Quantitative evaluation method and device for carbonate gas saturation degree based on gamma spectrum and element yield

Abstract

The application discloses a carbonate rock gas saturation quantitative evaluation method and device based on gamma spectrum and element yield, and relates to the technical field of mine geophysical logging. The application uses element yield information to construct a formation lithology correction factor, uses the formation lithology correction factor to correct the influence of formation lithology on the non-elastic capture gamma count ratio evaluation of gas saturation, and realizes the conversion of the non-elastic capture gamma count ratio of the carbonate rock reservoir and the limestone reservoir under the same pore condition. Based on a pure limestone reservoir gas saturation interpretation model, the application forms a carbonate rock reservoir gas saturation quantitative evaluation method, and provides an effective means for the carbonate rock reservoir gas evaluation.

Description

Technical Field

[0001] This invention relates to the field of geophysical logging technology in mines, and more specifically to a method and apparatus for quantitatively evaluating carbonate gas saturation based on gamma spectroscopy and elemental yield. Background Technology

[0002] Natural gas resources play a vital role in improving my country's energy structure and promoting a green energy transition. With the rapid development of the natural gas industry, carbonate reservoir natural gas resources have become an important component of my country's natural gas reserves. Intensifying the exploration and development of carbonate gas reservoirs is crucial for ensuring my country's energy security and optimizing its energy structure. However, domestic carbonate oil and gas reservoirs are characterized by deep burial, strong heterogeneity, complex mineral composition, and diverse pore structures. Most carbonate reservoirs exhibit the low porosity and low permeability characteristics of unconventional oil and gas reservoirs, resulting in weak logging response characteristics that reflect the pore fluid properties of the reservoirs. This poses a significant challenge to using logging technology for identifying the fluid properties and evaluating the saturation of carbonate reservoirs.

[0003] Currently, in the evaluation of gas-bearing reservoirs through logging, methods such as acoustic wave, resistivity, nuclear magnetic resonance, and neutron density are mainly used to identify and evaluate the gas-bearing capacity of the formation based on the response characteristics of different logging methods in gas-bearing formations.

[0004] In acoustic logging, parameters sensitive to gas formations, such as the time difference between P-waves and S-waves, are primarily used to determine the porosity and gas-bearing properties of the formation. In resistivity logging, due to factors such as the conductivity of bound water in tight gas formations, reservoir conductivity models are improved to reflect the complex fluid properties of the formation, thereby obtaining information on gas-bearing capacity. In nuclear magnetic resonance (NMR) logging, influenced by the complex pore structure and permeability of tight gas formations, NMR experiments are often combined to analyze the differences in NMR spectra under different pore structures, improving the accuracy of logging data interpretation.

[0005] There are two main methods for evaluating the gas-bearing capacity of tight reservoirs using nuclear magnetic resonance (NMR) logging: one is a comprehensive evaluation method based on conventional neutron-density logging combined with other logging information; the other is a gas-bearing evaluation method based on pulsed neutron logging technology. For comprehensive evaluation using multiple logging information, neutron porosity, resistivity, and density logging are often combined to amplify the response characteristics of different logging curves in gas-bearing layers, and qualitatively evaluate the gas-bearing layers based on characteristic parameters. Guo Zhenhua used a combination of neutron, resistivity, and density logging to amplify the response characteristics of different logging curves in gas-bearing layers, and established a qualitative evaluation chart of gas-bearing layers based on characteristic parameters. Soleyman et al. used different porosity and cementation coefficient response formulas to establish a cementation coefficient prediction model under tight gas conditions based on the conductivity of rock samples, and evaluated reservoir saturation based on Archie's formula. Yang Kebing et al. used the curve overlay method of array acoustic logging, including P-wave and S-wave transit time and wave velocity ratio, to qualitatively evaluate the gas-bearing capacity of tight reservoirs, and calculated gas saturation using bulk modulus and P-wave and S-wave velocity ratio methods. Al-Yaarubi et al. used NMR logging data to obtain bound water saturation and movable water saturation, and combined NMR with dielectric constant to interpret gas content in tight and complex formations.

[0006] Formation element logging technology can quantitatively evaluate the content of formation elements and minerals by recording the secondary gamma spectrum information generated by the reaction of neutrons with the formation medium. It has significant advantages in fine lithology identification, skeleton parameter determination and source rock evaluation, and plays an important role in the exploration and development of complex lithology and unconventional oil and gas reservoirs.

[0007] Since the introduction of elemental scanning logging (ESL) technology by Schlumberger, extensive field data collection and evaluation studies have been conducted both domestically and internationally in various reservoir types. Especially in the accurate calculation of mineral components and quantitative evaluation of physical properties in complex lithological oil and gas reservoirs such as shale (oil) and gas, volcanic rocks, and mixed layers of marine-continental sandstone, mudstone, and carbonate rocks, it has significant advantages over conventional logging. Wei Guo et al. used ESL data to conduct quantitative TOC evaluation and qualitative classification of sedimentary microfacies in complex lacustrine carbonate formations in the Qinghai Oilfield; Yan Xuehong et al., based on ESL data, conducted quantitative evaluation studies on shale and mudstone oil and gas reservoirs and igneous reservoirs in the Daqing Oilfield from the perspectives of mineral composition and reservoir quantitative calculation; Wu Yuyu et al. achieved good application results using ESL data in the lithology and reservoir quantitative evaluation of Permian volcanic clastic rocks in the Sichuan Basin. These methods and studies are mainly used for quantitative calculation of lithological components, quantitative evaluation of reservoir physical properties, and quantitative calculation of source rock TOC, which are also the main application directions of ESL. In fact, by deeply mining the information collected by lithology scanning logging, the measured chlorine element and thermal neutron capture cross section are related to the reservoir fluid. Therefore, lithology scanning logging can also be used to determine the properties of reservoir fluid.

[0008] For example, the invention patent application CN109521487A, published on March 26, 2019, entitled "A Method for Identifying Gas-Bearing Layers Using Elemental Gamma Spectrometry Logging," describes a method that uses a pulsed neutron source and a gamma detector. It determines the formation gas saturation based on the ratio of inelastic gamma counts to captured gamma counts recorded by the gamma detector, thus evaluating the formation's gas content. This invention proposes a method for identifying gas-bearing layers using elemental gamma spectroscopy logging, determining formation gas saturation based on the ratio of inelastic to captured gamma counts recorded by the gamma detector. This provides a solution for gas layer identification and quantitative evaluation, while also expanding the application of elemental gamma spectroscopy logging technology.

[0009] The method of identifying gas-bearing layers using elemental gamma spectroscopy logging is greatly affected by the formation mineral composition in practical applications, which affects the accuracy of quantitative evaluation of gas saturation. Summary of the Invention

[0010] To overcome the defects and shortcomings of the existing technologies, this invention provides a method and apparatus for quantitatively evaluating the gas saturation of carbonate reservoirs based on gamma-ray spectroscopy and elemental yield. The purpose of this invention is to address the problem that existing methods for quantitatively evaluating gas saturation using elemental gamma-ray spectroscopy logging are significantly affected by formation lithology. This invention proposes a method for quantitatively evaluating the gas saturation of carbonate reservoirs by combining elemental yield and inelastic captured gamma-ray count ratio. A formation lithology correction factor is constructed using elemental yield information to correct the influence of formation lithology on the evaluation of gas saturation using the inelastic captured gamma-ray count ratio, realizing the conversion of the inelastic captured gamma-ray count ratio between carbonate reservoirs and limestone reservoirs under the same pore conditions. Based on a gas saturation interpretation model for pure limestone reservoirs, a quantitative evaluation method for carbonate reservoir gas saturation is formed, providing an effective means for evaluating the gas content of carbonate reservoirs.

[0011] To address the problems existing in the prior art, the present invention is achieved through the following technical solution.

[0012] The first aspect of this invention provides a method for quantitatively evaluating the gas saturation of carbonates based on gamma spectroscopy and elemental yield, the evaluation method comprising the following steps:

[0013] S1. Simulate the non-elastic gamma spectrum and captured gamma spectrum of the target strata under different mineral compositions, porosity and gas saturation conditions;

[0014] S2. For the non-elastic gamma spectrum and captured gamma spectrum of the target formation under different mineral composition, porosity and gas saturation conditions obtained by simulation in step S1, use energy spectrum analysis technology to obtain the element yield information of the corresponding characteristic elements in the non-elastic gamma spectrum and captured gamma spectrum.

[0015] S3. Based on the mineral composition, porosity and gas saturation conditions of the target strata obtained in step S2, the lithological correction factor calculation model based on the main minerals of the target strata is established using multivariate nonlinear regression.

[0016] S4. The non-elastic gamma spectrum and captured gamma spectrum under different porosity and gas saturation conditions in the main mineral strata of the simulated pure target strata;

[0017] S5. Using the non-elastic gamma energy spectrum and captured gamma energy spectrum of the main mineral strata of the pure target strata obtained by step S4 simulation under different porosity and gas saturation conditions, the relationship between the non-elastic captured gamma count ratio and porosity and gas saturation is obtained, and a gas saturation calculation model is established.

[0018] S6. Obtain the inelastic gamma spectrum and captured gamma spectrum of the target formation. Based on the inelastic gamma spectrum and captured gamma spectrum of the target formation, and the lithology correction factor calculation model established in step S3, calculate the lithology correction factor of the target formation; calculate the inelastic captured gamma count ratio of the target formation; and combine the formation porosity parameters, lithology correction factor, inelastic captured gamma count ratio, and gas saturation calculation model established in step S5 to quantitatively evaluate the gas saturation of the target formation.

[0019] In a further preferred embodiment, in step S1, the FLUKA (FLUktuierende KAskade) multi-functional Monte Carlo numerical simulation program is used to construct an instrument-formation numerical calculation model based on elemental logging instruments. By adjusting the elemental composition and relative content of formation materials, the mineral content, porosity, and pore fluid type of the formation are modified. On this basis, the FLUKA multi-functional Monte Carlo numerical simulation program is used to simulate the non-elastic gamma spectrum and captured gamma spectrum of the target formation under different formation mineral compositions, porosity, and gas saturation conditions.

[0020] In a further preferred embodiment, in step S4, the non-elastic gamma spectrum and captured gamma spectrum under different porosity and gas saturation conditions in the main mineral strata of the pure target strata are simulated using the FLUKA (FLUktuierende KAskade) multifunctional Monte Carlo numerical simulation program.

[0021] More preferably, the main mineral of the target stratum is limestone.

[0022] Furthermore, in step S3, the lithology correction factor calculation model established based on limestone is as follows:

[0023] In the formula, ξ is the lithology correction factor; R carbThe non-elastic capture gamma count ratio of carbonate formations under the same porosity and gas saturation conditions; R lime The non-elastic captured gamma count ratio of pure limestone strata under the same porosity and gas saturation conditions; n1 is the number of element types in carbonate rock strata; n2 is the number of element types in pure limestone strata; y i y represents the elemental yield of the i-th element in carbonate rock formations; j a is the elemental yield of the j-th element in a pure limestone stratum; i For element yield y i The regression coefficient; b j For element yield y j The regression coefficients.

[0024] Furthermore, the regression coefficients of each element's yield in the lithology correction factor calculation model are obtained by using the elemental yields of carbonate rock formations and pure limestone formations under various known conditions, and applying a multivariate nonlinear regression method according to the lithology correction factor calculation model.

[0025] Specifically, for each carbonate stratum with known conditions, the inelastic capture gamma count ratio and elemental yield information can be obtained. Based on this, a set of characteristic ratios formed by the inelastic capture count ratios under different carbonate stratum conditions and the inelastic capture count ratios under the corresponding porosity of pure limestone strata are obtained. Simultaneously, a set of corresponding ratios of elemental yield combinations under different carbonate stratum conditions and the elemental yield combinations under the corresponding porosity of pure limestone strata are obtained, including the 'a' value in the lithology correction factor calculation model. i b j Regression coefficients; Based on the two sets of data above, the regression coefficients α are obtained by fitting parameters using a multivariate nonlinear regression method. i b j The value.

[0026] Further preferred, in step S5, the established gas saturation calculation model is as follows:

[0027]

[0028] In the formula, S g R represents gas saturation. log The non-ballistic capture gamma count ratio of the target formation; φ is the formation porosity of the target formation; A w B w and t w A represents the fitting coefficient of the cross-plot between the inelastic captured gamma count ratio and porosity in pure limestone formations with a gas saturation of 0%. g B g and t g, which is the fitting coefficient of the cross-plot of non-elastic captured gamma count ratio and porosity in pure limestone strata with a gas saturation of 100%.

[0029] In a further preferred embodiment, in step S2, the formation element yield is obtained by using the least squares spectral analysis method to obtain the non-elastic gamma spectrum and the captured gamma spectrum under different formation mineral composition, porosity and gas saturation conditions of the target formation simulated in step S1.

[0030] In a further preferred embodiment, in step S6, the inelastic gamma spectrum and the captured gamma spectrum of the target formation are obtained by measuring a formation element logging device, which includes a pulsed neutron source and a gamma detector.

[0031] More preferably, the distance between the gamma detector and the pulsed neutron source is 45-55 cm.

[0032] More preferably, the gamma detector is a LaBr3 crystal detector.

[0033] More preferably, the LaBr3 crystal detector records the total gamma spectrum in the 0-80 μs pulse emission period and the captured gamma spectrum in the 100-420 μs pulse emission period.

[0034] More preferably, the pulsed neutron source is a DT controllable neutron source with a pulse period of 420 μs. Within one pulse period, fast neutrons are emitted from 0 to 80 μs and fast neutron emission stops from 100 to 420 μs.

[0035] A second aspect of the present invention provides a device for quantitatively evaluating the gas saturation of carbonates based on gamma spectroscopy and elemental yield, the device comprising:

[0036] The first module is used to simulate the non-elastic gamma spectrum and captured gamma spectrum under different mineral composition, porosity and gas saturation conditions of the target strata, as well as to simulate the non-elastic gamma spectrum and captured gamma spectrum under different porosity and gas saturation conditions in the main mineral strata of the pure target strata.

[0037] The second module is used to obtain the element yield information of corresponding characteristic elements in the non-elastic gamma spectrum and captured gamma spectrum from the non-elastic gamma spectrum and captured gamma spectrum under different mineral composition, porosity and gas saturation conditions of the target strata simulated by the first module, using energy spectrum analysis technology.

[0038] The third module is used to establish a lithological correction factor calculation model based on the main minerals of the target strata under different strata mineral composition, porosity and gas saturation conditions output by the second module.

[0039] The fourth module is used to obtain the relationship between the inelastic gamma energy spectrum and the captured gamma energy spectrum under different porosity and gas saturation conditions in the main mineral strata of the pure target strata simulated in the first module, to obtain the relationship between the inelastic captured gamma count ratio and porosity and gas saturation, and to establish a gas saturation calculation model.

[0040] The fifth module is used to obtain the inelastic gamma spectrum and the captured gamma spectrum of the target formation; based on the inelastic gamma spectrum and the captured gamma spectrum of the target formation and the lithology correction factor calculation model output by the third module, it calculates the lithology correction factor of the target formation; it is used to calculate the inelastic captured gamma count ratio of the target formation; and it is used to quantitatively evaluate the gas saturation of the target formation by combining the formation porosity parameters, lithology correction factor, inelastic captured gamma count ratio, and the gas saturation calculation model established by the fourth module.

[0041] Further preferably, in the first module, the FLUKA (FLUktuierende KAskade) multi-functional Monte Carlo numerical simulation program is used to simulate the non-elastic gamma spectrum and captured gamma spectrum of the target stratum under different mineral compositions, porosity and gas saturation conditions; the FLUKA multi-functional Monte Carlo numerical simulation program is used to simulate the non-elastic gamma spectrum and captured gamma spectrum of the main mineral strata of the pure target stratum under different porosity and gas saturation conditions.

[0042] Furthermore, in the first module, the FLUKA (FLUktuierende KAskade) multi-functional Monte Carlo numerical simulation program is used to construct an instrument-formation numerical calculation model based on elemental logging instruments. By adjusting the elemental composition and relative content of formation materials, the mineral content, porosity, and pore fluid type of the formation are modified. On this basis, the non-elastic gamma spectrum and captured gamma spectrum of the target formation under different formation mineral compositions, porosity, and gas saturation conditions are simulated, as well as the non-elastic gamma spectrum and captured gamma spectrum of the main mineral strata of the pure target formation under different porosity and gas saturation conditions are simulated.

[0043] More preferably, the main mineral of the target stratum is limestone.

[0044] Furthermore, in the third module, the lithological correction factor calculation model based on limestone is established as follows:

[0045] In the formula, ξ is the lithology correction factor; R carb The non-elastic capture gamma count ratio of carbonate formations under the same porosity and gas saturation conditions; R limeThe non-elastic captured gamma count ratio of pure limestone strata under the same porosity and gas saturation conditions; n1 is the number of element types in carbonate rock strata; n2 is the number of element types in pure limestone strata; y i y represents the elemental yield of the i-th element in carbonate rock formations; j a is the elemental yield of the j-th element in a pure limestone stratum; i For element yield y i The regression coefficient; b j For element yield y j The regression coefficients.

[0046] Furthermore, the regression coefficients of each element's yield in the lithology correction factor calculation model are obtained by using the elemental yields of carbonate rock formations and pure limestone formations under various known conditions, and applying a multivariate nonlinear regression method according to the lithology correction factor calculation model.

[0047] Specifically, for each carbonate stratum with known conditions, the inelastic capture gamma count ratio and elemental yield information can be obtained. Based on this, a set of characteristic ratios formed by the inelastic capture count ratios under different carbonate stratum conditions and the inelastic capture count ratios under the corresponding porosity of pure limestone strata are obtained. Simultaneously, a set of corresponding ratios of elemental yield combinations under different carbonate stratum conditions and the elemental yield combinations under the corresponding porosity of pure limestone strata are obtained, including the 'a' value in the lithology correction factor calculation model. i b j Regression coefficients; Based on the two sets of data above, the regression coefficients α are obtained by fitting parameters using a multivariate nonlinear regression method. i b j The value.

[0048] Furthermore, in the fourth module, the established gas saturation calculation model is as follows:

[0049]

[0050] In the formula, S g R represents gas saturation. log The non-ballistic capture gamma count ratio of the target formation; φ is the formation porosity of the target formation; A w B w and t w A represents the fitting coefficient of the cross-plot between the inelastic captured gamma count ratio and porosity in pure limestone formations with a gas saturation of 0%. g B g and t g , which is the fitting coefficient of the cross-plot of non-elastic captured gamma count ratio and porosity in pure limestone strata with a gas saturation of 100%.

[0051] More preferably, in the second module, the formation element yield is obtained by using the least squares spectral analysis method to obtain the non-elastic gamma spectrum and the captured gamma spectrum under different formation mineral composition, porosity and gas saturation conditions of the target formation simulated by the first module.

[0052] Further preferably, the inelastic gamma spectrum and captured gamma spectrum of the target formation obtained by the fifth module are measured by a formation element logging measurement device, which includes a pulsed neutron source and a gamma detector.

[0053] More preferably, the distance between the gamma detector and the pulsed neutron source is 45-55 cm.

[0054] More preferably, the gamma detector is a LaBr3 crystal detector.

[0055] More preferably, the LaBr3 crystal detector records the total gamma spectrum in the 0-80 μs pulse emission period and the captured gamma spectrum in the 100-420 μs pulse emission period.

[0056] More preferably, the pulsed neutron source is a DT controllable neutron source with a pulse period of 420 μs. Within one pulse period, fast neutrons are emitted from 0 to 80 μs and fast neutron emission stops from 100 to 420 μs.

[0057] A third aspect of the present invention provides a computer device including a processor, an input device, an output device, and a memory, wherein the processor, the input device, the output device, and the memory are interconnected, wherein the memory is used to store a computer program, the computer program including program instructions, and the processor is configured to invoke the program instructions to perform some or all of the steps as described in the first aspect of the present invention.

[0058] A fourth aspect of the present invention provides a computer-readable storage medium storing a computer program for electronic data interchange, wherein the computer program causes a computer to perform some or all of the steps described in the first aspect of the present invention.

[0059] Compared with the prior art, the beneficial technical effects of the present invention are as follows:

[0060] 1. This invention, based on the theory of neutron secondary gamma ray distribution, utilizes formation element logging technology and combines lithology factors to quantitatively calculate the gas saturation of carbonate reservoirs, providing technical support for monitoring the gas saturation of complex lithological carbonate reservoirs. While the ratio of inelastic to captured gamma counts measured by a single detector in element logging instruments can reflect formation gas content well, it is significantly affected by formation mineral composition. To address the influence of formation lithology, formation element yields are obtained through gamma spectral analysis to establish formation lithology factors. The ratio of the carbonate reservoir gamma count ratio to the formation lithology factor represents the gamma count ratio under pure limestone reservoir conditions. Combined with a gas saturation interpretation model under pure limestone reservoir conditions, quantitative calculation of the gas saturation of carbonate reservoirs with mixed mineral compositions can be achieved.

[0061] 2. This invention, based on the elemental yields of stratigraphy and using limestone strata as a benchmark, establishes a lithology correction factor to eliminate the influence of stratigraphic mineral composition on the inelastic captured gamma count ratio. The gamma count ratio of carbonate strata without lithology correction is larger than that of limestone strata under corresponding porosity and gas saturation conditions. After correction, the gamma count ratio matches well with that of limestone strata. The absolute error of the gas saturation calculated using the inelastic captured gamma count ratio corrected by the lithology factor is controlled within 13.5%, verifying the effectiveness of the method. Attached Figure Description

[0062] Figure 1 This is a schematic diagram of the planar structure of the formation element logging measurement device used in this invention.

[0063] Figure 2 The non-elastic capture gamma count ratio response law is shown for limestone and dolomite strata under different gas saturation (0%-100%) and different porosity (0%-25%) conditions.

[0064] Figure 3 The non-elastic captured gamma count ratios calculated in three types of carbonate rocks before and after lithological correction, and the non-elastic captured gamma count ratio response in pure limestone strata;

[0065] Figure 4 Example diagram of simulated well application. Detailed Implementation

[0066] The technical solution of the present invention will be further described in detail below with reference to specific embodiments. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0067] Example 1

[0068] As a preferred embodiment of the present invention, a method for quantitatively evaluating the gas saturation of carbonate reservoirs based on gamma-ray spectroscopy and elemental yield is disclosed in detail herein. This evaluation method, through a series of rigorous and interrelated steps, aims to achieve an accurate quantitative assessment of the gas saturation of carbonate reservoirs. The specific steps are as follows:

[0069] S1. Simulate the non-elastic gamma spectrum and captured gamma spectrum of the target strata under different mineral compositions, porosity and gas saturation conditions;

[0070] In this step, in order to fully understand the gamma spectrum characteristics of the target formation under various possible conditions, we need to conduct detailed simulation work.

[0071] First, for the target stratum, it is necessary to conduct in-depth research on the various stratigraphic mineral components it may contain. The types, contents, and distribution of these mineral components have a significant impact on the generation of gamma-ray spectra. Common stratigraphic minerals include, but are not limited to, calcite, dolomite, and quartz. When different minerals interact with particles such as neutrons, they produce gamma rays with specific energies and intensities, thus forming unique gamma-ray spectra.

[0072] Meanwhile, formation porosity is also a key factor. The size of the porosity not only affects the physical properties of the formation but is also closely related to the storage and transport of fluids (such as gases and liquids) within it. Under different porosity conditions, the interaction between neutrons and formation materials will differ, resulting in different characteristics in the generated gamma-ray spectra.

[0073] Furthermore, gas saturation, as an important indicator reflecting the gas content in the formation, also plays a crucial role in the formation of the gamma spectrum. As gas saturation changes, the ratio of gas to other substances (such as minerals and pore fluids) in the formation changes, which inevitably affects the generation and propagation of gamma rays after neutrons interact with the formation material, thus causing corresponding changes in the gamma spectrum.

[0074] To accurately simulate the inelastic and captured gamma spectra under the aforementioned different conditions, we employed specialized simulation software or a self-constructed simulation model. These tools, based on nuclear physics principles, can consider the combined effects of various factors such as formation minerals, porosity, and gas saturation on the gamma spectrum. For example, by setting parameters such as the proportions of different mineral components, porosity values, and gas saturation values, we simulated the inelastic and captured gamma spectra produced by the formation under various practical possibilities, laying the foundation for subsequent analysis and evaluation.

[0075] As an example of this embodiment, in this step, the FLUKA (FLUktuierende KAskade) multi-functional Monte Carlo numerical simulation program is used to construct an instrument-formation numerical calculation model based on elemental logging instruments. By adjusting the elemental composition and relative content of formation materials, the mineral content, porosity, and pore fluid type of the formation are modified. On this basis, the FLUKA multi-functional Monte Carlo numerical simulation program is used to simulate the non-elastic gamma spectrum and captured gamma spectrum of the target formation under different formation mineral compositions, porosity, and gas saturation conditions.

[0076] S2. For the non-elastic gamma spectrum and captured gamma spectrum of the target formation under different mineral composition, porosity and gas saturation conditions obtained by simulation in step S1, use energy spectrum analysis technology to obtain the element yield information of the corresponding characteristic elements in the non-elastic gamma spectrum and captured gamma spectrum.

[0077] After obtaining the simulated gamma spectrum data, the next step is to use energy spectrum analysis technology to conduct in-depth analysis to determine the elemental yield of the formation.

[0078] Energy dispersive spectroscopy (EDS) is a technique based on the principles of nuclear physics reactions and mathematical analysis methods. When neutrons interact with atomic nuclei in the strata (such as inelastic scattering and neutron capture), gamma rays are produced. The gamma rays produced by the nuclei of different elements in these reactions have specific energy and intensity characteristics.

[0079] By performing a detailed analysis of the inertial gamma spectrum and the captured gamma spectrum obtained from the S1 step simulation, we can identify the various characteristic peaks in the spectrum. These characteristic peaks correspond to the gamma rays produced by different elements under specific nuclear reactions. By comparing them with known nuclear physics data (such as standard values ​​of the energy and intensity of gamma rays produced by different elements under specific nuclear reactions), we can determine the element corresponding to each characteristic peak.

[0080] Then, based on parameters such as the intensity and area of ​​characteristic peaks in the energy spectrum, and combined with the relationship model between nuclear reactions and elemental yields in nuclear physics theory, the elemental yields of the formation under different mineral compositions, porosity, and gas saturation conditions are calculated. For example, for a specific element, a higher intensity of its corresponding characteristic peak in the energy spectrum usually means a relatively high yield of that element in the formation; conversely, a lower intensity indicates a relatively low yield. In this way, we can accurately obtain the elemental yield information of the target formation under various conditions, which is of great significance for subsequent work such as establishing lithological correction factors and gas saturation calculation models.

[0081] S3. Based on the mineral composition, porosity and gas saturation conditions of the target strata obtained in step S2, the lithological correction factor calculation model based on the main minerals of the target strata is established using multivariate nonlinear regression.

[0082] Based on the abundant stratigraphic element yield data obtained in the previous steps, we set out to establish a lithological correction factor calculation model based on limestone.

[0083] As an example, limestone, as a common and representative carbonate rock, is often used as a reference standard in geological studies and reservoir evaluation. Therefore, in this example, a lithology correction factor calculation model based on limestone will be established. First, we extract the stratigraphic element yield information corresponding to the limestone sample separately from the stratigraphic element yield data under different stratigraphic conditions obtained in step S2, as the benchmark data for subsequent comparative analysis.

[0084] Then, for lithological samples other than limestone, we calculate their differences from limestone in various relevant parameters. These parameters include, but are not limited to, differences in the proportion of formation mineral components, porosity, gas saturation, and elemental yields. For example, for a sandstone sample, we calculate its differences from limestone in calcite content, quartz content, porosity, gas saturation, and calcium and silicon yields.

[0085] Using these differences as independent variables and the lithology correction factor as the dependent variable, a suitable multiple nonlinear regression model is selected for modeling. Common multiple nonlinear regression models include multinomial regression, exponential regression, and logarithmic regression. Depending on the characteristics of the data and the actual situation, we may choose one or more of these models to try. For example, if the data exhibits a relatively obvious nonlinear relationship, and preliminary analysis suggests that a multinomial regression model may be more suitable for describing this relationship, then we would choose a quadratic multinomial regression model (e.g., y = β0 + β1x1 + β2x2 + β3x1). 2 +β4x2 2 The model is constructed using the formula +β5x1x2, where y is the lithology correction factor, x1, x2, etc. are independent variables, and β0-β5 are the regression coefficients to be estimated.

[0086] By dividing the dataset into training and test sets, the model is fitted using the training set data. Mathematical methods such as least squares are employed to find a set of regression coefficients that minimize the sum of squared errors between the model's predicted values ​​and the actual values ​​in the training set data. After model fitting, estimated values ​​for each regression coefficient are obtained, thus determining the specific lithology correction factor calculation model. This model can accurately calculate the lithology correction factor value based on the differences in relevant parameters of different lithologies, providing an important correction basis for the subsequent accurate evaluation of gas saturation in carbonate reservoirs.

[0087] S4. Simulate the non-elastic gamma spectrum and captured gamma spectrum under different porosity and gas saturation conditions in the main mineral (limestone) strata of the pure target strata;

[0088] In this step, we focus on pure limestone formations and specifically simulate their inelastic and captured gamma spectra under different porosity and gas saturation conditions.

[0089] Pure limestone strata have a relatively simple mineral composition, mainly composed of calcite. This allows us to focus more on the effects of two key factors, porosity and gas saturation, on the gamma spectrum when simulating its gamma spectrum.

[0090] Similar to previous simulations of the target formation, we employed specialized simulation software or simulation models based on nuclear physics principles. By setting different porosity and gas saturation values, we simulated the inelastic and captured gamma-ray spectra of pure limestone formations under various possible combinations of porosity and gas saturation. For example, as porosity gradually increases, the propagation path of neutrons in the limestone formation and their interaction with calcite nuclei change, leading to corresponding alterations in the characteristics of the inelastic and captured gamma-ray spectra. Similarly, changes in gas saturation affect the generation and propagation of gamma-ray spectra, causing them to exhibit different characteristics. These simulated gamma-ray spectrum data will provide crucial foundational data for subsequent gas saturation calculation models.

[0091] S5. Using the non-elastic gamma spectrum and captured gamma spectrum obtained from the simulation of the main mineral (limestone) strata of the pure target strata under different porosity and gas saturation conditions in step S4, the relationship between the non-elastic captured gamma count ratio and porosity and gas saturation is obtained, and a gas saturation calculation model is established.

[0092] Based on the non-elastic gamma spectrum and captured gamma spectrum obtained from the S4 step simulation in pure limestone strata under different porosity and gas saturation conditions, we further analyzed them to establish a gas saturation calculation model.

[0093] First, the simulated inertial gamma spectrum and captured gamma spectrum are processed to calculate the inertial-captured gamma count ratio. The inertial-captured gamma count ratio refers to the ratio of the count in a specific energy range of the inertial gamma spectrum to the count in a specific energy range of the captured gamma spectrum. It is an important indicator that can comprehensively reflect the generation and propagation characteristics of gamma rays in the formation.

[0094] Then, through a systematic analysis of the inelastic captured gamma count ratio under different porosity and gas saturation conditions, the variation law of its change with porosity and gas saturation was observed. For example, when the porosity increases, the inelastic captured gamma count ratio may show a certain trend; similarly, when the gas saturation increases, the inelastic captured gamma count ratio will also change accordingly.

[0095] Based on these patterns of change, and combining nuclear physics theory and mathematical modeling methods, a gas saturation calculation model was established. This model can accurately calculate the gas saturation of a formation based on the in-situ gamma count ratio and known porosity values. For example, a mathematical function relationship can be established, such as gas saturation = f(in-situ gamma count ratio, porosity), where f is a specific mathematical function obtained through analysis and fitting of a large amount of simulation data. In this way, we have established a calculation model capable of accurately calculating the gas saturation of pure limestone formations, providing an important reference standard for subsequent evaluation of the gas saturation of target formations.

[0096] S6. Obtain the inelastic gamma spectrum and captured gamma spectrum of the target formation. Based on the inelastic gamma spectrum and captured gamma spectrum of the target formation, and the lithology correction factor calculation model established in step S3, calculate the lithology correction factor of the target formation; calculate the inelastic captured gamma count ratio of the target formation; and combine the formation porosity parameters, lithology correction factor, inelastic captured gamma count ratio, and gas saturation calculation model established in step S5 to quantitatively evaluate the gas saturation of the target formation.

[0097] In this final step, we integrate the information obtained from the previous steps and the established model to quantitatively evaluate the gas saturation of the target formation.

[0098] First, the inelastic gamma spectrum and captured gamma spectrum of the target strata are obtained through field measurements or other relevant methods. Then, based on these spectral data and the lithology correction factor model established in step S3, the lithology correction factor of the target strata is calculated. Specifically, the difference values ​​between the target strata and the limestone in various relevant parameters are substituted into the lithology correction factor model to obtain the specific lithology correction factor values.

[0099] Next, based on the non-elastic gamma energy spectrum and the captured gamma energy spectrum of the target stratum, the non-elastic captured gamma count ratio of the target stratum is calculated. The calculation method is similar to that used in step S5 to calculate the non-elastic captured gamma count ratio of the pure limestone stratum, that is, it is obtained by processing the counts in a specific energy range of the energy spectrum.

[0100] Finally, the formation porosity parameters of the target strata, the calculated lithology correction factor, the inelastic trapping gamma count ratio, and the gas saturation calculation model established in step S5 are integrated to quantitatively evaluate the gas saturation of the target strata. Specifically, these parameters are substituted into the gas saturation calculation model, and the gas saturation value of the target strata is accurately calculated according to the calculation method specified by the model. In this way, we have achieved a quantitative evaluation of the gas saturation of the target strata, providing important technical support for the exploration and development of carbonate reservoirs and related work.

[0101] Through the complete process of Example 1 above, we can use gamma spectroscopy and elemental yield correlation technology to accurately and quantitatively evaluate the gas saturation of carbonate reservoirs, providing an effective analysis and evaluation method for related geological work.

[0102] Example 2

[0103] As another preferred embodiment of the present invention, this embodiment further supplements and elaborates on the technical solution of the present invention based on the above-described embodiment 1. In this embodiment, in step S3, the lithological correction factor calculation model established based on limestone is as follows:

[0104] In the formula, ξ is the lithology correction factor; R carb The non-elastic capture gamma count ratio of carbonate formations under the same porosity and gas saturation conditions; R lime The non-elastic captured gamma count ratio of pure limestone strata under the same porosity and gas saturation conditions; n1 is the number of element types in carbonate rock strata; n2 is the number of element types in pure limestone strata; y i y represents the elemental yield of the i-th element in carbonate rock formations; j a is the elemental yield of the j-th element in a pure limestone stratum; i For element yield y i The regression coefficient; b j For element yield y j The regression coefficients.

[0105] The regression coefficients of each element's yield in the lithology correction factor calculation model are obtained by using the elemental yields of carbonate rock formations and pure limestone formations under various known conditions, and applying a multivariate nonlinear regression method according to the lithology correction factor calculation model.

[0106] Specifically, for each carbonate stratum with known conditions, the inelastic capture gamma count ratio and elemental yield information can be obtained. Based on this, a set of characteristic ratios formed by the inelastic capture count ratios under different carbonate stratum conditions and the inelastic capture count ratios under the corresponding porosity of pure limestone strata are obtained. Simultaneously, a set of corresponding ratios of elemental yield combinations under different carbonate stratum conditions and the elemental yield combinations under the corresponding porosity of pure limestone strata are obtained, including the 'a' value in the lithology correction factor calculation model. i b j Regression coefficients; Based on the two sets of data above, the regression coefficients α are obtained by fitting parameters using a multivariate nonlinear regression method. i b j The value.

[0107] Based on the above lithology correction factor calculation model, the gas saturation calculation model established in step S5 is as follows:

[0108]

[0109] In the formula, S g R represents gas saturation. log The non-ballistic capture gamma count ratio of the target formation; φ is the formation porosity of the target formation; A w B w and t w A represents the fitting coefficient of the cross-plot between the inelastic captured gamma count ratio and porosity in pure limestone formations with a gas saturation of 0%. g B g and t g , which is the fitting coefficient of the cross-plot of non-elastic captured gamma count ratio and porosity in pure limestone strata with a gas saturation of 100%.

[0110] As an example of this embodiment, in step S2, the formation element yield is obtained by using the least squares spectral analysis method to obtain the non-elastic gamma spectrum and the captured gamma spectrum under different formation mineral composition, porosity and gas saturation conditions of the target formation simulated in step S1.

[0111] Example 3

[0112] As another preferred embodiment of the present invention, this embodiment is a further detailed supplement and explanation of the technical solution of the present invention based on the above-described Embodiment 1 or Embodiment 2. In this embodiment, reference is made to the appendix to the specification. Figure 1As shown, in step S6, the inelastic gamma spectrum and captured gamma spectrum of the target formation are obtained by a formation element logging device, which includes a pulsed neutron source and a gamma detector. The distance between the gamma detector and the pulsed neutron source is 45-55 cm. The gamma detector is a LaBr3 crystal detector. The LaBr3 crystal detector records the total gamma spectrum from 0-80 μs of the pulse emission period and the captured gamma spectrum from 100-420 μs. The pulsed neutron source is a DT controlled neutron source with a pulse period of 420 μs. Within one pulse period, fast neutrons are emitted from 0-80 μs and fast neutron emission stops from 100-420 μs.

[0113] Example 4

[0114] As another preferred embodiment of the present invention, this embodiment provides a device for quantitatively evaluating the gas saturation of carbonates based on gamma-ray spectroscopy and elemental yield. This device includes:

[0115] The first module is used to simulate the non-elastic gamma spectrum and captured gamma spectrum under different mineral composition, porosity and gas saturation conditions of the target strata, as well as to simulate the non-elastic gamma spectrum and captured gamma spectrum under different porosity and gas saturation conditions in the main mineral (limestone) strata of the pure target strata.

[0116] As an example of this embodiment, in the first module, the FLUKA (FLUktuierende KAskade) multi-functional Monte Carlo numerical simulation program is used to simulate the non-elastic gamma-ray spectrum and captured gamma-ray spectrum of the target formation under different formation mineral compositions, porosity, and gas saturation conditions. The FLUKA multi-functional Monte Carlo numerical simulation program is also used to simulate the non-elastic gamma-ray spectrum and captured gamma-ray spectrum of the main mineral strata of the pure target formation under different porosity and gas saturation conditions. Specifically, using the FLUKA multi-functional Monte Carlo numerical simulation program, an instrument-formation numerical calculation model based on elemental logging instruments is constructed. By adjusting the elemental composition and relative content of formation materials, the mineral content, porosity, and pore fluid type of the formation are modified. Based on this, the non-elastic gamma-ray spectrum and captured gamma-ray spectrum of the target formation under different formation mineral compositions, porosity, and gas saturation conditions are simulated, as well as the non-elastic gamma-ray spectrum and captured gamma-ray spectrum of the main mineral strata of the pure target formation under different porosity and gas saturation conditions are simulated.

[0117] The second module is used to obtain the element yield information of corresponding characteristic elements in the non-elastic gamma spectrum and captured gamma spectrum from the non-elastic gamma spectrum and captured gamma spectrum under different mineral composition, porosity and gas saturation conditions of the target strata simulated by the first module, using energy spectrum analysis technology.

[0118] As an example of this embodiment, the formation element yield is obtained by using the least squares spectral analysis method to obtain the non-elastic gamma spectrum and the captured gamma spectrum under different formation mineral composition, porosity and gas saturation conditions of the target formation simulated by the first module.

[0119] The third module is used to establish a lithological correction factor calculation model based on the main minerals (limestone) of the target strata under different mineral composition, porosity and gas saturation conditions output by the second module.

[0120] As an example of this embodiment, the established lithology correction factor calculation model based on limestone is as follows:

[0121] In the formula, ξ is the lithology correction factor; R carb The non-elastic capture gamma count ratio of carbonate formations under the same porosity and gas saturation conditions; R lime The non-elastic captured gamma count ratio of pure limestone strata under the same porosity and gas saturation conditions; n1 is the number of element types in carbonate rock strata; n2 is the number of element types in pure limestone strata; y i y represents the elemental yield of the i-th element in carbonate rock formations; j a is the elemental yield of the j-th element in a pure limestone stratum; i For element yield y i The regression coefficient; b j For element yield y j The regression coefficients.

[0122] As another example of this embodiment, the regression coefficients of each element's yield in the lithology correction factor calculation model are obtained by using the elemental yields of carbonate rock formations and pure limestone formations under various known conditions, and applying the multivariate nonlinear regression method according to the lithology correction factor calculation model.

[0123] Specifically, for each carbonate stratum with known conditions, the inelastic capture gamma count ratio and elemental yield information can be obtained. Based on this, a set of characteristic ratios formed by the inelastic capture count ratios under different carbonate stratum conditions and the inelastic capture count ratios under the corresponding porosity of pure limestone strata are obtained. Simultaneously, a set of corresponding ratios of elemental yield combinations under different carbonate stratum conditions and the elemental yield combinations under the corresponding porosity of pure limestone strata are obtained, including the 'a' value in the lithology correction factor calculation model. i b j Regression coefficients; Based on the two sets of data above, the regression coefficients α are obtained by fitting parameters using a multivariate nonlinear regression method. i bj The value.

[0124] The fourth module is used to obtain the relationship between the inelastic gamma energy spectrum and the captured gamma energy spectrum under different porosity and gas saturation conditions in the main mineral strata of the pure target strata simulated in the first module, to obtain the relationship between the inelastic captured gamma count ratio and porosity and gas saturation, and to establish a gas saturation calculation model.

[0125] The established gas saturation calculation model is as follows:

[0126]

[0127] In the formula, S g R represents gas saturation. log The non-ballistic capture gamma count ratio of the target formation; φ is the formation porosity of the target formation; A w B w and t w A represents the fitting coefficient of the cross-plot between the inelastic captured gamma count ratio and porosity in pure limestone formations with a gas saturation of 0%. g B g and t g , which is the fitting coefficient of the cross-plot of non-elastic captured gamma count ratio and porosity in pure limestone strata with a gas saturation of 100%.

[0128] The fifth module is used to obtain the inelastic gamma spectrum and the captured gamma spectrum of the target formation; based on the inelastic gamma spectrum and the captured gamma spectrum of the target formation and the lithology correction factor calculation model output by the third module, it calculates the lithology correction factor of the target formation; it is used to calculate the inelastic captured gamma count ratio of the target formation; and it is used to quantitatively evaluate the gas saturation of the target formation by combining the formation porosity parameters, lithology correction factor, inelastic captured gamma count ratio, and the gas saturation calculation model established by the fourth module.

[0129] As an example of this embodiment, please refer to the appendix to the specification. Figure 1 As shown, the inelastic gamma spectrum and captured gamma spectrum of the target formation obtained by the fifth module were measured using a formation element logging device, which includes a pulsed neutron source and a gamma detector. The distance between the gamma detector and the pulsed neutron source is 45-55 cm. The gamma detector is a LaBr3 crystal detector. The LaBr3 crystal detector records the total gamma spectrum from 0-80 μs during the pulse emission period and the captured gamma spectrum from 100-420 μs. The pulsed neutron source is a DT controlled neutron source with a pulse period of 420 μs. Within one pulse period, fast neutrons are emitted from 0-80 μs and cease emission from 100-420 μs.

[0130] Example 5

[0131] As another preferred embodiment of the present invention, this embodiment further supplements and elaborates on the technical solutions of embodiments 1, 2, 3, and 4 above. This embodiment further illustrates the technical solutions of the present invention in conjunction with principle derivation and application examples.

[0132] According to the neutron-gamma coupling field theory, considering the formation mass attenuation coefficient and the average number of gamma photons generated by the inelastic scattering of a fast neutron with a formation element as constants, the inelastic scattered gamma flux received by the detector at the source distance is:

[0133]

[0134] In the formula, λ is the inelastic scattering gamma flux; S0 is the neutron source intensity. s For the free path of fast neutron scattering, Σ in It represents the inelastic scattering cross section of the formation.

[0135] Based on the thermal neutron distribution pattern in the neutron two-group diffusion theory, the captured gamma-ray flux received by a detector with a source distance of R is:

[0136]

[0137] In the formula, The captured gamma-ray flux; i′ is the intensity of the captured gamma-ray emitted by a thermal neutron in a capture reaction with an atomic nucleus; L f L is the deceleration length of a fast neutron. t denoted as the diffusion length of thermal neutrons.

[0138] As shown in the above equation, i and i′ appear as ratios and can be considered constants. The ratio of non-ballistic to captured gamma counts is mainly related to the source distance R and the fast neutron deceleration length L. f Thermal neutron diffusion length L t and the inelastic scattering cross section Σ of the formation in These parameters are related to the formation's mineral composition and porosity parameters. Therefore, the ratio R of the non-explosive gamma count to the captured gamma count recorded by the detector is:

[0139]

[0140] In the formula, φ and S g These represent formation porosity and gas saturation, respectively, with Lith representing formation lithology or mineral composition. Based on the elemental yields of the formation, and using limestone formations as a benchmark, a lithology correction factor is established to eliminate the influence of formation mineral composition on the inelastic captured gamma count ratio.

[0141]

[0142] In the formula, ξ is the lithology correction factor; R carb and R lime Let be the inelastic captured gamma count ratio of carbonate and pure limestone formations under the same porosity and gas saturation conditions; n1 and n2 are the number of element species in the carbonate and pure limestone formations, respectively; y is the element yield, and a and b are the regression coefficients of each element yield. Combining lithological factors and the inelastic captured gamma count ratio, the gas saturation of the formation can be expressed as:

[0143] S g =f -1 (ξ,R,φ)

[0144] As can be seen from the above formula, when using the inelastic captured gamma count ratio to quantitatively monitor the gas saturation of carbonate rocks, the stratigraphic lithology factor is an important parameter for eliminating the influence of stratigraphic minerals on the gamma count ratio. Therefore, the stratigraphic element yield and the inelastic captured gamma count ratio can be combined for quantitative monitoring of the gas saturation of carbonate rocks.

[0145] Figure 2 This figure shows the relationship between the inelastic gamma-ray trapping ratio and the formation gas saturation in limestone and dolomite formations, under conditions of 0%, 50%, and 100% gas saturation and porosity ranging from 0% to 25% in 5% increments. As can be seen from the figure, when the lithology is the same, the inelastic gamma-ray trapping ratio increases with increasing formation porosity and decreases linearly with increasing gas saturation. The inelastic gamma-ray trapping ratio can effectively reflect the gas-bearing properties of the formation. However, there is a significant difference in the inelastic gamma-ray trapping ratio between the two lithologies. This is mainly because the CaCO3 framework of limestone has a smaller inelastic scattering cross section and thermal neutron trapping cross section compared to the CaMg(CO3)2 framework of dolomite. This leads to different numerical values ​​and trends in the inelastic gamma-ray trapping ratio between limestone and dolomite reservoirs, with the inelastic gamma-ray trapping ratio in dolomite formations generally being higher than that in limestone formations. This indicates that lithology has a significant impact on reservoir gas-bearing capacity assessment. For carbonate rocks with complex mineral compositions, the differences in lithology and mineral type have a particularly significant effect on gas-bearing capacity assessment. Therefore, it is necessary to adjust the count ratio in real time according to mineral type to meet the requirements for quantitative calculation of gas saturation.

[0146] according to Figure 2 The relationship between the non-elastic captured gamma count ratio and gas saturation and porosity in pure limestone strata was investigated, and a gas saturation evaluation model for limestone under standard conditions was established.

[0147]

[0148] In the formula, S g R represents gas saturation. logThe non-ballistic captured gamma count ratio is the measured value; φ is the formation porosity; K w and b w K represents the fitting coefficient of the cross-plot between the inelastic captured gamma count ratio and porosity in a pure limestone formation with 0% gas saturation. g and b g The fitting coefficient of the cross-plot between the inelastic captured gamma count ratio and porosity in a pure limestone formation with 100% gas saturation is given. From the formula, the gas saturation S of the formation can be obtained. g Is the non-missile capture count ratio R log The lithology correction factor is a function of formation porosity φ and lithology correction factor ξ. Based on the non-elastic gamma and captured gamma energy spectrum information detected by the detector, the element yield is extracted to calculate the lithology correction factor. The non-elastic captured gamma count ratio after lithology correction can be used to quantitatively evaluate the gas saturation of carbonate rocks.

[0149] The stratigraphic sequence was set as a carbonate rock formation with mineral assemblages such as quartz, calcite, and dolomite. The non-explosive and captured gamma energy spectrum information recorded by the instrument under different combination ratios was simulated, and the non-explosive captured gamma count ratio and lithology correction factor were calculated. Figure 3 The figure shows the inelastic gamma-ray capture ratios under three stratigraphic conditions before and after lithological correction, and the response patterns of the count ratios in limestone strata. Specifically, carbonate stratum A has a porosity of 7.5%, a gas saturation of 50%, and a rock skeleton consisting of 70% calcite and 30% dolomite; carbonate stratum B has a porosity of 12.5%, a gas saturation of 75%, and a rock skeleton consisting of 20% calcite and 80% dolomite; carbonate stratum C has a porosity of 18.5%, a gas saturation of 100%, and a rock skeleton consisting of 50% calcite, 40% dolomite, and 10% quartz. As can be seen from the figure, due to the differences in the inelastic scattering and thermal neutron capture capabilities of different minerals, the measured inelastic gamma-ray capture ratios in carbonate rocks are generally higher than those in pure limestone strata, leading to significant errors when directly calculating gas saturation. After lithological correction, the non-elastic capture count ratio of carbonate rocks decreased by different proportions. The gas saturation results interpreted by the saturation calculation model were consistent with the model settings, verifying the necessity and effectiveness of lithological correction.

[0150] The Monte Carlo simulation method was used to simulate the continuous measurement process of elemental logging instruments in carbonate gas-bearing formations. During the logging process, the instrument slids against the well wall from top to bottom, recording the gamma energy spectrum and gamma count information of the gamma detector, calculating the non-elastic capture gamma count ratio and lithology correction factor, and combining the gas saturation interpretation model under the standard formation conditions of limestone to realize the calculation of gas saturation. Figure 4The simulation results are shown in the following diagrams: the first channel displays the original lithological profile information; the second channel displays the depth channel; the third channel shows the formation porosity and density curves; the fourth channel displays the inelastic capture count ratio curves, which include the actual measured original count ratio, the count ratio after lithological correction, and the theoretical count ratio in limestone formations under the same conditions; the fifth channel displays the model-set gas saturation and calculated gas saturation curves; and the sixth channel displays the gas saturation calculation error curve. To more broadly verify the application effect of the calculation method under carbonate rock conditions, the simulation well has six formations from top to bottom. Formation A has a porosity of 12%, a gas saturation of 75%, and a rock skeleton of 80% calcite and 20% dolomite; formation B has a porosity of 7%, a gas saturation of 45%, and a rock skeleton of 65% calcite, 25% quartz, and 10% kaolinite; formation C has a porosity of 15%, a gas saturation of 60%, and a rock skeleton of... The rock matrix consists of 75% calcite, 20% dolomite, and 5% quartz. Formation D has a porosity of 9%, a gas saturation of 85%, and a rock skeleton of 25% calcite and 75% dolomite. Formation E has a porosity of 13%, a gas saturation of 50%, and a rock skeleton of 25% calcite, 70% dolomite, and 5% quartz. Formation F has a porosity of 7.5%, a gas saturation of 75%, and a rock skeleton of 20% calcite, 70% dolomite, and 10% kaolinite. The interpretation results show that the gamma-ray count ratio of the carbonate formation without lithological factor correction is larger than that of the limestone formation under the corresponding porosity and gas saturation conditions. After correction, the gamma-ray count ratio matches well with that of the limestone formation. The absolute error of the gas saturation calculated using the non-elastic trap gamma-ray count ratio after lithological factor correction is controlled within 13.5%, verifying the effectiveness of the method.

[0151] Example 6

[0152] In another preferred embodiment of the present invention, in order to achieve the above objectives, according to another aspect of the present application, a computer device is also provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the above-described method for quantitative evaluation of carbonate gas saturation based on gamma energy spectrum and elemental yield.

[0153] In this embodiment, the processor can be a central processing unit (CPU). The processor can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or combinations of the above types of chips.

[0154] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer-executable programs, and units, such as the program module units corresponding to the above method embodiments of the present invention. The processor executes various functional applications and data processing of the processor by running the non-transitory software programs, instructions, and modules stored in the memory, thereby implementing the methods in the above method embodiments.

[0155] The memory may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created by the processor, etc. Furthermore, the memory may include high-speed random access memory and non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, the memory may optionally include memory remotely located relative to the processor, which can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0156] The one or more units are stored in the memory, and when executed by the processor, they perform the steps of Embodiment 1, Embodiment 2 or Embodiment 3 described above.

[0157] Example 7

[0158] As another preferred embodiment of the present invention, this embodiment discloses a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps in Embodiment 1, Embodiment 2 or Embodiment 3 above.

[0159] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for quantitatively evaluating carbonate gas saturation based on gamma-ray spectroscopy and elemental yield, characterized in that, The evaluation method includes the following steps: S1. Simulate the non-elastic gamma spectrum and captured gamma spectrum under different mineral compositions, porosity and gas saturation conditions of the target strata; S2. For the non-elastic gamma spectrum and captured gamma spectrum of the target formation under different mineral composition, porosity and gas saturation conditions obtained by simulation in step S1, use energy spectrum analysis technology to obtain the element yield information of the corresponding characteristic elements in the non-elastic gamma spectrum and captured gamma spectrum. S3. Based on the mineral composition, porosity and gas saturation conditions of the target strata obtained in step S2, the lithological correction factor calculation model based on the main minerals of the target strata is established using multivariate nonlinear regression. S4. Simulate the non-elastic gamma spectrum and captured gamma spectrum under different porosity and gas saturation conditions in the main mineral strata of the pure target strata; S5. Using the non-elastic gamma energy spectrum and captured gamma energy spectrum of the main mineral strata of the pure target strata obtained by step S4 simulation under different porosity and gas saturation conditions, the relationship between the non-elastic captured gamma count ratio and porosity and gas saturation is obtained, and a gas saturation calculation model is established. S6. Obtain the inelastic gamma spectrum and captured gamma spectrum of the target formation. Based on the inelastic gamma spectrum and captured gamma spectrum of the target formation, and the lithology correction factor calculation model established in step S3, calculate the lithology correction factor of the target formation; calculate the inelastic captured gamma count ratio of the target formation; and combine the formation porosity parameters, lithology correction factor, inelastic captured gamma count ratio, and gas saturation calculation model established in step S5 to quantitatively evaluate the gas saturation of the target formation.

2. The method for quantitatively evaluating carbonate gas saturation based on gamma spectroscopy and elemental yield as described in claim 1, characterized in that: In step S1, the FLUKA multi-functional Monte Carlo numerical simulation program is used to construct an instrument-formation numerical calculation model based on elemental logging instruments. By adjusting the elemental composition and relative content of formation materials, the mineral content, porosity, and pore fluid type of the formation are modified. On this basis, the FLUKA multi-functional Monte Carlo numerical simulation program is used to simulate the non-elastic gamma spectrum and captured gamma spectrum of the target formation under different formation mineral compositions, porosity, and gas saturation conditions.

3. The method for quantitatively evaluating carbonate gas saturation based on gamma spectroscopy and elemental yield as described in claim 2, characterized in that: In step S4, the FLUKA multifunctional Monte Carlo numerical simulation program is used to simulate the non-elastic gamma spectrum and captured gamma spectrum under different porosity and gas saturation conditions in the main mineral strata of the pure target strata.

4. The method for quantitatively evaluating carbonate gas saturation based on gamma spectroscopy and elemental yield as described in claim 3, characterized in that: The main mineral in the target stratum is limestone.

5. The method for quantitatively evaluating carbonate gas saturation based on gamma spectroscopy and elemental yield as described in claim 4, characterized in that: In step S3, the lithology correction factor calculation model based on limestone is established as follows: In the formula, ξ is the lithology correction factor; R carb The non-elastic capture gamma count ratio of carbonate formations under the same porosity and gas saturation conditions; R lime The non-elastic captured gamma count ratio of pure limestone strata under the same porosity and gas saturation conditions; n1 is the number of element types in carbonate rock strata; n2 is the number of element types in pure limestone strata; y i y represents the elemental yield of the i-th element in carbonate rock formations; j a is the elemental yield of the j-th element in a pure limestone stratum; i For element yield y i The regression coefficients; b j For element yield y j The regression coefficients.

6. The method for quantitatively evaluating carbonate gas saturation based on gamma spectroscopy and elemental yield as described in claim 5, characterized in that: The regression coefficients of each element's yield in the lithology correction factor calculation model are obtained by using the elemental yields of carbonate rock formations and pure limestone formations under various known conditions, and applying a multivariate nonlinear regression method according to the lithology correction factor calculation model.

7. The method for quantitatively evaluating carbonate gas saturation based on gamma spectroscopy and elemental yield as described in claim 6, characterized in that: For each carbonate stratum with known conditions, its inelastic capture gamma count ratio and elemental yield information can be obtained. Based on this, a set of characteristic ratios formed by the inelastic capture count ratios under different carbonate stratum conditions and the inelastic capture count ratios under the corresponding porosity of pure limestone strata are obtained. Simultaneously, a set of corresponding ratios of elemental yield combinations under different carbonate stratum conditions and the elemental yield combinations under the corresponding porosity of pure limestone strata are obtained, including the 'a' value in the lithology correction factor calculation model. i b j Regression coefficients; Based on the two sets of data above, the regression coefficients α are obtained by fitting parameters using a multivariate nonlinear regression method. i b j The value.

8. The method for quantitative evaluation of carbonate gas saturation based on gamma spectroscopy and elemental yield as described in any one of claims 1-7, characterized in that: In step S5, the established gas saturation calculation model is as follows: In the formula, S g R represents gas saturation. log The non-ballistic capture gamma count ratio of the target formation; φ is the formation porosity of the target formation; A w B w and t w A represents the fitting coefficient of the cross-plot between the inelastic captured gamma count ratio and porosity in pure limestone formations with a gas saturation of 0%. g B g and t g , which is the fitting coefficient of the cross-plot of non-elastic captured gamma count ratio and porosity in pure limestone strata with a gas saturation of 100%.

9. The method for quantitative evaluation of carbonate gas saturation based on gamma spectroscopy and elemental yield as described in any one of claims 1-7, characterized in that: In step S2, the formation element yield is obtained by using the least squares spectral analysis method to obtain the non-elastic gamma spectrum and the captured gamma spectrum under different formation mineral composition, porosity and gas saturation conditions of the target formation simulated in step S1.

10. The method for quantitatively evaluating carbonate gas saturation based on gamma spectroscopy and elemental yield as described in any one of claims 1-7, characterized in that: In step S6, the inelastic gamma spectrum and the captured gamma spectrum of the target formation are obtained by measuring the formation element logging device, which includes a pulsed neutron source and a gamma detector.

11. The method for quantitatively evaluating carbonate gas saturation based on gamma spectroscopy and elemental yield as described in claim 10, characterized in that: The distance between the gamma detector and the pulsed neutron source is 45-55 cm.

12. The method for quantitatively evaluating carbonate gas saturation based on gamma spectroscopy and elemental yield as described in claim 10, characterized in that: The gamma detector is a LaBr3 crystal detector.

13. The method for quantitatively evaluating carbonate gas saturation based on gamma spectroscopy and elemental yield as described in claim 12, characterized in that: The LaBr3 crystal detector records the total gamma spectrum in the 0-80 μs pulse emission period and the captured gamma spectrum in the 100-420 μs pulse emission period.

14. The method for quantitatively evaluating carbonate gas saturation based on gamma spectroscopy and elemental yield as described in claim 10, characterized in that: The pulsed neutron source is a DT controllable neutron source. The pulse period of the DT controllable neutron source is 420 μs. Within one pulse period, fast neutrons are emitted from 0 to 80 μs and fast neutron emission stops from 100 to 420 μs.

15. A device for quantitatively evaluating carbonate gas saturation based on gamma spectroscopy and elemental yield, characterized in that: The quantitative evaluation device includes, The first module is used to simulate the non-elastic gamma spectrum and captured gamma spectrum under different mineral composition, porosity and gas saturation conditions of the target strata, as well as to simulate the non-elastic gamma spectrum and captured gamma spectrum under different porosity and gas saturation conditions in the main mineral strata of the pure target strata. The second module is used to obtain the element yield information of corresponding characteristic elements in the non-elastic gamma spectrum and captured gamma spectrum from the non-elastic gamma spectrum and captured gamma spectrum under different mineral composition, porosity and gas saturation conditions of the target strata simulated by the first module, using energy spectrum analysis technology. The third module is used to establish a lithological correction factor calculation model based on the main minerals of the target strata under different strata mineral composition, porosity and gas saturation conditions output by the second module. The fourth module is used to obtain the relationship between the inelastic gamma energy spectrum and the captured gamma energy spectrum under different porosity and gas saturation conditions in the main mineral strata of the pure target strata simulated by the first module, to obtain the relationship between the inelastic captured gamma count ratio and porosity and gas saturation, and to establish a gas saturation calculation model. The fifth module is used to obtain the inelastic gamma spectrum and the captured gamma spectrum of the target formation; based on the inelastic gamma spectrum and the captured gamma spectrum of the target formation and the lithology correction factor calculation model output by the third module, it calculates the lithology correction factor of the target formation; it is used to calculate the inelastic captured gamma count ratio of the target formation; and it is used to quantitatively evaluate the gas saturation of the target formation by combining the formation porosity parameters, lithology correction factor, inelastic captured gamma count ratio, and the gas saturation calculation model established by the fourth module.

16. A computer device, characterized in that: The system includes a processor, an input device, an output device, and a memory, which are interconnected. The memory stores a computer program, which includes program instructions. The processor is configured to invoke the program instructions to execute the quantitative evaluation method for carbonate gas saturation based on gamma spectroscopy and elemental yield as described in any one of claims 1-14.

17. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, the computer program including program instructions, which, when executed by a processor, cause the processor to perform the quantitative evaluation method for carbonate gas saturation based on gamma energy spectrum and elemental yield as described in any one of claims 1-14.

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