Thickened oil reservoir thermal recovery flow field grading evaluation method

Abstract

This invention provides a method for classifying and evaluating the flow field after thermal recovery in heavy oil reservoirs. It classifies the water flooding field, mobility field, and abundance field into distinct levels. The water flooding field, mobility field, and abundance field are more significantly affected by a single factor than the comprehensive flow field. Regarding the water flooding field, the method quantitatively classifies the oil-water distribution within the flow field after the thermal recovery process and classifies the water flooding level. Regarding the mobility field, it classifies the oil-water flow in each grid after the thermal recovery process, determines the two-phase flow trend, and classifies the mobility level. Regarding the abundance field, it calculates the remaining reserves corresponding to each grid and classifies the reserves level. After defining the standards for the three fields, the method integrates their combined influence on the post-thermal recovery flow field of heavy oil reservoirs, establishing a classification and evaluation system for the flow field after thermal recovery in heavy oil reservoirs, thus forming a method for classifying and evaluating the flow field after thermal recovery in heavy oil reservoirs.

Description

Technical Field

[0001] This invention relates to the field of heavy oil reservoirs, and more particularly to a method for classifying and evaluating the flow field during thermal recovery in heavy oil reservoirs. Background Technology

[0002] Oil reservoir development is significantly influenced by the flow field, and heavy oil reservoirs are particularly affected by the physical properties of the crude oil. Compared to conventional waterflooding development, heavy oil reservoirs require more careful attention to the various effects of temperature on the flow field. After large-scale thermal recovery, the remaining oil in heavy oil reservoirs is dispersed and disordered, making it difficult to establish an effective understanding of the reservoir's dynamics. This leads to challenges in adjusting the flow field of heavy oil reservoirs, thus affecting the overall recovery rate.

[0003] Domestic scholars typically characterize flow fields by selecting appropriate flow field evaluation indicators and establishing relevant evaluation methods based on these indicators. However, some of the data selected in these methods are incomplete or contain irrelevant data. Foreign scholars often use streamline simulation to predict reservoir flow field distribution, but this method is greatly affected by complex geological conditions, ultimately leading to simulation failure and significant prediction bias.

[0004] For heavy oil reservoirs, it is impossible to observe underground fluid flow through direct observation. Therefore, it is necessary to use a reasonable combination of relevant indicators and calculations to simulate and evaluate changes in the underground flow field, thereby characterizing the distribution of remaining oil and development potential. This study focuses on describing the water flooding situation, fluid flow, and remaining reservoir reserves within heavy oil reservoirs. Based on this, a multi-factor flow field classification and evaluation system is constructed, and classification standards are determined. This leads to a complete set of reasonable classification and evaluation methods for characterizing and evaluating the post-thermal recovery seepage flow field of heavy oil reservoirs. Summary of the Invention

[0005] In view of the above problems, the present invention is proposed to provide a method for classifying and evaluating the flow field of thermal recovery in heavy oil reservoirs to overcome or at least partially solve the above problems.

[0006] According to one aspect of the present invention, a method for classifying and evaluating the flow field of thermal recovery in heavy oil reservoirs is provided, the evaluation method comprising:

[0007] Establish a geological model of the target heavy oil reservoir and record the basic parameter curves;

[0008] Based on the basic parameter curves, calculate the inter-grid fluid permeability and residual oil saturation of the geological model of the target heavy oil reservoir after thermal recovery, and calculate the temperature change;

[0009] The change in viscosity of the heavy oil is determined based on the temperature change.

[0010] Based on the flow of oil and water phases within the grid of the target heavy oil reservoir geological model, the fluid interpenetration between the grids and the viscosity variation, the water flooding field of the target heavy oil reservoir geological model is calculated, and the water cut distribution is obtained.

[0011] Based on the moisture content distribution, the K-Means clustering algorithm is used to obtain the moisture content classification boundaries, and the flooded fields are classified according to the moisture content classification boundaries.

[0012] Based on the fluid saturation variation and temperature variation of the target heavy oil reservoir geological model, the fluid distribution of the target heavy oil reservoir geological model is calculated, and a mobility field is constructed based on the fluid distribution.

[0013] The K-Means clustering analysis algorithm is used to obtain the classification boundaries of the mobility values, and the mobility field is classified according to the classification boundaries.

[0014] Based on the variations in grid thickness, fluid volume factor, density, and saturation of the target heavy oil reservoir geological model, the remaining reserve abundance of the target heavy oil reservoir geological model is calculated.

[0015] The abundance field of the geological model is constructed based on the remaining reserves abundance, and the abundance field is classified using the K-Means clustering analysis algorithm.

[0016] Optionally, the step of calculating the water flooding field of the target heavy oil reservoir geological model based on the flow of oil and water phases within the grids of the target heavy oil reservoir geological model, the fluid interpenetration between the grids, and the viscosity variation, and calculating the water cut distribution specifically includes:

[0017] Based on the fluid variation in the grid of the geological model of the target heavy oil reservoir, the water cut is calculated from the fluid phase permeability and viscosity variations between the grids. The water cut equation is as follows:

[0018]

[0019] in, These represent the relative permeability of oil and water within the grid. These represent the viscosity of oil and water within the grid, respectively.

[0020] Optionally, calculating the fluid distribution of the target heavy oil reservoir geological model based on the fluid saturation changes and temperature changes specifically includes:

[0021] Based on the temperature and seepage properties of the grid in the geological model of the target reservoir, the mobility is calculated using the viscosity and phase permeability relationship between the oil and water phases. The equation is as follows:

[0022]

[0023] The calculation of the manifold field requires normalization, which is achieved using a linear normalization equation:

[0024]

[0025] Normalized The value range is between 0 and 1. Classify into levels.

[0026] Optionally, calculating the remaining reserves abundance of the target heavy oil reservoir geological model based on the variations in grid thickness, fluid volume factor, density, and saturation of the target heavy oil reservoir geological model specifically includes:

[0027] The equation for reserves abundance is:

[0028]

[0029] in, For the remaining reserves abundance, For reservoir thickness, Porosity This represents the oil saturation of the grid after thermal recovery. This represents the residual oil volume factor in the grid after thermal recovery. Residual oil density;

[0030] The remaining reserves abundance was normalized using a normalization formula:

[0031]

[0032] in, This represents the dimensionless remaining reserves abundance value after normalization. This represents the value with the highest remaining reserves abundance in the geological model of heavy oil reservoirs;

[0033] The abundance field is hierarchically divided according to the remaining reserves abundance of different grids using the K-Means clustering analysis algorithm.

[0034] Optionally, the step of obtaining the water content grading boundaries using the K-Means clustering algorithm based on the water content distribution, and grading the flooded field according to the water content grading boundaries, specifically includes:

[0035] Equations were constructed based on production data from the target heavy oil reservoir during thermal recovery, combined with geological information;

[0036] The initial formula for Darcy's Law was:

[0037] (1)

[0038] In the formula, ρ is the flow rate through the sandstone; k is the sandstone permeability; For fluid viscosity; The cross-sectional area for seepage; To calculate the differential pressure; It is the distance between two seepage sections;

[0039] Converting gravity into pressure difference:

[0040] (2)

[0041] in, To calculate the pressure; The pressure of pressure energy; Because of gravity, therefore Pressure for potential energy, For quality, It is the acceleration due to gravity. For density, For volume, For area, For height;

[0042] Assume the oil layer forms an angle with the horizontal plane. The oil layer length is The flow direction is from 1 to 2, which is a one-dimensional flow, and the cross-sectional area of ​​the oil layer is a point;

[0043] Calculate the equivalent pressure at the injection end and the production end separately:

[0044] The injection end: (3)

[0045] in The calculated pressure at the injection end; The pressure of the injected end pressure energy; The height of the injection end;

[0046] Production side: (4)

[0047] In the formula This refers to the converted pressure at the production end; The pressure of the production end pressure energy; The height of the production end;

[0048] The equivalent pressure difference between the injection end and the production end is:

[0049] (5)

[0050] Assuming the injection end is the reference surface for calculating the equivalent pressure, the equivalent pressure is:

[0051] (6)

[0052] In the formula, To produce pressure differential, ; The height of the production end is given by taking the injection end as the reference plane. According to the Pythagorean theorem, this can be obtained... ;

[0053] Darcy's law considering gravity, The value is L:

[0054] (7)

[0055] Formula (7) can be written in differential form as in formula (8):

[0056] (8)

[0057] In the formula, For pressure gradient;

[0058] For multiphase flow problems, formula (8) is extended from single-phase to oil-water two-phase flow as shown in formula (9):

[0059] (9)

[0060] In the formula, These are the volumetric flow rates of oil and water, respectively. The absolute permeability of the rock; These are the relative permeabilities of oil and water, respectively. These are the viscosities of oil and water, respectively. This is the cross-sectional area for seepage. These are the pressures of the oil phase and the water phase, respectively. These are the densities of oil and water, respectively. It is the acceleration due to gravity; The angle between the oil layer and the horizontal plane;

[0061] After studying Darcy's two-phase law for gravitational potential energy, it is necessary to establish the flow-part equation considering two-phase flow:

[0062] Introducing oil fluidity and water flow As shown in (10):

[0063] (10)

[0064] Substituting formula (10) into formula (9), we get:

[0065] (11)

[0066] Divide both sides of the equation (11) by . Or We can obtain:

[0067] (12)

[0068] (13)

[0069] Subtracting formula (13) from formula (12) yields:

[0070] (14)

[0071] Introducing total volumetric flow rate ,but , ,in Moisture content; capillary force introduced. ,but Introducing density difference ,have to:

[0072] (15)

[0073] From the left half of formula (15), we can obtain:

[0074] (16)

[0075] Substituting formula (16) into the left side of the equal sign in formula (15), we get:

[0076] (17)

[0077] Formula (17) is the flow-part equation for a one-dimensional homogeneous stratum that takes into account capillary force and gravity. As can be seen from Formula (17), oil-water mobility, capillary force, gravity and other factors will affect the flow-part equation.

[0078] The moisture content within each grid is calculated using formula (18):

[0079] (18)

[0080] in, These are the mobility parameters for the oil and water phases, respectively. The cross-sectional area of ​​the grid; This represents the fluid flow rate through the grid per unit time. Capillary pressure within the grid;

[0081] Establish the flow rate equation that does not consider capillary force and gravity:

[0082] (19)

[0083] Formula (19) is a simplification of formula (17), neglecting capillary force. Since gravity is not considered, when the heavy oil reservoir is horizontal, ;

[0084] The moisture content is substituted into the K-Means clustering analysis algorithm to classify the flooding field levels;

[0085] The classification level of flooded areas was determined, ranging from 3 to 7. The K-Means clustering analysis algorithm was used iteratively to calculate the cluster boundaries, resulting in K-1 boundary values. The K-class flooded area level was obtained. The higher the water content, the higher the utilization level and the lower the development potential, and the lower the flooded area level. Conversely, the lower the utilization level and the higher the development potential, the higher the flooded area level.

[0086] Based on the upper and lower limits of each level of water flooding field, the classification criteria are imported into the geological model of heavy oil reservoirs after thermal recovery using the numerical simulation software CMG, and the classified water flooding field map is output.

[0087] Optionally, the step of using the K-Means clustering analysis algorithm to obtain the mobility value classification boundary, and classifying the mobility field according to the mobility classification boundary, specifically includes:

[0088] Collect production data from the target heavy oil reservoir during the thermal recovery process and construct equations based on geological information;

[0089] Based on the calculation of the flood field, the influence of temperature is introduced;

[0090] Substituting the fluidity of water and oil into formula (19), we get:

[0091] (20)

[0092] In the formula, Water saturation;

[0093] The formula for the mobility ratio is:

[0094] (twenty one)

[0095] Substituting formula (21) into formula (20), we get:

[0096] (twenty two)

[0097] Transforming formula (22) and obtaining the mobility ratio M, we get:

[0098] (twenty three)

[0099] The mobility ratio is linearly normalized to facilitate the graded evaluation of mobility field levels.

[0100] (twenty four)

[0101] In the formula, The oil-water two-phase mobility ratio within a single grid. This represents the minimum oil-water two-phase mobility ratio across all grids within the geological model. This represents the maximum mobility ratio of the oil and water phases across all grids within the geological model.

[0102] When the capillary force of the reservoir geological model has a small influence and the formation is horizontal, the fluidity field is inferred by formula (23) with the help of the water flooding field. In most complex reservoir geological models, the fluidity ratio is calculated by formula (21). After normalization by formula (24), the smaller the normalized fluidity value, the larger the affected area, the lower the development potential, and the lower the fluidity field level. The larger the fluidity value, the smaller the utilization area, the higher the development potential, and the higher the fluidity field utilization level.

[0103] Determine the classification level of the flow field, setting it to 3 to 7 classes. Use the K-Means clustering analysis algorithm iteratively to calculate the cluster boundaries, obtaining (K-1) boundary values ​​to obtain K classes of flow field levels. The number of classifications should be consistent with the classification level of the flooded field.

[0104] Based on the upper and lower limits of each level of the mobility field, the classification criteria are imported into the geological model of the heavy oil reservoir after thermal recovery using the numerical simulation software CMG, and the classified mobility field map is output.

[0105] Optionally, the step of constructing an abundance field for a geological model based on the remaining reserve abundance, and classifying the abundance field using the K-Means clustering analysis algorithm, specifically includes:

[0106] The thickness and porosity of the interlayers in the geological model of the heavy oil reservoir were collected, and the oil saturation, volume index and density of each grid in the model after the end of the thermal recovery process of the heavy oil reservoir were statistically analyzed.

[0107] Based on the data from each grid in the reservoir geological model after thermal recovery, the reserves of each grid are calculated, and a formula for calculating the remaining reservoir reserves within each grid after thermal recovery is established:

[0108] (25)

[0109] In the formula, A represents the remaining oil reservoir reserves; A represents the lateral area of ​​the grid. For the first Vertical thickness of the layer mesh; Porosity within the grid; This is the volume factor; The density of crude oil within the grid;

[0110] Calculate the remaining reserve abundance within the grid:

[0111] (26)

[0112] in, The remaining reserves abundance within this grid;

[0113] The calculated values ​​of remaining reserves abundance were normalized:

[0114] (27)

[0115] In the formula, This is the dimensionless remaining reserves abundance value after normalization, with a value range of 0 to 1; This represents the value with the highest remaining reserves abundance in the geological model of heavy oil reservoirs.

[0116] Optionally, the evaluation method further includes:

[0117] Scores are assigned based on the mobilization level types categorized into three physical locations. For example, if a mobilization level K is set... The first-level mobilization level is set with a score of 1 for the flood field, flow field, and abundance field. The first-level mobilization level indicates the highest level of mobilization, reaching the level of mobilization, with no further potential for development.

[0118] The scores for the flood field, flow field, and abundance field of the secondary mobilization level are all 2. The mobilization level of the secondary mobilization level is second only to the primary mobilization level, and its potential for tapping is slightly higher than that of the primary mobilization level.

[0119] The three-level mobilization level is set with a score of 3 for the flood field, flow field, and abundance field. The mobilization level of the third level is lower than that of the first-level and second-level mobilization levels.

[0120] Ultimately, the scores for flooding, flow, and abundance fields were all set to K at the K-level mobilization level. The K-level mobilization level has the lowest mobilization intensity and the greatest potential for tapping into potential.

[0121] Calculate the total mobilization level score = (1 / 3) * flood field mobilization level score + (1 / 3) * flow rate field mobilization level score + (1 / 3) * abundance field mobilization level score; round to obtain the total mobilization level of the flow field.

[0122] This invention provides a method for classifying and evaluating the flow field after thermal recovery in heavy oil reservoirs. It classifies the water-flooded field, mobility field, and abundance field separately, while comprehensively considering the "weakest link effect" of various factors on the flow field after thermal recovery. The water-flooded field, mobility field, and abundance field are more significantly affected by a single factor than the overall flow field. This invention can take into account multiple important factors affecting the flow field distribution, and significant deviations in each factor are directly fed back into the flow field classification. Regarding the water-flooded field, the method quantitatively classifies the oil-water distribution within the flow field after the thermal recovery process and classifies the water-flooding situation. Regarding the mobility field, it classifies the oil-water flow situation in each grid after the thermal recovery process, determines the two-phase flow trend, and classifies the mobility situation within the flow field. Regarding the abundance field, it calculates the remaining reserves corresponding to each grid in the geological model after the thermal recovery process and classifies the reserves within the flow field. After defining the three standards, the comprehensive impact of the three fields on the post-thermal recovery flow field of heavy oil reservoirs is integrated to establish a graded evaluation system for the thermal recovery flow field of heavy oil reservoirs, thus forming a graded evaluation method for the thermal recovery flow field of heavy oil reservoirs.

[0123] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and in order to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0124] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0125] Figure 1 A flowchart of a method for classifying and evaluating the flow field of thermal recovery in heavy oil reservoirs, provided as an embodiment of the present invention;

[0126] Figure 2 This is a schematic diagram illustrating the influence of dip angle and gravity on heavy oil reservoirs.

[0127] Figure 3 This is a schematic diagram of the water flooding field distribution after thermal recovery of heavy oil reservoirs in the example;

[0128] Figure 4 This is a schematic diagram of the flow field distribution after thermal recovery of a heavy oil reservoir, as shown in the example.

[0129] Figure 5 This is a schematic diagram of the abundance field distribution after thermal recovery of heavy oil reservoirs in the example;

[0130] Figure 6 This is a schematic diagram of the flow field distribution after thermal recovery of a heavy oil reservoir, as shown in the example. Detailed Implementation

[0131] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0132] The terms "comprising" and "having," and any variations thereof, in the specification, embodiments, claims, and drawings of this invention are intended to cover non-exclusive inclusion, such as including a series of steps or units.

[0133] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0134] This invention takes the M block of the H oilfield as an example, and evaluates the flow field classification of heavy oil reservoirs after thermal recovery based on the integration of three fields, characterizing its development potential, such as... Figure 1 The process shown includes the following steps:

[0135] Step 1: Establish a geological model of the target heavy oil reservoir and carry out thermal recovery development, and calculate the inter-grid fluid parameters after the development process is completed;

[0136] Step 2: After knowing the fluid parameters and reservoir grid thickness within the geological model of the heavy oil reservoir, calculate specific parameters such as water cut within each grid. The water cut equation is as follows: Figure 2 As shown, The angle between the oil layer and the horizontal plane;

[0137]

[0138] in, These are the mobility parameters for the oil and water phases, respectively, in units of... ; The cross-sectional area of ​​the grid is expressed in units of 1000 ppm. ; The fluid flow rate through the grid per unit time is expressed in units of 1. ; Capillary pressure within the grid, unit: The water content value within each grid is calculated using the above equations.

[0139] Based on the water content grid distribution, the floodfield distribution is calculated using CMG software. Then, combined with the floodfield classification standards, the floodfield classification can be obtained using CMG. Figure 3 As shown;

[0140] Step 3: Given the fluid parameters and temperature variations within the known geological model grid of the heavy oil reservoir, obtain the crude oil viscosity parameters from the viscosity-temperature curve, and calculate the mobility ratio. The equation for calculating the mobility ratio is:

[0141]

[0142] in, , representing the relative permeability of the oil and water phases, dimensionless; Viscosities of oil and water, respectively, in units of... ;

[0143] The mobility is normalized using a linear normalization formula to facilitate grading:

[0144]

[0145] Based on the distribution of the manifold grid, the manifold field distribution is calculated using CMG software. Then, combined with the manifold field classification standard, the manifold field classification can be obtained using CMG, such as... Figure 4 As shown;

[0146] Step 4: Given the fluid parameters and grid thickness distribution within the geological model grid of the heavy oil reservoir, calculate the remaining reserve abundance based on the saturation variation within the grid and relevant parameters of the formation model. The formula for calculating the remaining reserve abundance is as follows:

[0147]

[0148] in, The remaining reserves abundance within this grid, in units of ; For the first Vertical thickness of the layer mesh ; Porosity within the grid, dimensionless; This is the volume index, dimensionless; The density of crude oil within the grid is expressed in units of... ;

[0149] To facilitate the reasonable classification of remaining reserve abundance, the calculated numerical results of remaining reserve abundance are normalized:

[0150]

[0151] Based on the mobility grid distribution, the abundance field distribution is calculated using CMG software. The K-Means algorithm is then used to establish a classification standard for the abundance field, allowing the CMG to be used to obtain the abundance field classification. Figure 5 As shown;

[0152] Step 5: After using CMG to plot the distribution maps of the flooding field, mobility field and abundance field, the flow field of each grid in the heavy oil reservoir is assigned a score according to the graded scoring system, and the grid is divided according to the graded scoring standard.

[0153] This embodiment uses a three-level flow field as the classification type, and combines the scores of the water flooding field, mobility field, and abundance field to classify the post-thermal recovery flow field of heavy oil reservoirs:

[0154]

[0155] The final flow field division was obtained using CMG software, as shown below. Figure 6 As shown, the areas with a flow field level of level 3 and relatively concentrated in the geological model grid after thermal recovery of heavy oil reservoirs have greater development potential and need to be given attention in subsequent development adjustments; the areas with a flow field level of level 1 and relatively concentrated in the geological model have poor development potential.

[0156] Beneficial Effects: By classifying the water-flooded field, mobility field, and abundance field separately, and comprehensively considering the "weakest link effect" of various factors on the flow field after thermal recovery of heavy oil reservoirs, this invention addresses the fact that the water-flooded field, mobility field, and abundance field are more significantly affected by a single factor than the comprehensive flow field. This invention can take into account multiple important factors affecting the flow field distribution, and significant deviations in each factor are directly fed back into the flow field classification. Regarding the water-flooded field, the invention quantitatively classifies the oil-water distribution within the flow field after the thermal recovery process of heavy oil reservoirs and classifies the water-flooding situation within the flow field. Regarding the mobility field, the invention classifies the oil-water flow situation in each grid after the thermal recovery process of heavy oil reservoirs, determines the two-phase flow trend of oil and water, and classifies the mobility situation within the flow field. Regarding the abundance field, after the thermal recovery process, the invention calculates the remaining reserves corresponding to each grid in the geological model and classifies the reserves within the flow field. After defining the three standards, the comprehensive impact of the three fields on the post-thermal recovery flow field of heavy oil reservoirs is integrated to establish a graded evaluation system for the thermal recovery flow field of heavy oil reservoirs, thus forming a graded evaluation method for the thermal recovery flow field of heavy oil reservoirs.

[0157] The above specific embodiments further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for grading evaluation of thermal recovery flow field of heavy oil reservoirs, characterized in that, The evaluation methods include: Establish a geological model of the target heavy oil reservoir and record the basic parameter curves; Based on the basic parameter curves, calculate the inter-grid fluid permeability and residual oil saturation of the geological model of the target heavy oil reservoir after thermal recovery, and calculate the temperature change; The change in viscosity of the heavy oil is determined based on the temperature change. Based on the flow of oil and water phases within the grid of the target heavy oil reservoir geological model, the fluid interpenetration between the grids and the viscosity variation, the water flooding field of the target heavy oil reservoir geological model is calculated, and the water cut distribution is obtained. Based on the moisture content distribution, the K-Means clustering algorithm is used to obtain the moisture content classification boundaries, and the flooded fields are classified according to the moisture content classification boundaries. Based on the fluid saturation variation and temperature variation of the target heavy oil reservoir geological model, the fluid distribution of the target heavy oil reservoir geological model is calculated, and a mobility field is constructed based on the fluid distribution. The K-Means clustering analysis algorithm is used to obtain the classification boundaries of the mobility values, and the mobility field is classified according to the classification boundaries. Based on the variations in grid thickness, fluid volume factor, density, and saturation of the target heavy oil reservoir geological model, the remaining reserve abundance of the target heavy oil reservoir geological model is calculated. The abundance field of the geological model is constructed based on the remaining reserves abundance, and the abundance field is classified using the K-Means clustering analysis algorithm. Scores are assigned based on the mobilization level types categorized into three physical locations. For example, if a mobilization level K is set... The first-level mobilization level is set with a score of 1 for the flood field, flow field, and abundance field. The first-level mobilization level indicates the highest level of mobilization, reaching the level of mobilization, with no further potential for development. The scores for the flood field, flow field, and abundance field of the secondary mobilization level are all 2. The mobilization level of the secondary mobilization level is second only to the primary mobilization level, and its potential for tapping is slightly higher than that of the primary mobilization level. The three-level mobilization level is set with a score of 3 for the flood field, flow field, and abundance field. The mobilization level of the third level is lower than that of the first-level and second-level mobilization levels. Ultimately, the scores for flooding, flow, and abundance fields were all set to K at the K-level mobilization level. The K-level mobilization level has the lowest mobilization intensity and the greatest potential for tapping into potential. Calculate the total mobilization level score = (1 / 3) * flood field mobilization level score + (1 / 3) * flow rate field mobilization level score + (1 / 3) * abundance field mobilization level score; round to obtain the total mobilization level of the flow field.

2. The method for classifying and evaluating the flow field of thermal recovery in heavy oil reservoirs according to claim 1, characterized in that, The calculation of the water flooding field of the target heavy oil reservoir geological model based on the flow of oil and water phases within the grids of the target heavy oil reservoir geological model, the fluid interpenetration between the grids, and the viscosity variation, and the calculation of the water cut distribution specifically include: Based on the fluid variation in the grid of the geological model of the target heavy oil reservoir, the water cut is calculated from the fluid phase permeability and viscosity variations between the grids. The water cut equation is as follows: in, These represent the relative permeability of oil and water within the grid. These represent the viscosity of oil and water within the grid, respectively.

3. The method for classifying and evaluating the thermal recovery flow field of heavy oil reservoirs according to claim 1, characterized in that, The calculation of the fluid distribution of the target heavy oil reservoir geological model based on the fluid saturation changes and temperature changes specifically includes: Based on the temperature and seepage properties of the grid in the geological model of the target reservoir, the mobility is calculated using the viscosity and phase permeability relationship between the oil and water phases. The equation is as follows: in, The mobility ratio within a single grid; The viscosity of the oil within the grid; The viscosity of the water within the grid; The relative permeability of water; The relative permeability of the oil; The calculation of the manifold field requires normalization, which is achieved using a linear normalization equation: normalized mobility ratio the value range is between 0 and 1, and is graded; This represents the minimum oil-water two-phase mobility ratio across all grids within the geological model. This represents the maximum mobility ratio of the oil and water phases across all grids within the geological model.

4. The method according to claim 1, characterized in that, The calculation of the remaining reserves abundance of the target heavy oil reservoir geological model based on the variations in grid thickness, fluid volume factor, density, and saturation of the target heavy oil reservoir geological model specifically includes: The equation for reserves abundance is: in, For the remaining reserves abundance, For reservoir thickness, Porosity This represents the oil saturation of the grid after thermal recovery. This represents the residual oil volume factor in the grid after thermal recovery. Residual oil density; The remaining reserves abundance was normalized using a normalization formula: wherein, is the normalized dimensionless remaining reserves abundance value, is the maximum value of the remaining reserves abundance in the heavy oil reservoir geological model; The abundance field is hierarchically divided according to the remaining reserves abundance of different grids using the K-Means clustering analysis algorithm.

5. The method according to claim 1, characterized in that, The step of obtaining the water content grading boundaries using the K-Means clustering algorithm based on the water content distribution, and then grading the flooded field according to these boundaries, specifically includes: Equations were constructed based on production data from the target heavy oil reservoir during thermal recovery, combined with geological information; The initial formula for Darcy's Law was: (1) wherein is the flow rate through the sandstone; k is the sandstone permeability; is the fluid viscosity; is the seepage cross-sectional area; is the reduced differential pressure; is the distance between the two seepage cross-sections; Converting gravity into pressure difference: (2) wherein, is the reduced pressure; is the pressure of the pressure energy; is the gravitational force, thus is the pressure of the potential energy, is the mass, is the gravitational acceleration, is the density, is the volume, is the area, is the height; The oil layer is at an angle to the horizontal The oil layer has a length The flow direction is from 1 to 2, which is one-dimensional flow, and the cross-sectional area of the oil layer is a point; Calculate the equivalent pressure at the injection end and the production end separately: the injection end: (3) in The calculated pressure at the injection end; The pressure of the injected end pressure energy; The height of the injection end; Production side: (4) wherein is the reduced pressure at the production end; is the pressure of the pressure energy at the production end; is the height of the production end; The equivalent pressure difference between the injection end and the production end is: (5) Assuming the injection end is the reference surface for calculating the equivalent pressure, the equivalent pressure is: (6) In the formula, To produce pressure difference, ; The height of the production end when the injection end is the reference surface, which can be obtained according to the Pythagorean theorem ; Darcy's law in the case of gravity is considered, the value of L: (7) Formula (7) can be written in differential form as in formula (8): (8) In the formula, is the pressure gradient; For multiphase flow problems, formula (8) is extended from single-phase to oil-water two-phase flow as shown in formula (9): (9) In the formula, These are the volumetric flow rates of oil and water, respectively. The absolute permeability of the rock; These are the relative permeabilities of oil and water, respectively. These are the viscosities of oil and water, respectively. This is the cross-sectional area for seepage. These are the pressures of the oil phase and the water phase, respectively. These are the densities of oil and water, respectively. It is the acceleration due to gravity; The angle between the oil layer and the horizontal plane; After studying Darcy's two-phase law for gravitational potential energy, it is necessary to establish the flow-part equation considering two-phase flow: The flow rate of the oil and the flow rate of the water As shown in (10): (10) Substituting formula (10) into formula (9), we get: (11) Dividing both sides of equation (11) by or we obtain (12) (13) Subtracting formula (13) from formula (12) yields: (14) introduced total volumetric flow then , where is the water cut; introduced capillary force then ; introduced density difference gives: (15) From the left half of formula (15), we can obtain: (16) Substituting formula (16) into the left side of the equal sign in formula (15), we get: (17) Formula (17) is the flow-part equation for a one-dimensional homogeneous stratum that takes into account capillary force and gravity. As can be seen from Formula (17), oil-water mobility, capillary force, gravity and other factors will affect the flow-part equation. The moisture content within each grid is calculated using formula (18): (18) wherein, are the mobility parameters for the oil and water phases, respectively; is the cross-sectional area of the grid; is the fluid flow rate through the grid per unit time; is the capillary pressure within the grid; Establish the flow rate equation that does not consider capillary force and gravity: (19) Equation (19) is simplified from equation (17) without considering capillary force, ; since gravity is not considered, the heavy oil reservoir is horizontal, ; The moisture content is substituted into the K-Means clustering analysis algorithm to classify the flooding field levels; The classification level of flooded areas was determined, ranging from 3 to 7. The K-Means clustering analysis algorithm was used iteratively to calculate the cluster boundaries, resulting in K-1 boundary values. The K-class flooded area level was obtained. The higher the water content, the higher the utilization level and the lower the development potential, and the lower the flooded area level. Conversely, the lower the utilization level and the higher the development potential, the higher the flooded area level. Based on the upper and lower limits of each level of water flooding field, the classification criteria are imported into the geological model of heavy oil reservoirs after thermal recovery using the numerical simulation software CMG, and the classified water flooding field map is output.

6. The method for grading evaluation of thermal recovery flow field of heavy oil reservoir according to claim 1, characterized in that, The step of obtaining the mobility grading boundary using the K-Means clustering analysis algorithm and grading the mobility field according to the mobility grading boundary specifically includes: Collect production data from the target heavy oil reservoir during the thermal recovery process and construct equations based on geological information; Based on the calculation of the flood field, the influence of temperature is introduced; Substituting the fluidity of water and oil into formula (19), we get: (20) wherein is the water saturation; is the water cut; is the viscosity of water; is the viscosity of oil; K is the absolute permeability of the rock; is the relative permeability of water; is the relative permeability of oil; The formula for the mobility ratio is: (21) is the mobility ratio; Substituting formula (21) into formula (20), we get: (22) Transforming formula (22) and obtaining the mobility ratio M, we get: (23) The mobility ratio is linearly normalized to facilitate the graded evaluation of mobility field levels. (24) wherein is the oil-water two-phase mobility ratio within a single grid, is the oil-water two-phase minimum mobility ratio within all grids within the geological model, is the oil-water two-phase maximum mobility ratio within all grids within the geological model; When the capillary force of the reservoir geological model has a small influence and the formation is horizontal, the fluidity field is inferred by formula (23) with the help of the water flooding field. In most complex reservoir geological models, the fluidity ratio is calculated by formula (21). After normalization by formula (24), the smaller the normalized fluidity value, the larger the affected area, the lower the development potential, and the lower the fluidity field level. The larger the fluidity value, the smaller the utilization area, the higher the development potential, and the higher the fluidity field utilization level. Determine the classification level of the flow field, setting it to 3 to 7 classes. Use the K-Means clustering analysis algorithm iteratively to calculate the cluster boundaries, obtaining (K-1) boundary values ​​to obtain K classes of flow field levels. The number of classifications should be consistent with the classification level of the flooded field. Based on the upper and lower limits of each level of the mobility field, the classification criteria are imported into the geological model of the heavy oil reservoir after thermal recovery using the numerical simulation software CMG, and the classified mobility field map is output.

7. The method for classifying and evaluating the thermal recovery flow field of heavy oil reservoirs according to claim 1, characterized in that, The process of constructing an abundance field for a geological model based on the remaining reserve abundance, and then classifying the abundance field using the K-Means clustering algorithm, specifically includes: The thickness and porosity of the interlayers in the geological model of the heavy oil reservoir were collected, and the oil saturation, volume index and density of each grid in the model after the end of the thermal recovery process of the heavy oil reservoir were statistically analyzed. Based on the data from each grid in the reservoir geological model after thermal recovery, the reserves of each grid are calculated, and a formula for calculating the remaining reservoir reserves within each grid after thermal recovery is established: (25) In the formula, A represents the remaining oil reservoir reserves; A represents the lateral area of ​​the grid. For the first Vertical thickness of the layer mesh; Porosity within the grid; The residual oil volume factor in the grid after thermal recovery; Residual oil density; This represents the oil saturation of the grid after thermal recovery. Calculate the remaining reserve abundance within the grid: (26) in, The remaining reserves abundance within this grid; The calculated values ​​of remaining reserves abundance were normalized: (27) In the formula, This is the dimensionless remaining reserves abundance value after normalization, with a value range of 0 to 1; This represents the value with the highest remaining reserves abundance in the geological model of heavy oil reservoirs.

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