CN117270043A - Method for identifying beach dam sand - Google Patents
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Abstract
The invention provides a method for identifying beach-dam sand, which comprises the following steps: step 1, preprocessing seismic data; step 2, performing high-quality and high-precision horizon interpretation; step 3, establishing a high-precision lithology construction grid; step 4, performing paleomorphology analysis; step 5, slicing various seismic attributes on the high-precision lithology construction grid; step 6, carrying out seismic phase analysis under the control of various attributes; and 7, carving the geologic body under the control of various seismic attributes. The method for identifying the beach-dam sand greatly accelerates the interpretation speed and can greatly improve the interpretation precision, is a new flow of beach-dam sand thin reservoir prediction, is an effective technical method, and provides technical and method support for similar thin reservoir prediction.
Description
Technical Field
The invention relates to the technical field of oil and gas exploration, in particular to a method for identifying beach and dam sand.
Background
The beach dam sand is a thin interbed deposit developed in the high-energy environment of a shoreside shallow lake, and is formed by reforming, carrying and depositing a shoreside shallow water sand body such as a delta under the action of lake waves or coastal currents. Beach bar sand tends to have several characteristics: (1) The thickness of the single layer is small, generally less than 2m, and part is even less than 1m; (2) The physical properties of the reservoir are poor, and the reservoir belongs to a low-pore-ultra-low permeability reservoir as a whole; (3) Considerable reserves of oil and gas are available, but the oil layer productivity is low; (4) The sand body thickness is thin, and the transverse continuity is poor, and the discernment degree of difficulty is big.
Due to the characteristics, the beach bar sand body is very difficult to identify, and according to literature investigation, the current beach bar sand research situation is as follows:
1. in the aspect of beach dam sand deposition, the research focuses on palace and topography, more macroscopic rules of layer sequence sand control, and relatively less reservoir microphase distribution; 2. in the aspect of beach dam sand accumulation, focusing on macroscopic operation and aggregation rule research, and lacking multi-type oil reservoir difference enrichment research; 3. in terms of reservoir prediction, there is a lot of literature on thin reservoir prediction, but less research has been devoted to beach and dam sand.
Thus, reservoir prediction techniques for beach and dam sand are to be further deepened. For many years, we have grown accustomed to interpretation of the sediments in the well-shock bond, manually. It has proven difficult to resolve a large number of thin reservoirs on a seismic profile, and the sedimentary microphases of a single well site are difficult to interpret by the seismic profile, and therefore this approach is inefficient and inaccurate.
In application number: in the chinese patent application CN201210212603.9, a method for establishing a beach-dam sandstone microphase identification mode is related. The method comprises the steps of accurately identifying lithology according to a logging curve by combining the logging curve with geological logging data, dividing a small layer in a geological microphase layer section by combining the logging curve with lithology, extracting curve values and form values of the small layer, accurately dividing the lithology of the beach-dam sandstone sediment microphase according to the curve values, the form values and the lithology, and establishing a beach-dam sandstone sediment microphase modeling identification standard library and a beach-dam sandstone microphase identification mode. The beach bar sandstone microphase identification mode is adopted, based on a beach bar sandstone sediment microphase mode identification standard library, the beach bar sandstone sediment microphase can be accurately identified by combining the well logging curve value and the form value, the method can be suitable for all beach bar sandstone sediment microphase division, and a judgment basis can be provided for reservoir properties and oil gas content.
In application number: in CN202010691793.1, a method and a device for predicting a beach and dam sand reservoir are related, where the method includes: dividing a reservoir corresponding to logging data into a plurality of sand development periods of beach-dam sand according to the logging data of a target well in a beach-dam sand research area; selecting a development period with the largest oil gas content as a target interval based on the oil gas exploration results corresponding to each development period, and determining seismic horizon data of the target interval; and determining a beach bar sand development area in the beach bar sand research area according to the seismic horizon data of the target interval, and determining a beach bar sand reservoir distribution result of the beach bar sand research area based on the beach bar sand development area. The method and the device can effectively improve the identification precision of acquiring the beach-dam sand reservoir, can effectively improve the efficiency, reliability and accuracy of predicting the beach-dam sand reservoir, and can further provide an effective and accurate data basis for beach-dam sand exploration and oil gas exploitation.
In application number: in CN201910348690.2, the present invention relates to a method and a device for predicting a thin reservoir in a sand body of a lake-phase beach dam, where the method includes: according to the historical data and logging data in the target area, respectively dividing the sequence levels of different single wells; comparing sand bodies of different single wells at the same layer sequence level to obtain a sand body comparison result; preprocessing and standardizing a logging curve of a single well, and screening an inversion sample curve from the logging curve according to lithology data of the logging curve; according to the sand body comparison result, carrying out band-pass filtering on the seismic data to obtain thin reservoir seismic reflection data; performing waveform phase control random inversion according to the inversion sample curve, the acoustic curve and the density curve in the seismic data and the thin reservoir seismic reflection data to obtain a thin reservoir inversion result; and determining the position of the thin reservoir according to the inversion result of the thin reservoir. The method and the device can improve the precision of predicting the thin reservoir in the sand body of the lake-phase beach dam.
The prior art is greatly different from the method, the technical problem which is needed to be solved by the user cannot be solved, and the novel method for identifying beach-dam sand is invented.
Disclosure of Invention
The invention aims to provide a method for identifying beach-dam sand, which can more accurately and precisely identify beach-dam sand bodies.
The aim of the invention can be achieved by the following technical measures: a method of identifying beach-dam sand, the method comprising:
step 1, preprocessing seismic data;
step 2, performing high-quality and high-precision horizon interpretation;
step 3, establishing a high-precision lithology construction grid;
step 4, performing paleomorphology analysis;
step 5, slicing various seismic attributes on the high-precision lithology construction grid;
step 6, carrying out seismic phase analysis under the control of various attributes;
and 7, carving the geologic body under the control of various seismic attributes.
The aim of the invention can be achieved by the following technical measures:
in step 1, the preprocessing includes seismic energy equalization and denoising, the seismic energy equalization solves the problem of data variability, and the denoising aims to remove noise in the seismic data so as to improve the quality of the seismic data.
The seismic energy balance mainly considers that the energy unbalance of seismic data causes error recognition at different positions of the same seismic work area due to various reasons, and firstly, energy normalization processing is carried out on all data to ensure that the energy levels of all data are consistent, thereby solving the problem of data diversity.
In step 1, the denoising process is performed by first analyzing the noise generation cause through noise analysis, and performing the denoising process according to the noise generation cause without changing the geological structure reflected by the seismic data.
In step 2, the correlation between the artificial synthetic seismic record and the seismic channel is high, so that a specific geological interface and the seismic data have good comparability, and the layer with the best target layer section and the seismic event are interpreted according to the seismic grid precision of 1X 1.
In step 3, in the human-computer interaction intelligent high-frequency geological horizon interpretation process, each bin is connected to form a seismic reflection horizon of each isochronous interface for each bin of the grid bin model.
In step 3, the network structure of the stratum model considers the relation between the spatial resolution of the geologic model and the grid of the seismic model, a regular grid is calculated by using constants, each node represents stratum units with the constant, the stratum model modeling method adopts cost function minimization, based on quantitative analysis of the same phase axis of seismic data, the model grid is built according to the characteristics of the same phase axis of the seismic data, then based on cost function minimization, a previewable geologic model, namely a stratum grid model, is built, and then the previewable geologic model is properly edited and modified on the basis of overall stratum grid geological awareness, and finally the formal geologic model is obtained.
In the step 3, man-machine interaction intelligent high-frequency horizon interpretation is based on seismic data, the characteristics of a same-phase axis wave group of the seismic data are analyzed, and a global three-dimensional geological model is established by applying a global automatic interpretation principle; finally, based on the global three-dimensional model and combined with a fracture interpretation model, a high-precision sequence stratum grid in the earthquake work area is established; the man-machine interaction intelligent high-frequency horizon interpretation is that through continuously adding geological knowledge and repeatedly iterating, a global stratum model with relative geological ages is finally constructed, any horizon is picked up from the global stratum model, and different high-frequency geological horizon interfaces are represented according to well calibration.
In step 4, the paleo-topography analysis is performed to recognize the dynamic process of the structural superposition deformation and restore the paleo-structural morphology before the structural deformation.
In step 4, the deformation history and the deformation foundation of the trap or local construction unit are restored, and the accuracy of the restoration of the paleo-structure is affected due to the deposition compaction effect, the fault activity and the lifting and denudation effect, so that the paleo-structure is corrected in the restoration process, a series of analyses are performed on paleo-structure after accurate paleo-structure data are obtained, and the control effect of paleo-structure on beach and dam sand deposition is analyzed.
In step 5, on the basis of the high-precision lithology-construction grid, a plurality of seismic attribute volumes with obvious responses to beach and dam sand are calculated first, the responses are determined by comparing the well drilling data, and slices of a plurality of seismic attributes are generated based on the previous high-precision lithology-construction grid.
In step 6, the multi-attribute fusion analysis of the seismic facies improves the identification capacity of the seismic facies and realizes the fine classification of the seismic facies.
In step 7, the neural network is used for directly carving beach and dam sand bodies depending on the fusion of various information such as seismic attribute intersection, well drilling-logging-seismic combination, high-precision geologic body interface scanning, multi-seismic attribute RGB fusion, various sensitive seismic attributes and regional geological knowledge.
The method for identifying the beach-dam sand is used for finding a dam, and is based on the ancient landform study of high-precision layer sequence stratigraphy, so that the beach-dam sand sediment microphase and seismic geologic body characterization is developed on the basis, and the method is a new flow and an effective technical method for beach-dam sand thin reservoir prediction, and provides technical and method support for similar thin reservoir prediction. The beach and dam sand of the target interval of the research area is directly depicted based on geological information and seismic data through the processing of each step provided by the method. The method greatly accelerates the interpretation speed and can greatly improve the interpretation accuracy.
Drawings
FIG. 1 is a flow chart of a method of identifying beach bar sand according to an embodiment of the present invention;
FIG. 2 is a cross-sectional view of a seismic data pre-processing (energy equalization) performed in accordance with an embodiment of the invention;
FIG. 3 is a cross-sectional view of a seismic section after noise analysis and denoising, in accordance with an embodiment of the present invention;
FIG. 4 is a schematic illustration of a high-precision-high-quality seismic horizon interpretation in an embodiment of the invention;
FIG. 5 is a cross-sectional view of a high-precision lithology-construction lattice interpreted by human-machine interaction artificial intelligence in accordance with an embodiment of the present invention;
FIG. 6 is an ancient topography analysis chart according to an embodiment of the invention;
FIG. 7 is a schematic representation of a slice of a seismic attribute in accordance with an embodiment of the invention;
FIG. 8 is a seismic phase diagram of a plurality of seismic attributes fused in accordance with an embodiment of the invention;
FIG. 9 is a diagram of an artificial intelligence identified beach and dam sand distribution integrating various geological-geophysical information in an embodiment of the present invention.
Detailed Description
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the invention. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the present invention. As used herein, the singular forms also are intended to include the plural forms unless the context clearly indicates otherwise, and furthermore, it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, and/or combinations thereof.
According to the method for identifying beach-dam sand, according to geological recognition, well drilling-well logging data are combined, the advantages of various data and technologies are integrated, and beach-dam sand bodies are directly identified and drawn on the basis of seismic data; the method can also be used for researching petroleum geology or sedimentary geology; according to the result identified by the method, other geological factors are integrated, the characteristics of the petroleum geology or the sedimentary geology of the research area are explored, and the research of the petroleum geology and the sedimentary geology of the research area is promoted. Imaging is carried out on a plane through a plurality of slice layers, a deposition system is interpreted according to the deposition topographical features, and then the significance of the spatial layer sequence stratigraphy and the depositology is studied. The method greatly accelerates the interpretation speed and can greatly improve the interpretation accuracy.
The following are several embodiments of the invention
Example 1
In a specific embodiment 1 of the present invention, the method for identifying beach-dam sand includes the steps of:
(1) Seismic data preprocessing
Seismic data preprocessing mainly includes two aspects: seismic energy equalization and denoising:
the seismic energy balance solves the problem of data variability, noise in the seismic data is removed for denoising processing, and the seismic preprocessing aims at improving the quality of the seismic data.
(2) High quality-high precision horizon interpretation
The correlation between the artificial synthetic seismic record and the seismic channel is high. The specific geological interface and the seismic data have good comparability. And the horizon with the best comparison between the target interval and the seismic event is interpreted according to the seismic grid precision of 1X 1.
(3) High precision lithology-construction grid construction
In the human-computer interaction intelligent high-frequency geological horizon interpretation process, aiming at each surface element of the grid surface element model, each surface element is connected to form a seismic reflection horizon of each isochronous interface.
(4) Ancient landform analysis
The ultimate goal of the restoration of the paleo-structure is two, one is to recognize the dynamic process of the superimposed deformation of the structure and the other is to restore the paleo-structure morphology prior to the deformation of the structure.
(5) High precision seismic attribute slice
Slicing of multiple seismic attributes is performed in a high-precision lithology-construction grid.
(6) Seismic facies analysis under multiple attribute control
And the multi-attribute fusion analysis of the seismic facies improves the identification capacity of the seismic facies and realizes the fine classification of the seismic facies.
(7) Geologic body carving under control of various seismic attributes.
Seismic attribute intersection, well drilling-well logging-seismic combination, high-precision geologic body interface scanning, multi-seismic attribute RGB fusion, various sensitive seismic attributes, regional geological awareness and the like, and beach and dam sand bodies are directly carved by a neural network.
Example 2
In a specific embodiment 2 to which the present invention is applied, as shown in fig. 1, fig. 1 is a flowchart of a method for identifying beach-dam sand according to the present invention. The method for identifying beach-dam sand comprises the following steps:
(step 101) seismic data preprocessing
Seismic data preprocessing mainly includes two aspects: seismic energy equalization and denoising:
the seismic energy balance mainly considers that the energy unbalance of seismic data causes error recognition at different positions of the same seismic work area due to various reasons, and firstly, energy normalization processing is carried out on all data to ensure that the energy levels of all data are consistent, thereby solving the problem of data diversity.
The denoising process is performed by first analyzing noise, analyzing the cause of noise generation, and performing denoising process without changing the geological structure reflected by the seismic data according to the cause of noise generation.
The seismic preprocessing target improves the quality of seismic data, and facilitates subsequent steps.
(step 102) high quality horizon interpretation
And analyzing from the composite records, and matching the composite records of the seismic reflection phase shafts at the drilling positions as much as possible. The specific expression is that the correlation between the artificial synthetic seismic record and the seismic channel is high. The most clear horizon corresponding to the target interval related horizons is selected, the seismic event with the best continuity carries out 1X1 high-precision horizon interpretation, and the interpreted horizon is used as a marking layer or a constraint layer for subsequent research.
(step 103) high-precision lithology-construction grid establishment
The implementation method comprises the following steps: the network structure of the stratigraphic model takes into account the relationship between the spatial resolution of the geologic model and the seismic model grid. A regular grid is calculated with constants, each node representing a stratigraphic unit of constant size. The stratum model modeling method adopts cost function minimization, based on quantitative analysis of the same phase axis of the seismic data, a model grid is established according to the characteristics of the same phase axis of the seismic data, and then a previewable geological model (stratum sequence lattice model) is established based on cost function minimization. And then, on the basis of the geological knowledge of the global layer sequence grid, the geological model which can be previewed is properly edited and modified, and finally, the formal geological model is obtained.
The man-machine interaction intelligent high-frequency horizon interpretation is based on seismic data, the characteristics of a same-phase axis wave group of the seismic data are analyzed, and a global three-dimensional geological model is established by applying a global automatic interpretation principle; finally, based on the global three-dimensional model, a high-precision sequence stratigraphic framework in the earthquake work area is established by combining with the fracture interpretation model.
The man-machine interaction intelligent high-frequency horizon interpretation is that through continuously adding geological knowledge and repeatedly iterating, a global stratum model with relative geological ages is finally constructed, any horizon is picked up from the global stratum model, and different high-frequency geological horizon interfaces are represented according to well calibration.
(step 104) ancient topography analysis
The ultimate goal of the restoration of the paleo-structure is two, one is to recognize the dynamic process of the superimposed deformation of the structure and the other is to restore the paleo-structure morphology prior to the deformation of the structure. The primary emphasis is on restoring the deformation history and deformation basis for trapped or local building elements. However, the deposition compaction effect, the fault activity, the lifting and denudation effect and the like can have an influence on the accuracy of the recovery of the paleo-structure, and the paleo-structure is corrected in the recovery process. After accurate paleo-topography data are obtained, a series of analyses are performed on paleo-topography to analyze the control effect of paleo-topography on beach and dam sand deposition.
(step 105) high precision seismic attribute slicing
On the basis of the high-precision lithology-construction grid, firstly, a plurality of seismic attribute bodies with obvious responses to beach and dam sand are calculated, the responses are determined by comparing the drilling data, and the slices with a plurality of seismic attributes are generated based on the prior high-precision lithology-construction grid.
(step 106) seismic phase analysis under multiple attribute control
Compared with single attribute, the multi-seismic attribute fusion technology can greatly improve the identification capability of the seismic phases and realize fine classification of the seismic phases.
(step 107) carving the geologic body under the control of various seismic attributes.
Specifically, the geologic body is directly carved by a neural network according to various information fusion such as seismic attribute intersection, well drilling-logging-seismic combination, high-precision geologic body interface scanning, multi-seismic attribute fusion, regional geological knowledge and the like.
Example 3
In a specific embodiment 3 of the present invention, the study object is a sand layer system of four subsections of a sand-river street group in a northern area of a depression AA of a depression XX, which is a thin interbedded sediment developed in a high-energy environment of a shallow lake, and is formed by modifying, carrying and depositing a shallow water sand body of a near shore such as a delta under the action of a lake wave or coastal flow, wherein the sand of the sand-river street is affected and controlled by the ancient topography.
Through seismic data preprocessing, energy equalization processing (figure 2), denoising analysis and denoising processing (figure 3), high-precision horizon interpretation (figure 4) is carried out after a target horizon is calibrated through high-quality synthetic seismic records, a high-precision lithology-construction grid is established in a man-machine interaction mode through artificial intelligence (figure 5), the geomorphic characteristics formed by beach dam sand are explored through paleo-geomorphic restoration and analysis (figure 6), high-precision multi-attribute seismic attributes are cut through extraction of various sensitive seismic attributes (figure 7), seismic phase analysis is carried out through multi-seismic attribute fusion, and sediment phase characteristics formed by beach dam sand are explored under the conditions that water bodies change repeatedly and sediment phases are replaced frequently (figure 8). The neural network method is utilized to directly describe the beach-dam sand body of the target interval (figure 9) by integrating various information, so that the aim of identifying the beach-dam sand body of the target interval in a research area with high quality and high precision is achieved.
Finally, it should be noted that: the foregoing description is only a preferred embodiment of the present invention, and is not intended to limit the present invention, but although the present invention has been described in detail with reference to the foregoing embodiment, it will be apparent to those skilled in the art that modifications may be made to the technical solution described in the foregoing embodiment, or equivalents may be substituted for some of the technical features thereof. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Other than the technical features described in the specification, all are known to those skilled in the art.
Claims (13)
1. A method of identifying beach-dam sand, the method comprising:
step 1, preprocessing seismic data;
step 2, performing high-quality and high-precision horizon interpretation;
step 3, establishing a high-precision lithology construction grid;
step 4, performing paleomorphology analysis;
step 5, slicing various seismic attributes on the high-precision lithology construction grid;
step 6, carrying out seismic phase analysis under the control of various attributes;
and 7, carving the geologic body under the control of various seismic attributes.
2. The method for identifying beach and dam sand according to claim 1, wherein in step 1, the preprocessing includes seismic energy equalization and denoising, the seismic energy equalization solving the problem of data variability, the denoising processing aiming at removing noise from the seismic data to improve the quality of the seismic data.
3. The method for identifying beach dam sand according to claim 2, wherein in step 1, the seismic energy balance mainly considers that the energy imbalance of the seismic data causes error recognition at different positions of the same seismic work area for various reasons, and the energy normalization processing is performed on all data first to make the energy levels of all data consistent, so as to solve the problem of data variability.
4. The method of identifying beach and dam sand according to claim 2, wherein in step 1, the denoising process is first performed by noise analysis, analyzing the cause of noise generation, and performing the denoising process based on the cause of noise generation without changing the geological structure reflected by the seismic data.
5. The method for identifying beach and dam sand according to claim 1, wherein in step 2, the correlation between the artificial synthetic seismic record and the seismic trace is high, so that the specific geological interface and the seismic data have good comparability, and the horizon with the best comparability between the target interval and the same phase axis of the earthquake is interpreted according to the 1x1 seismic grid precision.
6. The method of identifying beach and dam sand of claim 1, wherein in step 3, during human-machine interaction intelligent high-frequency geologic horizon interpretation, each bin is connected for each bin of the grid bin model to form a seismic reflection horizon for each isochronous interface.
7. The method for identifying beach and dam sand according to claim 6, wherein in step 3, the network structure of the stratum model considers the relation between the spatial resolution of the geological model and the grid of the seismic model, a regular grid is calculated by using constants, each node represents stratum units with the size of the constants, the stratum model modeling method adopts cost function minimization, quantitative analysis based on the same phase axis of the seismic data is adopted, the model grid is established according to the characteristics of the same phase axis of the seismic data, then a previewable geological model, namely a stratum framework model, is established based on cost function minimization, and then the previewable geological model is properly edited and modified on the basis of overall stratum framework geological awareness, and finally a formal geological model is obtained.
8. The method for identifying beach-dam sand according to claim 7, wherein in step 3, human-computer interaction intelligent high-frequency horizon interpretation is based on seismic data, in-phase axis wave group characteristics of the seismic data are analyzed, and a global three-dimensional geological model is established by applying a global automatic interpretation principle; finally, based on the global three-dimensional model and combined with a fracture interpretation model, a high-precision sequence stratum grid in the earthquake work area is established; the man-machine interaction intelligent high-frequency horizon interpretation is that through continuously adding geological knowledge and repeatedly iterating, a global stratum model with relative geological ages is finally constructed, any horizon is picked up from the global stratum model, and different high-frequency geological horizon interfaces are represented according to well calibration.
9. The method of identifying beach bar sand according to claim 1, wherein in step 4, an paleo-topography analysis is performed, the dynamic process of the structural superposition deformation is recognized, and the paleo-structural morphology before the structural deformation is restored.
10. The method of identifying beach and dam sand according to claim 9, wherein in step 4, the deformation history and deformation basis of the trap or local construction unit are recovered, and the accuracy of the recovery of the paleo-structure is affected by the deposit compaction, the fault activity and the lifting and denudation, and the recovery is corrected, and after accurate paleo-topography data is obtained, a series of analyses are performed for paleo-topography, and the control effect of paleo-topography on beach and dam sand deposition is resolved.
11. The method of identifying beach-dam sand of claim 1, wherein in step 5, on the basis of the high-precision lithology-construction grid, a plurality of seismic attribute volumes are first calculated that have a significant response to beach-dam sand, the response being determined by comparing primarily in dependence on drilling data, and slices of the plurality of seismic attributes are generated based on the previous high-precision lithology-construction grid.
12. The method for identifying beach-dam sand according to claim 1, wherein in step 6, the multi-attribute fusion analyzes the seismic facies, improves the identification ability of the seismic facies, and realizes fine classification of the seismic facies.
13. The method of identifying beach-dam sand as claimed in claim 1, wherein in step 7, the beach-dam sand body is engraved directly from the neural network in dependence on the fusion of the seismic attributes, the well-logging-seismic combination, the high-precision geologic body interface scan, the fusion of multiple seismic attributes RGB, the multiple sensitive seismic attributes, the regional geological awareness of these multiple information fusion.
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