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WO2007058662A1 - Procede d’optimisation de production a pleine echelle - Google Patents

Procede d’optimisation de production a pleine echelle Download PDF

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Publication number
WO2007058662A1
WO2007058662A1 PCT/US2005/042470 US2005042470W WO2007058662A1 WO 2007058662 A1 WO2007058662 A1 WO 2007058662A1 US 2005042470 W US2005042470 W US 2005042470W WO 2007058662 A1 WO2007058662 A1 WO 2007058662A1
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Prior art keywords
well bores
constraint
fluid
equations
well
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PCT/US2005/042470
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English (en)
Inventor
Baris Guyaguler
James Thomas Byer
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Chevron USA Inc
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Chevron USA Inc
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Application filed by Chevron USA Inc filed Critical Chevron USA Inc
Priority to CA2630411A priority Critical patent/CA2630411C/fr
Priority to EA200801405A priority patent/EA014140B1/ru
Priority to PCT/US2005/042470 priority patent/WO2007058662A1/fr
Priority to BRPI0520693-6A priority patent/BRPI0520693A2/pt
Priority to EP05849297.6A priority patent/EP1955253A4/fr
Priority to AU2005338352A priority patent/AU2005338352B2/en
Priority to CN2005800525117A priority patent/CN101361080B/zh
Publication of WO2007058662A1 publication Critical patent/WO2007058662A1/fr
Anticipated expiration legal-status Critical
Priority to NO20082622A priority patent/NO20082622L/no
Ceased legal-status Critical Current

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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
    • E21B43/12—Methods or apparatus for controlling the flow of the obtained fluid to or in 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
    • E21B43/00—Methods or apparatus for obtaining oil, gas, water, soluble or meltable materials or a slurry of minerals from wells
    • E21B43/14—Obtaining from a multiple-zone well

Definitions

  • the present invention relates generally to methods for controlling hydrocarbon production from a field of wells, and more particularly, to methods for optimizing production by enhancing fluid flow rate allocations among the wells.
  • Field scale optimization is known which attempts to optimize or enhance the production of production fluids, including hydrocarbons, from a field containing one or more subterranean reservoirs.
  • Wells or well bores connect the reservoirs with surface facilities which collect and process the captured production fluids.
  • these production fluids include the components of oil, gas and water. Chokes or flow control devices are used to adjust the allocation of flow rates among the well bores in a field.
  • the relative quantities and ratios of production of the different components of oil, gas and water for an individual well bore can be controlled by adjusting a choke to change the pressure in a well bore.
  • Surface facilities are needed to produce and process the production fluids. These facilities may include apparatus such as separators, pumps, storage tanks, compressors, etc. Ideally, the capital expenditures on these facilities are minimized by employing the smallest and least expensive surface facilities possible.
  • fluid handling capacity should be sufficiently large so as not to unduly limit the production rate of the economically desirable oil and/or gas.
  • the allocation of fluid flow in the well bores is ideally optimized to maximize monetary return while meeting production constraints such as those imposed by the fluid handling capacities of the surface facilities. Optimization techniques are used predict the optimal allocation of fluid flows in well bores for a given set of production constraints.
  • a reservoir simulator is used to mathematically model the flow of fluids throughout a field including the reservoirs and well bores.
  • the simulated flow is used to establish component flow rate curves or rate equations for each well bore which describe how the flow rate of one component, such as water, relates to the flow rate of another component, i.e., oil.
  • an objective function is created which seeks to optimize an objective such as maximizing oil production or minimizing water production.
  • the objective function incorporates the flow rates from the well bores which are predicted by the reservoir simulation.
  • a set of production constraints such as oil production targets or gas or water production limitations for the field, are specified. Constraint equations are generated to meet these production constraints.
  • the fluid flow among the well bores must adhere to these production constraints.
  • the objective function is then optimized by a subroutine, referred to as an optimizer, to determine the optimal allocation of flow rates among the well bores.
  • the optimizer utilizes the well bore component flow rate equations and constraint equations in the optimization process.
  • a first shortcoming of typical field scale optimization schemes is that feasible solutions to an optimization may not be possible for specified production constraints. For example, a certain level of oil production may be desired while not producing more than a specified quantity of water. A feasible solution to the objective function with this set of constraints may not be possible. In this event, one or more of the constraints must be adjusted and the reservoir simulator and optimizer run again to determine when a feasible solution is possible. Such iterative runs in solving numerous optimizations of the objective function are computationally intensive and undesirable.
  • a second problem in some optimization schemes is that while a feasible solution to the optimization of the objective function may be achieved, the results may not be practical.
  • the optimizer may determine that a first well bore should produce at a high level while a second well bore is substantially closed down.
  • the optimizer may suggest that the second well bore produce at a high level while the first well bore is substantially shut down. Therefore, production from the well bores may oscillate if the suggested allocations from the optimizer are followed.
  • it is more practical if the production from well bores having similar fluid flow characteristics are at a consistent level. This would minimize the oscillations in production from the related well bores over time steps.
  • a third shortcoming is that creating component flow rate curves or equations for the production of fluids from a well bore can be computationally intensive.
  • One method of calculating these rate curves is to create a sub model of the well bores and surrounding reservoirs and iteratively solve for the production rates of the components, i.e., oil, gas and water, as the chokes are opened and the pressure draw downs between the reservoirs and the well bores are increased.
  • the components i.e., oil, gas and water
  • the pressure draw down in a well bore is related to how open is a choke controlling the well bore.
  • the present invention provides solutions to the above described shortcomings of conventional field scale optimization schemes.
  • First, an objective function and associated constraint equations are generated which can be solved in a single run of an optimizer to produce a feasible solution.
  • Second, constraint equations may be created which requires the rates of production from similar well bores to be related to prevent significant oscillation of well rates between time steps of a reservoir simulation.
  • Third, an efficient method of generating well bore component flow rate curves or equations relating production rates between fluid components of a well bore is described.
  • the present invention includes a method for enhancing the allocation of fluid flow rates among a plurality of well bores in fluid communication with at least one subterranean reservoir.
  • Fluid flow is simulated, using a numerical reservoir simulator, in at least one subterranean reservoir and in a number of well bores in fluid communication with the subterranean reservoir.
  • Component flow rate equations are generated from the simulated flow in the well bores.
  • Production constraints are selected with at least one of the production constraints ideally being a soft constraint which may be violated if necessary during an optimization process to provide a feasible solution. Constraint equations corresponding to the production constraints are also generated.
  • An objective function is generated which corresponds to the fluid flow in the well bores.
  • the objective function may also include constraint violation penalties which correspond to the soft constraints and soft constraint equations.
  • the objective function is then optimized utilizing the component flow rate equations and the constraint equations to determine an enhanced allocation of fluid flow rates among the well bores. If necessary, soft constraints may be violated to achieve a feasible solution to the optimizing of the objective function. The presence of the constraint violation penalties allows the soft constraints to be violated while still satisfying a corresponding constraint equation.
  • the fluid flow rates are then allocated among the well bores as determined by the optimizing of the objective function.
  • the soft constraints may be prioritized as to which of the soft constraints should be most difficult to violate if necessary to achieve a feasible solution to the optimization of the objective function.
  • Weighting scale factors may be associated with the constraint violation penalties in the objective function. The weighting scale factors may be weighted in accordance with the prioritization of the soft constraints to make higher priority soft constraints more difficult to violate than lower priority soft constraints.
  • Flow rates between select well bores may have their flow rates related.
  • well bores exhibiting similar flow characteristics such as gas-to-oil ratio ( GOR ) or water-to-oil ratio (WOR) may have their well rates related to one another.
  • GOR gas-to-oil ratio
  • WOR water-to-oil ratio
  • the simulated well bores include a plurality of completion elements and the reservoir or reservoirs include a plurality of reservoir elements.
  • the reservoir simulator is run to determine pressures in the reservoir elements and in the completion elements and to determine fluid flows in the completion elements of at least two components, i.e., oil and water, due to the pressure draw down between the reservoir elements and the completion elements.
  • Fluid flow component rate data points are then generated over a range of fluid flows for each well bore. The data points are ideally generated by scaling and summing the fluid flows in the completion elements based upon the component flow rates determined by an initial simulator run and in relation to an incremented range of pressure draw downs between the reservoir and completion elements.
  • FIG. 1 is a schematic drawing of an exemplary hydrocarbon producing field containing subterranean reservoirs which are fluidly connected by well bores to the surface of the field with chokes being used to control well bore pressures and flow rates so that production from the field may be optimized;
  • FIG. 2 is a flowchart of an exemplary method for field scale optimization made in accordance with this invention
  • FIGS. 3A and 3B illustrate component flow rates curves generated using a "quick rates" method made in accordance with the present invention and component flow rate curves generated using a computationally intensive iterative Newton method;
  • FIGS. 4A and 4B are graphs showing how well rates are related between a pair of well bores having similar fluid characteristics
  • Chokes or well control devices 54, 56, and 60 are used to control the flow of fluid into and out respective well bores 30, 32 and 34.
  • chokes 54, 56 and 60 also control the pressure profiles in respective well bores 30, 32 and 34.
  • well bores 30, 32 and 34 will fluidly connect with surface facilities such as oil/gas/water separators, compressors, storage tanks, pumps, pipelines, etc. The rate of flow of fluids through well bores 30, 32 and 34 may be limited by the fluid handling capacities of these surface facilities.
  • FIG. 2 shows a flowchart illustrating the general steps used in accordance with the field scale optimization method of the present invention.
  • Persons skilled in the art of reservoir simulation could easily develop computer software for performing the method outlined in FIG. 2 based on the teachings contained in this description of the invention.
  • a reservoir simulator is used to model the fluid flow in field 50 which includes the reservoirs and well bores (step 110).
  • a reservoir model will include thousands or even millions of discrete elements to carry out a numerical simulation.
  • These discrete elements comprise reservoir elements and well bore elements.
  • the well bore elements include specific completion elements which transfer fluid back and forth between adjacent reservoir elements and other well bore elements which are in fluid communication with the choke and the surface facilities (not shown).
  • Initial and boundary conditions are specified on the field model. These initial and boundary conditions include, by way of example and not limitation, the initial pressures and flow rates in the reservoir elements and well bore elements, fluid compositions, viscosities, etc.
  • a simulation run is performed on the field model to calculate reservoir and fluid flow characteristics for a time step. In particular, fluid flow rates between the reservoirs and the well bores are determined as are the pressures in the reservoir and well bore elements.
  • Producing well bores will receive producing fluids from the reservoirs, including oil, water and gas, which are delivered to the surface facilities of the field. Injection wells may be used to pressurize one or more of the reservoirs and/or to dispose of water. Also, gas may be injected into the well bores to provide gas assisted fluid production.
  • Those skilled in the art will appreciate that many other operations affecting production may be modeled with a reservoir simulator and these operations are included within the scope of this invention.
  • Component fluid flow rates may be determined in terms of oil, gas and water flow.
  • the fluid components for which flow is to be optimized could be compositional components such as light (C 3 -C 4 ), medium (C 5 -C 8 ) and heavy (>Cg) hydrocarbons.
  • other possible component combinations might include non-hydrocarbon components such as H 2 S and CO2.
  • Component flow rate equations for each of the well bores are next calculated (step 130.) These component flow rate equations describe the estimated flow of one fluid component relative to that of another fluid component over the anticipated range of flow rates for a well bore. Physically, the chokes on the well bores may be opened or closed to increase or decrease the overall fluid output or input relative to a well bore. Because of changing pressure profiles in the well bores, the relative ratios of oil, gas and water produced from a well bore may change with the opening and closing of a choke.
  • FIGS. 3A and 3B Examples of component flow rate curves for a well are shown in FIGS. 3A and 3B.
  • FlG. 3A the rate of production of gas in MSCF/D (million square cubic feet per day) is plotted against the rate of production of oil in STB/D (stock tank barrels/day).
  • STB/D the rate of production of water
  • the rate of production of gas versus oil is relatively linearly over a wide range of possible oil production rates.
  • the rate of water production is non-linear relative to the production rate of oil Much more water is produced at higher outputs of oil production than at lower rates of oil production. High production outputs correspond to a wide open choke position.
  • a "quick-rates" method is used to generate individual component rate data points which can then be used to quickly construct graphs or generate component flow rate equations. More details on the "quick-rates" method will be described below. Those skilled in the art will appreciate that other methods may be used in generating estimates of how the production of one component versus the rate of production of another component may vary over the overall output range of a well bore.
  • a user will specify production constraints (step 140) to be used in conjunction with the field model.
  • production constraints include (1) producing oil at a target level; (2) producing gas at a target level; (3) limiting gas production below a predetermined limit; (4) limiting water production below a predetermined limit; (5) limiting water injection to an amount related to the water produced from the well bores; and (6) limiting gas injection above a predetermined limit to provide gas assisted lift.
  • these targets and limitations may be combined or scaled relative to one another as well.
  • the production constraints may include hard or soft constraints.
  • Hard constraints are constraints which will not be allowed to be violated.
  • Soft constraints are constraints which may be violated if necessary to produce a feasible solution to an optimization problem.
  • the order in which the soft constraints are preferably allowed to be violated, if necessary to achieve a feasible solution, may also be specified.
  • Another aspect of the present invention includes optionally specifying (step 150) whether the well bore flow rates of certain well bores are to be related. For example, well bores having similar fluid characteristics such as gas-to-oil ratio [GOR ) or water-to-oil ratio (WOR), may be related to one another. The relating of production rates between well bores will insure that rates of production (or injection) between these well bores will not arbitrarily oscillate between time steps.
  • GOR gas-to-oil ratio
  • WOR water-to-oil ratio
  • Constraint equations are then generated (step 160) from the production constraints and the related well bore rates. Hard constraint equations are created for those constraints which are not allowed to be violated. Soft constraint equations corresponding to the soft constraints are generated which include constraint violation penalties. The constraint violation penalties allow the soft constraint equations to be satisfied even when the soft constraints must be violated so that an optimization may produce a feasible solution. The generation of this set of constraint equations will be described in further detail below.
  • An objective function is created in step 170 which seeks to optimize an objective, such as oil production from field 50.
  • the objective function ideally includes the component flow rates of the well bores and also the constraint violation penalties associated with the soft constraint equations. Weighting scale factors may be associated with the soft constraint penalties in the objective function. By appropriately weighting these weighting scale factors, the order in which related soft constraints may be violated, may be prioritized.
  • the objective function is then optimized (step 180) by an optimizing subroutine (optimizer) to produce an optimized allocation of fluid flow rates among the well bores.
  • the optimizer uses the component flow rate equations calculated in step 130 and the constraint equations set up in step 160 to optimize the objective function.
  • the optimized fluid flow rates, and other fluid flow characteristics determined from the optimizer such as constraint violation penalties, may then be allocated among the well bores and reservoir (step 190). These optimized flow rates and characteristics may then be imposed (step 200) as initial/boundary conditions in the next iterative time step in the reservoir simulation. Steps 120-200 are then repeated to provide enhanced field scale production over many time steps until a satisfactory period of time has elapsed and the simulation is then ended. More details on the above aforementioned steps will now be described.
  • a linear programming (LP) system is a set of linear equations and linear constraints.
  • a mixed integer programming (MIP) system is a set of linear or non-linear equations and constraints.
  • MIP mixed integer programming
  • a MIP system augments a LP system when a set of non-linear equations or constraints, represented by piecewise linear functions, needs to be solved to achieve an optimized objective.
  • An open source software package which uses LP and MIP techniques, is used in this exemplary embodiment to optimize the objective function.
  • the present invention uses a package entitled LP-Solve, which is available from http://packaqes.debian.org/stable/math/lp-solve.
  • the constraint equations, component flow rate equations, and the objective function are input into the optimizer.
  • the optimizer then outputs a feasible solution to the optimization problem including enhanced allocation of well bore flow rates. Values for the violation of any soft constraints necessary to achieve a feasible solution to the optimization are also ideally output. A user may then make appropriate changes to production constraints or to the capacity of surface facilities to reflect the value of the violation of the soft constraints.
  • a simple LP system may have the following form:
  • the main variables are well bore rates. That is, the rates at which components of fluid production, i.e., oil, water and gas, are produced from a well bore.
  • Component flow rate equations are preferably generated using a "quick rates" method which will be described below.
  • the component rate equations describe how much of one component is transported through a well bore as compared to another fluid component.
  • the rates of production of the components may remain linear with respect to one another or may be non-linear over the potential range of well bore production outputs.
  • the present invention ideally handles nonlinear scaling between component or phase rates through piecewise linear functions by formulating the system as a MIP problem.
  • Production constraints are set up as hard constraints, which are not allowed to be violated, and/or as soft constraints, which are allowed to be violated when necessary to achieve a solution.
  • the constraints may include target objectives and production limitations.
  • the objective function is setup from information provided by a user.
  • i number of fluid components in a well bore fluid
  • W j weighting scale factor for production of the i th fluid . component in a well bore
  • j the number of well bores
  • q tj quantity of the i th component produced by the j th well
  • Ic number of constraint violation penalties associated with the production constraints
  • w k weighting scale factor for the k th constraint violation penalty
  • CVP k k th constraint violation penalty.
  • a more specific exemplary objective function for the LP/MIP system might consist of the weighted sum of total production rates of oil, water and gas for a selected set of well bores.
  • the objective function may also include constraint violation penalty variables ( CVP k ) to accommodate the use of soft constraints.
  • CVP k constraint violation penalty variables
  • the weighting scale factors W 1 or well rate parameters may be specified by a user. For example, a user might specify:
  • the units of the objective function are a combination of STB/D and MSCF/D units. Normalization of the objective function components is ideally carried out to render the objective function non-dimensional. Another preferred way of handling this unit mismatch in the objective function is to make use of economical information, if available. For example, if oil revenues are 22$/STB/D, gas revenues are 3$/MSCF/D and every STB/D of water costs $3.5 to handle, then:
  • the units of the objective function are monetary ($) and are consistent. It is preferred to scale the weighting scale factors so that w 0 is 1.0, hence the previous well rate parameter values would be normalized by 22.0 to give:
  • Constraints may be based on physical limitations such as well production limits, injection rate limits or gas lift rate limits. Alternatively, constraints may be determined to meet engineering preferences such as production/injection targets for a group of wells. Other constraints by way of example and not limitation might include Gas to Oil Ratios (GOR), Water to Oi! Ratios (WOR), and constraints on a subset of wells or completions.
  • GOR Gas to Oil Ratios
  • WOR Water to Oi! Ratios
  • the LP/MIP system constraints are classified as hard and soft constraints. For example, hard constraints may be imposed on a pair of wells such that the combined maximum oil production is 5,000 STB/D. These hard constraints are translated into the following LP/MIP constraints:
  • Soft constraints are constraints that are allowed to be violated if-and-only-if there is no other way to honor the soft constraints while obtaining a feasible solution for the system. Ideally, this violation of constraints will be the minimum possible necessary for obtaining a solution. Constraint violations may occur when the system has conflicting limits/targets.
  • the optimizer will not report a no-solution but instead will allow the violation of one of the soft constraints.
  • a flag will be raised indicating that the constraint has been violated. Which constraint is chosen to be violated first may be determined by the user as well in this preferred embodiment of this invention.
  • constraint-1 q ⁇ om + q ⁇ 0D2 + CVP x > 7,500 (6)
  • constraint-2 q ⁇ om + q ⁇ om - CVP 2 ⁇ 7,500
  • constraint-3 ⁇ CVP, ⁇ 5,000
  • Constraint violation penalty CVP k variables are appended to the objective function:
  • the LP/MIP optimizer will prefer to scale back 1 the rates.
  • a first soft constraint may be given 8 the lowest priority
  • a second soft constraint is given a slightly higher priority
  • 9 and a third soft constraint is given the highest priority.
  • the weighting scale factors Wj are then given 1 values corresponding to 10 x 10 p where p is order of priority in which the soft 2 constraints may be violated.
  • p is order of priority in which the soft 2 constraints may be violated.
  • W 1 10 x 10 1
  • w 2 10 x 10 2
  • w 3 10 x 10 3 5 6
  • the objective function with weighting scale factors then becomes: 05J -IOxIO 2 CFF 2 -IOxIO 3 CFF 3 (13)
  • these coefficients are normalized to give values of between 0 and 1.
  • the normalization is partially based upon the potential range of a constraint violation penalty.
  • CVP k parameters are optimized along with the other parameters in the optimization system (production/injection rates). Since any positive value of CVP k imposes a penalty through the objective function, the system tries to keep CVP k values as zero. CVP k gains a positive value if and only if there is no other way to achieve a feasible solution.
  • LP/MIP systems are strictly mathematical and thus have no notion of the physics underlying the variables, equations and constraints. Therefore, in some cases, the LP/MIP results, although mathematically sound, may make little practical sense. Such a case may occur when the LP/MIP optimizer decides to significantly choke back only one well bore in a group of well bores that all have insignificant differences in their properties. This might result in large rate oscillations for individual wells between time steps. To prevent such an occurrence, the present invention provides the option that well rates of well bores with close characteristics be related.
  • RVP Rate Violation Penalty a value determining "strictness" of relation
  • w is chosen to be -10 in this particular example.
  • Another way to relate flow rates is through scaling flow rates in a group of well bores by the same factor.
  • the injection rates of all the injectors in a first injector well group and the production rates of all the producers in a first production group of well bores may be related.
  • This relation is not based on GOR or WOR in this case; the relation simply implies that when the rate of a well bore is scaled by a factor, the other wells in the related group will be scaled with the same factor.
  • a rate curve relates how the production of one component compares with the production of another. For example, as a choke or valve is opened on a well, oil, water and gas production will generally increase. The increase between any two of the components may be linear or non-linear over the range of overall fluid production. Referring again to FIGS. 3A and 3B, gas and oil production are shown to be generally linear while water and oil production are generally non- linear.
  • the rate curves are generated from a series of data points. Data points generated using an iterative Newton-Raphson procedure in conjuction with a sub-portion of the reservoir model are indicated by "x" marks. Data points indicated by "diamond” indicia were created using a "quick rates” method. Note that both methods provide similar results. However, the "quick rates” method is much more computationally efficient.
  • the quick-rates method utilizes the fact that at a fixed point in time, production from individual completion elements is generally linearly proportional to pressure draw down.
  • Pressure draw down is the pressure differential between the pressure in a well bore completion element and adjacent reservoir elements. It is this pressure differential which drives fluids into and out of the completion elements during respective production and injection operations.
  • a set of data points is generated.
  • a piecewise linear function that best fits these points is ideally constructed.
  • a component flow rate equation is then generated from this piecewise linear function which is to be used by the optimizer.
  • FIGS. 5A-D show the flow rates of individual completion elements for four different overall production outputs for a well bore. Also shown are the pressure profiles for the reservoir and well bore elements for these different production rates.
  • FIGS. 5A-D illustrate cases where oil production is being sequentially reduced, such as occurs when a well head choke valve of a well bore is being closed. Note as oil production is reduced, water production is reduced until almost no water is produced. While the rate of production is decreased, the well bore pressure profile of the well bore will increase. The pressure profile of the reservoir is assumed to remain constant at a given time step. This will result in the pressure draw down in the well decreasing as the well bore pressure profile increases toward the reservoir pressure profile. Note that the pressure at deeper completions will be greater than at shallower depth completions due to pressure head/gravity effects. Consequently, pressure draw down will be lower at greater depths where denser water underlies less dense layers of oil and gas.
  • the present invention exploits the linear rate scaling for individual well bore completions.
  • the total production rate of component p i.e., oil, water or gas, from a well is the sum of rates from its flowing completions:
  • I pT total quantity of flow from a well
  • M mri number of completion elements s in a well
  • q pi quantity of flow of a component from the i th well bore.
  • the baseline flow rate of each component at each individual completion is extracted from the reservoir simulation run at a particular time step and well production level. It is assumed that at a fixed point in time the completion rate for each individual well completion element is linearly proportional to the pressure draw down. Thus, if the pressure draw down in a well is reduced by an amount, c, individual completion rates will be scaled back accordingly and the new total rate will be given by:
  • IpT new total quantity of flow from a well; C reduction in pressure draw down;
  • equation 20 can be used to calculate the well rates of other components flowing in the well bore. The same procedure can be used for injection rates as well. Repeating this process, a number of component flow data points may be generated and a curve may be generated as has been considered previously with respect to FIGS. 3A and 3B.
  • Piecewise linear functions are generated which best represent these data point sets generated by the "quick-rates" method for each of the well bores.
  • the piecewise linear functions include a number of line segments and breakpoints. The number and location of the breakpoints are ideally selected using a least squares fit to the data set generated by the "quick-rates" method. In this exemplary embodiment, a Levenberg-Marquardt least squares fit method is preferably used to locate breakpoints.
  • curve or equation generating techniques may be used to represent the generated data points which is to be used by the optimizer.
  • an appropriate number of breakpoints as well as their optimum locations are determined.
  • the algorithm shown in FIG. 8 is used for the selection of the number of breakpoints.
  • the ⁇ f for this linear function is calculated
  • the breakpoint coordinates is optimized for minimum ⁇ f . If the fit is improved by more than a factor of f from the initial fit, then a new breakpoint is added and the process is repeated until the improvement is not significant. This algorithm keeps adding more breakpoints only if this improves the fit by the fraction f.
  • a better fit can be made by decreasing the value of fat the expense of having a larger number of segments.
  • This approach is generally robust. A check may be made to make sure that the breakpoints are always in the feasible region (first quadrant). This is ensured by penalizing (P) the solutions that fall into infeasible areas, as shown in FIG. 9.
  • the binary y indicates the segment that x belongs to. In this case, ⁇ 1 should be one and y 2 should be zero.

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Abstract

Cette invention concerne un procédé destiné à améliorer l’allocation de débits de fluide entre une pluralité de puits de forage en communication de fluide avec au moins un réservoir souterrain (24). On génère une fonction de décision et un système d’équations utilisant des pénalités de violation de contrainte liées à des contraintes douces. Les contraintes douces sont des contraintes pouvant être violées si nécessaire pour parvenir à une solution réalisable afin d’optimiser la fonction de décision et le système d’équations. L’allocation des débits de fluide entre les puits (30) s’avère ensuite possible tel que déterminé par l’optimisation de la fonction de décision et du système d’équations. Les débits de fluide entre les puits (30), en particulier ceux présentant des caractéristiques de fluide similaires, sont associables. Des débits initiaux d’éléments (pétrole, gaz et eau) et des pressions dans les puits (30) peuvent être déterminés par une simulation préalable.
PCT/US2005/042470 2005-11-21 2005-11-21 Procede d’optimisation de production a pleine echelle Ceased WO2007058662A1 (fr)

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CA2630411A CA2630411C (fr) 2005-11-21 2005-11-21 Procede d'optimisation de production a pleine echelle
EA200801405A EA014140B1 (ru) 2005-11-21 2005-11-21 Способ оптимизации добычи в масштабе месторождения
PCT/US2005/042470 WO2007058662A1 (fr) 2005-11-21 2005-11-21 Procede d’optimisation de production a pleine echelle
BRPI0520693-6A BRPI0520693A2 (pt) 2005-11-21 2005-11-21 método para otimização de produção em escala de campo
EP05849297.6A EP1955253A4 (fr) 2005-11-21 2005-11-21 Procede d'optimisation de production a pleine echelle
AU2005338352A AU2005338352B2 (en) 2005-11-21 2005-11-21 Method for field scale production optimization
CN2005800525117A CN101361080B (zh) 2005-11-21 2005-11-21 油气田规模生产优化的方法
NO20082622A NO20082622L (no) 2005-11-21 2008-06-12 Fremgangsmate for feltskalert produksjonsoptimalisering

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EP2811107A1 (fr) * 2013-06-06 2014-12-10 Repsol, S.A. Procédé de sélection et d'optimisation de commande de champ de pétrole d'un plateau de production
WO2015138805A1 (fr) * 2014-03-12 2015-09-17 Landmark Graphics Corporation Modèle d'huile noire modifié pour calculer le mélange de différents fluides dans un réseau de surface commun
WO2015138810A1 (fr) * 2014-03-12 2015-09-17 Landmark Graphics Corporation Modèles de composition simplifiés permettant de calculer des propriétés de fluides mélangés dans un réseau de surface commun
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WO2008150811A1 (fr) * 2007-05-31 2008-12-11 Baker Hughes Incorporated Appareil et procédé pour gérer l'alimentation d'additifs sur les emplacements de forage
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US8775361B2 (en) 2008-04-21 2014-07-08 Exxonmobil Upstream Research Company Stochastic programming-based decision support tool for reservoir development planning
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CN105452598A (zh) * 2013-06-06 2016-03-30 雷普索尔有限公司 选择和优化用于产量平台的油田控制的方法
WO2015138810A1 (fr) * 2014-03-12 2015-09-17 Landmark Graphics Corporation Modèles de composition simplifiés permettant de calculer des propriétés de fluides mélangés dans un réseau de surface commun
US9835012B2 (en) 2014-03-12 2017-12-05 Landmark Graphics Corporation Simplified compositional models for calculating properties of mixed fluids in a common surface network
WO2015138805A1 (fr) * 2014-03-12 2015-09-17 Landmark Graphics Corporation Modèle d'huile noire modifié pour calculer le mélange de différents fluides dans un réseau de surface commun
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WO2019152912A1 (fr) * 2018-02-02 2019-08-08 Schlumberger Technology Corporation Procédé d'obtention de contraintes uniques pour ajuster la régulation de débit dans un puits de forage
US11808117B2 (en) 2018-02-02 2023-11-07 Schlumberger Technology Corporation Method for obtaining unique constraints to adjust flow control in a wellbore
US12264562B2 (en) 2018-02-02 2025-04-01 Schlumberger Technology Corporation Method for obtaining unique constraints to adjust flow control in a wellbore
WO2020046392A1 (fr) * 2018-08-31 2020-03-05 Halliburton Energy Services, Inc. Déconvolution et inversion éparses pour propriétés de formation
US11269098B2 (en) 2018-08-31 2022-03-08 Halliburton Energy Services, Inc. Sparse deconvolution and inversion for formation properties

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AU2005338352B2 (en) 2012-05-24
CN101361080B (zh) 2011-12-14
EA014140B1 (ru) 2010-10-29
CA2630411C (fr) 2015-04-21
BRPI0520693A2 (pt) 2009-06-13
EP1955253A4 (fr) 2016-03-30
EA200801405A1 (ru) 2009-12-30
NO20082622L (no) 2008-08-13
CN101361080A (zh) 2009-02-04
EP1955253A1 (fr) 2008-08-13
AU2005338352A1 (en) 2007-05-24
CA2630411A1 (fr) 2007-05-24

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