aid as well in knowing which areas in the reservoir that require attention whether related to poor performance or lack of data.
  • placing wells in the right spots in gas reservoirs should include detailed phenomenon such as pseudo-pressure function and non-Darcy flow computations to make sure that wells placement are justified which will ensure effective production and increase the success rate of drilled or sidetracked wells in gas reservoirs.
  • the present invention is integrated into a practical application.
  • the present invention solves a technological problem.
  • the previously utilized gas productivity measures for gas condensate wells used for optimized placement of wells in reservoirs indicators have not realized that condensate saturation and pressure in gas condensate wells are not static or fixed values.
  • more accurate gas productivity measures are obtained, based on dynamic condensate saturation and pressure parameters that combine fluid and rock properties with pressure and condensate saturation as they change with time during production from gas condensate wells.
  • Non-Darcy flow and blockage coefficient for gas condensate are factored into the computations of a well productivity index.
  • the present invention highlights changes occurring during gas condensate well production due to gas expansion and non-Darcy flow effects on the productivity of gas condensate wells.
  • the present invention takes into account dynamic reservoir parameters when identifying sweet spots and energy- points.

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  • Fluid Mechanics (AREA)
  • General Life Sciences & Earth Sciences (AREA)
  • Geochemistry & Mineralogy (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

Des cellules de modèle de simulation d'un réservoir ayant une production de condensat de gaz à partir de couches d'un réservoir d'hydrocarbure souterrain sont déterminées par le procédé d'indice de productivité dynamique totale (TDPI). Les déterminations sont basées sur la physique des roches et des fluides, y compris un flux non-Darcy et une intégrale de pseudo-pression, qui sont responsables de la chute de pression à proximité de puits de forage dans les puits de condensat de gaz. Une matrice de grille tridimensionnelle (3D) est reliée à un algorithme d'optimisation de positionnement de puits pour cibler des points d'énergie de gaz élevée et augmenter la productivité, l'efficacité et la récupération à partir des puits de condensat de gaz.
PCT/US2021/034594 2020-06-02 2021-05-27 Quantification de la productivité de puits et de conditions de flux de puits de forage proches dans des réservoirs de gaz WO2021247378A1 (fr)

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