CN112649893A - Thin reservoir oriented multi-data multi-parameter fusion modeling method and system - Google Patents
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Abstract
The invention provides a thin reservoir oriented multi-data multi-parameter fusion modeling method and system, and belongs to the field of oil and gas geophysical exploration. The method comprises the following steps: firstly, establishing an initial geology-earthquake model; secondly, obtaining accurate one-dimensional speed and density; thirdly, obtaining a two-dimensional velocity initial model by using the accurate one-dimensional velocity and density; fourthly, carrying out inversion on the two-dimensional velocity initial model to obtain a fine two-dimensional velocity model; fifthly, reconstructing the fine two-dimensional velocity model by using the initial geology-earthquake model to obtain a reconstructed two-dimensional fine velocity model; and sixthly, obtaining a speed model required by the forward modeling by using the reconstructed two-dimensional fine speed model. The invention obtains the fine model of the thin reservoir required by the forward modeling by accurately depicting the thin reservoir, thereby greatly improving the similarity between the forward modeling data and the field actual seismic data.
Description
Technical Field
The invention belongs to the field of oil and gas geophysical exploration, and particularly relates to a thin reservoir-oriented multi-data multi-parameter fusion modeling method and system.
Background
With the transfer of oil and gas exploration to a complex reservoir, the seismic reflection homodromous axis on a seismic section and the underground real stratum interface do not have one-to-one correspondence, the description and the depiction of the reservoir by the conventional seismic interpretation method are difficult, a corresponding geological model is designed and established for a thin reservoir, and the complex reservoir characteristics are analyzed through forward modeling, so that the reservoir prediction becomes a main means.
At present, the difference between a forward modeling data processing result corresponding to a model established by a conventional modeling method and an actual seismic profile is very large, and the reflection characteristics of a thin reservoir cannot be completely reflected, particularly the change characteristics deposited on the seismic profile such as sand body overburden and the like are difficult to reflect, and the main reason is that the model cannot completely reflect the detail characteristics of a sand body model, so that the conventional modeling method is not feasible in solving the problems.
Disclosure of Invention
The invention aims to solve the problems that the conventional seismic geological modeling technology in the prior art is low in precision, cannot effectively describe a thin reservoir so as to reflect the change characteristics of sand body overburden and the like deposited on a seismic section, and cannot meet the seismic interpretation requirement of the current complex reservoir, and provides a thin reservoir oriented multi-data multi-parameter fusion modeling method and system, so that a fine model of the thin reservoir required by forward modeling is obtained, and the similarity between forward modeling data and field actual seismic data is improved by combining a high-precision forward modeling technology.
The invention is realized by the following technical scheme:
a thin reservoir-oriented multi-data multi-parameter fusion modeling method comprises the following steps:
firstly, establishing an initial geology-earthquake model;
secondly, obtaining accurate one-dimensional speed and density;
thirdly, obtaining a two-dimensional velocity initial model by using the accurate one-dimensional velocity and density;
fourthly, carrying out inversion on the two-dimensional velocity initial model to obtain a fine two-dimensional velocity model;
fifthly, reconstructing the fine two-dimensional velocity model by using the initial geology-earthquake model to obtain a reconstructed two-dimensional fine velocity model;
and sixthly, obtaining a speed model required by the forward modeling by using the reconstructed two-dimensional fine speed model.
The operation of the first step includes:
collecting geological data and seismic data;
and establishing an initial geological-seismic model by using the geological data and the seismic data.
The second step of operation includes:
collecting logging information;
testing a rock sample obtained from a known well region to obtain test data, wherein the test data comprises speed and density;
and correcting the logging data by using the test data to obtain accurate one-dimensional speed and density.
The operation of correcting the logging data by using the test data to obtain accurate one-dimensional speed and density comprises the following steps:
and supplementing the undetected speed and density in the logging information by using the speed and density in the test data, replacing abnormal values of the speed and density in the logging information by using the speed and density in the test data, and obtaining the speed and density of the well in the area as accurate one-dimensional speed and density after the supplementation and replacement are all completed.
The operation of the third step includes:
and performing transverse interpolation and longitudinal interpolation on the accurate one-dimensional velocity and density obtained in the second step through seismic horizon and fault constraint in the seismic data acquired in the field to obtain a two-dimensional velocity initial model.
The operation of the fourth step includes:
and (4) carrying out inversion on the two-dimensional velocity initial model obtained in the third step by using the seismic data acquired in the field to obtain a fine two-dimensional velocity model.
The operation of the fifth step includes:
reserving a target layer in the fine two-dimensional velocity model; and coarsening layers except the target layer in the fine two-dimensional velocity model by using the initial geology-earthquake model to obtain a reconstructed two-dimensional fine velocity model.
The operation of the sixth step includes:
converting the reconstructed two-dimensional fine velocity model from a time domain to a depth domain to obtain a depth domain two-dimensional fine velocity model;
and gridding the depth domain two-dimensional fine velocity model to obtain a velocity model required by the forward modeling finally.
The invention also provides a thin reservoir-oriented multi-data multi-parameter fusion modeling system, which comprises:
the data input unit is used for inputting geological data, seismic data, logging data, test data and seismic data;
the initial geology-earthquake model building unit is used for building an initial geology-earthquake model by utilizing geological data and earthquake data;
the accurate one-dimensional speed and density solving unit is used for correcting the logging information by using the test data to obtain accurate one-dimensional speed and density;
the two-dimensional velocity initial model establishing unit is used for carrying out transverse interpolation and longitudinal interpolation on accurate one-dimensional velocity and density by utilizing seismic horizon and fault constraint in seismic data to obtain a two-dimensional velocity initial model;
the fine two-dimensional velocity model establishing unit is used for utilizing the seismic data to carry out inversion on the two-dimensional velocity initial model to obtain a fine two-dimensional velocity model;
the two-dimensional fine velocity model reconstruction unit is used for reserving a target layer in the fine two-dimensional velocity model; coarsening layers except the target layer in the fine two-dimensional velocity model by using the initial geology-earthquake model to obtain a reconstructed two-dimensional fine velocity model;
a final model establishing unit, configured to convert the reconstructed two-dimensional fine velocity model from a time domain to a depth domain to obtain a depth domain two-dimensional fine velocity model; and gridding the depth domain two-dimensional fine velocity model to obtain a velocity model required by the forward modeling finally.
The present invention also provides a computer-readable storage medium storing at least one program executable by a computer, the at least one program, when executed by the computer, causing the computer to perform the steps of a thin-reservoir oriented multi-data multi-parameter fusion modeling method of the present invention.
Compared with the prior art, the invention has the beneficial effects that: the invention utilizes the theoretical characteristics based on model inversion, and aims at the problem that the complex thin reservoir is difficult to finely depict, adopts an inversion method to replace the conventional modeling horizon construction mode, solves the modeling problem caused by the fact that the seismic reflection homodromous axis on the seismic section and the underground real stratum interface do not have one-to-one correspondence, obtains the fine model of the thin reservoir required by forward simulation by accurately depicting the thin reservoir, and greatly improves the similarity between forward simulation data and field actual seismic data.
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FIG. 1 is a block diagram of the steps of the method of the present invention;
the raw seismic data and associated log velocity profiles in the embodiment of FIG. 2;
the initial geologic-seismic model in the embodiment of FIG. 3;
the model established by the method of the invention in the embodiment of fig. 4;
the model established in the embodiment of fig. 5 by using a conventional modeling method;
FIG. 6 is a block diagram of the components of the system of the present invention.
Detailed Description
The invention is described in further detail below with reference to the accompanying drawings:
the invention provides a seismic modeling method for precisely describing a thin reservoir through an inversion technology under a seismic geological model frame on the basis of conventional seismic modeling, the method obtains model speed through the inversion technology on the basis of seismic, geological and logging data and rock physical test data, reconstructs the form of an obtained longitudinal wave velocity model by using a seismic geological model, further obtains a fine model of the thin reservoir required by forward modeling, and performs complex reservoir wave field characteristic research by combining a high-precision forward modeling technology.
As shown in fig. 1, the method of the present invention comprises:
firstly, establishing an initial geologic-seismic model: and collecting geological data and seismic data, and establishing an initial geological-seismic model through a horizon and a fault in the geological data and the seismic data in the seismic data through software petrel. Petrel is a set of three-dimensional visual modeling software based on a Windows platform, and integrates seismic interpretation, structural modeling, lithofacies modeling, reservoir attribute modeling, reservoir numerical simulation display and virtual reality.
Secondly, obtaining accurate one-dimensional speed and density: collecting logging information, testing a rock sample obtained from a known well zone by a rock physics testing technology to obtain test data, wherein the test data comprises speed and density, correcting the speed and the density in the logging information according to the test data to obtain the corrected speed and density of the well in the region, namely the corrected speed and density in one dimension, and specifically, the supplementing and correcting means that: and supplementing the undetected speed and density in the logging data by using the speed and density in the test data, and replacing abnormal values of the speed and density in the logging data by using the speed and density in the test data.
Thirdly, performing horizontal and longitudinal interpolation on the accurate one-dimensional velocity and density obtained in the second step by using petrel software through seismic horizon and fault constraint in the seismic data acquired in the field to obtain a two-dimensional velocity and density profile, namely a two-dimensional velocity initial model.
And fourthly, utilizing the seismic data acquired in the field, carrying out inversion by using the two-dimensional velocity initial model obtained in the third step through a model-based inversion technology in the data to obtain a velocity model containing information such as earthquake, well logging, geology and the like and having higher longitudinal resolution, namely a fine two-dimensional velocity model.
And fifthly, reconstructing the fine two-dimensional velocity model obtained in the fourth step by using the initial geology-seismic model obtained in the first step, namely, coarsening the layers except the target layer in the fine two-dimensional velocity model by using the geology-seismic model (namely, reserving the target layer in the fine two-dimensional velocity model and filling the layers except the target layer in the fine two-dimensional velocity model with the geology-seismic model), and obtaining the velocity model with high longitudinal resolution of the required target thin reservoir and coarsened properties of other layers, namely the reconstructed two-dimensional fine velocity model. The purpose of coarsening is to improve the calculation speed, because if the fine two-dimensional velocity model obtained in the fourth step is directly used for calculation, the calculation speed is slow due to the fact that the model is very fine and the calculation amount is large, and after the layers except the target layer are filled with the initial geological-seismic model, the fineness of the layers can be reduced, and the calculation speed can be greatly improved.
And sixthly, converting the reconstructed two-dimensional fine velocity model obtained in the fifth step from a time domain to a depth domain to obtain a depth domain two-dimensional fine velocity model, and gridding the depth domain two-dimensional fine velocity model to obtain regular square gridding data required by forward modeling, namely obtaining the velocity model required by the final forward modeling.
As shown in fig. 6, the present invention further provides a thin reservoir-oriented multi-data multi-parameter fusion modeling system, which includes:
the data input unit 10 is used for inputting geological data, seismic data, logging data, test data and seismic data;
an initial geology-seismic model building unit 20 for building an initial geology-seismic model by using geological data and seismic data;
an accurate one-dimensional velocity and density calculating unit 30, configured to correct the logging data by using the test data to obtain an accurate one-dimensional velocity and density;
the two-dimensional velocity initial model establishing unit 40 is used for performing transverse interpolation and longitudinal interpolation on accurate one-dimensional velocity and density by using seismic horizon and fault constraint in seismic data to obtain a two-dimensional velocity initial model;
the fine two-dimensional velocity model establishing unit 50 is used for inverting the two-dimensional velocity initial model by utilizing the seismic data to obtain a fine two-dimensional velocity model;
a two-dimensional fine velocity model reconstruction unit 60 for preserving a target layer in the fine two-dimensional velocity model; coarsening layers except the target layer in the fine two-dimensional velocity model by using the initial geology-earthquake model to obtain a reconstructed two-dimensional fine velocity model;
a final model establishing unit 70, configured to convert the reconstructed two-dimensional fine velocity model from a time domain to a depth domain to obtain a depth domain two-dimensional fine velocity model; and gridding the depth domain two-dimensional fine velocity model to obtain a velocity model required by the forward modeling finally.
The examples of the invention are as follows:
taking a certain block of the Subei basin as an example, the method provided by the invention is used for carrying out fine thin reservoir modeling.
Fig. 2 shows seismic data of an embodiment block, and it can be seen that there are more sandstone-shale thin interbeddes in the work area in the time period of 1200-2200ms, and three dense places in fig. 2 represent logging acoustic data projected onto the seismic data.
Step 1, establishing a simplified initial geologic-seismic model for information such as seismic horizons, faults and the like shown in fig. 2, as shown in fig. 3.
And 2, correcting and supplementing the speed in the logging data shown in the figure 2 through rock physical test rock sample results.
And 3, carrying out transverse and longitudinal interpolation on well data under the information constraints of seismic horizons, faults and the like shown in the figure 2 to obtain a two-dimensional velocity and density profile, and taking the two-dimensional velocity and density profile as an inversion initial model.
And 4, obtaining a fine velocity model of the target thin reservoir by using the seismic data shown in the figure 2 and taking the result in the step 3 as an initial model through an inversion technology based on the model.
And 5, reconstructing the fine velocity model obtained in the step 4 by using the geology-earthquake model obtained in the step 1, and obtaining a velocity model with high longitudinal resolution of the target thin reservoir and coarsened properties of other layers.
And 6, performing time depth conversion on the velocity model obtained in the step 5, and meshing the velocity model to obtain a velocity model required by the final forward modeling, as shown in fig. 4.
Fig. 4 is a seismic thin reservoir model required for forward modeling established by the method of the present invention, and fig. 5 is a seismic thin reservoir model required for forward modeling established by a conventional modeling technique, and it can be seen by comparison that the longitudinal resolution of fig. 4 is significantly higher than that of fig. 5 in a target layer outlined by a dashed line frame, so that the method of the present invention has an absolute advantage for fine delineation of a thin reservoir in a target region, has a higher longitudinal resolution, and is closer to the actual seismic data situation in the field.
The method carries out seismic modeling by only utilizing information such as seismic horizon, fault and the like in the original conventional modeling technology through model-based inversion substitution, combines seismic acquisition data information to carry out fine modeling of a thin reservoir in a target horizon, and then reconstructs the structural form of the thin reservoir by utilizing a known geological seismic model so as to obtain a fine model of the thin reservoir required by forward simulation.
The above-described embodiment is only one embodiment of the present invention, and it will be apparent to those skilled in the art that various modifications and variations can be easily made based on the application and principle of the present invention disclosed in the present application, and the present invention is not limited to the method described in the above-described embodiment of the present invention, so that the above-described embodiment is only preferred, and not restrictive.
Claims (10)
1. A thin reservoir-oriented multi-data multi-parameter fusion modeling method is characterized by comprising the following steps: the method comprises the following steps:
firstly, establishing an initial geology-earthquake model;
secondly, obtaining accurate one-dimensional speed and density;
thirdly, obtaining a two-dimensional velocity initial model by using the accurate one-dimensional velocity and density;
fourthly, carrying out inversion on the two-dimensional velocity initial model to obtain a fine two-dimensional velocity model;
fifthly, reconstructing the fine two-dimensional velocity model by using the initial geology-earthquake model to obtain a reconstructed two-dimensional fine velocity model;
and sixthly, obtaining a speed model required by the forward modeling by using the reconstructed two-dimensional fine speed model.
2. The thin-reservoir-oriented multi-data multi-parameter fusion modeling method of claim 1, characterized in that: the operation of the first step includes:
collecting geological data and seismic data;
and establishing an initial geological-seismic model by using the geological data and the seismic data.
3. The thin-reservoir-oriented multi-data multi-parameter fusion modeling method of claim 1, characterized in that: the second step of operation includes:
collecting logging information;
testing a rock sample obtained from a known well region to obtain test data, wherein the test data comprises speed and density;
and correcting the logging data by using the test data to obtain accurate one-dimensional speed and density.
4. The thin-reservoir-oriented multi-data multi-parameter fusion modeling method of claim 3, characterized in that: the operation of correcting the logging data by using the test data to obtain accurate one-dimensional speed and density comprises the following steps:
and supplementing the undetected speed and density in the logging information by using the speed and density in the test data, replacing abnormal values of the speed and density in the logging information by using the speed and density in the test data, and obtaining the speed and density of the well in the area as accurate one-dimensional speed and density after the supplementation and replacement are all completed.
5. The thin-reservoir-oriented multi-data multi-parameter fusion modeling method of claim 1, characterized in that: the operation of the third step includes:
and performing transverse interpolation and longitudinal interpolation on the accurate one-dimensional velocity and density obtained in the second step through seismic horizon and fault constraint in the seismic data acquired in the field to obtain a two-dimensional velocity initial model.
6. The thin-reservoir-oriented multi-data multi-parameter fusion modeling method of claim 1, characterized in that: the operation of the fourth step includes:
and (4) carrying out inversion on the two-dimensional velocity initial model obtained in the third step by using the seismic data acquired in the field to obtain a fine two-dimensional velocity model.
7. The thin-reservoir-oriented multi-data multi-parameter fusion modeling method of claim 1, characterized in that: the operation of the fifth step includes:
reserving a target layer in the fine two-dimensional velocity model; and coarsening layers except the target layer in the fine two-dimensional velocity model by using the initial geology-earthquake model to obtain a reconstructed two-dimensional fine velocity model.
8. The thin-reservoir-oriented multi-data multi-parameter fusion modeling method of claim 1, characterized in that: the operation of the sixth step includes:
converting the reconstructed two-dimensional fine velocity model from a time domain to a depth domain to obtain a depth domain two-dimensional fine velocity model;
and gridding the depth domain two-dimensional fine velocity model to obtain a velocity model required by the forward modeling finally.
9. A thin reservoir-oriented multi-data multi-parameter fusion modeling system is characterized in that: the system comprises:
the data input unit is used for inputting geological data, seismic data, logging data, test data and seismic data;
the initial geology-earthquake model building unit is used for building an initial geology-earthquake model by utilizing geological data and earthquake data;
the accurate one-dimensional speed and density solving unit is used for correcting the logging information by using the test data to obtain accurate one-dimensional speed and density;
the two-dimensional velocity initial model establishing unit is used for carrying out transverse interpolation and longitudinal interpolation on accurate one-dimensional velocity and density by utilizing seismic horizon and fault constraint in seismic data to obtain a two-dimensional velocity initial model;
the fine two-dimensional velocity model establishing unit is used for utilizing the seismic data to carry out inversion on the two-dimensional velocity initial model to obtain a fine two-dimensional velocity model;
the two-dimensional fine velocity model reconstruction unit is used for reserving a target layer in the fine two-dimensional velocity model; coarsening layers except the target layer in the fine two-dimensional velocity model by using the initial geology-earthquake model to obtain a reconstructed two-dimensional fine velocity model;
a final model establishing unit, configured to convert the reconstructed two-dimensional fine velocity model from a time domain to a depth domain to obtain a depth domain two-dimensional fine velocity model; and gridding the depth domain two-dimensional fine velocity model to obtain a velocity model required by the forward modeling finally.
10. A computer-readable storage medium characterized by: the computer-readable storage medium stores at least one program executable by a computer, the at least one program, when executed by the computer, causing the computer to perform the steps of the thin-reservoir oriented multi-data multi-parameter fusion modeling method of any one of claims 1-8.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115421181A (en) * | 2022-07-27 | 2022-12-02 | 北京超维创想信息技术有限公司 | Three-dimensional geological model phase control attribute modeling method based on deep learning |
| CN115421181B (en) * | 2022-07-27 | 2023-10-20 | 北京超维创想信息技术有限公司 | Three-dimensional geological model phase control attribute modeling method based on deep learning |
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