Coalbed methane reservoir classification evaluation method

Abstract

The invention relates to the technical field of coalbed methane exploration and development, in particular to a coalbed methane reservoir classification evaluation method which comprises the following steps: S1, data arrangement preparation; s2, resetting the depth of the rock core; s3, standardizing the logging curve; s4, on the basis of geophysical logging response characteristic ranges of various coals and lithological characters, coal seam recognition sensitive factors Fcs are constructed, and a coal seam is quickly recognized; s5, establishing corresponding relations between the coal seam industrial component parameters, the gas content, the Langmuir volume, the Langmuir pressure, the coal seam microscopic organic matter components, the vitrinite combination content and the logging curve; s6, establishing a coal body structure index, and carrying out coal body structure identification; and S7, establishing a comprehensive classification parameter K of the coal reservoir, and carrying out reservoir classification and evaluation on the coal bed gas. According to the method, single-well longitudinal coal seam classification evaluation is realized, and technical support is provided for subsequent layer selection and layer change; in the transverse direction, multi-well evaluation and well-seismic combination can be carried out, and large-scale efficient exploration and development of the coal bed gas layer are promoted.

Description

Technical Field

[0001] The invention relates to the technical field of coalbed methane exploration and development, and is a coalbed methane reservoir classification evaluation method. Background Art

[0002] With the advent of the global low-carbon economy, especially the current rapid development of the international and domestic economies, the increasing demand for energy, coal mine safety production and environmental protection, etc., coalbed methane, as a high-quality clean energy, is more urgently needed to be developed quickly and efficiently. Studies have shown that my country's coalbed methane is widely distributed, and the recoverable resources below 2,000 meters are equivalent to the conventional natural gas resources. The total recoverable resources are about 10 trillion cubic meters, and the exploration and development potential is huge. Coalbed methane reservoirs are often distributed in very limited parts of the basin. The prediction of the sweet spot area, the site selection of coalbed methane wells, and the selection of favorable sections of single wells are the basis for realizing the efficient development of coalbed methane. At present, various methods are relatively single, and no unified method suitable for the efficient development of medium and low-rank coalbed methane has been formed. Coalbed development well site deployment is mainly based on seismic data. Combined with the geological environment, the main selection is the coal seam with large thickness, stable distribution on the plane, relatively simple and traceable structure, good hydrogeological conditions, good Jingliu area or weak pressure zone, and relatively good gas content inspection. Perforation is mainly selected for coal seams with high gas content, active gas logging, high resistivity, high acoustic time difference and other characteristics. The disadvantage is that the coal seams are not classified vertically, resulting in poor selection of the layer sections. Therefore, an economical and practical coalbed methane reservoir classification and evaluation method is urgently needed to solve the above problems. Summary of the invention

[0003] The present invention provides a coalbed methane reservoir classification and evaluation method, which overcomes the shortcomings of the above-mentioned prior art and can effectively solve the problem that the coalbed methane evaluation method is relatively single and cannot meet the needs of medium and low-level coalbed methane development.

[0004] The technical solution of the present invention is achieved by the following measures: A coalbed methane reservoir classification evaluation method is carried out according to the following steps: S1, data sorting and preparation;

[0005] S2, core depth return;

[0006] S3, well logging curve standardization;

[0007] S4, based on the geophysical logging response characteristic range of various coals and lithologies, the coal seam identification sensitivity factor Fcs is constructed to quickly identify coal seams;

[0008] S5, in the identified coal seam area, establish the corresponding relationship between the coal seam industrial component parameters, gas content, Langmuir volume, Langmuir pressure, coal seam microscopic organic matter components, vitrinite combination content and well logging curve;

[0009] S6, based on the macroscopic coal structure analysis and corresponding logging response values, establish the coal structure index and carry out coal structure identification;

[0010] S7, based on the calculation and identification results of step S5 and step S6, select parameters related to the analyzed gas content as the main parameters for coal reservoir quality evaluation, establish a comprehensive classification parameter K of the coal reservoir, and classify and evaluate the coalbed methane reservoir according to the comprehensive classification parameter K of the coal reservoir.

[0011] The following are further optimizations and / or improvements to the above technical solutions:

[0012] In the above step S1, the data include coal seam formations of the target layer in the study area, core sampling and analytical data of the coal seams, macroscopic coal body structure and logging data. The analytical data of the coal seams include the industrial components of the coal seams (including ash, moisture, volatile matter and fixed carbon), coalbed methane content, coal seam microscopic components and the vitrinite composition content of the coal.

[0013] In the above step S2, the specific operation of the core depth return is: according to the industrial analysis component data of the coal seam, the matching relationship analysis between the industrial component and the logging data is carried out to perform the depth return of the core data.

[0014] The specific operation of the above step S3 is: taking the mud layer with stable thickness near the target layer as the marker layer, and using the histogram or trend surface method to standardize the logging curve, the logging curve includes the wellbore CAL curve, the natural gamma GR curve, the compensated acoustic wave DTC curve, the compensated density ZDEN curve, the compensated neutron CNL curve and the original formation resistivity RT curve.

[0015] In the above step S4, the calculation expression of the coal seam identification sensitivity factor Fcs is:

[0016] Fcs=k×(DTC×CNL) / ZDEN Formula 1

[0017] Where, Fcs is the coal seam identification sensitivity factor, dimensionless. When Fcs is greater than 10, it is a coal seam, otherwise it is a non-coal seam.

[0018] k is the area coefficient, dimensionless;

[0019] DTC is the standardized longitudinal wave time difference, μs / f;

[0020] CNL, normalized compensated neutron response, %;

[0021] ZDEN, normalized compensated density logging response, g / cm 3 .

[0022] The specific operations of the above step S5 are:

[0023] S51, establish the coal seam quality sensitive parameter Ls, whose expression is:

[0024] Ls=DTC / (ZDEN×GR) Formula 2

[0025] Wherein, Ls is the sensitive parameter of coal seam quality, dimensionless; DTC is the standardized P-wave time difference logging response value, ZDEN is the standardized compensated density logging response value, GR is the standardized natural gamma logging response value;

[0026] S52, based on the constructed coal seam quality sensitive parameters, establish the corresponding relationship between industrial component parameters, gas content, Langmuir volume, Langmuir pressure, vitrinite combination content and logging parameters:

[0027] Vdaf=a1×ln(Ls)+b1 Formula 3

[0028] Aad=a2×Vdaf 2 +b2×Vdaf+c1 Formula 4

[0029] Vl=a3×Ls 2 +b3×Ls+c2 Formula 5

[0030] LP=a4×RT 2 +b4×RT+c3 Formula 6

[0031] Vgas=a5×Ls+b4 Formula 7

[0032]

[0033] Vi=a7Vgas+b7 Formula 9

[0034] Wherein, Vdaf is the volatile matter content of coal seam, %; Aad is the ash content of coal seam, %; Vl is the Langmuir volume, g / cm 3 ; LP is the Langmuir pressure, MPa; Vgas is the analyzed gas content of the coal seam; Sg is the gas saturation of the coal reservoir, %; Vi is the vitrinite content, %; a1 to a7, b1 to b7, c1 to c3 are regional empirical coefficients respectively;

[0035] S53, based on the established corresponding relationship between industrial component parameters, gas content, Langmuir volume, Langmuir pressure, coal seam microscopic organic matter component content and logging parameters, the coal seam ash content, analytical gas content, vitrinite combination content and Langmuir pressure in the study area are calculated.

[0036] The specific operations of the above step S6 are:

[0037] S61, based on the macroscopic coal structure analysis and corresponding logging response values, establish the coal structure index:

[0038] GSI=CNL×Ls Formula 10

[0039] ICAL=(CAL-BIT) / BIT Formula 11

[0040] Wherein, GSI is the coal structure index, dimensionless; CNL is the compensated neutron logging response value, %; Ls is the coal seam quality sensitive parameter; ICAL is the coal seam borehole expansion rate, %; CAL is the caliper logging response value, in; BIT is the drill bit diameter, in.

[0041] S62, based on the results of macroscopic coal structure identification of coal seams, cross plots were made with GSI and ICAL as horizontal and vertical coordinates to obtain a coal structure identification map, on which the primary structure area, primary-fragmented structure area, and fragmented structure area were divided, and coal structure identification was carried out in the study area.

[0042] In the above step S7, the expression of the coal reservoir comprehensive classification parameter K is:

[0043] K=f·K Aad ·K Vgas ·K gs ·K Vi ·K LP Formula 12

[0044] In the formula, K is the comprehensive classification function, f is the regional experience coefficient, K aad To calculate the ash content, K vgas To calculate the analytical gas content, K vi To calculate the vitrine content, K LP To calculate the Langmuir pressure, K aad , K vgas , K vi and K LP All of them are calculated by the corresponding relationship formulas of industrial component parameters, gas content, Langmuir volume, Langmuir pressure, coal seam microscopic organic matter component content and logging parameters established in step S5, K gs This is the result of coal body structure judgment. During calculation, the original structure Kgs is assigned a value of 3, the original-fragmented structure Kgs is assigned a value of 2, and the fragmented structure Kgs is assigned a value of 1.

[0045] In the above step S7, the coal seams are divided into three types according to the comprehensive classification parameter K of the coal reservoir, wherein the first type of coal seams has K≥2, the second type of coal seams has 1≤K<2, and the third type of coal seams has K<1.

[0046] Aiming at the deficiencies in the existing coalbed methane evaluation technology, the present invention relies on well logging data, establishes the relationship between coal seam quality and well logging data through parameter construction and other methods, integrates key parameters such as coal seam reservoir industrial composition, gas content, microscopic organic matter, Langmuir pressure and coal body structure, and constructs a coal seam reservoir classification indicator curve to achieve coal seam classification evaluation in the vertical direction of a single well, providing technical support for subsequent layer selection and modification; in the horizontal direction, multi-well evaluation and well-seismic combination can be carried out to carry out prediction of favorable reservoir zones, improve reservoir prediction accuracy, reduce the deployment of low-yield wells and ineffective wells, and promote large-scale and efficient exploration and development of coalbed methane layers. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Attached Figure 1 This is a coal body structure discrimination diagram of the ST well area of ​​Tuha Oilfield in Example 10 of the present invention.

[0048] Attached Figure 2 This is a comprehensive classification result diagram of coal seams in the ST well area of ​​Tuha Oilfield in Example 10 of the present invention. DETAILED DESCRIPTION

[0049] The present invention is not limited by the following embodiments, and specific implementation methods can be determined based on the technical solution of the present invention and actual conditions.

[0050] The present invention will be further described below in conjunction with embodiments:

[0051] Embodiment 1: The coalbed methane reservoir classification evaluation method is carried out according to the following steps:

[0052] S1, data collection and preparation;

[0053] S2, core depth return;

[0054] S3, well logging curve standardization;

[0055] S4, based on the geophysical logging response characteristic range of various coals and lithologies, the coal seam identification sensitivity factor Fcs is constructed to quickly identify coal seams;

[0056] S5, in the identified coal seam area, establish the corresponding relationship between the coal seam industrial component parameters, gas content, Langmuir volume, Langmuir pressure, coal seam microscopic organic matter components, vitrinite combination content and well logging curve;

[0057] S6, based on the macroscopic coal structure analysis and corresponding logging response values, establish the coal structure index and carry out coal structure identification;

[0058] S7, based on the calculation and identification results of step S5 and step S6, select parameters related to the analyzed gas content as the main parameters for coal reservoir quality evaluation, establish a comprehensive classification parameter K of the coal reservoir, and classify and evaluate the coalbed methane reservoir according to the comprehensive classification parameter K of the coal reservoir.

[0059] Example 2: As an optimization of the above example, in step S1, the data include coal seam formations of the target layer in the study area, core sampling and analytical data of the coal seams, macroscopic coal body structure and logging data. The analytical data of the coal seams include the industrial components of the coal seams (including ash, moisture, volatile matter and fixed carbon), coalbed methane content, coal seam microscopic components and the vitrinite composition content of the coal.

[0060] Embodiment 3: As an optimization of the above embodiment, in step S2, the specific operation of core depth homing is: according to the industrial analysis component data of the coal seam, the matching relationship analysis between the industrial component and the logging data is carried out to perform depth homing of the core data.

[0061] Example 4: As an optimization of the above example, the specific operation of step S3 is: taking the mud layer with stable thickness near the target layer as the marker layer, and using the histogram or trend surface method to standardize the logging curve, the logging curve includes the wellbore CAL curve, the natural gamma GR curve, the compensated acoustic wave DTC curve, the compensated density ZDEN curve, the compensated neutron CNL curve and the original formation resistivity RT curve.

[0062] Embodiment 5: As an optimization of the above embodiment, in step S4, the calculation expression of the coal seam identification sensitivity factor Fcs is:

[0063] Fcs=k×(DTC×CNL) / ZDEN Formula 1

[0064] Where, Fcs is the coal seam identification sensitivity factor, dimensionless. When Fcs is greater than 10, it is a coal seam, otherwise it is a non-coal seam.

[0065] k is the area coefficient, dimensionless;

[0066] DTC is the standardized longitudinal wave time difference, μs / f;

[0067] CNL, normalized compensated neutron response, %;

[0068] ZDEN, normalized compensated density logging response, g / cm 3 .

[0069] In the present invention, the geophysical logging response characteristic ranges of various coals and lithologies based on which the coal seam identification sensitivity factor Fcs is constructed are shown in Table 1.

[0070] Embodiment 6: As an optimization of the above embodiment, the specific operation of step S5 is:

[0071] S51, establish the coal seam quality sensitive parameter Ls, whose expression is:

[0072] Ls=DTC / (ZDEN×GR) Formula 2

[0073] Wherein, Ls is the sensitive parameter of coal seam quality, dimensionless; DTC is the standardized P-wave time difference logging response value, ZDEN is the standardized compensated density logging response value, GR is the standardized natural gamma logging response value;

[0074] S52, based on the constructed coal seam quality sensitive parameters, establish the corresponding relationship between industrial component parameters, gas content, Langmuir volume, Langmuir pressure, vitrinite combination content and logging parameters:

[0075] Vdaf=a1×ln(Ls)+b1 Formula 3

[0076] Aad=a2×Vdaf 2 +b2×Vdaf+c1 Formula 4

[0077] Vl=a3×Ls 2 +b3×Ls+c2 Formula 5

[0078] LP=a4×RT 2 +b4×RT+c3 Formula 6

[0079] Vgas=a5×Ls+b4 Formula 7

[0080]

[0081] Vi=a7Vgas+b7 Formula 9

[0082] Wherein, Vdaf is the volatile matter content of coal seam, %; Aad is the ash content of coal seam, %; Vl is the Langmuir volume, g / cm 3 ; LP is the Langmuir pressure, MPa; Vgas is the analyzed gas content of the coal seam; Sg is the gas saturation of the coal reservoir, %; Vi is the vitrinite content, %; a1 to a7, b1 to b7, c1 to c3 are regional empirical coefficients respectively;

[0083] S53, based on the established corresponding relationship between industrial component parameters, gas content, Langmuir volume, Langmuir pressure, coal seam microscopic organic matter component content and logging parameters, the coal seam ash content, analytical gas content, vitrinite combination content and Langmuir pressure in the study area are calculated.

[0084] Embodiment 7: As an optimization of the above embodiment, the specific operation of step S6 is:

[0085] S61, based on the macroscopic coal structure analysis and corresponding logging response values, establish the coal structure index:

[0086] GSI=CNL×Ls Formula 10

[0087] ICAL=(CAL-BIT) / BIT Formula 11

[0088] Wherein, GSI is the coal structure index, dimensionless; CNL is the compensated neutron logging response value, %; Ls is the coal seam quality sensitive parameter; ICAL is the coal seam borehole expansion rate, %; CAL is the caliper logging response value, in; BIT is the drill bit diameter, in.

[0089] S62, based on the results of macroscopic coal structure identification of coal seams, cross plots were made with GSI and ICAL as horizontal and vertical coordinates to obtain a coal structure identification map, on which the primary structure area, primary-fragmented structure area, and fragmented structure area were divided, and coal structure identification was carried out in the study area.

[0090] Embodiment 8: As an optimization of the above embodiment, in step S7, the expression of the coal reservoir comprehensive classification parameter K is:

[0091] K=f·K Aad ·K Vgas ·K gs ·K Vi ·K LP Formula 12

[0092] In the formula, K is the comprehensive classification function, f is the regional experience coefficient, K aad To calculate the ash content, K vgas To calculate the analytical gas content, K vi To calculate the vitrine content, K LP To calculate the Langmuir pressure, K aad , K vgas , K vi and K LP All of them are calculated by the corresponding relationship formulas of industrial component parameters, gas content, Langmuir volume, Langmuir pressure, coal seam microscopic organic matter component content and logging parameters established in step S5, K gs This is the result of coal body structure judgment. During calculation, the original structure Kgs is assigned a value of 3, the original-fragmented structure Kgs is assigned a value of 2, and the fragmented structure Kgs is assigned a value of 1.

[0093] Embodiment 9: As an optimization of the above embodiment, in step S7, the coal seams are divided into three types according to the comprehensive classification parameter K of the coal reservoir, wherein the first type of coal seams has K≥2, the second type of coal seams has 1≤K<2, and the third type of coal seams has K<1. Specifically, the corresponding ash content, gas content, coal body structure, vitrinite content and gas saturation index ranges of the three types of coal seams according to the National Standard of the People's Republic of China for Coal Ash Classification (GB / T15224.1-94) are shown in Table 2.

[0094] Example 10: The specific implementation process of the coalbed methane reservoir classification evaluation method in the ST well area of ​​Tuha Oilfield is as follows:

[0095] S1, data collation and preparation. The data include the target coal seam formation, the core sampling and analytical data of the coal seam, the macroscopic coal body structure, and the logging data. The analytical data include the industrial components of the coal seam (ash, moisture, volatile matter and fixed carbon), the seam gas content, the microscopic components of the coal seam, and the vitrinite content of the coal. The experimental analysis data statistics of the ST well area of ​​Tuha Oilfield are shown in Table 3.

[0096] S2, core depth return, according to the industrial analysis component data of the coal seam, carry out the matching relationship analysis between the industrial component and the logging data, make the core depth consistent with the logging depth, and eliminate the depth error between the two.

[0097] S3, standardization of logging curves, select the mud layer with stable thickness at the top of the coal seam as the marker layer, and use the histogram method to standardize the logging curves, including wellbore CAL, natural gamma GR, compensated acoustic wave DTC, compensated density ZDEN, compensated neutron CNL and deep detection resistivity RT;

[0098] S4, coal seam identification, using logging and coring data analysis to obtain the characteristic range of logging response values ​​of common lithologies (Table 4), and construct the coal seam identification sensitivity factor Fcs to quickly identify coal seams;

[0099] Fcs=k×(DTC×CNL) / ZDEN Formula 1

[0100] Fcs is the coal seam identification sensitivity factor, dimensionless, DTC, the normalized P-wave time difference, μs / f, CNL, the normalized compensated neutron response value, %, ZDEN, the normalized compensated density logging response value, g / cm 3 In this embodiment, based on the comparative analysis of the logging parameters of the coal seam and the surrounding rock, the K value is 1. The area with an Fcs value greater than 10 is a coal seam, otherwise it is a non-coal seam. The following steps S5 to S7 further study the area determined to be a coal seam by the Fcs value.

[0101] S5. In the identified coal seam area, the corresponding relationship between the coal seam industrial component parameters, gas content, Langmuir volume, Langmuir pressure, coal seam microscopic organic matter components, vitrinite combination content and logging curve is established.

[0102] S51, establish the coal seam quality sensitive parameter Ls, whose expression is:

[0103] Ls=DTC / (ZDEN×GR) Formula 2

[0104] Wherein, Ls is the sensitive parameter of coal seam quality, dimensionless; DTC is the standardized P-wave time difference logging response value, ZDEN is the standardized compensated density logging response value, and GR is the standardized natural gamma logging response value.

[0105] S52, based on the constructed coal seam quality sensitive parameters, establish the corresponding relationship between industrial component parameters, gas content, Langmuir volume, Langmuir pressure, vitrinite content and logging parameters. In this embodiment, based on the industrial component parameters, gas content, Langmuir volume, Langmuir pressure, coal seam microscopic organic component content and logging parameter values ​​of the ST well area of ​​Tuha Oilfield statistically analyzed in step S1, the expression of the series of corresponding relationship equations obtained by fitting is:

[0106] Vdaf=-9.032×ln(Ls)+24.427

[0107] Aad=0.0629×Vdaf 2 -1.2973×Vdaf+16.035

[0108] VL=-2.0175×Ls 2 +9.5066×Ls+6.7268

[0109] PL=-0.0000006×Rt 2 +0.0021RT+0.8796

[0110] Vgas=5.1706×Ls+5.4265

[0111] Sg=0.143×Vgas 0.7953

[0112] Vi=-1.5366×Vdaf+105.3

[0113] Wherein, Vdaf is the volatile matter content of coal seam, %; Aad is the ash content of coal seam, %; Vl is the Langmuir volume, g / cm 3 ; LP is the Langmuir pressure, MPa; Vgas is the analyzed gas content of the coal seam; Sg is the gas saturation of the coal reservoir, %; Vi is the vitrinite content of the coal seam, %.

[0114] S53, according to the above expressions, the ash content, analytical gas content, vitrinite content and Langmuir pressure of the coal seams in the study area are calculated.

[0115] S6, based on the macroscopic coal structure analysis and corresponding logging response values, establish the coal structure index and carry out coal structure identification;

[0116] S61, based on the macroscopic coal structure analysis and corresponding logging response values, establish the coal structure index:

[0117] GSI=CNL×Ls Formula 9

[0118] ICAL=(CAL-BIT) / BIT Formula 10

[0119] Where GSI is the coal structure index, dimensionless, CNL is the compensated neutron logging response value, %, Ls is the coal seam quality sensitive parameter, ICAL is the coal seam wellbore expansion rate, %, CAL is the caliper logging response value, in, and BIT is the drill bit diameter, in.

[0120] S62, based on the results of macroscopic coal body structure identification of coal seams, cross plots were made with GSI and ICAL as horizontal and vertical coordinates to obtain coal body structure identification charts, on which the original structure area, original-fragmented structure area, and fragmented structure area were divided, and coal body structure identification was carried out in the study area. Coal body structure identification charts for ST well area of ​​Tuha Oilfield are shown in Figure 1 .

[0121] S7, based on the calculation and identification results of step S5 and step S6, select the parameters related to the analytical gas content as the main parameters for coal reservoir quality evaluation, construct the coal reservoir comprehensive classification parameter K according to formula 12, and classify and evaluate the coalbed methane reservoir according to the coal reservoir comprehensive classification parameter K. Figure 2 This is the coal reservoir classification result map of ST-1 well in ST well area. Figure 2 It can be seen that the first channel is the depth channel, the second to fourth channels are logging curve channels, the fifth to tenth channels are parameter calculation channels, which are calculated based on S52 respectively, the eleventh channel is the coal seam identification conclusion channel, which is divided by the LS indicator curve. Ls greater than 10 is a coal seam, and less than 10 is a non-coal seam. The twelfth channel is determined by the calculation parameters and comprehensive classification parameters to determine the reservoir classification. A full grid is a Class I reservoir, corresponding to a K value greater than 2, a half grid is a Class II reservoir, corresponding to a K value between 1 and 2, and a third of a grid is a Class III reservoir, corresponding to a K value less than 1.

[0122] In summary, the present invention provides a coalbed methane reservoir classification and evaluation method, which comprehensively considers key coalbed evaluation indicators such as coal seam ash content, gas content, vitrinite combination content, Langmuir pressure and coal body structure, and establishes the relationship between coal seam quality and logging data through parameter construction and other methods. The method of use is simple and efficient, avoiding the multi-solution caused by a single factor. At the same time, the comprehensive parameters and the single evaluation index correspond to each other, which improves the reliability of the comprehensive classification of the reservoir and maximizes the coalbed methane development efficiency and single well production capacity; at the same time, the classification result of the present invention is directly reflected in the form of a curve, which can be directly related to the seismic data, to carry out favorable reservoir zone prediction, improve prediction accuracy, reduce the deployment of low-yield wells and invalid wells, and provide technical support for large-scale exploration, development and evaluation of coalbed reservoirs.

[0123] The above technical features constitute the embodiments of the present invention, which have strong adaptability and implementation effect. Non-essential technical features can be added or reduced according to actual needs to meet the requirements of different situations.

[0124] Table 1

[0125]

[0126]

[0127] Table 2

[0128] Classification basis Class I coal seam Class II coal seam Three types of coal seams Ash <20％ 20% < Ash content < 30% >30％ Gas content >10 5<Vgas<10 Vgas<5 Coal body structure Native structure Primary-fragmented structure Fracture structure Vitrinite content >60％ 30% < vitrinite < 60% Vitrinite <30% Gas Saturation >80％ 50%<Sg<80% Sg<50% K Value ≥2 1≤K<2 <1

[0129] Table 3

[0130]

[0131] Table 4

[0132] Lithology density neutron Sound Wave Natural Gamma Deep Lateral Limestone 2.45-2.86 1.7-49.8 46.2-70.3 34.8-122.0 28.7-2045 Coal seam 1.28-1.84 41.7-63 49-123.8 42.1-291.2 23.2-2096.2 sandstone 2.46-2.81 5.2-38.7 53.6-73.6 43.3-109.8 10.9-331.4 Mudstone 2.26-2.82 12-43.6 60.8-86.7 93.6-179.5 16.8-236.7

Claims

1. A coalbed methane reservoir classification and evaluation method, characterized in that Follow these steps: S1, data collection and preparation; S2, core depth homing; S3, well logging curve standardization; S4, based on the geophysical logging response characteristic range of various coals and lithologies, the coal seam identification sensitivity factor Fcs is constructed to quickly identify coal seams; S5, in the identified coal seam area, establish the corresponding relationship between the coal seam industrial component parameters, gas content, Langmuir volume, Langmuir pressure, coal seam microscopic organic matter components, vitrinite combination content and well logging curve; S6, based on the macroscopic coal structure analysis and corresponding logging response values, establish the coal structure index and carry out coal structure identification; S7, based on the calculation and identification results of step S5 and step S6, select parameters related to the analyzed gas content as the main parameters for coal reservoir quality evaluation, establish a comprehensive classification parameter K of the coal reservoir, and classify and evaluate the coalbed methane reservoir according to the comprehensive classification parameter K of the coal reservoir.

2. The coalbed methane reservoir classification and evaluation method according to claim 1, characterized in that In step S1, the data include coal seam formations of the target layer in the study area, core sampling and analytical data of the coal seams, macroscopic coal body structure and logging data. The analytical data of the coal seams include industrial components of the coal seams, coalbed methane content, microscopic components of the coal seams and vitrinite content of the coal.

3. The coalbed methane reservoir classification and evaluation method according to claim 1 or 2, characterized in that In step S2, the specific operation of core depth retrieval is: according to the industrial analysis component data of the coal seam, the matching relationship analysis between the industrial component and the logging data is carried out to perform the depth retrieval of the core data.

4. The coalbed methane reservoir classification and evaluation method according to any one of claims 1 to 3, characterized in that The specific operation of step S3 is: taking the mud layer with stable thickness near the target layer as the marker layer, and using the histogram or trend surface method to standardize the logging curve, the logging curve includes the wellbore CAL curve, the natural gamma GR curve, the compensated acoustic wave DTC curve, the compensated density ZDEN curve, the compensated neutron CNL curve and the original formation resistivity RT curve.

5. The coalbed methane reservoir classification and evaluation method according to any one of claims 1 to 4, characterized in that In step S4, the calculation expression of the coal seam identification sensitivity factor Fcs is: Fcs=k×(DTC×CNL) / ZDEN Formula 1 Where, Fcs is the coal seam identification sensitivity factor, dimensionless. When Fcs is greater than 10, it is a coal seam, otherwise it is a non-coal seam. k is the area coefficient, dimensionless; DTC is the standardized longitudinal wave time difference, μs / f; CNL, normalized compensated neutron response, %; ZDEN, normalized compensated density logging response, g / cm 3 .

6. The coalbed methane reservoir classification and evaluation method according to any one of claims 1 to 5, characterized in that The specific operations of step S5 are: S51, establish the coal seam quality sensitive parameter Ls, whose expression is: Ls=DTC / (ZDEN×GR) Formula 2 Wherein, Ls is the sensitive parameter of coal seam quality, dimensionless; DTC is the standardized P-wave time difference logging response value, ZDEN is the standardized compensated density logging response value, GR is the standardized natural gamma logging response value; S52, based on the constructed coal seam quality sensitive parameters, establish the corresponding relationship between industrial component parameters, gas content, Langmuir volume, Langmuir pressure, vitrinite combination content and logging parameters: Vdaf=a1×ln(Ls)+b1 Formula 3 Aad = a2 × Vdaf 2 + b2 × Vdaf + c1 Equation 4 Vl=a3×Ls 2 +b3×Ls+c2 Formula 5 LP=a4×RT 2 +b4×RT+c3 Formula 6 Vgas=a5×Ls+b4 Formula 7 Vi=a7Vgas+b7 Formula 9 Wherein, Vdaf is the volatile matter content of coal seam, %; Aad is the ash content of coal seam, %; Vl is the Langmuir volume, g / cm 3 ; LP is the Langmuir pressure, MPa; Vgas is the analyzed gas content of the coal seam; Sg is the gas saturation of the coal reservoir, %; Vi is the vitrine content, %; a1 to a7, b1 to b7, c1 to c3 are regional empirical coefficients respectively; S53, based on the established corresponding relationship between industrial component parameters, gas content, Langmuir volume, Langmuir pressure, coal seam microscopic organic matter component content and logging parameters, the coal seam ash content, analytical gas content, vitrinite combination content and Langmuir pressure in the study area are calculated.

7. The coalbed methane reservoir classification and evaluation method according to any one of claims 1 to 6, characterized in that The specific operations of step S6 are: S61, based on the macroscopic coal structure analysis and corresponding logging response values, establish the coal structure index: GSI=CNL×Ls Formula 10 ICAL=(CAL-BIT) / BIT Formula 11 Wherein, GSI is the coal structure index, dimensionless; CNL is the compensated neutron logging response value, %; Ls is the coal seam quality sensitive parameter; ICAL is the coal seam borehole expansion rate, %; CAL is the caliper logging response value, in; BIT is the drill bit diameter, in. S62, based on the results of macroscopic coal structure identification of coal seams, cross plots were made with GSI and ICAL as horizontal and vertical coordinates to obtain a coal structure identification map, on which the primary structure area, primary-fragmented structure area, and fragmented structure area were divided, and coal structure identification was carried out in the study area.

8. The coalbed methane reservoir classification and evaluation method according to any one of claims 1 to 7, characterized in that In step S7, the expression of the coal reservoir comprehensive classification parameter K is: K=f·K Aad ·K Vgas ·K gs ·K Vi ·K LP Formula 12 In the formula, K is the comprehensive classification function, f is the regional experience coefficient, K aad To calculate the ash content, K vgas To calculate the analytical gas content, K vi To calculate the vitrine content, K LP To calculate the Langmuir pressure, K aad , K vgas , K vi and K LP They are all calculated by the corresponding relationship formula of industrial component parameters, gas content, Langmuir volume, Langmuir pressure, coal seam microscopic organic matter component content and logging parameters established in step S5. Kgs is the result of coal body structure judgment. During the calculation, the original structure Kgs is assigned a value of 3, the original-fragmented structure Kgs is assigned a value of 2, and the fragmented structure Kgs is assigned a value of 1.

9. The coalbed methane reservoir classification and evaluation method according to any one of claims 1 to 8, characterized in that In step S7, the coal seams are divided into three types according to the comprehensive classification parameter K of the coal reservoir, wherein the first type of coal seams has K≥2, the second type of coal seams has 1≤K<2, and the third type of coal seams has K<1.

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