A rapid detection method for abnormal value sections in well logging acoustic time difference data

Abstract

The present invention relates to the field of rock physics and geology, and in particular to a method for rapidly detecting outlier intervals in well logging acoustic transit time data. The method comprises calculating porosity interpretations from acoustic transit time, density, and neutron logging; calculating and analyzing the differences between the acoustic transit time porosity conclusions and two other types of porosity conclusions; and determining the outlier intervals in the well logging acoustic transit time data, completing the detection process. This method effectively addresses the problem of acoustic transit time distortion caused by lamellar development in shale formations, effectively identifying lamellar development intervals and recovering the true acoustic transit time values of the formation.

Description

Technical Field

[0001] The present invention relates to the technical field of rock physics and geology, and in particular to a method for quickly detecting abnormal value well sections of well logging sonic time difference data. Background Art

[0002] Acoustic logging, a technique developed since the 1950s, is used to measure key acoustic properties of rock, including acoustic wave propagation velocity, acoustic wave attenuation characteristics, and frequency variations. Acoustic logging can effectively delineate rock strata and identify lithology; determine rock porosity; locate fractures and karst caves in carbonate rocks; and, in conjunction with other techniques, calculate rock mechanical properties. It also provides rock acoustic wave propagation velocity data for seismic exploration and provides the foundation for synthetic seismic records.

[0003] As one of the most important types of acoustic logging, transit time logging (TTL) determines the composition and porosity of formations by measuring the time it takes for an acoustic wave, generated by an acoustic transmitter onboard, to travel through a specified length of formation and be captured by a receiver. However, due to certain unique circumstances, the amplitude of the first wave received by a receiver located far from the transmitter may be very small. This prevents the surface recording TTL from being triggered, and the circuit is triggered only by subsequent waves that lag one to several cycles behind the first wave. This causes the TTL to increase dramatically, often by an integer multiple of the acoustic signal period (e.g., 30μs). Failure to effectively monitor TTL data anomalies can lead to erroneous reservoir evaluation using data with significant errors.

[0004] In the prior art, a Chinese invention patent document with authorization publication number CN105301657B and authorization publication date July 24, 2017 has been proposed. The technical solution disclosed in the patent document is as follows: a curve correction method based on rock physics significance, comprising: establishing a rock physics model based on actual test data; forward modeling to obtain an initial P-wave velocity and density curve intersection template, and combining it with the wellbore curve to determine the P-wave velocity and density curve to be corrected; dividing the curve to be corrected into multiple time windows; setting a correction coefficient, and using the weighted average Raymer rock physics correction method and the empirical formula method to correct the curves in the multiple time windows respectively to obtain variable coefficient weighted correction results; based on the variable coefficient weighted correction results, using the wellbore-seismic correlation coefficient as a basis and referring to the convergence of the anomaly point to determine whether the correction result is reasonable; when the wellbore-seismic correlation coefficient is lower than a set threshold or the anomaly point has not converged, the correction coefficient is modified and the variable coefficient weighted correction result is recalculated; when the wellbore-seismic correlation coefficient is equal to or higher than the set threshold and the anomaly point has converged, the correction result is reasonable, and the corrected curve is output.

[0005] The above technical solution may cause the following problems during actual use:

[0006] The technical point of this invention is to use the logging rock physics model to correct seismic data. Its analysis and judgment of abnormal points (values) are relatively vague, which is also the norm for similar technologies in the industry. It focuses on the specific methods of outlier solutions or corrections, but does not mention the determination and quantitative analysis of outliers.

[0007] Prior art has proposed a Chinese invention patent document with authorization publication number CN105301657B, published on March 15, 2019. The patent discloses the following technical solution: a method and apparatus for correcting acoustic logging data. The method comprises: acquiring wellbore logging data; determining a distortion location of the wellbore based on the logging data; determining the distortion level of the wellbore at the distortion location; and correcting the acoustic transit time curve corresponding to the distortion location based on the distortion level to obtain a corrected acoustic transit time curve for the wellbore. This method can improve the accuracy of acoustic logging data correction.

[0008] The above technical solution may cause the following problems during actual use:

[0009] The above scheme adopts the acoustic wave correction method. Although it classifies abnormal situations, it lacks comprehensive consideration means. It adopts the qualitative observation method, which is poor in accuracy and quantifiability. In addition, the scheme adopts the fitting method and relies on data points and work area data characteristics, which makes it difficult to promote and apply in other blocks. Summary of the Invention

[0010] In order to solve the above technical problems, the present invention proposes a method for quickly detecting well sections with abnormal values of logging acoustic wave time difference data, which can effectively solve the problem of acoustic wave time difference distortion caused by the development of lamellae in shale layers, and can effectively identify the lamellae development layers and restore the true acoustic wave time difference value of the formation.

[0011] The present invention is achieved by adopting the following technical solutions:

[0012] A method for quickly detecting abnormal value sections of well logging acoustic time difference data, characterized by comprising the following steps:

[0013] a. Calculate the porosity interpretation conclusions of acoustic transit time, density and neutron logging;

[0014] b. Calculate and analyze the differences between the conclusions of acoustic transit time porosity and the other two types of porosity conclusions;

[0015] c. Based on the conclusion of step b, determine the abnormal well section of the logging acoustic wave time difference data and complete the detection process.

[0016] The step a specifically includes:

[0017] a1. Obtain the acoustic transit time, neutron logging and density logging data for the well section to be analyzed;

[0018] a2. Obtain porosity interpretation conclusions based on acoustic time difference, neutron logging, and density logging at each depth point in the well section to be analyzed.

[0019] The step b specifically includes:

[0020] b1. Draw a superimposed chart of sonic transit time, density logging and neutron logging porosity;

[0021] b2. Calculate the difference between the sonic transit time porosity and the other two types of porosity.

[0022] The step c specifically includes:

[0023] c1. Establish a model to distinguish the difference between transit-time porosity and neutron and density logging porosity;

[0024] c2. Determine whether the acoustic time difference is abnormal and the extent of the abnormality based on the discriminant model to complete the detection process.

[0025] In step a2, the sonic transit time porosity, neutron logging porosity, and density logging porosity are calculated as follows:

[0026] φAC i =(AC f -ACR i ) / (ACF i -ACR i )

[0027] φDEN i =(DEN i -DENR i ) / (DENF i -DENR i )

[0028] φCNL i =(CNL i -CNLR i -0.5*Vsh*Nsh)*0.01,

[0029] Among them, φAC i is the sonic transit time porosity, AC i ACR is the acoustic time difference reading corresponding to a certain depth of the well section to be tested. i ACF is the theoretical reading of acoustic wave transit time corresponding to unit volume of rock skeleton. i is the theoretical reading of acoustic wave transit time corresponding to unit volume of pore fluid, φDEN i is the density logging porosity, DEN i DENR is the density logging reading per unit volume at the same depth.i DENF is the theoretical logging reading of rock skeleton density per unit volume. i is the theoretical logging reading of the corresponding unit volume pore fluid density, φCNL i is the neutron logging porosity, CNL i The CNLR is the neutron logging reading per unit volume at the same depth. i is the theoretical neutron logging reading corresponding to unit volume of rock skeleton, Vsh is the mud content of the well section to be tested, and Nsh is the neutron characteristic value of the mudstone in the well section to be tested.

[0030] In step b1, the elements of the superimposed trace include well depth, well logging curve and porosity interpretation conclusion.

[0031] In step b2, the difference between the calculated acoustic transit time porosity and the other two types of porosity conclusions specifically refers to:

[0032] Δ iAC-CNL =φCNL i ′-φAC i

[0033] Δ iAC-DEN =φDEN i ′-φAC i ,

[0034] in,

[0035] φCNL i ′=(((φCNL i *φAC imin ) / φCNL imin )*(1ABS(φCNL imin φCNL iaver ) / ABS(φCNL imax -φCNL iaver ))+((φCNL i *φAC imax ) / φCNL imax ′))*(1-ABS(φCNL imin -φCNL iaver ) / ABS(φCNL imax -φCNL iaver )) / 2

[0036] φDEN i ′=(((φDEN i *φAC imin ) / φDEN imin )*(1-ABS(φDENi min -φφDEN iaver ) / ABS(φDENi max-φDENi aver ))+((φDENi*φAC imax ) / φDENi max ′))*(1-ABS(φDENi min -φDENi aver ) / ABS(φDENi max -φDENi aver )) / 2;

[0037] Among them, Δ iAC-CNL is the difference between the sonic transit time porosity and the neutron porosity, Δ iAC-DEN is the difference between acoustic transit time porosity and density porosity, φCNL i ′ is the neutron logging data for the superimposed trace, φAC i Calculate the porosity of the well section to be tested by acoustic time difference, φAC imin The minimum porosity value calculated by the acoustic time difference of the well section to be tested, φAC imax The maximum porosity value calculated by the acoustic time difference of the well section to be tested, φCNL iaver Calculate the average porosity of the well section to be tested based on the acoustic time difference.

[0038] The establishment of the discrimination model for the degree of difference between the sonic transit time porosity and the neutron and density logging porosity in step c1 specifically refers to: using neutron logging porosity to participate in the establishment of an abnormal well section identification model for measuring the sonic transit time porosity. For the i-th meter, the discrimination equation for the degree of abnormality of the sonic transit time data is as follows:

[0039] δ 总 =(φCNL i +(Δ iAC-DEN +Δ iAC-cNL ) / 2) / φAC i ;

[0040] The judgment method in step c2 is: 总 <1 means that the acoustic time difference data is not abnormal; when δ 总 >1 means that the acoustic time difference data is significantly abnormal.

[0041] Compared with the prior art, the present invention has the following beneficial effects:

[0042] 1. This application provides a method for quickly detecting well sections with abnormal values of acoustic time difference data in well logging. According to the difference between the acoustic time difference data and density logging and neutron logging, the quality of acoustic time difference logging data is judged, and the abnormal situation of acoustic time difference logging data in a certain well section in the oil and gas field is detected, providing the necessary conditions for subsequent data correction work. This application detects abnormal well sections with acoustic time difference by combining neutron and density logging for comprehensive judgment. It contains more information and has more reliable conclusions. It can effectively solve the problem of acoustic time difference distortion caused by the development of shales. It can effectively identify the layers with developed shales and restore the true acoustic time difference value of the formation. It is of great significance to the subsequent development of a series of related technologies such as reservoir prediction, reservoir evaluation, and reservoir transformation.

[0043] 2. Since density logging is greatly affected by the diagenetic mineral composition of the well section to be tested, and neutron logging is more significantly affected by the properties of the fluid in the pore space, the neutron logging porosity is used to participate in the measurement of acoustic time difference to establish the identification mode of the abnormal well section, making the model establishment more accurate.

[0044] 3. This application adopts a quantitative calculation method, which is more excellent in terms of accuracy and quantifiability.

[0045] 4. This application selects discrete petroleum logging data to perform forward calculations on the porosity benchmark to facilitate promotion and application.

[0046] 5. This application significantly improves the application rate and level of logging parameters. Traditional technologies usually simply combine or fit the acoustic, density and neutron logging. This patent achieves the data "1+1+1>3", improves the parameter fusion effect, and makes the acoustic logging parameters more accurate and reliable.

[0047] 6. This application detects and identifies abnormal values of acoustic logging, the most widely used and important type of conventional "three-porosity" logging, thereby improving data reliability and stability and providing more powerful data support for oil and gas exploration decision-making and evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments, wherein:

[0049] Figure 1 It is a schematic diagram of the process of the present invention;

[0050] Figure 2 This is an illustration of the application effect of the present invention Figure 1 ;

[0051] Figure 3 This is an illustration of the application effect of the present invention Figure 2 . DETAILED DESCRIPTION

[0052] Example 1

[0053] As the basic embodiment of the present invention, refer to the attached Figure 1 The present invention includes a method for quickly detecting abnormal value sections of well logging acoustic time difference data, comprising the following steps:

[0054] a. Calculation of sonic transit time, density and porosity interpretation conclusions from neutron logging:

[0055] a1. Obtain the acoustic transit time, neutron logging and density logging data for the well section to be analyzed;

[0056] a2. Obtain porosity interpretation conclusions based on acoustic time difference, neutron logging, and density logging at each depth point in the well section to be analyzed.

[0057] b. Calculate and analyze the differences between the conclusions of acoustic transit time porosity and the other two types of porosity conclusions:

[0058] b1. Draw a superimposed chart of sonic transit time, density logging and neutron logging porosity;

[0059] b2. Calculate the difference between the sonic transit time porosity and the other two types of porosity.

[0060] c. According to the conclusion of step b, determine the abnormal well section of the logging acoustic time difference data and complete the detection process:

[0061] c1. Establish a model to distinguish the difference between transit-time porosity and neutron and density logging porosity;

[0062] c2. Determine whether the acoustic time difference is abnormal and the extent of the abnormality based on the discriminant model to complete the detection process.

[0063] Example 2

[0064] As a preferred embodiment of the present invention, the present invention includes a method for rapidly detecting outlier intervals in well logging acoustic transit time data. This method was implemented and applied in a specific well. Patented numerical models were used to identify neutron logging and density logging characteristics, and the acoustic logging data was corrected accordingly. Outlier identification was performed by drawing an overlay chart and combining numerical methods. Specifically, the following steps were included:

[0065] a. Calculate the sonic time difference, density and porosity interpretation conclusions of neutron logging.

[0066] Assume that the acoustic wave time difference reading corresponding to a certain depth of the well section to be tested is AC i , the theoretical reading of the acoustic wave time difference per unit volume of rock skeleton is ACR i , the theoretical reading of the acoustic wave time difference per unit volume of pore fluid is ACF i , the porosity calculated by acoustic time difference is φAC i, assume that the density logging reading corresponding to the unit volume at the same depth is DEN i , the corresponding unit volume rock skeleton density logging theoretical reading is DENR i , the corresponding unit volume pore fluid density logging theoretical reading is DENF i , density logging calculates porosity as φDEN i , assuming that the neutron logging reading corresponding to the unit volume at the same depth is CNL i , the corresponding unit volume rock skeleton neutron logging theoretical reading is CNLR i The corresponding unit volume pore fluid neutron logging theoretical reading is CNLF i , the porosity calculated by neutron logging is φCNL i , then the sonic transit time porosity, neutron logging porosity and density logging porosity are calculated as follows:

[0067] AC i =ACR i *(1-φAC i )+ACFi*φAC i

[0068] Then there is

[0069] φAC i =(AC i -ACR i ) / (ACF i -ACR i ) (Formula 1)

[0070] Similarly,

[0071] DEN i =DEN i *(1-φDEN i )+DENF i *φDEN i (Formula 2)

[0072] Then there is

[0073] φDEN i =(DEN i -DENR i ) / (DENF i -DENR i ) (Formula 3)

[0074] Similarly,

[0075] CNL i =CNL i *(1-φCNL i )+CNLF i *φCNL i(Formula 4)

[0076] Then there is

[0077] φCNL i =(CNL i -CNLR i ) / (CNLF i -CNLR i ). (Formula 5)

[0078] b. Calculate and analyze the differences between the sonic transit time porosity conclusions and the other two types of porosity conclusions.

[0079] First, draw the superposition of acoustic transit time, density logging and neutron logging, whose elements include well depth, logging curve and porosity interpretation conclusion. Since porosity can be calculated by all three types of logging data, and there are certain numerical differences, the porosity φAC is calculated based on the acoustic transit time of the well section to be tested. i , minimum value φAC imin 、Maximum value φφAC imax As the unified upper and lower limits of the graph, φCNL iaver is the average value, then for density logging and neutron logging, the plotted data for the superimposed trace is φCNL i ′ and φDEN i φ can be set as follows:

[0080] φCNL i ′=(((φCNL i *φAC imin ) / φCNL imin )*(1-ABS(φCNL imin -φCNL iaver ) / ABS(φCNL imax -φCNL iaver ))+((φCNL i *φAC imax ) / φCNL imax ′))*(1-ABS(CNL imin -φCNL iaver ) / ABS(φCNL imax -φCNL iaver )) / 2

[0081] φDEN i ′=(((φDEN i *φAC imin ) / φDENi min )*(1-ABS(φDENi min -φφDEN iaver ) / ABS(φDENi max -φDENiaver ))+((φDEN i *φAC imax ) / φDEN imax ′))*(1-ABS(φDENi min -φDEN iaver ) / ABS(φDENi max -φDENi aver )) / 2

[0082] thus,

[0083] Δ iAC-CNL =φCNL i ′-φAC i

[0084] Δ iAC-DEN =φDEN i ′-φAC i

[0085] In summary, Δ iAC-CNL and Δ iAC-DEN The differences between CNL' and DEN' and AC under the superimposed trace conditions can be approximately represented.

[0086] c. Based on the conclusion of step b, determine the abnormal well section of the logging acoustic time difference data and complete the detection process.

[0087] Refer to the instruction manual Figure 2 Since density logging is greatly affected by the diagenetic mineral composition of the well section to be tested, and neutron logging is more significantly affected by the properties of the fluid in the pore space, it is considered to use neutron logging porosity to participate in the measurement of acoustic time difference to establish the recognition model of the abnormal well section.

[0088] Then, for the i-th meter, the discriminant equation for the abnormality of the acoustic time difference data is as follows:

[0089] δ 总 =(φCNL i +(Δ iAC-DEN +Δ iAC-CNL ) / 2) / φAC i ,

[0090] When δ 总 <1 means that the abnormality of the acoustic time difference data is not significant.

[0091] When δ 总 >1 means that the acoustic time difference data is significantly abnormal.

[0092] For this well, δ 总 <1, indicating that the abnormality of the acoustic time difference data is not significant. For details, please refer to the appendix of the manual. Figure 3 and the following application example data sheets:

[0093] Depth (m) AC CNL DEN 3469 71.508 19.535 2.704 3470 73.871 19.695 2.697 3471 76.5 19.898 2.691 3472 77.796 19.84 2.673 3473 78.455 18.381 2.679 3474 77.106 19.811 2.653 3475 79.026 20.109 2.743 3476 77.524 18.539 2.673 3477 77.902 18.985 2.765 3478 74.789 17.161 2.649 3479 61.493 14.489 2.663 3480 69.215 14.174 2.655 3481 73.524 17.366 2.652 3482 72.804 18.038 2.645 3483 73.721 18.462 2.634 3484 76.401 17.668 2.666 3485 78.023 15.884 2.615 3486 79.855 18.76 2.633 3487 79.631 19.958 2.638 3488 77.418 20.224 2.64 3489 78.134 19.057 2.64 3490 75.673 18.184 2.638 3491 75.82 19.089 2.69 3492 78.479 19.894 2.631 3493 80.614 20.783 2.636 3494 78.227 19.218 2.628 3495 74.174 17.897 2.603 3496 74.03 17.326 2.634 3497 72.715 17.223 2.635 3498 75.203 18.29 2.624 3499 76.428 17.278 2.603 3500 75.618 15.67 2.611

[0094] In summary, after reading the present invention document, ordinary technicians in this field can make various other corresponding transformation schemes based on the technical solutions and technical concepts of the present invention without creative mental work, which all fall within the scope of protection of the present invention.

Claims

1. A method for rapidly detecting abnormal value sections in well logging acoustic time difference data, characterized by: The following steps are involved: a. Calculate the sonic transit time, density and porosity interpretations from neutron logging; b. Calculate and analyze the differences between the sonic transit time porosity and the other two types of porosity; c. Determine the abnormal well section of the logging acoustic time difference data based on the conclusion of step b and complete the detection process; The step b specifically includes: b1. Draw a superimposed chart of sonic transit time, density logging, and neutron logging porosity; b2. Differences between the calculated sonic transit time porosity and the other two types of porosity: ， in, is the difference between the sonic transit time porosity and the neutron porosity, is the difference between sonic transit time porosity and density porosity, For neutron logging, the data for the superimposed trace is drawn. Calculate the porosity of the well section to be tested using the acoustic time difference.

2. The method for rapidly detecting abnormal value sections of well logging acoustic time difference data according to claim 1, characterized in that: The step a specifically includes: a1. Obtaining the acoustic transit time, neutron logging, and density logging data for the well section to be analyzed; a2. Obtain porosity interpretation conclusions from acoustic transit time, neutron logging, and density logging at each depth point in the well section to be analyzed.

3. The method for rapidly detecting abnormal value sections of well logging acoustic time difference data according to claim 1, characterized in that: The step c specifically includes: c1. Establish a model to distinguish the difference between transit-time porosity and neutron and density logging porosity; c2. Determine whether the acoustic time difference is abnormal and the extent of the abnormality based on the discriminant model to complete the detection process.

4. The method for rapidly detecting abnormal value sections of well logging acoustic time difference data according to claim 2, characterized in that: In step a2, the sonic transit time porosity, neutron logging porosity, and density logging porosity are calculated as follows: ， in, Calculate the porosity of the well section to be tested by using the acoustic time difference. The time difference reading of the acoustic wave corresponding to a certain depth of the well section to be tested is: is the theoretical reading of acoustic wave time difference corresponding to unit volume of rock skeleton, is the theoretical reading of the acoustic time difference of the corresponding unit volume of pore fluid, is the density logging porosity, The density logging reading for unit volume at the same depth is: is the theoretical logging reading of the rock skeleton density per unit volume, is the theoretical logging reading corresponding to the unit volume of pore fluid density, is the neutron logging porosity, The neutron logging readings per unit volume at the same depth are: is the theoretical neutron logging reading per unit volume of rock skeleton, is the mud content of the well section to be measured, is the neutron characteristic value of mudstone in the well section to be tested.

5. The method for rapidly detecting abnormal value sections of well logging acoustic time difference data according to claim 3, characterized in that: In step b1, the elements of the superimposed trace include well depth, well logging curve and porosity interpretation conclusion.

6. A method for rapidly detecting abnormal value sections of well logging acoustic time difference data according to claim 5, characterized in that: ； in, For neutron logging, the data for the superimposed trace is drawn. Calculate the minimum porosity value for the well section to be tested using the acoustic time difference. Calculate the maximum porosity of the well section to be tested by using the acoustic time difference. is the average porosity of neutron logging; is the density logging porosity, is the neutron logging porosity.

7. A method for rapidly detecting abnormal value sections of well logging acoustic time difference data according to claim 6, characterized in that: The establishment of the discrimination model for the degree of difference between the sonic transit time porosity and the neutron and density logging porosity in step c1 specifically refers to: using neutron logging porosity to participate in the establishment of an abnormal well section identification model for measuring the sonic transit time porosity. For the i-th meter, the discrimination equation for the degree of abnormality of the sonic transit time data is as follows: 。 8. The method for rapidly detecting abnormal value sections of well logging acoustic time difference data according to claim 7, characterized in that: The determination method in step c2 is: <1 means that the abnormality of the acoustic time difference data is not significant; when >1 means that the acoustic time difference data is significantly abnormal.

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