Reservoir probability volume determination method and apparatus

Abstract

This invention provides a method and apparatus for determining reservoir probability volumes. The method includes: determining the boundary values ​​between reservoirs and non-reservoirs using multiple original logging curves from drilled wells; establishing an intersection diagram using the original logging curves and a porosity curve reflecting reservoir quality, and calculating the correlation coefficient between the original logging curves and the porosity curve; establishing a basic model using the original logging curves, using seismic data as a plane constraint, and inverting the attribute volumes of other original logging curves in the target area based on the basic model; calculating the reservoir prediction volumes of other original logging curves using the boundary values ​​between reservoirs and non-reservoirs determined based on each original logging curve and the inverted attribute volumes of each original logging curve; determining the weighting coefficients of other original logging curves based on the correlation coefficients; and calculating the reservoir development probability volume based on the weighting coefficients and the reservoir prediction volumes of other original logging curves. Using this invention, the accuracy of reservoir prediction can be effectively improved.

Description

Technical Field

[0001] This invention relates to the field of oil and gas field exploration technology, specifically to a method and apparatus for determining reservoir probability volumes. Background Technology

[0002] In oilfield development, for target blocks with complex underground conditions, poor resource grades, and large target layer depths—such as complex oil and gas reservoirs including carbonate rocks, tight sandstone, and buried hills—there are significant technical challenges, including the diversity of reservoir rock types, strong heterogeneity, and the difficulty in accurately predicting the spatial distribution patterns of reservoirs. Reservoir prediction not only affects the accuracy of exploration target prediction and the accuracy of oil and gas resource scale description, but also directly impacts the success rate of exploration well deployment. Therefore, how to accurately predict reservoir distribution patterns is of great significance for rapid and efficient exploration work in domestic and international exploration blocks.

[0003] Currently, the mainstream method for reservoir prediction is the application of acoustic impedance reservoir inversion. This method has at least the following problems: In areas with a limited number of drilled wells and complex geological conditions, reservoir inversion is performed using only a single well logging curve and seismic data. Since a single well logging curve from a drilled well cannot accurately predict reservoir development, and in most cases, the correlation between 3D seismic data and a single well logging curve is generally poor, the reservoir prediction results suffer from multiple solutions, leading to inaccurate reservoir predictions. Summary of the Invention

[0004] This invention provides a method and apparatus for determining reservoir probability volumes, which solves the problem of inaccurate reservoir prediction caused by limited drilling data and complex geological conditions in the early stages of exploration, and improves the accuracy of prediction results.

[0005] This invention provides a method for determining reservoir probability volumes, the method comprising:

[0006] Determine the boundary between reservoir and non-reservoir using multiple original logging curves from drilled wells;

[0007] An intersection diagram is established using the original logging curve and the porosity curve, which reflects the reservoir quality, and the correlation coefficient between the original logging curve and the porosity curve is calculated.

[0008] A basic model is established using the original well logging curves, and the seismic data volume is used as a plane constraint. The attribute volumes of other original well logging curves in the target area are obtained by inversion based on the basic model.

[0009] Using the reservoir and non-reservoir boundary values ​​determined based on each original logging curve and the attribute volume of each original logging curve obtained by inversion, the reservoir prediction volume of the other original logging curves is calculated.

[0010] The weighting coefficients of the other original logging curves are determined based on the correlation coefficients. Based on the weighting coefficients and the reservoir prediction volume of the other original logging curves, the reservoir development probability volume is calculated.

[0011] Optionally, the original logging curve is obtained by measuring the characteristics of the drilled well using logging instruments.

[0012] Optionally, the features include any one or more of the following: acoustic features, electrical features, and radioactive features.

[0013] Optionally, determining the boundary value between reservoir and non-reservoir using multiple original logging curves from drilled wells includes:

[0014] A histogram is established using multiple original logging curves from drilled wells and underground reservoir distribution data. The histogram is then used to determine logging curves that can identify reservoirs.

[0015] The boundary between reservoirs and non-reservoirs is determined based on the well logging curves that can identify reservoirs.

[0016] Optionally, the method further includes: determining a porosity curve reflecting the quality of the reservoir through core analysis experiments and well logging curve interpretation.

[0017] Optionally, obtaining the correlation coefficient between the original well logging curve and the porosity curve includes: obtaining the correlation coefficient between the original well logging curve and the porosity curve through fitting.

[0018] Optionally, the step of establishing a basic model using the original well logging curve includes: performing a planar spatial difference calculation using the original well logging curve, and establishing a basic model based on the difference result.

[0019] Optionally, the step of calculating the reservoir prediction body for each original logging line using the reservoir and non-reservoir boundary values ​​determined based on each original logging curve and the attribute volume of each original logging curve obtained through inversion includes:

[0020] By using the reservoir and non-reservoir boundary values ​​determined based on each original logging curve, data in the attribute volume of each inverted original logging curve that are greater than or equal to the boundary values ​​are modified to 1, and data that are less than the boundary values ​​are modified to 0, thus obtaining the reservoir prediction volume for each original logging curve; or

[0021] By using the boundary values ​​of reservoir and non-reservoir determined based on each original logging curve, the data in the attribute body of each original logging curve that are greater than or equal to the boundary value are modified to 0, and the data that are less than the boundary value are modified to 1, thus obtaining the reservoir prediction body of each original logging curve.

[0022] The present invention also provides a reservoir probability volume determination apparatus, the apparatus comprising:

[0023] The boundary value determination module is used to determine the boundary value between reservoir and non-reservoir using multiple original logging curves from drilled wells.

[0024] The correlation coefficient calculation module is used to establish an intersection diagram using the original logging curve and the porosity curve reflecting the reservoir quality, and to calculate the correlation coefficient between the original logging curve and the porosity curve.

[0025] The inversion module is used to establish a basic model using the original well logging curves, and to obtain the attribute volumes of other original well logging curves in the target area based on the seismic data volume as a plane constraint.

[0026] The prediction module is used to calculate the reservoir prediction body of the other original logging curves by using the reservoir and non-reservoir boundary values ​​determined based on each original logging curve and the attribute body of each original logging curve obtained by inversion.

[0027] The probability calculation module is used to determine the weighting coefficients of the other original logging curves based on the correlation coefficients, and to calculate the reservoir development probability volume based on the weighting coefficients and the reservoir prediction volume of the other original logging curves.

[0028] Optionally, the boundary value determination module includes:

[0029] The histogram building unit is used to build a histogram using multiple original logging curves from drilled wells and underground reservoir distribution data, and to use the histogram to determine logging curves that can identify reservoirs;

[0030] The boundary value determination unit is used to determine the boundary value between the reservoir and the non-reservoir based on the well logging curve that can identify the reservoir.

[0031] The reservoir probability volume determination method and apparatus provided by this invention establishes the relationship between original well logging curves and seismic data volumes, inverts the attribute volumes of different original well logging curves, and calculates reservoir prediction volumes for multiple original curves. Simultaneously, it calculates the correlation coefficient between the original well logging curves and the porosity curve, which reflects reservoir quality, and determines the weighting coefficients based on the correlation coefficients to calculate the reservoir development probability volume for multiple reservoir prediction volumes. Using this invention, the accuracy of reservoir prediction can be significantly improved, effectively guiding the selection of favorable exploration targets and well location deployment in the study area, and achieving efficient exploration in the study area. Attached Figure Description

[0032] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly described below. Obviously, the drawings described below are merely some embodiments of the present invention, and those skilled in the art can obtain other drawings based on these drawings without creative effort.

[0033] Figure 1 This is a flowchart of a reservoir probability volume determination method provided in an embodiment of the present invention;

[0034] Figure 2 This is a schematic diagram of four different original logging curves in an embodiment of the present invention;

[0035] Figure 3 This is a schematic diagram of a histogram established based on the original well logging curves and underground reservoir distribution data in an embodiment of the present invention;

[0036] Figure 4 This is an intersection diagram established using the original well logging curve and the porosity curve in an embodiment of the present invention;

[0037] Figure 5 This is a schematic diagram of a reservoir probability determination device provided in an embodiment of the present invention. Detailed Implementation

[0038] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0039] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0040] To address the technical challenge of inaccurate reservoir prediction caused by limited drilling data and complex geological conditions in the early stages of exploration, this invention provides a method and apparatus for determining reservoir probability volumes. This method integrates multiple original curve attribute volumes to achieve accurate reservoir prediction, thereby improving the success rate of well deployment, saving drilling costs, and enhancing exploration efficiency.

[0041] like Figure 1 The diagram shown is a flowchart of a reservoir probability volume determination method provided by an embodiment of the present invention, including the following steps:

[0042] Step 101: Determine the boundary between reservoir and non-reservoir using multiple original logging curves from the drilled well.

[0043] The original logging curve is obtained by measuring the characteristics of the drilled well using logging instruments. These characteristics may include, but are not limited to, any one or more of the following: acoustic characteristics, electrical characteristics, radioactive characteristics, etc.

[0044] The original logging curves may include, for example, curves for sonic transit time, density, and neutrons.

[0045] Specifically, a histogram can be established using multiple original logging curves from drilled wells and underground reservoir distribution data. The histogram can then be used to determine logging curves that can identify reservoirs. Finally, the boundary between reservoirs and non-reservoirs can be determined based on the logging curves that can identify reservoirs.

[0046] A subsurface reservoir refers to a reservoir layer underground capable of storing fluids such as oil, gas, and water. For reservoir development layers in a target block that have already been drilled, the original logging curves can be obtained through drilling, logging, well logging, and testing methods and technologies.

[0047] like Figure 2 The diagram shown is a schematic diagram of four different original logging curves in an embodiment of the present invention.

[0048] MD represents the underground measurement depth of the well. GR, ZDEN, RD, and DT curves are natural gamma ray logging curves, density logging curves, resistivity logging curves, and sonic transit-time logging curves, respectively. These logging curves can indirectly reflect the quality of underground reservoirs and are obtained through specialized logging instruments.

[0049] In practice, a histogram can be created for all original logging curves and reservoir data from completed wells. This allows for the identification of original logging curves that distinguish between reservoirs and non-reservoirs. Ideally, there should be at least three logging curves. The histogram can then be used to determine the boundary values ​​for identifying reservoirs and non-reservoirs.

[0050] For example, for Figure 3 The figure shown is a schematic diagram of a histogram established based on the original well logging curves and underground reservoir distribution data in an embodiment of the present invention.

[0051] Figure 3 The example shown is a histogram of the original sonic transit time curve and the reservoir distribution. The horizontal axis represents the range of sonic transit time values, and the vertical axis represents the probability of the logging curve distribution at a certain sonic transit time value. The green line represents the range of reservoir values, and the brown line represents the range of non-reservoir values.

[0052] If the overlap between the two color ranges is very small (e.g., less than 30%), and can be distinguished by each value of the horizontal axis (the boundary value of this area is around 0.00029), then the logging curve can be considered to be able to distinguish between reservoirs and non-reservoirs, and this logging curve can be selected as the logging curve that can identify reservoirs; otherwise, the corresponding logging curve cannot distinguish between reservoirs and non-reservoirs, and it is not selected.

[0053] As can be seen from the histogram, in this example, the acoustic transit time can be used to effectively identify reservoirs and non-reservoirs.

[0054] Step 102: Establish an intersection diagram using the original logging curve and the porosity curve reflecting the reservoir quality, and calculate the correlation coefficient between the original logging curve and the porosity curve.

[0055] A porosity curve reflects the porosity of a reservoir. Porosity is one of the important parameters that directly reflects the quality of a reservoir, and it can be obtained through core analysis experiments and comprehensive interpretation of well logging curves.

[0056] like Figure 4 The diagram shown is an intersection plot established using the original logging curve and porosity curve in an embodiment of the present invention.

[0057] The horizontal axis represents the range of the porosity curve, and the vertical axis represents the range of the natural gamma curve. Through linear fitting, the correlation coefficient is 0.65.

[0058] Step 103: Establish a basic model using the original well logging curves, use the seismic data volume as a plane constraint, and invert the attribute volumes of other original well logging curves in the target area based on the basic model.

[0059] Specifically, a basic model can be established by performing planar spatial difference calculations using the original curves from multiple drilled wells. This basic model serves as the data foundation for inverting the attribute volume of the original well logging curves. The grid density of the basic model should not be too large; the grid spacing in the X, Y, and Z directions should be as small as possible (50m × 50m × 5m) to improve the accuracy of the curve attribute volume.

[0060] The original logging curve attribute volume refers to a three-dimensional data volume that reflects the characteristics of the original logging curve, which is obtained from the logging curves of multiple drilled wells and the three-dimensional seismic data volume.

[0061] A seismic data volume is a three-dimensional data volume reflecting the properties of underground rock strata, obtained by artificially generating seismic waves into the ground, receiving seismic signals with ground instruments, and processing them. A seismic data volume is also a three-dimensional data network, with each grid having a different amplitude value. However, its vertical resolution is very low, approximately 25 meters × 25 meters × 25 meters per three-dimensional grid. Therefore, its numerical variations can serve as a spatial trend constraint.

[0062] Information about subsurface reservoirs can also be obtained from seismic data volumes. Since seismic data volumes themselves represent seismic waves reflecting subsurface information, inversion involves interpreting this seismic data volume information to obtain actual reservoir data. Simultaneously, different curves can be inverted to obtain other original well logging curve attribute volumes for the target area. Specific inversion methods can employ existing technologies, and this embodiment of the invention does not limit the specific methods used.

[0063] The original logging curve attribute volume can be regarded as a data description of the original logging curve, which is the interpolation prediction result of the original logging curve in three-dimensional space.

[0064] Step 104: Using the reservoir and non-reservoir boundary values ​​determined based on each original logging curve and the attribute volume of each original logging curve obtained by inversion, the reservoir prediction volume of the other original logging curves is calculated.

[0065] The reservoir prediction volume refers to a three-dimensional data volume that reflects whether there is a reservoir underground. The value of a reservoir is set to 1, and the value of a non-reservoir is set to 0. That is, the reservoir prediction volume is a three-dimensional data volume with only 0 and 1 values.

[0066] Specifically, the reservoir and non-reservoir boundary values ​​determined based on each original logging curve can be used to change data points in the logging curve attribute body obtained in step 103 that are greater than or less than the boundary values ​​to 1, and vice versa to 0, thus obtaining the reservoir prediction body of each original logging curve.

[0067] Step 105: Determine the weighting coefficients of the other original logging curves based on the correlation coefficients, and calculate the reservoir development probability volume based on the weighting coefficients and the reservoir prediction volume of the other original logging curves.

[0068] The reservoir development probability volume refers to a three-dimensional data volume that reflects the probability of whether a reservoir exists, with a value range of 0-1.

[0069] Specifically, the weight coefficient of each reservoir prediction body can be determined based on the correlation coefficient between the original logging curve obtained in step 102 and the porosity curve, and the weight coefficients of all reservoir prediction bodies are added together to get 1.

[0070] The weighting coefficients of each reservoir prediction body can be calculated using the following formula (1):

[0071]

[0072] Among them, B i Let a be the weighting coefficient of the i-th original logging curve. i Let be the correlation coefficient of the i-th original curve.

[0073] The formula for calculating the reservoir development probability volume is as follows:

[0074] Z = B1×C1 + B2×C2 + … + B i ×C i (2)

[0075] Where: Z is the reservoir probability, B i C is the weighting coefficient of the i-th original logging curve. i is the reservoir prediction body for the i-th original logging curve.

[0076] Accordingly, the present invention also provides a reservoir probability volume determination device, such as... Figure 5 The diagram shown is a structural schematic of the device.

[0077] The reservoir probability volume determination device 500 includes the following modules:

[0078] The boundary value determination module 501 is used to determine the boundary value between reservoir and non-reservoir using multiple original logging curves from drilled wells.

[0079] The correlation coefficient calculation module 502 is used to establish an intersection diagram using the original logging curve and the porosity curve reflecting the reservoir quality, and to calculate the correlation coefficient between the original logging curve and the porosity curve.

[0080] The inversion module 503 is used to establish a basic model using the original well logging curves, and to obtain the attribute volume of other original well logging curves in the target area based on the seismic data volume as a plane constraint.

[0081] The prediction module 504 is used to calculate the reservoir prediction body of the other original logging curves by using the reservoir and non-reservoir boundary values ​​determined based on each original logging curve and the attribute body of each original logging curve obtained by inversion.

[0082] The probability calculation module 505 is used to determine the weighting coefficients of the other original logging curves based on the correlation coefficients, and to calculate the reservoir development probability volume based on the weighting coefficients and the reservoir prediction volume of the other original logging curves.

[0083] A specific structure of the aforementioned limit value determination module 501 may include the following units:

[0084] The histogram building unit is used to build a histogram using multiple original logging curves from drilled wells and underground reservoir distribution data, and to use the histogram to determine logging curves that can identify reservoirs;

[0085] The boundary value determination unit is used to determine the boundary value between the reservoir and the non-reservoir based on the well logging curve that can identify the reservoir.

[0086] The specific implementation of each module and unit in the reservoir probability volume determination device of the present invention can be referred to the description in the previous method embodiment of the present invention, and will not be repeated here.

[0087] The reservoir probability volume determination method and apparatus provided in this invention establishes the relationship between original well logging curves and seismic data volumes, inverts the attribute volumes of different original well logging curves, and calculates reservoir prediction volumes for multiple original curves. Simultaneously, it calculates the correlation coefficient between the original well logging curves and the porosity curve, which reflects reservoir quality, and determines the weighting coefficients based on the correlation coefficients to calculate the reservoir development probability volume for multiple reservoir prediction volumes. Using this invention, the accuracy of reservoir prediction can be significantly improved, effectively guiding the selection of favorable exploration targets and well location deployment in the study area, thus achieving efficient exploration in the study area.

[0088] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the present invention is not limited to the described order of actions, because according to the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to the present invention.

[0089] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0090] In the several embodiments provided by the present invention, it should be understood that the disclosed apparatus can be implemented in other ways.

[0091] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless means.

[0092] The embodiments of the present invention have been described in detail above. Specific implementation methods have been used to illustrate the present invention. The descriptions of the embodiments above are only for the purpose of helping to understand the methods and systems of the present invention, and are merely some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention, and the content of this specification should not be construed as a limitation of the present invention. Therefore, 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 determining the probability volume of a reservoir, characterized in that, The method includes: Determine the boundary between reservoir and non-reservoir using multiple original logging curves from drilled wells; An intersection diagram is established using the original logging curve and the porosity curve, which reflects the reservoir quality, and the correlation coefficient between the original logging curve and the porosity curve is calculated. A basic model is established using the original well logging curves, and the seismic data volume is used as a plane constraint. The attribute volumes of other original well logging curves in the target area are obtained by inversion based on the basic model. Using the reservoir and non-reservoir boundary values ​​determined based on each original logging curve and the attribute volume of each original logging curve obtained by inversion, the reservoir prediction volume of the other original logging curves is calculated. The weighting coefficients of the other original logging curves are determined based on the correlation coefficients. Based on the weighting coefficients and the reservoir prediction volume of the other original logging curves, the reservoir development probability volume is calculated.

2. The method for determining reservoir probability volume according to claim 1, characterized in that, The original logging curve is obtained by measuring the characteristics of the drilled well using logging instruments.

3. The method for determining reservoir probability volume according to claim 2, characterized in that, The features include any one or more of the following: Acoustic characteristics, electrical characteristics, and radioactive characteristics.

4. The method for determining reservoir probability volume according to claim 1, characterized in that, The method of determining the boundary value between reservoirs and non-reservoirs using multiple original logging curves from drilled wells includes: A histogram is established using multiple original logging curves from drilled wells and underground reservoir distribution data. The histogram is then used to determine logging curves that can identify reservoirs. The boundary between reservoirs and non-reservoirs is determined based on the well logging curves that can identify reservoirs.

5. The method for determining reservoir probability volume according to claim 1, characterized in that, The method further includes: The porosity curve, which reflects the quality of the reservoir, was determined through core analysis experiments and well logging curve interpretation.

6. The method for determining reservoir probability volume according to claim 1, characterized in that, The correlation coefficient between the original well logging curve and the porosity curve is obtained by: The correlation coefficient between the original logging curve and the porosity curve is obtained by fitting.

7. The method for determining reservoir probability volume according to claim 1, characterized in that, The process of establishing a basic model using the original well logging curves includes: The original logging curves are used to perform planar spatial difference calculations, and a basic model is established based on the difference results.

8. The method for determining reservoir probability volume according to claim 1, characterized in that, The reservoir prediction volume for each original logging line is calculated using the reservoir and non-reservoir boundary values ​​determined based on each original logging curve and the attribute volume of each original logging curve obtained through inversion. This includes: By using the reservoir and non-reservoir boundary values ​​determined based on each original logging curve, data in the attribute volume of each inverted original logging curve that are greater than or equal to the boundary values ​​are modified to 1, and data that are less than the boundary values ​​are modified to 0, thus obtaining the reservoir prediction volume for each original logging curve; or By using the boundary values ​​of reservoir and non-reservoir determined based on each original logging curve, the data in the attribute body of each original logging curve that are greater than or equal to the boundary value are modified to 0, and the data that are less than the boundary value are modified to 1, thus obtaining the reservoir prediction body of each original logging curve.

9. A reservoir probability determination device, characterized in that, The device includes: The boundary value determination module is used to determine the boundary value between reservoir and non-reservoir using multiple original logging curves from drilled wells. The correlation coefficient calculation module is used to establish an intersection diagram using the original logging curve and the porosity curve reflecting the reservoir quality, and to calculate the correlation coefficient between the original logging curve and the porosity curve. The inversion module is used to establish a basic model using the original well logging curves, and to obtain the attribute volumes of other original well logging curves in the target area based on the seismic data volume as a plane constraint. The prediction module is used to calculate the reservoir prediction body of the other original logging curves by using the reservoir and non-reservoir boundary values ​​determined based on each original logging curve and the attribute body of each original logging curve obtained by inversion. The probability calculation module is used to determine the weighting coefficients of the other original logging curves based on the correlation coefficients, and to calculate the reservoir development probability volume based on the weighting coefficients and the reservoir prediction volume of the other original logging curves.

10. The reservoir probability determination device according to claim 9, characterized in that, The boundary value determination module includes: The histogram building unit is used to build a histogram using multiple original logging curves from drilled wells and underground reservoir distribution data, and to use the histogram to determine logging curves that can identify reservoirs; The boundary value determination unit is used to determine the boundary value between the reservoir and the non-reservoir based on the well logging curve that can identify the reservoir.

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