Method and system for predicting earthquake of effective reservoir of aluminiferous rock series gas reservoir

Abstract

The invention belongs to the technical field of tight oil-gas exploration and development, and discloses an aluminum-containing rock series gas reservoir effective reservoir earthquake prediction method and system, and the method comprises the steps: obtaining three-dimensional seismic data, logging data, logging data, test analysis data and other geological data in a work area for the discrimination means of an aluminum-containing rock series effective reservoir lacking; the method comprises the following steps: performing comprehensive analysis on logging data, logging data, test analysis data and other geological data, establishing an aluminum-containing rock series effective reservoir identification standard, clearly identifying an effective reservoir sensitive curve, and obtaining an effective reservoir characteristic curve through a characteristic curve fitting reconstruction technology; analyzing the three-dimensional seismic data, and determining effective reservoir distribution by applying a geostatistics reconstruction inversion technology; and determining favorable area distribution in the work area according to the effective reservoir distribution characteristics, and performing natural gas exploration. The method is applied to ancient life exploration in the Ning County-Ning region of the Ordos Basin, achieves a good effect, is high in applicability, and can be applied and popularized.

Description

Technical Field

[0001] The present invention belongs to the technical field of tight oil and gas exploration and development, and in particular relates to a method and system for seismic prediction of effective reservoirs in aluminum-containing rock series gas reservoirs. Background Art

[0002] Aluminous rock series is a special type of sediment formed by weathering and leaching, which is widely distributed in my country and even in the world. The traditional view is that aluminous rock series has a high clay mineral content and a dense lithology, and cannot be used as a reservoir for oil and gas storage, and is often evaluated as a cap rock for oil and gas reservoirs.

[0003] In recent years, my country has made major breakthroughs in the exploration of natural gas reservoirs in the aluminous rock series in the Ordos Basin, changing the traditional understanding that aluminous rock series cannot be used as reservoirs, and opening up new areas of natural gas exploration in my country and around the world. However, in exploration practice, the lithology of aluminous rock series gas reservoirs is complex and the physical properties vary greatly, making it difficult to predict effective reservoirs, which restricts the exploration and development of aluminous rock series gas reservoirs. No effective solution has yet been proposed.

[0004] There are two major problems in predicting effective reservoirs in aluminous rock systems. First, the characteristics of aluminous rock system reservoirs are different from those of conventional reservoirs, and there is a lack of means to identify effective reservoirs in aluminous rock systems. Second, traditional seismic wave impedance inversion technology is not suitable for predicting aluminous rock system, a new type of oil and gas reservoir, and there is a lack of seismic prediction methods for effective reservoirs in aluminous rock systems. Summary of the invention

[0005] In order to make up for the shortcomings of the existing technology, the present invention provides a method and system for seismic prediction of effective reservoirs in aluminous rock series gas reservoirs for the first time. In view of the above two problems, three-dimensional seismic data, well logging data and various geological data are used to identify effective reservoirs in aluminous rock series and conduct seismic prediction. The prediction results can guide the exploration and development of aluminous rock series gas reservoirs, providing strong support for large-scale reserve increase and efficient production.

[0006] A method for seismic prediction of effective reservoirs in aluminum-bearing rock series gas reservoirs, comprising the following steps:

[0007] Step 101, obtaining 3D seismic data, well logging data, mud logging data, laboratory analysis data and other geological data in the work area;

[0008] Step 102, analyzing the well logging data, mud logging data, chemical analysis data and other geological data, and applying characteristic curve fitting and reconstruction technology to establish effective reservoir characteristic curves of the aluminum-bearing rock series in the work area on the basis of clarifying the characteristics of the aluminum-bearing rock series reservoir;

[0009] Step 103, analyzing the three-dimensional seismic data, combining with the effective reservoir characteristic curve, applying geostatistical characteristic curve inversion, and clarifying the effective reservoir distribution;

[0010] Step 104, based on the distribution characteristics of effective reservoirs, determine the distribution of favorable areas in the work area and conduct exploration deployment.

[0011] Furthermore, the three-dimensional seismic data in step 101 is post-stack time migration data, including post-stack migration data volume data and velocity volume.

[0012] Furthermore, the logging data in step 101 includes at least nine curves: natural gamma, wellbore diameter, natural potential, deep lateral resistivity, shallow lateral resistivity, flushing zone resistivity, sonic time difference, density, and compensated neutron.

[0013] Furthermore, the logging data in step 101 at least includes lithology, oil and gas display, and gas logging data.

[0014] Furthermore, the laboratory analysis data in step 101 at least include rock thin section identification, cast thin section image analysis, physical property analysis, conventional mercury injection analysis, whole rock quantitative analysis, X-ray diffraction whole rock quantitative analysis, scanning electron microscopy and particle size analysis.

[0015] Furthermore, other geological data in step 101 include oil and gas testing data and regional geological data.

[0016] Furthermore, in step 103, the geostatistical characteristic curve inversion adopts a strict Markov chain-Monte Carlo algorithm.

[0017] The present invention also provides an effective reservoir seismic prediction system for aluminum-containing rock series gas reservoirs, the system comprising: a data acquisition module, used to acquire three-dimensional seismic data, well logging data, mud logging data, chemical analysis data and other geological data in a work area; an effective reservoir determination module, used to determine the effective reservoir characteristic curve of the aluminum-containing rock series in the work area; an effective reservoir acquisition module, used to acquire the distribution characteristics of the effective reservoirs of the aluminum-containing rock series in the work area; an exploration module, used to acquire the distribution of favorable areas in the work area according to the distribution characteristics of the effective reservoirs in the work area, and conduct natural gas exploration.

[0018] Compared with the prior art, the present invention brings the following beneficial effects:

[0019] The present invention aims at the two major problems of predicting effective reservoirs in aluminum-bearing rock series. It establishes and applies characteristic curve fitting reconstruction technology and geostatistical characteristic curve inversion technology for the first time, and forms a pioneering seismic prediction method for effective reservoirs in aluminum-bearing rock series gas reservoirs. The present invention has been applied in the exploration of the Upper Paleozoic in the Ningxian-Zhengning area of ​​the Ordos Basin and achieved good results. The favorable area of ​​aluminum-bearing rock series gas reservoirs is 685km2. 2, the natural gas resources are predicted to be 23.6 billion cubic meters, 11 exploration wells are deployed, and multiple exploration wells are implemented in advance to obtain industrial gas flow, opening up the field of large-scale exploration of aluminous rock series gas reservoirs, providing strong support for large-scale reserve increase and efficient production. At the same time, the invention has strong applicability, and aluminous rock series are widely developed in other parts of my country. Good exploration signs have been seen, and it can be applied and promoted. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The drawings described herein are used to provide a further understanding of the present invention, constitute a part of the present invention, and do not constitute a limitation of the present invention. In the drawings:

[0021] Figure 1 This is a flow chart of a method for seismic prediction of effective reservoirs in aluminum-bearing rock series gas reservoirs according to an embodiment of the present invention;

[0022] Figure 2 It is a structural block diagram of an effective reservoir seismic prediction system for aluminum-containing rock series gas reservoirs according to an embodiment of the present invention. DETAILED DESCRIPTION

[0023] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments and the accompanying drawings. Here, the illustrative embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0024] Aluminum-bearing rock series gas reservoirs belong to new fields and new types of gas reservoirs. Their formation mechanism, mineral composition, reservoir type, logging characteristics and reservoir-forming conditions are significantly different from those of conventional gas reservoirs. Traditional technical means are not suitable for identifying and predicting this type of gas reservoirs. There is a lack of research and technology related to reservoir prediction at home and abroad. On the premise of clarifying the characteristics of the aluminum-bearing rock series reservoirs, two technologies suitable for the prediction of effective reservoirs in aluminum-bearing rock series are preferably applied. Combined with the actual effect analysis, the effective reservoir prediction compliance rate reaches 86%, and good application effects are achieved, which proves that the present invention has important significance and role in the exploration and development of aluminum-bearing rock series gas reservoirs.

[0025] Example 1

[0026] Specifically, in this example, a method for seismic prediction of effective reservoirs in aluminum-bearing rock series gas reservoirs is provided, such as Figure 1 As shown, the following steps may be included:

[0027] Step 101, obtaining three-dimensional seismic data, well logging data, mud logging data, laboratory analysis data and other geological data in the work area.

[0028] In the embodiment of the present invention, the three-dimensional seismic data is post-stack time migration data, including post-stack migration data volume data and velocity volume. Well logging data includes at least nine curves, including natural gamma, well diameter, natural potential, deep lateral resistivity, shallow lateral resistivity, flushing zone resistivity, acoustic time difference, density, and compensated neutron. Well logging data includes at least lithology, oil and gas display, and gas measurement data. Laboratory analysis data includes at least rock thin section identification, cast thin section image analysis, physical property analysis, conventional mercury injection analysis, whole rock quantitative analysis, X-ray diffraction whole rock quantitative analysis, scanning electron microscopy, and grain size analysis. Other geological data include oil and gas test data, regional geological data, etc.

[0029] Step 102, analyzing the logging data, well logging data, chemical analysis data and other geological data, and applying characteristic curve fitting and reconstruction technology on the basis of clarifying the reservoir characteristics of the aluminum-bearing rock series, to establish the effective reservoir characteristic curve of the aluminum-bearing rock series in the work area, so as to lay a foundation for seismic prediction of effective reservoirs.

[0030] In the embodiment of the present invention, according to the analysis of regional geological data, the aluminum-bearing rock system is a type of rock rich in aluminum minerals, mainly chemically deposited, and also has mechanical deposition characteristics. In the rock, Al2O3:SiO2>1, and the main mineral components are gibbsite, boehmite and diaspore. my country's aluminum-bearing rock systems mainly include karst type and laterite type, and the embodiment of the present invention is laterite type. Regional geological data show that the development of aluminum-bearing rock systems is controlled by karst grooves and surface effects. Its parent material comes from aluminosilicate rocks, carbonate rocks, and volcanic materials. The parent material is weathered to form a laterite layer enriched in elements such as Al, Fe, and Si. Through sedimentation, the aluminum-rich laterite layer is transported to the karst trough and then deposited to form a bauxite layer. Through diagenesis, the bauxite layer is dehydrated and consolidated into rock, aluminum oxide minerals are recrystallized, and gibbsite and boehmite are transformed into diaspore. Finally, it undergoes uplift, and through surface effects, impurities such as Fe and Si are lost due to weathering and leaching, and Al is enriched to form a porous bauxite layer.

[0031] In the embodiment of the present invention, according to the analysis of logging data, chemical analysis data and other geological data, the lithology of the aluminum-bearing rock system is mainly bauxite, bauxite mudstone, siderite mudstone, mudstone, sandstone, tuff and coal seam, and the vertical direction shows the enrichment rule of Fe, Al, Si and C from bottom to top. The bauxite layer is generally located in the middle and upper part of the aluminum-bearing rock system. According to the analysis of chemical analysis data, the physical properties of bauxite are the best and it is an effective reservoir. The content of diaspore in bauxite is greater than 80%, and the lithology is relatively pure. The pore types are mainly residual intergranular pores, intergranular solution pores, feldspar clay intercrystalline pores, etc., and microcracks are developed, which is a pore-crack type reservoir. The porosity is between 0.2% and 28.7%, with an average value of 10.65%, and the permeability is between 0.0041 and 38.55mD, with an average value of 4.04mD. The mercury injection curve shows that the displacement pressure is relatively low, generally between 0.18 and 4.67 MPa. The high mercury withdrawal saturation indicates a small pore-throat ratio and good pore connectivity. The gentle mercury injection curve indicates good pore throat sorting. The median throat radius is between 0.10 and 0.77 μm, and the maximum throat radius is between 0.98 and 3.35 μm, indicating that the throat sorting is poor. Overall, it belongs to a low-porosity, ultra-low-permeability, fine-throat type reservoir.

[0032] In the embodiment of the present invention, according to the analysis of logging data and well logging data, the gas content of bauxite in the aluminum-bearing rock system is the best, the gas logging total hydrocarbon peak is greater than 5%, and the peak-to-base ratio is greater than 10. The electrical characteristics have "three highs", namely high natural gamma, high density, and high compensated neutron, which are significantly different from the electrical characteristics of mudstone, sandstone, and carbonate rock. Through the above data analysis, the effective reservoir identification standard of the aluminum-bearing rock system is established: the natural gamma is greater than 350API, the density is greater than 2.65g / cm 3 , compensated neutron greater than 45%, gas test total hydrocarbon peak greater than 5%, peak-to-base ratio greater than 10, and the diaspore content in the mineral composition greater than 80%.

[0033] In the embodiment of the present invention, according to the analysis of the three-dimensional seismic data, the three-dimensional seismic data of the aluminum-bearing rock system in the work area has a main frequency of 30Hz, a bandwidth between 3 and 48Hz, an average velocity of 4300m / s, and can distinguish a bauxite reservoir with a thickness of 36m. The thickness of the bauxite in the work area is relatively thin, between 5 and 15m, so the vertical resolution of the three-dimensional seismic data is low and the thickness of the bauxite cannot be accurately predicted. At the same time, based on the joint analysis of the well logging data and the three-dimensional seismic data, the wave impedance differences of bauxite, bauxite mudstone, siderite mudstone, mudstone and sandstone in the aluminum-bearing rock system are very small, and the traditional wave impedance inversion cannot accurately predict the distribution of bauxite and effective reservoirs.

[0034] Therefore, in the embodiment of the present invention, the natural gamma curve and density curve are selected through the effective reservoir identification standard of the aluminum-bearing rock series, and the characteristic curve fitting and reconstruction technology is applied to establish a better effective reservoir characteristic curve, so as to perform high-precision effective reservoir prediction.

[0035] In the embodiment of the present invention, a fitting reconstruction technology of effective reservoir characteristic curves of aluminum-bearing rock series is proposed and applied for the first time. The purpose of characteristic curve reconstruction is to obtain a new curve that conforms to geological laws and can well distinguish effective reservoirs. Characteristic curve reconstruction is an effective technology to improve the effect of logging constrained seismic inversion in areas where the velocity difference between reservoir and surrounding rock is not obvious. In the embodiment of the present invention, a single curve cannot meet the prediction of effective reservoirs. The natural gamma curve can effectively distinguish different lithologies, such as bauxite, sandstone, mudstone and coal seam, but cannot distinguish physical property changes and cannot identify effective reservoirs; the density curve can distinguish physical property changes, but the effect of distinguishing bauxite, mudstone and sandstone is not good. Therefore, the density curve can be used to distinguish the characteristics of effective reservoirs of bauxite, and the density curve is a wave impedance parameter curve, which is used as a low-frequency curve in reconstruction; the natural gamma curve has the characteristics of strong ability to distinguish multiple lithologies and has a high resolution, which is used as a high-frequency curve in reconstruction. Fitting reconstruction is performed to obtain a reconstructed density curve that can both identify effective reservoirs and predict thin reservoirs, laying the foundation for the next step of geostatistical characteristic curve inversion.

[0036] Step 103, analyzing the three-dimensional seismic data, combining with the effective reservoir characteristic curve, applying the geostatistical characteristic curve inversion technology, and clarifying the effective reservoir distribution.

[0037] The geostatistical characteristic curve inversion adopts a strict Markov Chain Monte Carlo algorithm (MCMC), which combines constrained sparse pulse inversion with geostatistical simulation technology to become a new statistical inversion algorithm. This algorithm is suitable for the prediction of various lithological reservoirs because it takes into account both seismic and geostatistical information and is more rigorous in the calculation process. Therefore, the Markov Chain Monte Carlo algorithm can effectively solve the problem of global optimization of reservoir prediction. In addition, the algorithm also has fast convergence capability.

[0038] In geostatistical reconstruction and inversion, by combining information such as seismic lithofacies, reservoir characteristic logging curves, probability density functions and variograms, predictions are made within the effective bandwidth of seismic data through the built-in constrained sparse pulse inversion engine. Since geostatistical reconstruction and inversion provide a large amount of details that exceed the bandwidth of seismic data, and the trends are exactly the same as those of seismic data, a perfect balance is achieved between qualitative waveform interpretation and quantitative reservoir interpretation. Therefore, geostatistical reconstruction and inversion can solve the problem of thin layers higher than the seismic resolution, the problem of complex lithology superposition and multi-solution, and can accurately predict reservoir variables to resolve effective reservoirs.

[0039] Geostatistical reconstruction and inversion can be divided into the following two parts: geostatistical parameter analysis and simulation, and geostatistical reconstruction and inversion, as follows:

[0040] Part I: Geostatistical parameter analysis and simulation.

[0041] Geostatistical parameters mainly refer to two parameters: probability density function (PDF, which describes the probability distribution of a certain attribute in space) and variogram (which describes the change of the spatial distribution characteristics of a certain attribute with distance, which is a function of distance).

[0042] The probability distribution function describes the possibility of the distribution of elastic parameters corresponding to a specific rock. The probability density function can separate discrete attribute PDF and continuous attribute PDF. The discrete attribute PDF comes from the effective reservoir data after the actual fine logging evaluation, and the continuous attribute PDF comes from the characteristic curve reconstruction curve. It should be noted that the number of sample points used in the calculation, all statistical calculations should be based on the appropriate number of sample points. For each lithology, generally more than 50 sample points are required. The PDF calculated with less than 20 sample points should be used with caution, and the PDF can be edited if necessary. In the embodiment of the present invention, the discrete attribute PDF of the effective reservoir of bauxite obtained by logging interpretation of 18 wells is 31%. Since the development of bauxite is jointly controlled by karst grooves and surface effects, there is a certain degree of randomness in the distribution. In order to further improve the prediction accuracy, two other sets of discrete attribute PDFs are taken in combination with the regional bauxite distribution characteristics for subsequent parameter simulation. Therefore, a total of three sets of discrete attribute PDFs are obtained and applied, namely: Low_21%, Initial_31%, and High_41%. At the same time, the continuous attribute PDF (P-Imp_RHOB) is obtained through the histogram and cross plot of the reconstructed density curve and longitudinal wave impedance curve of the 18 wells. Since a total of 74 sample points are obtained from the 18 wells, which is greater than 50, it is considered that the continuous attribute PDF (P-Imp_RHOB) is credible, so this group of continuous attribute PDF (P-Imp_RHOB) is selected in the subsequent parameter simulation.

[0043] The variogram describes the structure and characteristic scale of the horizontal and vertical geological characteristics. That is, the spatial distribution form and change scale of different lithofacies and their attributes. It is a three-dimensional function that describes the spatial correlation of different lithofacies data. Through test analysis, the range of vertical Z and horizontal X and Y directions can be obtained. The variogram can separate the discrete attribute variogram and the continuous attribute variogram. The discrete attribute variogram should use the Gaussian model, and the important parameter is the range, which is divided into the longitudinal variogram range and the transverse variogram range. By setting these two ranges to match the variogram range obtained from the effective reservoir of the actual well and the variogram range obtained from the regional lithology, its accuracy is verified by lithology simulation. The continuous attribute variogram should use the exponential model, and the important parameter is also the range, which is also divided into the longitudinal variogram range and the transverse variogram range. By setting these two ranges to match the variogram range obtained from the reconstruction characteristic curve of the actual well and the variogram range obtained from the deterministic impedance. In the embodiment of the present invention, the discrete attribute variation function of the effective reservoir of bauxite is obtained by combining the logging interpretation of 18 wells with the regional bauxite distribution trend (2.5ms, 800m). Since the development of bauxite is jointly controlled by karst grooves and surface effects, there is a certain randomness in the distribution. In order to further improve the accuracy of the predicted distribution, another two groups of discrete attribute variation functions are taken for subsequent parameter simulation. Therefore, a total of three groups of discrete attribute variation functions are obtained and applied, namely: G_short (2.0ms, 600m), G_initial (3.0ms, 800m), and G_long (4.0ms, 1000m). At the same time, the continuous attribute variation function E_initial(2.5ms,700m) was obtained by analyzing the experimental variogram obtained by reconstructing the density curve, P-wave impedance curve and deterministic P-wave impedance of 18 wells. Since the reconstructed density curve, P-wave impedance curve and deterministic P-wave impedance are continuous and clear, it is considered that the continuous attribute variation function E_initial(2.5ms,700m) is credible. Therefore, this set of continuous attribute variation function E_initial(2.5ms,700m) is selected in the subsequent parameter simulation.

[0044] In the embodiment of the present invention, after obtaining the probability density function and the variogram, the geostatistical parameter simulation is performed to test whether the determined geostatistical parameters are reasonable. In combination with the characteristics of the obtained parameters, there are three groups of discrete attribute PDFs, one group of continuous attribute PDFs, three groups of discrete attribute variograms, and one group of continuous attribute variograms. Therefore, the discrete attribute parameters are mainly tested and divided into three groups, as shown in the following table:

[0045] Table 1 Discrete attribute parameter test table

[0046]

[0047]

[0048] In the embodiment of the present invention, a total of 8 wells are used as prior wells for geostatistical parameter simulation. According to the reservoir prediction compliance statistics at home and abroad, combined with the geological characteristics of the working area of ​​the embodiment of the present invention, it is believed that the compliance rate of the effective bauxite reservoir reaches more than 70%. This group of parameters has a good accuracy rate. Geostatistical parameter simulation is performed on 9 groups of parameters, and the compliance rate is as follows:

[0049] Table 2 Discrete attribute parameter simulation compliance table

[0050] Parameter group name Compliance rate BXInitial_initialG 76％ BXLow_initialG 59％ BXHigh_initialG 62％ BXInitial_shortG 49％ BXLow_shortG 46％ BXHigh_shortG 37％ BXInitial_longG 81％ BXLow_longG 55％ BXHigh_longG 68％

[0051] It can be seen from the above table that the BXInitial_longG parameter group has the highest compliance rate, so the geostatistical parameters finally selected by the embodiment of the present invention are Initial_31% (31 / 69), P-Imp_RHOB, G_long (4.0ms, 1000m), and E_initial (2.5ms, 700m).

[0052] Part II: Geostatistical Reconstruction and Inversion

[0053] Geostatistical reconstruction inversion is to use actual seismic data and parameters to perform real inversion after obtaining important parameters through geostatistical parameter analysis. Therefore, there are three important parameters in geostatistical inversion: PDF function, variogram and seismic weight (input signal-to-noise ratio). The first two parameters have been obtained through the above steps, and seismic information is introduced by setting the signal-to-noise ratio. The signal-to-noise ratio calculation formula is as follows:

[0054]

[0055] Where σ represents the standard deviation

[0056] In the formula, noise can be understood as residual (i.e., the difference between seismic data and synthetic records, or the part where synthetic records do not match seismic data). Because seismic data is certain, the higher the signal-to-noise ratio, the smaller the residual (i.e., the weaker the noise, the more similar the seismic data is to the synthetic record); and vice versa. Therefore, the introduced seismic information and the correlation between synthetic records and earthquakes can be controlled by controlling the size of the signal-to-noise ratio.

[0057] In the embodiment of the present invention, the seismic signal-to-noise ratio is obtained by the Signal-to-noise ratio in the deterministic longitudinal wave impedance inversion. The Signal-to-noise ratio in the inversion result shows that the signal-to-noise ratio of the working area of ​​the embodiment ranges from 5 to 20 dB, and most of the signal-to-noise ratios in the working area are around 11 dB, so the seismic weight coefficient is selected as 11 dB.

[0058] After further testing in the previous step, the most ideal input parameters are determined, and then the wells are added for constraint, and the geological geostatistical P-wave impedance inversion is performed to obtain the P-wave impedance body. This step requires multiple rounds of inversion iterations, and each round of inversion needs to generate multiple realizations. Multiple realizations and multiple iterations are used to offset the randomness generated by the geostatistical inversion. In the embodiment of the present invention, 8 realizations are generated in each round, and a total of 4 rounds are iterated to finally obtain a P-wave impedance body that is consistent with the actual well and regional geology.

[0059] After obtaining the P-wave group antibody, the last step of inversion is carried out - geostatistical reconstruction collaborative simulation. On the basis of obtaining a reasonable P-wave impedance body, a reconstructed density body reflecting the characteristics of the effective reservoir is further obtained. The P-wave impedance body has been obtained, and the relationship between the P-wave impedance and the reconstructed density in the geostatistical parameters is combined to calculate the effective reservoir reconstruction density body on the basis of the two. After that, the final inversion result is quality controlled. Quality control includes: input and output PDF function, lithology ratio quality control, comparison of geostatistical inversion results with constrained sparse pulse inversion results, well pumping inspection, lithology body / attribute body slice inspection along the layer, checking the lithology probability body and checking the maximum likelihood lithology probability body. In the embodiment of the present invention, a total of 8 wells are used as prior wells, which do not participate in the inversion. The above-mentioned inspection means are used for inspection. The effective reservoir prediction compliance rate of the 8 wells is 86%, and the compliance rate reaches more than 70%, which proves that the final inversion result is accurate and reliable. According to the inversion results, the effective reservoir plane distribution map is extracted.

[0060] Step 104, based on the distribution characteristics of effective reservoirs, determine the distribution of favorable areas in the work area and conduct exploration deployment.

[0061] In the embodiment of the present invention, the effective reservoir plane distribution map is used to determine the most favorable reservoir-forming area, optimize the exploration target, and make exploration deployment. 13 favorable traps with a favorable area of ​​685km2 have been confirmed in the aluminum-bearing rock formations in the work area. 2 The natural gas resources are predicted to be 23.6 billion cubic meters, and 11 pre-exploration wells have been deployed.

[0062] From the above description, it can be seen that the embodiments of the present invention form a series of seismic prediction technologies for effective reservoirs in aluminum-bearing rock series gas reservoirs, including the following two technologies: ① characteristic curve fitting and reconstruction technology, which establishes characteristic curves of effective reservoirs in aluminum-bearing rock series and lays the foundation for effective reservoir prediction; ② geostatistical reconstruction and inversion technology, which clarifies the distribution characteristics of effective reservoirs in aluminum-bearing rock series.

[0063] Based on the same inventive concept, an effective reservoir seismic prediction system for aluminum-containing rock series gas reservoirs is also provided in an embodiment of the present invention. Since the principle of solving the problem by the effective reservoir seismic prediction system for aluminum-containing rock series gas reservoirs is similar to that of the effective reservoir seismic prediction method for aluminum-containing rock series gas reservoirs, the implementation of the effective reservoir seismic prediction system for aluminum-containing rock series gas reservoirs can refer to the implementation of the effective reservoir seismic prediction method for aluminum-containing rock series gas reservoirs, and the repeated parts will not be repeated. As used below, the term "unit" or "module" can be a combination of software and / or hardware that implements predetermined functions. Although the system described in the following embodiments is better implemented by software, it is also possible to implement it by hardware or a combination of software and hardware. Figure 2 : is a structural block diagram of an effective reservoir seismic prediction system for aluminum-bearing rock series gas reservoirs according to an embodiment of the present invention. Figure 2 As shown, it includes: a data acquisition module 201, an effective reservoir determination module 202, an effective reservoir acquisition module 203, and an exploration module 204. The structure is described below.

[0064] Data acquisition module 201, acquires 3D seismic data, well logging data, mud logging data, chemical analysis data and other geological data in the work area;

[0065] In the embodiment of the present invention, the post-stack time migration data is used to obtain the post-stack migration data volume data and velocity volume. The 3700 series and 5700 series logging data are used to obtain at least nine types of curve data, including natural gamma, well diameter, natural potential, deep lateral resistivity, shallow lateral resistivity, flushing zone resistivity, acoustic time difference, density, and compensated neutron. The conventional logging data are used to obtain at least lithology, color, oil and gas display, and gas logging data. The rock thin section and casting data were obtained by using Leica DM750 polarizing microscope (Leica, Germany), the mercury injection data were obtained by using AutoPore V9620 high pressure mercury injection instrument (Micromeritics, USA), the physical property data were obtained by using KX-90F gas porosity tester and DX-07G gas permeability tester, the X-ray diffractometer D8ADVANCE (AXS, Bruker, Germany) was used to obtain X-ray diffraction whole rock data, and the scanning electron microscope data were obtained by using FEI Quan 400 FEG field emission environmental scanning electron microscope (FEI, Czech Republic), etc. Other geological data including at least oil and gas test data and regional geological data were obtained.

[0066] The effective reservoir determination module 202 is used to determine the effective reservoir identification standard in the work area and establish the effective reservoir characteristic curve of the aluminum-bearing rock series;

[0067] In the embodiment of the present invention, the effective reservoir determination module includes: using the logging data interpretation module in the CIFLog logging processing and interpretation integrated software platform (China Petroleum Exploration and Development Research Institute) to obtain the effective reservoir identification standard. Using the rock physical characteristic curve module in the seismic waveform indication inversion software SMI (Beijing Zhongheng Lihua Petroleum Technology Research Institute) to obtain the effective reservoir characteristic curve.

[0068] The effective reservoir acquisition module 203 is used to obtain the distribution characteristics of the effective reservoir in the work area;

[0069] In the embodiment of the present invention, the effective reservoir acquisition module includes: using post-stack geostatistical inversion StatMod software (CGG GeoSoftware China) to obtain effective reservoir distribution characteristics.

[0070] The exploration module 204 is used to obtain the distribution of favorable areas in the work area according to the distribution characteristics of effective reservoirs in the work area and conduct natural gas exploration.

[0071] From the above description, it can be seen that the present invention proposes a new method and system for high-precision prediction of effective reservoirs in aluminous rock series gas reservoirs using three-dimensional seismic data, which fills the gap in the current oil exploration and development industry in the field of seismic prediction of effective reservoirs in aluminous rock series gas reservoirs. By predicting the distribution of effective reservoirs in aluminous rock series gas reservoirs with high precision, the favorable reservoir-forming areas in the work area are identified and exploration deployment is carried out. 13 favorable closures have been implemented in the aluminous rock series strata in the work area, with a favorable area of ​​685km 2 , the natural gas resources are predicted to be 23.6 billion cubic meters, and 11 pre-exploration wells are deployed. The Ninggu 3 well, which has been drilled, has obtained a high-yield industrial gas flow of 130,000 cubic meters per day in the aluminum-bearing rock formation, and the Ninggu 13 well, which has been drilled, has obtained a daily production of 23,000 cubic meters of industrial gas flow in the aluminum-bearing rock formation, achieving good exploration results, providing strong support for large-scale reserve increase and efficient production. At the same time, aluminum-bearing rock formations are widely developed in other parts of my country, and good exploration signs have been seen in some areas, which are important natural gas exploration fields, and the application prospects of this invention are broad.

[0072] Obviously, those skilled in the art should understand that the modules 202, 203 and 204 of the above-mentioned embodiment of the present invention can be implemented by a general-purpose computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, optionally, they can be implemented by a program code executable by a computing device, so that they are stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be performed in a different order from that here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the embodiment of the present invention is not limited to any specific combination of hardware and software.

[0073] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the embodiments of the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for seismic prediction of effective reservoirs in aluminum-bearing rock series gas reservoirs, characterized in that: The following steps are involved: Step 101, obtaining 3D seismic data, well logging data, mud logging data, laboratory analysis data and other geological data in the work area; Step 102, analyzing the well logging data, mud logging data, chemical analysis data and other geological data, and applying characteristic curve fitting and reconstruction technology to establish effective reservoir characteristic curves of the aluminum-bearing rock series in the work area on the basis of clarifying the characteristics of the aluminum-bearing rock series reservoir; Step 103, analyzing the three-dimensional seismic data, combining with the effective reservoir characteristic curve, applying geostatistical characteristic curve inversion, and clarifying the effective reservoir distribution; Step 104, based on the distribution characteristics of effective reservoirs, determine the distribution of favorable areas in the work area and conduct exploration deployment.

2. The method for seismic prediction of effective reservoirs in aluminum-bearing rock series gas reservoirs according to claim 1, characterized in that: The three-dimensional seismic data in step 101 is post-stack time migration data, including post-stack migration data volume data and velocity volume.

3. The method for seismic prediction of effective reservoirs in aluminum-bearing rock series gas reservoirs according to claim 1, characterized in that: The logging data in step 101 include at least nine curves: natural gamma, well diameter, natural potential, deep lateral resistivity, shallow lateral resistivity, flushing zone resistivity, acoustic time difference, density, and compensated neutron.

4. The method for seismic prediction of effective reservoirs in aluminum-bearing rock series gas reservoirs according to claim 1, characterized in that: The logging data in step 101 at least includes lithology, oil and gas display, and gas logging data.

5. The method for seismic prediction of effective reservoirs in aluminum-bearing rock series gas reservoirs according to claim 1, characterized in that: The laboratory analysis data in step 101 at least include rock thin section identification, casting thin section image analysis, physical property analysis, conventional mercury injection analysis, whole rock quantitative analysis, X-ray diffraction whole rock quantitative analysis, scanning electron microscopy and particle size analysis.

6. The method for seismic prediction of effective reservoirs in aluminum-bearing rock series gas reservoirs according to claim 1, characterized in that: In step 101, other geological data include oil and gas testing data and regional geological data.

7. The method for seismic prediction of effective reservoirs in aluminum-bearing rock series gas reservoirs according to claim 1, characterized in that: In step 103, the geostatistical characteristic curve inversion adopts a strict Markov chain-Monte Carlo algorithm.

8. An effective reservoir seismic prediction system for aluminum-bearing rock series gas reservoirs, characterized in that: include: The data acquisition module is used to acquire 3D seismic data, well logging data, mud logging data, chemical analysis data and other geological data in the work area; the effective reservoir determination module is used to determine the effective reservoir characteristic curve of the aluminum-bearing rock series in the work area; the effective reservoir acquisition module is used to acquire the distribution characteristics of the effective reservoir of the aluminum-bearing rock series in the work area; The exploration module is used to obtain the distribution of favorable areas in the work area according to the distribution characteristics of effective reservoirs in the work area and conduct natural gas exploration.

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