Formation density calculation method and device of variable energy window gamma density logging and storage medium

Abstract

The application discloses a formation density calculation method and device of a variable-energy window gamma density logging and a storage medium, and relates to the field of well logging. The method comprises the following steps: acquiring a response energy spectrum of a gamma density logging instrument in different known formation environments; changing the energy window boundary of the response energy spectrum to acquire density counts in different energy window ranges; calculating different density counts to obtain apparent density; screening based on the apparent density and the true density of the formation to obtain an optimal energy window combination; repeating the above operation to record data in different known formation environments and connecting the data to obtain a "formation environment-energy spectrum-optimal energy window combination database"; acquiring a response energy spectrum of a formation environment to be measured; screening the recorded response energy spectrum to obtain a response energy spectrum with the highest similarity to the response energy spectrum to be measured; and querying the optimal energy window boundary combination of the response energy spectrum with the highest similarity in the "formation environment-energy spectrum-optimal energy window combination database" and calculating the optimal energy window boundary combination to obtain a final calculation result. The method can improve the accuracy of formation density calculation.

Description

Technical Field

[0001] This invention belongs to the field of nuclear logging formation density technology, and particularly relates to a method, device and storage medium for calculating formation density in variable energy window gamma density logging. Background Technology

[0002] In nuclear logging, when using a gamma density logging tool, a gamma source emits gamma photons into the formation. After scattering and absorption by the formation, some of these scattered photons are received by two gamma-ray detectors at different distances from the source. Different formation densities result in different scattering and absorption capacities for gamma photons, leading to different readings recorded by the detectors. The interactions between gamma rays and matter primarily involve the Compton effect, photoelectric effect, and electron-electron pair effect. Only the Compton effect is directly proportional to formation density. Based on this principle, a compensated gamma density logging formula exists, which can be used to calculate the formation density.

[0003] Currently, the fixed energy window method is mostly used to obtain formation density. In formation environments without heavy mud cake, the fixed energy window method can match the part of the energy spectrum that reflects the Compton effect, i.e., the falling edge of the peak. However, when heavy mud cake is present in the formation, the peak of the energy spectrum shifts significantly, causing the energy window and the falling edge of the peak to not match well, thus making it impossible to accurately calculate the formation density. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a method, apparatus, and storage medium for calculating formation density using variable energy window gamma density logging. This method primarily obtains a reasonable energy window range in the energy spectrum of each formation by changing the energy window boundary, thereby obtaining a more accurate formation density.

[0005] This invention is achieved through the following technical solution:

[0006] On the one hand, the present invention provides a method for calculating formation density in variable energy window gamma density logging, comprising the following steps:

[0007] Step S1: Obtain the response energy spectrum of the gamma density logging tool under different known formation environments, and record each formation environment and response energy spectrum as the "formation environment-energy spectrum database";

[0008] Step S2: Use a traversal algorithm to change the energy window boundary of the response energy spectrum, traverse the energy corresponding to the channel address of the response energy spectrum and divide the response energy spectrum into energy channel addresses, and obtain different combinations of energy window boundaries and density counts under the combination.

[0009] Step S3: Using the compensated density calculation formula of the gamma density logging tool, perform density calculations for different energy window boundary combinations and density counts under these combinations, and use the calculation results as the apparent density under different energy window boundary combinations.

[0010] Step S4: Based on the calculated apparent density and true density of the formation, a benchmark screening is performed to eliminate random results. The selected energy window boundary combinations that meet the conditions are then used to find the energy window boundary combination with the smallest relative error by taking the minimum value. This is recorded as the optimal energy window boundary combination for that formation.

[0011] Step S5: Apply steps S1 to S4 to different known stratigraphic environments, store the optimal energy window boundary combination corresponding to each stratigraphic environment into the "Stratigraphic-Optimal Energy Window Combination Database", and link the "Stratigraphic-Optimal Energy Window Combination Database" with the "Stratigraphic Environment-Energy Spectrum Database" to obtain the "Stratigraphic Environment-Energy Spectrum-Optimal Energy Window Combination Database".

[0012] Step S6: Obtain the response energy spectrum of the gamma density logging tool under formation environment as the response energy spectrum to be tested;

[0013] Step S7: Use the data similarity comparison method to compare the similarity between the response energy spectrum to be tested and the existing recorded response energy spectrum, and select the existing response energy spectrum with the highest similarity.

[0014] Step S8: Query the "Formation Environment-Energy Spectrum-Optimal Energy Window Combination Database" to obtain the optimal energy window boundary combination corresponding to the existing response energy spectrum with the highest similarity, and use it as the new energy window range. Calculate the apparent density of the new energy window range using the compensation density calculation formula to obtain the final calculation result.

[0015] In step S1, the methods for obtaining the response energy spectrum of the gamma density logging tool under different known formation environments include: obtaining it through calibration well experiments or by establishing density logging models of different formation environments on simulation software.

[0016] Furthermore, the formation environment includes mud cake density, mud cake thickness, lithology, formation density, mud slurry gaps, and mud slurry density; the response energy spectrum includes the energy spectrum of long-source-distance detectors and the energy spectrum of short-source-distance detectors; each formation environment and its long-source-distance detector energy spectrum and short-source-distance detector energy spectrum are recorded and denoted as the "formation environment-energy spectrum database".

[0017] Furthermore, the energy spectra of the long-source-pitch detector and the short-source-pitch detector both have different energy window ranges, and these energy window ranges have high-energy boundaries E. high and low-energy boundary E low Wherein: in the energy window region of the energy spectrum of the long-source-spacing detector, the high-energy boundary is LE. high The low-energy boundary is LE low The high-energy boundary in the energy window region of the short-source-pitch detector energy spectrum is SE. high The low-energy boundary is SE low .

[0018] In step S2, the energy window boundary of the response energy spectrum is changed using an traversal algorithm, which includes the following steps:

[0019] S201: Finding the peak E in the response energy spectrum m E m This is the energy corresponding to the channel with the highest count rate in the entire response energy spectrum;

[0020] S202: Initialize the low-energy boundary E low High-energy boundary E high E low =E m E high =E low ;

[0021] S203: E slides one step to the right low E high You need to slide to the right several times until you reach E. high Greater than 0.662 MeV;

[0022] S204: Each time E is swiped high All operations must calculate the sum of counts within the current energy window range, i.e., the density count N, and record the current energy window boundary combination and density count, i.e., E. low E high and N;

[0023] S205: Until E low If the energy is greater than 0.662 MeV, the traversal ends.

[0024] Furthermore, by performing the above-mentioned traversal of the energy window boundaries on the response energy spectra of long and short source distance detectors respectively, different combinations of energy window boundaries for long and short source distance detectors and density counts under such combinations can be obtained.

[0025] In step S3, the formula for calculating the compensated density of the gamma density logging tool is as follows: In the formula, ρ b For the calculated apparent density, A L A s These are known coefficients related to long and short source distance detectors and the geological environment, respectively, B. L B S These are known coefficients related to long and short source distance detectors and the geological environment, respectively, N. L N S , respectively, are the sum of density count rates within specific energy ranges in the energy spectra of long and short source distance detectors in well logging, and k is the fitting coefficient between long and short source distance detectors and the formation environment.

[0026] The calculated apparent density ρ for each combination of long and short source distance energy window boundaries is calculated using the compensated density calculation formula of this density logging tool. b .

[0027] In step S4, the benchmarking and screening based on the calculated apparent density and the true density of the formation includes the following steps:

[0028] Step S401: Set the power window width to be greater than 10 address widths, i.e., E high -E low >10×0.003198MeV;

[0029] Step S402: Calculate the relative error e between the energy window boundary combination and the true density that meets the conditions;

[0030] Step S403: Select the energy window boundary combination with the smallest relative error e and denote it as the optimal energy window boundary combination under this formation.

[0031] Furthermore, the relative error e is calculated as follows: In the formula, ρ is the known true density value of the stratum. b The apparent density is the density obtained from the gamma density logging response spectrum, whether obtained through calibration experiments or from density logging models of different formation environments built on simulation software. The true density value of the formation is known.

[0032] In step S7, the data similarity comparison method employs a norm-based approach, specifically including:

[0033] Step S701: Calculate the norm between the response energy spectrum of the unrecorded stratigraphic environment and all existing response energy spectra;

[0034] Step S702: Using the calculated norm, find the energy spectrum with the smallest norm that is obtained from the unrecorded stratigraphic environment in this acquisition, and use it as the existing response energy spectrum with the highest similarity.

[0035] Furthermore, the specific calculation formula for the method of solving the norm is as follows: In the formula, d represents the norm between the response energy spectrum of the unrecorded stratigraphic environment and all existing response energy spectra, and N... i This indicates that the density count of the i-th channel of the energy spectrum is already available, and n i This represents the density count of the i-th channel of the measured response energy spectrum.

[0036] On one hand, the present invention provides a formation density calculation device for variable energy window gamma density logging, comprising: a data acquisition module, an energy window revision module, a density calculation module, an optimal energy window screening module, an optimal energy window storage module, a test data acquisition module, a similarity judgment module, and a solution module;

[0037] The data acquisition module is used to acquire the response energy spectrum of the gamma density logging tool under different known formation environments, and to record the response energy spectrum of each known formation environment as the "formation environment-energy spectrum database".

[0038] The energy window revision module is used to change the energy window boundary of the response energy spectrum. It traverses the energy corresponding to the channel address of the response energy spectrum through a traversal algorithm and divides the response energy spectrum into energy channel addresses to obtain different combinations of energy window boundaries and density counts under the combination.

[0039] The density calculation module is used to calculate the density for different combinations of energy window boundaries and the density count under such combinations, and use the calculation results as the apparent density under different combinations of energy window boundaries.

[0040] The optimal energy window screening module performs benchmarking and screening based on the calculated apparent density and the true density of the formation, eliminates random results, and selects the energy window boundary combinations that meet the conditions. The module then uses the minimum value method to find the energy window boundary combination with the smallest relative error, which is denoted as the optimal energy window boundary combination under that formation.

[0041] The optimal energy window storage module is used to store the optimal energy window boundary combination corresponding to each stratigraphic environment in different known stratigraphic environments into the "Stratigraphic-Optimal Energy Window Combination Database". The "Stratigraphic-Optimal Energy Window Combination Database" is linked with the "Stratigraphic Environment-Energy Spectrum Database" to obtain the "Stratigraphic Environment-Energy Spectrum-Optimal Energy Window Combination Database".

[0042] The data acquisition module is used to acquire the response energy spectrum of the gamma density logging tool under formation environment.

[0043] The similarity judgment module is used to compare the similarity between the response energy spectrum to be tested and the existing recorded response energy spectrum, and select the existing response energy spectrum with the highest similarity.

[0044] The solution module is used to query the "stratum environment-energy spectrum-optimal energy window combination database" to obtain the optimal energy window boundary combination corresponding to the existing response energy spectrum with the highest similarity, which is used as the new energy window range. The apparent density of the new energy window range is calculated using the compensation density calculation formula to obtain the final calculation result.

[0045] On the one hand, the present invention also provides an electronically readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of a formation density calculation method for a variable energy window gamma density logging as described above.

[0046] Compared with the prior art, the present invention has the following advantages:

[0047] 1. This invention employs a variable energy window and the most similarity calculation method, which can change the energy window range when the energy spectrum shifts due to the presence of mud cake in the logging environment, thereby improving the measurement accuracy of the density logging tool.

[0048] 2. This invention is applicable to both simulated and measured spectra, and also has an adjustable parameter function, which allows for targeted parameter adjustments for logging instruments with different densities, thereby further improving the accuracy of density calculation.

[0049] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures pointed out in the description, claims and drawings. Attached Figure Description

[0050] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0051] Figure 1 A flowchart illustrating a method for calculating formation density using variable energy window gamma density logging is shown.

[0052] Figure 2 Another flowchart illustrating a method for calculating formation density in variable energy window gamma density logging is shown.

[0053] Figure 3 A schematic diagram of the traversal algorithm flow for a formation density calculation method using variable energy window gamma density logging is shown.

[0054] Figure 4 A comparison chart showing the error effects of simulation data from fixed energy windows and sliding energy windows is presented.

[0055] Figure 5 A comparison chart showing the error effects of experimental data from fixed energy windows and sliding energy windows is presented. Detailed Implementation

[0056] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0057] Example 1.

[0058] In one embodiment, please refer to Figure 1 , Figure 2 and Figure 3 This invention provides a method for calculating formation density using variable energy window gamma density logging, comprising the following steps:

[0059] Step S1: Obtain the response energy spectrum of the gamma density logging tool under different known formation environments, and record each formation environment and response energy spectrum as the "formation environment-energy spectrum database";

[0060] Furthermore, methods for obtaining the response energy spectrum of the gamma density logging tool under different known formation environments include: obtaining it through calibration well experiments or by establishing density logging models of different formation environments on simulation software.

[0061] Furthermore, the formation environment includes mud cake density, mud cake thickness, lithology, formation density, mud slurry gaps, and mud slurry density; the response energy spectrum includes the energy spectrum of long-source-distance detectors and the energy spectrum of short-source-distance detectors; each formation environment and its long-source-distance detector energy spectrum and short-source-distance detector energy spectrum are recorded and denoted as the "formation environment-energy spectrum database".

[0062] Furthermore, the energy spectra of the long-source-pitch detector and the short-source-pitch detector both have different energy window ranges, and these energy window ranges have high-energy boundaries E. high and low-energy boundary E low Wherein: in the energy window region of the energy spectrum of the long-source-spacing detector, the high-energy boundary is LE. high The low-energy boundary is LE low The high-energy boundary in the energy window region of the short-source-pitch detector energy spectrum is SE. high The low-energy boundary is SE low .

[0063] Step S2: Use a traversal algorithm to change the energy window boundary of the response energy spectrum, traverse the energy corresponding to the channel address of the response energy spectrum and divide the response energy spectrum into energy channel addresses, and obtain different combinations of energy window boundaries and density counts under the combination.

[0064] For further details, please refer to Figure 3The traversal algorithm includes the following steps:

[0065] S201: Finding the peak E in the response energy spectrum m E m This is the energy corresponding to the channel with the highest count rate in the entire response energy spectrum;

[0066] S202: Initialize the low-energy boundary E low High-energy boundary E high E low =E m E high =E low ;

[0067] S203: E slides one step to the right low E high You need to slide to the right several times until you reach E. high Greater than 0.662 MeV;

[0068] S204: Each time E is swiped high All operations must calculate the sum of counts within the current energy window range, i.e., the density count N, and record the current energy window boundary combination and density count, i.e., E. low E high and N;

[0069] S205: Until E low If the energy is greater than 0.662 MeV, the traversal ends.

[0070] Furthermore, by performing the above-mentioned traversal of the energy window boundaries on the response energy spectra of long and short source distance detectors respectively, different combinations of energy window boundaries for long and short source distance detectors and density counts under such combinations can be obtained.

[0071] Step S3: Using the compensated density calculation formula of the gamma density logging tool, perform density calculations for different energy window boundary combinations and density counts under these combinations, and use the calculation results as the apparent density under different energy window boundary combinations.

[0072] Furthermore, the formula for calculating the compensated density of the gamma density logging tool is as follows: In the formula, ρ b For the calculated apparent density, A L A s These are known coefficients related to long and short source distance detectors and the geological environment, respectively, B. L B S These are known coefficients related to long and short source distance detectors and the geological environment, respectively, N. L N S, respectively, are the sum of density count rates within specific energy ranges in the energy spectra of long and short source distance detectors in well logging, and k is the fitting coefficient between long and short source distance detectors and the formation environment.

[0073] The calculated apparent density ρ for each combination of long and short source distance energy window boundaries is calculated using the compensated density calculation formula of this density logging tool. b .

[0074] Step S4: Based on the calculated apparent density and true density of the formation, a benchmark screening is performed to eliminate random results. The selected energy window boundary combinations that meet the conditions are then used to find the energy window boundary combination with the smallest relative error by taking the minimum value. This is recorded as the optimal energy window boundary combination for that formation.

[0075] Furthermore, the benchmarking and screening based on the calculated apparent density and the true density of the formation includes the following steps:

[0076] Step S401: Set the power window width to be greater than 10 address widths, i.e., E high -E low >10×0.003198MeV;

[0077] Step S402: Calculate the relative error e between the energy window boundary combination and the true density that meets the conditions;

[0078] Step S403: Select the energy window boundary combination with the smallest relative error e and denote it as the optimal energy window boundary combination under this formation.

[0079] Furthermore, the relative error e is calculated as follows: In the formula, ρ is the known true density value of the stratum. b The apparent density is the density obtained from the gamma density logging response spectrum, whether obtained through calibration experiments or from density logging models of different formation environments built on simulation software. The true density value of the formation is known.

[0080] Step S5: Apply steps S1 to S4 to different formation environments, store the optimal energy window boundary combination corresponding to each formation environment into the "formation-optimal energy window combination database", and link the "formation-optimal energy window combination database" with the "formation environment-energy spectrum database" to obtain the "formation environment-energy spectrum-optimal energy window combination database".

[0081] Step S6: Obtain the response energy spectrum of the gamma density logging tool under formation environment as the response energy spectrum to be tested;

[0082] Step S7: Use the data similarity comparison method to compare the similarity between the response energy spectrum to be tested and the existing recorded response energy spectrum, and select the existing response energy spectrum with the highest similarity.

[0083] Furthermore, the data similarity comparison method employs a norm-based approach, specifically including:

[0084] Step S701: Calculate the norm between the response energy spectrum of the unrecorded stratigraphic environment and all existing response energy spectra;

[0085] Step S702: Using the calculated norm, find the energy spectrum with the smallest norm that is obtained from the unrecorded stratigraphic environment in this acquisition, and use it as the existing response energy spectrum with the highest similarity.

[0086] Furthermore, the specific calculation formula for the method of solving the norm is as follows: In the formula, d represents the norm between the response energy spectrum of the unrecorded stratigraphic environment and all existing response energy spectra, and N... i This indicates that the density count of the i-th channel of the energy spectrum is already available, and n i This represents the density count of the i-th channel of the measured response energy spectrum.

[0087] Step S8: Query the "Formation Environment-Energy Spectrum-Optimal Energy Window Combination Database" to obtain the optimal energy window boundary combination corresponding to the existing response energy spectrum with the highest similarity, and use it as the new energy window range. Calculate the apparent density of the new energy window range using the compensation density calculation formula to obtain the final calculation result.

[0088] Example 2.

[0089] In one embodiment, please refer to Figure 4 and Figure 5 .

[0090] Figure 4 The comparison chart of simulation data error effects between fixed energy window and sliding energy window is obtained by acquiring the energy spectrum of the logging instrument under different formation environments in density logging models built on Monte Carlo simulation software, using the method used in this invention.

[0091] Figure 5 The diagram shows a comparison of the error effects of experimental data from fixed energy windows and sliding energy windows. The data was obtained by using the method described in this invention to acquire the energy spectrum of the logging tool under different formation environments during a calibration well experiment.

[0092] Furthermore, regardless of whether the energy spectrum of the logging tool is obtained in simulation software under different formation environments, or in calibration well experiments under different formation environments, the experimental operation process after obtaining the energy spectrum is the same, and the specific implementation operation is as follows:

[0093] The response energy spectra of the lithology density LDLT1550 logging tool under different mud cake thicknesses and densities were obtained. The response energy spectra include the energy spectra of short-spacing detectors and the energy spectra of long-spacing detectors. Each formation environment and its short-spacing detector energy spectrum and long-spacing detector energy spectrum were recorded and denoted as the "formation environment-energy spectrum database" of the lithology density LDLT1550 logging tool.

[0094] An ergonomic algorithm was used to modify the energy window boundaries for short and long source-pitch detectors. The energy spectrum of the short-pitch and long-pitch detectors was traversed, corresponding to the energy levels of the channel addresses. The energy spectrum was then divided into 207 channels, with the 0-0.662 MeV range divided into these channels. The short and long source-pitch detectors have different energy window ranges, each with a high-energy boundary E. high and low-energy boundary E low Long source distance, high energy window, boundary LE high Long source distance, low energy boundary LE low Short source distance, high energy window, SE high Short source distance, low energy boundary SE low By performing the above-mentioned traversal of the energy window boundaries on the response energy spectra of the long and short source distance detectors respectively, the lithological density of the LDLT1550 logging tool can be obtained by combining different energy window boundaries of the long and short source distance detectors and the density counts under such combinations.

[0095] Apparent density is calculated based on density counting within different energy windows; the formula for calculating the compensated density of mud cake using the LDLT1550 logging tool is as follows: In the formula, ρ b To calculate density, N L N S These are the sums of density count rates within specific energy ranges in the energy spectra of long and short source-spacing detectors in well logging, respectively; the calculated density ρ is then calculated using this compensation density calculation formula for each combination of long and short source-spacing energy window boundaries. b ;

[0096] Based on apparent density, the relative error between apparent density and actual density is calculated, and the optimal combination of energy windows is selected.

[0097] The above steps are applied to different stratigraphic environments. The optimal energy window boundary combination corresponding to each stratigraphic environment is recorded and denoted as the "Stratigraphic-Optimal Energy Window Combination Database". This database is then linked with the "Stratigraphic Environment-Energy Spectrum Database" to obtain the "Stratigraphic Environment-Energy Spectrum-Optimal Energy Window Combination Database".

[0098] Density energy spectrum data of 9 formation densities and 8 types of light and heavy mud cakes were obtained as the energy spectrum data to be measured. The energy spectrum data to be measured is based on the "formation environment-energy spectrum-optimal energy window combination database" obtained by establishing density logging models of different formation environments on Monte Carlo simulation software, and the "formation environment-energy spectrum-optimal energy window combination database" obtained in calibration well experiments. The following operations were performed respectively:

[0099] Compare the similarity between the energy spectrum data to be measured and the existing recorded response energy spectra, and select the existing response energy spectrum with the highest similarity.

[0100] The database "Formation Environment-Energy Spectrum-Optimal Energy Window Combination" is consulted to obtain the optimal energy window boundary combination corresponding to the existing response energy spectrum with the highest similarity. This combination is used as the new energy window range. The apparent density of the new energy window range is calculated using the compensation density calculation formula to obtain the final calculation result.

[0101] The results are as follows Figure 4 and Figure 5 Data comparison chart.

[0102] from Figure 4 and Figure 5 The data comparison shows that, using the calculation method of this invention, and verifying the measured density spectral data of 9 formation densities and 8 types of light and heavy mud cakes, the measurement accuracy of this invention is improved by one order of magnitude.

[0103] Example 3.

[0104] In one embodiment, a formation density calculation device for variable energy window gamma density logging is provided, comprising: a data acquisition module, an energy window revision module, a density calculation module, an optimal energy window screening module, an optimal energy window storage module, a test data acquisition module, a similarity judgment module, and a solution module;

[0105] The data acquisition module is used to acquire the response energy spectrum of the gamma density logging tool under different known formation environments, and to record the response energy spectrum of each known formation environment as the "formation environment-energy spectrum database".

[0106] The energy window revision module is used to change the energy window boundary of the response energy spectrum. It traverses the energy corresponding to the channel address of the response energy spectrum through a traversal algorithm and divides the response energy spectrum into energy channel addresses to obtain different combinations of energy window boundaries and density counts under the combination.

[0107] The density calculation module is used to calculate the density for different combinations of energy window boundaries and the density count under such combinations, and use the calculation results as the apparent density under different combinations of energy window boundaries.

[0108] The optimal energy window screening module performs benchmarking and screening based on the calculated apparent density and the true density of the formation, eliminates random results, and selects the energy window boundary combinations that meet the conditions. The module then uses the minimum value method to find the energy window boundary combination with the smallest relative error, which is denoted as the optimal energy window boundary combination under that formation.

[0109] The optimal energy window storage module is used to store the optimal energy window boundary combination corresponding to each stratigraphic environment in different known stratigraphic environments into the "Stratigraphic-Optimal Energy Window Combination Database". The "Stratigraphic-Optimal Energy Window Combination Database" is linked with the "Stratigraphic Environment-Energy Spectrum Database" to obtain the "Stratigraphic Environment-Energy Spectrum-Optimal Energy Window Combination Database".

[0110] The data acquisition module is used to acquire the response energy spectrum of the gamma density logging tool under formation environment.

[0111] The similarity judgment module is used to compare the similarity between the response energy spectrum to be tested and the existing recorded response energy spectrum, and select the existing response energy spectrum with the highest similarity.

[0112] The solution module is used to query the "stratum environment-energy spectrum-optimal energy window combination database" to obtain the optimal energy window boundary combination corresponding to the existing response energy spectrum with the highest similarity, which is used as the new energy window range. The apparent density of the new energy window range is calculated using the compensation density calculation formula to obtain the final calculation result.

[0113] Example 4.

[0114] In one embodiment, an electronically readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of a formation density calculation method for a variable energy window gamma density logging as described above.

[0115] Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for calculating formation density in variable energy window gamma density logging, characterized in that: Obtain the response energy spectrum of the gamma density logging tool under different known formation environments, and record the response energy spectrum of each formation environment as the "formation environment-energy spectrum database"; The energy window boundaries of the response energy spectrum are changed by traversing the energy corresponding to the channel address of the response energy spectrum and dividing the response energy spectrum into energy channel addresses to obtain different combinations of energy window boundaries and density counts under the combination. Using the compensation density calculation formula of the gamma density logging tool, density calculations are performed on different energy window boundary combinations and density counts under these combinations, and the calculation results are used as the apparent density under different energy window boundary combinations. Based on the calculated apparent density and the true density of the formation, the energy window boundary combination that meets the conditions is selected by benchmarking and screening to eliminate random results. The energy window boundary combination with the smallest relative error is obtained by taking the minimum value and is recorded as the optimal energy window boundary combination under the formation. Apply the above four steps to different known stratigraphic environments, store the optimal energy window boundary combination corresponding to each stratigraphic environment into the "Stratigraphic-Optimal Energy Window Combination Database", and link the "Stratigraphic-Optimal Energy Window Combination Database" with the "Stratigraphic Environment-Energy Spectrum Database" to obtain the "Stratigraphic Environment-Energy Spectrum-Optimal Energy Window Combination Database". The response energy spectrum of the gamma density logging tool under formation environment is obtained as the response energy spectrum to be tested; The data similarity comparison method is used to compare the similarity between the response energy spectrum to be tested and the existing recorded response energy spectrum, and the existing response energy spectrum with the highest similarity is selected. Query the "Formation Environment-Energy Spectrum-Optimal Energy Window Combination Database" to obtain the optimal energy window boundary combination corresponding to the existing response energy spectrum with the highest similarity. Use this combination as the new energy window range and calculate the apparent density of the new energy window range using the compensation density calculation formula to obtain the final calculation result.

2. The method for calculating formation density in variable energy window gamma density logging as described in claim 1, characterized in that, The method for obtaining the response energy spectrum of the gamma density logging tool under different known formation environments includes: Obtained through calibration well experiments; Density data were obtained by establishing density logging models for different formation environments using simulation software.

3. The formation density calculation method for variable energy window gamma density logging as described in claim 1, characterized in that: The geological environment includes mud cake density, mud cake thickness, lithology, formation density, mud slurry gaps, and mud slurry density; The response energy spectrum includes the energy spectrum of long-source-distance detectors and the energy spectrum of short-source-distance detectors.

4. The formation density calculation method for variable energy window gamma density logging as described in claim 3, characterized in that: The energy spectra of the long-source-pitch detector and the short-source-pitch detector both have different energy window ranges, and these energy window ranges have high-energy boundaries E. high and low-energy boundary E low ,in: Within the energy window region of the long-source-pitch detector's energy spectrum, the high-energy boundary is LE. high The low-energy boundary is LE low The high-energy boundary in the energy window region of the short-source-pitch detector energy spectrum is SE. high The low-energy boundary is SE low .

5. The formation density calculation method for variable energy window gamma density logging as described in claim 4, characterized in that, The traversal algorithm specifically includes: Find the peak E in the response energy spectrum m E m This is the energy corresponding to the channel with the highest count rate in the entire response energy spectrum; Initialize the low-energy boundary E low High-energy boundary E high E low =E m E high =E low ; Each step E slides to the right low E high You need to slide to the right several times until you reach E. high Greater than 0.662 MeV; Each slide E high All operations must calculate the sum of counts within the current energy window range, i.e., the density count N, and record the current energy window boundary combination and density count, i.e., E. low E high and N; Until E low If the energy is greater than 0.662 MeV, the traversal ends.

6. The formation density calculation method for variable energy window gamma density logging as described in claim 4, characterized in that, The apparent density is calculated as follows: In the formula, ρ b For the calculation of apparent density, A L A s These are known coefficients related to long and short source distance detectors and the geological environment, respectively, B. L B S These are known coefficients related to long and short source-spacing detectors and the geological environment, respectively, N. L N S , respectively, are the sum of density count rates within specific energy ranges in the energy spectra of long and short source distance detectors in well logging, and k is the fitting coefficient between long and short source distance detectors and the formation environment.

7. The method for calculating formation density in variable energy window gamma density logging as described in claim 4, characterized in that, The benchmarking and screening based on the calculated apparent density and the true density of the formation includes the following criteria: Set the power window width to be greater than 10 channel addresses, i.e., E high -E low >10×0.003198MeV; Calculate the relative error e between the energy window boundary combination that meets the conditions and the true density; The energy window boundary combination that minimizes the relative error e is denoted as the optimal energy window boundary combination for that formation.

8. The formation density calculation method for variable energy window gamma density logging as described in claim 7, characterized in that: The relative error e is calculated as follows: In the formula, ρ is the known true density value of the stratum. b For apparent density.

9. The formation density calculation method for variable energy window gamma density logging as described in claim 4, characterized in that: The data similarity comparison method involves solving for the norm, specifically including: The norm is calculated between the response energy spectrum of unrecorded stratigraphic environments and all existing response energy spectra; By calculating the norm, the energy spectrum with the smallest norm between the energy spectrum and the response energy spectrum of the unrecorded stratigraphic environment is identified, and this spectrum is taken as the existing response energy spectrum with the highest similarity.

10. The method for calculating formation density in variable energy window gamma density logging as described in claim 9, characterized in that, The formula for calculating the norm is as follows: In the formula, d represents the norm between the response energy spectrum of the unrecorded stratigraphic environment and all existing response energy spectra, and N... i This indicates that the density count of the i-th channel of the energy spectrum is already available, and n i This represents the density count of the i-th channel of the measured response energy spectrum.

11. A formation density calculation device for variable energy window gamma density logging, characterized in that, include: The data acquisition module is used to acquire the response energy spectrum of the gamma density logging tool under different known formation environments, and to record the response energy spectrum of each known formation environment as the "formation environment-energy spectrum database". The energy window revision module is used to change the energy window boundary of the response energy spectrum. It traverses the energy corresponding to the energy address of the response energy spectrum through the traversal algorithm and divides the response energy spectrum into energy addresses to obtain different energy window boundary combinations and density counts under the combination. The density calculation module is used to calculate the density for different combinations of energy window boundaries and the density count under those combinations, and the calculation results are used as the apparent density under different combinations of energy window boundaries. The optimal energy window screening module performs benchmarking and screening based on the calculated apparent density and the true density of the formation, eliminates random results, and selects the energy window boundary combinations that meet the conditions. The module then uses the minimum value method to find the energy window boundary combination with the smallest relative error, which is denoted as the optimal energy window boundary combination for that formation. The optimal energy window storage module is used to store the optimal energy window boundary combination corresponding to each stratigraphic environment in different known stratigraphic environments. The optimal energy window combination combination is stored in the "Stratigraphic-Optimal Energy Window Combination Database". The "Stratigraphic-Optimal Energy Window Combination Database" is linked with the "Stratigraphic Environment-Energy Spectrum Database" to obtain the "Stratigraphic Environment-Energy Spectrum-Optimal Energy Window Combination Database". The test data acquisition module is used to acquire the response energy spectrum of the gamma density logging tool under formation environment. The similarity judgment module is used to compare the similarity between the response energy spectrum to be tested and the existing recorded response energy spectrum, and select the existing response energy spectrum with the highest similarity. The solution module is used to query the "strata environment-energy spectrum-optimal energy window combination database" to obtain the optimal energy window boundary combination corresponding to the existing response energy spectrum with the highest similarity, which is used as the new energy window range. The apparent density of the new energy window range is calculated using the compensation density calculation formula to obtain the final calculation result.

12. An electronically readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of a formation density calculation method for variable energy window gamma density logging as described in any one of claims 1 to 10.

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