A method for predicting distribution of remaining oil in top barrier layer of oil sand SAGD development
By integrating static and dynamic data, utilizing lithofacies identification and shading layer discrimination coefficients, and combining dynamic monitoring and three-dimensional models, the problem of quantitative prediction of remaining oil in the top shading layer was solved, thereby improving the recovery rate and economic benefits of SAGD development.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CNOOC ENERGY TECHNOLOGY & SERVICES LTD
- Filing Date
- 2026-03-10
- Publication Date
- 2026-06-12
AI Technical Summary
Existing technologies cannot accurately identify and quantitatively predict the effectiveness of the top shielding layer in SAGD development, making it difficult to quantify the remaining oil at the top and affecting recovery rate and economic benefits.
By integrating static and dynamic data, utilizing lithofacies identification and shading layer discrimination coefficients, and combining dynamic monitoring and three-dimensional models, the distribution of remaining oil in the top shading layer is accurately predicted, and the thickness of remaining oil is quantitatively calculated using the Kriging interpolation algorithm.
It enables accurate quantitative prediction of remaining oil in the top shielding layer, providing a direct basis for decision-making on deploying adjustment wells and optimizing production parameters, thereby improving recovery rate and economic benefits.
Smart Images
Figure CN122196670A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of steam-assisted gravity drainage (SAGD) development technology for heavy oil reservoirs, specifically relating to a method for predicting the distribution of residual oil in the top shielding layer during SAGD development of oil sands. Background Technology
[0002] Steam-assisted gravity drainage (SAGD) is a highly efficient thermal recovery technology for developing extra-heavy oil and oil sands resources. During SAGD, steam forms a steam chamber above the injection well, and the heated crude oil flows downwards to the production well under gravity. Theoretically, the steam chamber should extend uniformly upwards and laterally. However, under actual geological conditions, low-permeability blocking layers such as mudstone and silty mudstone often develop at the top of the reservoir. These blocking layers hinder the vertical migration of steam, leading to the formation of "attic oil" or "top residual oil," severely impacting the recovery rate and economic benefits of SAGD.
[0003] Currently, the identification and prediction of residual oil at the top of the steam vent faces the following challenges: Static data has limitations. While static data such as core samples and well logs can identify the presence of a shielding layer, they cannot accurately determine the effectiveness of this shielding layer under dynamic thermal conditions (i.e., whether it can effectively seal off steam in the long term). Dynamic data also has limitations. While dynamic data such as temperature monitoring and 4D seismic data can depict the macroscopic morphology and temperature field of the steam cavity, they cannot directly and quantitatively provide the thickness and distribution range of the residual oil.
[0004] Therefore, there is an urgent need in this field for a new method that can deeply integrate dynamic and static data to achieve accurate and quantitative prediction of the remaining oil in the top shielding layer of SAGD. Summary of the Invention
[0005] This invention is proposed to solve the problem of the inability to quantitatively predict the distribution of residual oil at the top in the existing technology. Its purpose is to provide a method for predicting the distribution of residual oil in the top shading layer of oil sands based on the fusion of dynamic and static data in SAGD development.
[0006] This invention is achieved through the following technical solution: A method for predicting the distribution of residual oil in the top shading layer during SAGD development of oil sands includes the following steps: S1. Identify lithofacies based on static data and establish quantitative standards for effective shielding layers: The static data in step S1 includes core sampling and lithological description, as well as well logging curves for clay content. The method for identifying lithofacies involves classifying sandstone and mudstone types based on static data. The specific calculation formula for identifying lithofacies is as follows: LithFacies=IF(Vsh≤a,4,IF(Vsh≤b,3,IF(Vsh≤c,2,1))) In the formula: LithFacies is the lithofacies, Vsh is the argillaceous content, a is the argillaceous content limit of pure sandstone, b is the argillaceous content limit of argillaceous sandstone SandIHS, and c is the argillaceous content limit of sandy mudstone MudIHS. a, b, and c are all rock electrical response limits, which are determined based on the logging-core calibration of the target area. When LithFacies=1, the lithofacies is pure mudstone; When LithFacies=2, the lithofacies is sandy mudstone MudIHS; When LithFacies=3, the lithofacies is argillaceous sandstone SandIHS; When LithFacies=4, the lithofacies is pure sandstone; The boundary for lithofacies division in the calculation formula for lithofacies identification can be adjusted according to the division scheme of the target area; In step S1, the quantitative standard for the effective shielding layer is the determination of the lower limit of the key parameters of the shielding layer that can effectively block the penetration of steam or heat. The shielding layer discrimination coefficient is a prerequisite for determining the key parameters of the shielding layer. The shielding layer discrimination coefficient is the identification standard for effective shielding after the expansion of the steam cavity is relatively stable within a predetermined time t. The predetermined time period t years is generally 5 years, and can be defined according to the situation of the target area; The key parameters of the shielding layer that can effectively block the penetration of steam or heat are the clay content or the effective shielding layer thickness. The quantitative standard for the effective shielding layer is the static geological basis for judging whether the shielding layer is "effective"; the thickness and mud content of the effective shielding layer are statistically analyzed to form a lower limit discrimination chart for the target area; The formula for calculating the discrimination coefficient of the shading layer is: Q =IF((LithFacies=1 or LithFacies=2)and( ≥10 and ≥5) in: In the above formula: Q For depth The discrimination coefficient of the shielding layer at the location, under lithofacies conditions where shielding is required, is either LithFacies=1 or LithFacies=2, when Q =1 indicates an effective shielding layer; Assuming the expansion of the steam chamber is within a relatively stable given time t, Temperature difference at depth, in °C; For depth range [ , The average geothermal gradient within the area, in °C / m; In order to be in Temperature measured at depth, in °C; In depth The temperature measured at the location is in °C. The average temperature of the temperature plateau (a temperature plateau is a relatively flat, nearly horizontal shape in a continuous temperature measurement curve, in stark contrast to the large temperature gradients above and below it; the average temperature of the temperature plateau is the average value of that segment), is expressed in °C. <1, = ; Assuming production and steam injection conditions remain constant, from year one to year five (times t=1 to t=5), if Q... If the value is always 1, then the occlusion layer is effective at that depth; S2. Generate a three-dimensional model of the steam cavity based on dynamic monitoring data; The dynamic data includes temperature monitoring data and four-dimensional seismic monitoring data; The steam cavity three-dimensional model is a three-dimensional qualitative temperature volume, which is based on the temperature-sensitive characteristics of oil sand and establishes a quantitative relationship between oil sand sensitive parameters and temperature. By using four-dimensional seismic inversion to retrieve the steam drive sensitive parameter three-dimensional volume, and taking temperature monitoring wells as a basis, the temperature of the three-dimensional volume is calibrated to predict high-temperature, medium-temperature, and low-temperature zones. The steam cavity three-dimensional model can reflect the high-temperature, medium-temperature, and low-temperature zones. S3. Identify the potential area at the top of the effective shielding layer through the fusion and expansion analysis of dynamic and static data; The specific method for the external expansion analysis of dynamic and static data fusion is as follows: S31. Determine the matching relationship between the boundary positions of the high-temperature zone, medium-temperature zone, and low-temperature zone of the steam chamber of the temperature measuring well and the effective shielding layer, according to industry practice. S32. By comparing the sedimentation cycles of the unmeasured wells and the measured wells, and combining the shielding layer discrimination coefficient with the boundary positions of the low temperature zone, medium temperature zone and high temperature zone of the steam cavity determined in step S31, the top of the steam cavity of the evaluation wells other than the measured wells is identified on the profile. S33. Taking the temperature measurement well as the center, extrapolate from the inside to the outside in three-dimensional space until the unshielded layer develops or the three-dimensional model of the steam cavity drops to the low temperature zone, thereby delineating the range of the potential area at the top of the effective shielding layer. S4. Under the constraint of the planar boundary of the steam cavity, use spatial interpolation algorithm to predict and generate the distribution data of the remaining oil thickness in the potential zone at the top of the effective shielding layer; The spatial interpolation algorithm in step S4 is the Kriging interpolation algorithm; The Kriging interpolation algorithm is performed in Petrel, a commonly used software in the petroleum industry. During the interpolation process, the potential area at the top of the effective shielding layer is used as the boundary constraint condition, and the interpolation calculation is only performed within this area. The interpolation variable in the Kriging interpolation algorithm is the remaining oil thickness at the wellpoint from the depth of the steam chamber top to the top of the upper formation, which is determined by a combination of well and seismic testing. The specific calculation formula is as follows: In the above formula: Lithofacies factors, It is either pure sandstone or dipping interbedded argillaceous sandstone (SandIHS); Water saturation factor To meet the reservoir condition factor, It is an oil layer; The remaining oil thickness in the potential zone at the top of the effective shielding layer, in meters; The sampling interval for well logging is in meters (m).
[0007] The beneficial effects of this invention are: This invention provides a method for predicting the distribution of remaining oil in the top shielding layer of oil sands SAGD based on the fusion of dynamic and static data. By combining geological static characteristics with production dynamic response, it achieves deep fusion of multiple data and high-precision prediction, which can accurately delineate the potential area of "effective shielding" and quantitatively predict the distribution of remaining oil thickness, providing a direct and reliable decision-making basis for subsequent deployment of adjustment wells (such as top drilling wells) or optimization of production parameters. Attached Figure Description
[0008] Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is the lower limit identification diagram of the occlusion layer in this invention; Figure 3 This is a three-dimensional model of the temperature chamber and steam chamber in this invention; Figure 4 This is a schematic cross-sectional view (connected well profile + temperature chamber) of the potential identification area by fusing dynamic and static data in this invention. Figure 5 This is a plan view showing the final predicted distribution of the remaining oil thickness at the top in this invention.
[0009] For those skilled in the art, other related figures can be obtained from the above figures without any creative effort. Detailed Implementation
[0010] To enable those skilled in the art to better understand the technical solution of the present invention, the technical solution of the present invention will be further described below with reference to the accompanying drawings and specific embodiments.
[0011] Example 1 After five years of production, the recovery rate of a certain SAGD development block was lower than expected. Geological analysis indicated that discontinuous mudstone shielding layers were prevalent at the top of the reservoir, suspected to be the main reason for the remaining oil at the top. Based on logging data from 50 wells in the block, 26 core and temperature monitoring wells, and a three-dimensional volume of steam drive sensitive parameters derived from 4D seismic inversion, this study selected the areas containing wells A (core and temperature monitoring well), B (core and non-temperature monitoring well), and C (core and non-temperature monitoring well) as examples to predict the distribution of remaining oil in the top shielding layer of oil sands SAGD based on the fusion of dynamic and static data. Figure 1 As shown, it specifically includes the following: S1. Identify lithofacies based on static data and establish quantitative standards for effective shielding layers: Logging data from 50 wells within the block (focusing on gamma ray GR curves) and core descriptions from 26 cored wells were collected; a clay content (Vsh) calculation model was established using core calibration logging. LithFacies=IF(Vsh≤10,4,IF(Vsh≤30,3,IF(Vsh≤50,2,1))) In the formula: LithFacies represents lithofacies, and Vsh represents argillaceous content; When LithFacies=1, the lithofacies is pure mudstone; When LithFacies=2, the lithofacies is MudIHS, an inclined interbedded sandy mudstone. When LithFacies=3, the lithofacies is inclined interbedded argillaceous sandstone SandIHS; When LithFacies=4, the lithofacies is pure sandstone; Based on the mud content, the above formula was used to classify the lithofacies, and the classification results are shown in Table 1.
[0012] Table 1: Lithofacies classification and example of data from Well A Establish a standard for "effective shielding layer": Analyze 26 temperature monitoring wells with 5 years of production history within the block, statistically analyze the mud content and thickness of the shielding layer that can effectively block steam, and form a lower limit discrimination chart for the target area. Figure 2 );when ≥10℃, When the value is ≥5, it is defined as an effective occlusion layer Q, and the discrimination formula is as follows: Q =IF((LithFacies=1 or LithFacies=2)and( >=10 and >=5), 1, 0) Q = IF(Q =1, 1, 0) (t∈[1,5]) Wherein: the expansion of the steam chamber occurs at a relatively stable time t. for Temperature difference at depth, in °C; exist Temperature measured at depth, in °C; when <1, = This is the average temperature of the temperature plateau, expressed in °C. In the depth range [ , The average geothermal gradient within the area, in °C / m; In depth The temperature measured at the location is in °C. In depth The temperature measured at Q is in °C. For depth The discrimination coefficient of the shielding layer at the location, under lithofacies conditions where shielding is required, is either LithFacies=1 or LithFacies=2, when Q =1 constitutes a shielding layer; assuming production and steam injection conditions remain constant, from the first year to the fifth year (time t=1 to t=5), if Q If the value is always 1, then the shielding layer is effective at that depth. The statistical data of the effective shielding layer in well A are shown in Table 2. Table 2: Statistical analysis of effective shielding layer identification data (taking well A as an example) Based on the statistical results, a chart for identifying the lower limit of the occlusion layer was drawn. Figure 2It was found that when the clay content of a certain layer is greater than or equal to 60% and the continuous thickness is >1.5m, it can effectively block steam penetration within a development period of 5 years. The quantitative standard for the "effective shielding layer" of this block was determined as follows: Continuous thickness H ≥ 1.5 meters Average clay content (Vsh) ≥ 60% During the 5 years of production, the temperature difference ΔT at its top remained ≥ 10℃. S2. Generate a dynamic steam chamber model: Integrating downhole temperature monitoring data from 26 wells in the block and 4D seismic differential data, and based on the block's oil sand temperature-sensitive characteristics, a quantitative relationship between oil sand sensitive parameters and temperature was established. Using specialized software, a 3D volume of steam drive sensitive parameters was inverted via 4D seismic analysis. Based on temperature monitoring wells, the temperature of the 3D volume was calibrated. Referring to the block's viscosity-temperature curve, the high-temperature, medium-temperature, and low-temperature zones were predicted, generating a 3D model of the steam cavity. Figure 3 The data clearly shows that the steam chamber expands well laterally, but has an uneven top, suggesting it may be segmented and suppressed by a shielding layer. The steam chamber temperature zoning criteria are shown in Table 3.
[0013] Table 3: Definition of Temperature Zones in the Steam Chamber S3, Dynamic and Static Integration to Define Potential Zones S31, Sectional Analysis ( Figure 4 ): A well profile connecting A1 (temperature measuring well), B2 (non-temperature measuring well), and C3 (non-temperature measuring well) will be selected for illustration: First, observe the temperature-monitoring wells: At well A, static data shows a 2-meter-thick mudstone layer at the top (meeting the criteria for a shielding layer), while temperature monitoring shows a temperature of 60℃ (low-temperature zone). This indicates that the shielding layer here is "effective," and there is residual oil above it. Mark this point as the core potential point. Then, extend the extrapolation: Using well A as the center, trace laterally along the shielding layer. In the adjacent well B (no temperature monitoring), static data also shows a qualified shielding layer. Combined with the 3D temperature volume, this location is also within the low-temperature zone. Therefore, the area around well B is also included in the potential zone. Continuing to extrapolate to area C, although the static shielding layer is developed, the 3D temperature volume shows that this area has become a high-temperature zone, indicating that steam has broken through the shielding layer. This area is not considered a potential zone.
[0014] Table 4: Dynamic and Static Fusion Analysis of Well Profiles (Data in the table below needs to be verified) S32. Three-Dimensional Spatial Expansion: Using a temperature-measuring well like Well A as the core control point, the model traces outwards along the standard shielding layer. The tracing boundary is set as follows: the boundary of the temperature zone (80℃) in the vapor chamber on the inner boundary; and the location where the shielding layer pinches out or its thickness / mud content becomes substandard on the outer boundary. Through this point-to-surface fusion analysis, a remaining oil potential zone of approximately 1.84 km² is ultimately delineated in three-dimensional space, with the top being an effective shielding layer. 2 .
[0015] S4. Kriging interpolation prediction of remaining oil thickness: S41. Calculate the remaining oil thickness at the well point: Within the delineated potential zone, 15 wells are located. For these wells, calculate the thickness from the top boundary of the steam cavity (interpreted by the dynamic model) to the top boundary of the upper formation (interpreted by the static model), as the remaining oil thickness at that well point, using the following formula: In the above formula: Lithofacies factors, It is either pure sandstone or dipping interbedded argillaceous sandstone (SandIHS); Water saturation factor To meet the reservoir condition factor, It is an oil layer; The effective thickness of the oil layer is expressed in meters (m). The sampling interval for well logging is in meters (m).
[0016] The remaining oil thickness data for some well points within the potential zones of each well are shown in Table 5 below. Table 5: Remaining oil thickness data at some well points in the potential zone S42. In Petrel software, the potential zone boundary is used as a constraint polygon, and the data in Table 5 is used as input points. A standard Kriging algorithm is then used for grid interpolation. This ultimately generates a planar map showing the distribution of the remaining oil thickness at the top. Figure 5 The figure shows that the remaining oil thickness in the central area of the potential zone can reach 10m, which is the "sweet spot" area with the richest remaining oil, while the thickness in the edge area is only 2m to 4m.
[0017] This embodiment successfully quantifies the distribution of remaining oil in the top shielding layer of SAGD through deep fusion of dynamic and static data, providing an intuitive basis for oilfield development decisions.
[0018] The applicant declares that the above description is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Those skilled in the art should understand that any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention fall within the protection and disclosure scope of the present invention.
Claims
1. A method for predicting the distribution of residual oil in the top shading layer during SAGD development of oil sands, characterized in that: Includes the following steps: S1. Identify lithofacies based on static data and establish quantitative standards for effective shielding layers: S2. Generate a three-dimensional model of the steam cavity based on dynamic monitoring data; S3. Identify the potential area at the top of the effective shielding layer through the fusion and expansion analysis of dynamic and static data; S4. Under the constraint of the steam cavity planar boundary, use spatial interpolation algorithm to predict and generate the distribution data of the remaining oil thickness in the potential zone at the top of the effective shielding layer.
2. The method for predicting the distribution of residual oil in the top shading layer of oil sands SAGD development according to claim 1, characterized in that: The static data in step S1 includes core sampling and lithological description, as well as well logging curves for clay content.
3. The method for predicting the distribution of residual oil in the top shading layer of oil sands SAGD development according to claim 1, characterized in that: The method for identifying lithofacies in step S1 is to classify sandstone and mudstone types based on static data. The specific calculation formula for identifying lithofacies is as follows: LithFacies=IF(Vsh≤a,4,IF(Vsh≤b,3,IF(Vsh≤c,2,1))) In the formula: LithFacies is the lithofacies, Vsh is the mud content, and a, b, and c are the limits of rock electrical response; When LithFacies=1, the lithofacies is mudstone; When LithFacies=2, the lithofacies is sandy mudstone MudIHS; When LithFacies=3, the lithofacies is argillaceous sandstone SandIHS; When LithFacies=4, the lithofacies is pure sandstone.
4. The method for predicting the distribution of residual oil in the top shading layer of oil sands SAGD development according to claim 1, characterized in that: The quantitative standard for the effective shielding layer in step S1 is the lower limit of the key parameters of the shielding layer that can effectively block the penetration of steam or heat within a predetermined time t after the expansion of the steam cavity has reached a relatively stable state.
5. The method for predicting the distribution of residual oil in the top shading layer of oil sands SAGD development according to claim 4, characterized in that: The key parameters of the shielding layer that can effectively block the penetration of steam or heat are the clay content or the effective shielding layer thickness. The formula for calculating the discrimination coefficient of the shading layer is: Q =IF((LithFacies=1 or LithFacies=2)and( ≥10 and ≥5) in: In the above formula: Q For depth The discrimination coefficient of the shielding layer at the location, under lithofacies conditions where shielding is required, is 1 or 2, when Q =1 indicates an effective shielding layer; Assuming the expansion of the steam chamber is within a relatively stable given time t, Temperature difference at depth, in °C; For depth range [ , The average geothermal gradient within the area, in °C / m; In order to be in Temperature measured at depth, in °C; In depth The temperature measured at the location is in °C. The average temperature of the temperature platform, in °C. <1, = ; Assuming production and steam injection conditions remain unchanged, within a given time period t, if Q If the value is always 1, then the occlusion layer is effective at that depth.
6. The method for predicting the distribution of residual oil in the top shading layer of oil sands SAGD development according to claim 1, characterized in that: The dynamic data in step S2 includes temperature monitoring data and four-dimensional seismic monitoring data; the three-dimensional model of the steam cavity in step S2 is a three-dimensional qualitative temperature volume, which is a quantitative relationship between oil sand sensitive parameters and temperature established based on the temperature-sensitive characteristics of oil sand. By inverting the three-dimensional volume of steam drive sensitive parameters through four-dimensional seismic inversion, and using the temperature monitoring well as a basis, the temperature of the three-dimensional volume is calibrated, thereby predicting the high-temperature zone, the medium-temperature zone and the low-temperature zone.
7. The method for predicting the distribution of residual oil in the top shading layer of oil sands SAGD development according to claim 1, characterized in that: The specific method for the dynamic and static data fusion and expansion analysis in step S3 is as follows: S31. Determine the boundary locations of the high-temperature zone, medium-temperature zone, and low-temperature zone of the steam chamber of the temperature measuring well, and their matching relationship with the effective shielding layer, according to industry practice. S32. Compare the sedimentation cycles of the unmeasured wells and the measured wells, and combine the shielding layer standard with the boundary positions of the low temperature zone, medium temperature zone and high temperature zone of the steam cavity determined in step S31 to identify the top of the steam cavity of the evaluation wells other than the measured wells on the profile. S33. Centered on the temperature measurement well, extrapolate from the inside out in three-dimensional space until the unshielded layer develops or the temperature of the steam cavity drops to a low temperature zone, thereby delineating the range of the potential area at the top of the effective shielding layer.
8. The method for predicting the distribution of residual oil in the top shading layer of oil sands SAGD development according to claim 1, characterized in that: The spatial interpolation algorithm in step S4 is the Kriging interpolation algorithm; The Kriging interpolation algorithm is performed in Petrel, a commonly used software in the petroleum industry. During the interpolation process, the top remaining oil potential zone is used as the boundary constraint condition, and the interpolation calculation is only performed within this zone. The interpolation variable in the Kriging interpolation algorithm is the remaining oil thickness at the wellpoint from the depth of the steam chamber top to the top of the upper formation, which is determined by a combination of well and seismic testing. The specific calculation formula is as follows: In the above formula: Lithofacies factors, It is either pure sandstone or dipping interbedded argillaceous sandstone (SandIHS); Water saturation factor To meet the reservoir condition factor, It is an oil layer; The effective thickness of the oil layer is expressed in meters (m). The sampling interval for well logging is in meters (m).