Earthquake comprehensive prediction method for shallow complex oil and gas type identification

Abstract

The invention discloses an earthquake comprehensive prediction method for shallow complex oil and gas type identification, and the method comprises the steps: building a rock physical model suitable for a research region for fluid replacement through the statistics of drilled well data in the research region on the basis of an AVO technology; then, a P-G cross plot is made by using a reflection coefficient approximation formula in combination with the logging data and the fluid replacement data; performing linear fitting on the discrete sample points according to different fluid types, determining the positions of a waterline, an oil line and a gas line, and defining the fluid types in different areas; and finally, qualitatively determining the fluid property according to the position distribution of the target layer sample points in the research area on the template. According to the method, seismic data and logging data are comprehensively utilized, the AVO template suitable for a research area is established, the requirement for logging data is lowered, fluid types can be rapidly identified before drilling only through elastic information of strata, meanwhile, the transverse and longitudinal distribution range of oil and gas is predicted, efficiency and identification accuracy are greatly improved, and the method is suitable for popularization and application. And meanwhile, the production cost is greatly reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of oil and gas seismic exploration and development, and particularly relates to a seismic comprehensive prediction method for identifying shallow complex oil and gas types. Background Art

[0002] The existing fluid type identification methods are mainly realized based on well logging and mud logging technologies and AVO bright spot technology. Well logging technology refers to directly using well logging data such as natural gamma, neutron density, resistivity, and saturation to establish crossplot templates for lithology and reservoir fluid identification. This method can identify oil and gas relatively quickly, but establishing crossplot templates using well logging data requires analyzing different well logging data, with a large workload. Mud logging technology includes methods such as gas logging, geochemical logging, and quantitative fluorescence logging. Gas logging distinguishes oil and gas reservoirs by using the correlation between hydrocarbon gas components and abundances detected on the ground and fluids in underground reservoirs; geochemical logging makes hydrocarbon chromatograms for fluid type identification; quantitative fluorescence logging makes fluorescence logging diagrams of crude oil concentration varying with well depth for fluid type identification according to the relationship that fluorescence intensity is proportional to crude oil concentration, which can better solve some problems such as the inability to be identified by the naked eye and being affected by human factors in conventional fluorescence logging. Mud logging technology can accurately identify fluid types, but using mud logging technology to identify oil and gas requires a large amount of laboratory analysis and has low efficiency. At the same time, well logging and mud logging technologies both identify fluids after drilling, cannot give judgments before drilling, cannot guide pre-drilling deployment, affect drilling deployment, and have high drilling costs.

[0003] AVO bright spot technology mainly analyzes through the amplitude response characteristics of seismic data. AVO bright spot technology can directly identify amplitude anomalies on seismic profiles. Gas-bearing reservoirs show obvious strong amplitudes, while oil-bearing reservoirs have less obvious amplitude anomalies. Gas layers have obvious strong bright spots, and oil layers have weak bright spots or no bright spot characteristics. However, there are many false images and interferences in seismic bright spots. High-porosity sandstones can form seismic bright spots regardless of the type of fluid they contain. In addition, due to the high gas-oil ratio, shallow light oil can also form strong seismic bright spots. Therefore, simply using seismic bright spots to identify fluid properties has great uncertainty and multiple solutions, resulting in many influencing factors for AVO bright spot technology. Therefore, AVO bright spot technology can identify fluids to a certain extent but cannot distinguish fluid types.

[0004] Based on the above situation, there is an urgent need for a method that can conveniently and accurately identify fluid types. Summary of the Invention

[0005] The present invention is proposed to solve the problems existing in the prior art, and its purpose is to provide a seismic comprehensive prediction method for identifying shallow complex oil and gas types.

[0006] The present invention is realized through the following technical solutions:

[0007] A seismic comprehensive prediction method for shallow complex oil and gas type identification, comprising the following steps:

[0008] S1. Select the logging data of a single well or multiple wells in the study area, conduct petrophysical crossplot analysis based on the logging data, and statistically analyze the formation elastic and physical properties characteristics under the background of the entire study area according to the results of the petrophysical crossplot analysis, so as to obtain the distribution law of the formation elastic and physical properties characteristics in the study area;

[0009] The logging data includes acoustic data, density data, Gamma data, porosity data, etc.;

[0010] S2. Establish a petrophysical model according to the distribution law of the formation elastic and physical properties characteristics obtained in step S1, conduct seismic forward modeling to verify the accuracy of the petrophysical model, use the petrophysical model as a fluid substitution template, and perform fluid substitution using the fluid substitution template to calculate the elastic parameter data of the formation under different pore and different fluid property conditions;

[0011] The fluid substitution template is based on the petrophysical model, and a petrophysical model applicable to the target layer in the study area can be established according to specific conditions; in the present invention, according to logging data or laboratory data, the rock mineral components of the target layer in the study area are analyzed, the dry rock frame bulk modulus is calculated using the V-R-H averaging theory, the porosity of the target layer in the study area is analyzed using logging data, and fluid substitution is performed using the Gassmann equation to obtain the rock elastic parameters under different fluid saturation states, and the porosity is changed to obtain the rock elastic parameters under different porosity fluid saturation states;

[0012] S3. Rearrange the Shuey reflection coefficient approximation formula to obtain the calculation formulas for P and G;

[0013] The Shuey reflection coefficient approximation formula is the Shuey approximation commonly used in AVO quantitative analysis and hydrocarbon detection;

[0014] The Shuey approximation formula is rearranged to obtain the mathematical expression forms of P and G in terms of elastic parameters, and the rearranged reflection coefficient approximation formula form is:

[0015] R(θ) = P + G * sin 2 (θ)

[0016]

[0017] In the formula: R is the reflection coefficient, dimensionless; θ is the incident angle, unit is °; Δv p is the longitudinal wave velocity difference between the upper and lower media, unit is m / s; v p is the average longitudinal wave velocity of the upper and lower media, unit is m / s; Δvs is the shear wave velocity difference, with the unit of m / s; v s is the average shear wave velocity, with the unit of m / s; Δρ is the density difference, with the unit of g / cm 3 ; ρ is the average density, with the unit of g / cm 3 ;

[0018] S4. Substitute the logging data obtained in step S1 and the elastic parameter data obtained by fluid substitution in step S2 into the P and G calculation formulas obtained in step S3 to create a P-G attribute crossplot;

[0019] The specific method is: Take the data of two adjacent sampling points in the logging data obtained in step S1 as two adjacent horizons, substitute the elastic parameter data obtained by fluid substitution in step S2 into the calculation formulas of P and G obtained in step S3 to obtain a P-G attribute crossplot;

[0020] S5. According to the distribution law of different fluids on the P-G attribute crossplot, fit different fluids to obtain three trend lines representing different types of fluids. The one in the upper right is the water line, the middle one is the oil line, and the one in the lower left is the gas line;

[0021] S6. According to the three trend lines obtained by fitting in step S5, divide the background into four regions, define the fluid types within the regions, and obtain a P-G attribute template for fluid identification in the study area;

[0022] S7. Determine the fluid type based on the distances from the sample points projected by the P and G calculated from the formation elastic parameters of the target layer to each line on the fluid identification P-G attribute template obtained in step S6, and realize the qualitative judgment of the fluid type.

[0023] The specific method is: Substitute the formation elastic parameters of the target layer into the P and G calculation formulas, project the sample points onto the established P-G attribute template, and qualitatively distinguish the fluid type according to its distribution on the template.

[0024] The formation elastic parameters of the target layer are obtained by seismic inversion using seismic data and drilled well data before drilling.

[0025] The beneficial effects of the present invention are:

[0026] The present invention provides a seismic comprehensive prediction method for identifying shallow complex oil and gas types. Starting from traditional AVO analysis, by statistically studying the logging data in the study area, an AVO attribute crossplot template suitable for the study area is established. Crossplots are made using the logging data, linear fitting is performed on the discrete points to determine the water line position, and the distance of the target layer from the water line is determined based on the seismic reflection characteristics to further determine the fluid type. The method of the present invention reduces the dependence on logging data for oil and gas layer discrimination, reduces production costs, and has high prediction accuracy. It can use seismic data and regional laws to judge the properties of un-drilled target oil and gas before drilling, quickly identify the fluid type, provide a calculation basis for the economic efficiency of drilling deployment, and guide offshore oil drilling work. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is a schematic flow chart of the seismic comprehensive prediction method for oil and gas identification of the present invention;

[0028] Figure 2 It is a logging curve graph of the fluid replacement section in Embodiment 1 of the present invention;

[0029] Figure 3 It is a P-G attribute crossplot made according to the logging curves and AVO analysis in the study area in Embodiment 1 of the present invention;

[0030] Figure 4 The P-G attribute template graph suitable for the study area made in Embodiment 1 of the present invention.

[0031] For those of ordinary skill in the art, without creative efforts, other related drawings can be obtained based on the above drawings. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0032] In order to enable those skilled in the art of the present technology to better understand the technical solution of the present invention, the technical solution of the present invention will be further described below in conjunction with the drawings in the specification and through specific embodiments.

[0033] Embodiment 1

[0034] Taking the oil and gas identification of a certain oilfield ** structure as an example, the target layer in the study area is buried relatively shallow, and the traditional AVO bright spot technology is affected by too many factors, so seismic data cannot be directly used for fluid type identification. There are multiple drilled wells around this structure, and the fluid types of the drilled horizons have been clearly determined. Using their logging data, a P-G attribute crossplot of the study area can be made. According to the characteristics of the P-G attribute crossplot, a P-G template for fluid identification in the study area is established, and a well is selected to verify the accuracy of the template. Using the P-G template for fluid identification, the fluid type of the target layer of the newly drilled well in the study area can be quickly identified. The specific implementation process is as Figure 1 shown, including the following steps:

[0035] S1. There are multiple drilled wells near the study area, all of which have discovered oil and gas, and the fluid types of the horizons drilled have been determined. Select the logging data of the fluid intervals in a single well or multiple wells near the study area, conduct petrophysical crossplot analysis based on the logging data, and statistically analyze the formation elastic and physical properties under the background of the entire study area according to the results of the petrophysical crossplot analysis to obtain the distribution laws of the formation elastic and physical properties in the study area;

[0036] Analyze and statistically analyze the formation elastic properties under the background of the entire study area based on the logging data to obtain the changing trends of the formation velocity and density in the study area;

[0037] The logging data includes acoustic data, density data, Gamma data, porosity data, etc.;

[0038] S2. Since the data of the fluid intervals drilled is limited, it is necessary to use a petrophysical model to expand the data through fluid substitution. Specifically: establish a petrophysical model according to the distribution laws of the formation elastic and physical properties in the study area obtained in step S1, conduct seismic forward modeling to verify the accuracy of the petrophysical model, use the petrophysical model as a fluid substitution template, and perform fluid substitution using the fluid substitution template to calculate the elastic parameter data of the formation under different porosities and different fluid properties;

[0039] In this embodiment, the following method is used to establish a petrophysical model: measure the rock frame bulk modulus through laboratory analysis, use the logging data to analyze the porosity of the target layer in the study area, conduct fluid substitution using the Gassmann equation to obtain the rock elastic parameters under different fluid saturation states, and change the porosity to obtain the rock elastic parameters under different porosities and fluid saturation states, Figure 2 is the fluid substitution interval;

[0040] S3. Rearrange the Shuey reflection coefficient approximation formula to obtain the P and G calculation formulas;

[0041] The Shuey reflection coefficient approximation formula is the common Shuey approximation for AVO quantitative analysis and hydrocarbon detection;

[0042] Rearrange the Shuey approximation formula to obtain the P and G mathematical expression forms in terms of elastic parameters. The rearranged reflection coefficient approximation formula form is:

[0043] R(θ) = P + G * sin 2 (θ)

[0044]

[0045] where: R is the reflection coefficient, dimensionless; θ is the incident angle, unit is °; Δv p is the P-wave velocity difference between the upper and lower media, unit is m / s; v pis the average longitudinal wave velocity of the upper and lower media, with the unit of m / s; Δv s is the shear wave velocity difference, with the unit of m / s; v s is the average shear wave velocity, with the unit of m / s; Δρ is the density difference, with the unit of g / cm 3 ; ρ is the average density, with the unit of g / cm 3 ;

[0046] S4. Substitute the logging data obtained in step S1 and the elastic parameter data obtained by fluid substitution in step S2 into the P and G calculation formulas obtained in step S3 to create a P-G attribute crossplot;

[0047] The specific method is as follows: Take the data of two adjacent sampling points in the logging data obtained in step S1 as two adjacent horizons, substitute the elastic parameter data obtained by fluid substitution in step S2 into the calculation formulas of P and G obtained in step S3 to obtain a P-G attribute crossplot, as Figure 3 shown. A total of 17 sample points from three wells are selected;

[0048] S5. Since there are few gas layer sample points, add fluid substitution data to expand the sample points. Specifically: According to the distribution law of different fluids on the P-G attribute crossplot, fit different fluids to obtain three boundary lines for different types of fluids. The upper right one is the water line, the middle one is the oil line, and the lower left one is the gas line;

[0049] S6. According to the three trend lines fitted in step S5, the three trend lines divide the background into four regions (as Figure 4 shown), define the fluid types within the regions to obtain a fluid identification P-G attribute template for the study area;

[0050] S7. Determine the fluid type based on the distances from the sample points where the P and G projected from the formation elastic parameters of the target layer to the lines on the fluid identification P-G attribute template obtained in step S6, and realize the qualitative judgment of the fluid type;

[0051] S8. Use another well in the work area to verify the fluid identification P-G attribute template. The distribution of the sample points of the target layer on the fluid identification P-G attribute template is as Figure 4 shown. The sample point in the upper right is close to the water line and is judged as a water layer but is actually an oil layer. Except for this point, the rest of the sample points are successfully identified, and the identification accuracy reaches 75%, verifying the accuracy of the template.

[0052] In the practical application of the present invention, the accuracy rate of fluid identification reaches 75%. Using this template, the fluid types can be accurately distinguished. The present invention can predict the underground fluid types before drilling, has high prediction accuracy, and can guide the drilling deployment before drilling.

[0053] The applicant declares that the above description is only a specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Those skilled in the art should understand that any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed by the present invention fall within the protection scope and the disclosure scope of the present invention.

Claims

1. A seismic comprehensive prediction method for identifying shallow complex oil and gas types, characterized in that: It includes the following steps: S1. Select the logging data of a single well or multiple wells in the study area. Conduct petrophysical crossplot analysis based on the logging data, and statistically analyze the formation elastic and physical properties under the background of the entire study area according to the results of the petrophysical crossplot analysis to obtain the distribution law of the formation elastic and physical properties in the study area; S2. Establish a petrophysical model according to the distribution law of the formation elastic and physical properties obtained in step S1, conduct seismic forward modeling to verify the accuracy of the petrophysical model, use the petrophysical model as a fluid substitution template, perform fluid substitution using the fluid substitution template, and calculate the elastic parameter data of the formation under different pore and different fluid property conditions; S3. Rearrange the Shuey reflection coefficient approximate formula to obtain the P and G calculation formulas; S4. Substitute the logging data obtained in step S1 and the elastic parameter data obtained by fluid substitution in step S2 into the P and G calculation formulas obtained in step S3 to produce a P-G attribute crossplot; S5. According to the distribution law of different fluids on the P-G attribute crossplot, fit different fluids to obtain three trend lines representing different types of fluids. The one in the upper right is the water line, the middle one is the oil line, and the one in the lower left is the gas line; S6. According to the three trend lines obtained by fitting in step S5, divide the background into four regions, define the fluid types within the regions, and obtain the fluid identification P-G attribute template for the study area; S7. Determine the fluid type based on the distances from the sample points of the P and G projections calculated from the formation elastic parameters of the target layer to each line on the fluid identification P-G attribute template obtained in step S6, and realize the qualitative judgment of the fluid type.

2. The seismic comprehensive prediction method for shallow complex oil and gas type identification according to claim 1, characterized in that: The logging data in step S1 includes acoustic data, density data, Gamma data, and porosity data.

3. The seismic comprehensive prediction method for shallow complex oil and gas type identification according to claim 1, characterized in that: The specific method for obtaining the elastic parameter data in step S2 is as follows: Analyze the rock mineral components of the target layer in the study area based on the logging data or laboratory data, calculate the dry rock frame bulk modulus using the V-R-H averaging theory, analyze the porosity of the target layer in the study area using the logging data, perform fluid substitution using the Gassmann equation to obtain the rock elastic parameters under different fluid saturation states, and change the porosity to obtain the rock elastic parameters under different porosity fluid saturation states.

4. The seismic comprehensive prediction method for shallow complex oil and gas type identification according to claim 1, characterized in that: The Shuey reflection coefficient approximate formula is the commonly used Shuey approximation for AVO quantitative analysis and hydrocarbon detection.

5. The seismic comprehensive prediction method for shallow complex oil and gas type identification according to claim 1, characterized in that: The P and G calculation formulas are: R(θ) = P + G * sin 2 (θ) Where: R is the reflection coefficient, dimensionless; θ is the incident angle, with the unit of °; Δv p is the longitudinal wave velocity difference between the upper and lower media, with the unit of m / s; v p is the average longitudinal wave velocity of the upper and lower media, with the unit of m / s; Δv s is the shear wave velocity difference, with the unit of m / s; v s is the average shear wave velocity, with the unit of m / s; Δρ is the density difference, with the unit of g / cm 3 ; ρ is the average density, with the unit of g / cm 3 .

6. The seismic comprehensive prediction method for identifying shallow complex oil and gas types according to claim 1, characterized in that: The specific method in step S4 is as follows: Take the data of two adjacent sampling points in the logging data obtained in step S1 as two adjacent horizons, substitute the elastic parameter data obtained by fluid substitution in step S2 into the P and G calculation formulas obtained in step S3 to obtain a P-G attribute crossplot.

7. The seismic comprehensive prediction method for shallow complex oil and gas type identification according to claim 1, characterized in that: The specific method in step S7 is as follows: Substitute the formation elastic parameters of the target layer into the P and G calculation formulas, project the sample points onto the fluid identification P-G attribute template established in step S6, and qualitatively distinguish the fluid type according to its distribution on the template.

8. The seismic comprehensive prediction method for shallow complex oil and gas type identification according to claim 1, characterized in that: The formation elastic parameters of the target layer in step S7 are obtained by seismic inversion using seismic data and drilled well data before drilling.

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