Compact rock stratum logging curve environment correction method and application thereof

Abstract

The invention provides a dense rock stratum logging curve environment correction method and application thereof, and belongs to the technical field of oil exploration and development. According to the dense rock stratum logging curve environment correction method, well section characteristics of a to-be-corrected section in a verification well and a correction well are collected, a marker bed in the well section of the verification well is determined, and the well section characteristics of the to-be-corrected section and the marker bed are determined according to a BP neural network algorithm; calculating the interval transit time of the correction section, and further adopting a VSP interval transit time compaction mode to complete the environment correction of the dense rock stratum logging curve; the invention also provides the application of the device in production of logging equipment. Compared with the prior art, the method has the advantages that the influence of lithology and compaction on the interval transit time can be fully considered, and the accuracy is further improved; the method has a good application effect in a compact rock stratum, and has a wide popularization and application prospect.

Description

Technical Field

[0001] The present invention relates to the technical field of oil exploration and development, and particularly relates to a method for environmental correction of logging curves in tight rock formations and its application. Background Art

[0002] During the process of oil and gas exploration and development, logging curves have always been regarded as one of the important tools for oil and gas exploration. These curves provide key information about underground rock formations, including formation depth, rock properties, and possible reservoir locations, etc. Especially in aspects such as seismic synthetic record calibration, high-quality reservoir prediction, and hydrocarbon detection, the accuracy of logging curves plays a crucial role in the success of the entire exploration work.

[0003] During conventional logging, due to factors such as differences in compaction of the original formation, drill bit vibration, and long mud soaking time, wellbore formation collapse has become a common phenomenon. Wellbore collapse will cause deviations in logging curves, thus affecting the reliability evaluation of logging curve data. In the case of less or local wellbore collapse, the influence of these factors on logging curves is relatively small, and these influences can be eliminated by environmental correction methods.

[0004] However, when the wellbore collapses severely, this will directly lead to distortion of logging data such as density and acoustic travel time. For example, the collapsed rock may change the propagation path and speed of sound waves, thus affecting the accuracy of acoustic travel time data. Similarly, density measurement may also be affected because the collapsed rock may cause the density measurement value to deviate from the true value. These distorted logging data are difficult to truly reflect the actual situation of the formation, and further affect the rationality and accuracy of subsequent logging interpretation, horizon calibration, rock physics modeling, reservoir prediction, and hydrocarbon detection and other related work.

[0005] With the continuous deepening of exploration work, high-quality acoustic and density logging curves play an increasingly important role in the production process. Therefore, environmental correction for wellbore collapse has become an important link in the logging correction process. Through this link, the influence of wellbore collapse on logging curves can be effectively eliminated, the accuracy and reliability of logging data can be improved, so as to meet the requirements of fine exploration production. This is crucial for the development and production of oil and gas fields. Accurate logging data can help better understand the properties of underground rock formations and reservoir conditions, and provide key information and support for the development and production of oil and gas fields.

[0006] To better correct logging curves, for example, CN111610575A discloses a logging curve environmental correction method, system and device. By applying spectral analysis technology, the time-domain logging curve is converted into a frequency-domain amplitude-frequency curve. The periodically regular change characteristics on the time-domain logging curve will be converted into amplitude anomalies in the high-frequency band of the frequency-domain curve. Combining signal filtering technology to eliminate this anomaly, and then converting the frequency-domain curve back into the time-domain curve to complete the correction of the influence of the threaded wellbore on the logging curve. The filtering method is more reasonable, the influence of the threaded wellbore on the logging curve is completely filtered out, and the useful signals in the high-frequency band are retained. Reconstruct the signal with the abnormal part filtered out, retain the signal integrity, and quickly and effectively eliminate the correction method of the influence of the threaded wellbore on the logging curve, while retaining the useful signals.

[0007] CN116165716A also discloses a method, device, equipment, medium and product for logging environment correction. It distinguishes the shale section and the sandstone section according to the logging depth curve and the corresponding first spontaneous potential curve. Based on the borehole diameter curve of the collapsed section in the shale section, the first density change relationship is obtained to correct the density curve of the whole well section. On the basis of the above correction, based on the porosity curve of the sandstone section, the second density change relationship is obtained to correct the density curve of the sandstone section. By combining the curves corrected twice, the overall correction of the whole well section and the key correction of the sandstone section are realized, and accurate correction of the density curves of sandstone and shale can be achieved under the conditions of poor quality of conventional logging curves, large-area borehole collapse, no standard layer and standard curves, so as to effectively improve the quality of logging data.

[0008] However, the environmental correction methods provided in the above patents are all for shallow clastic rocks. In such an environment, there is little compaction in the shallow layer, and conventional methods are highly applicable. However, for tight formations, due to their deep burial depth, the porosity is affected by both lithology and burial depth, and the change in porosity is directly reflected in the acoustic travel time curve, resulting in the problem of abnormal acoustic travel time not being solved. There are also few generally recognized and efficient solutions to this problem in the existing technology.

[0009] Therefore, how to solve the problem of abnormal acoustic travel time caused by borehole collapse in tight rock formations and provide a logging curve environmental correction method applicable to tight rock formations is a key issue for those skilled in the art to study. Summary of the Invention

[0010] Aiming at the lack of a correction method for logging curve environment applicable to tight rock formations in the prior art, the present invention provides a logging curve environmental correction method for tight rock formations and its application. It adopts neural network calculation and a compaction correction method based on VSP data, fully considering the influence of lithology and burial depth on the curve. It has achieved good application effects in tight rock formations and has broad prospects for popularization and application.

[0011] To achieve the above object, the technical solution adopted by the present invention is as follows:

[0012] A method for environmental correction of logging curves in tight rock formations, comprising the following steps:

[0013] S101: Collect the well section characteristics of the section to be corrected in the verification well and the correction well respectively;

[0014] S201: Determine the marker bed in the well section of the verification well according to the well section characteristics of the section to be corrected in the verification well and the correction well collected in step S101;

[0015] S301: Calculate the shale content of the section to be corrected in step S101 and the marker bed determined in step S201 by using the well section characteristics of the section to be corrected in the verification well and the correction well collected in step S101;

[0016] S401: According to the well section characteristics of the verification well collected in step S101 and the shale content of the marker bed obtained in step S301, use a BP neural network to calculate the acoustic travel time of the marker bed to obtain a BP neural network model;

[0017] S501: Import the well section characteristics of the section to be corrected in the correction well collected in step S101 and the shale content of the section to be corrected calculated in step S301 into the BP neural network model obtained in step S401, and calculate the fitted acoustic travel time of the section to be corrected;

[0018] S601: Extract the low-frequency trend data of the fitted acoustic travel time of the section to be corrected obtained in step S501, and subtract it from the VSP acoustic travel time in the well section characteristics of the section to be corrected in the correction well in step S101 to obtain the compaction trend difference of the section to be corrected;

[0019] S701: Add the fitted acoustic travel time of the section to be corrected obtained in step S501 and the compaction trend difference of the section to be corrected obtained in step S601 to obtain the acoustic travel time with compaction effect, that is, complete the environmental correction of the logging curves in the tight rock formation.

[0020] Preferably, the well section characteristics of the verification well in step S101 include: resistivity, potassium-thorium characteristics and spontaneous potential.

[0021] Preferably, the well section characteristics of the section to be corrected in the correction well in step S101 include: resistivity, potassium-thorium characteristics, spontaneous potential and VSP acoustic travel time.

[0022] Preferably, the well section characteristics of the section to be corrected in the verification well and the correction well collected in step S101 in step S201 both include: resistivity and natural gamma.

[0023] Further preferably, the method for determining the marker bed in the verification well section in step S201 is as follows:

[0024] Based on the resistivity and potassium thorium of the section to be corrected in the verification well and calibration well collected in step S101, the part where the resistivity and potassium thorium in the verification well are consistent with the resistivity and natural gamma of the section to be corrected in the calibration well is taken as the marker bed.

[0025] Preferably, the well section characteristics of the section to be corrected in the verification well and calibration well collected in step S301 in step S301 include: potassium thorium characteristics.

[0026] Further preferably, the potassium thorium characteristics include: potassium thorium log values of pure lithology formations and potassium thorium log values of pure shale formations.

[0027] Preferably, the method for calculating the shale content in step S301 includes the step of calculating the relative value of potassium thorium, and the formula for calculating the relative value of potassium thorium is:

[0028]

[0029] In the formula, SH is the relative value of potassium thorium; KTu is the potassium thorium curve; KTu max is the potassium thorium log value of the pure lithology formation; KTu min is the potassium thorium log value of the pure shale formation.

[0030] Further preferably, the method for calculating the shale content further includes the step of calculating the shale content using the relative value of potassium thorium, and the formula for calculating the shale content is:

[0031]

[0032] In the formula, V sh is the shale content; GCUR is an empirical coefficient related to the formation age, usually taken as 2.

[0033] Preferably, the well section characteristics of the verification well in step S401 include resistivity and spontaneous potential.

[0034] Further preferably, the BP neural network in step S401 calculates the acoustic travel time of the marker bed, which requires the resistivity and spontaneous potential in the well section characteristics of the verification well;

[0035] Most preferably, the BP neural network in step S401 calculates the acoustic travel time of the marker bed, which also requires the shale content of the marker bed obtained in step S301.

[0036] Preferably, the well section characteristics of the section to be corrected in the calibration well in step S501 include resistivity and spontaneous potential.

[0037] The present invention also provides an application of the logging curve environmental correction method for tight rock formations in production logging equipment.

[0038] The present invention also provides a logging device, which measures logging curves by using the logging curve environmental correction method for tight rock formations provided by the present invention.

[0039] Compared with the prior art, the present invention has the following beneficial effects:

[0040] By adopting the BP neural network calculation, the compaction correction method based on VSP data, and combining the characteristics of specific well sections and specific correction steps in the verification well and the correction well, the present invention realizes the correction of abnormal curves in tight rock formations, can fully consider the influence of lithology and compaction on acoustic travel time, and further improves the accuracy; it has achieved good application effects in tight rock formations and has a broad prospect of popularization and application. Description of the Drawings

[0041] Figure 1 It is a flowchart of a logging curve environmental correction method for tight rock formations provided by the present invention;

[0042] Figure 2 It is a comparison diagram of spontaneous potential and resistivity curves of the marker bed and the section to be corrected in the correction well in step S201 of the embodiment;

[0043] Figure 3 It is a shale content curve diagram of the marker bed calculated in step S301 of the embodiment;

[0044] Figure 4 It is a schematic diagram of the construction of the BP neural network model in step S401;

[0045] Figure 5 It is an error diagram of the fitted acoustic travel time and the measured acoustic travel time of the section to be corrected calculated by using the BP neural network model in step S501;

[0046] Figure 6 It is a compaction trend difference and its calculation data in step S601 and an acoustic travel time diagram with compaction effect obtained by completing the logging curve environmental correction of tight rock formations in step S701.

[0047] Figure 7 By interpreting the oil layer diagram with acoustic travel time corrected by the compaction trend, in this area, an acoustic travel time greater than 100 indicates an oil layer, and between 90 and 100 indicates a dry layer. Detailed Embodiments

[0048] Embodiment A logging curve environmental correction method for tight rock formations

[0049] S101: Collect the well section characteristics of the section to be corrected in the verification well and the calibration well respectively. The well section characteristics of the verification well include: resistivity, natural gamma ray, potassium-thorium characteristic and spontaneous potential. The well section characteristics of the section to be corrected in the calibration well include: resistivity, natural gamma ray, potassium-thorium characteristic, spontaneous potential and VSP acoustic time difference;

[0050] S201: Based on the resistivity and natural gamma ray of the section to be corrected in the verification well and the calibration well collected in step S101, take the part where the resistivity and natural gamma ray in the verification well are consistent with those of the section to be corrected in the calibration well as the marker bed, as Figure 2 shown;

[0051] S301: Use the potassium-thorium log values of the pure lithology formation and the pure shale formation in the section to be corrected in the verification well and the calibration well collected in step S101 to calculate the shale content of the section to be corrected in step S101 and the marker bed determined in step S201, including the following steps:

[0052] Calculate the relative potassium-thorium value:

[0053]

[0054] In the formula, SH is the relative potassium-thorium value; KTu is the potassium-thorium curve; KTu max is the potassium-thorium log value of the pure lithology formation; KTu min is the potassium-thorium log value of the pure shale formation;

[0055] Calculate the shale content:

[0056]

[0057] In the formula, V sh is the shale content; GCUR is an empirical coefficient related to the formation age, usually taken as 2; the result is as Figure 3 shown.

[0058] S401: Based on the resistivity and spontaneous potential of the verification well collected in step S101 and the shale content of the marker bed obtained in step S301, use the BP neural network to calculate the acoustic time difference of the marker bed to obtain the BP neural network model. The construction process of step S401 is as Figure 4 shown;

[0059] S501: Import the resistivity and spontaneous potential of the section to be corrected in the calibration well collected in step S101 and the shale content of the section to be corrected calculated in step S301 into the BP neural network model obtained in step S401 to calculate the fitted acoustic time difference of the section to be corrected, as Figure 5 shown;

[0060] S601: Extract the low-frequency trend data of the fitting acoustic travel time of the section to be corrected obtained in step S501, and subtract it from the VSP acoustic travel time in the section characteristics of the section to be corrected in the calibration well in step S101 to obtain the compaction trend difference of the section to be corrected as Figure 6 shown;

[0061] S701: Add the fitting acoustic travel time of the section to be corrected obtained in step S501 to the compaction trend difference of the section to be corrected obtained in step S601 to obtain the acoustic travel time with compaction effect (compaction trend correction), that is, complete the logging curve environmental correction of the tight rock formation, as Figure 6 shown.

[0062] During the calibration process of the above embodiment, after completing the logging curve environmental correction, dry layers and oil layers in the calibration area were successfully identified based on the analysis of acoustic travel time, which is consistent with the actual production process, as Figure 7 shown.

[0063] Finally, it should be noted that the above content is only used to illustrate the technical solution of the present invention, rather than limiting the protection scope of the present invention. Any simple modification or equivalent replacement of the technical solution of the present invention by those of ordinary skill in the art shall not depart from the essence and scope of the technical solution of the present invention.

Claims

1. A method for environmental correction of logging curves in tight rock formations, characterized in that, it includes the following steps: S101: Collect the well section characteristics of the section to be corrected in the verification well and the correction well respectively; S201: Determine the marker bed in the well section of the verification well according to the well section characteristics of the section to be corrected in the verification well and the correction well collected in step S101; S301: Calculate the shale content of the section to be corrected in step S101 and the marker bed determined in step S201 by using the well section characteristics of the section to be corrected in the verification well and the correction well collected in step S101; S401: According to the well section characteristics of the verification well collected in step S101 and the shale content of the marker bed obtained in step S301, use the BP neural network to calculate the acoustic travel time of the marker bed to obtain the BP neural network model; S501: Import the well section characteristics of the section to be corrected in the correction well collected in step S101 and the shale content of the section to be corrected calculated in step S301 into the BP neural network model obtained in step S401 to calculate the fitted acoustic travel time of the section to be corrected; S601: Extract the low-frequency trend data of the fitted acoustic travel time of the section to be corrected obtained in step S501, and subtract it from the VSP acoustic travel time in the well section characteristics of the section to be corrected in the correction well in step S101 to obtain the compaction trend difference of the section to be corrected; S701: Add the fitted acoustic travel time of the section to be corrected obtained in step S501 and the compaction trend difference of the section to be corrected obtained in step S601 to obtain the acoustic travel time with compaction effect, that is, complete the environmental correction of the logging curves in the tight rock formation.

2. The method for environmental correction of logging curves in tight rock formations according to claim 1, characterized in that: The well section characteristics of the verification well in step S101 include: resistivity, natural gamma ray, potassium-thorium characteristic and spontaneous potential.

3. The method for environmental correction of logging curves in tight rock formations according to claim 1, characterized in that: The well section characteristics of the section to be corrected in the correction well in step S101 include: resistivity, natural gamma ray, potassium-thorium characteristic, spontaneous potential and VSP acoustic travel time.

4. The method for environmental correction of logging curves in tight rock formations according to claim 1, characterized in that: The well section characteristics of the section to be corrected in the verification well and the correction well collected in step S101 in step S201 both include: resistivity and natural gamma ray.

5. The method for environmental correction of logging curves in tight rock formations according to claim 4, characterized in that: The method for determining the marker bed in the well section of the verification well in step S201 is: According to the resistivity and natural gamma ray of the section to be corrected in the verification well and the correction well collected in step S101; take the part where the resistivity and natural gamma ray in the verification well are the same as those of the section to be corrected in the correction well as the marker bed.

6. The method for environmental correction of logging curves in tight rock formations according to claim 1, characterized in that: The well section characteristics of the section to be corrected in the verification well and the correction well collected in step S301 in step S301 include: potassium-thorium characteristic.

7. The method for environmental correction of logging curves in tight rock formations according to claim 6, characterized in that: The potassium-thorium characteristics include: the potassium-thorium log value of the pure lithology formation and the potassium-thorium log value of the pure shale formation.

8. The logging curve environmental correction method for tight rock formations according to claim 1, wherein: The method for calculating the shale content in step S301 includes the step of calculating the relative potassium-thorium value, and the formula for calculating the relative potassium-thorium value is: Where SH is the relative value of potassium and thorium; KTu is the potassium-thorium curve; KTu max is the potassium-thorium logging value of a pure lithologic formation; KTu min is the potassium-thorium logging value of a pure shale formation.

9. The logging curve environmental correction method for tight rock formations according to claim 8, wherein: The method for calculating the shale content further includes the step of calculating the shale content using the relative potassium-thorium value, and the formula for calculating the shale content is: where V sh is the shale content; GCUR is an empirical coefficient related to the formation age, usually taken as 2.

10. The logging curve environmental correction method for tight rock formations according to claim 1, wherein: The well section characteristics of the verification well in step S401 include resistivity and spontaneous potential.

11. The logging curve environmental correction method for tight rock formations according to claim 10, wherein: Calculating the acoustic travel time of the marker bed by the BP neural network in step S401 requires the resistivity and spontaneous potential in the well section characteristics of the verification well.

12. The logging curve environmental correction method for tight rock formations according to claim 11, wherein: Calculating the acoustic travel time of the marker bed by the BP neural network in step S401 further requires the shale content of the marker bed obtained in step S301.

13. The logging curve environmental correction method for tight rock formations according to claim 1, wherein: The well section characteristics of the section to be corrected in the correction well in step S501 include resistivity and spontaneous potential.

14. Application of the logging curve environmental correction method for tight rock formations according to any one of claims 1-13 in production logging equipment.

15. A logging device, wherein, The logging curve is measured by using the logging curve environmental correction method for tight rock formations according to any one of claims 1-13.

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