Comprehensive earthquake prediction method for high-quality gas-bearing reservoir

Abstract

The invention discloses a comprehensive earthquake prediction method for a high-quality gas-bearing reservoir. The method comprises the following steps: manually collecting and processing to obtain an earthquake data volume; analyzing to obtain seismic elastic parameters sensitive to the high-quality gas-bearing reservoir; fine well seismic calibration is carried out, and a time-depth relation and seismic wavelets are obtained; performing time-frequency analysis to extract a high-frequency-band attenuation area, and qualitatively predicting a gas-bearing region; constructing a low-frequency fusion model conforming to the deposition characteristics of the high-quality gas-bearing reservoir; carrying out seismic pre-stack simultaneous inversion to obtain a sensitive parameter inversion body; and performing probability analysis to construct a high-quality gas-bearing reservoir identification chart, quantitatively predicting the distribution of the high-quality gas-bearing reservoir, and comprehensively and optimally selecting a favorable region of the high-quality gas-bearing reservoir. Compared with the prior art, the method has the advantages that aiming at the problems of low exploration degree, small number of drilled wells, non-uniform well location distribution, high transverse anisotropism of the reservoir and the like of the gas-bearing area, the method can obtain more accurate spatial distribution characteristics of the high-quality gas-bearing reservoir by combining well logging, horizon and pre-stack and post-stack seismic data, and the multiplicity of a prediction result is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas exploration and development, and more specifically to a method for comprehensive seismic prediction of high-quality gas-bearing reservoirs, targeting scenarios with low exploration levels, few wells or uneven well distribution, and dramatic lateral changes in gas-bearing reservoirs. Background Art

[0002] With the continuous growth of global energy demand, natural gas, as a clean and efficient energy source, has attracted more and more attention from the oil exploration and development industry. In order to ensure the stability of energy supply, it is particularly important to find high-quality gas-bearing reservoirs. The effective detection and evaluation of gas-bearing reservoirs can not only improve the success rate of traditional oil and gas exploration, but also promote the development of unconventional resources such as shale gas and coalbed methane. Traditional geological exploration methods often face complex geological environments and uncertainties, while seismic exploration, as an important geophysical technology, has the advantages of high precision and high efficiency. Through the propagation characteristics of seismic waves in underground media, seismic exploration can provide rich information to help identify and describe the geometric morphology and physical properties of gas-bearing reservoirs. However, with the continuous deepening of the exploration and development of oil and gas resources, the difficulty of discovering oil and gas continues to increase, and a single seismic method is often difficult to meet the actual exploration needs. In view of the reservoir prediction problems such as low exploration degree, small number of wells, uneven distribution of well locations and strong lateral heterogeneity of reservoirs in gas-bearing development areas, single methods such as seismic attribute analysis and seismic inversion have certain shortcomings, resulting in unsatisfactory prediction results. Therefore, it is urgent to establish a comprehensive seismic prediction method for high-quality gas-bearing reservoirs, explore the distribution rules of high-quality reservoirs, and improve the overall success rate of exploration wells. Summary of the invention

[0003] The purpose of the embodiment of the present invention is to provide a high-quality gas-bearing reservoir seismic comprehensive prediction method to improve the accuracy of the prediction of high-quality gas-bearing reservoirs.

[0004] To achieve the above objectives, the present invention provides a method for comprehensive seismic prediction of high-quality gas-bearing reservoirs. The present invention includes:

[0005] Manually collect 3D seismic data and process them indoors to obtain pre-stack and post-stack pure wave seismic data volumes and seismic velocity volumes;

[0006] The histogram statistics method is used to correct the systematic error among multiple wells by fine-tuning the main peak position.

[0007] Analyze logging data to obtain seismic elastic parameters E that are sensitive to high-quality gas-bearing reservoirs a and E b ;

[0008] Use well logging and prestack seismic data to perform fine well-seismic calibration and obtain time-depth relationships and prestack seismic wavelets;

[0009] Using the stratigraphic and post-stack seismic data, based on the wavelet transform time-frequency analysis method, the attenuation area attribute of the high frequency band of the spectrum is extracted to qualitatively predict the gas-bearing area A;

[0010] Analyze the spectrum characteristics of seismic data, velocity volume data and logging data, and determine the low-frequency compensation spectrum range of different data;

[0011] Under the constraints of the fine seismic stratigraphic framework, based on the mathematical interpolation algorithm, the logging data is interpolated and extrapolated to obtain a conventional well interpolation full-band high-resolution model;

[0012] Quality control of the seismic velocity body and its correlation with the well logging curve, extraction of the P-wave velocity curve at the well point, intersection and fitting with the actual well logging P-wave velocity, correction of the deviation between the seismic velocity body and the well logging data;

[0013] Extract the well point velocity of the seismic correction velocity body, establish the relationship between it and the well logging density and shear wave velocity, and convert it into the longitudinal wave impedance, shear wave impedance and density model;

[0014] The compaction trend of the study area and the compaction trend curve on the wells are obtained by using the P-wave impedance of multiple wells and time intersection after well seismic calibration.

[0015] The decompaction trend curve on the well is obtained by subtracting the well logging longitudinal wave impedance from the compaction trend curve, and the decompaction trend curve is interpolated into the decompaction trend body through the interpolation and extrapolation algorithm;

[0016] The compaction trend body and the decompacted trend body of the study area are added together to obtain the compaction trend corrected P-wave impedance model. The density and S-wave impedance compaction trend correction model is constructed in the same way.

[0017] According to the frequency band range of various data, the well interpolation model, seismic velocity field conversion model and compaction trend correction body are combined in the frequency domain to construct a low-frequency fusion model that conforms to the sedimentary characteristics of high-quality gas-bearing reservoirs;

[0018] Carry out pre-stack seismic simultaneous inversion to obtain E sensitive to high-quality reservoirs a and E b Inversion data volume;

[0019] Using E a and E b The inversion volume and the classification conclusion of the gas-bearing reservoir in the well logging are used to perform the probability analysis of the gas-bearing reservoir and construct the high-quality reservoir identification plate;

[0020] Based on the high-quality reservoir identification plate, the spatial distribution of high-quality gas-bearing reservoirs is quantitatively predicted to obtain gas-bearing area B;

[0021] By using the qualitative prediction results and the quantitative prediction results, the favorable areas of high-quality gas-bearing reservoirs are selected. Combining all the above processes, a comprehensive seismic prediction method for high-quality gas-bearing reservoirs is formed.

[0022] The embodiment of the present invention has the following advantages: Compared with the attribute analysis method using post-stack seismic data, the method proposed by the present invention combines the seismic inversion method to reduce the multi-solution of the prediction results. Compared with the conventional pre-stack seismic inversion method, the method first uses the time-frequency domain seismic attribute analysis method to qualitatively detect the gas-bearing area, and then conducts seismic inversion based on the low-frequency fusion model, which integrates more geological information, improves the accuracy and resolution of the prediction results in the far-well area, and improves the accuracy of the lateral feature recognition of the target layer in the near-well control area, improves the overall prediction effect, and greatly improves the interpretability of the prediction results of high-quality gas layers. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. It is obvious that the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0024] Figure 1 This is a flow chart of a comprehensive seismic prediction method for high-quality gas-bearing reservoirs according to an embodiment of the present invention.

[0025] Figure 2 This is a cross-sectional diagram of original seismic data of the target layer according to an embodiment of the present invention.

[0026] Figure 3 This is a plane distribution diagram of the qualitative prediction of the gas-bearing zone A according to an embodiment of the present invention.

[0027] Figure 4 This is a cross-sectional view of the well line of the longitudinal wave impedance low-frequency fusion model according to an embodiment of the present invention.

[0028] Figure 5 It is a plan view of the longitudinal wave impedance low-frequency fusion model of an embodiment of the present invention.

[0029] Figure 6 This is a high-quality reservoir identification chart according to an embodiment of the present invention.

[0030] Figure 7 This is a quantitative prediction of the planar distribution of the gas-bearing area B according to an embodiment of the present invention.

[0031] Figure 8 This is a comprehensive predicted distribution map of high-quality gas-bearing reservoir development areas according to an embodiment of the present invention. DETAILED DESCRIPTION

[0032] The technical scheme in the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiment of the present invention. Obviously, the described embodiment is only a part of the embodiment of the present invention, not all of the embodiments. Based on the embodiment of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0033] Figure 1 The flowchart of the comprehensive seismic prediction method for high-quality gas-bearing reservoirs according to an embodiment of the present invention.

[0034] like Figure 1 As shown, the method comprises the following steps:

[0035] S101. In the study area, a set of three-dimensional seismic data is collected. Specifically, a three-dimensional seismic data obtained by three-dimensional acquisition is selected for the underground exploration target in a certain work area in a certain place, and the application of the embodiment of the present invention is demonstrated. The area is a deep-water area in a certain sea in my country. The underground exploration target includes a set of shallow natural gas sections, which are continental slope deposits with multi-layer reservoirs developed from bottom to top. The seismic data is affected by the law of shallow gas enrichment, with fast lateral changes in energy and strong heterogeneity. The seismic data of the shallow gas section of the target is intercepted, Figure 2 This is the seismic data of the shallow gas section in the embodiment of the present invention. The location of the wells is marked above. There are only three wells in the area, and all of them are located in a corner of the study area, with extremely uneven distribution.

[0036] S102, using the histogram statistics method, correcting the systematic error among the three wells by fine-tuning the main peak position. In this embodiment, the well logging data includes the longitudinal wave impedance, the shear wave impedance and the density curve.

[0037] S103. Analyze logging data to obtain seismic elastic parameters E that are sensitive to high-quality gas-bearing reservoirs a and Eb. In this example, the longitudinal wave impedance cannot effectively distinguish between reservoirs and non-reservoirs, so the seismic post-stack inversion method is not applicable, and the pre-stack inversion should be selected. The sensitive parameter analysis results of the embodiment of the present invention show that the longitudinal and transverse wave velocity ratio (Vp / Vs) and the Lame constant multiplied by the density (λρ) are extremely sensitive to gas layers. Low Vp / Vs and low λρ point to high-quality gas-bearing reservoirs with high porosity, high gas saturation, high permeability and low mud content. Vp / Vs and λρ are preferably used as sensitive elastic parameters of high-quality gas-bearing reservoirs, and pre-stack simultaneous inversion is performed.

[0038] S104, perform fine well-seismic calibration using well logging and pre-stack seismic data to obtain time-depth relationship and seismic wavelets. In this embodiment, the pre-stack seismic data is three partial angle stacking data, namely 0-20 degrees, 20-40 degrees and 40-60 degrees, corresponding to three comprehensive wavelets.

[0039] S105. Using the stratigraphic and post-stack seismic data, based on the wavelet transform time-frequency analysis method, the attenuation area attribute of the high frequency band of the spectrum is extracted to qualitatively predict the gas-bearing area A, such as Figure 3 shown.

[0040] S106. Analyze the spectrum characteristics of seismic data, velocity volume data and logging data, and determine the spectrum range of low-frequency compensation for different data. In this example, the shallow gas reservoir is shallowly buried, the low-frequency component of the seismic data signal is strong, the effective frequency band is 7-60Hz, and the low-frequency information below 7hz is missing; the effective frequency band of the velocity field is 0-10Hz. In order to combine the advantages of various types of data, the seismic velocity field conversion model, compaction trend correction model and conventional well interpolation model compensate for 0-4Hz, 4-6Hz and 6-10hz low-frequency energy respectively.

[0041] S107. Under the constraints of the fine seismic stratigraphic grid, based on the mathematical interpolation algorithm, the logging data is interpolated and extrapolated to obtain a conventional well interpolation full-band high-resolution model. In this example, there are only three well data, so based on the local weighted interpolation algorithm, the P-wave impedance, S-wave impedance and density models are obtained.

[0042] S108, quality control velocity body quality and its correlation with well logging curve, extract the P-wave velocity curve at the well point, intersect and fit with the actual well logging P-wave velocity, and correct the error between the seismic velocity body data and the well logging data. In this example, the relationship is in the form of a linear function:

[0043] y=825.221+0.66794x (Formula 1) where x is the velocity field velocity of the well point, and y is the measured longitudinal wave velocity, both in units of m / s.

[0044] S109, extract the well point velocity of the seismic correction velocity body, and establish relationship expressions with the density and shear wave velocity of the logging data. In this example, the relationship expressions are all in the form of linear functions:

[0045] y=1.86778+0.00015075x1 (Formula 2)

[0046] y=195.681+0.366869x2 (Formula 3)

[0047] Where x1 is the density in g / cm 3 , x2 is the shear wave velocity, y is the corrected body well point velocity, all in m / s. The longitudinal wave impedance, shear wave impedance and density model are obtained by conversion using formulas (1)-(3).

[0048] S110, using the multi-well P-wave impedance and time intersection after well seismic calibration to calculate the uniform compaction trend in the region. In this example, the compaction trend relationship is:

[0049] y=2035.23+3013.76t (Formula 4)

[0050] Where t is the double-layer travel time in seconds and y is the longitudinal wave impedance in g / cm 3 m / s; the compaction trend body in the study area and the compaction trend curve on the well are calculated according to formula (4).

[0051] S111, subtracting the well logging longitudinal wave impedance from the compaction trend curve to obtain a decompaction trend curve on the well, and interpolating it into a decompaction trend body through an interpolation and extrapolation algorithm;

[0052] S112. Add the compaction trend body and the decompacted trend body of the study area to calculate the compaction trend correction longitudinal wave impedance model. The density and shear wave impedance compaction trend correction model construction method is the same. In this example, the density, shear wave impedance and time intersection fitting relationship is:

[0053] y1=1.89931+0.314055t (Formula 5)

[0054] y2=348.158+1940.9t (Formula 6)

[0055] Where t is the round trip travel time in seconds and y1 is the density in g / cm 3 , y2 is the shear wave impedance, unit is g / cm 3 m / s.

[0056] S113. According to the frequency band range of various data, the well interpolation model, seismic velocity field conversion model and compaction trend correction body are merged in the frequency domain to construct a low-frequency fusion model that conforms to the depositional characteristics of high-quality gas-bearing reservoirs. Figure 4 The longitudinal wave impedance low-frequency fusion model constructed for the embodiment of the present invention is connected to the well line profile. The fusion model incorporates more geological information, the vertical characteristics are more reasonable, the well and the well curve are highly consistent, and the wells are in line with the regional change trend. Figure 5 A plan view of the low-frequency fusion model of longitudinal wave impedance constructed in an embodiment of the present invention. The fusion model reduces the lateral patterning and uncertainty caused by the extrapolation of the well in the far well area, improves the "bull's eye" phenomenon around the well in the compaction trend correction model, and improves the accuracy and rationality of the inversion initial model.

[0057] S114. Carry out pre-stack simultaneous inversion to obtain Vp / Vs and λρ inversion data volumes that are sensitive to high-quality gas-bearing reservoirs.

[0058] S115. Probability analysis of gas-bearing reservoirs is performed using Vp / Vs and λρ inversion bodies and well logging gas-bearing reservoir classification conclusions. Based on Bayesian fuzzy discrimination theory, a high-quality reservoir identification chart is constructed. In this example, the well logging gas-bearing reservoir classification conclusions of five lithologies (class 1 high-quality reservoir, class 2 high-quality reservoir, class 3 high-quality reservoir, dry layer and mudstone) are considered. Intersection analysis is carried out based on the two elastic parameters of Vp / Vs and λρ. The two-dimensional probability density function of class 1 high-quality reservoir and other layers is fitted using Gaussian function, corresponding to Figure 6 The closer the two-color ellipse is to the center of the ellipse, the greater the possibility of lithofacies or fluid development, and a high-quality reservoir identification plate is established.

[0059] S116. Based on the high-quality reservoir identification plate, the spatial distribution of the gas-bearing reservoir is quantitatively predicted to obtain the gas-bearing area B. In this example, the high-quality reservoir identification plate is applied to the Vp / Vs and λρ data volumes obtained by prestack inversion to calculate the probability volume of the high-quality reservoir, and the probability range of 0.7-1 is used to characterize it as a high-quality gas-bearing reservoir. Figure 7 To quantitatively predict the spatial distribution of gas-bearing areas B.

[0060] S117, using the qualitative prediction results and the quantitative prediction results, the favorable area of ​​high-quality gas-bearing reservoir is selected to form a high-quality gas-bearing reservoir seismic comprehensive prediction method. In this example, the qualitative prediction result A and the quantitative prediction result B are superimposed and displayed, and the B area included in the A range is preferably the comprehensive high-quality gas-bearing reservoir development area ( Figure 8 ).

[0061] Although the present invention has been described in detail above by general description and specific embodiments, it is obvious to those skilled in the art that some modifications or improvements can be made to the present invention. Therefore, these modifications or improvements made without departing from the spirit of the present invention all belong to the scope of protection claimed by the present invention.

Claims

1. A comprehensive seismic prediction method for high-quality gas-bearing reservoirs, characterized in that The steps include: (1) Manually collect 3D seismic data and process them indoors to obtain pre-stack and post-stack pure wave seismic data volumes and seismic velocity volumes; (2) Using the histogram statistics method, the systematic error among multiple wells is corrected by fine-tuning the main peak position; (3) Analyze logging data to obtain the seismic elastic parameter E that is sensitive to high-quality gas-bearing reservoirs a and E b ; (4) Use well logging and prestack seismic data to perform fine well-seismic calibration to obtain the time-depth relationship and each prestack seismic wavelet; (5) Using the stratigraphic and post-stack seismic data, based on the wavelet transform time-frequency analysis method, the attenuation area attributes of the high-frequency band of the spectrum are extracted to qualitatively predict the gas-bearing area A; (6) Analyze the frequency spectrum characteristics of seismic data, velocity volume data, and well logging data to determine the low-frequency compensation frequency spectrum range of different data; (7) Under the constraints of the fine seismic stratigraphic framework, based on the mathematical interpolation algorithm, the logging data is interpolated and extrapolated to obtain a conventional well interpolation full-band high-resolution model; (8) Quality control of the seismic velocity body and its correlation with the well logging curve, extraction of the P-wave velocity curve at the well point, cross fitting with the actual well logging P-wave velocity, and correction of the deviation between the seismic velocity body data and the well logging data; (9) Extracting the well point velocity of the seismic corrected velocity body, establishing relationships with the well logging density and shear wave velocity, and converting them to obtain the longitudinal wave impedance, shear wave impedance and density model; (10) Using the P-wave impedance of multiple wells after well seismic calibration and time intersection to calculate the unified compaction trend in the region, the compaction trend body of the study area and the compaction trend curve on the wells are obtained; (11) Subtracting the well logging P-wave impedance from the compaction trend curve to obtain the decompaction trend curve on the well, and interpolating it into a decompaction trend body through an interpolation and extrapolation algorithm; (12) Add the compaction trend body and the decompacted trend body of the study area to obtain the compaction trend corrected longitudinal wave impedance model. The density and shear wave impedance compaction trend correction model is constructed in the same way; (13) According to the frequency band range of various data, the well interpolation model, seismic velocity field conversion model and compaction trend correction body are merged in the frequency domain to construct a low-frequency fusion model that conforms to the sedimentary characteristics of high-quality gas-bearing reservoirs; (14) Carry out pre-stack seismic simultaneous inversion to obtain E sensitive to high-quality reservoirs a and E b Inversion data volume; (15) Using E a and E b The inversion volume and the classification conclusion of the gas-bearing reservoir in the well logging are used to perform the probability analysis of the gas-bearing reservoir and construct the high-quality reservoir identification plate; (16) Based on the high-quality reservoir identification plate, quantitatively predict the spatial distribution of high-quality gas-bearing reservoirs and obtain gas-bearing area B; (17) By using the qualitative prediction results and the quantitative prediction results, favorable areas of high-quality gas-bearing reservoirs are selected to form a comprehensive seismic prediction method for high-quality gas-bearing reservoirs.

2. The method according to claim 1, characterized in that the artificial earthquake in step (1) is an earthquake artificially stimulated for the purpose of exploration; When artificial seismic data is collected in the field by using a seismograph, three-dimensional data is collected on the ground, and the seismic data obtained is three-dimensional data; The process of processing 3D seismic data includes preprocessing, field static correction, denoising, deconvolution, velocity analysis, dynamic correction, residual static correction, counter-dynamic correction and migration imaging steps.

3. The method according to claim 1 is characterized in that the correction processing described in step (2) is performed separately on the longitudinal wave impedance curve, the transverse wave impedance curve and the density curve.

4. The method according to claim 1 is characterized in that the high-quality gas-bearing reservoir described in step (3) refers to a gas-bearing reservoir with high porosity, high gas saturation, high permeability and low mud content.

5. The method according to claim 1 is characterized in that the high-quality reservoir identification chart described in step (15) consists of one cross-plot and two histograms, and the ones involved in the analysis are E a 、E b Well logging curves and logging gas-bearing reservoir classification conclusions.

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