Energy spectrum logging-combined marine shale gas reservoir quality classification evaluation method

Abstract

The invention discloses a marine shale gas reservoir quality classification evaluation method in combination with energy spectrum logging, and belongs to the technical field of shale reservoir geological engineering evaluation. According to the method, original logging curve information is fully mined, an energy spectrum logging curve is combined for the first time, the influence of organic matter, porosity and total gas content on the logging curve is amplified through curve combination, multiple judgment parameters are obtained, evaluation factors are obtained through the judgment parameters, the reservoir quality evaluation factors are established by using logging original data, and the reservoir quality evaluation efficiency is improved. According to the method, the middle process of modeling according to a rock physical experiment result is omitted, data errors generated by parameter modeling are avoided, the quality of the shale gas reservoir can be classified and evaluated timely, quickly and accurately, and'geological desserts' can be found. Practical application effects show that the method has the characteristics of high accuracy and strong operability, and can provide powerful technical support and guarantee for efficient exploration and development of an oil field.

Description

Technical Field

[0001] The invention belongs to the technical field of shale reservoir geological engineering evaluation in petroleum exploration and development, and specifically relates to a marine shale gas reservoir quality classification evaluation method combined with energy spectrum logging. Background Art

[0002] As a new type of clean unconventional natural gas resource, shale gas has been increasingly valued by oil and gas fields in the context of continuous optimization of energy structure. Shale has the reservoir characteristics of "low porosity and low permeability". Therefore, finding "geological sweet spots" has become one of the important contents of shale gas exploration and development and shale gas reservoir quality evaluation. Geological sweet spots refer to areas with high free gas and adsorbed gas content and good physical properties, which are the basis and prerequisite for the effective implementation of shale gas development. The evaluation indicators of geological sweet spots for shale gas include source rock quality, physical quality and gas quality. The main characterization parameter of source rock quality is organic carbon (TOC) content; the main characterization parameter of physical quality is porosity (POR); the main characterization parameter of gas quality is the total gas content.

[0003] There are many studies on shale reservoir quality evaluation: Zhang Jianping et al. disclosed a method for evaluating shale gas reservoir quality through logging data, using TOC as the X-axis and POR as the Y-axis to establish an evaluation plate, and then projecting the corresponding parameters of the well to be evaluated onto the plate for judgment; Li Xia et al. disclosed a method for evaluating shale gas reservoir quality using logging data, which uses logging data to calculate reservoir TOC content, clay content, formation pressure coefficient, gas content and other parameters, and then determines the effective thickness of the reservoir; according to the pre-stored shale gas well data and gas reservoir production capacity, respectively determine the weight values ​​corresponding to the organic carbon content, clay content, formation pressure coefficient, total gas content, effective thickness and burial depth; evaluate the shale gas reservoir quality according to the weight values, the organic carbon content, clay content, formation pressure coefficient, total gas content, effective thickness and burial depth; Zhang Jianping et al. disclosed a shale quality evaluation method based on organic porosity and total organic carbon content, through the intersection of organic porosity and organic carbon of known wells, establish an evaluation plate, and project the corresponding parameters of the well to be evaluated onto the plate. The data points are projected onto the map to evaluate the reservoir quality. Hu Dongfeng et al. disclosed a method for quantitatively evaluating shale gas sweet spots, which determines the geologically sensitive sweet spot parameters and engineering sweet spot parameters based on basic geological data, logging data and seismic data, and conducts geophysical prediction based on the confirmed geologically sensitive sweet spot parameters and engineering sweet spot parameters to obtain prediction results. A shale gas sweet spot quantitative evaluation model Q is established. The shale gas sweet spot quantitative evaluation factor Qsweet is determined. The favorable shale gas exploration area is determined according to the numerical range of the evaluation factor Qsweet. Liao Dongliang et al. disclosed an evaluation method for identifying shale formation sweet spots, which determines the kerogen volume content, gas porosity, gas saturation and total organic matter content of the shale formation according to the logging data, and obtains the geological sweet spot coefficient of the shale formation by using the radar chart analysis method. The maximum horizontal effective stress value, pore structure index and brittleness index of the shale formation are determined according to the logging data, and the engineering sweet spot coefficient of the shale formation is obtained by using the radar chart analysis method. The shale formation sweet spot is identified according to the geological sweet spot coefficient and the engineering sweet spot coefficient.

[0004] In the above-mentioned invention patents, the reservoir quality evaluation parameters involved are all indirect parameters calculated from logging data such as organic carbon content, porosity, and gas content. There are many calculation models for these parameters, but there are still some problems.

[0005] TOC evaluation methods include core test method, well logging curve interpretation method and seismic data analysis method. Core test mainly obtains TOC content of core through organic geochemical analysis; well logging curve interpretation method mainly predicts TOC by using statistical modeling method by exploring the relationship between well logging data and TOC content. This method needs to be calibrated with core data. At present, more mature methods include △LogR method studied by Exxon and Esso in 1979, multivariate regression method of density curve and energy spectrum curve, etc.;) Seismic data analysis method, that is, establishing a quantitative relationship between seismic rock physical properties and TOC content, and using this relationship to predict TOC content in areas with seismic data, but limited by the distribution of seismic data and low seismic resolution, the accuracy of this method is poor;

[0006] The evaluation methods of POR mainly include regression analysis, special logging method and volume model method. The regression analysis method mainly establishes the regional empirical formula for calculating the porosity of shale reservoirs through the relationship between core experimental data and logging curves; the special logging method mainly obtains reservoir porosity through special logging technologies such as nuclear magnetic resonance logging. Ding Yujiao and others used nuclear magnetic resonance logging technology to evaluate the porosity of shale reservoirs, and Shu Zhiguo and others used Schlumberger's CMR-Plus combinable nuclear magnetic resonance logging instrument in the Jiaoshiba area to measure the formation porosity value; the volume model method mainly calculates reservoir porosity through the rock volume physical model and the skeleton information of different minerals, including the variable skeleton density method derived on this basis.

[0007] The calculation method of total gas content is mainly to calculate the free gas content and adsorbed gas content of the reservoir separately, and the sum of the two is the total gas content of the reservoir. The adsorbed gas content is usually calculated using the Langmuir equation. Gou Qiyang et al. corrected the Langmuir volume and Langmuir pressure for mineral composition and temperature, and obtained the corrected Langmuir equation; Nie Xin et al. used the depth to be detected and TOC value to obtain the adsorbed gas content of shale, and the adsorbed gas of the reservoir can be obtained without core experiments; free gas usually uses the shale free gas content calculation method proposed by Lewis et al. Ambrose et al. considered that adsorbed gas would occupy part of the free gas volume space, corrected the free gas content for adsorbed gas, and proposed a corrected free gas calculation model; Lu Jing et al. determined the shale free gas saturation based on the porosity occupied by unit volume of shale adsorbed gas, the total porosity of the shale reservoir and the invalid porosity, and calculated the shale free gas content using the shale free gas saturation.

[0008] The above-mentioned evaluation methods for reservoir quality parameters basically need to be calibrated with rock physics experimental data. However, in the actual core experiment process, due to different experimental methods and different experimental conditions, there are great differences in the results: in the porosity POR measurement process, different core samples such as plunger rock samples and crushed samples, and different measurement methods such as gas measurement and liquid measurement, all have large differences in measurement results; in the total gas content test process, the existing method is mainly the core field desorption experiment, and most of them are conventional desorption. In the process of core from the coring barrel to the desorption tank, some gas will be lost, resulting in the obtained gas content cannot reflect the true situation of the reservoir; another method for obtaining gas content is pressure-maintained coring. However, the currently used pressure-maintained coring method has not been able to be applied on a large scale on site due to its immature technology, high cost and low success rate, which brings certain difficulties to the accurate determination of gas content. In addition, in the process of processing core desorption data, the calculation of lost gas is a difficult point in the accurate evaluation of reservoir gas content. Most of them use regression calculation methods. However, the amount of lost gas obtained by regression using different methods varies greatly. The difference between the monomial regression result and the polynomial regression result can be more than 2 times. Therefore, when using experimental data obtained by different experimental methods for parameter modeling, there will be large differences in the calculation results, which cannot reflect the actual reservoir conditions and lead to incorrect classification of reservoir types. Therefore, there is an urgent need for an evaluation method that can reduce the error in reservoir parameter evaluation, quickly and accurately classify reservoirs, and find "geological sweet spots" to help oil and gas field exploration and development. Summary of the invention

[0009] The purpose of the present invention is to overcome the shortcomings of the above-mentioned prior art and provide a marine shale gas reservoir quality classification and evaluation method combined with spectral logging to solve the problem that the results obtained by different experimental methods and different experimental conditions are quite different and difficult to reflect the real reservoir conditions. It can quickly and accurately evaluate the geological quality of reservoirs, classify reservoirs, find "geological sweet spots", better serve oil fields, and provide technical support for shale gas exploration and development in oil fields.

[0010] In order to achieve the above object, the present invention adopts the following technical solutions:

[0011] A method for classifying and evaluating the quality of marine shale gas reservoirs combined with spectral logging comprises the following steps:

[0012] S1, based on the target layer energy spectrum logging results, obtain the uranium curve; based on the target layer logging data, obtain the acoustic time difference curve, density logging curve, longitudinal wave time difference curve and shear wave time difference curve;

[0013] S2. The uranium curve is normalized to obtain the UC curve, the forward normalized curve of the envelope area is obtained based on the acoustic time difference curve and the density logging curve, and the reverse normalized curve of the longitudinal and transverse wave velocity ratio is obtained based on the longitudinal time difference curve and the transverse wave time difference curve; the UC curve, the forward normalized curve of the envelope area and the reverse normalized curve of the longitudinal and transverse wave velocity ratio constitute the judgment parameters;

[0014] S3. Obtain the importance of each judgment parameter, obtain a judgment matrix based on the importance, obtain a weight coefficient of each judgment parameter through the judgment matrix, obtain a reservoir quality evaluation factor curve through the weight coefficient and the three judgment parameter values, and judge the marine shale gas reservoir quality of the target layer through the reservoir quality evaluation factor curve;

[0015] Each element in the judgment matrix is ​​the ratio of the importance of two judgment parameters.

[0016] A further improvement of the present invention is:

[0017] Preferably, in S2, the calculation formula of the UC curve is:

[0018]

[0019] Where UC is the normalized uranium curve, dimensionless; U is the uranium curve obtained by logging, ppm; U min is the minimum value of the uranium curve, ppm; U max is the maximum value of the uranium curve, ppm.

[0020] Preferably, in S2, the forward normalized curve calculation formula of the envelope area is:

[0021]

[0022] Where DASC is the normalized envelope area of ​​acoustic time difference logging curve and density logging curve, dimensionless; DAS max DAS is the maximum envelope area of ​​the two curves, dimensionless; min It is the minimum value of the envelope area of ​​the two curves and is dimensionless.

[0023] Preferably, in S2, the calculation process of DAS is:

[0024]

[0025] Where DAS is the envelope area of ​​the acoustic transit time logging curve and the density logging curve, dimensionless; AC is the acoustic transit time logging curve, μs / ft; AC left is the left scale value of the acoustic transit time logging curve, μs / ft; AC right is the right scale value of the acoustic transit time logging curve, μs / ft; DEN is the density logging curve, g / cm 3 ;DEN左 Left scale value of density logging curve, g / cm 3 ;DEN 右 is the right scale value of the density logging curve, g / cm 3 .

[0026] Preferably, in S2, before calculating the envelope area, the acoustic time difference logging curve and the density logging curve are reversely scaled, and the left and right scales of the two curves are adjusted so that the two curves overlap in the upper tight reservoir or non-reservoir section of the target layer.

[0027] Preferably, in S2, the calculation formula of the reverse normalized curve of the longitudinal and transverse wave velocity ratio is:

[0028]

[0029] Where RCSC is the normalized longitudinal and transverse wave velocity ratio, dimensionless; RCS min is the minimum value of the ratio of longitudinal and transverse wave speeds, dimensionless; RCS max is the maximum value of the ratio of longitudinal and transverse wave speeds, dimensionless.

[0030] Preferably, the calculation formula of the longitudinal and transverse wave speed ratio is:

[0031]

[0032] Where RCS is the ratio of longitudinal and transverse wave velocities, dimensionless; DTS is the transverse wave time difference, μs / ft; DTC is the longitudinal wave time difference, μs / ft.

[0033] Preferably, the importance of the three judgment parameters is obtained through experience, and the importance is divided into equal importance, slightly important, relatively important, strongly important and extremely important, and a corresponding numerical value is assigned to each importance, and the numerical value increases as the importance increases.

[0034] Preferably, in S3, the calculation method of the reservoir quality evaluation factor curve RQF is:

[0035]

[0036] Where RQF is the reservoir quality evaluation factor, dimensionless; Ri is the weight coefficient of the i-th parameter, dimensionless; Zi is the normalized value of the i-th parameter, dimensionless.

[0037] Preferably, the formation with RQF less than 0.4 is a non-reservoir, and the formation with RQF greater than or equal to 0.4 is an effective reservoir; when RQF is 0.4-0.65, it is a Class III reservoir; when RQF is 0.65-0.75, it is a Class II reservoir; when RQF is greater than 0.75, it is a Class I reservoir.

[0038] Compared with the prior art, the present invention has the following beneficial effects:

[0039] The present invention discloses a method for classifying and evaluating the quality of marine shale gas reservoirs combined with energy spectrum logging. The method fully exploits the original logging curve information, combines the energy spectrum logging curve for the first time, and amplifies the influence of organic matter, porosity, and total gas content on the logging curve through curve combination, obtains multiple judgment parameters, and obtains evaluation factors through judgment parameters. The method uses the original logging data to establish reservoir quality evaluation factors, omits the intermediate process of modeling according to rock physics experimental results, avoids the data errors caused by parameter modeling, and can timely, quickly and accurately classify and evaluate the quality of shale gas reservoirs to find "geological sweet spots". The actual application effect shows that the present invention has the characteristics of high accuracy and strong operability, and can provide strong technical support and guarantee for the efficient exploration and development of oil fields; at the same time, the present invention can reduce the cost of conducting rock physics experiments in oil fields, which is of great significance to reducing costs and increasing efficiency in oil fields. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 Flow chart of shale gas reservoir quality evaluation method based on conventional well logging curves;

[0041] Figure 2 Hierarchical analysis model;

[0042] Figure 3 Comparison of the classification evaluation results of Well A using this method and the classification evaluation results using reservoir parameters. DETAILED DESCRIPTION

[0043] The present invention is further described in detail below with reference to the accompanying drawings and specific embodiments:

[0044] In the description of the present invention, it should be noted that the orientations or positional relationships indicated by the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inside", "outside", etc. are based on the orientations or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention; the terms "first", "second", and "third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance; in addition, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection or a detachable connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the internal communication of two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0045] Shale gas reservoirs have the characteristics of low porosity and low permeability, which means that they must be fractured to obtain economic benefits. Therefore, finding the "sweet spot" has become one of the important contents of shale gas exploration and development. Reservoir quality evaluation is an important research content in shale gas reservoir evaluation. The existing research methods are mainly based on parameter modeling based on rock physics experiments, and most of them use indirect means to evaluate reservoir quality. In addition, in the process of parameter modeling, the results obtained under different experimental conditions and different experimental methods have certain differences, which brings certain difficulties to reservoir evaluation.

[0046] The purpose of the present invention is to provide a method for classifying and evaluating the quality of marine shale gas reservoirs combined with energy spectrum logging, so as to improve the accuracy of reservoir parameter interpretation, classify reservoirs, and find "geological sweet spots". Therefore, this study deeply mines the most original logging curve information reflecting reservoir quality, establishes reservoir quality evaluation factors, and thus evaluates the geological quality of the reservoir, eliminating the intermediate process of modeling based on rock physics experimental results, avoiding the data errors caused by parameter modeling, and timely, quickly and accurately realizing the classification and evaluation of shale gas reservoir quality, finding "geological sweet spots", and providing strong technical support and guarantee for efficient exploration and development of oil fields.

[0047] See also Figure 1 One embodiment of the present invention provides a method for classifying and evaluating the quality of marine shale gas reservoirs combined with spectral logging, comprising the following steps:

[0048] Step 1. Obtain the uranium curve through the energy spectrum logging results in the study area; obtain the acoustic time difference curve and density logging curve through the compensated acoustic wave data in the conventional logging project; obtain the longitudinal wave time difference curve and shear wave time difference curve through the array acoustic wave logging data in the special logging project.

[0049] Step 2. Perform forward normalization processing on the uranium curve of spectral logging to obtain the UC curve;

[0050] The calculation formula of UC curve is:

[0051]

[0052] Where UC is the normalized uranium curve, dimensionless; U is the uranium curve obtained by logging, ppm; U min is the minimum value of the uranium curve, ppm; U max is the maximum value of the uranium curve, ppm.

[0053] Step 3. Obtaining the positive normalized curve DACS of the envelope area DAS includes the following two sub-steps:

[0054] Step 3.1. Perform reverse envelopment on the acoustic logging curve and the density logging curve to obtain the envelope area DAS. The specific method is:

[0055] Reverse scale the acoustic time difference logging curve and the density logging curve to find the upper tight reservoir or non-reservoir section of the target layer. By adjusting the left and right scales of the two curves, the two curves are basically overlapped in the upper tight reservoir or non-reservoir section, and the envelope area DAS of the two logging curves is obtained. The calculation formula is:

[0056]

[0057] Where DAS is the envelope area of ​​the acoustic transit time logging curve and the density logging curve, dimensionless; AC is the acoustic transit time logging curve, μs / ft; AC left is the left scale value of the acoustic transit time logging curve, μs / ft; AC right is the right scale value of the acoustic transit time logging curve, μs / ft; DEN is the density logging curve, g / cm 3 ;DEN 左 Left scale value of density logging curve, g / cm 3 ;DEN 右 is the right scale value of the density logging curve, g / cm 3 .

[0058] It should be understood that because the acoustic time difference logging curve and the density logging curve are two different curves, the overlap here is a basic overlap, or the area DAS is calculated when the overlap of the two curves is the highest. In actual application, it is manually judged whether the curve overlap meets the requirements.

[0059] Step 3.2. Perform forward normalization processing on the envelope area DAS to obtain the curve DASC;

[0060] The calculation method of DASC is:

[0061]

[0062] Where DASC is the normalized envelope area of ​​acoustic time difference logging curve and density logging curve, dimensionless; DAS max DAS is the maximum envelope area of ​​the two curves, dimensionless; min It is the minimum value of the envelope area of ​​the two curves and is dimensionless.

[0063] Step 4. Obtain the inverse normalized curve RCSC of the longitudinal and transverse wave speed ratio RCS.

[0064] Step 4.1. Obtain the longitudinal and transverse wave velocity ratio RCS through the longitudinal wave time difference and transverse wave time difference curves;

[0065] The calculation method of RCS is:

[0066]

[0067] Where RCS is the ratio of longitudinal and transverse wave velocities, dimensionless; DTS is the transverse wave time difference, μs / ft; DTC is the longitudinal wave time difference, μs / ft.

[0068] Step 4.2. Reverse normalize the longitudinal and transverse wave velocity ratio RCS to obtain the curve RCSC;

[0069] The calculation method of RCSC is:

[0070]

[0071] Where RCSC is the normalized longitudinal and transverse wave velocity ratio, dimensionless; RCS min is the minimum value of the ratio of longitudinal and transverse wave speeds, dimensionless; RCS max is the maximum value of the ratio of longitudinal and transverse wave speeds, dimensionless.

[0072] In step 2 and step 3, three judgment parameters are considered, namely, uranium curve, envelope area and longitudinal and transverse wave velocity ratio. Among the three parameters, the uranium curve and envelope area are positively correlated with the final calculated evaluation factor curve RQF, so positive correlation normalization is performed here; the longitudinal and transverse wave velocity ratio and the evaluation factor curve RQF are negatively correlated, so negative correlation normalization is performed here; while normalizing, positive and negative correlations are considered, making subsequent calculations simpler on the basis of dimensionless calculation.

[0073] It should be understood that, in this embodiment, steps 2 to 4 calculate three different parameters. For the sake of clarity, the steps are divided into different orders. In fact, in the specific calculation process, the calculation process of the three parameters is not divided into different orders.

[0074] Step 5. Obtain the weight coefficients of different characterization parameters through the hierarchical analysis method;

[0075] The method for obtaining the weight coefficients of different characterization parameters is as follows:

[0076] This time, the hierarchical analysis method is used to obtain the weight coefficients of different characterization parameters. First, a hierarchical structure analysis model is established (see Appendix Figure 2), construct a judgment matrix, the reservoir type of the target layer A is related to the organic matter, porosity and gas content of the criterion layer B. The judgment parameter corresponding to the organic matter is the uranium curve, the judgment parameter corresponding to the porosity is the envelope area, and the judgment parameter corresponding to the longitudinal and transverse wave velocity ratio is the longitudinal and transverse wave velocity ratio. Therefore, they are compared with each other to determine their importance. When determining the importance of the b-layer factor, considering the actual situation of the marine shale reservoir, the target layer will have two situations: normal resistivity and low resistivity. The low-resistivity reservoir is of poor quality and cannot be used as a high-quality reservoir for exploration and development. Compared with the marine shale reservoir with normal resistivity, the low-resistivity reservoir has a significantly lower gas content, a lower porosity to a certain extent, and the organic matter is basically unchanged. Therefore, when evaluating the type of marine shale reservoir, the gas content has the greatest impact, followed by the porosity, and the organic matter has the least impact. Based on this standard, combined with expert opinions, the three factors of the criterion layer are judged in pairs. The importance of gas content relative to porosity is slightly important, the importance relative to organic matter is relatively important, and the importance of porosity relative to organic matter is slightly important. The corresponding values ​​are determined according to the scale table and the judgment matrix is ​​constructed.

[0077] All comparison results are expressed by the judgment matrix Z = b(i,j)n×n, see Appendix 2, n is the number of criterion layer factors, b(i,j) is the ratio of the influence on the target layer A, b(i,j) indicates the relative importance of the i-th factor and the j-th factor, conversely, the ratio of the influence of b(j,i) on A is 1 / b(i,j). The determination method of b(i,j) generally adopts the grade classification evaluation method, where the grade classification quantitative value is shown in Appendix 1; the second step is to calculate the weight, and obtain the maximum eigenvalue λmax of the judgment matrix Z and its corresponding eigenvector ω, so as to obtain the weight of the factor of this level in the previous level (Appendix 3). The actual second step calculation process is realized by the YAAHP software in the computer.

[0078] Table 1 Scale table of AHP

[0079]

[0080] Table 2 AHP judgment matrix

[0081] Influencing factors RCSC UC DASC RCSC 1 5 3 UC 1 / 5 1 1 / 3 DASC 1 / 3 3 1

[0082] Table 3 Hierarchy analysis calculation results

[0083] Influencing factors RCSC UC DASC Weight coefficient 0.6370 0.1047 0.2583

[0084] Step 8. Obtain the reservoir quality evaluation factor curve RQF by weighted analysis method;

[0085] The calculation method of reservoir quality evaluation factor curve RQF is:

[0086]

[0087] Where RQF is the reservoir quality evaluation factor, dimensionless; Ri is the weight coefficient of the i-th parameter, dimensionless; Zi is the normalized value of the i-th parameter, dimensionless.

[0088] Step 9. Use the reservoir quality evaluation factor curve RQF obtained in step 8 to classify and evaluate the shale reservoir quality.

[0089] RQF can comprehensively reflect the quality of shale reservoirs. The U element can indicate the content of organic matter. On the one hand, the humic acid produced during hydrocarbon generation in the marine reducing environment will reduce uranium ions to water-insoluble uranium elements and fix them in organic matter; on the other hand, due to the large number of micropores in organic matter, more U elements will be adsorbed; therefore, the uranium content in organic-rich layers will increase. The envelope of the acoustic time difference logging curve and the density curve can reflect the size of the porosity because the increase in porosity will lead to an increase in the acoustic time difference and a decrease in the density logging value. The reverse scale of the two curves will amplify the impact of porosity on the two curves and more intuitively determine the size of the reservoir porosity. The ratio of the longitudinal and transverse wave velocities can reflect the quality of the reservoir because longitudinal waves can propagate in fluids, while transverse waves can only propagate in rock skeletons. When the formation contains gas, the longitudinal wave velocity decreases significantly, while the transverse wave velocity remains basically unchanged, resulting in a significant decrease in the longitudinal and transverse wave velocity ratio.

[0090] In some embodiments of the present invention, a formation with an RQF less than 0.4 is a non-reservoir, and a formation with an RQF greater than or equal to 0.4 is an effective reservoir; when the RQF is 0.4-0.65, it is a Class III reservoir; when the RQF is 0.65-0.75, it is a Class II reservoir; and when the RQF is greater than 0.75, it is a Class I reservoir.

[0091] Example

[0092] The results are attached Figure 3 As shown in the figure, the reservoir classification of Well A was carried out by this method, and the reservoir parameters (TOC, POR, total gas content) obtained by rock physics experimental modeling were used for classification. The classification standard adopted the reservoir evaluation standard of Longmaxi Formation shale gas of PetroChina. By comparing the two experimental results, it can be found that the classification results of the two have good consistency, with a degree of agreement of more than 90%, which verifies the reliability of this method.

[0093] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for classifying and evaluating the quality of marine shale gas reservoirs combined with spectral logging, characterized in that: The following steps are involved: S1, based on the spectral logging results of the target layer, the uranium curve is obtained; Acoustic time difference curve, density logging curve, P-wave time difference curve and S-wave time difference curve are obtained based on the target layer logging data; S2. The uranium curve is normalized to obtain the UC curve, the forward normalized curve of the envelope area is obtained based on the acoustic time difference curve and the density logging curve, and the reverse normalized curve of the longitudinal and transverse wave velocity ratio is obtained based on the longitudinal time difference curve and the transverse wave time difference curve; the UC curve, the forward normalized curve of the envelope area and the reverse normalized curve of the longitudinal and transverse wave velocity ratio constitute the judgment parameters; S3. Obtain the importance of each judgment parameter, obtain a judgment matrix based on the importance, obtain a weight coefficient of each judgment parameter through the judgment matrix, obtain a reservoir quality evaluation factor curve through the weight coefficient and the three judgment parameter values, and judge the marine shale gas reservoir quality of the target layer through the reservoir quality evaluation factor curve; Each element in the judgment matrix is ​​the ratio of the importance of two judgment parameters.

2. The method for classifying and evaluating the quality of marine shale gas reservoirs combined with spectral logging according to claim 1, characterized in that: In S2, the calculation formula of the UC curve is: Where UC is the normalized uranium curve, dimensionless; U is the uranium curve obtained by logging, ppm; U min is the minimum value of the uranium curve, ppm; U max is the maximum value of the uranium curve, ppm.

3. The method for classifying and evaluating the quality of marine shale gas reservoirs combined with spectral logging according to claim 1, characterized in that: In S2, the forward normalized curve calculation formula of the envelope area is: Where DASC is the normalized envelope area of ​​acoustic time difference logging curve and density logging curve, dimensionless; DAS max DAS is the maximum envelope area of ​​the two curves, dimensionless; min It is the minimum value of the envelope area of ​​the two curves and is dimensionless.

4. The method for classifying and evaluating the quality of marine shale gas reservoirs combined with spectral logging according to claim 3, characterized in that: In S2, the calculation process of DAS is: Where DAS is the envelope area of ​​the acoustic transit time logging curve and the density logging curve, dimensionless; AC is the acoustic transit time logging curve, μs / ft; AC left is the left scale value of the acoustic transit time logging curve, μs / ft; AC right is the right scale value of the acoustic transit time logging curve, μs / ft; DEN is the density logging curve, g / cm 3 ;DEN 左 Left scale value of density logging curve, g / cm 3 ;DEN 右 is the right scale value of the density logging curve, g / cm 3 .

5. The method for classifying and evaluating the quality of marine shale gas reservoirs combined with spectral logging according to claim 1, characterized in that: In S2, before envelope area calculation, the sonic time difference logging curve and the density logging curve are reversely scaled, and the left and right scales of the two curves are adjusted so that the two curves overlap in the upper tight reservoir or non-reservoir section of the target layer.

6. The method for classifying and evaluating the quality of marine shale gas reservoirs combined with spectral logging according to claim 1, characterized in that: In S2, the calculation formula of the reverse normalized curve of the longitudinal and transverse wave velocity ratio is: Where RCSC is the normalized longitudinal and transverse wave velocity ratio, dimensionless; RCS min is the minimum value of the ratio of longitudinal and transverse wave speeds, dimensionless; RCS max is the maximum value of the ratio of longitudinal and transverse wave speeds, dimensionless.

7. The method for classifying and evaluating the quality of marine shale gas reservoirs combined with spectral logging according to claim 6, characterized in that: The calculation formula of the longitudinal and transverse wave velocity ratio is: Where RCS is the ratio of longitudinal and transverse wave velocities, dimensionless; DTS is the transverse wave time difference, μs / ft; DTC is the longitudinal wave time difference, μs / ft.

8. The method for classifying and evaluating the quality of marine shale gas reservoirs combined with spectral logging according to claim 1, characterized in that: The importance of the three judgment parameters is obtained through experience. The importance is divided into equal importance, slightly important, relatively important, strongly important and extremely important. A corresponding value is assigned to each importance. As the importance increases, the value increases.

9. The method for classifying and evaluating the quality of marine shale gas reservoirs combined with spectral logging according to claim 1, characterized in that: In S3, the calculation method of the reservoir quality evaluation factor curve RQF is: Where RQF is the reservoir quality evaluation factor, dimensionless; Ri is the weight coefficient of the i-th parameter, dimensionless; Zi is the normalized value of the i-th parameter and is dimensionless.

10. The method for classifying and evaluating the quality of marine shale gas reservoirs combined with energy spectrum logging according to claim 9, characterized in that: The formations with RQF less than 0.4 are non-reservoirs, and the formations with RQF greater than or equal to 0.4 are effective reservoirs; when RQF is between 0.4-0.65, it is a Class III reservoir; when RQF is between 0.65-0.75, it is a Class II reservoir; when RQF is greater than 0.75, it is a Class I reservoir.

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