Method and device for predicting reservoir thickness based on multi-attribute fusion of forward simulation

Abstract

This invention provides a method and apparatus for predicting reservoir thickness based on multi-attribute fusion using forward modeling. The method includes: interpreting the seismic sequence constraining reservoir units based on well-seismic calibration results; performing forward modeling on a geological model of the reservoir in the study area to obtain the seismic response characteristics of the reservoir; determining multiple sensitive seismic attributes that characterize reservoir thickness based on the seismic response characteristics; determining a fitting relationship between the values ​​of the multiple sensitive seismic attributes and the reservoir thickness value in the geological model; and predicting the actual reservoir thickness in the study area based on the fitting relationship. This method solves the problem of low prediction accuracy caused by the lack of seismic attribute screening before multi-attribute fusion in existing technologies; it improves the accuracy of reservoir thickness prediction and reduces errors in the reservoir thickness prediction process.

Description

Technical Field

[0001] This invention relates to the field of carbonate reservoir prediction in the petroleum industry, and particularly to a method and apparatus for predicting reservoir thickness based on multi-attribute fusion using forward modeling. Background Technology

[0002] Seismic attributes are geophysical parameters derived mathematically from seismic data, characterizing the geometric morphology, kinematics, dynamics, and statistical features of various seismic waves. They provide a good description of the lithology, physical properties, and hydrocarbon content of actual strata, representing a comprehensive response to various geophysical information within seismic data. Analyzing seismic attribute parameters allows for a direct understanding of the characteristics of different reservoirs, enabling qualitative reservoir analysis.

[0003] However, the relationship between seismic attributes and reservoir thickness is not a simple one-to-one correspondence, but a complex nonlinear relationship involving multiple factors. For reservoirs with strong heterogeneity, the factors affecting useful seismic information will increase, resulting in greater uncertainty. Therefore, it is difficult to accurately predict reservoir thickness by considering only one seismic attribute. Summary of the Invention

[0004] In view of the above problems, the present invention is proposed to provide a method and apparatus for predicting reservoir thickness based on forward modeling multi-attribute fusion to overcome or at least partially solve the above problems.

[0005] In a first aspect, embodiments of the present invention provide a method for predicting reservoir thickness based on multi-attribute fusion using forward modeling, comprising:

[0006] Interpret the seismic sequence of the constrained reservoir units based on the well seismic calibration results;

[0007] Forward modeling was performed on the geological model of the reservoir in the study area to obtain the seismic response characteristics of the reservoir;

[0008] Based on the seismic response characteristics of the reservoir, several sensitive seismic attributes that can characterize the reservoir thickness are determined.

[0009] Based on the values ​​of multiple sensitive seismic attributes and reservoir thickness in the geological model, the fitting relationship between the values ​​of sensitive seismic attributes and reservoir thickness is determined; based on the fitting relationship, the actual reservoir thickness in the study area is predicted.

[0010] In one embodiment, interpreting the seismic sequence of the constrained reservoir unit based on the well seismic calibration results includes:

[0011] The geological model is established by comparing well profiles. Forward modeling simulations are performed on the geological model using forward wavelets of different frequencies. The forward wavelet that makes the seismic reflection characteristics of the geological model clearest is selected from the forward wavelets of different frequencies as the preferred forward wavelet. The original seismic body is frequency-divided using the preferred forward wavelet to obtain the preferred frequency body. Based on the well-seismic calibration results, seismic sequence identification is carried out on the preferred frequency body to complete the seismic sequence interpretation of the constrained reservoir units.

[0012] In one embodiment, the forward modeling of the geological model of the reservoir in the study area to obtain the seismic response characteristics of the reservoir includes:

[0013] Based on the formation velocity parameters of different sedimentary types in the geological model, the preferred forward wavelet is used to conduct forward modeling simulations on geological models with different reservoir development patterns to obtain the seismic response characteristics of the reservoirs.

[0014] In one embodiment, determining multiple sensitive seismic attributes characterizing reservoir thickness based on the seismic response characteristics of the reservoir includes:

[0015] From the seismic attributes corresponding to the seismic response characteristics, multiple seismic attributes that have a correlation with reservoir thickness higher than a preset correlation threshold and belong to different seismic attribute types are selected as the sensitive seismic attributes; the seismic attribute types include: amplitude attribute, frequency attribute and phase attribute.

[0016] In one embodiment, determining the fitting relationship between the values ​​of the multiple sensitive seismic attributes and the reservoir thickness based on the values ​​of the reservoir thickness in the geological model includes:

[0017] For each sensitive attribute, the value of the sensitive seismic attribute in the geological model is initially fitted to the value of the reservoir thickness according to the fitting function corresponding to multiple preset fitting relationships. The correlation value between the value of the sensitive seismic attribute and the reservoir thickness under each fitting relationship is calculated. The fitting relationship with the highest correlation value is selected as the initial fitting relationship between the value of the sensitive seismic attribute and the reservoir thickness. The multiple fitting relationships include any one or more of the following combinations: linear function relationship, exponential function relationship and logarithmic function relationship.

[0018] In one embodiment, after selecting the fitting relationship with the highest correlation value as the initial fitting relationship between the sensitive seismic attribute value and the reservoir thickness, the method further includes:

[0019] Based on the values ​​of the sensitive attributes, the planar distribution of the fitted reservoir thickness is obtained through the preliminary fitting relationship;

[0020] The absolute errors between the actual reservoir thickness values ​​from drilling and those calculated using various fitted relationships are statistically analyzed. The absolute errors are then weighted and averaged to obtain the average error.

[0021] The average error is substituted into the preliminary fitting relationship for correction to obtain the fitting relationship between the value of the sensitive seismic attribute and the reservoir thickness.

[0022] In one embodiment, predicting the actual reservoir thickness in the study area based on the fitting relationship includes:

[0023] The correlation values ​​of the fitting relationship between the values ​​of multiple sensitive seismic attributes and reservoir thickness are added together to obtain the sum of the correlation values;

[0024] Divide the correlation value of each sensitive earthquake attribute by the sum value to obtain the weight coefficient of each sensitive earthquake attribute;

[0025] Based on the weight coefficients of each sensitive seismic attribute and the fitting relationship corresponding to each sensitive seismic attribute, a multi-attribute weighted fusion formula for predicting reservoir thickness using sensitive seismic attributes is determined.

[0026] The values ​​of the seismically sensitive attributes of the study area are substituted into the multi-attribute weighted fusion formula to predict the actual reservoir thickness in the study area.

[0027] Secondly, embodiments of the present invention provide an apparatus for predicting reservoir thickness based on multi-attribute fusion using forward modeling, comprising:

[0028] The parameter acquisition module is used to interpret the seismic sequence of constrained reservoir units based on the well seismic calibration results;

[0029] The forward modeling module is used to perform forward modeling of geological models of reservoirs in the study area to obtain the seismic response characteristics of the reservoirs.

[0030] The preferred feature module is used to determine multiple sensitive seismic attributes that can characterize the reservoir thickness based on the seismic response characteristics of the reservoir.

[0031] The fitting module is used to determine the fitting relationship between the values ​​of the sensitive seismic attributes and the reservoir thickness based on the values ​​of the multiple sensitive seismic attributes and the values ​​of the reservoir thickness in the geological model.

[0032] The prediction module is used to predict the thickness of the actual reservoir in the study area based on the fitting relationship.

[0033] Thirdly, embodiments of the present invention provide a computing device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the program executed by the processor is a method for predicting reservoir thickness based on forward modeling multi-attribute fusion.

[0034] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the method for predicting reservoir thickness based on forward modeling multi-attribute fusion.

[0035] The beneficial effects of the above-described technical solutions provided in the embodiments of the present invention include at least the following:

[0036] This invention provides a method and apparatus for predicting reservoir thickness based on multi-attribute fusion using forward modeling. The method involves interpreting the seismic sequence constraining reservoir units based on well-seismic calibration results; conducting forward modeling on a geological model of the reservoir in the study area to obtain the reservoir's seismic response characteristics; identifying multiple sensitive seismic attributes that characterize reservoir thickness based on these characteristics; determining the fitting relationship between the values ​​of these sensitive seismic attributes and the reservoir thickness value in the geological model; and predicting the actual reservoir thickness in the study area based on this fitting relationship. This invention utilizes the seismic response characteristics obtained from forward modeling to select sensitive seismic attributes that are sensitive to reservoir thickness, and then weights and fuses these sensitive seismic attributes. This ensures that seismic attributes sensitive to formation thickness are selected before the seismic attribute fusion, achieving accurate reservoir thickness prediction. This improves the accuracy of reservoir thickness prediction and reduces errors in the prediction process.

[0037] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings.

[0038] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0039] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0040] Figure 1 A flowchart of a method for predicting reservoir thickness based on multi-attribute fusion using forward modeling, provided in an embodiment of the present invention;

[0041] Figure 2 Forward modeling of Carboniferous reservoirs in a certain area of ​​a basin provided in this embodiment of the invention;

[0042] Figure 3This invention provides a relationship between the width of the bottom trough of the Qixia Formation overlying the Carboniferous system and the reservoir thickness in a certain area of ​​a basin.

[0043] Figure 4 This invention provides a relationship between the peak amplitude of the Carboniferous top boundary wave and the reservoir thickness in a certain area of ​​a basin.

[0044] Figure 5 A thickness map of Carboniferous reservoirs in a certain area of ​​a basin provided for an embodiment of the present invention (predicted based on the amplitude of the trough width of the Qixia Formation base);

[0045] Figure 6 A thickness map of Carboniferous reservoirs in a certain area of ​​a basin provided for an embodiment of the present invention (based on multi-attribute fusion prediction);

[0046] Figure 7 A structural block diagram of a device for predicting reservoir thickness based on multi-attribute fusion using forward modeling, provided in an embodiment of the present invention;

[0047] Figure 8 This is another flowchart of the method for predicting reservoir thickness based on multi-attribute fusion using forward modeling provided in an embodiment of the present invention. Detailed Implementation

[0048] 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.

[0049] Before describing the embodiments of the present invention, the definition of seismic attributes should be explained. Seismic attributes refer to specific parameter values ​​derived from seismic data that are related to the geometry, kinematics, statistics, and statistical characteristics of seismic waves.

[0050] To address the problem of formation thickness prediction in existing technologies, this invention provides a method for predicting reservoir thickness based on multi-attribute fusion using forward modeling, the flowchart of which is shown below. Figure 1 As shown, it includes:

[0051] S11. Based on the well seismic calibration results, interpret the seismic sequence of the constrained reservoir units;

[0052] S12. Forward modeling of the geological model of the reservoir in the study area was carried out to obtain the seismic response characteristics of the reservoir;

[0053] S13. Based on the seismic response characteristics of the reservoir, determine multiple sensitive seismic attributes that can characterize the reservoir thickness;

[0054] S14. Based on the values ​​of multiple sensitive seismic attributes and reservoir thickness in the geological model, determine the fitting relationship between the values ​​of sensitive seismic attributes and reservoir thickness;

[0055] S15. Based on the fitting relationship, predict the thickness of the actual reservoir in the study area.

[0056] In step S11 above, in one embodiment, the step of interpreting the seismic sequence of the constrained reservoir unit based on the well seismic calibration results can be implemented in the following way:

[0057] The geological model was established by comparing the well profiles; forward modeling was performed on the geological model using forward wavelets of different frequencies, and the forward wavelet that makes the seismic reflection characteristics of the geological model clearest was determined from the forward wavelets of different frequencies, which was then selected as the preferred forward wavelet.

[0058] The original seismic volume is frequency-divided using the preferred forward wavelet to obtain a preferred frequency volume; based on the well-seismic calibration results, seismic sequence identification is carried out on the preferred frequency volume to complete the seismic sequence interpretation of the constrained reservoir unit.

[0059] For example, in one embodiment, a forward modeling simulation of a Carboniferous reservoir is performed in a certain area of ​​a basin, and the results obtained are as follows: Figure 2 As shown, the top surface of the Qixia Formation, the top surface of the Carboniferous System, and the bottom surface of the Carboniferous System are marked.

[0060] Therefore, the purpose of the aforementioned seismic sequence interpretation of constrained reservoir units is to finely calibrate the reservoir, thereby distinguishing the target layer from other layers using the top and bottom interfaces, and determining the preferred frequency of the forward wavelet.

[0061] The inventors of this invention have discovered that seismic reservoir prediction involves many uncertainties, significantly influenced by factors such as the selection of seismic attributes, the choice of prediction method, and the resolution and signal-to-noise ratio of seismic data. To improve the accuracy of reservoir thickness prediction, it is essential to accurately select sensitive seismic attributes and determine the fusion weights of individual sensitive attributes before performing fusion, thereby reducing the ambiguity of reservoir prediction.

[0062] Fine-grained reservoir calibration is fundamental for seismic attribute extraction and optimization. Based on this calibration, the seismic response characteristics of the reservoirs in the study area can be clearly defined. However, due to limitations imposed by the number of wells and their planar distribution, it is necessary to establish a theoretical geological model and conduct forward modeling based on the geological conditions of the study area. This will allow for a more comprehensive determination of the seismic response characteristics that reflect reservoir thickness, thereby enabling better optimization of sensitive seismic attributes.

[0063] Specifically, in step S12, based on the formation velocity parameters of different sedimentary types in the geological model, the preferred forward wavelet can be used to conduct forward modeling simulations on geological models with different reservoir development patterns to obtain the seismic response characteristics of the reservoir.

[0064] The geological model can be obtained, for example, in the following manner:

[0065] Based on drilling data and the geological background of reservoir development in the study area, we summarize the macroscopic patterns of reservoir distribution and establish geological models for different reservoir development patterns.

[0066] In this embodiment of the invention, based on its seismic response characteristics, and following the principles of "high correlation between seismic attributes and reservoir thickness, independence of each seismic attribute, and as few sensitive seismic attribute types as possible," sensitive seismic attributes that can characterize reservoir thickness are selected.

[0067] Based on this, in step S13, in one embodiment, determining multiple sensitive seismic attributes that can characterize the reservoir thickness according to the seismic response characteristics of the reservoir includes:

[0068] From the seismic attributes corresponding to the seismic response characteristics, select multiple seismic attributes that have a higher correlation with reservoir thickness than a preset correlation threshold and belong to different seismic attribute types.

[0069] Specifically, earthquake attributes can include, for example, amplitude attributes, frequency attributes, and phase attributes.

[0070] The reason for selecting different types of seismic attributes is that if multiple seismic attributes of the same type are used in the calculation, they will be highly correlated and lack independence. They will be interconnected and influence each other during the calculation process, which is not conducive to the judgment of the true reservoir thickness.

[0071] The aforementioned correlation threshold should be determined according to actual requirements. For example, if the accuracy requirement for reservoir thickness prediction is not high during the exploration stage, the correlation threshold can be set lower. On the other hand, if the accuracy requirement for reservoir thickness prediction is high during the development stage, the correlation threshold can be set relatively higher. In practice, the process of determining the selection of seismic sensitive attributes can be implemented through computer programs.

[0072] In step S14 above, determining the fitting relationship between the values ​​of multiple sensitive seismic attributes and the reservoir thickness based on the values ​​of the geological model can be achieved in the following way:

[0073] For each sensitive attribute, the value of the sensitive seismic attribute in the geological model is initially fitted to the value of the reservoir thickness according to the fitting function corresponding to multiple preset fitting relationships. The correlation value between the value of the sensitive seismic attribute and the reservoir thickness under each fitting relationship is calculated. The fitting relationship with the highest correlation value is selected as the initial fitting relationship between the value of the sensitive seismic attribute and the reservoir thickness.

[0074] The aforementioned fitting relationships include any one or a combination of the following: linear function relationships, exponential function relationships, and logarithmic function relationships, etc. The embodiments of the present invention are not limited to the above-mentioned fitting relationships.

[0075] An example of the fitting relationship between the values ​​of sensitive seismic attributes and reservoir thickness can be found in [reference]. Figure 3 , Figure 4 As shown;

[0076] Figure 3 This describes the relationship between the width of the trough at the base of the Qixia Formation overlying the Carboniferous system and the reservoir thickness in a certain area of ​​a basin. Figure 3 In the equation y = 1.8472x + 4.7553, the initial fitting relationship formula (linear fitting relationship) is given by R. 2 It refers to the relevance; 0.5558 is the relevance value.

[0077] Figure 4 This describes the relationship between the peak amplitude of the Carboniferous upper boundary wave and the reservoir thickness in a certain area of ​​a basin. Figure 4 In the equation y = 0.0008 + 19.424, we get the initial fitting relationship formula (also a linear fitting relationship), R... 2 It refers to the relevance; 0.4272 is the relevance value.

[0078] After selecting the fitting relationship with the highest correlation value from the calculation results of multiple fitting relationships as the preliminary fitting relationship between the sensitive seismic attribute value and the reservoir thickness, the following steps can be performed to correct the preliminary fitting formula and further improve the accuracy of the sensitive attribute in predicting reservoir thickness:

[0079] Based on the values ​​of the sensitive attributes, the planar distribution of the fitted reservoir thickness is obtained through the preliminary fitting relationship;

[0080] The absolute errors between the actual reservoir thickness values ​​from drilling and those calculated using various fitted relationships are statistically analyzed. The absolute errors are then weighted and averaged to obtain the average error.

[0081] The average error is substituted into the preliminary fitting relationship for correction, resulting in the corrected fitting relationship between the sensitive seismic attribute value and the reservoir thickness.

[0082] Integrating the above process of determining the fitting relationship between the values ​​of multiple sensitive seismic attributes and reservoir thickness in the geological model, for example, the preliminary fitting relationship formula is H... i =a i +b i x i Formula 1

[0083] In Formula 1 above, H i The reservoir thickness corresponding to a certain sensitive seismic attribute; x i For a certain sensitive seismic attribute value; a i b i These are the empirical coefficients for a specific sensitive seismic attribute.

[0084] The reservoir thickness planar distribution is calculated based on the fitted relationship formula established between each individual sensitive attribute and the reservoir thickness. The absolute errors between the actual drilled reservoir thickness and the thickness at the well point on the calculated reservoir thickness planar distribution map are statistically analyzed, and these absolute errors are weighted and averaged to obtain the average error E. To further improve prediction accuracy, the average error E is substituted into the above fitted relationship formula for correction, resulting in a new reservoir thickness prediction formula based on each sensitive seismic attribute, namely H. i =a i +b i x i +E i E in the formula i It is the average error of the i-th sensitive seismic attribute.

[0085] Specifically, in step S15 above, the thickness of the actual reservoir in the study area is predicted based on the fitting relationship, which can be achieved in the following way:

[0086] The correlation values ​​of the fitting relationship between the values ​​of multiple sensitive seismic attributes and reservoir thickness are added together to obtain the sum of the correlation values;

[0087] Divide the correlation value of each sensitive earthquake attribute by the sum value to obtain the weight coefficient of each sensitive earthquake attribute;

[0088] Based on the weight coefficients of each sensitive seismic attribute and the fitting relationship corresponding to each sensitive seismic attribute, a multi-attribute weighted fusion formula for predicting reservoir thickness using sensitive seismic attributes is determined.

[0089] The values ​​of the seismically sensitive attributes of the study area are substituted into the multi-attribute weighted fusion formula to predict the actual reservoir thickness in the study area.

[0090] The formula for calculating the aforementioned weighting coefficients could be, for example, W: i =C i / (C1+C2+C3+…+C n Formula 2;

[0091] Substituting the above formula into Formula 2 above, the calculation formula for multi-attribute fusion can be, for example, as: H = W1(a1 + b1x1 + E1) + W2(a2 + b2x2 + E2) + W3(a3 + b3x3 + E3) + ... + W i (a i +b i x i +E i ).

[0092] In Formula 2 above, W i C is the fusion weighting coefficient for a specific sensitive seismic attribute; i The correlation coefficients for a specific sensitive seismic attribute are calculated; C1, C2, C3, C n These are the correlation coefficients of the first, second, third, and nth sensitive seismic attributes, respectively; (a1+b1x1+E1), (a2+b2x2+E2), (a3+b3x3+E3), (a i +b i x i +E i ) are the fitting relationship formulas for the i-th sensitive seismic attribute, and H is the reservoir thickness.

[0093] To demonstrate the advantages of the above method, for example, the method of multi-attribute fusion prediction of reservoir thickness based on forward modeling can be used to conduct a methodological breakthrough experiment in a certain area of ​​a basin.

[0094] The methodology experiment compared the prediction of reservoir thickness using multi-attribute fusion with single-attribute prediction. The experimental data are as follows:

[0095] The reservoir thickness distribution map predicted by the above method of multi-attribute fusion prediction can be, for example, a prediction of the Carboniferous strata in a certain area of ​​a basin. The predicted Carboniferous reservoir thickness distribution in that area of ​​the basin is as follows: Figure 6 As shown.

[0096] This invention also provides a control group for predicting reservoir thickness using a single attribute. For example, the reservoir thickness can be predicted based on the amplitude of the trough width of the Qixia Formation floor in a certain area of ​​a basin, as shown in the example above. Figure 5 As shown.

[0097] Table 1 below compares the reservoir thickness predicted based on the width of the bottom trough of the Qixia Formation with the actual reservoir thickness; and compares the reservoir thickness predicted based on multi-attribute fusion with the actual reservoir thickness. The multi-attribute fusion method for predicting reservoir thickness based on forward modeling provided in this embodiment of the invention has achieved good application results in the study area. This is mainly reflected in the fact that the reservoir thickness predicted using the new multi-attribute fusion method matches the actual drilled well thickness well, with an absolute error of 0.2m to 1.9m, while the reservoir thickness predicted using a single attribute method matches the actual drilled well thickness less well, with an absolute error of 0.8m to 8.9m. The method has higher accuracy and better application performance.

[0098] Table 1

[0099]

[0100] Taking an example of a method flow for predicting reservoir thickness based on forward modeling using multi-attribute fusion, as provided in an embodiment of the present invention, such as... Figure 8 As shown, geological, well logging, and seismic data are used for seismic sequence interpretation and reservoir thickness prediction, respectively.

[0101] Reference Figure 8 As shown on the left, the steps of seismic sequence interpretation can be divided into seismic data frequency division processing steps and seismic data sequence interpretation steps. Among them, the seismic data frequency division processing steps include: geological model establishment, forward modeling of the geological model, and frequency division processing of the geological model; the seismic data sequence interpretation steps include: well-seismic calibration and sequence interpretation steps.

[0102] On the right, the steps for reservoir thickness prediction are divided into two steps: selecting sensitive attributes and fusing sensitive seismic attributes. The step of selecting sensitive seismic attributes involves forward modeling of the geological model based on the well-seismic calibration results to obtain the seismic response. The step of fusing sensitive seismic attributes includes: establishing fitting relationships, statistical error, determining weighting coefficients, and multi-attribute weighted fusion.

[0103] Based on the same inventive concept, this invention provides a device for predicting reservoir thickness using multi-attribute fusion based on forward modeling, as shown in the following diagram. Figure 7 As shown, it includes:

[0104] The parameter acquisition module 71 is used to interpret the seismic sequence of the constrained reservoir unit based on the well seismic calibration results;

[0105] Forward modeling module 72 is used to perform forward modeling of the geological model of the reservoir in the study area to obtain the seismic response characteristics of the reservoir;

[0106] The preferred feature module 73 is used to determine multiple sensitive seismic attributes that can characterize the reservoir thickness based on the seismic response characteristics of the reservoir.

[0107] The fitting module 74 is used to determine the fitting relationship between the values ​​of the sensitive seismic attributes and the reservoir thickness based on the values ​​of the multiple sensitive seismic attributes and the values ​​of the reservoir thickness in the geological model.

[0108] The prediction module 75 is used to predict the thickness of the actual reservoir in the study area based on the fitting relationship.

[0109] Based on the same inventive concept, this embodiment of the invention also provides a computing device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the program executed by the processor is the method for predicting reservoir thickness based on forward modeling multi-attribute fusion.

[0110] Based on the same inventive concept, embodiments of the present invention also provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the method for predicting reservoir thickness based on forward modeling multi-attribute fusion.

[0111] Since the principle behind these devices is similar to the aforementioned method for predicting reservoir thickness using multi-attribute fusion based on forward modeling, the implementation of these devices can be found in the implementation of the aforementioned method, and the repetitions will not be repeated.

[0112] This invention provides a method and apparatus for predicting reservoir thickness based on multi-attribute fusion using forward modeling. The method involves interpreting the seismic sequence constraining reservoir units based on well-seismic calibration results; conducting forward modeling on a geological model of the reservoir in the study area to obtain the reservoir's seismic response characteristics; identifying multiple sensitive seismic attributes that characterize reservoir thickness based on these characteristics; determining the fitting relationship between the values ​​of these sensitive seismic attributes and the reservoir thickness value in the geological model; and predicting the actual reservoir thickness in the study area based on this fitting relationship. This invention utilizes the seismic response characteristics obtained from forward modeling to select sensitive seismic attributes that are sensitive to reservoir thickness, and then weights and fuses these sensitive seismic attributes. This ensures that seismic attributes sensitive to formation thickness are selected before the seismic attribute fusion, achieving accurate reservoir thickness prediction. This improves the accuracy of reservoir thickness prediction and reduces errors in the prediction process.

[0113] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0114] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0115] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0116] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the functions specified in one or more boxes. Obviously, those skilled in the art can make various modifications and variations to this invention without departing from the spirit and scope of the invention. Therefore, if these modifications and variations of the invention fall within the scope of the claims of the invention and their equivalents, the invention is also intended to include these modifications and variations.

Claims

1. A method for predicting reservoir thickness based on multi-attribute fusion using forward modeling, characterized in that, include: Interpret the seismic sequence of the constrained reservoir units based on the well seismic calibration results; Forward modeling was performed on the geological model of the reservoir in the study area to obtain the seismic response characteristics of the reservoir; Based on the seismic response characteristics of the reservoir, several sensitive seismic attributes that can characterize the reservoir thickness are determined. Based on the values ​​of multiple sensitive seismic attributes and reservoir thickness in the geological model, the fitting relationship between the values ​​of sensitive seismic attributes and reservoir thickness is determined; Based on the fitting relationship, the thickness of the actual reservoir in the study area is predicted; Based on the values ​​of multiple sensitive seismic attributes and reservoir thickness in the geological model, the fitting relationship between the values ​​of sensitive seismic attributes and reservoir thickness is determined, including: For each sensitive attribute, the value of the sensitive seismic attribute in the geological model and the value of the reservoir thickness are initially fitted according to the fitting function corresponding to multiple preset fitting relationships. The correlation value between the value of the sensitive seismic attribute and the reservoir thickness under each fitting relationship is calculated. The fitting relationship with the highest correlation value is selected as the initial fitting relationship between the value of the sensitive seismic attribute and the reservoir thickness. Furthermore, after selecting the fitting relationship with the highest correlation value as the initial fitting relationship between the sensitive seismic attribute value and the reservoir thickness, the planar distribution of the fitted reservoir thickness is obtained based on the value of the sensitive attribute through the initial fitting relationship. The absolute errors between the actual reservoir thickness values ​​from drilling and those calculated using various fitted relationships are statistically analyzed. The absolute errors are then weighted and averaged to obtain the average error. The average error is substituted into the preliminary fitting relationship for correction to obtain the fitting relationship between the value of the sensitive seismic attribute and the reservoir thickness; The step of predicting the actual reservoir thickness in the study area based on the fitting relationship includes: The correlation values ​​of the fitting relationship between the values ​​of multiple sensitive seismic attributes and reservoir thickness are added together to obtain the sum of the correlation values; Divide the correlation value of each sensitive earthquake attribute by the sum value to obtain the weight coefficient of each sensitive earthquake attribute; Based on the weight coefficients of each sensitive seismic attribute and the fitting relationship corresponding to each sensitive seismic attribute, a multi-attribute weighted fusion formula for predicting reservoir thickness using sensitive seismic attributes is determined. The values ​​of the seismically sensitive attributes of the study area are substituted into the multi-attribute weighted fusion formula to predict the actual reservoir thickness in the study area.

2. The method as described in claim 1, characterized in that, The interpretation of the seismic sequence of constrained reservoir units based on well seismic calibration results includes: The geological model was established by comparing well profiles. Forward modeling simulations of geological models were performed using forward wavelets of different frequencies. The forward wavelet that best reveals the seismic reflection characteristics of the geological model was selected from the forward wavelets of different frequencies and was chosen as the preferred forward wavelet. The original seismic volume is frequency-divided using the preferred forward wavelet to obtain the preferred frequency volume; Based on the well seismic calibration results, seismic sequence identification is carried out on the preferred frequency volume to complete the seismic sequence interpretation of the constrained reservoir unit.

3. The method as described in claim 2, characterized in that, The forward modeling of the geological model of the reservoir in the study area yielded the seismic response characteristics of the reservoir, including: Based on the formation velocity parameters of different sedimentary types in the geological model, the preferred forward wavelet is used to conduct forward modeling simulations on geological models with different reservoir development patterns to obtain the seismic response characteristics of the reservoirs.

4. The method as described in claim 1, characterized in that, The determination of multiple sensitive seismic attributes characterizing reservoir thickness based on the seismic response characteristics of the reservoir includes: From the seismic attributes corresponding to the seismic response characteristics, multiple seismic attributes that have a correlation with reservoir thickness higher than a preset correlation threshold and belong to different seismic attribute types are selected as the sensitive seismic attributes; the seismic attribute types include: amplitude attribute, frequency attribute and phase attribute.

5. The method according to any one of claims 1-4, characterized in that, The multiple fitting relationships include any one or a combination of the following: linear function relationship, exponential function relationship, and logarithmic function relationship.

6. A device for predicting reservoir thickness based on multi-attribute fusion using forward modeling, characterized in that, include: The parameter acquisition module is used to interpret the seismic sequence of constrained reservoir units based on the well seismic calibration results; The forward modeling module is used to perform forward modeling of geological models of reservoirs in the study area to obtain the seismic response characteristics of the reservoirs. The preferred feature module is used to determine multiple sensitive seismic attributes that can characterize the reservoir thickness based on the seismic response characteristics of the reservoir. The fitting module is used to determine the fitting relationship between the values ​​of multiple sensitive seismic attributes and the reservoir thickness in the geological model; for each sensitive attribute, the values ​​of the sensitive seismic attribute and the reservoir thickness in the geological model are initially fitted according to the fitting functions corresponding to multiple preset fitting relationships; the correlation value between the values ​​of the sensitive seismic attribute and the reservoir thickness under each fitting relationship is calculated; the fitting relationship with the highest correlation value is selected as the initial fitting relationship between the values ​​of the sensitive seismic attribute and the reservoir thickness; and after selecting the fitting relationship with the highest correlation value as the initial fitting relationship between the values ​​of the sensitive seismic attribute and the reservoir thickness, the planar distribution of the fitted reservoir thickness is obtained through the initial fitting relationship based on the values ​​of the sensitive attribute. The absolute errors between the actual reservoir thickness values ​​from drilling and those calculated using various fitting relationships are statistically analyzed. The absolute errors are then weighted and averaged to obtain the average error. This average error is then substituted into the preliminary fitting relationship for correction, resulting in the fitting relationship between the sensitive seismic attribute values ​​and the reservoir thickness. The prediction module is used to predict the actual reservoir thickness in the study area based on the fitting relationship. The step of predicting the actual reservoir thickness in the study area based on the fitting relationship includes: adding the correlation values ​​of multiple sensitive seismic attributes and the reservoir thickness fitting relationship to obtain the sum of the correlation values; The correlation values ​​of each sensitive seismic attribute are divided by the sum of the values ​​to obtain the weight coefficients of each sensitive seismic attribute. Based on the weight coefficients of each sensitive seismic attribute and the fitting relationship corresponding to each sensitive seismic attribute, a multi-attribute weighted fusion formula for predicting reservoir thickness using sensitive seismic attributes is determined. The values ​​of the sensitive seismic attributes in the study area are substituted into the multi-attribute weighted fusion formula to predict the actual reservoir thickness in the study area.

7. A computing device, characterized in that, include: The memory, the processor, and the computer program stored in the memory and executable on the processor, wherein the program executed by the processor implements the method for multi-attribute fusion prediction of reservoir thickness based on forward modeling as described in any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method for predicting reservoir thickness based on multi-attribute fusion using forward modeling as described in any one of claims 1-5.

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