A method for evaluating quality classification of marine shale gas reservoirs in combination with energy spectrum logging

Abstract

The application discloses a kind of marine shale gas reservoir quality classification evaluation methods combined with energy spectrum logging, belong to shale reservoir geological engineering evaluation technical field.The method of the present application fully exploits original logging curve information, for the first time combined with energy spectrum logging curve, through curve combination, the influence of organic matter, porosity, total gas content on logging curve is amplified, obtains multiple judgment parameters, obtains evaluation factor through judgment parameter, the method uses well logging original data to establish reservoir quality evaluation factor, saves the intermediate process of modeling according to rock physical experiment results, avoids the data error generated by parameter modeling, can timely, quickly and accurately classify and evaluate shale gas reservoir quality, find "geological sweet spot".The actual application effect shows that the application has the characteristics of high accuracy and strong operability, can provide strong technical support and guarantee for oilfield efficient exploration and development.

Description

Technical Field

[0001] This invention belongs to the field of geological engineering evaluation technology of shale reservoirs in petroleum exploration and development, specifically involving a method for classifying and evaluating the quality of marine shale gas reservoirs by combining energy spectrum logging. Background Technology

[0002] Shale gas, as a new and clean unconventional natural gas resource, is receiving increasing attention from oil and gas fields against the backdrop of continuous optimization of the energy structure. Shale is characterized by "low porosity and low permeability" reservoirs; therefore, finding "geological sweet spots" has become a crucial aspect of shale gas exploration and development, as well as shale gas reservoir quality evaluation. Geological sweet spots refer to areas with high free and adsorbed gas content and favorable physical properties, serving as the foundation and prerequisite for effective shale gas development. Evaluation indicators for shale gas geological sweet spots include source rock quality, physical property quality, and gas-bearing quality. The main characteristic parameter of source rock quality is total organic carbon (TOC) content; the main characteristic parameter of physical property quality is porosity (POR); and the main characteristic parameter of gas-bearing quality is the total gas content.

[0003] There are numerous existing studies on shale reservoir quality evaluation: Zhang Jianping et al. disclosed a method for evaluating shale gas reservoir quality using well logging data. This method uses TOC as the X-axis and POR as the Y-axis to establish an evaluation chart, then projects the corresponding parameters of the wells to be evaluated onto the chart for judgment. Li Xia et al. disclosed a method for evaluating shale gas reservoir quality using well logging data. This method calculates parameters such as reservoir TOC content, clay content, formation pressure coefficient, and gas content using well logging data, then determines the effective reservoir thickness. Based on pre-stored shale gas well data and reservoir production capacity, it determines the weight values ​​corresponding to organic carbon content, clay content, formation pressure coefficient, total gas content, effective thickness, and burial depth. The shale gas reservoir quality is evaluated based on these 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. This method establishes an evaluation chart by interpolating the organic porosity and organic carbon content of known wells, and then projects the corresponding parameters of the wells to be evaluated onto the chart. Data points are projected onto a chart for reservoir quality evaluation. Hu Dongfeng et al. disclosed a method for quantitatively evaluating shale gas sweet spots. Based on basic geological data, well logging data, and seismic data, they determined geologically sensitive sweet spot parameters and engineering sweet spot parameters. Based on these confirmed parameters, geophysical predictions were conducted to obtain prediction results. A quantitative evaluation model Q for shale gas sweet spots was established. The quantitative evaluation factor Qsweet for shale gas sweet spots was determined. Favorable shale gas exploration areas were identified based on the numerical range of the evaluation factor Qsweet. Liao Dongliang et al. disclosed an evaluation method for identifying sweet spots in shale formations. Based on well logging data, they determined the kerogen volume content, gas porosity, gas saturation, and total organic matter content of shale formations. The geological sweet spot coefficient of the shale formation was obtained using radar chart analysis. The maximum horizontal effective stress, pore structure index, and brittleness index of the shale formation were determined based on well logging data. The engineering sweet spot coefficient of the shale formation was obtained using radar chart analysis. Sweet spots in shale formations were identified based on the geological and engineering sweet spot coefficients.

[0004] The reservoir quality evaluation parameters involved in the aforementioned invention patents are all indirect parameters calculated from well logging data such as organic carbon content, porosity, and gas content. There are many calculation models for these parameters, but some problems still exist.

[0005] TOC (Total Organic Carbon) evaluation methods include core testing, well logging interpretation, and seismic data analysis. Core testing primarily obtains TOC content through organic geochemical analysis. Well logging interpretation mainly explores the relationship between well logging data and TOC content, using statistical modeling to predict TOC. This method requires calibration using core data. Currently, relatively mature methods include the ΔLogR method developed by Exxon and Esso in 1979, and multiple regression methods for density curves and energy spectrum curves. Seismic data analysis establishes a quantitative relationship between seismic rock physical properties and TOC content, using this relationship to predict TOC content in areas with seismic data. However, due to limitations in seismic data distribution and low seismic resolution, this method has relatively poor accuracy.

[0006] The evaluation methods for Porosity of Shale Reservoirs (POR) mainly include regression analysis, special logging methods, and volumetric modeling. Regression analysis primarily establishes regional empirical formulas for calculating shale reservoir porosity by relating core experimental data to logging curves. Special logging methods mainly use specialized logging techniques such as nuclear magnetic resonance (NMR) logging to obtain reservoir porosity. Ding Yujiao et al. applied NMR logging technology to evaluate shale reservoir porosity, while Shu Zhiguo et al. used Schlumberger's CMR-Plus combinable NMR logging tool to measure formation porosity values ​​in the Jiaoshiba area. Volumetric modeling methods mainly use rock volumetric physical models to calculate reservoir porosity based on the framework information of different minerals, including the variable framework density method derived from this.

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

[0008] The aforementioned evaluation methods for reservoir quality parameters all require calibration using rock physics experimental data. However, in actual core experiments, the results vary significantly due to different experimental methods and conditions. For example, in porosity (POR) measurement, different core samples (such as plunger samples and fragmented samples) and different measurement methods (such as gas analysis and liquid analysis) yield substantial discrepancies. In total gas content (TGC) testing, current methods primarily involve in-situ core desorption experiments, mostly using conventional methods. During the transfer of the core from the core sampler to the desorption tank, some gas is lost, resulting in the obtained TGC not reflecting the true reservoir condition. Another method for obtaining TGC is pressure-controlled core sampling; however, the currently used pressure-controlled core sampling method is technically immature, costly, and has a low success rate, preventing its large-scale application in the field and posing challenges to accurate TGC determination. Furthermore, calculating gas loss is a key challenge in accurately evaluating reservoir gas content during the processing of core desorption data. Regression methods are commonly used, but the resulting gas loss values ​​vary significantly depending on the regression method used; for example, the difference between univariate and multivariate regression results can be more than double. Therefore, using experimental data obtained from different methods for parameter modeling leads to substantial discrepancies in the calculated results, failing to reflect the true reservoir conditions and resulting in incorrect reservoir classification. Thus, there is an urgent need for an evaluation method that can reduce errors in reservoir parameter evaluation, quickly and accurately classify reservoirs, and identify "geological sweet spots" to aid in oil and gas field exploration and development. Summary of the Invention

[0009] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method for classifying and evaluating the quality of marine shale gas reservoirs by combining energy dispersive spectroscopy logging. This addresses the problem that existing experimental methods and conditions yield significantly different results, making it difficult to reflect the true reservoir conditions. The invention aims to rapidly and accurately evaluate reservoir geological quality, classify reservoirs, identify "geological sweet spots," better serve oilfields, and provide technical support for shale gas exploration and development.

[0010] To achieve the above objectives, the present invention employs the following technical solution:

[0011] A method for classifying and evaluating the quality of marine shale gas reservoirs by combining energy dispersive spectroscopy logging includes the following steps:

[0012] S1. Based on the energy spectrum logging results of the target layer, the uranium curve is obtained; based on the logging data of the target layer, the sonic transit time curve, density logging curve, P-wave transit time curve and S-wave transit time curve are obtained.

[0013] S2. Normalize the uranium curve to obtain the UC curve. Obtain the positive normalization curve of the envelope area based on the sonic transit time curve and the density logging curve. Obtain the negative normalization curve of the P-wave / S-wave velocity ratio based on the longitudinal transit time curve and the transverse transit time curve. The UC curve, the positive normalization curve of the envelope area, and the negative normalization curve of the P-wave / S-wave velocity ratio constitute the judgment parameters.

[0014] S3. Obtain the importance of each judgment parameter, obtain the judgment matrix based on the importance, obtain the weight coefficient of each judgment parameter through the judgment matrix, obtain the 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 each pair of judgment parameters.

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

[0017] Preferably, in S2, the formula for calculating the UC curve is:

[0018]

[0019] In the formula, UC is the normalized uranium curve, dimensionless; U is the uranium curve obtained from well logging, in ppm; U min The minimum value of the uranium curve, ppm; U max ppm represents the maximum value of the uranium curve.

[0020] Preferably, in S2, the formula for calculating the positive normalization curve of the envelope area is:

[0021]

[0022] In the formula, DASC is the normalized envelope area of ​​the sonic transit time logging curve and the density logging curve, which is dimensionless; DAS max The maximum area under the envelope of the two curves is dimensionless; DAS min It is the minimum area of ​​the envelope of the two curves, and is dimensionless.

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

[0024]

[0025] In the formula, DAS is the envelope area of ​​the sonic transit time logging curve and the density logging curve, dimensionless; AC is the sonic transit time logging curve, μs / ft; AC left is the left scale value of the sonic transit time logging curve, μs / ft; AC right is the right scale value of the sonic transit time logging curve, μs / ft; DEN is the density logging curve, g / cm³. 3 ;DEN左 This represents the left scale value of the density logging curve, in g / cm³. 3 ;DEN 右 This represents the right-hand scale value of the density logging curve, in g / cm³. 3 .

[0026] Preferably, in S2, before calculating the envelope area, the sonic transit time logging curve and the density logging curve are reversed 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 formula for calculating the inverse normalized curve of the P-wave velocity ratio is:

[0028]

[0029] In the formula, RCSC is the normalized P-wave / S-wave velocity ratio, which is dimensionless; RCS min The minimum P-wave velocity to S-wave velocity ratio, dimensionless; RCS max This represents the maximum value of the longitudinal and transverse wave velocity ratio, which is dimensionless.

[0030] Preferably, the formula for calculating the P-wave velocity ratio is:

[0031]

[0032] In the formula, RCS is the P-wave / S-wave velocity ratio, which is dimensionless; DTS is the S-wave time difference, in μs / ft; and DTC is the P-wave time difference, in μs / ft.

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

[0034] Preferably, in S3, the reservoir quality evaluation factor curve RQF is calculated as follows:

[0035]

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

[0037] Preferably, formations with an RQF less than 0.4 are non-reservoirs, and formations with an RQF greater than or equal to 0.4 are effective reservoirs; formations with an RQF between 0.4 and 0.65 are classified as Class III reservoirs; formations with an RQF between 0.65 and 0.75 are classified as Class II reservoirs; and formations with an RQF greater than 0.75 are classified as Class I reservoirs.

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

[0039] This invention discloses a method for classifying and evaluating the quality of marine shale gas reservoirs by combining energy dispersive spectroscopy (EDS) logging. This method fully utilizes the information from original logging curves and, for the first time, combines EDS logging curves. Through curve combination, it amplifies the influence of organic matter, porosity, and total gas content on the logging curves, obtaining multiple judgment parameters. Evaluation factors are derived from these parameters. This method establishes reservoir quality evaluation factors using raw logging data, eliminating the intermediate process of modeling based on rock physics experimental results and avoiding data errors caused by parameter modeling. It can classify and evaluate shale gas reservoir quality in a timely, rapid, and accurate manner, identifying "geological sweet spots." Practical application results show that this invention has the characteristics of high accuracy and strong operability, providing strong technical support and guarantee for efficient oilfield exploration and development. At the same time, this invention can reduce the cost of conducting rock physics experiments in oilfields, which is of great significance for cost reduction and efficiency improvement in oilfields. Attached Figure Description

[0040] Figure 1 Flowchart of a shale gas reservoir quality evaluation method based on conventional logging curves;

[0041] Figure 2 Hierarchical structure analysis model;

[0042] Figure 3 Comparison chart of the classification and evaluation results of well A using the basic method and the classification and evaluation results using reservoir parameters. Detailed Implementation

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

[0044] In the description of this invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. The terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance. Furthermore, unless otherwise explicitly specified and limited, the terms "installed," "connected," and "linked" should be interpreted broadly. For example, they can refer to a fixed connection or a detachable connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal connection of two elements. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0045] Shale gas reservoirs are characterized by low porosity and low permeability, necessitating fracturing to achieve economic benefits. Therefore, finding "sweet spots" has become a crucial aspect of shale gas exploration and development. Reservoir quality evaluation is a vital research area in shale gas reservoir assessment. Existing research methods primarily rely on parametric modeling based on rock physics experiments, often employing indirect methods for reservoir quality evaluation. Furthermore, the results obtained under different experimental conditions and methods during parametric modeling exhibit variations, posing challenges to reservoir evaluation.

[0046] The purpose of this invention is to provide a method for classifying and evaluating the quality of marine shale gas reservoirs by combining energy dispersive spectroscopy logging, thereby improving the accuracy of reservoir parameter interpretation, classifying reservoirs, and identifying "geological sweet spots." Therefore, this study deeply mines the most original logging curve information reflecting reservoir quality to establish reservoir quality evaluation factors, thus evaluating the geological quality of the reservoirs. This eliminates the intermediate process of modeling based on rock physics experimental results, avoids data errors caused by parameter modeling, and enables timely, rapid, and accurate classification and evaluation of shale gas reservoir quality, identifying "geological sweet spots," and providing strong technical support and guarantee for efficient oilfield exploration and development.

[0047] See Figure 1 One embodiment of the present invention provides a method for classifying and evaluating the quality of marine shale gas reservoirs by combining energy dispersive spectroscopy logging, comprising the following steps:

[0048] Step 1. Obtain the uranium curve using the energy spectrum logging results in the studied area; obtain the sonic transit time curve and density logging curve using the compensated sonic data in the conventional logging project; obtain the P-wave transit time curve and S-wave transit time curve using the array sonic logging data in the special logging project.

[0049] Step 2. The uranium logging curves are positively normalized to obtain the UC curves;

[0050] The formula for calculating the UC curve is:

[0051]

[0052] In the formula, UC is the normalized uranium curve, dimensionless; U is the uranium curve obtained from well logging, in ppm; U min The minimum value of the uranium curve, ppm; U max ppm represents the maximum value of the uranium curve.

[0053] Step 3. Obtain the positively normalized curve DAS of the envelope area DAS, which includes the following two sub-steps:

[0054] Step 3.1. Obtain the envelope area (DAS) by reverse-envelope analysis of the sonic logging curve and the density logging curve. The specific method is as follows:

[0055] Reverse calibration is performed on the sonic transit time logging (STL) and density logging (DTL) curves to locate the upper tight reservoir or non-reservoir section of the target layer. By adjusting the left and right scales of the two curves, they are made to basically overlap in the upper tight reservoir or non-reservoir section, thus obtaining the DAS envelope area of ​​the two logging curves. The calculation formula is as follows:

[0056]

[0057] In the formula, DAS is the envelope area of ​​the sonic transit time logging curve and the density logging curve, dimensionless; AC is the sonic transit time logging curve, μs / ft; AC left is the left scale value of the sonic transit time logging curve, μs / ft; AC right is the right scale value of the sonic transit time logging curve, μs / ft; DEN is the density logging curve, g / cm³. 3 ;DEN 左 This represents the left scale value of the density logging curve, in g / cm³. 3 ;DEN 右 This represents the right-hand scale value of the density logging curve, in g / cm³. 3 .

[0058] It is important to understand that since the sonic transit time logging curve and the density logging curve are two different curves, the overlap here is basically the same, or the calculation includes the area DAS when the overlap between the two curves is at its highest. In actual application, the overlap between the curves is judged manually to see if the requirements are met.

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

[0060] The DASC is calculated as follows:

[0061]

[0062] In the formula, DASC is the normalized envelope area of ​​the sonic transit time logging curve and the density logging curve, which is dimensionless; DAS max The maximum area under the envelope of the two curves is dimensionless; DAS min It is the minimum area of ​​the envelope of the two curves, and is dimensionless.

[0063] Step 4. Obtain the inverse normalized curve RCSC of the P-wave velocity ratio (RCS).

[0064] Step 4.1. Obtain the P-wave / S-wave velocity ratio (RCS) through the P-wave time difference and S-wave time difference curves;

[0065] The calculation method for RCS is as follows:

[0066]

[0067] In the formula, RCS is the P-wave / S-wave velocity ratio, which is dimensionless; DTS is the S-wave time difference, in μs / ft; and DTC is the P-wave time difference, in μs / ft.

[0068] Step 4.2. Perform reverse normalization on the P-wave / S-wave velocity ratio (RCS) to obtain the curve RCSC;

[0069] The calculation method for RCSC is as follows:

[0070]

[0071] In the formula, RCSC is the normalized P-wave / S-wave velocity ratio, which is dimensionless; RCS min The minimum P-wave velocity to S-wave velocity ratio, dimensionless; RCS max This represents the maximum value of the longitudinal and transverse wave velocity ratio, which is dimensionless.

[0072] In steps 2 and 3, three judgment parameters were considered: the uranium curve, the envelope area, and the P-wave velocity ratio. Among these three parameters, the uranium curve and the envelope area are positively correlated with the final calculated evaluation factor curve RQF, so positive correlation normalization was performed here. The P-wave velocity ratio and the evaluation factor curve RQF are negatively correlated, so negative correlation normalization was performed here. The normalization process takes into account both positive and negative correlations, making subsequent calculations simpler based on dimensionless calculations.

[0073] It should be understood that in this embodiment, steps 2 to 4 calculate three different parameters. For clarity, the steps are divided into sequences here. In actual calculation, the calculation of the three parameters is not in any particular order.

[0074] Step 5. Obtain the weight coefficients of different characterization parameters using the analytic hierarchy process (AHP).

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

[0076] This study uses the analytic hierarchy process (AHP) to obtain the weight coefficients of different representation parameters. First, a hierarchical structure analysis model is established (see attached diagram). Figure 2A judgment matrix was constructed. The reservoir type of target layer A is related to the organic matter, porosity, and gas content of the criterion layer b. The judgment parameter for organic matter is the uranium curve, the judgment parameter for porosity is the envelope area, and the judgment parameter for the P-wave velocity ratio is the P-wave velocity ratio. Therefore, these parameters are compared to determine their importance. When determining the importance of factors in layer b, considering the actual situation of marine shale reservoirs, the target layer may exhibit both normal resistivity and low resistivity. Low resistivity reservoirs are of poor quality and cannot be considered high-quality reservoirs for exploration and development. Compared with marine shale reservoirs with normal resistivity, low resistivity reservoirs have significantly lower gas content and porosity to some extent, while organic matter remains essentially unchanged. Therefore, when evaluating the type of marine shale reservoir, gas content has the greatest impact, followed by porosity, and organic matter has the least impact. Based on this standard, and in conjunction with expert opinions, pairwise judgments were made on the three factors of the criterion layer. Gas content is slightly more important than porosity, and more important than organic matter. Porosity is slightly more important than organic matter. Determine the corresponding values ​​based on the scaling table and construct a judgment matrix.

[0077] All comparison results are represented by the judgment matrix Z = b(i,j)n×n, as shown in Appendix Table 2. n is the number of factors in the criterion layer, and b(i,j) is the ratio of their 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 method for determining b(i,j) generally adopts a hierarchical classification evaluation method, where the hierarchical classification quantification values ​​are shown in Appendix Table 1. The second step is to calculate the weights by obtaining the maximum eigenvalue λmax of the judgment matrix Z and its corresponding eigenvector ω. This yields the weight of the factor in the previous layer (Appendix Table 3). The actual second step calculation is implemented using the YAAHP software on a computer.

[0078] Table 1. Scale Table for Analytic Hierarchy Process (AHP)

[0079]

[0080] Table 2. Analytical Hierarchy Process (AHP) Judgment Matrix

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

[0082] Table 3. Results of Hierarchical Analysis

[0083] Weighting coefficient 0.6370 0.1047 0.2583

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

[0085] The method for calculating the reservoir quality evaluation factor curve (RQF) is as follows:

[0086]

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

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

[0089] RQF (Recovery Quality Forecast) comprehensively reflects the quality of shale reservoirs. U (uranium) indicates the level of organic matter content. This is because, in marine reducing environments, humic acids produced during hydrocarbon generation reduce uranium ions to water-insoluble uranium, which is then fixed in the organic matter. Furthermore, the numerous micropores in organic matter lead to the adsorption of more uranium; therefore, uranium content is higher in organic-rich zones. The envelope of the sonic transit time (SRT) and density logging curves reflects porosity because increased porosity leads to increased SRT and decreased density. The inverse scale of the two curves amplifies the impact of porosity, providing a more intuitive assessment of reservoir porosity. The P-wave / S-wave velocity ratio reflects reservoir quality because P-waves can propagate in fluids, while S-waves can only propagate within the rock framework. When the formation contains gas, the P-wave velocity decreases significantly, while the S-wave velocity remains relatively constant, resulting in a substantial decrease in the P-wave / S-wave velocity ratio.

[0090] In some embodiments of the present invention, formations with an RQF less than 0.4 are non-reservoirs, and formations with an RQF greater than or equal to 0.4 are effective reservoirs; when the RQF is between 0.4 and 0.65, it is a Class III reservoir; when the RQF is between 0.65 and 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, this method was used to classify the reservoir in Well A. Simultaneously, reservoir parameters (TOC, POR, and total gas content) obtained from rock physics experimental modeling were also used for classification. The classification standard adopted was the reservoir evaluation standard for shale gas in the Longmaxi Formation from China National Petroleum Corporation (CNPC). Comparison of the two experimental results shows good consistency, with a degree of agreement exceeding 90%, verifying 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 principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for classifying and evaluating the quality of marine shale gas reservoirs using energy dispersive spectroscopy logging, characterized in that, Includes the following steps: S1, based on the energy spectrum logging results of the target layer, the uranium curve is obtained; Based on the target layer logging data, sonic transit time curves, density logging curves, P-wave transit time curves, and S-wave transit time curves were obtained; S2. Normalize the uranium curve to obtain the UC curve. Obtain the positive normalization curve of the envelope area based on the sonic transit time curve and the density logging curve. Obtain the negative normalization curve of the P-wave / S-wave velocity ratio based on the longitudinal transit time curve and the transverse transit time curve. The UC curve, the positive normalization curve of the envelope area, and the negative normalization curve of the P-wave / S-wave velocity ratio constitute the judgment parameters. In S2, the formula for calculating the positive normalized curve of the envelope area is: (3) In the formula, DASC is the normalized envelope area of ​​the sonic transit time logging curve and the density logging curve, which is dimensionless; DAS max The maximum area under the envelope of the two curves is dimensionless; DAS min The minimum area enclosed by the two curves is dimensionless. In S2, the calculation process of DAS is as follows: (2) In the formula, DAS is the envelope area of ​​the sonic transit time logging curve and the density logging curve, which is dimensionless; AC is the sonic transit time logging curve, in μs / ft; AC 左 The left scale value of the sonic transit time logging curve is in μs / ft; AC 右 DEN represents the right-hand scale value of the sonic transit time logging curve, in μs / ft; DEN represents the density logging curve, in g / cm³. 左 The left-hand graduation value of the density logging curve, in g / cm³; DEN 右 This represents the right-hand scale value of the density logging curve, in g / cm³. In S2, before calculating the envelope area, the sonic transit time logging curve and the density logging curve are reversed 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. S3. Obtain the importance of each judgment parameter, obtain the judgment matrix based on the importance, obtain the weight coefficient of each judgment parameter through the judgment matrix, obtain the 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 each pair of judgment parameters.

2. The method for classifying and evaluating the quality of marine shale gas reservoirs by combining energy dispersive spectroscopy logging according to claim 1, characterized in that, In S2, the formula for calculating the UC curve is: (1) In the formula, UC is the normalized uranium curve, dimensionless; U is the uranium curve obtained from well logging, in ppm; U min The minimum value of the uranium curve, ppm; U max ppm represents the maximum value of the uranium curve.

3. The method for classifying and evaluating the quality of marine shale gas reservoirs by combining energy dispersive spectroscopy logging according to claim 1, characterized in that, In S2, the formula for calculating the inverse normalized curve of the P-wave velocity ratio is: (5) In the formula, RCSC is the normalized P-wave / S-wave velocity ratio, which is dimensionless; RCS min The minimum P-wave velocity to S-wave velocity ratio, dimensionless; RCS max This represents the maximum value of the longitudinal and transverse wave velocity ratio, which is dimensionless.

4. The method for classifying and evaluating the quality of marine shale gas reservoirs by combining energy dispersive spectroscopy logging according to claim 3, characterized in that, The formula for calculating the longitudinal and transverse wave velocity ratio is: (4) In the formula, RCS is the P-wave / S-wave velocity ratio, which is dimensionless; DTS is the S-wave time difference, in μs / ft; and DTC is the P-wave time difference, in μs / ft.

5. The method for classifying and evaluating the quality of marine shale gas reservoirs by combining energy dispersive spectroscopy logging according to claim 1, characterized in that, The importance of three judgment parameters is determined through experience. The importance is divided into equal importance, slightly important, relatively important, strongly important, and extremely important. Each importance is assigned a corresponding value, and the value increases as the importance increases.

6. The method for classifying and evaluating the quality of marine shale gas reservoirs by combining energy dispersive spectroscopy logging according to claim 1, characterized in that, In S3, the reservoir quality evaluation factor curve RQF is calculated as follows: (6) In the formula, RQF is the reservoir quality evaluation factor, which is dimensionless; Ri is the weight coefficient of the i-th parameter, which is dimensionless. Zi is the normalized value of the i-th parameter, which is dimensionless.

7. The method for classifying and evaluating the quality of marine shale gas reservoirs by combining energy dispersive spectroscopy logging according to claim 6, characterized in that, Formations with an RQF less than 0.4 are non-reservoirs, while those with an RQF greater than or equal to 0.4 are effective reservoirs. Formations with an RQF between 0.4 and 0.65 are classified as Class III reservoirs, those with an RQF between 0.65 and 0.75 are classified as Class II reservoirs, and those with an RQF greater than 0.75 are classified as Class I reservoirs.

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