Phase body constrained thin reservoir drilling rate improving method and device

Abstract

The invention discloses a phase body constrained thin reservoir drilling rate improving method and device, and belongs to the technical field of petroleum and natural gas exploration, the phase body constrained thin reservoir drilling rate improving method comprises the following steps: screening effective frequency bands of seismic data to obtain seismic data of a target frequency band; the method comprises the steps of acquiring seismic data of a target frequency band, extracting a phase body based on the seismic data of the target frequency band, constructing an inversion framework based on horizon interpretation of the phase body, dividing the seismic data into discrete data volumes according to oil components based on the inversion framework, and performing geostatistical inversion by adopting different prior functions based on the discrete data volumes. The method comprises the following steps: screening seismic data to obtain seismic data of a target frequency band, and extracting a phase body based on the seismic data of the target frequency band to realize phase body-based grillwork constraint; and after seismic data are divided into discrete data bodies according to oil components, geostatistical inversion is carried out by adopting different prior functions, so that quantitative prediction of the thin sand body is realized.

Description

Technical Field

[0001] The invention belongs to the technical field of oil and natural gas exploration, and in particular relates to a method and a device for improving the drilling rate of thin reservoirs constrained by a phase body. Background Art

[0002] As a large number of oil fields shift from the exploration stage to the development stage, the requirements for the accuracy of reservoir description are becoming increasingly higher, and the research target is shifting from the study of thick sand groups to thin sand bodies. Conventional inversion methods are the main means of identifying thin sand bodies, but due to the randomness of the algorithm, multiple solutions will be caused to the sand body prediction results.

[0003] Georgy et al. applied phase spectrum to the analysis of pinch-out characteristics of thin interlayers. Compared with amplitude spectrum, phase spectrum can provide more accurate information for determining pinch-out points; Cai Hanpeng et al. applied instantaneous phase spectrum within the seismic frequency band to construct a target function for estimating stratum thickness; Wang Peng et al. estimated the thickness of thin layers by deriving the relationship between seismic phase and stratum thickness; although direct application of phase information can realize qualitative and quantitative identification of some thin layers, due to the limitation of seismic resolution, the resolution improvement brought by phase is not obvious and the effect is unstable. Summary of the invention

[0004] The object of the present invention is to provide a method and device for improving the drilling rate of thin reservoirs constrained by a phase body, so as to solve the limitations of the existing thin sand body prediction method proposed in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solution: a method for improving the drilling rate of thin reservoirs constrained by phase body, comprising:

[0006] Screening effective frequency bands of seismic data to obtain seismic data of a target frequency band, and extracting a phase volume based on the seismic data of the target frequency band;

[0007] Construct an inversion grid based on the horizon interpretation of the phase volume;

[0008] Based on the inversion grid, the seismic data are divided into discrete data volumes according to the oil composition, and based on the discrete data volumes, geostatistical inversion is performed using different prior functions.

[0009] Preferably, the extraction of the phase volume based on the seismic data of the target frequency band is achieved by wavelet transformation.

[0010] Preferably, the construction of the inversion grid based on the phase volume-based horizon interpretation includes:

[0011] The phase value corresponding to each layer is converted to 0 or extreme value, and the layer interpretation is carried out based on the converted phase volume data.

[0012] Preferably, the oil group division of the seismic data is achieved based on the phase value range distinction of the phase body.

[0013] Preferably, before dividing the seismic data into discrete data volumes according to the oil components based on the inversion grid, the method further comprises:

[0014] The phase value range of the phase body is linearly changed by 2nπ;

[0015] The phase volume is recalculated based on the transformed phase values.

[0016] Preferably, the recalculation of the phase body based on the transformed phase value is implemented based on a deep learning algorithm.

[0017] Preferably, the screening conditions for the effective frequency band of seismic data include resolution and stratum contact information.

[0018] On the other hand, the present application proposes a thin reservoir drilling rate improvement device constrained by a phase body, comprising:

[0019] An extraction module configured to screen the effective frequency band of the seismic data to obtain the seismic data of the target frequency band, and extract a phase volume based on the seismic data of the target frequency band;

[0020] a construction module configured to construct an inversion grid based on horizon interpretation of a phase volume;

[0021] The inversion module is configured to divide the seismic data into discrete data volumes according to the oil composition based on the inversion grid, and perform geostatistical inversion based on the discrete data volumes using different prior functions.

[0022] Preferably, in the inversion module, the oil group division of the seismic data is achieved based on the phase value range distinction of the phase body.

[0023] Compared with the prior art, the present invention has the following beneficial effects:

[0024] The present application screens seismic data to obtain seismic data of a target frequency band, and extracts a phase body based on the seismic data of the target frequency band to implement a grid constraint based on the phase body; and after dividing the seismic data into discrete data bodies according to oil groups, different prior functions are used to carry out geostatistical inversion to achieve quantitative prediction of thin sand bodies. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 This is a flow chart of the application method;

[0026] Figure 2 is the wavelet transformed phase volume profile;

[0027] Figure 3 is the phase volume distribution histogram;

[0028] Figure 4 An inversion grid established for horizon interpretation schemes based on raw seismic data;

[0029] Figure 5 An inversion grid established for phase volume-based horizon interpretation schemes;

[0030] Figure 6 Conduct sensitivity curve intersection analysis for target layer section in target work area;

[0031] Figure 7 It is the phase volume section of the well before and after the phase value range transformation;

[0032] Figure 8 The lithology profile of the wells is shown in Figure 1. The upper part uses a single probability density function, and the lower part uses a piecewise probability density function.

[0033] Fig. 9 This is the predicted planar distribution of sand bodies in each sub-layer of the JIII oil formation in the target work area. DETAILED DESCRIPTION

[0034] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0035] A method for improving the drilling rate of thin reservoirs constrained by phase body, comprising the following steps:

[0036] S100: Screening effective frequency bands of seismic data to obtain seismic data of a target frequency band, and extracting a phase volume based on the seismic data of the target frequency band;

[0037] In conventional post-stack seismic data, low-frequency data contains stratigraphic contact relationships, but has a low resolution, and high-frequency data has a high resolution, but loses stratigraphic contact relationships. Based on this, conventional post-stack seismic data need to be processed to obtain seismic data that contains stratigraphic contact relationships and has a high resolution. Specifically, in step S100, firstly, a spectrum analysis is performed on the seismic data, and based on the result of the spectrum analysis, seismic data within an effective frequency band is obtained. Based on the resolution and stratigraphic contact information, the seismic data within the effective frequency band is analyzed and screened to obtain seismic data in a target frequency band. In some embodiments, the target frequency band is constituted as an effective frequency band range. The high-frequency part within the range, within this frequency band, the seismic data has a higher apparent resolution than the original seismic data. At the same time, after obtaining the seismic data of the target frequency band, the phase body is correspondingly extracted based on the seismic data of the target frequency band. In some embodiments, the method of extracting the phase body from the seismic data can be one of short-time Fourier transform, generalized S transform and wavelet transform. In a preferred embodiment, the phase body data is preferably extracted by wavelet transform. Wavelet is a special waveform with a limited length and an average value of 0. The exact definition of the wavelet function is: φ(t) is set to a square integrable function, that is, φ(t)∈L2(R), if its Fourier transform φ(ω) satisfies the following conditions:

[0038]

[0039] In formula (1), φ^(ω) is the Fourier spectrum of φ(t); ω is the frequency; R represents the set of real numbers; and C represents the set of complex numbers.

[0040] Then φ(t) is a basic wavelet or wavelet mother function, and equation (1) is the admissible condition of the wavelet function.

[0041] From the definition of wavelet function, we can know that it has two characteristics: ① "Small" means that it has compact support or approximately compact support in both time domain and frequency domain. Although in principle, any function in L2(R) space that meets the admissible conditions can be used as a wavelet mother function, real or complex functions with compact support or approximately compact support (with locality in time domain) and regularity (with locality in frequency domain) are generally selected as wavelet mother functions. Such wavelet mother functions have good local characteristics in both time and frequency domains. ② "Wavelet" alternating between positive and negative, that is, the DC component is zero. The sine wave used in Fourier analysis has no time limit (from negative infinity to positive infinity), but wavelets tend to be irregular and asymmetric. Fourier analysis decomposes the signal into a superposition of a series of sine waves of different frequencies. Similarly, wavelet analysis decomposes the signal into a superposition of a series of wavelet functions, and these wavelet functions are all obtained by translation and scaling of a mother wavelet function.

[0042] Based on this, refer to Figure 2, is the phase body extracted by wavelet transformation. It can be seen from the figure that the phase body obtained by wavelet transformation can accurately reflect the contact relationship of the strata and the lateral changes of the thin sand body.

[0043] S200, constructing inversion grid based on the horizon interpretation of phase volume;

[0044] In step S200, an inversion grid is constructed based on the stratum interpretation of the phase body, that is, a grid constraint based on the phase body is implemented based on the phase body obtained in step S100. Specifically, in step S200, based on the seismic data and the phase body, combined with the sequence interpretation, according to the stratification results of the single well, the phase value corresponding to each stratification in the single well is counted, and the stratification of the single well is determined based on the distribution of the phase value (such as Figure 3 As shown), the inversion grid is constructed;

[0045] In some embodiments, in view of the limitations of the software horizon interpretation, it is necessary to convert the phase value corresponding to each layer to 0 or an extreme value, convert the phase volume data into quasi-seismic data, and carry out the horizon interpretation work based on the converted phase volume data;

[0046] Reference Figure 4 and 5 , which are respectively the inversion grid established based on the sequence interpretation scheme of the original seismic data and the inversion grid established based on the stratigraphic interpretation of the phase body. It can be seen from the figure that the inversion grid established based on the stratigraphic interpretation of the phase body has the advantages of higher vertical resolution and better consistency with the lateral distribution of thin sand bodies.

[0047] S300: dividing the seismic data into discrete data volumes according to the oil composition based on the inversion grid, and performing geostatistical inversion based on the discrete data volumes using different prior functions;

[0048] Reference Figure 6 ,By using sensitivity curves to perform intersection analysis on multiple oil groups in the target work area, it can be seen that there are certain differences in the probability density functions of sandstone and mudstone between different oil groups. As a result, when sampling is carried out based on the same probability density function, the sandstone and mudstone cannot be accurately distinguished in the final lithology results. Therefore, in the process of analyzing the lithology of different oil groups, different probability density functions need to be used for sampling. In some embodiments, in probability theory, Bayes' theorem is a theoretical framework for calculating conditional probability density distribution. It is applied to geophysical inversion methods and can be used to build the conditional probability density distribution of model parameters m, so as to use observation data d and prior information to estimate the probability of model parameters. Among them, the obtained model parameter conditional probability density distribution function is called the posterior probability density distribution function (PPDF), denoted as P(m|d,I), which represents the probability of the model parameter vector m under the conditions of data vector d and geological background information I. The expression is as follows.

[0049]

[0050] Where P(m|d,I) is the posterior distribution of the model parameters, P(d|m,I) is the likelihood function describing the relationship between the observed data d and the model parameters m, and P(m|I) is the selected prior distribution. When only the shape of the posterior distribution is considered, the normalization function P(d|I) can be ignored, that is, PPDF can be written as follows:

[0051] P(m|d,I)∝P(d|m,I)·P(m|I)

[0052] When the likelihood function P(d|m,I) obeys Gauss distribution, the posterior distribution P(m|d,I) also obeys Gauss distribution. The value corresponding to the distribution vertex is the optimal solution of the model parameters, and the distribution width represents uncertainty. Based on this, in geological inversion operations, different probability density functions, that is, different prior functions, are used to calculate the lithology of different oil groups.

[0053] Specifically, geostatistical inversion can be understood as adding statistical ideas to seismic inversion; its process is mainly random simulation and the process of optimizing multiple simulation results based on the geological knowledge of the actual study area; when building a reservoir model for reservoir prediction, we need to predict the reservoir properties between wells. First, we use the lithology information in the logging data to establish scatter points or histograms to analyze the probability range of reservoir parameters and obtain the distribution function of related physical properties or lithology; then, because the number of wells used to determine the reservoir properties between wells is far from enough relative to the scope of the study area, the farther away from the well, the less accurate the reservoir information is. In order to understand the relationship between the change in distance and the accuracy of reservoir property prediction, the variogram is introduced; the variogram of the variogram can tell us a safe and reasonable inter-well interpolation range. Under the constraints of the above statistical parameters, the inversion data body is obtained through random simulation related algorithms.

[0054] In some embodiments, in step S300, the oil group division of seismic data is realized based on the phase value domain distinction of the phase body. Preferably, when the seismic data is divided with reference to the phase body, the phase body needs to be preprocessed. Specifically, the preprocessing process is based on the interactive analysis results of sandstone and mudstone of different oil groups. When there are obvious differences in the probability density functions of sandstone and mudstone between different oil groups, correspondingly, a 2nπ linear change of the phase value domain is realized for the oil groups with obvious differences in the probability density functions, and the phase body is recalculated based on the phase value after the linear transformation. In some embodiments, the recalculation of the phase body based on the phase value after the linear transformation can be realized by algorithms such as deep learning, thereby obtaining phase body data containing probability density function difference information (see Appendix 1). Figure 7 ).

[0055] After obtaining the phase volume data containing the probability density function difference information, the phase volume data is applied to the calculation of the lithology body. Specifically, according to the different value ranges of the phase, the seismic data is divided into different oil groups. Different probability density functions are applied to the calculation of different oil groups to achieve effective distinction between sandstone and mudstone (see Appendix Figure 8 ); and thereby determine the vertical position of the target layer, select the appropriate time window, extract the porosity attributes of the target layer, and predict the plane distribution law of high-quality reservoirs (see Appendix Fig. 9 ).

[0056] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for improving the drilling rate of thin reservoirs with phase body constraints, characterized by: include: Screening effective frequency bands of seismic data to obtain seismic data of a target frequency band, and extracting a phase volume based on the seismic data of the target frequency band; Construct an inversion grid based on the horizon interpretation of the phase volume; Based on the inversion grid, the seismic data are divided into discrete data volumes according to the oil composition, and based on the discrete data volumes, geostatistical inversion is performed using different prior functions.

2. The method for improving the drilling rate of thin reservoirs with phase body constraints according to claim 1, characterized in that: The extraction of phase volume based on seismic data of target frequency band is realized by wavelet transformation.

3. A method for improving the drilling rate of thin reservoirs with phase body constraints according to claim 1 or 2, characterized in that: The construction of the inversion grid based on the phase body layer interpretation includes: The phase value corresponding to each layer is converted to 0 or extreme value, and the layer interpretation is carried out based on the converted phase volume data.

4. The method for improving the drilling rate of thin reservoirs with phase body constraints according to claim 1, characterized in that: The oil group division of the seismic data is achieved based on the phase value range distinction of the phase body.

5. The method for improving the drilling rate of thin reservoirs with phase body constraints according to claim 4, characterized in that: Before dividing the seismic data into discrete data volumes according to the oil components based on the inversion grid, the method further includes: The phase value range of the phase body is linearly changed by 2nπ; The phase volume is recalculated based on the transformed phase values.

6. The method for improving the drilling rate of thin reservoirs with phase body constraints according to claim 5, characterized in that: The recalculation of the phase volume based on the transformed phase value is implemented based on a deep learning algorithm.

7. The method for improving the drilling rate of thin reservoirs with phase body constraints according to claim 1, characterized in that: The screening conditions for the effective frequency band of seismic data include resolution and stratum contact information.

8. A phase body constrained thin reservoir drilling rate improvement device, characterized by: include: An extraction module configured to screen the effective frequency band of the seismic data to obtain the seismic data of the target frequency band, and extract a phase volume based on the seismic data of the target frequency band; a construction module configured to construct an inversion grid based on horizon interpretation of a phase volume; The inversion module is configured to divide the seismic data into discrete data volumes according to the oil composition based on the inversion grid, and perform geostatistical inversion based on the discrete data volumes using different prior functions.

9. The device for improving the drilling rate of thin reservoirs constrained by a phase body according to claim 8, characterized in that: In the inversion module, the oil group division of the seismic data is realized based on the phase value range distinction of the phase body.

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