CN107829718A - Oil reservoir well pattern and injection-production program Optimization Design based on balanced water drive theory - Google Patents
Oil reservoir well pattern and injection-production program Optimization Design based on balanced water drive theory Download PDFInfo
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- 238000013461 design Methods 0.000 title claims abstract description 31
- 238000005457 optimization Methods 0.000 claims abstract description 74
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- 238000006073 displacement reaction Methods 0.000 claims abstract description 19
- 238000012407 engineering method Methods 0.000 claims abstract description 9
- 238000004364 calculation method Methods 0.000 claims abstract description 8
- 238000002360 preparation method Methods 0.000 claims abstract description 4
- 238000002347 injection Methods 0.000 claims description 29
- 239000007924 injection Substances 0.000 claims description 29
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- 238000001764 infiltration Methods 0.000 claims description 2
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- 238000011084 recovery Methods 0.000 abstract description 2
- 239000009671 shengli Substances 0.000 description 21
- 238000010586 diagram Methods 0.000 description 10
- 239000007788 liquid Substances 0.000 description 9
- 238000004088 simulation Methods 0.000 description 6
- 239000003129 oil well Substances 0.000 description 4
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- 238000005553 drilling Methods 0.000 description 1
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- 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
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- E—FIXED CONSTRUCTIONS
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- 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
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Abstract
The present invention provides a kind of oil reservoir well pattern and injection-production program Optimization Design based on balanced water drive theory, including：Step 1, collect and arrange block geology and develop related data；Step 2, setting optimization relevant parameter, complete well pattern note and adopt optimization preparation；Step 3, using reservoir engineering method, predict that current well location and note adopt displacement situation and quantitative assessment in all directions under parameter；Step 4, generate new well location/note using global random searching algorithm, optimization and adopt parameter, predict and evaluate new well location note and adopt the displacement situation respectively noted and adopted on direction under parameter；Step 5, optimum results are arranged, well pattern note is formed and adopts design, put into field conduct.This method is combined by the waterflooding development index calculating method established based on reservoir engineering theories with Optimum Theory, use a kind of global random searching algorithm automatic calculation, it ensure that and the well pattern to match with actual oil reservoir and injection-production program are obtained while computational efficiency, improve oil recovery.
Description
Technical field
The present invention relates to oil-gas field development field, especially relate to a kind of oil reservoir well pattern based on balanced water drive theory and
Injection-production program Optimization Design.
Background technology
Well pattern and injection-production program are the important contents in oil reservoir development scheme, directly influence effect of reservoir development.How
It is one of problem in current oilfield development program design to make optimal well pattern and injection-production program.Currently used well pattern and
Injection-production program Optimization Design mainly has：Well pattern and injection-production program design are carried out by artificial experience and based on Optimum Theory
Well pattern and injection-production program optimization design.By artificial experience progress well pattern and injection-production program design, it is necessary to which Consideration is numerous,
Subjective factor influences greatly, while is difficult to obtain real optimal well pattern and injection-production program；Well pattern and note based on Optimum Theory
Scheme optimization is adopted, the quality of each scheme in optimization process is evaluated using numerical simulation, for actual extensive oil reservoir, amount of calculation
Greatly, time-consuming, and conventional computer is difficult to.And method is broadly divided into well net optimization method at present and note adopts optimization method two
Separate part, lacks combination.When being solved using gradient algorithm, gradient asks for complexity, and Restriction condition treat is complicated, together
When be easy to be absorbed in local optimum and real optimal case can not be found.For this we have invented a kind of based on balanced water drive theory
Oil reservoir well pattern and injection-production program Optimization Design, solve above technical problem.
The content of the invention
It is an object of the invention to provide the well pattern and note that acquisition matches with actual oil reservoir while a kind of computational efficiency to adopt
Scheme, improve the oil reservoir well pattern and injection-production program Optimization Design based on balanced water drive theory of oil recovery.
The purpose of the present invention can be achieved by the following technical measures：Oil reservoir well pattern and note based on balanced water drive theory are adopted
Scheme optimization design method, oil reservoir well pattern and injection-production program Optimization Design that should be based on balanced water drive theory include：Step
1, collect and arrange block geology and develop related data；Step 2, setting optimization relevant parameter, it is accurate that completion well pattern note adopts optimization
Standby work；Step 3, using reservoir engineering method, predict current well location and note adopts under parameter in all directions displacement situation and quantitative
Evaluation；Step 4, using global random searching algorithm, optimization generates new well location note and adopts parameter, predicts and evaluates new well location/note
Adopt the displacement situation respectively noted and adopted on direction under parameter；Step 5, optimum results are arranged, well pattern note is formed and adopts design, input is existing
Implement field.
The purpose of the present invention can be also achieved by the following technical measures：
In step 1, the data of collection includes：Static data：Permeability Distribution, porosity distribution, sand thickness, net hair
Than distribution；Dynamic data：Saturation distribution, pressure distribution, profit density, profit viscosity, permeability saturation curve, existing oil
Well well location.
In step 2, setting optimization relevant parameter includes：Setting optimization type, optimization type includes well net optimization, note is adopted
Optimization, well pattern note adopt combined optimization；Well to be optimized is specified, generates optimized variable；The constraints of each optimized variable is specified, for
Well net optimization, constraints include section residing for the X-coordinate of well, section, reservoir boundary constraint residing for Y-coordinate；Adopted for note excellent
Change, constraints includes the overall note amount of adopting, the individual well note amount of the adopting upper limit, the individual well note amount of adopting lower limit；Set initial well pattern/note side of adopting
Case；Set derivation algorithm relevant parameter, including initial ranging step-length, initial sample number, end condition.
The oil reservoir well pattern and injection-production program Optimization Design based on balanced water drive theory also include, after step 2,
Initial well location/the note for generating well to be optimized adopts parameter, including：Whether the initial well pattern/injection-production program of setting of judgement in step 2
Perform；If performing, initial scheme is used as using its input scheme；If being not carried out, set according to the constraints in step 2,
Each optimized variable is generated at random, as initial scheme.
In step 3, according to oil-water well well location, injection-production relation matrix is generated；According to the angle of two groups of injection-production well lines
Oil reservoir is splitted into multiple notes and adopts control unit by bisector；Each note adopts control unit intrinsic parameter equivalent process, and parameter includes infiltration
Rate, porosity, net-gross ratio, sand thickness, saturation degree, equivalent way are weighted average；Use theoretical calculation of reservoir engineering
Current time walks each equivalent filtrational resistance of control unit；Each control unit is walked using theoretical calculation of reservoir engineering current time to inject
Speed；Calculate each control unit average staturation after current time walks；Renewal time, t=t+ Δ t, wherein t are current time,
Δ t is time step, computes repeatedly the equivalent filtrational resistance of each control unit, each each control unit of control unit injection rate is averaged
These steps of saturation degree, until reaching the prediction end time；Calculate the standard deviation of each flooding unit average staturation.
In step 3, calculate current time using equation below and walk each equivalent filtrational resistance of control unit：
In formula：RiFor the filtrational resistance between water injection well and i-th mouthful of producing well, mPas/ (μm2·cm)；I compiles for producing well
Number；For the mean permeability between water injection well and i-th mouthful of producing well, 10-3μm2；It is flat between water injection well and i-th mouthful of producing well
Equal reservoir thickness, m；For the angle between the angular bisector of two adjacent groups injection-production well line and the injection-production well line；rfTo drive
For leading edge distance, m；rwFor the radius of water injection well, m；KroFor oil relative permeability；μoFor oil viscosity, mPas；Krw
For aqueous phase relative permeability；μwFor stratum water viscosity, mPas；SwcFor irreducible water saturation；diProduced for water injection well and i-th mouthful
The well spacing of well, m；SweFor exit-end water saturation.
In step 3, calculate current time using equation below and walk each control unit injection rate：
qi=Δ pi/Ri
In formula：ΔpiFor the pressure difference between water injection well and i-th mouthful of producing well, MPa；qiFor flooding unit where i-th mouthful of producing well
Injection rate, m3/d。
In step 3, each control unit average staturation after current time walks is calculated：
In formula：R is displacement distance, m；SwFor water saturation；T is the displacement time, d；fwFor moisture content.
In step 4, the standard deviation of saturation degree under each scheme is calculated；Sorted from small to large by standard deviation；According to saturation degree
The minimum scheme of standard deviation, by the filial generation generation strategy in global random searching algorithm, generates well pattern of new generation/note side of adopting
Case, global random searching algorithm include genetic algorithm, particle cluster algorithm, covariance matrix evolution algorithm；Judge newly-generated well
Whether net/injection-production program meets constraints, is recalculated if not satisfied, going to previous step；Judge newly-generated well pattern/note
Adopt whether scheme reaches optimization end condition, when meeting to optimize end condition, optimization terminates, and otherwise returns and calculates current iteration
Walk the standard deviation of average staturation under each scheme.
The oil reservoir well pattern and injection-production program Optimization Design based on balanced water drive theory in the present invention, based on optimization
Theoretical and automatic technology, the influence of the subjective factor of people when avoiding solution formulation, mitigate the working strength of people；This method is based on
Balanced waterflooding development theory, substitute numerical simulation using reservoir engineering method and carry out scheme evaluation, avoid in numerical simulation
Large-scale matrix is iterated to calculate, and shortens scheme evaluation time so that extensive oil reservoir optimization is possibly realized；This method uses global
Random search algorithm asks for optimal case, can easily handle all kinds of constraintss, and the related mechanism in searching algorithm can be protected
Card obtains globally optimal solution；This method optimizes note on the basis of optimized well pattern and adopts parameter, and then obtains the optimal well pattern note side of adopting
Case, ensure that well pattern and note adopt it is mutually coordinated between parameter.Each factor of oil reservoir can be considered by this method, automatically
Optimal well pattern and injection-production program are obtained, technical support is provided for oil field development, improves waterflooding development effect.
Brief description of the drawings
Fig. 1 is that oil reservoir well pattern and note of the present invention based on balanced water drive theory adopt Optimization Design schematic flow sheet；
Fig. 2 is Optimal Parameters setting procedure schematic flow sheet；
Fig. 3 is that reservoir engineering calculates development index and evaluates displacement balance degree schematic flow sheet；
Fig. 4 adopts control unit for note and splits a point schematic diagram；
Fig. 5 is that Optimized model solves schematic flow sheet；
Fig. 6 is Shengli Oil Field A block part data maps；
Fig. 7 is Shengli Oil Field A blocks well well location restriction range schematic diagram to be optimized；
Fig. 8 is the initial well pattern schematic diagram of Shengli Oil Field A Block Sets；
Fig. 9 is that method for numerical simulation and reservoir engineering method calculate time comparison diagram under different scales oil reservoir；
Interative computation is respectively for minimum saturation standard deviation figure when Figure 10 is Shengli Oil Field A block well net optimizations；
Interative computation is respectively for minimum saturation standard deviation figure when Figure 11 adopts optimization for Shengli Oil Field A blocks note；
Figure 12 is well pattern arrangement schematic diagram after the optimization of Shengli Oil Field A blocks well location；
Figure 13 is that Shengli Oil Field A blocks oil well note adopts liquid measure comparison diagram before and after optimization；
Figure 14 is that Shengli Oil Field A blocks well note adopts liquid measure comparison diagram before and after optimization；
Figure 15 is that Shengli Oil Field A blocks well pattern and note adopt prioritization scheme and tire out oil-producing comparison diagram with artificial program prediction.
Embodiment
For enable the present invention above and other objects, features and advantages become apparent, it is cited below particularly go out embodiment, and
Coordinate institute's accompanying drawings, be described in detail below.
As shown in figure 1, Fig. 1 is oil reservoir well pattern based on balanced water drive theory and the injection-production program optimization design side of the present invention
The flow chart of one specific embodiment of method.
In step 101, collect and arrange block geology and develop related data.The data of collection includes：Static data：Ooze
Saturating rate distribution, porosity distribution, sand thickness, net-gross ratio distribution；Dynamic data：Saturation distribution, pressure distribution, profit are close
Degree, profit viscosity, permeability saturation curve, existing oil-water well well location.Flow enters step 102.
In step 102, setting optimization relevant parameter, complete well pattern/note and adopt optimization preparation.As shown in Fig. 2 setting is excellent
Changing relevant parameter includes：Setting optimization type, optimization type includes well net optimization, note adopts optimization, well pattern note adopts combined optimization；Refer to
Fixed well to be optimized, generates optimized variable；The constraints of each optimized variable is specified, for well net optimization, constraints includes well
X-coordinate residing for section, section, reservoir boundary constraint residing for Y-coordinate；Optimization is adopted for note, constraints includes overall note and adopted
Amount, the individual well note amount of the adopting upper limit, the individual well note amount of adopting lower limit；Set initial well pattern/injection-production program (optional)；It is related to set derivation algorithm
Parameter, including initial ranging step-length, initial sample number, end condition.Flow enters step 103.
In step 103, the initial well location/note for generating well to be optimized adopts parameter.Judge setting initial well in a step 102
Whether net/injection-production program (optional) performs；If performing, initial scheme is used as using its input scheme；If being not carried out, according to step
Constraints setting in rapid 102, generates each optimized variable, as initial scheme at random.Flow enters step 104.
In step 104, using reservoir engineering method, predict current well location and note adopts under parameter in all directions displacement situation simultaneously
Quantitative assessment.As shown in figure 3, according to oil-water well well location, injection-production relation matrix is generated；As shown in figure 4, adopted according to two groups of notes
Oil reservoir is splitted into multiple notes and adopts control unit by the angular bisector of well line；Each note adopts control unit intrinsic parameter equivalent process, joins
Number includes permeability, porosity, net-gross ratio, sand thickness, saturation degree, and equivalent way is weighted average；Use oil reservoir work
Journey theoretical calculation current time walks each equivalent filtrational resistance of control unit；Each control is walked using theoretical calculation of reservoir engineering current time
Unit injection rate processed；Calculate each control unit average staturation after current time walks；Renewal time, t=t+ Δ t, wherein t are
Current time, Δ t are time step, compute repeatedly the equivalent filtrational resistance of each control unit, each control unit injection rate is respectively controlled
These steps of cell-average saturation degree processed, until reaching the prediction end time；Calculate the mark of each flooding unit average staturation
It is accurate poor.
Current time, which is calculated, using equation below walks each equivalent filtrational resistance of control unit：
In formula：RiFor the filtrational resistance between water injection well and i-th mouthful of producing well, mPas/ (μm2·cm)；I compiles for producing well
Number；For the mean permeability between water injection well and i-th mouthful of producing well, 10-3μm2；It is flat between water injection well and i-th mouthful of producing well
Equal reservoir thickness, m；For the angle between the angular bisector of two adjacent groups injection-production well line and the injection-production well line；rfTo drive
For leading edge distance, m；rwFor the radius of water injection well, m；KroFor oil relative permeability；μoFor oil viscosity, mPas；Krw
For aqueous phase relative permeability；μwFor stratum water viscosity, mPas；SwcFor irreducible water saturation；diProduced for water injection well and i-th mouthful
The well spacing of well, m；SweFor exit-end water saturation.
Current time, which is calculated, using equation below walks each control unit injection rate：
qi=Δ pi/Ri
In formula：ΔpiFor the pressure difference between water injection well and i-th mouthful of producing well, MPa；qiFor flooding unit where i-th mouthful of producing well
Injection rate, m3/d。
Calculate each control unit average staturation after current time walks：
In formula：R is displacement distance, m；SwFor water saturation；T is the displacement time, d；fwFor moisture content.
Flow enters step 105.
In step 105, judge whether to reach optimization end condition, if not satisfied, flow enters step 106；If satisfied, stream
Journey enters step 107；
In step 106, as shown in figure 5, using global random searching algorithm, optimization generates new well location/note and adopts parameter, in advance
Survey and evaluate new well location/note and adopt the displacement situation respectively noted and adopted on direction under parameter.Calculate the standard deviation of saturation degree under each scheme；Press
Standard deviation sorts from small to large；According to the scheme that saturation degree standard deviation is minimum, given birth to by the filial generation in global random searching algorithm
Into strategy, well pattern/injection-production program of new generation is generated, global random searching algorithm includes but is not limited to genetic algorithm, population is calculated
Method, covariance matrix evolution algorithm；Judge whether newly-generated well pattern/injection-production program meets constraints, judge newly-generated
Whether well pattern/injection-production program reaches optimization end condition, and when meeting to optimize end condition, optimization terminates, and otherwise returns and calculates
Current iteration walks the standard deviation of average staturation under each scheme.Flow enters step 104.
In step 107, optimum results are arranged, well pattern/note is formed and adopts design, put into field conduct.Flow terminates.
To enable the above of the present invention to become apparent, below by taking Shengli Oil Field A blocks as an example, using based on equilibrium
The oil reservoir well pattern and injection-production program Optimization Design of waterflooding development theory, are described in detail below：
1st, collect and arrange the block geology and develop related data：
Data source can have diversified forms, derived digitlization can be provided from existing geological model or numerical model
Material, can also be digitized processing according to related geologic development map and obtain.
The data of collection includes：
(1) static data：Permeability Distribution, porosity distribution, sand thickness, net-gross ratio distribution；
(2) dynamic data：Saturation distribution, pressure distribution, profit density, profit viscosity, permeability saturation curve, deposit
In oil-water well well location.
Shengli Oil Field A blocks Permeability Distribution, porosity distribution, saturation distribution and existing well well location as shown in fig. 6,
Wherein, Fig. 6 a are Permeability Distribution figure, and Fig. 6 b are porosity distribution map, and Fig. 6 c are saturation distribution figure and existing well well location.
2nd, setting optimization relevant parameter, complete well pattern/note and adopt optimization preparation.Its specific implementation step is as follows：
(1) setting optimization type：
Shengli Oil Field A blocks should carry out well net optimization, carry out note again and adopt optimization, therefore it is well pattern to optimize type set
Note adopts combined optimization；
(2) well to be optimized is specified, generates optimized variable：
After initial analysis, it is determined that 4 mouthfuls of drilling new well is needed, including new 2 mouthfuls of well, 2 mouthfuls of grease hole.Well location optimization is treated excellent
Change 4 mouthfuls of wells that well is appointed as newly boring, liquid measure optimizes well to be optimized and is appointed as block whole oil-water well.
(3) each optimized variable constraints is specified：
It is actual with reference to oil reservoir, specify the well spacing scope of 4 mouthfuls of wells in well net optimization as shown in Figure 7.Note adopts each well in optimization
Injection rate lower limit be 0, the upper limit is 2 times of current maximum individual-well injection rate；The produced quantity lower limit of each oil well is 0, and the upper limit is to work as
2 times of preceding maximum individual well produced quantity.
(4) initial well pattern/injection-production program is set：
Initial scheme can not be set, and for Shengli Oil Field A blocks, initial well location such as Fig. 8 is set based on previous work.If
The fixed old initial liquid measure of well is that stoste amount is constant, sets the initial liquid measure of new well as the average liquid measure of block.
(5) derivation algorithm relevant parameter is set：
For Shengli Oil Field A blocks, the particle cluster algorithm in global random searching algorithm is selected, sets initial population number
50 generations, other specification take algorithm default value itself.
3rd, the initial well location/note for generating well to be optimized adopts parameter：
Shengli Oil Field A blocks treat that well location optimization amounts to 4 mouthfuls of wells, and every mouthful of well includes two X-coordinate, Y-coordinate variables, altogether 8
Individual optimized variable；Treat that note adopts optimization and amounts to 13 mouthfuls of wells, optimized variable is liquid measure, altogether 13 variables.Set according in step 2- (4)
Fixed initial scheme, initialization assignment is carried out to each variable.
4th, using reservoir engineering method, predict current well location and note is adopted under parameter displacement situation in all directions and quantitatively commented
Valency：
Shengli Oil Field A blocks include 13 mouthfuls of wells, and injection-production relation matrix is 13*13 0-1 matrixes, wherein 0 value represents
Without corresponding relation between two mouthfuls of wells, 1 value has corresponding relation between representing two mouthfuls of wells.Predicted time is set as 15 years, iteration time
Step-length is 30 days, solves each note and adopts average staturation on line, and takes standard deviation.Fig. 9 is reservoir engineering method and numerical simulation
Method predicts operation time under different scales oil reservoir, it can be seen that reservoir engineering method efficiency is significantly higher than numerical simulation side
Method.The saturation degree variance that initial scheme is calculated is shown in Table 1.
The saturation degree variance table of the Shengli Oil Field A block initial schemes of table 1
5th, generate new well location/note using global random searching algorithm, optimization and adopt parameter, predict and evaluate new well location/note
Adopt the displacement situation respectively noted and adopted on direction under parameter：
(1) it is raw by the filial generation generation strategy in global random searching algorithm according to the minimum scheme of saturation degree standard deviation
Into well pattern/injection-production program of new generation：
The global random searching algorithm that Shengli Oil Field A blocks are chosen is particle cluster algorithm, population invariable number 50, generates filial generation
During population, the minimum individual of saturation degree variance can be retained, other 49 individuals lean on according to translational speed to this generation optimum individual
Hold together, produce certain displacement, so as to generate offspring individual, and form progeny population.
(2) judge whether newly-generated well pattern/injection-production program meets constraints, if not satisfied, going to step 5- (1) weights
It is new to calculate, until meeting constraints；
(3) calculate current iteration and walk average staturation standard deviation under each scheme.
6th, interative computation, until seeking obtaining optimal case, comprise the following steps that：
(1) judge whether to reach optimization end condition, if not satisfied, repeat step 5-6, bar is terminated until reaching optimization
Part；
(2) optimum results are arranged, well pattern/note is formed and adopts design, put into field conduct.
Interative computation is respectively for minimum saturation standard deviation figure such as Figure 10 when Shengli Oil Field A blocks well location optimizes；Shengli Oil Field A
Interative computation is respectively for minimum saturation standard deviation figure such as Figure 11 when block note adopts optimization；After optimization terminates, the optimal well spacing of formation
Scheme such as Figure 12；The specific data of well location are shown in Table 2 after optimization；Each oil well liquid measure comparison diagram such as Figure 13 before and after optimization；It is each before and after optimization
Well liquid measure comparison diagram such as Figure 14；Compared to the scheme by engineer, prioritization scheme prediction can increase oily 1.87 ten thousand steres more,
Amplification 37.54%, significant effect, as shown in figure 15.
Well location table after the optimization of the Shengli Oil Field A blocks of table 2
Claims (9)
1. oil reservoir well pattern and injection-production program Optimization Design based on balanced water drive theory, it is characterised in that should be based on equilibrium
The oil reservoir well pattern and injection-production program Optimization Design of water drive theory include：
Step 1, collect and arrange block geology and develop related data；
Step 2, setting optimization relevant parameter, complete well pattern note and adopt optimization preparation；
Step 3, using reservoir engineering method, predict current well location and note is adopted under parameter displacement situation in all directions and quantitatively commented
Valency；
Step 4, using global random searching algorithm, optimization generates new well location note and adopts parameter, predicts and evaluates new well location/note and adopts
Each note adopts the displacement situation on direction under parameter；
Step 5, optimum results are arranged, well pattern note is formed and adopts design, put into field conduct.
2. oil reservoir well pattern and injection-production program Optimization Design according to claim 1 based on balanced water drive theory, its
It is characterised by, in step 1, the data of collection includes：Static data：It is Permeability Distribution, porosity distribution, sand thickness, net
Hair is than distribution；Dynamic data：It is saturation distribution, pressure distribution, profit density, profit viscosity, permeability saturation curve, existing
Oil-water well well location.
3. oil reservoir well pattern and injection-production program Optimization Design according to claim 1 based on balanced water drive theory, its
It is characterised by, in step 2, setting optimization relevant parameter includes：Setting optimization type, optimization type include well net optimization, note
Adopt optimization, well pattern note adopts combined optimization；Well to be optimized is specified, generates optimized variable；The constraints of each optimized variable is specified, it is right
In well net optimization, constraints includes section residing for the X-coordinate of well, section, reservoir boundary constraint residing for Y-coordinate；Adopted for note
Optimization, constraints include the overall note amount of adopting, the individual well note amount of the adopting upper limit, the individual well note amount of adopting lower limit；Set initial well pattern/note side of adopting
Case；Set derivation algorithm relevant parameter, including initial ranging step-length, initial sample number, end condition.
4. oil reservoir well pattern and injection-production program Optimization Design according to claim 1 based on balanced water drive theory, its
Be characterised by, should oil reservoir well pattern based on balanced water drive theory and injection-production program Optimization Design also include, step 2 it
Afterwards, the initial well location/note for generating well to be optimized adopts parameter, including：Judge the initial well pattern/injection-production program of setting in step 2
Whether perform；If performing, initial scheme is used as using its input scheme；If being not carried out, set according to the constraints in step 2
It is fixed, each optimized variable is generated at random, as initial scheme.
5. oil reservoir well pattern and injection-production program Optimization Design according to claim 1 based on balanced water drive theory, its
It is characterised by, in step 3, according to oil-water well well location, generates injection-production relation matrix；According to the angle of two groups of injection-production well lines
Oil reservoir is splitted into multiple notes and adopts control unit by bisector；Each note adopts control unit intrinsic parameter equivalent process, and parameter includes infiltration
Rate, porosity, net-gross ratio, sand thickness, saturation degree, equivalent way are weighted average；Use theoretical calculation of reservoir engineering
Current time walks each equivalent filtrational resistance of control unit；Each control unit is walked using theoretical calculation of reservoir engineering current time to inject
Speed；Calculate each control unit average staturation after current time walks；Renewal time, t=t+ Δ t, wherein t are current time,
Δ t is time step, computes repeatedly the equivalent filtrational resistance of each control unit, each each control unit of control unit injection rate is averaged
These steps of saturation degree, until reaching the prediction end time；Calculate the standard deviation of each flooding unit average staturation.
6. oil reservoir well pattern and injection-production program Optimization Design according to claim 5 based on balanced water drive theory, its
It is characterised by, in step 3, calculates current time using equation below and walk each equivalent filtrational resistance of control unit：
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In formula：RiFor the filtrational resistance between water injection well and i-th mouthful of producing well, mPas/ (μm2·cm)；I numbers for producing well；
For the mean permeability between water injection well and i-th mouthful of producing well, 10-3μm2；For the average storage between water injection well and i-th mouthful of producing well
Thickness degree, m；For the angle between the angular bisector of two adjacent groups injection-production well line and the injection-production well line；rfBefore displacement
Edge distance, m；rwFor the radius of water injection well, m；KroFor oil relative permeability；μoFor oil viscosity, mPas；KrwFor water
Phase relative permeability；μwFor stratum water viscosity, mPas；SwcFor irreducible water saturation；diFor water injection well and i-th mouthful of producing well
Well spacing, m；SweFor exit-end water saturation.
7. oil reservoir well pattern and injection-production program Optimization Design according to claim 5 based on balanced water drive theory, its
It is characterised by, in step 3, calculates current time using equation below and walk each control unit injection rate：
qi=Δ pi/Ri
In formula：ΔpiFor the pressure difference between water injection well and i-th mouthful of producing well, MPa；qiFor the note of flooding unit where i-th mouthful of producing well
Enter speed, m3/d。
8. oil reservoir well pattern and injection-production program Optimization Design according to claim 5 based on balanced water drive theory, its
It is characterised by, in step 3, calculates each control unit average staturation after current time step：
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In formula：R is displacement distance, m；SwFor water saturation；T is the displacement time, d；fwFor moisture content.
9. oil reservoir well pattern and injection-production program Optimization Design according to claim 1 based on balanced water drive theory, its
It is characterised by, in step 4, calculates the standard deviation of saturation degree under each scheme；Sorted from small to large by standard deviation；According to saturation degree
The minimum scheme of standard deviation, by the filial generation generation strategy in global random searching algorithm, generates well pattern of new generation/note side of adopting
Case, global random searching algorithm include genetic algorithm, particle cluster algorithm, covariance matrix evolution algorithm；Judge newly-generated well
Whether net/injection-production program meets constraints, is recalculated if not satisfied, going to previous step；Judge newly-generated well pattern/note
Adopt whether scheme reaches optimization end condition, when meeting to optimize end condition, optimization terminates, and otherwise returns and calculates current iteration
Walk the standard deviation of average staturation under each scheme.
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| CN109816182A (en) * | 2019-03-27 | 2019-05-28 | 常州大学 | What water-drive pool note adopted dynamic regulation splits a point optimization method step by step |
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| CN109816182A (en) * | 2019-03-27 | 2019-05-28 | 常州大学 | What water-drive pool note adopted dynamic regulation splits a point optimization method step by step |
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| CN110941890B (en) * | 2019-09-27 | 2022-11-01 | 中国海洋石油集团有限公司 | Offshore oil reservoir dynamic real-time production optimization method based on optimal control theory |
| CN110924908A (en) * | 2019-11-08 | 2020-03-27 | 中国石油大学(华东) | Water drive reservoir injection-production parameter determination method and computer readable storage medium |
| CN110924908B (en) * | 2019-11-08 | 2021-10-08 | 中国石油大学(华东) | Water drive reservoir injection-production parameter determination method and computer readable storage medium |
| CN111119815A (en) * | 2020-01-03 | 2020-05-08 | 中国石油化工股份有限公司 | Method for determining production well production allocation ratio through balanced displacement |
| CN111119815B (en) * | 2020-01-03 | 2022-07-26 | 中国石油化工股份有限公司 | Method for determining production well production allocation ratio through balanced displacement |
| CN113137212A (en) * | 2020-01-17 | 2021-07-20 | 中国石油天然气股份有限公司 | Multi-layer heterogeneous oil reservoir water injection method |
| CN111222252B (en) * | 2020-01-20 | 2023-05-12 | 东北石油大学 | Method and system for predicting oil-water two-phase post-pressure capacity of low-saturation oil reservoir |
| CN111222252A (en) * | 2020-01-20 | 2020-06-02 | 东北石油大学 | Method and system for predicting oil-water two-phase post-pressure productivity of low-saturation oil reservoir |
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| CN112070269B (en) * | 2020-07-31 | 2024-05-14 | 中海油能源发展股份有限公司 | Injection and production balance adjustment method based on depth traversal |
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| CN115906675B (en) * | 2023-03-09 | 2023-05-26 | 中国石油大学(华东) | Well position and injection and production parameter joint optimization method based on time sequence multi-target prediction model |
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