Method and device for predicting reservoir thickness through multi-attribute fusion based on forward modeling

Abstract

The invention provides a method and device for predicting the thickness of a reservoir through multi-attribute fusion based on forward modeling, and the method comprises the steps: explaining and constraining a seismic sequence of a reservoir unit according to a well seismic calibration result; carrying out forward modeling on the geological model of the reservoir in the research area to obtain seismic response characteristics of the reservoir; determining a plurality of sensitive seismic attributes capable of representing the thickness of the reservoir according to the seismic response characteristics of the reservoir; according to the values of the plurality of sensitive seismic attributes and the value of the reservoir thickness in the geologic model, determining a fitting relationship between the values of the sensitive seismic attributes and the reservoir thickness; and according to the fitting relationship, predicting the actual reservoir thickness of the research area. The problem of low prediction precision caused by the absence of a process of screening seismic attributes before multi-attribute fusion in the prior art is solved. The reservoir thickness prediction precision is improved, and errors in the reservoir thickness prediction process are reduced.

Description

Technical Field

[0001] The present invention relates to the field of carbonate reservoir prediction in the petroleum industry, and in particular to a method and a device for predicting reservoir thickness based on multi-attribute fusion of forward simulation. Background Art

[0002] Seismic attributes are geophysical parameters derived from seismic data by mathematical methods that can characterize the geometric forms, kinematics, dynamics and statistical characteristics of various seismic waves. It has a good description of the lithology, physical properties, oil and gas content of the actual strata, and is a comprehensive response to various geophysical information in seismic data. By analyzing seismic attribute parameters, we can intuitively understand the characteristics of different reservoirs and conduct qualitative analysis of reservoirs.

[0003] However, there is not a simple one-to-one correspondence between seismic attributes and reservoir thickness, but a multivariate and complex nonlinear relationship. For reservoirs with strong heterogeneity, there will be more and more factors affecting the useful seismic information, and there is great uncertainty. Based on this, 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 device for predicting reservoir thickness based on multi-attribute fusion of forward simulation, which overcomes the above problems or at least partially solves the above problems.

[0005] In a first aspect, an embodiment of the present invention provides a method for predicting reservoir thickness by multi-attribute fusion based on forward simulation, comprising:

[0006] Based on the well-seismic calibration results, interpret the seismic sequence that constrains the reservoir unit;

[0007] Carry out forward simulation on the geological model of the reservoir in the study area to obtain the seismic response characteristics of the reservoir;

[0008] Determining a plurality of sensitive seismic attributes capable of characterizing the thickness of the reservoir according to the seismic response characteristics of the reservoir;

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

[0010] In one embodiment, interpreting the seismic sequence constraining the reservoir unit according to the well-seismic calibration results includes:

[0011] The geological model is established by comparing the wells in sections; forward modeling wavelets of different frequencies are used to perform forward simulation on the geological model, and a forward modeling wavelet that can make the seismic reflection characteristics of the geological model clearest is determined from the forward modeling wavelets of different frequencies as the preferred forward modeling wavelet; the original seismic body is subjected to frequency division processing using the preferred forward modeling wavelet to obtain a preferred frequency body; and seismic sequence identification is carried out on the preferred frequency body according to the well-seismic calibration results to complete the seismic sequence interpretation of the constrained reservoir unit.

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

[0013] According to the formation velocity parameters of different sedimentary types in the geological model, the preferred forward wavelet is used to carry out forward simulation on geological models of different reservoir development modes to obtain the seismic response characteristics of the reservoir.

[0014] In one embodiment, the determining of a plurality of sensitive seismic attributes capable of characterizing the thickness of the reservoir according to the seismic response characteristics of the reservoir comprises:

[0015] From the seismic attributes corresponding to the seismic response characteristics, multiple seismic attributes having a correlation with reservoir thickness higher than a preset correlation threshold and belonging 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 a fitting relationship between the value of the sensitive seismic attribute and the reservoir thickness according to the values ​​of the plurality of sensitive seismic attributes and the value 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 and the value of the reservoir thickness are preliminarily fitted according to the fitting functions corresponding to the preset multiple fitting relationships, and the correlation values ​​of the value of the sensitive seismic attribute and the reservoir thickness under each fitting relationship are calculated respectively, and the fitting relationship with the highest correlation value is selected as the preliminary 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 preliminary fitting relationship between the value of the sensitive seismic attribute and the reservoir thickness, the following further comprises:

[0019] According to the value of the sensitive attribute, obtaining the plane distribution of the fitted reservoir thickness through the preliminary fitting relationship;

[0020] The absolute errors between the actual wellbore reservoir thickness values ​​and the reservoir thickness values ​​calculated by the fitting relationships are counted respectively, and the absolute errors are weighted averaged to obtain the average error;

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

[0022] In one embodiment, predicting the actual reservoir thickness of the study area according to the fitting relationship includes:

[0023] Adding the correlation values ​​of the values ​​of the plurality of sensitive seismic attributes and the reservoir thickness fitting relationship to obtain a sum of the correlation values;

[0024] Dividing the correlation values ​​of each sensitive seismic attribute by the sum value respectively to obtain a weight coefficient of each sensitive seismic attribute;

[0025] Determine a multi-attribute weighted fusion formula for predicting reservoir thickness using sensitive seismic attributes according to the weight coefficients of the sensitive seismic attributes and the fitting relationships corresponding to the sensitive seismic attributes;

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

[0027] In a second aspect, an embodiment of the present invention provides a device for predicting reservoir thickness based on multi-attribute fusion of forward simulation, comprising:

[0028] Obtain parameter module, used to interpret the seismic sequence constraining the reservoir unit based on the well-seismic calibration results;

[0029] The forward modeling module is used to conduct forward simulation on the geological model of the reservoir in the study area to obtain the seismic response characteristics of the reservoir;

[0030] A preferred feature module is used to determine a plurality of sensitive seismic attributes capable of characterizing the thickness of the reservoir according to the seismic response characteristics of the reservoir;

[0031] A fitting module, used for determining a fitting relationship between a value of a sensitive seismic attribute and a reservoir thickness according to the values ​​of the plurality of sensitive seismic attributes and the value of the reservoir thickness in the geological model;

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

[0033] In a third aspect, an embodiment of the present invention 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 implements the method for predicting reservoir thickness by multi-attribute fusion based on forward simulation.

[0034] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for predicting reservoir thickness by multi-attribute fusion based on forward simulation is implemented.

[0035] The beneficial effects of the above technical solution provided by the embodiment of the present invention include at least:

[0036] The embodiment of the present invention provides a method and device for predicting reservoir thickness based on multi-attribute fusion of forward simulation. According to the well-seismic calibration results, the seismic sequence of the constrained reservoir unit is interpreted; the geological model of the reservoir in the study area is forward simulated to obtain the seismic response characteristics of the reservoir; according to the seismic response characteristics of the reservoir, multiple sensitive seismic attributes that can characterize the reservoir thickness are determined; according to the values ​​of the multiple sensitive seismic attributes in the geological model and the value of the reservoir thickness, the fitting relationship between the value of the sensitive seismic attribute and the reservoir thickness is determined; according to the fitting relationship, the thickness of the actual reservoir in the study area is predicted. The present invention uses the seismic response characteristics obtained by forward simulation to select sensitive seismic attributes that are sensitive to reservoir thickness, and then weightedly fuses the sensitive seismic attributes, so that the seismic attributes that are sensitive to the formation thickness are selected before the seismic attributes are fused, so as to accurately predict the reservoir thickness. The accuracy of predicting reservoir thickness is improved, and the error in the reservoir thickness prediction process is reduced.

[0037] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings.

[0038] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0040] Figure 1 A flow chart of a method for predicting reservoir thickness based on multi-attribute fusion of forward simulation provided by an embodiment of the present invention;

[0041] Figure 2 A forward modeling of a Carboniferous reservoir in a certain area of ​​a certain basin provided by an embodiment of the present invention;

[0042] Figure 3The relationship between the bottom trough width and reservoir thickness of the Qixia Formation overlying the Carboniferous in a certain area of ​​a certain basin provided by the embodiment of the present invention;

[0043] Figure 4 The relationship between the peak amplitude of the Carboniferous top boundary and the reservoir thickness in a certain area of ​​a basin provided by the embodiment of the present invention;

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

[0045] Figure 6 A Carboniferous reservoir thickness map of a certain area in a certain basin provided by 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 of forward simulation provided by an embodiment of the present invention;

[0047] Figure 8 Another flow chart of a method for predicting reservoir thickness by multi-attribute fusion based on forward simulation provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0048] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0049] Before describing the embodiments of the present invention, the definition of seismic attributes is explained first. The so-called seismic attributes refer to specific parameter values ​​related to seismic wave geometry, kinematics, statistics and statistical characteristics derived from seismic data.

[0050] In order to solve the problem of formation thickness prediction in the prior art, the embodiment of the present invention provides a method for predicting reservoir thickness based on multi-attribute fusion of forward simulation, and its flow chart is as follows: Figure 1 As shown, including:

[0051] S11. Based on the well-seismic calibration results, interpret the seismic sequence constraining the reservoir unit;

[0052] S12. Carry out forward simulation on the geological model of the reservoir in the study area to obtain the seismic response characteristics of the reservoir;

[0053] S13, determining a plurality of sensitive seismic attributes capable of characterizing the thickness of the reservoir according to the seismic response characteristics of the reservoir;

[0054] S14, determining a fitting relationship between the value of the sensitive seismic attribute and the reservoir thickness according to the values ​​of the plurality of sensitive seismic attributes and the value of the reservoir thickness in the geological model;

[0055] S15. Predicting the actual reservoir thickness in the study area based on the fitting relationship.

[0056] In the above step S11, in one embodiment, the interpretation of the seismic sequence constraining the reservoir unit according to the well-seismic calibration results can be implemented in the following manner:

[0057] The geological model is established by comparing the well sections; forward modeling wavelets of different frequencies are used to perform forward simulation on the geological model, and a forward modeling wavelet that can make the seismic reflection characteristics of the geological model clearest is determined from the forward modeling wavelets of different frequencies as the preferred forward modeling wavelet;

[0058] The preferred forward wavelet is used to perform frequency division processing on the original seismic body to obtain a 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 unit.

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

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

[0061] The inventors of the present invention have found that there are many uncertain factors in seismic reservoir prediction, which are greatly affected by factors such as the selection of seismic attributes, the choice of prediction methods, and the resolution and signal-to-noise ratio of seismic data. In order to improve the prediction accuracy of reservoir thickness, it is necessary to accurately select sensitive seismic attributes and determine the fusion weight of a single sensitive attribute, and then perform fusion to reduce the multiple solutions of reservoir prediction.

[0062] Fine calibration of reservoirs is the basis for seismic attribute extraction and seismic attribute optimization. Based on the fine calibration of reservoirs, the seismic response characteristics of the reservoirs in the study area are clarified. However, due to the limitation of the number of wells and their distribution on the plane, it is necessary to establish a theoretical geological model and conduct forward simulation based on the geological conditions of the study area to more comprehensively determine the seismic response characteristics that can reflect the thickness of the reservoir, so as to better optimize the sensitive seismic attributes.

[0063] Specifically, in step S12, the preferred forward wavelet may be used to perform forward simulation on geological models of different reservoir development modes according to the formation velocity parameters of different sedimentary types of the geological model to obtain the seismic response characteristics of the reservoir.

[0064] The geological model can be obtained, for example, by the following method:

[0065] Based on the drilling data and geological background of reservoir development in the study area, the macroscopic laws of reservoir distribution are summarized and geological models of different reservoir development modes are established.

[0066] In the embodiment of the present invention, based on its seismic response characteristics, in accordance with the principle of "seismic attributes have a high correlation with reservoir thickness, each seismic attribute is independent of each other, and there are as few types of sensitive seismic attributes as possible", sensitive seismic attributes that can characterize reservoir thickness are selected.

[0067] Based on this, in step S13, in one embodiment, the determining of a plurality of sensitive seismic attributes capable of characterizing the thickness of the reservoir according to the seismic response characteristics of the reservoir includes:

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

[0069] Specifically, the types of seismic attributes may include, for example, amplitude attributes, frequency attributes, phase attributes, and the like.

[0070] The reason for selecting different types of seismic attributes as much as possible is that if multiple seismic attributes of the same type are involved in the calculation, since the seismic attributes of the same type are often highly correlated and lack independence, they will be correlated 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, when the prediction accuracy of reservoir thickness is not required to be very high during the exploration phase, the correlation threshold can be set lower. For another example, when the prediction accuracy of reservoir thickness is required to be very high during the development phase, the correlation threshold can be set relatively high. In specific implementation, the process of determining the selection of seismic sensitive attributes can be realized through a computer program.

[0072] In the above step S14, the fitting relationship between the value of the sensitive seismic attribute and the reservoir thickness is determined according to the values ​​of the multiple sensitive seismic attributes and the reservoir thickness in the geological model. In specific implementation, it can be achieved in the following way:

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

[0074] The above-mentioned multiple fitting relationships include any one or a combination of the following: a linear function relationship, an exponential function relationship, a logarithmic function relationship, etc. The embodiment of the present invention is not limited to the above-mentioned several fitting relationships.

[0075] For example, see the example of the relationship between the value of sensitive seismic attribute and reservoir thickness fitting. Figure 3 , Figure 4 As shown;

[0076] Figure 3 It is the relationship between the trough width at the bottom of the Qixia Formation overlying the Carboniferous and the reservoir thickness in a certain area of ​​a basin. Figure 3 Where y=1.8472x+4.7553 is the preliminary fitting relationship formula (linear fitting relationship), R 2 It is the correlation, and 0.5558 is the correlation value;

[0077] Figure 4 The relationship between the peak amplitude of the Carboniferous top boundary and the reservoir thickness in a certain area of ​​a basin is shown in Figure 2. Figure 4 Where y=0.0008+19.424 is the preliminary fitting relationship formula (also a linear fitting relationship), R 2 It is the correlation, and 0.4272 is the correlation value.

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

[0079] According to the value of the sensitive attribute, obtaining the plane distribution of the fitted reservoir thickness through the preliminary fitting relationship;

[0080] The absolute errors between the actual wellbore reservoir thickness values ​​and the reservoir thickness values ​​calculated by the fitting relationships are counted respectively, and the absolute errors are weighted averaged to obtain the average error;

[0081] The average error is substituted into the preliminary fitting relationship for correction, so as to obtain a corrected fitting relationship between the value of the sensitive seismic attribute and the reservoir thickness.

[0082] The above process of determining the fitting relationship between the value of the sensitive seismic attribute and the reservoir thickness according to the values ​​of the multiple sensitive seismic attributes and the reservoir thickness in the geological model is integrated. For example, the preliminary fitting relationship formula is H i =a i +b i x i ; Formula 1

[0083] In the above formula 1, H i is the reservoir thickness corresponding to a certain sensitive seismic attribute; x i is a certain sensitive earthquake attribute value; a i 、b i are respectively the empirical coefficients of a certain sensitive seismic attribute.

[0084] The plane distribution of reservoir thickness is obtained by calculating the fitting relationship formula established between each single sensitive attribute and reservoir thickness; the absolute error between the actual reservoir thickness of drilling and the thickness at the well point on the reservoir thickness plane distribution map obtained by calculation is counted, and these absolute errors are weighted averaged to obtain the average error E. In order to further improve the prediction accuracy, the average error E is substituted into the above fitting relationship formula for correction, and a new reservoir thickness prediction formula based on each sensitive seismic attribute is obtained, namely H i =a i +b i x i +E i , where E i is the average error of the ith sensitive seismic attribute.

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

[0086] Adding the correlation values ​​of the values ​​of the plurality of sensitive seismic attributes and the reservoir thickness fitting relationship to obtain a sum of the correlation values;

[0087] Dividing the correlation values ​​of each sensitive seismic attribute by the sum value respectively to obtain a weight coefficient of each sensitive seismic attribute;

[0088] Determine a multi-attribute weighted fusion formula for predicting reservoir thickness using sensitive seismic attributes according to the weight coefficients of the sensitive seismic attributes and the fitting relationships corresponding to the sensitive seismic attributes;

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

[0090] The calculation formula of the above confirmation weight coefficient can be, for example: i =C i / (C 1 +C 2 +C 3 +…+C n );Formula 2;

[0091] Substituting the above formula into the above formula 2, the calculation formula for multi-attribute fusion can be, for example: H = W 1 (a 1 +b 1 x 1 +E 1 )+W 2 (a 2 +b 2 x 2 +E 2 )+W 3 (a 3 +b 3 x 3 +E 3 )+…+W i (a i +b i x i +E i ).

[0092] In the above formula 2, W i is the fusion weight coefficient of a sensitive seismic attribute calculated; C i is the correlation coefficient of a sensitive seismic attribute calculated; C 1 , C 2 , C 3 , C n are the correlation coefficients of the first, second, third, and nth sensitive seismic attributes respectively; (a 1 +b 1 x 1 +E 1 )、(a 2 +b 2 x 2 +E 2 )、(a 3 +b 3 x 3 +E 3 )、(a i +b i x i +E i ) are the fitting relationship formulas of the ith sensitive seismic attribute, and H is the reservoir thickness.

[0093] In order to demonstrate the advantages of the above method, for example, the above method of predicting reservoir thickness by multi-attribute fusion based on forward simulation can be used to carry out method research experiments in a certain basin and a certain area.

[0094] Methods The research experiment conducted a comparative experiment on the prediction of reservoir thickness by multi-attribute fusion and single attribute. The experimental data are as follows:

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

[0096] The embodiment of the present invention also provides a control group for predicting reservoir thickness with a single attribute. For example, the reservoir thickness can be predicted based on the amplitude of the bottom trough width of the Qixia Formation in a certain area of ​​the above-mentioned basin. The reservoir thickness distribution is as follows: Figure 5 shown.

[0097] Table 1 below is a comparison of the reservoir thickness value predicted based on the bottom trough width of the Qixia Formation and the actual reservoir thickness value; and a comparison of the reservoir thickness value predicted based on multi-attribute fusion and the actual reservoir thickness value. The method for predicting reservoir thickness based on multi-attribute fusion of forward simulation provided by the embodiment of the present invention has achieved good application results in the study area, which is mainly reflected in that the reservoir thickness predicted by the new multi-attribute fusion method is consistent with the actual drilling thickness, with an absolute error of 0.2m to 1.9m, while the reservoir thickness predicted by a single attribute is less consistent with the actual drilling thickness, with an absolute error of 0.8m to 8.9m, and the accuracy is higher. The application effect of this method is better.

[0098] Table 1

[0099]

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

[0101] Reference Figure 8 As shown, on the left side, 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 simulation of the geological model and frequency division processing of the geological model; seismic data sequence interpretation includes: well-seismic calibration and sequence interpretation steps.

[0102] On the right side, the steps of reservoir thickness prediction are divided into the steps of selecting sensitive attributes and fusing sensitive seismic attributes, wherein: the step of selecting sensitive seismic attributes performs forward simulation of the geological model according to the well-seismic calibration results to obtain the seismic response; the step of fusing sensitive seismic attributes includes: establishing a fitting relationship, statistical error, determining weight coefficients and multi-attribute weighted fusion steps.

[0103] Based on the same inventive concept, an embodiment of the present invention provides a device for predicting reservoir thickness based on multi-attribute fusion of forward simulation, and the device structure diagram is shown in FIG. Figure 7 As shown, including:

[0104] A parameter acquisition module 71 is used to interpret the seismic sequence of the constrained reservoir unit according to the well-seismic calibration results;

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

[0106] A preferred feature module 73 is used to determine a plurality of sensitive seismic attributes capable of characterizing the thickness of the reservoir according to the seismic response characteristics of the reservoir;

[0107] A fitting module 74, for determining a fitting relationship between the value of the sensitive seismic attribute and the reservoir thickness according to the values ​​of the plurality of sensitive seismic attributes and the value of the reservoir thickness in the geological model;

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

[0109] Based on the same inventive concept, an embodiment of the present 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 implements the method for predicting reservoir thickness by multi-attribute fusion based on forward simulation.

[0110] Based on the same inventive concept, an embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for predicting reservoir thickness by multi-attribute fusion based on forward simulation is implemented.

[0111] Since the principles of the problems solved by these devices are similar to the aforementioned method of predicting reservoir thickness by multi-attribute fusion based on forward simulation, the implementation of the device can refer to the implementation of the aforementioned method, and the repeated parts will not be repeated here.

[0112] The embodiment of the present invention provides a method and device for predicting reservoir thickness based on multi-attribute fusion of forward simulation. According to the well-seismic calibration results, the seismic sequence of the constrained reservoir unit is interpreted; the geological model of the reservoir in the study area is forward simulated to obtain the seismic response characteristics of the reservoir; according to the seismic response characteristics of the reservoir, multiple sensitive seismic attributes that can characterize the reservoir thickness are determined; according to the values ​​of the multiple sensitive seismic attributes in the geological model and the value of the reservoir thickness, the fitting relationship between the value of the sensitive seismic attribute and the reservoir thickness is determined; according to the fitting relationship, the thickness of the actual reservoir in the study area is predicted. The present invention uses the seismic response characteristics obtained by forward simulation to select sensitive seismic attributes that are sensitive to reservoir thickness, and then weightedly fuses the sensitive seismic attributes, so that the seismic attributes that are sensitive to the formation thickness are selected before the seismic attributes are fused, so as to accurately predict the reservoir thickness. The accuracy of predicting reservoir thickness is improved, and the error in the reservoir thickness prediction process is reduced.

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

[0114] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0115] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0116] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The present invention is a block or a plurality of blocks. Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for predicting reservoir thickness based on multi-attribute fusion of forward simulation, characterized in that: include: Based on the well-seismic calibration results, interpret the seismic sequence that constrains the reservoir unit; Carry out forward simulation on the geological model of the reservoir in the study area to obtain the seismic response characteristics of the reservoir; Determining a plurality of sensitive seismic attributes capable of characterizing the thickness of the reservoir according to the seismic response characteristics of the reservoir; Determining a fitting relationship between the value of the sensitive seismic attribute and the reservoir thickness according to the values ​​of the plurality of sensitive seismic attributes and the value of the reservoir thickness in the geological model; The actual reservoir thickness of the study area is predicted based on the fitting relationship.

2. The method according to claim 1, characterized in that The interpretation of the seismic sequence constraining the reservoir unit based on the well-seismic calibration results includes: Establishing the geological model by comparing the well sections; Using forward modeling wavelets of different frequencies to perform forward modeling on the geological model, and determining a forward modeling wavelet that can make the seismic reflection characteristics of the geological model clearest from the forward modeling wavelets of different frequencies as the preferred forward modeling wavelet; Using the preferred forward wavelet to perform frequency division processing on the original seismic volume to obtain a preferred frequency volume; According to 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 unit.

3. The method according to claim 2, characterized in that The forward simulation of the geological model of the reservoir in the study area is carried out to obtain the seismic response characteristics of the reservoir, including: According to the formation velocity parameters of different sedimentary types in the geological model, the preferred forward wavelet is used to carry out forward simulation on geological models of different reservoir development modes to obtain the seismic response characteristics of the reservoir.

4. The method according to claim 1, characterized in that Determining a plurality of sensitive seismic attributes capable of characterizing the thickness of the reservoir according to the seismic response characteristics of the reservoir comprises: 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 to 4, characterized in that: Determining a fitting relationship between the value of the sensitive seismic attribute and the reservoir thickness according to the values ​​of the plurality of sensitive seismic attributes and the value of the reservoir thickness in the geological model includes: For each sensitive attribute, the value of the sensitive seismic attribute in the geological model and the value of the reservoir thickness are preliminarily fitted according to the fitting functions corresponding to the preset multiple fitting relationships, and the correlation values ​​of the value of the sensitive seismic attribute and the reservoir thickness under each fitting relationship are calculated respectively, and the fitting relationship with the highest correlation value is selected as the preliminary 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.

6. The method according to claim 5, characterized in that After selecting the fitting relationship with the highest correlation value as the preliminary fitting relationship between the value of the sensitive seismic attribute and the reservoir thickness, it also includes: According to the value of the sensitive attribute, obtaining the plane distribution of the fitted reservoir thickness through the preliminary fitting relationship; The absolute errors between the actual wellbore reservoir thickness values ​​and the reservoir thickness values ​​calculated by the fitting relationships are counted respectively, and the absolute errors are weighted averaged to obtain the average error; The average error is substituted into the preliminary fitting relationship for correction, thereby obtaining a fitting relationship between the value of the sensitive seismic attribute and the reservoir thickness.

7. The method according to claim 5, characterized in that The predicting of the actual reservoir thickness in the study area according to the fitting relationship includes: Adding the correlation values ​​of the values ​​of the plurality of sensitive seismic attributes and the reservoir thickness fitting relationship to obtain a sum of the correlation values; Dividing the correlation values ​​of each sensitive seismic attribute by the sum value respectively to obtain a weight coefficient of each sensitive seismic attribute; Determine a multi-attribute weighted fusion formula for predicting reservoir thickness using sensitive seismic attributes according to the weight coefficients of the sensitive seismic attributes and the fitting relationships corresponding to the sensitive seismic attributes; The values ​​of the sensitive seismic attributes of the study area are substituted into the multi-attribute weighted fusion formula to predict the actual reservoir thickness of the study area.

8. A device for predicting reservoir thickness based on multi-attribute fusion of forward simulation, characterized in that: include: Obtain parameter module, used to interpret the seismic sequence constraining the reservoir unit based on the well-seismic calibration results; The forward modeling module is used to conduct forward simulation on the geological model of the reservoir in the study area to obtain the seismic response characteristics of the reservoir; A preferred feature module is used to determine a plurality of sensitive seismic attributes capable of characterizing the thickness of the reservoir according to the seismic response characteristics of the reservoir; A fitting module, used for determining a fitting relationship between a value of a sensitive seismic attribute and a reservoir thickness according to the values ​​of the plurality of sensitive seismic attributes and the value of the reservoir thickness in the geological model; The prediction module is used to predict the actual reservoir thickness of the study area according to the fitting relationship.

9. A computing device, characterized in that include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the program executed by the processor implements the method for predicting reservoir thickness by multi-attribute fusion based on forward simulation as described in claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for predicting reservoir thickness based on multi-attribute fusion of forward simulation according to claims 1-7 is implemented.

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