CN111173507B - High-water-cut oil field residual oil prediction method - Google Patents
High-water-cut oil field residual oil prediction method Download PDFInfo
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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
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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
- 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
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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
- 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
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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
- E21B47/00—Survey of boreholes or wells
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- 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
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.: the Chinese patent application 201710882860.6 relates to a method for predicting the distribution of residual oil in a high-water-cut reservoir of a water-drive reservoir, and the method comprises the following steps of: 1) Determining characteristic parameters required for representing the distribution of the residual oil according to the geological research data of the oil deposit, wherein the characteristic parameters comprise stratum, petrophysical parameters and fluid property parameters of the oil deposit; 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, which relates to a rapid identification method for secondary enrichment of residual oil in the later period of high water content of a complex fault block oil reservoir, and the method comprises the following steps: s10, measuring a target oil reservoir to obtain geological parameters and well pattern parameters of the target oil reservoir; 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 an 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; s13, based on the corrected physical model, calculating vertical enrichment and horizontal enrichment of each node in the residual oil enrichment process to obtain reservoir saturation and 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 oilfield residual oil prediction method capable of realizing quantitative evaluation of residual oil in the later development stage of a high-water-content oilfield.
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
Permeability (k), crude oil viscosity (μ) o ) Relative permeability (k) of oil phase ro ) Relative permeability of aqueous phase (k) rw ) And the formation dip angle (alpha) are produced to the water content by adopting a constant pressure mode>95% of water (N) for statistics i ) Oil production (N) o ) And water content (f) w ) And (6) data, and establishing a production dynamic sample library.
In step 1, the oil saturation (S) is measured o ) Calculating the result, performing statistics, and determining oil saturation limit value (S) for partition by clustering analysis algorithm o1 、S o3 ) The plane area is divided into strong displacement areas (3 areas: s. the o ≤S o1 ) Weak displacement region (region 2: s o1 ＜S o ＜S o2 ) And an unused region (region 1: s o ≥S o2 ) Respectively counting the boundary point distances of the three areas, wherein the boundary point distance between area 1 and area 2 is L c1 And the distance between the boundary points of the 2 zone and the 3 zone is L c2 Thereby establishing a partition boundary sample library, and respectively establishing f through a deep learning algorithm 1 (x)、f 2 (x) Two partition boundary prediction models:
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 including oil deposit thickness, porosity and permeability, fluid physical parameters and well pattern conversion data, and production dynamic data of each well includes 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 ，S 2 ，...，S n ) According to the corresponding relation of injection and productionFor a plurality of injection-production well pairs (w) 1 ，w 2 ，...，w n ) And (4) sorting the accumulated injected water amount of each well pair, and marking the direction of an injection-production arrow 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 ～f W ) And accumulated water and accumulated oil scatter diagram (N) i ～N 0 ) 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 adopted 1 (x)、f 2 (x) Well pair parameters obtained according to step 3
Obtaining a partition location parameter (L) for each well c1 ，L c2 ) 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 obtained i )；
S i ＝max{w 1 ，w 2 ，...，w n } (3)
When the same position is covered by two stages, the maximum values of the elements corresponding to the two stage matrixes are superposed (formula 4), so that a final partition matrix (S) is obtained;
S＝max{S 1 ，S 2 ，...，S n } (4)。
in step 6, N development adjustment schemes are designed, the partition result of the new scheme is 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 zone partitioning in accordance with 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.
Fig. 3 a-the first phase, fig. 3 b-the second phase, fig. 3 c-the third phase.
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.
A typical conceptual model is established by adopting an oil reservoir numerical simulation method, generally a water injection well and an oil production well model (figure 2), and the distance (L) between the water injection well and the oil production well, the thickness (h) of the stratum and the porosity are changed
Permeability (k), crude oil viscosity (μ) o ) Relative permeability curve (k) ro 、k rw ) And the formation dip angle (alpha) and the like, and the water content is produced by adopting a constant pressure mode>95% of water (N) for statistics i ) Oil (N) produced by tired production o ) 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 algorithm o1 、S o2 ) The plane area is divided into strong displacement areas (3 areas: s o ≤S o1 ) Weak displacement zone (zone 2: s o1 ＜S o ＜S o2 ) And an unused region (region 1: s o ≥S o2 ) 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 counted c1 ，L c2 ) So as to establish a 'partition boundary sample library', and respectively establish f through a deep learning algorithm 1 (x)、f 2 (x) Two partition boundary prediction models.
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 ，S 2 ，...，S n ) Dividing the well into a plurality of injection-production well pairs (w) according to the injection-production corresponding relation 1 ，w 2 ，...，w n ) And (3) sorting the accumulated injected water quantity of each well pair, marking the direction of an injection-production arrow and the accumulated injected water quantity data of each well pair by taking the water injection well as a starting point and the oil production well as an end point (figure 3).
104, calibrating the effective thickness of the stratum
The thickness of the oil deposit generally refers to the static thickness of geology, however, the actual flow is only a part of the static thickness of geology, and in order to obtain a relatively accurate oil deposit 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 ～f w ) And accumulated water and accumulated oil scatter diagram (N) i ～N o ) 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.
The partition boundary prediction model f obtained by training in step 101 1 (x)、f 2 (x) Inputting the 'well pair' parameter obtained in step 103
Obtaining respective well pair zone boundary position parameters (L) c1 ，L c2 ) 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 the partition matrix (S) of the stage is obtained i )；
S i ＝max{w 1 ，w 2 ，...，w n } (3)
When the same position is covered by two stages, the maximum values of the elements corresponding to the two stage matrixes are superposed (formula 4), so that a final partition matrix (S) is obtained;
S＝max{S 1 ，S 2 ，...，S n } (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.
N development adjustment schemes are designed, the new scheme is calculated in steps 102-105, the area of a region 1 (or a region 1+ 2) is counted, the scheme has the best effect when the area is the smallest, and the control effect of the well pattern on the residual oil is the strongest.
In one embodiment of the present invention, the method comprises the following steps:
Analyzing the oil saturation data by adopting a clustering algorithm to obtain oil saturation limit values S for the subareas o1 ＝0.35，S o2 =0.56, so as to divide the plan area of each scheme into three areas of strong drive (3 areas), weak drive (2 areas) and motionless (1 area), and respectively count the distance of the 'demarcation point' (L) of each scheme c1 ，L c2 ) So as to establish a ' partition boundary sample library ', and adopt deep learning algorithm training to obtain the relationship between 7 parameters and the distance of the boundary point to form ' partitionsA boundary prediction model ".
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 content i ～f w ) Relation diagram of accumulated water and accumulated oil (N) i ～N o ). 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 pair c1 ，L c2 ) Three regions are reconstructed according to the parameters (fig. 5), corresponding region values (3, 2, 1) are respectively assigned to obtain corresponding partition matrixes (w) i )。
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).
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 a high-water-content old oil field, provides a sample library establishment 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 (1)
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;
step 6, adjusting scheme design and carrying out effect evaluation;
in step 1, a typical concept model is established by adopting an oil reservoir numerical simulation method, and for a model of one oil production well of one water injection well, the distance L from the water injection well to the oil production well, the stratum thickness h and the porosity are changed
Permeability k, crude oil viscosity μ o Relative permeability of oil phase k ro Relative permeability of aqueous phase k rw And the formation dip angle alpha are produced to reach the water content by adopting a constant pressure mode>95% of water, and counting accumulated water N i Oil produced by the oil production process N 0 Water content f w Data, establishing a production dynamic sample library;
for degree of oil saturation S o Carrying out statistics on the calculation results, and determining an oil saturation limit value (S) for the partition by a cluster analysis algorithm o1 、S o2 ) Dividing the plane area into a strong displacement area 3 area: s o ≤S o1 And a weak displacement area 2: s o1 ＜S o ＜S o2 Region 1 for unused region: s o ≥S o2 Three regions, wherein the boundary point distance between region 1 and region 2 is L c1 And the distance between the boundary points of the zone 2 and the zone 3 is L c2 Thereby establishing a partition boundary sample library, and respectively establishing f through a deep learning algorithm 1 (x)、f 2 (x) Two partition boundary prediction models:
in step 2, the geological static and development historical data of the research area are collated, which comprises the following steps: the method comprises the following steps of (1) carrying out oil deposit boundary data, well position coordinates and depth data, wherein oil deposit geological parameters comprise oil deposit thickness, porosity and 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 ，S 2 ，...，S n ) Dividing the well into a plurality of injection-production well pairs (w) according to the injection-production corresponding relation 1 ，w 2 ，...，w n ) The accumulated injected water amount of each well pair is arranged, the injection and production arrow direction and the accumulated injected water amount data of each well pair are marked 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 ～f w ) And accumulated water and accumulated oil scatter diagram (N) i ～N o ) Searching a 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 as the calibrated effective thickness;
in step 5, the partition boundary prediction model f obtained by training in step 1 is adopted 1 (x)、f 2 (x) Well pair parameters obtained according to step 3
Obtaining a partition location parameter (L) for each well c1 ，L c2 ) 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 of the corresponding elements of the two matrixes is taken as the formula 3, so that the partition matrix (S) of the stage is obtained i )；
S i ＝max{w 1 ，w 2 ，...，w n } (3)
When the same position is covered by two stages, taking the maximum value of the corresponding elements of the two stage matrixes to carry out superposition 4, thereby obtaining a final partition matrix (S);
S＝max{S 1 ，S 2 ，...，S n } (4)；
in step 6, N development adjustment schemes are designed, the partitioning result of the new scheme is calculated in steps 2-5, the area of the area 1 or the area 1+2 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.
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