CN111173507A - High-water-cut oil field residual oil prediction method - Google Patents

High-water-cut oil field residual oil prediction method Download PDF

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CN111173507A
CN111173507A CN202010189293.8A CN202010189293A CN111173507A CN 111173507 A CN111173507 A CN 111173507A CN 202010189293 A CN202010189293 A CN 202010189293A CN 111173507 A CN111173507 A CN 111173507A
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well
production
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CN111173507B (en
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卜亚辉
杨勇
张世明
邴绍献
曹小朋
吕琦
薛玉荣
秦健飞
刘祖鹏
孙强
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China Petroleum and Chemical Corp
Exploration and Development Research Institute of Sinopec Shengli Oilfield Co
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Exploration and Development Research Institute of Sinopec Shengli Oilfield Co
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    • E—FIXED CONSTRUCTIONS
    • E21—EARTH DRILLING; MINING
    • E21B—EARTH DRILLING, e.g. DEEP DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
    • E21B49/00—Testing the nature of borehole walls; Formation testing; Methods or apparatus for obtaining samples of soil or well fluids, specially adapted to earth drilling or wells
    • E—FIXED CONSTRUCTIONS
    • E21—EARTH DRILLING; MINING
    • E21B—EARTH DRILLING, e.g. DEEP 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
    • E21B43/16—Enhanced recovery methods for obtaining hydrocarbons
    • E21B43/20—Displacing by water
    • E—FIXED CONSTRUCTIONS
    • E21—EARTH DRILLING; MINING
    • E21B—EARTH DRILLING, e.g. DEEP DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
    • E21B43/00—Methods or apparatus for obtaining oil, gas, water, soluble or meltable materials or a slurry of minerals from wells
    • E21B43/30—Specific pattern of wells, e.g. optimizing the spacing of wells
    • E—FIXED CONSTRUCTIONS
    • E21—EARTH DRILLING; MINING
    • E21B—EARTH DRILLING, e.g. DEEP DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
    • E21B47/00—Survey of boreholes or wells
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A10/00—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE at coastal zones; at river basins
    • Y02A10/40—Controlling or monitoring, e.g. of flood or hurricane; Forecasting, e.g. risk assessment or mapping

Abstract

The invention provides a method for predicting residual oil in a high-water-cut oil field, which comprises the following steps: step 1, establishing a sample library and carrying out model training; step 2, arranging geological static and development historical data of a research area; step 3, carrying out development stage and well pair division; step 4, calibrating the effective thickness of the stratum; step 5, carrying out partition boundary prediction and superposition; and 6, adjusting scheme design and evaluating the effect. The method for predicting the residual oil in the high-water-cut oil field can realize quantitative evaluation of the residual oil in the later development stage of the high-water-cut oil field, provides effective guidance for oil reservoir production management decision-making, and has wide application prospect in development, adjustment and deployment of the high-water-cut old oil field.

Description

High-water-cut oil field residual oil prediction method
Technical Field
The invention relates to the technical field of oilfield development, in particular to a method for predicting residual oil in a high-water-cut oilfield.
Background
At present, old oil fields in the east of China generally enter development stages with high water content and ultrahigh water content, the distribution of residual oil is complex, and the development effect is continuously poor. The efficiency and the precision of the distribution position of the residual oil are predicted, and the efficiency and the implementation effect of development and adjustment work are directly determined. However, due to the influence of factors such as complex development history and development conditions, the main control factor of the distribution of the residual oil is difficult to judge, the seepage and driving mechanism of the high-water-content old oil field is complex, the residual oil prediction model is difficult to establish, although the numerical simulation method can realize quantitative evaluation, the operation process is complicated and time-consuming, the prediction result has hysteresis, and the requirements of local and scattered development and adjustment of a mine field are difficult to meet.
In the application No.: 201710882860.6, relates to a method for predicting the distribution of residual oil in a high-water-cut reservoir of a water-drive oil reservoir, which comprises the following steps: 1) determining characteristic parameters required for representing the distribution of the residual oil according to the geological research data of the oil reservoir, wherein the characteristic parameters comprise stratum, rock physical parameters and fluid property parameters of the oil reservoir; 2) and establishing a migration model of oil and gas in the oil reservoir high-water-cut reservoir according to the determined characteristic parameters, and determining the quantity of the oil and gas accumulated in the trap during the production stopping period of the oil well according to the established migration model. The method treats the average of single trap as a group of parameters, an oil and gas migration velocity model is deduced and established through Darcy's law, migration results of multiple traps are calculated to determine favorable trap positions, and the method cannot meet the requirement of space refined residual oil research.
In the application No.: 201710080315.5, relates to a method for rapidly identifying secondary enrichment of residual oil in a late period of high water content of a complex fault block oil reservoir, which comprises the following steps: step S10, measuring the target oil deposit to obtain the geological parameters and well pattern parameters of the target oil deposit; step S11, establishing a primary physical model of the target oil reservoir according to the geological parameters and the well pattern parameters of the target oil reservoir; step S12, based on the preliminary physical model of the target oil reservoir, performing fitting calculation according to a streamline flow pipe method to obtain dynamic fitting characteristics of the oil production well and a saturation field before secondary enrichment of residual oil, and correcting the preliminary physical model of the target oil reservoir to obtain a corrected physical model; step S13, based on the corrected physical model, calculating the vertical enrichment and the horizontal enrichment of each node in the residual oil enrichment process respectively to obtain the reservoir saturation and the water content of each node; and finishing the identification of the secondary enrichment of the residual oil in the later period of high water content of the target oil reservoir. The method aims at rapid prediction of residual oil in an oil-water natural separation process of an inclined angle fault block oil reservoir, and obtains a corrected saturation prediction result through theoretical calculation of a flow pipe method so as to make up for the defect that a conventional residual oil prediction method is not suitable for considering a secondary enrichment condition, and the prediction method is also based on calculation of a theoretical formula.
Therefore, a novel method for predicting the residual oil in the high-water-cut oil field is invented, and the technical problems are solved.
Disclosure of Invention
The invention aims to provide a high-water-content oil field residual oil prediction method capable of realizing quantitative evaluation of residual oil in the later development stage of a high-water-content oil field.
The object of the invention can be achieved by the following technical measures: the method for predicting the residual oil in the high-water-cut oil field comprises the following steps: step 1, establishing a sample library and carrying out model training; step 2, arranging geological static and development historical data of a research area; step 3, carrying out development stage and well pair division; step 4, calibrating the effective thickness of the stratum; step 5, carrying out partition boundary prediction and superposition; and 6, adjusting scheme design and evaluating the effect.
The object of the invention can also be achieved by the following technical measures:
in step 1, a typical concept model is established by adopting an oil reservoir numerical simulation method, and for a production well model of one water injection well, the distance (L) from the water injection well to the production well, the stratum thickness (h) and the porosity are changed
Figure BDA0002414733910000021
Permeability (k), crude oil viscosity (μ)o) Relative permeability (k) of oil phasero) Relative permeability of aqueous phase (k)r w ) and the formation dip angle (alpha) are produced in a constant pressure mode until the water content is more than 95 percent, and the accumulative water (N) is countedi) Oil (N) produced by tired productiono) Water content (f)w) And (6) data, and establishing a production dynamic sample library.
In step 1, the oil saturation (S) is adjustedo) Calculating the result, performing statistics, and determining oil saturation limit value (S) for partition by clustering analysis algorithmo1、So2) The plane area is divided into strong displacement areas (3 areas: so≤So1) Weak displacement region (region 2: so1＜So＜So2) And an unused region (region 1: so≥So2) Respectively counting the boundary point distances of the three regions, wherein the boundary point distance between the region 1 and the region 2 is Lc1And the distance between the boundary points of the 2 zone and the 3 zone is Lc2Thereby establishing a partition boundary sample library, and respectively establishing f through a deep learning algorithm1(x)、f2(x) Two partition boundary prediction models:
Figure BDA0002414733910000032
Figure BDA0002414733910000033
in step 2, the geological static and development historical data of the research area are collated, which comprises the following steps: the system comprises oil deposit boundary data, well position coordinates and depth data, oil deposit geological parameters comprise oil deposit thickness, porosity, permeability, fluid physical property parameters and well pattern conversion data, and production dynamic data of each well comprise oil production, oil production and water injection.
In step 3, the target zone development history is divided into a number of well pattern adjustment phases (S)1，S2，...，Sn) Dividing the well into a plurality of injection-production well pairs (w) according to the injection-production corresponding relation1，w2，...，wn) And arranging the accumulated injected water amount of each well pair, marking the injection and production arrow direction and the accumulated injected water amount data of each well pair by taking the water injection well as a starting point and the oil production well as an end point.
In step 4, a production dynamic curve calibration method is adopted to count the actual production data of each oil production well and make a cumulative water and water content scatter diagram (N)i～fw) And accumulated water and accumulated oil scatter diagram (N)i～No) And (4) searching the parameter combination with the highest morphological similarity from the sample library established in the step (1), and taking the average value of the multi-well thickness (h) to obtain the calibrated effective thickness.
In step 5, the partition boundary prediction model f obtained by training in step 1 is adopted1(x)、f2(x) Well pair parameters obtained according to step 3
Figure BDA0002414733910000031
Obtaining a partition location parameter (L) for each wellc1，Lc2) Reconstructing three areas according to the parameters, and respectively assigning corresponding area numbers (3, 2 and 1) to obtain a partition matrix (w)i)；
When the same position is covered by two well pairs, the maximum corresponding element of the two matrixes is takenValue (equation 3) to obtain the partition matrix (S) at this stagei)；
Si＝max{w1，w2，...，wn} (3)
When the same position is covered by two stages, the maximum values of the corresponding elements of the two stage matrixes are superposed (formula 4), so that a final partition matrix (S) is obtained;
S＝max{S1，S2，...，Sn} (4)。
in step 6, N development and adjustment schemes are designed, the partition results of the new scheme are calculated in steps 2-5, the area of the 1 region or the 1+2 region is counted, the smallest area represents the best effect of the scheme, and the control effect of the well pattern on the residual oil is strongest.
The method for predicting the residual oil in the high-water-cut oil field adopts a method of combining numerical simulation and mine field statistics, establishes sample banks of displacement action areas of injection wells under different injection and production conditions, establishes a relation model of influence factors and action ranges through a deep learning algorithm, and produces a residual oil prediction result after overlapping the displacement action ranges of a plurality of well groups of an oil reservoir, thereby overcoming the defects of insufficient quantification and low efficiency of the numerical simulation method in the traditional dynamic analysis method. The method for predicting the residual oil in the high-water-cut oil field can realize quantitative evaluation of the residual oil in the later development stage of the high-water-cut oil field, provides effective guidance for oil reservoir production management decision-making, and has wide application prospect in development, adjustment and deployment of the high-water-cut old oil field.
Drawings
FIG. 1 is a flow chart of an embodiment of a high water cut field remaining oil prediction method of the present invention;
FIG. 2 is a schematic diagram illustrating exemplary model displacement area partitioning in an embodiment of the present invention;
FIG. 3 is a schematic diagram of development stages and well pair division in an embodiment of the present invention;
FIG. 4 is a schematic illustration of effective thickness calibration in an embodiment of the present invention;
FIG. 5 is a diagram illustrating partition boundary prediction and overlay according to an embodiment of the present invention.
Detailed Description
In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
As shown in fig. 1, fig. 1 is a flow chart of the method for predicting residual oil in a high-water-cut oil field according to the present invention.
Step 101, establishing a sample base and training a model
A typical concept model is established by adopting an oil reservoir numerical simulation method, which is usually a model of a single oil production well of a water injection well (figure 2), and the distance (L) between the water injection well and the oil production well, the thickness (h) of a stratum and the porosity are changed
Figure BDA0002414733910000041
Permeability (k), crude oil viscosity (μ)o) Relative permeability curve (k)ro、krw) and formation dip angle (α) and the like, and the production is carried out in a constant pressure mode until the water content is more than 95%, and the accumulated water (N) is countedi) Oil (N) produced by tired productiono) Water content (f)w) And (4) establishing a production dynamic sample library.
For oil saturation (S)o) Calculating the result, performing statistics, and determining oil saturation limit value (S) for partition by clustering analysis algorithmo1、So2) The plane area is divided into strong displacement areas (3 areas: so≤So1) Weak displacement region (region 2: so1＜So＜So2) And an unused region (region 1; so≥So2) Three regions.
In the high water content production stage, the displacement boundary between the oil and water wells usually presents a relatively stable form such as a spindle shape, an oval shape and the like (figure 2), so that approximate reconstruction of the boundary region can be realized by only obtaining the distance (boundary point distance) from the midpoint of the boundary to the midpoint of the oil and water well connecting line, and the boundary point distances (L) of the three regions are respectively countedc1，Lc2) Thereby establishing a 'partition boundary sample library' and respectively establishing f through a deep learning algorithm1(x)、f2(x) Two partition boundary prediction models.
Figure BDA0002414733910000051
Figure BDA0002414733910000052
Step 102, study area data preparation
The method for organizing geological static and development historical data of a research area comprises the following steps: reservoir boundary data, well site coordinates and depth data, reservoir geological parameters (reservoir thickness, porosity, permeability, etc.), fluid physical parameters, well pattern conversion data, and production dynamic data (oil production, water injection, etc.) for each well.
And 103, dividing development stages and well pairs.
Dividing the development history of the target zone into a number of well pattern adjustment stages (S)1，S2，...，Sn) Dividing the well into a plurality of injection-production well pairs (w) according to the injection-production corresponding relation1，w2，...，wn) And (3) arranging the accumulated injected water quantity of each 'well pair', taking the water injection well as a starting point and the oil production well as an end point, and marking the injection and production arrow direction and the accumulated injected water quantity data of each 'well pair' (figure 3).
104, calibrating the effective thickness of the stratum
The reservoir thickness usually refers to geological static thickness, but the actual flow is only a part of the geological static thickness, and in order to obtain a relatively accurate reservoir thickness parameter, a production dynamic curve calibration method is adopted.
The actual production data of each oil production well is counted (figure 4), and a cumulative water and water content scatter diagram is made (N)i～fw) And accumulated water and accumulated oil scatter diagram (N)i～No) And (3) searching the parameter combination with the highest morphological similarity from the 'production dynamic sample library' established in the step (1), and taking the average value of the multi-well thickness (h), namely the calibrated effective thickness.
Step 105, partition boundary prediction and superposition
The 'partition boundary prediction model' f obtained by training in step 1011(x)、f2(x) To transportGo to step 103 to obtain the "well pair" parameter
Figure BDA0002414733910000061
Obtaining respective well pair zone boundary position parameters (L)c1，Lc2) Three regions are reconstructed from the parameters (fig. 5), and corresponding region numbers (3, 2, 1) are assigned to the regions, respectively, to obtain a partition matrix (w)i)。
When the same position is covered by two well pairs, the maximum value (3 formula) of the corresponding elements of the two matrixes is taken, so that a partition matrix (S) of the stage is obtainedi)；
Si＝max{w1，w2，...，wn} (3)
When the same position is covered by two stages, the maximum values of the corresponding elements of the two stage matrixes are superposed (formula 4), so that a final partition matrix (S) is obtained;
S＝max{S1，S2，...，Sn} (4)
the zonal results may direct remaining oil submergence adjustments, for example, the unused zone (zone 1) is the target for scattered new wells and the weak displacement zone (zone 2) is the target for injection and production adjustments.
Step 106, adjusting scheme design and effect evaluation
Designing N development adjustment schemes, calculating the partition result of the new scheme by adopting steps 102-105, counting the area of the 1 region (or 1+2 regions), wherein the smallest area represents the best effect of the scheme, and the control effect of the well pattern on the residual oil is strongest.
In one embodiment of the present invention, the method comprises the following steps:
step 1, establishing an injection-production conceptual model (figure 2) by adopting numerical reservoir simulation software, and designing and considering 7 parameters such as reservoir and fluid
Figure BDA0002414733910000062
2-5 levels of simulated calculation of each parameter, a total of 2500 solutions were obtained. Carrying out simulation calculation on the model, finishing production until the water content is more than 95 percent, and counting oil saturation data of each scheme ([So]) And establishing a production dynamic sample library according to production dynamic data (accumulated water, accumulated oil, water content and the like).
Analyzing the oil saturation data by adopting a clustering algorithm to obtain oil saturation threshold values S for the subareaso1＝0.35、So2Dividing each scheme plane area into three areas of strong drive (3 areas), weak drive (2 areas) and motionless (1 area) and respectively counting the 'dividing point distance' (L) of each schemec1，Lc2) Therefore, a 'partition boundary sample library' is established, and a deep learning algorithm is adopted for training to obtain the relation between 7 parameters and the distance of the boundary point, so that a 'boundary point prediction model' is formed.
Step 2, collating geological static and development historical data of the research area, comprising the following steps: reservoir boundary data, well site coordinates and depth data, reservoir geological parameters (reservoir thickness, porosity, permeability, etc.), fluid physical parameters, well pattern conversion data, and production dynamic data (oil production, water injection, etc.) for each well.
Step 3, dividing development stage and well pair (fig. 3), wherein production of a certain block can be divided into three stages historically: in the first stage (fig. 3a), the well pair I1-P1 is filled with 3.6 ten thousand of water, the well pair I1-P2 is filled with 1.2 ten thousand of water, and the well pair I2-P2 is filled with 2.2 ten thousand of water; in the second stage (fig. 3b), 1.6 ten thousand of water is injected into the I1-P1 well pair, and 1.9 ten thousand of water is injected into the I2-P1 well pair; in the third stage (FIG. 3c), wells I2-P1 are paired with 2.6 ten thousand squares, and wells P2-P1 are paired with 1.9 ten thousand squares.
Step 4, calibrating the effective thickness of the stratum, counting the actual production data of each well group (figure 4), and making a relation graph (N) of the accumulated water and the water contenti～fw) And relation graph of accumulated water and accumulated oil (N)i～No). And (4) searching the parameter combination with the highest morphological similarity from the 'production dynamic sample library' established in the step 101, and taking the average value of the thicknesses (h) as the effective thickness.
Step 5, substituting the ' demarcation point prediction model ' established in the step 1 into the parameters of each ' well pair ', thereby obtaining the zoning limit position (L) of each ' well pairc1，Lc2) Three regions are reconstructed according to the parameters (fig. 5), corresponding region values (3, 2, 1) are respectively assigned, and corresponding partition matrixes are obtained(wi)。
When the same position is covered by two well pairs or two stages, the maximum values of the corresponding elements of the two matrixes are superposed, and the calculation process is shown in the formula (5).
Figure BDA0002414733910000071
And 6, designing 3 development and adjustment schemes, respectively calculating partition results of the schemes by adopting 2-5 calculation processes, and counting the area of the unused area (1 area), wherein the minimum area is the optimal scheme.
The invention solves the problem of prediction of residual oil in high-water-content old oil fields, provides a sample library establishing method combining numerical simulation and mine field statistics, establishes a displacement effect partition boundary prediction model through a deep learning algorithm, and obtains the residual oil distribution of the whole block after the partition results of all well components are superposed.

Claims (8)

1. The method for predicting the residual oil in the high-water-cut oil field is characterized by comprising the following steps of:
step 1, establishing a sample library and carrying out model training;
step 2, arranging geological static and development historical data of a research area;
step 3, carrying out development stage and well pair division;
step 4, calibrating the effective thickness of the stratum;
step 5, carrying out partition boundary prediction and superposition;
and 6, adjusting scheme design and evaluating the effect.
2. The method for predicting the residual oil in the high-water-cut oil field according to claim 1, wherein in the step 1, a typical conceptual model is established by using a numerical reservoir simulation method, and for a production well model of one water injection well, the distance (L) from the water injection well to the production well, the thickness (h) of the stratum and the porosity are changed
Figure FDA0002414733900000013
Permeability (k), crude oil viscosity (μ)0) Relative permeability (k) of oil phasero) Relative permeability of the aqueous phase (K)rw) and the formation dip angle (α) are produced to the water content by adopting a constant pressure mode>95% of water (N) for statisticsi) Oil (N) produced by tired productiono) Water content (f)w) And (6) data, and establishing a production dynamic sample library.
3. The method for predicting residual oil in high-water-cut oilfield according to claim 2, wherein in step 1, the oil saturation (S) is measuredo) Calculating the result, performing statistics, and determining oil saturation limit value (S) for partition by clustering analysis algorithmo1、So2) The plane area is divided into strong displacement areas (3 areas: so≤So1) Weak displacement region (region 2: so1＜So＜So2) And an unused region (region 1: so≥So2) Three regions, wherein the distance between the boundary points of the region 1 and the region 2 is Lc1And the distance between the boundary points of the 2 zone and the 3 zone is Lc2Thereby establishing a partition boundary sample library, and respectively establishing f through a deep learning algorithm1(x)、f2(x) Two partition boundary prediction models:
Figure FDA0002414733900000011
Figure FDA0002414733900000012
4. the method for predicting the residual oil in the high-water-cut oil field according to the claim 1, wherein in the step 2, the geological static and development historical data of the research area are collated, and the method comprises the following steps: the system comprises oil deposit boundary data, well position coordinates and depth data, oil deposit geological parameters comprise oil deposit thickness, porosity, permeability, fluid physical property parameters and well pattern conversion data, and production dynamic data of each well comprise oil production, oil production and water injection.
5. The method of predicting residual oil in high-water-cut oilfield according to claim 1, wherein in step 3, the target zone development history is divided into a plurality of well pattern adjustment stages (S)1，S2，...，Sn) Dividing the well into a plurality of injection-production well pairs (w) according to the injection-production corresponding relation1，w2，...，wn) And arranging the accumulated injected water amount of each well pair, marking the injection and production arrow direction and the accumulated injected water amount data of each well pair by taking the water injection well as a starting point and the oil production well as an end point.
6. The method for predicting residual oil in high-water-cut oilfield according to claim 1, wherein in step 4, the actual production data of each oil production well is counted by using a production dynamic curve calibration method to make an accumulated water and water content scatter diagram (N)i～fw) And accumulated water and accumulated oil scatter diagram (N)i～No) And (4) searching the parameter combination with the highest morphological similarity from the sample library established in the step (1), and taking the average value of the multi-well thickness (h) to obtain the calibrated effective thickness.
7. The method for predicting residual oil in high-water-cut oil field according to claim 1, wherein in step 5, the partition boundary prediction model f obtained by training in step 1 is adopted1(x)、f2(x) Well pair parameters obtained according to step 3
Figure FDA0002414733900000021
Obtaining a partition location parameter (L) for each wellc1，Lc2) Reconstructing three areas according to the parameters, and respectively assigning corresponding area numbers (3, 2 and 1) to obtain a partition matrix (w)i)；
When the same position is covered by two well pairs, the maximum value (3 formula) of the corresponding elements of the two matrixes is taken, so that a partition matrix (S) of the stage is obtainedi)；
Si＝max{w1，w2，...，wn} (3)
When the same position is covered by two stages, the maximum values of the corresponding elements of the two stage matrixes are superposed (formula 4), so that a final partition matrix (S) is obtained;
S＝max{S1，S2，...，Sn} (4)。
8. the method for predicting the residual oil in the high-water-cut oil field according to claim 1, wherein in step 6, N development adjustment schemes are designed, the partition results of the new schemes are calculated by adopting steps 2-5, the area of the 1 region or the 1+2 region is counted, the smallest area represents the best effect of the schemes, and the control effect of the well pattern on the residual oil is the strongest.
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CN112282714A (en) * 2020-11-30 2021-01-29 河海大学 Full well network water injection development optimization method based on deep learning and graph theory
CN112287279A (en) * 2020-09-27 2021-01-29 中国石油天然气股份有限公司 Residual oil prediction method and device
CN112360411A (en) * 2020-11-30 2021-02-12 河海大学 Local well pattern water injection development optimization method based on graph neural network
CN115841181A (en) * 2022-12-02 2023-03-24 昆仑数智科技有限责任公司 Residual oil distribution prediction method, device, equipment and storage medium

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