Method for quantitatively identifying dominant seepage channel of medium-high permeability oil field
Through quantitative identification methods, including obtaining historical well network data, establishing a fuzzy comprehensive evaluation method model and using the ensemble Kalman filtering assimilation method, the dominant seepage channels of medium and high permeability oil fields were successfully identified, solving the problem of low oilfield recovery rate, and achieving more efficient oilfield development.
Patent Information
- Application Number
- CN202510036921.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-01-09
- Publication Date
- 2025-02-28
AI Technical Summary
The prior art is difficult to effectively identify the dominant seepage channels of medium and high permeability oil fields, which makes it difficult to improve the oil field recovery rate.
Quantitative identification method is adopted to obtain historical well network adjustment data, determine the water production of well networks and oil wells, establish a quantitative identification model based on fuzzy comprehensive judgment method, and verify the model using the ensemble Kalman filter assimilation method to achieve quantitative identification of advantageous seepage channels.
It improves the identification effect of the advantageous seepage channels of medium and high permeability oil fields, is easy to operate and promote, and provides a theoretical basis for the later-stage deep drive adjustment or well network adjustment of the oil field.
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Figure CN119531866A_ABST
Abstract
Description
Technical Field
[0001] The invention relates to the technical field of oilfield development, and in particular to a method for quantitatively identifying dominant seepage channels in medium- and high-permeability oilfields. Background Art
[0002] When the oil reservoir enters water drive exploitation, due to the different permeabilities of various parts of the reservoir, the injected water will preferentially flow to the high permeability layer with smaller seepage resistance, and through long-term water injection to flush and soak the reservoir, the rock particles in the reservoir skeleton will fall and migrate. Under the action of hydrodynamics, the rock skeleton structure of the high permeability layer changes, causing the parameters of the entire reservoir to change accordingly. Due to the change in reservoir parameters, the seepage will be different. Over a long period of time, the difference will become larger and larger, and most of the fluid will only flow to the main channel with smaller seepage resistance, and the water drive exploitation effect will gradually decrease, thus forming a dominant seepage channel.
[0003] Predominant seepage channels are also called large pores, cross-flow channels, and high-permeability strips. Predominant seepage channels can lead to ineffective circulation. At the same time, the dominant flow field and inter-well connectivity can also show the development of the dominant seepage channels. Studies have shown that the dispersion and migration of core particles are the main reasons for the formation of the dominant seepage channels. The dominant seepage channels have the following characteristics: reduced clay mineral and mud content; changes in wettability and hydrophilicity, and phase permeability curves. Long-term water flooding has caused changes in the wettability and phase permeability of reservoir rocks; low oil saturation, close to residual oil saturation; large flow rate, large cumulative water overflow multiples; increased crude oil viscosity. The viscosity and relative density of crude oil gradually increase with the increase in the amount of injected water during the development process. At present, most oil fields in my country have entered a high water content period, and the dominant seepage channels between oil and water wells are generally developed, which seriously restricts the improvement of oil field recovery. How to effectively identify the dominant seepage channels in medium and high permeability oil fields is a problem that still needs to be solved.
[0004] Therefore, it is an urgent problem for those skilled in the art to propose a method for quantitatively identifying the dominant seepage channels in medium and high permeability oil fields to solve the difficulties existing in the prior art. Summary of the invention
[0005] In view of this, the present invention provides a method for quantitatively identifying dominant seepage channels in medium- and high-permeability oil fields, which effectively improves the identification effect of dominant seepage channels in medium- and high-permeability oil fields, is easy to operate and promote, and can provide a theoretical basis for later deep flooding or well network adjustment in oil fields.
[0006] In order to achieve the above object, the present invention adopts the following technical solution:
[0007] A method for quantitatively identifying dominant seepage channels in medium- and high-permeability oil fields comprises the following steps:
[0008] Obtain historical well network adjustment data for the area to be identified to determine the injection-production well network, determine the water production of oil wells in the injection-production well network, and determine the standard for dividing the dominant seepage channels;
[0009] Establish a quantitative identification model based on fuzzy comprehensive evaluation method;
[0010] The established quantitative identification model and the determined division criteria of dominant seepage channels were verified based on the ensemble Kalman filter assimilation method, and the quantitative identification of dominant seepage channels was achieved.
[0011] The above method optionally includes counting water injection wells and oil production wells to determine the injection and production well network, and selecting the initial permeability and oil washing efficiency as two indicators to determine the standard for dividing the dominant seepage channels.
[0012] In the above method, optionally, the specific content of determining the initial permeability index is:
[0013] The larger the initial permeability, the larger the water flow multiple under the condition of uniform well pattern. When the permeability change multiple is 2.8, the corresponding value x is taken as the initial permeability index of the dominant seepage channel.
[0014] The specific contents of the oil washing efficiency index are as follows:
[0015] Count the initial oil saturation, current oil saturation, original permeability, and current permeability of all grids;
[0016] Divide the saturation intervals, and then classify all grids according to the saturation intervals. Each saturation interval is a class, and the permeability value of a saturation interval is the average permeability value of all grids in this saturation interval;
[0017] Calculate the average permeability change multiple of the grid in each saturation interval, and convert the saturation interval into a grid oil washing efficiency interval. The oil saturation statistics are in a certain interval, and the middle value of the oil saturation interval is taken to replace the interval. The value y when the saturation of the dominant seepage channel is close to or even lower than the residual oil saturation is taken as the oil washing efficiency index of the dominant seepage channel.
[0018] The above method is optional. When the oil washing efficiency is ≥y and the initial permeability is ≥x, the comprehensive discrimination coefficient is 1, otherwise it is 0; when the comprehensive discrimination coefficient is 1, the grid is a dominant seepage channel, and when the comprehensive discrimination coefficient is 0, the grid is not a dominant seepage channel.
[0019] The above method is optional, and the specific content of establishing a quantitative identification model based on the fuzzy comprehensive evaluation method is:
[0020] Obtain a first influencing factor and a second influencing factor to obtain an influencing factor parameter set;
[0021] Based on the coefficient of variation method, the weight of the influencing factor parameter set is determined to obtain the first weight value H j and the second weight value H d ;
[0022] The influencing factor parameter set is standardized based on the minimum-maximum standardization method;
[0023] The first weight value and the second weight value are multiplied and then added, which is the first comprehensive evaluation index S of the dominant seepage channel. j The second comprehensive evaluation index S is obtained by the same method d , multiply the first comprehensive evaluation index and the second comprehensive evaluation index by their respective weights and add them together to obtain the comprehensive evaluation index R, as shown in the following formula:
[0024] R=S j H j +S d H d .
[0025] In the above method, optionally, the first influencing factor includes permeability, porosity, cumulative water injection per unit thickness, permeability difference, permeability variation coefficient, permeability breakthrough coefficient, fluid conduction between injection and production wells, permeability change value, water saturation change value, and water injection efficiency;
[0026] The second influencing factors include water content, daily liquid production, liquid production intensity, cumulative liquid production per unit thickness, and effective reservoir thickness.
[0027] The above method can optionally verify the established quantitative identification model and the determined dominant seepage channel division standard based on the ensemble Kalman filter assimilation method, and the specific content of the quantitative identification of the dominant seepage channel is as follows:
[0028] The ensemble Kalman filter assimilation method is introduced, and the history fitting solution of unknown parameters is performed with the help of Matlab. The ensemble Kalman filter assimilation method is divided into two steps, the prediction step and the update step; among them,
[0029] The parameters of the quantitative identification model are updated based on the established quantitative identification model. In the updating step, the parameters of the quantitative identification model are corrected based on the predicted results and the actual observed values. By comparing the difference between the result values of the quantitative identification model and the actual values, the quantitative identification model and the determined dominant seepage channel division criteria are verified, thereby realizing the quantitative identification of the dominant seepage channel.
[0030] It can be seen from the above technical solution that, compared with the prior art, the present invention provides a method for quantitatively identifying the dominant seepage channels in medium- and high-permeability oil fields, which has the following beneficial effects:
[0031] (1) The present invention obtains the historical well network adjustment data of the area to be identified to determine the injection and production well network, and determines the water production of the oil wells in the injection and production well network, and determines the division standard of the dominant seepage channel; a quantitative identification model is established based on the fuzzy comprehensive evaluation method; the established quantitative identification model and the determined division standard of the dominant seepage channel are verified based on the ensemble Kalman filter assimilation method, thereby realizing the quantitative identification of the dominant seepage channel.
[0032] (2) The present invention effectively improves the recognition effect of the dominant seepage channels in medium- and high-permeability oil fields, is easy to operate and promote, and can provide a theoretical basis for the later deep flooding or well network adjustment of the oil field. BRIEF DESCRIPTION OF THE DRAWINGS
[0033] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.
[0034] Figure 1 A flow chart of a method for quantitatively identifying dominant seepage channels in medium- and high-permeability oil fields provided by the present invention. DETAILED DESCRIPTION
[0035] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.
[0036] Reference Figure 1 As shown, the present invention discloses a method for quantitatively identifying the dominant seepage channels in medium- and high-permeability oil fields, comprising the following steps:
[0037] Obtain historical well network adjustment data for the area to be identified to determine the injection-production well network, determine the water production of oil wells in the injection-production well network, and determine the standard for dividing the dominant seepage channels;
[0038] Establish a quantitative identification model based on fuzzy comprehensive evaluation method;
[0039] The established quantitative identification model and the determined division criteria of dominant seepage channels were verified based on the ensemble Kalman filter assimilation method, and the quantitative identification of dominant seepage channels was achieved.
[0040] Furthermore, statistics of water injection wells and oil production wells were conducted to determine the injection and production well network, and the initial permeability and oil washing efficiency were selected as two indicators to determine the standard for dividing the dominant seepage channels.
[0041] Furthermore, the specific content of the initial permeability index is determined as follows:
[0042] The larger the initial permeability, the larger the water flow multiple under the condition of uniform well pattern. When the permeability change multiple is 2.8, the corresponding value x is taken as the initial permeability index of the dominant seepage channel.
[0043] The specific contents of the oil washing efficiency index are as follows:
[0044] Count the initial oil saturation, current oil saturation, original permeability, and current permeability of all grids;
[0045] Divide the saturation intervals, and then classify all grids according to the saturation intervals. Each saturation interval is a class, and the permeability value of a saturation interval is the average permeability value of all grids in this saturation interval;
[0046] Calculate the average permeability change multiple of the grid in each saturation interval, and convert the saturation interval into a grid oil washing efficiency interval. The oil saturation statistics are in a certain interval, and the middle value of the oil saturation interval is taken to replace the interval. The value y when the saturation of the dominant seepage channel is close to or even lower than the residual oil saturation is taken as the oil washing efficiency index of the dominant seepage channel.
[0047] Furthermore, when the oil washing efficiency ≥ y and the initial permeability ≥ x, the comprehensive discriminant coefficient is 1, otherwise it is 0; when the comprehensive discriminant coefficient is 1, the grid is a dominant seepage channel, and when the comprehensive discriminant coefficient is 0, the grid is not a dominant seepage channel.
[0048] Specifically, y is taken as 63% as the oil washing efficiency judgment condition of the dominant seepage channel, and x is taken as 2.4 μm. 2 .
[0049] Furthermore, the specific contents of establishing a quantitative identification model based on the fuzzy comprehensive evaluation method are as follows:
[0050] Obtain a first influencing factor and a second influencing factor to obtain an influencing factor parameter set;
[0051] Based on the coefficient of variation method, the weight of the influencing factor parameter set is determined to obtain the first weight value H j and the second weight value H d ;
[0052] The influencing factor parameter set is standardized based on the minimum-maximum standardization method;
[0053] The first weight value and the second weight value are multiplied and then added, which is the first comprehensive evaluation index S of the dominant seepage channel. j The second comprehensive evaluation index S is obtained by the same method d, multiply the first comprehensive evaluation index and the second comprehensive evaluation index by their respective weights and add them together to obtain the comprehensive evaluation index R, as shown in the following formula:
[0054] R=S j H j +S d H d .
[0055] Specifically, the coefficient of variation method is an objective value assignment method that directly uses the given data for mathematical calculations to determine the weight ratio of different attribute indicators in the comprehensive evaluation, avoiding the interference of human factors in the weighting process.
[0056] The minimum-maximum standardization method assumes that minA and maxA are the minimum and maximum values of parameter A, respectively. One of the parameter values x of A is mapped to the range of [0, 1] through "minimum-maximum standardization" to obtain a new value. The formula is:
[0057]
[0058] After the above standardization processing, the original data can be converted into comparable dimensionless values, that is, the values of each parameter are in the same order of magnitude.
[0059] Furthermore, the first influencing factors include permeability, porosity, cumulative water injection per unit thickness, permeability difference, permeability variation coefficient, permeability breakthrough coefficient, fluid conduction between injection and production wells, permeability change value, water saturation change value, and water injection efficiency;
[0060] The second influencing factors include water content, daily liquid production, liquid production intensity, cumulative liquid production per unit thickness, and effective reservoir thickness.
[0061] Furthermore, the established quantitative identification model and the determined division criteria of the dominant seepage channels were verified based on the ensemble Kalman filter assimilation method, and the specific contents of the quantitative identification of the dominant seepage channels were realized as follows:
[0062] The ensemble Kalman filter assimilation method is introduced, and the history fitting solution of unknown parameters is performed with the help of Matlab. The ensemble Kalman filter assimilation method is divided into two steps, the prediction step and the update step; among them,
[0063] The parameters of the quantitative identification model are updated based on the established quantitative identification model. In the updating step, the parameters of the quantitative identification model are corrected based on the predicted results and the actual observed values. By comparing the difference between the result values of the quantitative identification model and the actual values, the quantitative identification model and the determined dominant seepage channel division criteria are verified, thereby realizing the quantitative identification of the dominant seepage channel.
[0064] In a specific embodiment, the following contents are included:
[0065] A block in a certain oil field belongs to a layered anticline with a gas cap. With the continuous advancement of the development of the fault area, the three major contradictions of the plane, interlayer and intralayer of the oil reservoir are increasingly intensified, and the development effect of the oil reservoir is declining. In order to reverse the passive situation of the block's production, the quantitative identification method of the dominant seepage channel of the present invention is adopted to determine that the block is flooded and there are multiple dominant seepage channels between the injection and production wells.
[0066] By determining the water production of oil wells in the injection-production well network, the division standard of dominant seepage channels is determined; a quantitative identification model is established based on the fuzzy comprehensive evaluation method; the established quantitative identification model and the determined division standard of dominant seepage channels are verified based on the ensemble Kalman filter assimilation method, thus realizing the quantitative identification of dominant seepage channels.
[0067] By observing the injection and production dynamics in real time, the water injection plan can be further optimized and the water injection volume can be adjusted, which can reduce the ineffective water injection volume, reduce production costs, and improve oil production efficiency and economic benefits.
[0068] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.
[0069] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.
Claims
1. A method for quantitatively identifying the dominant seepage channels in medium- and high-permeability oil fields, characterized in that: The following steps are involved: Obtain historical well network adjustment data for the area to be identified to determine the injection-production well network, determine the water production of oil wells in the injection-production well network, and determine the standard for dividing the dominant seepage channels; Establish a quantitative recognition model based on fuzzy comprehensive evaluation method; Based on the ensemble Kalman filter assimilation method, the established quantitative identification model and the determined dominant seepage channel division criteria were verified, and the quantitative identification of dominant seepage channels was achieved.
2. The method for quantitatively identifying the dominant seepage channels in medium- and high-permeability oil fields according to claim 1, characterized in that: The water injection wells and oil production wells are counted to determine the injection and production well network, and the initial permeability and oil washing efficiency are selected as two indicators to determine the standard for dividing the dominant seepage channels.
3. The method for quantitatively identifying the dominant seepage channels in medium- and high-permeability oil fields according to claim 2, characterized in that: The specific contents of the initial permeability index are as follows: The larger the initial permeability, the larger the water flow multiple under the condition of uniform well pattern. When the permeability change multiple is 2.8, the corresponding value x is taken as the initial permeability index of the dominant seepage channel. The specific contents of the oil washing efficiency index are as follows: Count the initial oil saturation, current oil saturation, original permeability, and current permeability of all grids; Divide the saturation intervals, and then classify all grids according to the saturation intervals. Each saturation interval is a class, and the permeability value of a saturation interval is the average permeability value of all grids in this saturation interval; Calculate the average permeability change multiple of the grid in each saturation interval, and convert the saturation interval into a grid oil washing efficiency interval. The oil saturation statistics are in a certain interval, and the middle value of the oil saturation interval is taken to replace the interval. The value y when the saturation of the dominant seepage channel is close to or even lower than the residual oil saturation is taken as the oil washing efficiency index of the dominant seepage channel.
4. The method for quantitatively identifying the dominant seepage channels in medium- and high-permeability oil fields according to claim 3, characterized in that: When the oil washing efficiency is ≥y and the initial permeability is ≥x, the comprehensive discriminant coefficient is 1, otherwise it is 0; when the comprehensive discriminant coefficient is 1, the grid is a dominant seepage channel, and when the comprehensive discriminant coefficient is 0, the grid is not a dominant seepage channel.
5. The method for quantitatively identifying the dominant seepage channels in medium- and high-permeability oil fields according to claim 1, characterized in that: The specific contents of establishing a quantitative identification model based on the fuzzy comprehensive evaluation method are as follows: Obtain a first influencing factor and a second influencing factor to obtain an influencing factor parameter set; Based on the coefficient of variation method, the weight of the influencing factor parameter set is determined to obtain the first weight value H j and the second weight value H d ; The influencing factor parameter set is standardized based on the minimum-maximum standardization method; The first weight value and the second weight value are multiplied and then added, which is the first comprehensive evaluation index S of the dominant seepage channel. j The second comprehensive evaluation index S is obtained by the same method d , multiply the first comprehensive evaluation index and the second comprehensive evaluation index by their respective weights and add them together to obtain the comprehensive evaluation index R, as shown in the following formula: R=S j H j +S d H d 。 6. The method for quantitatively identifying the dominant seepage channels in medium- and high-permeability oil fields according to claim 5, characterized in that: The first influencing factors include permeability, porosity, cumulative water injection per unit thickness, permeability difference, permeability variation coefficient, permeability breakthrough coefficient, fluid conduction between injection and production wells, permeability change value, water saturation change value, and water injection efficiency; The second influencing factors include water content, daily liquid production, liquid production intensity, cumulative liquid production per unit thickness, and effective reservoir thickness.
7. The method for quantitatively identifying the dominant seepage channels in medium- and high-permeability oil fields according to claim 1, characterized in that: Based on the ensemble Kalman filter assimilation method, the established quantitative identification model and the determined division criteria of the dominant seepage channels are verified, and the specific contents of the quantitative identification of the dominant seepage channels are as follows: The ensemble Kalman filter assimilation method is introduced, and the history fitting solution of unknown parameters is performed with the help of Matlab. The ensemble Kalman filter assimilation method is divided into two steps, the prediction step and the update step; among them, The parameters of the quantitative identification model are updated based on the established quantitative identification model. In the updating step, the parameters of the quantitative identification model are corrected based on the predicted results and the actual observed values. By comparing the difference between the result values of the quantitative identification model and the actual values, the quantitative identification model and the determined dominant seepage channel division criteria are verified, thereby realizing the quantitative identification of the dominant seepage channel.