Coal rock gas reservoir logging evaluation method based on oxidation-ash-coal quantity-paleoclimate parameters

Abstract

The present application belongs to the technical field of oil drilling, and particularly relates to a coal rock gas reservoir logging evaluation method based on oxidation-ash-coal quantity-paleoclimate parameters, which collects coal rock gas formation tested oil well data, classifies the data, determines key logging parameters, and establishes a coal rock logging identification function; six parameters, including oxidation, ash, sulfur, chlorine, coal quantity and paleoclimate, are established; some parameters are subjected to correlation analysis with fixed carbon content to form a fixed carbon content calculation model; some parameters are subjected to correlation analysis with porosity interpreted from well logging to form a porosity calculation model; three models are integrated to obtain a coal rock gas reservoir evaluation model and a classification function is fitted to establish a coal rock gas reservoir logging evaluation mode. The present application not only fully incorporates current coal rock gas reservoir evaluation standards and parameters, but also focuses on the study of coal rock characteristics and the creation of oxidation, coal quantity and paleoclimate parameters as the basis for method establishment, so that the method is more applicable.

Description

Technical Field

[0001] This invention belongs to the field of petroleum drilling technology, and specifically relates to a logging evaluation method for coal and gas reservoirs based on oxidation-ash content-coal quantity-paleoclimate parameters. Background Technology

[0002] Coalbed methane (CBM) is a new type of natural gas resource, falling between conventional gas and coalbed methane, possessing unique reservoir characteristics and development potential. However, my country's CBM exploration and development is still in its early stages, with exploration levels generally low. CBM reservoirs are deeply buried, with complex coal and rock compositions, exhibiting strong heterogeneity, self-generation and self-storage, and the coexistence of free and adsorbed gas. This results in poor reservoir response to logging parameters, making logging evaluation difficult. Existing logging evaluation technologies have been tentatively applied to CBM reservoir evaluation, but the results have been less than ideal. Based on currently published literature, domestic research on CBM reservoir logging evaluation technologies is significantly lacking. Changqing Logging Company has made some explorations, establishing a CBM reservoir logging classification and evaluation method based on the curve method and intersection method. However, this is a qualitative method with insufficient accuracy, making it difficult to meet the needs of oil testing.

[0003] In recent years, my country's coal exploration and development has shown a positive trend, particularly with significant breakthroughs in the Ordos Basin and Junggar Basin, leading to the successful commercial exploitation of coal gas. Initial results have also been achieved in the Sichuan-Chongqing region, making it an important new exploration area for capacity replacement. Therefore, research and innovation in coal gas reservoir logging evaluation methods to fill this gap is particularly important. Summary of the Invention

[0004] This invention addresses the problem of complex coal-gas reservoir properties and the inapplicability of existing logging evaluation methods. It creates a coal-gas reservoir logging evaluation method based on oxidation, coal quantity, and paleoclimate parameters. The invention innovatively establishes oxidation, coal quantity, and paleoclimate parameters. Based on these new parameters, a factor model is established through correlation analysis. Then, the factor model is used as a new factor to fit and construct a calculation model and evaluation standard for key parameters of coal-gas reservoirs. Combined with the coal logging identification model, a coal-gas reservoir logging evaluation method is formed.

[0005] To achieve the above-mentioned technical effects, the technical solution of this application is as follows:

[0006] A well logging evaluation method for coal-gas reservoirs based on oxidation, ash, coal content, and paleoclimate parameters is characterized by: collecting well test data from coal-gas formations; classifying the well logging data; determining key well logging parameters based on the correlation coefficients between the classification categories and different logging parameters; establishing a coal-gas logging identification function; establishing a coal-gas reservoir well logging evaluation model; establishing oxidation, ash, sulfur, chloride, coal content, and paleoclimate parameters; performing correlation analysis between ash, coal content, and paleoclimate parameters and fixed carbon content to form a fixed carbon content calculation model; establishing a gas content calculation model; performing correlation analysis between oxidation, sulfur, and chloride parameters and porosity interpreted from well logging; integrating multi-factor functions to form a porosity calculation model; integrating the three models of fixed carbon content, gas content, and porosity to obtain a coal-gas reservoir evaluation model and fitting a classification function to finally form the evaluation method.

[0007] Specifically, the steps include the following:

[0008] Step a: Collect logging data, well logging interpretation data, reservoir classification, experimental analysis data, and oil testing data from wells tested in the coal-rock gas formation within the study area;

[0009] Step b: Based on the well logging interpretation data and oil testing data, classify the coal-rock gas formations into coal-rock and non-coal-rock categories based on the well logging data;

[0010] Step c: Perform a difference analysis on the logging data of coal and non-coal rocks to determine the correlation between different lithologies and logging parameters. Based on the magnitude of the correlation coefficients between coal and non-coal rocks and different logging parameters, determine the key logging parameters. Then, establish a coal logging identification function through classification and discrimination.

[0011] Step d: Establish oxidation parameter U 能谱 / Th 能谱 U 能谱 / GR 能谱 Establish ash content parameter 2×Fe 元素 +Ca 元素 +Mg 元素 / 2×Al 元素 +Si 元素 Obtain sulfur content parameter S 元素 Obtain the chloride parameter Cl 元素 Establish coal quantity parameters 2×Al 元素 +Si 元素 Establish paleoclimate parameter Mg 元素 / Al 元素 Mg 元素 / Ca 元素 Obtaining manganese parameters Mn 元素 ;

[0012] U 能谱 ,Th 能谱 GR 能谱 The content of U, Th, and K in the rock cuttings was determined by gamma-ray spectroscopy logging; Fe... 元素 Ca 元素 Mg 元素 Al 元素 Si 元素 S 元素 Cl 元素 Mn 元素 These represent the contents of Fe, Ca, Al, Si, S, Cl, and Mn in the rock cuttings as determined by elemental logging.

[0013] Step e: Adjust the ash content parameter 2×Fe 元素 +Ca 元素 +Mg 元素 / 2×Al 元素 +Si 元素 Coal quantity parameter 2×Al 元素 +Si 元素 Paleoclimate parameter Mg 元素 / Al 元素 Mg 元素 / Ca 元素 Manganese parameter Mn 元素 Correlation analysis was performed on the fixed carbon content in the well logging interpretation data, and the multi-factor functions were integrated through factor transformation to form a fixed carbon content calculation model.

[0014] Step f: Standardize the total hydrocarbons measured during drilling, and perform correlation analysis between the standardized total hydrocarbons measured during drilling and the gas content interpreted from well logging to establish a gas content calculation model.

[0015] Step g, set oxidation parameter U 能谱 / Th 能谱 and U 能谱 / GR 能谱 Sulfur content parameter S 元素 Chlorine parameter Cl 元素 Correlation analysis was performed on the porosity interpreted from well logging, and the multi-factor functions were integrated through factor transformation to form a porosity calculation model;

[0016] Step h: Integrate the calculation model of three parameters, namely carbon content, gas content and porosity, to obtain the coal-rock gas reservoir evaluation model. Based on the logging data of coal and rock of different reservoir quality obtained in step b, the classification function is obtained by classification fitting.

[0017] Step i: Establish a logging evaluation model for coal-rock gas reservoirs using classification functions;

[0018] Step j: Based on the coal and rock logging identification function established in step c and the reservoir evaluation standard set by the coal and rock gas reservoir evaluation model obtained in step i, an evaluation method is formed, which is used to evaluate and identify the coal and rock gas formation in new wells.

[0019] Furthermore, the logging data in step a includes conventional logging data and special logging data. Conventional logging data includes drilling time, gas logging, and lithological profiles, while special logging data includes natural gamma ray spectroscopy logging data and elemental logging data.

[0020] Well logging data includes: conventional well logging data and energy dispersive spectroscopy (EDS) well logging data;

[0021] Well logging interpretation results include: fixed carbon content, porosity, gas content, reservoir segmentation, and reservoir evaluation results;

[0022] Experimental analysis data includes: fixed carbon content, porosity, and gas content from core analysis;

[0023] The oil testing data includes: the testing well section, the test results, and the fracturing effect.

[0024] Furthermore, the logging parameters in step c refer to gamma-ray spectral logging parameters and elemental logging parameters, and the key logging parameters refer to the parameters among the gamma-ray spectral logging parameters and elemental logging parameters that have a correlation coefficient with coal and rock lithology greater than 0.5.

[0025] Furthermore, in step c, coal and rock identification is mainly based on microscopic observation, with a weight of 70%, combined with the key parameter lithology discrimination function based on special logging data, with a weight of 30%, and the two are combined to identify coal and rock. The key parameter lithology discrimination function is a multi-parameter discrimination function established based on well logging interpretation of coal and rock and non-coal and rock.

[0026] Coal seam identification = 0.7 × f1 (microscopic observation) + 0.3 × f2 (A) 能谱录井 A 元素录井 Formula 1

[0027] In Equation 1, f1 (microscopic observation) is the human judgment after microscopic observation, and the result is yes or no, corresponding to a value of 1 or 0; f2 (A 能谱录井 A 元素录井 ) represents the key parameter lithology discriminant function; A 能谱录井 The natural gamma ray spectral logging parameter set determined for correlation analysis has a correlation coefficient > 0.5; A 元素录井 The natural gamma spectral logging parameter set determined by correlation analysis has a correlation coefficient > 0.5.

[0028] Furthermore,

[0029] f2(A 能谱录井 A元素录井 )=a1×X1+a2×X2+a3×X3+… Equation 2

[0030] In Equation 2, a1, a2, a3, ... are the discrimination coefficients; X1, X2, X3, ... are the key parameters of natural gamma spectral logging and elemental logging determined based on correlation analysis.

[0031] Furthermore, the specific calculation model for the fixed carbon content in step e is as follows:

[0032]

[0033] In Equation 4, C 测井 For the fixed carbon content of core experiments or the fixed carbon content of well logging, if both data are obtained simultaneously, the fixed carbon content of core experiments shall be used first; each f() is a sub-model of fixed carbon content and ash content, coal content and paleoclimate parameters constructed based on the fixed carbon experimental data of core experiments or the fixed carbon of well logging.

[0034] Using the sub-models as factors, a multi-factor function model for calculating fixed carbon content is constructed:

[0035] C=b1×f(2×Fe 元素 +Ca 元素 +Mg 元素 / 2×Al 元素 +Si 元素 )+b2×f(2×

[0036] Al 元素 +Si 元素 )+b3×f(Mg 元素 / Al 元素 )+b4×f(Mg 元素 / Ca 元素 )+b5×

[0037] f(Mn 元素 )+b6 Equation 5

[0038] In Equation 5, b1, b2, b3, b4, b5, and b6 are the coefficients of the fixed carbon content model obtained by multivariate fitting;

[0039] C is the final fixed carbon content calculation model constructed, and C-logging is the fixed carbon data in the logging interpretation results.

[0040] Furthermore, the gas content calculation model in step f is as follows:

[0041] TG 标准 =TG×t / t 标准 Formula 6

[0042] Q 测井 =f)TG标准 Formula 7

[0043] In Equation 6, TG represents all hydrocarbons. 标准 For standardized total hydrocarbons, t represents drilling time. 标准 The standard drilling time is the average value of the selected area.

[0044] In Equation 7, Q 测井 To interpret gas content in well logging, f(U) 标准 A model is constructed to compare gas content with well logging measurements based on well logging interpretation.

[0045] Furthermore, the porosity calculation model in step g is as follows:

[0046]

[0047] In Equation 8, φ 测井 To interpret porosity from well logging, each of the above f() is a porosity relation to U constructed based on well logging porosity or core experimental porosity. 能谱 / Th 能谱 U 能谱 / GR 能谱 S 元素 Cl 元素 Sub-model,

[0048] Using the sub-models as factors, a multi-factor function model for porosity calculation is constructed:

[0049]

[0050] In Equation 9, c1, c2, c3, c4, and c5 are the porosity model coefficients obtained from multivariate fitting.

[0051] Furthermore, in step h, substituting equations 5, 7, and 9 into equation 3 yields equation 11, which is the coal-rock gas reservoir evaluation function.

[0052] Coal-rock gas reservoir evaluation function = F(B 能谱 B 元素 ,TG,t)=l×C+m×Q+n×φ Equation 11

[0053] In Equation 10, B 能谱 For all the natural gamma-ray spectral logging parameters previously involved, B 元素 For all the elements involved previously, the logging parameters are used;

[0054] Based on the logging data of coal and rock of different reservoir quality obtained in step b, the values ​​of l, m, and n are obtained by classification fitting, and the classification function, which is also the coal and rock gas reservoir evaluation function, can be obtained.

[0055] Furthermore, in step i, the evaluation of the coal-rock gas reservoir uses static parameters to fix carbon content, gas content, and porosity. Based on these parameters, a logging evaluation model is constructed:

[0056]

[0057] In Equation 3, C represents the fixed carbon content; Q represents the gas content. For porosity; l, m, and n are weight indices of fixed carbon, gas content, and porosity in the evaluation formula, constructed based on core test results and well logging reservoir evaluation results.

[0058] Set the reservoir evaluation criteria as follows: and Right now At that time, it is a Class I layer; At that time, it is a Class II layer; At that time, it is a Class III layer.

[0059] Furthermore, in step j, based on the logging parameters, the coal and rock are identified by the coal and rock logging identification function established in step c, and then the coal and rock are evaluated for coal and rock gas reservoirs by the reservoir evaluation criteria set by the coal and rock gas reservoir evaluation function obtained in step i.

[0060] This application has the following technical effects:

[0061] 1. This application innovatively establishes oxidation, ash, coal content, and paleoclimate parameters and introduces them into the logging evaluation system for coalbed methane reservoirs. Previous logging evaluations of coalbed methane reservoirs mainly focused on four aspects: lithology, brittleness, source rock characteristics, and gas content. This differs from current coalbed methane reservoir evaluation standards and parameters, making the effectiveness difficult to guarantee. This application fully integrates current coalbed methane reservoir evaluation standards and parameters while emphasizing the study of coal and lithological characteristics and specifically creating oxidation, coal content, and paleoclimate parameters as the foundation for the method, making the method more applicable.

[0062] 2. This application integrates multi-factor functions using factor transformation. Previously, the combined application of multiple parameters often employed curve methods, intersection methods, or multi-parameter fitting to establish functions, with inconsistent results. In this application, based on newly created parameters, a factor model is established through correlation analysis. This factor model is then used to fit new factors to construct a calculation model and evaluation criteria for key parameters of coal-gas reservoirs. Combined with a coal logging identification model, this forms a coal-gas reservoir logging evaluation method, resulting in significantly higher reliability.

[0063] 3. With the expansion of coal gas exploration and development, significant breakthroughs have been achieved in some parts of my country, with initial successes seen in the Sichuan-Chongqing region. However, due to the complex composition, strong heterogeneity, and diverse natural gas occurrence forms of coal gas reservoirs, as well as the poor reservoir responsiveness of logging parameters, some existing logging evaluation techniques are not suitable for coal gas reservoir logging evaluation. This method innovatively establishes oxidation, coal quantity, and paleoclimate parameters. Based on these new parameters, a factor model is established through correlation analysis. The factor model is then used to fit new factors to construct a calculation model and evaluation standard for key parameters of coal gas reservoirs. Combined with the coal logging identification model, a logging evaluation method specifically for coal gas reservoirs is formed. Therefore, this method has significantly higher reliability for coal gas reservoir evaluation. Furthermore, the method is constructed by creating new parameters based on coal characteristics, making it universally applicable to coal gas reservoir logging evaluation. It can be applied to coal gas reservoir logging evaluation in different regions; only the corresponding evaluation parameters need to be adjusted according to regional characteristics to establish their own evaluation standards and methods. Attached Figure Description

[0064] Figure 1 This is a flowchart of the method. Detailed Implementation

[0065] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0066] Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0067] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0068] In the description of this application, it should be noted that the terms "upper," "vertical," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship commonly used when the product is in use, or the orientation or positional relationship commonly understood by those skilled in the art. They are used only for the convenience of describing this application and 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, and therefore should not be construed as a limitation on this application. In addition, the terms "first," "second," etc., are only used to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0069] In the description of this application, it should also be noted that, unless otherwise expressly specified and limited, the terms "set," "install," and "connect" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0070] Example 1

[0071] like Figure 1 As shown, a well logging evaluation method for coal gas reservoirs based on oxidation, ash, coal content, and paleoclimate parameters is proposed. This method collects well test data from coal gas formations, classifies the well logging data, determines key logging parameters based on the correlation coefficients between the classification categories and different logging parameters, and establishes a coal logging identification function. Oxidation, ash, sulfur, chloride, coal content, and paleoclimate parameters are established. Correlation analysis is performed on ash, coal content, and paleoclimate parameters with fixed carbon content to form a fixed carbon content calculation model. A gas content calculation model is also established. Correlation analysis is performed on oxidation, sulfur, and chloride parameters with porosity interpreted from well logging, and multi-factor functions are integrated to form a porosity calculation model. The three models of fixed carbon content, gas content, and porosity are integrated to obtain a coal gas reservoir evaluation model, and a classification function is fitted to establish a coal gas reservoir well logging evaluation mode. Finally, an evaluation method is formed.

[0072] Example 2

[0073] like Figure 1 As shown, a well logging evaluation method for coal and gas reservoirs based on oxidation, coal quantity, and paleoclimate parameters includes the following steps:

[0074] Step a: Collect logging data, well logging interpretation data, reservoir classification, experimental analysis data, and oil testing data from wells tested in the coal-rock gas formation within the study area;

[0075] Step b: Based on the well logging interpretation data and oil testing data, classify the coal gas formation according to coal and non-coal rock. The target layer of the coal gas well has complex lithology, including shale, siltstone, etc., in addition to coal. However, other lithologies are not relevant to coal evaluation and can be ignored. In order to highlight coal evaluation, the lithology is divided into coal and non-coal rock, and non-coal rock is the lithology other than coal.

[0076] Step c: Perform a difference analysis on the logging data of coal and non-coal rocks to determine the correlation between different lithologies and logging parameters. Based on the magnitude of the correlation coefficients between coal and non-coal rocks and different logging parameters, determine the key logging parameters. Then, establish a coal logging identification function through classification and discrimination.

[0077] Step d: Establish oxidation parameter U 能谱 / Th 能谱 U 能谱 / GR 能谱 Establish ash content parameter 2×Fe 元素 +Ca 元素 +Mg 元素 / 2×Al 元素 +Si 元素 Obtain sulfur content parameter S 元素 Obtain the chloride parameter Cl 元素 Establish coal quantity parameters 2×Al 元素 +Si 元素 Establish paleoclimate parameter Mg 元素 / Al 元素 Mg 元素 / Ca 元素 Obtaining manganese parameters Mn 元素 ;

[0078] U 能谱 ,Th 能谱 GR 能谱 The content of U, Th, and K in the rock cuttings was determined by gamma-ray spectroscopy logging; Fe... 元素 Ca 元素 Mg 元素 Al 元素 Si 元素 S 元素 Cl 元素 Mn 元素 These represent the contents of Fe, Ca, Al, Si, S, Cl, and Mn in the rock cuttings as determined by elemental logging.

[0079] Oxidation parameters, coal quantity parameters, and paleoclimate parameters are new evaluation parameters constructed based on the inherent properties of the coal and rock, combined with key logging parameters derived from coal and rock reservoir evaluation standards and analysis. Sulfur and chloride parameters are content measured by elemental logging. All of the above parameters are either natural gamma-ray spectroscopy logging parameters or elemental logging parameters.

[0080] Step e: Adjust the ash content parameter 2×Fe 元素 +Ca 元素 +Mg 元素 / 2×Al 元素 +Si 元素 Coal quantity parameter 2×Al 元素 +Si 元素 Paleoclimate parameter Mg 元素 / Al 元素 Mg 元素 / Ca 元素 Manganese parameter Mn 元素 Correlation analysis was performed on the fixed carbon content in the well logging interpretation data to establish relevant formulas. The multi-factor functions were then integrated through factor transformation to form a fixed carbon content calculation model. This step uses the fixed carbon in the well logging interpretation data. If there is fixed carbon data from core experiments, then the core experiment data can be used.

[0081] Step f: Standardize the total hydrocarbons measured during drilling, and perform correlation analysis between the standardized total hydrocarbons measured during drilling and the gas content interpreted from well logging to establish a gas content calculation model.

[0082] Step g, set oxidation parameter U 能谱 / Th 能谱 and U 能谱 / GR 能谱 Sulfur content parameter S 元素 Chlorine parameter Cl 元素 Correlation analysis was performed on the porosity interpreted from well logging, relevant formulas were established, and multi-factor functions were integrated through factor transformation to form a porosity calculation model;

[0083] Step h: Integrate the calculation model of three parameters, namely carbon content, gas content and porosity, to obtain the coal-rock gas reservoir evaluation model. Based on the logging data of coal and rock of different reservoir quality obtained in step b, the classification function is obtained by classification fitting.

[0084] Step i: Based on the evaluation standards for coal-rock gas reservoirs, establish a well logging evaluation model for coal-rock gas reservoirs;

[0085] Step j: Based on the coal and rock logging identification function established in step c and the reservoir evaluation standard set by the coal and rock gas reservoir evaluation model obtained in step i, an evaluation method is formed, which is used to evaluate and identify the coal and rock gas formation in new wells.

[0086] Example 3

[0087] like Figure 1 As shown, a well logging evaluation method for coal and gas reservoirs based on oxidation, coal quantity, and paleoclimate parameters includes the following steps:

[0088] Step a: Collect logging data, well logging interpretation data, reservoir classification, experimental analysis data, and oil testing data from wells tested in the coal-rock gas formation within the study area;

[0089] Step b: Based on the well logging interpretation data and oil testing data, classify the coal gas formation according to coal and non-coal rock. The target layer of the coal gas well has complex lithology, including shale, siltstone, etc., in addition to coal. However, other lithologies are not relevant to coal evaluation and can be ignored. In order to highlight coal evaluation, the lithology is divided into coal and non-coal rock, and non-coal rock is the lithology other than coal.

[0090] Step c: Perform a difference analysis on the logging data of coal and non-coal rocks to determine the correlation between different lithologies and logging parameters. Based on the magnitude of the correlation coefficients between coal and non-coal rocks and different logging parameters, determine the key logging parameters. Then, establish a coal logging identification function through classification and discrimination.

[0091] Step d: Establish oxidation parameter U 能谱 / Th 能谱 U 能谱 / GR 能谱 Establish ash content parameter 2×Fe 元素 +Ca 元素 +Mg 元素 / 2×Al 元素 +Si 元素 Obtain sulfur content parameter S 元素 Obtain the chloride parameter Cl 元素 Establish coal quantity parameters 2×Al 元素 +Si 元素 Establish paleoclimate parameter Mg 元素 / Al 元素 Mg 元素 / Ca 元素 Obtaining manganese parameters Mn 元素 ;

[0092] U 能谱 ,Th 能谱 GR 能谱 The content of U, Th, and K in the rock cuttings was determined by gamma-ray spectroscopy logging; Fe... 元素 Ca 元素 Mg 元素 Al 元素 Si元素 S 元素 Cl 元素 Mn 元素 These represent the contents of Fe, Ca, Al, Si, S, Cl, and Mn in the rock cuttings as determined by elemental logging.

[0093] Oxidation parameters, coal quantity parameters, and paleoclimate parameters are new evaluation parameters constructed based on the inherent properties of the coal and rock, combined with key logging parameters derived from coal and rock reservoir evaluation standards and analysis. Sulfur and chloride parameters are content measured by elemental logging. All of the above parameters are either natural gamma-ray spectroscopy logging parameters or elemental logging parameters.

[0094] Step e: Adjust the ash content parameter 2×Fe 元素 +Ca 元素 +Mg 元素 / 2×Al 元素 +Si 元素 Coal quantity parameter 2×Al 元素 +Si 元素 Paleoclimate parameter Mg 元素 / Al 元素 Mg 元素 / Ca 元素 Manganese parameter Mn 元素 Correlation analysis was performed on the fixed carbon content in the well logging interpretation data to establish relevant formulas. The multi-factor functions were then integrated through factor transformation to form a fixed carbon content calculation model. This step uses the fixed carbon in the well logging interpretation data. If there is fixed carbon data from core experiments, then the core experiment data can be used.

[0095] Step f: Standardize the total hydrocarbons measured during drilling, and perform correlation analysis between the standardized total hydrocarbons measured during drilling and the gas content interpreted from well logging to establish a gas content calculation model.

[0096] Step g, set oxidation parameter U 能谱 / Th 能谱 and U 能谱 / GR 能谱 Sulfur content parameter S 元素 Chlorine parameter Cl 元素 Correlation analysis was performed on the porosity interpreted from well logging, relevant formulas were established, and multi-factor functions were integrated through factor transformation to form a porosity calculation model;

[0097] Step h: Integrate the calculation model of three parameters, namely carbon content, gas content and porosity, to obtain the coal-rock gas reservoir evaluation model. Based on the logging data of coal and rock of different reservoir quality obtained in step b, the classification function is obtained by classification fitting.

[0098] Step i: Based on the evaluation standards for coal-rock gas reservoirs, establish a well logging evaluation model for coal-rock gas reservoirs;

[0099] Step j: Based on the coal and rock logging identification function established in step c and the reservoir evaluation standard set by the coal and rock gas reservoir evaluation model obtained in step i, an evaluation method is formed, which is used to evaluate and identify the coal and rock gas formation in new wells.

[0100] Furthermore, the logging data in step a includes conventional logging data and special logging data. Conventional logging data includes drilling time, gas logging, and lithological profiles, while special logging data includes natural gamma ray spectroscopy logging data and elemental logging data.

[0101] Well logging data includes: conventional well logging data and energy dispersive spectroscopy (EDS) well logging data;

[0102] Well logging interpretation data includes: fixed carbon content, porosity, gas content, reservoir segmentation, and reservoir evaluation results;

[0103] Experimental analysis data includes: fixed carbon content, porosity, and gas content from core analysis;

[0104] The oil testing data includes: the testing well section, the test results, and the fracturing effect.

[0105] Furthermore, the logging parameters in step c refer to gamma-ray spectral logging parameters and elemental logging parameters, and the key logging parameters refer to the parameters among the gamma-ray spectral logging parameters and elemental logging parameters that have a correlation coefficient with coal and rock lithology greater than 0.5.

[0106] Furthermore, in step c, coal and rock identification is mainly based on microscopic observation, with a weight of 70%, combined with the key parameter lithology discrimination function based on special logging data, with a weight of 30%, and the two are combined to identify coal and rock. The key parameter lithology discrimination function is a multi-parameter discrimination function established based on well logging interpretation of coal and rock and non-coal and rock.

[0107] Coal seam identification = 0.7 × f1 (microscopic observation) + 0.3 × f2 (A) 能谱录井 A 元素录井 Formula 1

[0108] In Equation 1, f1 (microscopic observation) is the human judgment after microscopic observation, and the result is yes or no, corresponding to a value of 1 or 0; f2 (A 能谱录井 A 元素录井 ) represents the key parameter lithology discriminant function; A 能谱录井 The natural gamma ray spectral logging parameter set determined for correlation analysis has a correlation coefficient > 0.5; A 元素录井 The natural gamma spectral logging parameter set determined by correlation analysis has a correlation coefficient > 0.5.

[0109] Furthermore,

[0110] f2(A 能谱录井 A 元素录井 )=a1×X1+a2×X2+a3×X3+… Equation 2

[0111] In Equation 2, a1, a2, a3, ... are the discrimination coefficients; X1, X2, X3, ... are the key parameters of natural gamma spectral logging and elemental logging determined based on correlation analysis.

[0112] Furthermore, the specific calculation model for the fixed carbon content in step e is as follows:

[0113]

[0114] In Equation 4, C 测井 For the fixed carbon content of core experiments or the fixed carbon content of well logging, if both data are obtained simultaneously, the fixed carbon content of core experiments shall be used first; each f() is a sub-model of fixed carbon content and ash content, coal content and paleoclimate parameters constructed based on the fixed carbon experimental data of core experiments or the fixed carbon of well logging.

[0115] Using the sub-models as factors, a multi-factor function model for calculating fixed carbon content is constructed:

[0116] C=b1×f(2×Fe 元素 +Ca 元素 +Mg 元素 / 2×Al 元素 +Si 元素 )+b2×f(2×

[0117] Al 元素 +Si 元素 )+b3×f(Mg 元素 / Al 元素 )+b4×f(Mg 元素 / Ca 元素 )+b5×

[0118] f(Mn 元素 )+b6 Equation 5

[0119] In Equation 5, b1, b2, b3, b4, b5, and b6 are the coefficients of the fixed carbon content model obtained by multivariate fitting;

[0120] C is the calculation model for the final fixed carbon content. By using the constructed core fixed carbon or well logging fixed carbon and sub-models of ash, coal quantity, and paleoclimate as factors, a multi-factor calculation model for the final fixed carbon is established. C well logging refers to the fixed carbon data in the well logging interpretation results. If core experimental data is available, the core fixed carbon experimental results are used.

[0121] Furthermore, the gas content calculation model in step f is as follows:

[0122] TG 标准 =TG×t / t 标准 Formula 6

[0123] Q 测井 =f(TG) 标准 Formula 7

[0124] In Equation 6, TG represents all hydrocarbons. 标准 For standardized total hydrocarbons, t represents drilling time. 标准 The standard drilling time is generally selected as the regional average value.

[0125] In Equation 7, Q 测井 To interpret gas content in well logging, f(TG) 标准 A model is constructed to compare gas content with well logging measurements based on well logging interpretation.

[0126] Furthermore, the porosity calculation model in step g is as follows:

[0127]

[0128] In Equation 8, φ 测井 To interpret porosity from well logging, each of the above f() is a porosity and U constructed based on well logging porosity or core experimental porosity. 能谱 / Th 能谱 U 能谱 / GR 能谱 S 元素 Cl 元素 Sub-model,

[0129] Using the sub-models as factors, a multi-factor function model for porosity calculation is constructed:

[0130]

[0131] In Equation 9, c1, c2, c3, c4, and c5 are the porosity model coefficients obtained from multivariate fitting.

[0132] Further, in step h, substituting equations 5, 7, and 9 into equation 3 yields equation 11, which is the coal-rock gas reservoir evaluation function: Coal-rock gas reservoir evaluation function = F(B 能谱 B 元素 ,TG,t)=l×C+m×Q+n×φ Equation 11

[0133] In Equation 10, B 能谱 For all the natural gamma-ray spectral logging parameters involved previously, B 元素 For all the elements involved previously, the logging parameters are used;

[0134] Based on the logging data of coal and rock of different reservoir quality obtained in step b, the values ​​of l, m, and n are obtained by classification fitting, and the classification function, which is also the coal and rock gas reservoir evaluation function, can be obtained.

[0135] Furthermore, in step i, the evaluation of coal-rock gas reservoirs mainly adopts static parameters to fix carbon content, gas content, and porosity. Based on these parameters, a logging evaluation model is constructed:

[0136]

[0137] In Equation 3, C represents the fixed carbon content; Q represents the gas content. For porosity; l, m, and n are weight indices of fixed carbon, gas content, and porosity in the evaluation formula, constructed based on core test results and well logging reservoir evaluation results.

[0138] Set the reservoir evaluation criteria as follows: and Right now At that time, it is a Class I layer; At that time, it is a Class II layer; At that time, it is a Class III layer.

[0139] Furthermore, in step j, based on the logging parameters, the coal and rock are identified by the coal and rock logging identification function established in step c, and then the coal and rock are evaluated for coal and rock gas reservoirs by the reservoir evaluation criteria set by the coal and rock gas reservoir evaluation function obtained in step i.

[0140] Example 4

[0141] This invention was used to evaluate coal-gas reservoirs in a certain region through logging. Lithological characteristics were observed under a microscope; the f1 (microscopic observation) result for coal-gas was 1, while the f1 (microscopic observation) result for non-coal-gas was 0. Correlation analysis determined that the key logging parameter for natural gamma ray spectroscopy of coal-gas was Th, and the key logging parameters for elemental analysis of coal-gas were Al, Si, S, and Cl. A discriminant function was established using linear regression to classify coal-gas and non-coal-gas reservoirs. The discriminant function is:

[0142] Coal seam identification = 0.7 × f1 (microscopic observation) + 0.3 × (0.031Si + 0.154S + 0.619Cl + 0.012Th - 0.096Al + 0.39) (1)

[0143] Based on coal and petrology identification and using coal gas reservoir evaluation standards, a well logging evaluation model for coal gas reservoirs is established. Correlation analysis of ash content parameters, coal quantity parameters, paleoclimate parameters, and fixed carbon (C) is used to establish relevant formulas.

[0144]

[0145] Through multivariate fitting, a calculation model for fixed carbon content can be obtained:

[0146]

[0147] After standardization of total hydrocarbons from gas logging, correlation analysis was performed between the gas content Q from well logging interpretation to establish a gas content interpretation model. Based on the average drilling time for coal and rock in the region, t was obtained. 标准 It is 5.5 minutes, therefore:

[0148] TG 标准 =TG×t / 5.5 (4)

[0149] Q = 0.3239ln(TG) 标准 )+26.104 (5)

[0150] Oxidation parameter U 能谱 / Th 能谱 and U 能谱 / GR 能谱 Sulfur content parameter S 元素 Chlorine parameter Cl 元素 Correlation analysis was performed between the porosity interpreted from well logging and the porosity, and relevant formulas were established:

[0151]

[0152] Through multivariate fitting, a model for calculating porosity content can be obtained:

[0153]

[0154] By integrating a calculation model with fixed carbon content, gas content, and porosity, a classification function, which is also the evaluation function for coal-rock gas reservoirs, can be obtained through classification fitting.

[0155]

[0156] The evaluation coefficients are set to 2.5 and 2. Reservoir evaluation parameters ≥ 2.5 are classified as Class I reservoirs; 2.5 > 2 are classified as Class II reservoirs; and 2 > 2 are classified as Class III reservoirs.

[0157] Specific embodiments have been used to illustrate the principles and implementation methods of this invention. The descriptions of the embodiments above are only for the purpose of helping to understand the method and core ideas of this invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this invention. Therefore, the content of this specification should not be construed as a limitation of this invention.

[0158] Those skilled in the art will recognize that the embodiments described herein are intended to help the reader understand the principles of the invention, and should be understood that the scope of protection of the invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific modifications and combinations based on the technical teachings disclosed in this invention without departing from the spirit of the invention, and these modifications and combinations are still within the scope of protection of this invention.

Claims

1. A well logging evaluation method for coal-gas reservoirs based on oxidation-ash content-coal quantity-paleoclimate parameters, characterized in that, Data from tested wells in coal-rock gas formations were collected, and the logging data was classified. Key logging parameters were determined based on the correlation coefficients between the classification categories and different logging parameters, and a coal-rock logging identification function was established. A coal-rock gas reservoir logging evaluation model was established. Oxidation, ash, sulfur, chloride, coal content, and paleoclimate parameters were established. Correlation analysis was performed on ash, coal content, and paleoclimate parameters with fixed carbon content to form a fixed carbon content calculation model. A gas content calculation model was established. Correlation analysis was performed on oxidation, sulfur, and chloride parameters with porosity interpreted from logging, and multi-factor functions were integrated to form a porosity calculation model. The three models of fixed carbon content, gas content, and porosity were integrated to obtain a coal-rock gas reservoir evaluation model, and a classification function was fitted to obtain the final evaluation method.

2. The method for evaluating coal and gas reservoirs based on oxidation-ash content-coal quantity-paleoclimate parameters according to claim 1, characterized in that, Specifically, the steps include the following: Step a: Collect logging data, well logging interpretation data, reservoir classification, experimental analysis data, and oil testing data from wells that have been tested in coal and gas formations; Step b: Based on the well logging interpretation data and oil testing data, classify the coal-rock gas formations into coal-rock and non-coal-rock categories based on the well logging data; Step c: Perform differential analysis on the logging data, determine the key logging parameters based on the magnitude of the correlation coefficients between coal and non-coal rock and different logging parameters, and establish a coal logging identification function through classification and discrimination; Step d: Establish oxidation parameters, establish ash parameters, obtain sulfur parameters, obtain chlorine parameters, establish coal quantity parameters, establish paleoclimate parameters, and obtain manganese parameters; Step e: Perform correlation analysis between the ash content parameters, coal quantity parameters, paleoclimate parameters, and manganese parameters and the fixed carbon content in the well logging interpretation data to form a fixed carbon content calculation model; Step f: Standardize the total hydrocarbon content measured during drilling and perform correlation analysis with the gas content interpreted from the well log to establish a gas content calculation model. Step g: Perform correlation analysis on oxidation parameters, sulfur parameters, chloride parameters and porosity interpreted from well logging, and integrate the multi-factor functions through factor transformation to form a porosity calculation model; Step h: Integrate the calculation model with three parameters of fixed carbon content, gas content and porosity to obtain the coal-rock gas reservoir evaluation model, and obtain the classification function through classification fitting; Step i: Establish a logging evaluation model for coal-rock gas reservoirs using classification functions; Step j: Based on the coal and rock logging identification function established in step c and the coal and rock gas reservoir evaluation model obtained in step i, the reservoir evaluation standard is set to form an evaluation method.

3. The method for evaluating coal and gas reservoirs based on oxidation-ash content-coal quantity-paleoclimate parameters according to claim 2, characterized in that: The oxidation parameter established in step e is U 能谱 / Th 能谱 U 能谱 / GR 能谱 The ash content parameter is 2×Fe 元素 +Ca 元素 +Mg 元素 / 2×Al 元素 +Si 元素 The sulfur content parameter is S 元素 The chlorine parameter is Cl. 元素 The coal quantity parameter is 2×Al 元素 +Si 元素 The paleoclimate parameter is Mg. 元素 / Al 元素 Mg 元素 / Ca 元素 Mn 元素 ; U 能谱 ,Th 能谱 GR 能谱 The content of U, Th, and K in the rock cuttings was determined by gamma-ray spectroscopy logging; Fe... 元素 Ca 元素 Mg 元素 Al 元素 Si 元素 S 元素 Cl 元素 Mn 元素 These represent the contents of Fe, Ca, Al, Si, S, Cl, and Mn in the rock cuttings as determined by elemental logging.

4. The method for evaluating coal and gas reservoirs based on oxidation-ash content-coal quantity-paleoclimate parameters according to claim 2, characterized in that, The logging data in step a includes conventional logging data and special logging data. Conventional logging data includes drilling time, gas logging, and lithological profiles. Special logging data includes natural gamma ray spectroscopy logging data and elemental logging data. Well logging data includes: conventional well logging data and energy dispersive spectroscopy (EDS) well logging data; Well logging interpretation data includes: fixed carbon content, porosity, gas content, reservoir segmentation, and reservoir evaluation results; Experimental analysis data includes: fixed carbon content, porosity, and gas content from core analysis; The oil testing data includes: the testing well section, the test results, and the fracturing effect.

5. The method for evaluating coal and gas reservoirs based on oxidation-ash content-coal quantity-paleoclimate parameters according to claim 2, characterized in that, The logging parameters in step c refer to gamma-ray spectral logging parameters and elemental logging parameters. The key logging parameters refer to those parameters among the gamma-ray spectral logging parameters and elemental logging parameters that have a correlation coefficient with coal and rock lithology greater than 0.

5.

6. The method for evaluating coal and gas reservoirs based on oxidation-ash content-coal quantity-paleoclimate parameters according to claim 2, characterized in that, In step c, coal and rock identification is mainly based on microscopic observation, with a weight of 70%, combined with the key parameter lithology discrimination function based on special logging data, with a weight of 30%. The two are combined to identify coal and rock. The key parameter lithology discrimination function is a multi-parameter discrimination function established based on well logging interpretation of coal and rock and non-coal and rock. Coal seam identification = 0.7 × f1 (microscopic observation) + 0.3 × f2 (A) 能谱录井 A 元素录井 Formula 1 In Equation 1, f1 (microscopic observation) is the human judgment after microscopic observation, and the result is yes or no, corresponding to a value of 1 or 0; f2 (A 能谱录井 A 元素录井 ) represents the key parameter lithology discriminant function; A 能谱录井 The natural gamma ray spectral logging parameter set determined for correlation analysis has a correlation coefficient > 0.5; A 元素录井 The natural gamma spectral logging parameter set determined by correlation analysis has a correlation coefficient > 0.

5.

7. The method for logging and evaluating coal and gas reservoirs based on oxidation-ash content-coal quantity-paleoclimate parameters according to claim 6, characterized in that, f2(A 能谱录井 ,A 元素录井 )=a1×X1+a2×X2+a3×X3+…Formula 2 In Equation 2, a1, a2, a3, ... are the discrimination coefficients; X1, X2, X3, ... are the key parameters of natural gamma spectral logging and elemental logging determined based on correlation analysis.

8. The method for evaluating coal and gas reservoirs based on oxidation-ash content-coal quantity-paleoclimate parameters according to claim 2, characterized in that, In step d, the evaluation of coal-rock gas reservoirs uses static parameters to fix carbon content, gas content, and porosity. Based on these parameters, a logging evaluation model is constructed. In Equation 3, C represents the fixed carbon content; Q represents the gas content. For porosity; l, m, and n are weight indices of fixed carbon, gas content, and porosity in the evaluation formula, constructed based on core test results and well logging reservoir evaluation results. Set the reservoir evaluation criteria as follows: and That is when When, it is layer I; when When, it is a Class II layer; when At that time, it is a Class III layer.

9. The method for logging and evaluating coal and gas reservoirs based on oxidation-ash content-coal quantity-paleoclimate parameters according to claim 2, characterized in that, The specific model for calculating the fixed carbon content in step f is as follows: In Equation 4, C 测井 For the fixed carbon content of core experiments or the fixed carbon content of well logging, if both data are obtained simultaneously, the fixed carbon content of core experiments shall be used first; each f() is a sub-model of fixed carbon content and ash content, coal content and paleoclimate parameters constructed based on the fixed carbon experimental data of core experiments or the fixed carbon of well logging. Using the sub-models as factors, a multi-factor function model for calculating fixed carbon content is constructed: C = b1×f(2×Fe 元素 +Ca 元素 +Mg 元素 / 2×Al 元素 +Si 元素 ) + b2×f(2×Al 元素 +Si 元素 ) + b3×f(Mg 元素 / Al 元素 ) + b4×f(Mg 元素 / Ca 元素 ) + b5×f(Mn 元素 ) + b6 Equation 5 In Equation 5, b1, b2, b3, b4, b5, and b6 are the coefficients of the fixed carbon content model obtained by multivariate fitting; C is the final fixed carbon content calculation model constructed, and C-logging is the fixed carbon data in the logging interpretation results.

10. The method for logging and evaluating coal and gas reservoirs based on oxidation-ash content-coal quantity-paleoclimate parameters according to claim 9, characterized in that, The gas content calculation model in step g is as follows: TG 标准 =TG×t / t 标准 Formula 6 Q 测井 =f(TG) 标准 Formula 7 In Equation 6, TG represents all hydrocarbons. 标准 For standardized total hydrocarbons, t represents drilling time. 标准 The standard drilling time is the average value of the region. In Equation 7, Q 测井 To interpret gas content in well logging, f(TG) 标准 A model is constructed to compare gas content with well logging measurements based on well logging interpretation.

11. The method for evaluating coal and gas reservoirs based on oxidation-ash content-coal quantity-paleoclimate parameters according to claim 10, characterized in that, The porosity calculation model in step h is as follows: In Equation 8, φ 测井 To interpret porosity from well logging, each of the above f() is a porosity relation to U constructed based on well logging porosity or core experimental porosity. 能谱 / Th 能谱 U 能谱 / GR 能谱 S 元素 Cl 元素 Sub-model, Using the sub-models as factors, a multi-factor function model for porosity calculation is constructed: In Equation 9, c1, c2, c3, c4, and c5 are the porosity model coefficients obtained from multivariate fitting.

12. The method for evaluating coal and gas reservoirs based on oxidation-ash content-coal quantity-paleoclimate parameters according to claim 11, characterized in that, In step I, substituting equations 5, 7, and 9 into equation 3 yields equation 11, which is the coal-rock gas reservoir evaluation function. Coal-rock gas reservoir evaluation function = F(B 能谱 B 元素 ,TG,t)=l×C+m×Q+n×φ Equation 11 In Equation 10, B 能谱 For all natural gamma-ray spectral logging parameters involved, B 元素 For all elements involved, the logging parameters are specified.

13. The method for evaluating coal and gas reservoirs based on oxidation-ash content-coal quantity-paleoclimate parameters according to claim 12, characterized in that, Based on the logging data of coal and rock of different reservoir qualities obtained in step b, the values ​​of l, m, and n are obtained by classification fitting, and the classification function can be obtained.

14. The method for logging and evaluating coal and gas reservoirs based on oxidation-ash content-coal quantity-paleoclimate parameters according to claim 13, characterized in that, In step j, based on the logging parameters, the coal and rock are identified by the coal and rock logging identification function established in step c, and then the coal and rock are evaluated for coal and rock gas reservoirs by the reservoir evaluation criteria set by the coal and rock gas reservoir evaluation function obtained in step i.

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