A method for predicting vertical sweet spots in high-siliceous shale

Abstract

This invention provides a method for predicting vertical sweet spots in high-silica shale, comprising the following steps: S1, collecting shale core samples and obtaining measured experimental data and well logging data of the shale; wherein the measured experimental data of the shale includes measured TOC, NMR porosity, and relative content of silica minerals; S2, establishing the relationship between measured TOC, NMR porosity, and relative content of silica minerals, and determining the lower limits of relative content of silica minerals, TOC, and NMR porosity in the gas-producing section; S3, establishing a calculation model for the relative content of silica minerals in well logging; S4, determining the sweet spot section of the shale; S5, verifying the accuracy of the sweet spot section of the shale. This invention uses the relative content of silica minerals as the key parameter, which is simple and clear; the relative content of silica minerals is also the most important brittle mineral in shale. The higher the relative content of silica minerals, the better the brittleness of the shale, and the more conducive it is to fracturing. Therefore, the high silica content section is also the engineering sweet spot section. This method takes into account the prediction of both geological and engineering sweet spots.

Description

Technical Field

[0001] This invention belongs to the field of petroleum exploration and development technology, specifically relating to a method for predicting vertical sweet spots in high-silica shale. Background Technology

[0002] Shale is typically used as a source rock for hydrocarbons. The higher the organic matter abundance, the greater the amount of oil and gas generated. At the same time, shale also has a certain reservoir capacity. Therefore, shale sweet spots must have a relatively high total organic carbon (TOC) content and a high degree of porosity development. In addition, considering the need for later fracturing, it also needs to have high brittleness. Brittle minerals mainly include siliceous minerals (quartz) and carbonate minerals.

[0003] The "sweet spot" for shale gas refers to the optimal area or stratigraphic position for shale gas exploration and development. Its typical characteristics include: large shale thickness (greater than 30m), located within a "gas window," high TOC (total thermal equilibrium), high content of brittle minerals such as quartz (high compressibility), low montmorillonite content, overpressure, gas logging anomalies, adjacency to conventional oil and gas reservoirs, and favorable surface conditions. Currently, methods for predicting shale gas geological sweet spots primarily rely on in-well measurements and seismic multi-attribute inversion to determine and predict these areas.

[0004] Existing solutions, such as the invention patent with publication number CN104749651A, provide a method for quantitatively identifying sweet spots in shale and mudstone through phase-controlled multi-level reconstruction logging. This method involves finely dividing the different lithofacies of shale and mudstone, clarifying the logging response characteristics of each lithofacies, and establishing a quantitative identification model for sweet spots in different lithofacies of shale and mudstone under phase-controlled constraints through multi-level reconstruction fusion. This method achieves quantitative identification of shale sweet spots, but it mainly focuses on lithofacies identification and does not address key parameters such as source-reservoir conditions and fracturing in shale.

[0005] The invention patent published (announcement) number CN112922591A provides a method and system for predicting the "sweet spot" of shale reservoir facies. This method obtains relevant mineral content attribute parameters based on the intersection of shale reservoir facies property parameters and daily oil production per meter. A prediction model is constructed based on these parameters, and the "sweet spot" is determined by combining the model with the daily oil production per meter. While this method comprehensively considers multiple shale parameters, it does not employ well logging techniques for prediction, thus limiting its applicability. Summary of the Invention

[0006] The purpose of this invention is to provide a method for predicting the vertical sweet spot of high-silica shale, so as to solve the problems of selecting gas testing sections and horizontal well target points in shale gas exploration wells, improve the drilling rate of gas-bearing sections in horizontal sections, and increase the production of single wells.

[0007] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0008] A method for predicting vertical sweet spots in high-silica shale, characterized by comprising the following steps:

[0009] S1, shale core samples were systematically collected at certain sampling intervals in the concentrated shale development section, and measured experimental data and well logging data of shale were obtained; among them, the measured experimental data of shale included measured total organic carbon content (TOC), measured nuclear magnetic porosity, and measured relative content of siliceous minerals;

[0010] S2. The relationship between measured TOC, measured NMR porosity and measured relative content of siliceous minerals in the shale development section was established, and the lower limit values ​​of relative content of siliceous minerals, TOC and NMR porosity in the gas-producing section were determined, thereby clarifying the distribution range of favorable areas for shale sweet spots.

[0011] S3. Based on the logging data obtained in step S1, a calculation model for the relative content of logging silica minerals is established, and the relative content of logging silica minerals is obtained.

[0012] S4, Identify the sweet spot segment of shale.

[0013] The relative content of silica minerals calculated by well logging in step S3 is statistically analyzed. Data segments with relative silica mineral content greater than the lower limit of the measured relative silica mineral content obtained in step S2 are selected. Combined with the lower limit of the measured TOC and the lower limit of the nuclear magnetic porosity obtained in step S2, the vertical sweet spot segment of shale is determined.

[0014] S5 verifies the accuracy of the sweet spot segment of shale.

[0015] Furthermore, the shale logging data in step S1 includes shale formation density, neutron porosity, natural gamma, and lithology coefficient.

[0016] Further, step S2 determines the lower limits of the relative content of silica minerals, TOC, and NMR porosity in the gas-producing section, thereby clarifying the distribution range of the favorable area for shale gas sweet spots. The method is as follows: the minimum values ​​of TOC, NMR porosity, and relative content of silica minerals in the gas-producing section are obtained by using the scatter plot intersection method, which are the lower limits of the measured TOC, NMR porosity, and relative content of silica minerals corresponding to the shale sweet spot section; the shale distribution range exceeding the lower limits of the relative content of silica minerals, TOC, and NMR porosity in the gas-producing section is determined as the favorable area for shale gas sweet spots.

[0017] Preferably, the method for determining the lower limits of the relative content of silica minerals, TOC, and NMR porosity in the gas-producing section in step S2 is as follows:

[0018] First, based on the cross-plot of measured TOC values ​​and single-well production, the lower limit of TOC corresponding to industrial production capacity is determined.

[0019] Then, based on the cross-plot of measured TOC values ​​and measured relative content of silica minerals, the relative content of silica minerals corresponding to the lower limit of TOC is determined, which is the lower limit of the relative content of silica minerals in the gas-producing section.

[0020] Finally, based on the cross-plot of the measured relative content of silica minerals and the measured NMR porosity, the NMR porosity corresponding to the lower limit of the relative content of silica minerals is determined to be the lower limit of the NMR porosity of the gas-producing section.

[0021] Furthermore, S3 establishes a calculation model for the relative content of siliceous minerals in well logging, including the following steps:

[0022] S301, the method for calculating the total clay content (sh) is as follows:

[0023] First, calculate the relative value of natural gamma based on the measured natural gamma value from well logging, and then determine the total clay content in the formation.

[0024] S302. Based on the logging data obtained in step S1, establish a set of equations and solve the set of equations to obtain the relative content of each mineral, and then obtain the relative content of siliceous minerals in shale.

[0025] Further, the formula for calculating the relative value of natural gamma in step S301 is as follows: ;

[0026] In the formula, GR: the natural gamma value measured in the well logging;

[0027] GR min Minimum natural gamma value in shale sections;

[0028] GR max : Maximum natural gamma value of shale section.

[0029] Furthermore, the formula for calculating the total clay content in the strata in step S301 is as follows:

[0030]

[0031] Where: GR: Natural gamma value measured in well logging;

[0032] GCUR: Hilch index, an empirical index, with a value of 2 for older strata and 3.7 for newer strata.

[0033] Furthermore, the system of equations described in step S302 is as follows:

[0034]

[0035] In the formula: a, b, c, and d are unknown values, representing illite content, relative content of siliceous minerals, calcite content, and dolomite content, respectively;

[0036] : Shale formation density measured by well logging;

[0037] The characteristic density value of illite is generally taken as 2.53 (g / cm³). 3 );

[0038] The characteristic value of quartz density is generally taken as 2.65 (g / cm³). 3 );

[0039] The characteristic density value of calcite is generally taken as 2.71 (g / cm³). 3 );

[0040] The characteristic density value of dolomite is generally taken as 2.87 (g / cm³). 3 );

[0041] : Neutron porosity of shale measured by well logging;

[0042] The characteristic value of illite porosity is generally taken as 21 (pu).

[0043] : Characteristic value of quartz porosity, generally taken as 0 (pu);

[0044] : Characteristic value of calcite porosity, generally taken as 0 (pu);

[0045] : The characteristic value of porosity of dolomite, generally taken as 0 (pu);

[0046] : Shale lithology coefficient measured by well logging;

[0047] Characteristic value of illite lithology coefficient, generally taken as 3.45 (b / e);

[0048] : Characteristic value of quartz lithology coefficient, generally taken as 1.81 (b / e);

[0049] Characteristic value of calcite lithology coefficient, generally taken as 5.08 (b / e);

[0050] The characteristic value of the dolomite lithology coefficient is generally taken as 3.14 (b / e).

[0051] Furthermore, in step S5, verifying the accuracy of the shale sweet spot segment includes:

[0052] S501, compare the relative content data of silica minerals measured in the laboratory in step S1 with the relative content data of silica minerals calculated by well logging in step S3 to see if they match, so as to determine whether the calculation method of relative content of silica minerals is effective.

[0053] S502, compare the sweet spot calculated in step S4 with the high value area of ​​the measured TOC value and measured nuclear magnetic porosity obtained in step S2 (i.e. the favorable area of ​​shale sweet spot) to see if they correspond, so as to determine whether the sweet spot is accurate.

[0054] Furthermore, in step S1, the logging data is required to correspond one-to-one with the well depth and other data of the collected shale core sample, that is, the logging data at the same depth point and the measured experimental data correspond one-to-one.

[0055] By adopting the above technical solution, the present invention has the following beneficial effects:

[0056] 1. This invention uses silica content as a key parameter. On the one hand, the relative content of silica minerals is positively correlated with TOC value and porosity, and can be used as a key parameter to determine whether the source and reservoir conditions are superior. On the other hand, the relative content of silica minerals is also the most important brittle mineral in shale. The higher the relative content of silica minerals, the better the brittleness of shale, and the more conducive it is to fracturing. Therefore, the high silica content range is also the sweet spot for engineering. This method is a prediction that takes into account both geological and engineering sweet spots.

[0057] 2. In actual production, it is impossible to cor the entire section of all exploration wells and conduct measured TOC values, measured nuclear magnetic porosity, and measured relative content of siliceous minerals. However, logging is performed on each exploration well. This method is based on logging data to predict the distribution range of vertical sweet spots in shale, providing technical support for the next enrichment zone.

[0058] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, the preferred embodiments of the present invention are described in detail below with reference to the accompanying drawings. Attached Figure Description

[0059] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other design solutions and drawings can be obtained based on these drawings without creative effort.

[0060] Figure 1 A flowchart illustrating the steps of a method for predicting vertical sweet spots in high-silica shale, as provided in an embodiment of the present invention;

[0061] Figure 2 This is a cross-plot of measured total organic carbon (TOC) content and single-well production provided in an embodiment of the present invention.

[0062] Figure 3 This is a cross-plot of measured total organic carbon (TOC) content and measured relative content of siliceous minerals provided in an embodiment of the present invention.

[0063] Figure 4 This is a cross-plot of the measured relative content of siliceous minerals and the measured NMR porosity provided in an embodiment of the present invention;

[0064] Figure 5 This is a vertical sweet spot distribution and verification diagram of high-siliceous shale in Well L1 provided for an embodiment of the present invention.

[0065] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. Detailed Implementation

[0066] The invention can be further understood in conjunction with the following detailed description of preferred embodiments and included examples. Unless otherwise stated, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. If any definition of a specific term disclosed in the prior art differs from any definition provided herein, the definition provided herein shall prevail.

[0067] Current technologies for predicting vertical sweet spots in shale primarily rely on lithofacies analysis. From a geological sweet spot perspective, this approach lacks direct correlation with hydrocarbon source conditions (TOC value) and reservoir conditions (porosity). From an engineering sweet spot perspective, it completely ignores crucial factors such as brittleness. Therefore, referring to... Figure 1 This invention provides a method for predicting the vertical sweet spot of high-siliceous shale, comprising the following steps:

[0068] S1, shale core samples were systematically collected at certain sampling intervals in the concentrated shale development section, and experimental and logging data of shale were obtained. Among them, the experimental data of shale included the measured total organic carbon content (TOC), measured nuclear magnetic porosity, and measured relative content of siliceous minerals; the logging data of shale included shale formation density, neutron porosity, natural gamma ray, and lithology coefficient.

[0069] S2. The relationship between measured TOC, measured NMR porosity and measured relative content of siliceous minerals in shale sweet spots was established, and the lower limits of relative content of siliceous minerals, TOC and NMR porosity in gas-producing sections were determined, thereby clarifying the distribution range of favorable areas of shale sweet spots.

[0070] S3. Based on the logging data obtained in step S1, a calculation model for the relative content of logging silica minerals is established, and the distribution curve of the relative content of logging silica minerals is obtained.

[0071] S4, Identify the sweet spot segment of shale.

[0072] The relative content of silica minerals calculated by well logging in step S3 is statistically analyzed. Data segments with relative silica mineral content greater than the lower limit of the measured relative silica mineral content obtained in step S2 are selected. Combined with the lower limit of the measured TOC and the lower limit of the nuclear magnetic porosity obtained in step S2, the vertical sweet spot segment of shale is determined.

[0073] S5 verifies the accuracy of the sweet spot segment of shale.

[0074] Furthermore, step S2 clarifies the distribution range of favorable areas for shale gas sweet spots by obtaining the minimum values ​​of TOC, porosity, and relative content of siliceous minerals in the gas-producing section (i.e., the sweet spot section) using the scatter plot intersection method. These minimum values ​​are the lower limits of the measured TOC, measured NMR porosity, and measured relative content of siliceous minerals corresponding to the shale sweet spot section. The shale distribution range exceeding the lower limits of the relative content of siliceous minerals, TOC, and NMR porosity in the gas-producing section is determined as the favorable area for shale gas sweet spots.

[0075] Furthermore, the method for obtaining the lower limits of measured TOC, measured NMR porosity, and measured relative content of siliceous minerals corresponding to sweet spots in shale using the scatter plot intersection method is as follows:

[0076] First, based on the cross-plot of measured TOC values ​​and single-well production, the lower limit of TOC corresponding to industrial production capacity is determined.

[0077] Then, based on the cross-plot of measured TOC values ​​and measured relative content of silica minerals, the lower limit of the relative content of silica minerals in the gas-producing section is determined.

[0078] Finally, based on the cross-plot of the measured relative content of siliceous minerals and the measured porosity, the lower limit of the measured porosity content in the gas-producing section was determined.

[0079] Further, step S3 establishes a calculation model for the relative content of silica minerals in well logging, including the following steps:

[0080] S301, the method for calculating the total clay content (sh) is as follows:

[0081] First, calculate the relative value of natural gamma, and then determine the total clay content in the formation;

[0082] S302. Based on the logging data obtained in step S1, establish a set of equations and solve the set of equations to obtain the relative content of each mineral, thereby obtaining the logging curve distribution of the relative content of siliceous minerals.

[0083] Furthermore, the formula for calculating the relative value of natural gamma in step S301 is as follows: ;

[0084] In the formula, GR: the natural gamma value measured in the well logging;

[0085] GR min Minimum natural gamma value in shale sections;

[0086] GR max : Maximum natural gamma ray in shale sections;

[0087] Furthermore, the formula for calculating the total clay content in the strata is:

[0088]

[0089] Where: GR: Natural gamma value measured in well logging;

[0090] GCUR: Hilch index, an empirical index, with a value of 2 for older strata and 3.7 for newer strata.

[0091] Furthermore, the system of equations described in step S302 is as follows:

[0092]

[0093] In the formula: a, b, c, and d are unknown values, representing illite content, relative content of siliceous minerals, calcite content, and dolomite content, respectively;

[0094] : Shale formation density measured by well logging;

[0095] The characteristic density value of illite is generally taken as 2.53 (g / cm³). 3 );

[0096] The characteristic value of quartz density is generally taken as 2.65 (g / cm³). 3 );

[0097] The characteristic density value of calcite is generally taken as 2.71 (g / cm³). 3 );

[0098] The characteristic density value of dolomite is generally taken as 2.87 (g / cm³). 3 );

[0099] : Neutron porosity of shale measured by well logging;

[0100] The characteristic value of illite porosity is generally taken as 21 (pu).

[0101] : Characteristic value of quartz porosity, generally taken as 0 (pu);

[0102] : Characteristic value of calcite porosity, generally taken as 0 (pu);

[0103] : The characteristic value of porosity of dolomite, generally taken as 0 (pu);

[0104] : Shale lithology coefficient measured by well logging;

[0105] Characteristic value of illite lithology coefficient, generally taken as 3.45 (b / e);

[0106] : Characteristic value of quartz lithology coefficient, generally taken as 1.81 (b / e);

[0107] Characteristic value of calcite lithology coefficient, generally taken as 5.08 (b / e);

[0108] The characteristic value of the dolomite lithology coefficient is generally taken as 3.14 (b / e).

[0109] Furthermore, in step S5, verifying the accuracy of the shale sweet spot specifically includes:

[0110] S501, compare the relative content data of silica minerals measured in the laboratory in step S1 with the relative content data of silica minerals calculated by well logging in step S3 to see if they match, so as to determine whether the calculation method of relative content of silica minerals is effective.

[0111] S502, compare the sweet spot calculated in step S4 with the distribution range of the favorable area of ​​shale gas sweet spot obtained in step S2 to see if they correspond, so as to determine whether the sweet spot is accurate.

[0112] Compared with existing technologies, this invention uses the relative content of silica minerals as a key parameter. On the one hand, the relative content of silica minerals is positively correlated with TOC value and porosity, and can be used as a key parameter to determine whether the source and reservoir conditions are superior. On the other hand, the relative content of silica minerals is also the most important brittle mineral in shale. The higher the relative content of silica minerals, the better the brittleness of shale, and the more conducive it is to fracturing. Therefore, the high silica content range is also the sweet spot for engineering. This method takes into account the prediction of both geological and engineering sweet spots.

[0113] This invention provides a method for predicting the vertical sweet spot of high-siliceous shale, specifically implemented through the following steps:

[0114] Step 1: Select one shale gas exploration well (Well L1), ensuring it has obtained industrial gas flow and undergone full coring. Systematically collect shale core samples from Well L1 at 1.0m sampling intervals within the concentrated shale development zone. After collection, send the shale core samples to the laboratory for total organic carbon (TOC), nuclear magnetic resonance (NMR), and whole-rock X-ray diffraction (WXD) analyses to obtain measured TOC, measured NMR porosity, and measured relative silica content data. Then, calibrate the collected core samples onto the L1 well logging composite column to obtain the corresponding depth shale density (DEN), neutron porosity (CNL), natural gamma ray (GR), and lithology coefficient (Pe).

[0115] Special attention should be paid to ensuring that the logging data obtained above corresponds one-to-one with the well depth and other data of the collected shale core samples. That is, the same depth point must correspond one-to-one with the logging data of DEN, CNL, GR and Pe and the experimental data of TOC, nuclear magnetic porosity and relative content of silica minerals, so as to facilitate subsequent regularity analysis.

[0116] Step 2: Establish the relationship between measured TOC, measured NMR porosity, and measured relative content of siliceous minerals in the shale development section, determine the lower limit values ​​of relative content of siliceous minerals, TOC, and NMR porosity in the gas-producing section, and thus clarify the distribution range of favorable areas for shale gas sweet spots;

[0117] The determination of the lower limits of measured TOC, measured NMR porosity, and relative silica mineral content in shale core samples from the gas-producing section of industrial gas flow wells was conducted. The minimum values ​​of TOC, porosity, and relative silica mineral content in the gas-producing section (i.e., the sweet spot section) were obtained primarily using the scatter plot cross plot method. These minimum values ​​represent the lower limits of measured TOC, measured NMR porosity, and measured relative silica mineral content corresponding to the sweet spot section of shale. Scatter plots were created with measured relative silica mineral content on the X-axis and measured TOC or measured NMR porosity on the Y-axis. The results showed a positive correlation between the relative silica mineral content and TOC and porosity; that is, the higher the relative silica mineral content, the higher the TOC and porosity.

[0118] Specifically, the method for determining the lower limits of the relative content of siliceous minerals, TOC, and NMR porosity in the gas-producing section is as follows:

[0119] First, determine the lower limit of TOC in the gas-producing section (i.e., the sweet spot section). Based on the cross-plot of TOC value and output, determine the lower limit of TOC corresponding to the industrial capacity (daily output exceeding 40,000 cubic meters).

[0120] Then, determine the lower limit of the relative content of silica minerals in the gas-producing section (i.e., the sweet spot section). Based on the cross-plot of TOC value and relative content of silica minerals, determine the relative content of silica minerals corresponding to the lower limit of TOC, which is the lower limit of relative content of silica minerals.

[0121] Finally, the lower limit of porosity content in the gas-producing section (i.e., the sweet spot section) is determined. According to the cross-plot of relative content of silica minerals and porosity, the porosity corresponding to the lower limit of relative content of silica minerals is the lower limit of porosity.

[0122] Therefore, shale distribution sections exceeding the relative content of siliceous minerals, TOC, and porosity are considered sweet spots.

[0123] Step 3: Establish a calculation model for the relative content of silica minerals in well logging.

[0124] (1) Calculate the total clay content sh

[0125] First calculate the relative value of natural gamma.

[0126] Next, determine the total clay content in the strata.

[0127] Note: In the formula, GR is the natural gamma value measured in the well logging.

[0128] GR min Minimum natural gamma value in shale sections;

[0129] GR max : Maximum natural gamma ray in shale sections;

[0130] GCUR: Hillich index, an empirical index, with a value of 2 for older strata and 3.7 for younger strata; the study area is an older stratum, so the value is 2.

[0131] (2) Solve the system of equations to obtain the relative content of each mineral, and then obtain the relative content b of siliceous minerals in the shale.

[0132]

[0133] Note: In the formula, a, b, c, and d are unknown values, representing illite content, relative content of siliceous minerals, calcite content, and dolomite content, respectively.

[0134] : Shale formation density measured by well logging;

[0135] The characteristic density value of illite is generally taken as 2.53 (g / cm³). 3 );

[0136] The characteristic value of quartz density is generally taken as 2.65 (g / cm³).3 );

[0137] The characteristic density value of calcite is generally taken as 2.71 (g / cm³). 3 );

[0138] The characteristic density value of dolomite is generally taken as 2.87 (g / cm³). 3 );

[0139] : Neutron porosity of shale measured by well logging;

[0140] The characteristic value of illite porosity is generally taken as 21 (pu).

[0141] : Characteristic value of quartz porosity, generally taken as 0 (pu);

[0142] : Characteristic value of calcite porosity, generally taken as 0 (pu);

[0143] : The characteristic value of porosity of dolomite, generally taken as 0 (pu);

[0144] : Shale lithology coefficient measured by well logging;

[0145] Characteristic value of illite lithology coefficient, generally taken as 3.45 (b / e);

[0146] : Characteristic value of quartz lithology coefficient, generally taken as 1.81 (b / e);

[0147] Characteristic value of calcite lithology coefficient, generally taken as 5.08 (b / e);

[0148] The characteristic value of the dolomite lithology coefficient is generally taken as 3.14 (b / e).

[0149] This formula fully considers the three logging data most closely related to lithology among the three logging series: shale density (DEN), neutron porosity (CNL), and lithology coefficient (Pe). Therefore, this formula is effective in determining the relative content of silica minerals. Based on this formula, the logging curve distribution of the relative content of silica minerals was obtained.

[0150] Step 4: Determine the sweet spot of the shale.

[0151] The relative content of siliceous minerals calculated in step 3 is statistically analyzed, and the data segments with relative siliceous mineral content greater than the lower limit (as determined in step 2) are selected as shale sweet spots.

[0152] Step 5: Verify the accuracy of the shale sweet spot segment.

[0153] (1) Compare the laboratory measured relative content data of silica minerals with the relative content data of silica minerals calculated by well logging in step 3 to see if they match, so as to determine whether the calculation method of relative content of silica minerals is effective.

[0154] (2) In step 4, the accuracy of the sweet spot segment can be determined by whether the well logging calculation corresponds to the high value area of ​​TOC and porosity.

[0155] Significance of this invention: In actual production, it is impossible to cor the entire section of every exploration well and conduct measured TOC, measured NMR porosity, and measured relative content analysis of silica minerals, etc. However, logging is performed on every exploration well. This method is based on logging data to predict the distribution range of vertical sweet spots in shale, providing technical support for the next enrichment zone.

[0156] In one specific embodiment:

[0157] The method for predicting the vertical sweet spot of high-silica shale in the 4246-4302m interval of well L1 includes the following steps:

[0158] ① For the well section with a depth of 4246-4302m, systematic core sampling was carried out and sent to the laboratory for experimental analysis of measured TOC, measured NMR porosity, and measured relative content of silica minerals, in order to obtain measured TOC, measured NMR porosity, and measured relative content of silica minerals data;

[0159] ②Use cross-plot analysis to determine the lower limits of relative siliceous mineral content, TOC, and porosity.

[0160] First, determine the lower limit of TOC in the gas-producing section (i.e., the sweet spot section).

[0161] Reference Figure 2 Based on the TOC value versus output cross plot, the lower limit of TOC corresponding to industrial capacity (daily output exceeding 40,000 cubic meters) is determined, with TOC = 0.6%.

[0162] Then, determine the lower limit of the relative content of silica minerals in the gas-producing section (i.e., the sweet spot section).

[0163] Reference Figure 3 Based on the cross-plot of TOC value and relative content of silica minerals, the relative content of silica minerals corresponding to TOC=0.6% was determined to be 43%.

[0164] Finally, the lower limit of porosity content in the gas-producing section (i.e., the sweet spot section) was determined.

[0165] Reference Figure 4 According to the cross-plot of relative silica mineral content and porosity, a relative silica mineral content of 43% corresponds to a porosity of 3.2%. Therefore, the distribution segment of shale in the L1 well section from 4246 to 4302m with relative silica mineral content >43%, TOC >0.6%, and porosity >3.2% is identified as the sweet spot segment.

[0166] ③ Calculate the relative content of siliceous minerals in the L1 well section at a depth of 4246-4302m.

[0167] To better demonstrate the results, the relative content of silica minerals at five depths (A, B, C, D, and E) in the 4246-4302m well section was determined through logging. First, based on the natural GR (gravimetric analysis), the clay content (sh) at depths A, B, C, D, and E was calculated. Then, using the equation from step 3 and the corresponding logging parameters, the relative content (b) of silica minerals at depths A, B, C, D, and E, calculated from the logging data, was determined. 测井A =35.13%, b 测井B =40.25%, b 测井C =34.37%, b 测井D =63.21%, b 测井E =38.76%

[0168] ④ In the L1 well section at depths of 4246-4302m, based on the relative content of silica minerals calculated from well logging, measured TOC, and measured NMR porosity, and according to their respective lower limits (TOC > 0.6%, measured NMR porosity > 3.2%), such as... Figure 5 As shown, the sweet spot segment of shale is divided, that is, the range of vertical sweet spot distribution of shale is greater than the lower limit (4289-4292m).

[0169] ⑤ Finally, the predicted vertical sweet spots of the shale were verified.

[0170] The measured relative content of silica minerals corresponding to the five depth points A, B, C, D, and E, i.e., b 实测A =35.36%, b 实测B =41.53%, b 实测C =34.03%, b 实测D =63.11%, b 实测E =38.65%, and compared with the relative content of siliceous minerals calculated by well logging in ③, if the measured results match the calculated results.

[0171] The application of this method in shale gas exploration in the Uralik Formation in the western Ordos Basin guided the prediction of sweet spots in four vertical wells, identified the fracturing zone, and subsequently obtained high-yield industrial gas flows. One horizontal well also identified the sweet spot zone, achieving a gas layer encounter rate of 87% and a high production of 264,800 cubic meters per day during gas testing, significantly increasing the production of a single well.

[0172] In addition, the advantages of this invention are: (1) It is mainly a method for predicting sweet spots using well logging technology. Well logging is performed on every exploration well, the data is easy to obtain, and the application range is wide; (2) The relative content of siliceous minerals is used as the main parameter, which is simple and clear; (3) For high-siliceous shale, it takes into account both geological sweet spots and engineering sweet spots.

[0173] The above description is only a preferred embodiment of the present invention. It should be noted that, for those skilled in the art, many other modifications and implementations can be designed without departing from the technical principles of the present invention. These modifications and implementations will fall within the scope and spirit of the principles disclosed in this application.

Claims

1. A method for predicting vertical sweet spots in high-silica shale, characterized in that, Includes the following steps: S1, shale core samples were systematically collected in the concentrated shale development section at 1.0m sampling intervals, and experimental and logging data of the shale were obtained. Among them, the experimental data of the shale included the measured TOC, measured NMR porosity, and measured relative content of silica minerals. The logging data were required to correspond one-to-one with the well depth and other data of the collected shale core samples, that is, the logging data and the measured experimental data at the same depth point corresponded one-to-one. S2. Establish the relationship between measured TOC, measured NMR porosity, and measured relative content of siliceous minerals in the shale development section, determine the lower limits of relative content of siliceous minerals, TOC, and NMR porosity in the gas-producing section, and thus clarify the distribution range of favorable areas for shale sweet spots; the method for determining the lower limits of relative content of siliceous minerals, TOC, and NMR porosity in the gas-producing section is as follows: First, based on the cross-plot of measured TOC values ​​and single-well production, the lower limit of TOC corresponding to industrial production capacity is determined. Then, based on the cross-plot of measured TOC values ​​and measured relative content of silica minerals, the relative content of silica minerals corresponding to the lower limit of TOC is determined, which is the lower limit of the relative content of silica minerals in the gas-producing section. Finally, based on the cross-plot of the measured relative content of silica minerals and the measured NMR porosity, the NMR porosity corresponding to the lower limit of the relative content of silica minerals is determined to be the lower limit of the NMR porosity of the gas-producing section. S3. Based on the logging data obtained in step S1, a calculation model for the relative content of logging silica minerals is established, and the relative content of logging silica minerals is obtained. S4, Identify the sweet spot segment of shale. The relative content of silica minerals calculated by well logging in step S3 is statistically analyzed. Data segments with relative silica mineral content greater than the lower limit of the measured relative silica mineral content obtained in step S2 are selected. Combined with the lower limit of the measured TOC and the lower limit of the nuclear magnetic porosity obtained in step S2, the vertical sweet spot segment of shale is determined. S5 verifies the accuracy of the sweet spot segment of shale.

2. The method for predicting vertical sweet spots in high-silica shale as described in claim 1, characterized in that: The well logging data for shale in step S1 includes shale formation density, neutron porosity, natural gamma, and lithology coefficient.

3. The method for predicting vertical sweet spots in high-silica shale as described in claim 1, characterized in that, The S3 method establishes a model for calculating the relative content of silica minerals in well logging, including the following steps: S301, the method for calculating the total clay content (sh) is as follows: First, calculate the relative value of natural gamma based on the measured natural gamma value from well logging, and then determine the total clay content in the formation. S302. Based on the logging data obtained in step S1, establish a set of equations and solve the set of equations to obtain the relative content of each mineral, and then obtain the relative content of siliceous minerals in shale.

4. The method for predicting vertical sweet spots in high-silica shale as described in claim 3, characterized in that: The formula for calculating the relative value of natural gamma in step S301 is as follows: In the formula, GR: the natural gamma value measured in the well logging; GRmin: Minimum natural gamma ray in shale formations; GRmax: The maximum natural gamma ray in the shale section.

5. The method for predicting vertical sweet spots in high-silica shale as described in claim 4, characterized in that: The formula for calculating the total clay content in the strata in step S301 is as follows: Where: GR: Natural gamma value measured in well logging; GCUR: Hilch index, an empirical index, with a value of 2 for older strata and 3.7 for newer strata.

6. The method for predicting vertical sweet spots in high-silica shale as described in claim 3, characterized in that: The system of equations mentioned in step S302 is as follows: In the formula: a, b, c, and d are unknown values, representing illite content, relative content of siliceous minerals, calcite content, and dolomite content, respectively; : Shale formation density measured by well logging; The characteristic density value of illite is generally taken as 2.53 (g / cm³). 3 ); The characteristic value of quartz density is generally taken as 2.65 (g / cm³). 3 ); The characteristic density value of calcite is generally taken as 2.71 (g / cm³). 3 ); The characteristic density value of dolomite is generally taken as 2.87 (g / cm³). 3 ); : Neutron porosity of shale measured by well logging; The characteristic value of illite porosity is generally taken as 21 (pu). : Characteristic value of quartz porosity, generally taken as 0 (pu); : Characteristic value of calcite porosity, generally taken as 0 (pu); : The characteristic value of porosity of dolomite, generally taken as 0 (pu); : Shale lithology coefficient measured by well logging; Characteristic value of illite lithology coefficient, generally taken as 3.45 (b / e); : Characteristic value of quartz lithology coefficient, generally taken as 1.81 (b / e); Characteristic value of calcite lithology coefficient, generally taken as 5.08 (b / e); The characteristic value of the dolomite lithology coefficient is generally taken as 3.14 (b / e).

7. The method for predicting vertical sweet spots in high-silica shale as described in claim 1, characterized in that, In step S5, verifying the accuracy of the shale sweet spot segment includes: S501, compare the relative content data of silica minerals measured in the laboratory in step S1 with the relative content data of silica minerals calculated by well logging in step S3 to see if they match, so as to determine whether the calculation method of relative content of silica minerals is effective. S502, compare the sweet spot calculated in step S4 with the high value area of ​​the measured TOC value and measured NMR porosity obtained in step S2 to see if they correspond, so as to determine whether the sweet spot is accurate.

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