CN109763809B - Method for optimizing parameters in horizontal well subsection liquid flow control completion section - Google Patents
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Abstract
The invention discloses a method for optimizing horizontal well subsection liquid flow control completion section internal parameters, which comprises the following steps: establishing a parameter calculation model; determining an optimization result target and a parameter to be optimized associated with the optimization result target; determining a calculation relation between the optimization result target and the parameter to be optimized according to the parameter calculation model; determining an ideal value of the optimization result target; and using specific numerical value combinations of the parameters to be optimized as individuals, using a plurality of different specific numerical value combinations to form a population, using the calculated value of the optimization result target calculated according to the individuals as a screening condition, and using a genetic algorithm for a plurality of iterations to optimize the parameters. The method can greatly improve the execution effect of the horizontal well subsection liquid flow control well completion technical scheme; compared with the prior art, the method has the advantages of simple process, low implementation difficulty, small consumed workload, higher practical value and higher popularization value.
Description
Technical Field
The invention relates to the field of oil and gas exploitation, in particular to a method for optimizing parameters in a horizontal well subsection liquid flow control completion section.
Background
In the field of oil and gas exploitation, the horizontal well technology is a quite advanced important technology. In particular, a horizontal well is a special well having a maximum well deviation angle of up to or near 90 ° (generally not less than 86 °) and maintaining a horizontal well section of a certain length in the zone of interest. Sometimes the angle of inclination may exceed 90 deg., for certain special needs, "upturned". Generally, horizontal wells are suitable for thin hydrocarbon reservoirs or fractured hydrocarbon reservoirs with the aim of increasing the exposed area of the hydrocarbon reservoir.
In the horizontal well technology, one of the key technologies is the horizontal well subsection liquid flow control completion technology. The horizontal well subsection fluid flow control well completion is a well completion scheme which can be arranged in a targeted mode according to oil reservoir characteristics, fluid characteristics, well hole conditions and the like of different intervals of a horizontal well, and therefore effective exploitation of oil and gas is achieved. The technology can slow down the water and gas burst of the bottom water reservoir or the gas cap reservoir by adjusting the inflow dynamic of the fluid to the well, so that the fluid is uniformly distributed along the shaft, and the production life of the oil well is prolonged. The horizontal well subsection liquid flow control completion has various forms: the system comprises selective perforation well completion, variable density screen pipe well completion, flow control water screen pipe well completion, flow control device (ICD) well completion, intelligent well completion and the like, wherein the common point of the modes is that the liquid outlet balance is achieved by controlling the flow pressure drop of each section, and the specific modes are different.
In the execution process of the horizontal well subsection liquid flow control completion technical scheme, the selection of parameters in the horizontal well subsection liquid flow control completion section is particularly important, and the final execution effect of the technical scheme is directly determined. However, in the control system for horizontal well subsection fluid control completion, the number of parameters is numerous, the relationship among the parameters is quite complex, and uncertainty of geology related parameters in the implementation process of the scheme is added, so in the prior art, parameters in the horizontal well subsection fluid control completion section can be determined only according to historical real-time records of the technical scheme and by means of personal experience, and finally the implementation effect of the technical scheme is far from reaching an ideal level.
Disclosure of Invention
The invention provides a method for optimizing parameters in a horizontal well subsection liquid flow control completion section, which comprises the following steps:
step 1, establishing a parameter calculation model;
step 2, determining an optimization result target and a parameter to be optimized associated with the optimization result target;
step 3, determining a calculation relation between the optimization result target and the parameter to be optimized according to the parameter calculation model;
step 4, determining an ideal value of the optimization result target;
and 5, taking the specific numerical combination of the parameters to be optimized as an individual, forming a population by combining a plurality of different specific numerical combinations, taking the calculated value of the optimization result target calculated according to the individual as a screening condition, and adopting a genetic algorithm for multiple iterations to obtain the specific numerical combination of the parameters to be optimized, which can calculate the calculated value closest to the ideal value in the multiple iteration processes.
In one embodiment, a genetic algorithm is employed for a plurality of iterations, including:
step one, setting the number fn of individuals in a population, and iteratively calculating the times in;
randomly selecting a specific numerical combination of parameters to be optimized, and initializing a population;
calculating the calculation value of the optimization result target corresponding to the specific numerical combination of all the parameters to be optimized in the population;
selecting specific numerical value combinations of the parameters to be optimized corresponding to the first k calculated values closest to the ideal value;
step five, performing cross and variation calculation on the selected k groups of specific numerical value combinations to generate fn new specific numerical value combinations to form a new population;
and step six, repeating the step three to the step five until iteration is performed in times.
In one embodiment, the selected k groups of specific value combinations are crossed and subjected to variation calculation, wherein the genetic crossing of the individuals comprises:
a cross point is randomly set in the individual string, and when the cross is executed, the partial structures of two individuals before or after the cross point are interchanged to generate two new individuals.
In one embodiment, the selected k groups of specific value combinations are subjected to crossover and mutation calculation, wherein the genetic mutation of an individual includes:
judging whether to perform mutation or not for all individuals in the population according to a preset mutation probability;
randomly selecting mutation sites for mutation of individuals.
In one embodiment, the genetic algorithm is used for a plurality of iterations, further comprising:
and setting the numerical value upper and lower limits and the adjustment step length of the parameter to be optimized, and in the second step and the fifth step, selecting, crossing and mutating specific numerical value combinations in the numerical value upper and lower limit ranges based on the adjustment step length.
In an embodiment, the method further comprises:
dividing the horizontal section of the horizontal well into multiple sections, and performing parameter optimization based on steps 1-5 for each section needing to be opened.
In one embodiment, the horizontal well section is divided into sections according to a permeability profile of the reservoir along the horizontal wellbore.
In one embodiment, the horizontal well is a segmented nozzle type ICD completion, and the parameter calculation model is a segmented flow control completion reservoir and wellbore coupling flow model.
In one embodiment, in the segmented flow control well completion oil reservoir and wellbore coupled flow model, the well completion inflow pressure drop calculation method adopts a nozzle type ICD pressure drop calculation method.
In one embodiment, the staged fluid flow control completion reservoir and wellbore coupled flow model comprises:
an oil reservoir seepage model;
a horizontal well oil pipe internal pressure drop calculation model;
nozzle/orifice type ICD pressure drop calculation model.
According to the method, the execution effect of the horizontal well subsection liquid flow control well completion technical scheme can be greatly improved; compared with the prior art, the method has the advantages of simple process, low implementation difficulty, small consumed workload, higher practical value and higher popularization value.
Additional features and advantages of the invention will be set forth in the description which follows. Also, some of the features and advantages of the invention will be apparent from the description, or may be learned by practice of the invention. The objectives and some of the advantages of the invention may be realized and attained by the process particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a flow diagram of a method according to an embodiment of the invention;
fig. 2 is a flow diagram of a portion of a method according to an embodiment of the present invention.
Detailed Description
The following detailed description will be provided for the embodiments of the present invention with reference to the accompanying drawings and examples, so that the practitioner of the present invention can fully understand how to apply the technical means to solve the technical problems, achieve the technical effects, and implement the present invention according to the implementation procedures. It should be noted that, as long as there is no conflict, the embodiments and the features of the embodiments of the present invention may be combined with each other, and the technical solutions formed are within the scope of the present invention.
In the horizontal well technology, one of the key technologies is the horizontal well subsection liquid flow control completion technology. The horizontal well subsection fluid flow control well completion is a well completion scheme which can be arranged in a targeted mode according to oil reservoir characteristics, fluid characteristics, well hole conditions and the like of different intervals of a horizontal well, and therefore effective exploitation of oil and gas is achieved. The technology can slow down the water and gas burst of the bottom water reservoir or the gas cap reservoir by adjusting the inflow dynamic of the fluid to the well, so that the fluid is uniformly distributed along the shaft, and the production life of the oil well is prolonged. The horizontal well subsection liquid flow control completion has various forms: the system comprises selective perforation well completion, variable density screen pipe well completion, flow control water screen pipe well completion, flow control device (ICD) well completion, intelligent well completion and the like, wherein the common point of the modes is that the liquid outlet balance is achieved by controlling the flow pressure drop of each section, and the specific modes are different.
In the execution process of the horizontal well subsection liquid flow control completion technical scheme, the selection of parameters in the horizontal well subsection liquid flow control completion section is particularly important, and the final execution effect of the technical scheme is directly determined. However, in the control system for horizontal well subsection fluid control completion, the number of parameters is numerous, the relationship among the parameters is quite complex, and uncertainty of geology related parameters in the implementation process of the scheme is added, so in the prior art, parameters in the horizontal well subsection fluid control completion section can be determined only according to historical real-time records of the technical scheme and by means of personal experience, and finally the implementation effect of the technical scheme is far from reaching an ideal level.
Aiming at the problems, the invention provides a method for optimizing parameters in a horizontal well subsection liquid flow control completion section. Specifically, in the invention, based on a genetic algorithm, the relatively optimal parameter value is determined through specific detailed calculation, so that the optimization of the parameters in the horizontal well subsection fluid flow control completion section is realized. In the method, the combination closest to the optimization target can be found out from infinite parameter combination samples to the maximum extent, and the interference of artificial subjective factors on the parameter optimization process is avoided. According to the method, the execution effect of the horizontal well subsection liquid flow control well completion technical scheme can be greatly improved; compared with the prior art, the method has the advantages of simple process, low implementation difficulty, small consumed workload, higher practical value and higher popularization value.
Next, an implementation process of the embodiment of the present invention is described in detail based on the flowchart. The steps shown in the flow chart of the figure may be performed in a computer system containing, for example, a set of computer executable instructions. Although a logical order of steps is illustrated in the flowcharts, in some cases, the steps illustrated or described may be performed in an order different than presented herein.
As shown in fig. 1, in one embodiment, a method comprises:
step S110, establishing a parameter calculation model;
step S120, determining an optimization result target (a result parameter finally achieved by the technical scheme) and a parameter to be optimized associated with the optimization result target;
step S130, determining a calculation relation between an optimization result target and a parameter to be optimized according to the parameter calculation model;
step S140, determining an ideal value of the optimization result target (a final achievable result value of the technical solution in an ideal state);
and S150, taking the specific numerical combinations of the parameters to be optimized as individuals, forming a population by a plurality of different specific numerical combinations, taking the calculated values of the optimization result target calculated according to the individuals as the screening conditions, adopting a genetic algorithm for multiple iterations, obtaining the specific numerical combinations of the parameters to be optimized which can calculate the calculated values closest to the ideal values in the multiple iteration processes, and finally combining the obtained specific numerical combinations into the optimization results.
Specifically, as shown in fig. 2, in an embodiment, the process of performing multiple iterations using a genetic algorithm to optimize parameters includes:
step S151, setting the number fn of individuals in the population and iterative computation times in;
s152, randomly selecting a specific numerical value combination of parameters to be optimized, and initializing a population;
step S153, calculating values of optimization result targets corresponding to specific numerical combinations of all parameters to be optimized in the population;
step S154, selecting the specific numerical value combination of the parameters to be optimized corresponding to the first k calculated values closest to the ideal value;
step S155, carrying out cross and variation calculation on the selected k groups of specific numerical value combinations to generate fn new specific numerical value combinations to form a new population;
and finally, repeating the steps S153-155 until iterating for in times.
Further, in an embodiment, fn, in and k in the above steps are non-zero natural numbers. Specifically, in one embodiment, the specific values of fn, in, and k are determined based on the current optimization accuracy requirements, optimization time capability, and computational power of the genetic computing system.
Further, in one embodiment, in step S155, the genetic crossing of the individuals comprises:
a cross point is randomly set in the individual string, and when the cross is executed, the partial structures of two individuals before or after the cross point are interchanged to generate two new individuals.
For example:
individual a was 1001111 and individual B was 0011000.
Setting the 4 th bit and the fifth bit of the individual string as the intersection point, the intersection of the individual A and the individual B is represented as:
individual a: 1001 ↓111 → 1001000 new individual;
individual B: 0011 ↓ 000 → 0011111 new individual.
Further, in one embodiment, in step S155, the genetic variation of the individual includes:
judging whether to perform mutation or not for all individuals in the population according to a preset mutation probability;
randomly selecting mutation sites for mutation of individuals.
Further, in the parameter value crossing and mutation operation, the original parameter value is adjusted to generate a new parameter value, but if the adjustment range is too small, the fluctuation range of the calculated value of the final optimization result target is not too large, and in this case, genetic iteration is performed, so that higher calculation accuracy is required. Although the above embodiment may bring about a more accurate optimization result, it also brings about a larger amount of calculation and a demand for calculation accuracy. Therefore, in one embodiment, the adjustment step size of the parameter to be optimized is set according to the current optimization accuracy requirement and the computing system computing power. In order to reduce the computational pressure in the genetic iterative calculations.
Further, in the iterative computation, the selection of the initial value of the parameter to be optimized and the subsequent parameter value crossing and mutation operations have certain randomness, that is, the selected parameter value or the parameter value obtained by the crossing and mutation computation may be a value (unreasonable value) that cannot exist in a real scene. Performing the next calculation operation based on these unreasonable values is inevitably an inefficient calculation operation that wastes calculation power. Thus, in an embodiment, in order to avoid a waste of computing power, a range of values of the parameter to be optimized is defined in the iterative calculation. Specifically, in an embodiment, a reasonable range (upper and lower limits) of the parameter to be optimized is defined according to the actual application environment (for example, the number adjustment of the nozzles cannot take a negative value, and the maximum value of the number adjustment cannot exceed the maximum number of nozzles that can be accommodated by the actual system), so that unreasonable parameter values can be prevented from participating in the iterative computation.
Further, in an actual application scenario, an actual physical condition of the system may define a specific value of some parameters, so in an embodiment, in the parameter value crossing and mutation operation, an adjustment step size of the parameter to be optimized is defined according to an actual application environment (for example, the number adjustment of the nozzles may only be a natural number).
Specifically, in one embodiment, the upper and lower numerical limits of the parameter to be optimized and the adjustment step size are set, and in steps S152 and S155, the selection, intersection and variation of the specific numerical combination is performed within the range of the upper and lower numerical limits based on the adjustment step size.
Further, in practical applications, the specifications and control requirements of different locations on the horizontal well are different. In one embodiment, the horizontal section of the horizontal well is divided into a plurality of sections, and parameter optimization based on steps S110-150 is carried out on each section needing to be opened.
Further, in one embodiment, when the horizontal well is segmented, the horizontal well segment is segmented according to the permeability distribution of the reservoir along the horizontal shaft.
Further, in one embodiment, when the horizontal well is a staged jet ICD completion, the parametric calculation model controls a reservoir-to-wellbore coupled flow model for staged flow control completion.
Specifically, in an embodiment, the horizontal well is a segmented nozzle type ICD well completion, the production mode is production in a heliostat production mode (p tons/day) (not limited to the segmented well completion mode and the production regime), and the optimization of parameters in the segmented flow control well completion segment of the horizontal well comprises the following steps:
(1) and establishing a coupling flow model of the segmented liquid flow control well completion oil reservoir and the shaft, wherein the inflow pressure drop calculation method of the well completion device adopts a nozzle type ICD pressure drop calculation method.
Specifically, in one embodiment, the segmented flow control completion reservoir and wellbore coupled flow model comprises: the method comprises an oil reservoir seepage model, a horizontal well oil pipe internal pressure drop calculation model and a nozzle/hole type ICD pressure drop calculation model.
(a) Specifically, in one embodiment, the reservoir permeability model is:
A1X1＝b1 (1)
in formula 1:
s is the skin coefficient of each segment, peIs the reservoir boundary or drainage boundary pressure, psandface，jIs the pressure value at the sand surface of the jth horizontal segment, qin，jIs the input flow of the j-th section horizontal section, phii，jCan be modified into different forms according to the type of the oil reservoir.
In particular, for a bottom water reservoir phii，jThe expression is as follows:
n is the number of specular reflections, LiIs the length of the i-th horizontal segment, KijIs the equivalent permeability of the ith to jth sections.
In particular, in one embodiment,
(b) specifically, in an embodiment, the calculation model of the pressure drop in the horizontal well oil pipe (ignoring gravity pressure drop) is:
A2X2＝b2 (5)
wherein:
fjis the friction coefficient of the j section, ptube，jIs the pressure in the j section oil pipe, qin，jThe flow rate of the j section entering the oil pipe is shown, and qw, j is the flow rate in the j section of the infinitesimal pipe. The boundary conditions of the oil pipe pressure distribution are as follows:
ptube,0＝pwf (7)
pwfthe pressure in the end tubing, namely the bottom hole flowing pressure.
(c) Specifically, in one embodiment, since the pressure drop is primarily due to throttling, the nozzle/orifice ICD pressure drop calculation model is:
wherein:
qin,jfor the j horizontal section inflow, djFor the j horizontal section ICD nozzle diameter, njThe number of nozzles in the j horizontal section is rho fluid density CDThe ICD flow coefficient is measured by experiments.
(2) Dividing the horizontal section into n sections according to the permeability distribution of a reservoir along the horizontal shaft, and numbering the sections needing to be opened in the n sections by 1,2 and 3 … … m;
(3) assuming that the length of the horizontal segment is L (unit m), calculating the open m-segment uniform inflow profile f as (p/L) x (m/n), unit: ton/day/m;
(4) selecting a parameter optimization target, taking the maximum cumulative yield after d days as an example, wherein the target value t is d × p, unit: ton;
(5) setting the upper limit and the lower limit of each parameter of the ICD and the adjustment step length, wherein if the upper limit of the number (unit: number) of nozzles is n _ max, the lower limit is n _ min, and the adjustment step length is 1; the upper limit of the diameter (unit: mm) of the nozzle is dia _ max, the lower limit is dia _ min, and the adjustment step length is 1; the upper limit of the nozzle flow coefficient is cof _ max, and the lower limit is cof _ min;
(6) optimizing the kth segment (k is more than or equal to 1 and less than or equal to m) by a genetic algorithm, and comprising the following steps of:
a) setting the number fn of individuals in the population and the iterative computation times in;
b) randomly selecting the combination of the number of the nozzles in the fn group, the diameter of the nozzles and the flow coefficient, and initializing the population;
c) calculating inflow profiles in the section of all the combinations of the population, and selecting the front k groups of combinations closest to the target profile;
d) and c) carrying out cross and variation calculation on the k groups of combinations in the step c) to generate new combinations of the density and the diameter of the holes of the fn groups.
e) And repeating the steps c) and d) until the iteration is performed in times.
(7) And (6) repeating the step until all the m sections are calculated.
The following describes an embodiment of the present invention in detail with reference to a specific implementation scenario.
The length of the horizontal section of the oil well in a bottom water oil reservoir is 800 m. The well is completed by a perforated sieve tube, the diameter adjusting range of a nozzle is 2-8mm, and the adjusting precision is 1 mm; hole(s)The eye density adjustment range is 2-6, and the adjustment precision is 1; the flow coefficient adjusting range is 0.8-1, and the adjusting precision is 0.1. Dividing the shaft into 9 sections according to the permeability distribution, wherein the bottom hole flowing pressure is 28MPa, and the fixed production is 250m3And d, taking the maximum anhydrous oil extraction period as a target, and naturally extracting without artificial lifting. The maximum error of the inflow section of the optimal scheme of the nozzle type ICD (interface control document) can be obtained by adopting the optimization result of the method, namely 0.16m3The daily yield of 243.88m3/d under the condition of 28MPa and the inflow velocity of each section are close to the target profile, thereby proving the correctness of the method.
Although the embodiments of the present invention have been described above, the above description is only for the convenience of understanding the present invention, and is not intended to limit the present invention. There are various other embodiments of the method of the present invention. Various corresponding changes or modifications may be made by those skilled in the art without departing from the spirit of the invention, and these corresponding changes or modifications are intended to fall within the scope of the appended claims.
Claims (4)
1. A method for optimizing parameters in a staged fluid control completion section of a horizontal well, the method comprising:
step 1, establishing a parameter calculation model, wherein the horizontal well is a segmented nozzle type ICD well completion, the parameter calculation model is a segmented flow control well completion oil reservoir and shaft coupling flow model, in the segmented flow control well completion oil reservoir and shaft coupling flow model, a nozzle type ICD pressure drop calculation method is adopted as a well completion device inflow pressure drop calculation method, and the segmented flow control well completion oil reservoir and shaft coupling flow model comprises the following steps: an oil reservoir seepage model; a horizontal well oil pipe internal pressure drop calculation model; a nozzle/orifice type ICD pressure drop calculation model;
step 2, determining an optimization result target and a parameter to be optimized associated with the optimization result target;
step 3, determining a calculation relation between the optimization result target and the parameter to be optimized according to the parameter calculation model;
step 4, determining an ideal value of the optimization result target;
step 5, taking the specific numerical combination of the parameters to be optimized as an individual, forming a population by combining a plurality of different specific numerical combinations, taking the calculated value of the optimization result target calculated according to the individual as a screening condition, and adopting a genetic algorithm for multiple iterations to obtain the specific numerical combination of the parameters to be optimized, which is calculated to be the closest to the calculated value of the ideal value in the multiple iteration processes;
the horizontal well is completed by the segmented nozzle type ICD, the production mode is production in a heliostat yield mode, and the heliostat yield p, unit: ton, the length of a horizontal segment is L, the horizontal segment is divided into n segments according to the permeability distribution of a reservoir along a horizontal shaft, and the unit of an opened m-segment uniform inflow profile f is calculated as (p/L) x (m/n): ton/day/m;
selecting an optimization result target, wherein the maximum cumulative yield t after d days is d × p, unit: ton;
setting the upper and lower limits of each parameter of the ICD and the adjustment step length, the number of nozzles and the unit: the upper limit is n _ max, the lower limit is n _ min, and the adjustment step length is 1; nozzle diameter, unit: mm, the upper limit is dia _ max, the lower limit is dia _ min, and the adjustment step length is 1; the upper limit of the nozzle flow coefficient is cof _ max, and the lower limit is cof _ min;
and (3) optimizing the k section by using a genetic algorithm, wherein k is more than or equal to 1 and less than or equal to m, and the steps are as follows:
a. setting the number fn of individuals in the population and the iterative computation times in;
b. randomly selecting the combination of the number of the nozzles in the fn group, the diameter of the nozzles and the flow coefficient, and initializing the population;
c. calculating inflow profiles in the section of all the combinations of the population, and selecting the front k groups of combinations closest to the target profile;
d. c, performing crossing and variation calculation on the k groups of combinations in the step c to generate new nozzle number and nozzle diameter combinations of the fn groups;
e, repeating the steps c and d until iteration is performed for in times;
and carrying out crossover and variation calculation on the selected k groups of specific value combinations, wherein the genetic crossover of the individuals comprises: randomly setting a cross point in the individual string, and exchanging partial structures of two individuals before or after the cross point when the cross is carried out to generate two new individuals;
and carrying out cross and variation calculation on the selected k groups of specific value combinations, wherein the genetic variation of the individuals comprises: judging whether to perform mutation or not for all individuals in the population according to a preset mutation probability; randomly selecting mutation sites for mutation of individuals.
2. The method of claim 1, wherein the plurality of iterations using the genetic algorithm further comprises:
and setting the numerical value upper and lower limits and the adjustment step length of the parameter to be optimized, and in the steps 2 and 5, selecting, crossing and mutating specific numerical value combinations in the numerical value upper and lower limit ranges based on the adjustment step length.
3. The method of claim 1, further comprising:
dividing the horizontal section of the horizontal well into multiple sections, and performing parameter optimization based on steps 1-5 for each section needing to be opened.
4. The method of claim 3, wherein the horizontal well section is divided into sections according to a reservoir permeability profile along a horizontal wellbore.
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