Method for identifying and evaluating remote well crack of horizontal well by using sound wave remote detection imaging technology

Abstract

The invention discloses a method for identifying and evaluating a remote well crack of a horizontal well by using a sound wave remote detection imaging technology, which comprises the following steps of: 1, acquiring sound wave remote detection data, and importing the data into processing software; step 2, performing data preprocessing on the data; step 3, carrying out band-pass filtering processing on the preprocessed data; step 4, performing wave field separation, including linear prediction, F-K filtering, median filtering and the like; 5, performing imaging processing on the processed data, wherein the imaging processing comprises common midpoint superposition, inclination angle superposition and the like; step 6, performing migration imaging on the processing result, judging whether the imaging result is clear or not, and if not, adjusting the previous parameters until the imaging result is clear; and step 7, performing noise reduction on an imaging result to obtain a final processing result. According to the method, the problem of identification and evaluation of the horizontal well far-well fracture-cavity body can be solved.

Description

Technical Field

[0001] The invention belongs to the field of geophysical logging and oil and natural gas exploitation and is suitable for processing and interpreting data on reservoir characteristics and block noise characteristics of karst fracture-cavity type and fault-controlled fracture-cavity type. Background Art

[0002] In the process of oil and gas exploration and development, fracture identification and evaluation is one of the important links in reservoir transformation and oil and gas production management.

[0003] The research on acoustic remote detection technology is based on the quality evaluation method of the target block remote detection raw data and the interpretation model of karst fracture-cavity reservoirs. However, the current processing flow and parameter selection are not specifically optimized for fracture-cavity reservoirs, nor do they fully consider the elimination of noise in block data. Therefore, when different processing methods and parameters are selected, the results may vary greatly, and in the process of processing and interpretation, the causes of noise and false phenomena still need to be deeply analyzed. Summary of the invention

[0004] The purpose of the present invention is to propose a method for identifying and evaluating far-well fractures in horizontal wells using acoustic wave long-range detection imaging technology. Based on the basic principles of acoustic wave long-range detection technology, this technology is applied to horizontal wells to solve the problem of identifying and evaluating far-well fractures and caves in horizontal wells.

[0005] To implement the above method, the present invention adopts the following processing scheme:

[0006] A method for identifying and evaluating fractures in horizontal wells using acoustic wave remote detection imaging technology.

[0007] Step 1: Obtain raw sound wave data from the site and import the data into data processing software;

[0008] Step 2: Preprocess the data to ensure the accuracy and consistency of the data, optimize the quality of the acoustic remote detection logging data, and improve the accuracy of the logging interpretation;

[0009] Step 3: Filter the preprocessed data to remove burr interference that is irrelevant to the formation properties and reduce interference factors of automatic correction to achieve the effect of improving the signal-to-noise ratio;

[0010] Step 4: Perform wave field separation, including linear prediction, FK filtering, median filtering, etc. In the actual process of acoustic logging, due to the different propagation paths, the travel time difference between the wellbore direct wave and the reflected wave outside the well is large. Therefore, the linear prediction wave field separation method is proposed based on this premise. Its basic idea is: using the known wellbore direct wave time difference, formula (1) uses the least squares method to estimate the wellbore direct wave amplitude on each receiver in the well, and formula (2) subtracts the wellbore direct wave of the corresponding receiver from the actual logging waveform, and the remaining part is the reflected wave.

[0011]

[0012] In the process of image processing, it is usually necessary to moderately reduce the noise of the data before further processing such as edge detection. Median filtering is a nonlinear signal processing technology based on the neighborhood operation method of sorting statistics theory. Its processing method is to replace the value of a certain point in a digital image or digital sequence with the median of all point values ​​in the neighborhood of the point, and use it as the output, discarding the original value, thereby eliminating isolated noise points and improving the signal-to-noise ratio of the signal.

[0013] For the common receiver array of logging data, the arrival time of the direct wave in the well is linearly related to the depth, while the arrival time of the reflected wave is hyperbolic to the depth. Based on this feature, combined with the median filtering technology, the reflected shear wave can be effectively extracted from the original waveform data while suppressing the influence of the direct wave.

[0014] Step 5: Perform imaging processing on the processed data, including common center point stacking, dip stacking, etc.

[0015] The common center point superposition principle is as follows Figure 2 As shown. For the reflection interface parallel to the well axis, the reflection point position of each set of data in the common center point gather is the same. For the inclined reflection interface, the reflection point can be corrected to the same self-excited and self-receiving point through dynamic correction, so that the subsequent stacking processing has physical meaning. Although each data in the common center point gather is less than the gather data in seismic processing due to the small number of receivers in the logging instrument, the actual processing results show that the use of the common center point gather can still effectively enhance the intensity of the reflected wave and meet the expected processing requirements.

[0016] For high-angle reflectors, when the background noise is large, the inclination of the reflector can be roughly estimated first, and then the inclination stacking can be performed according to the principle of coherent stacking to enhance the amplitude and energy of the reflected wave. The inclination value used for stacking is an approximate value and can be estimated by different methods. If there is conventional inclination logging data at the logging site, we can directly use the inclination value measured on site. If conventional inclination logging is not performed on site and only dipole shear wave far-detection imaging logging data is available, you can experiment with different inclination values ​​and select an inclination value that can achieve the best imaging effect.

[0017]

[0018] like Figure 3 As shown, given the inclination value α, according to the reflection wave at the nth channel in the receiver array, the distance from the intersection of the reflection interface and the well axis to the transmitter is calculated using formula (5), and then the arrival time of the reflection wave at the mth channel different from the nth channel is calculated according to formulas (6) and (7), and the mth channel waveform is superimposed on the nth channel waveform. If the time shift between the two waveforms is within a quarter of the main period of the waveform, the energy of the nth channel reflection waveform is enhanced based on the coherent superposition principle. Therefore, the allowable error range of the estimated inclination value α depends on the wavelength and the distance from the intersection of the reflection interface and the well axis to the transmitter.

[0019] Step 6: Perform offset imaging on the processing result to determine whether the imaging result is clear. If it is not clear, adjust the previous parameters until the imaging result is clear.

[0020] Step 7: De-noise the imaging result to obtain the final imaging result.

[0021] Step 8: Evaluate the far-well fracture-cavity bodies in horizontal well imaging.

[0022] The step four is specifically as follows: according to the significant characteristics of the reflected wave, which is short duration, small amplitude, and easy to be mixed with the mode wave, it is necessary to perform wave field separation on the pre-processed data to extract the reflected wave, and then separate the upgoing wave and the downgoing wave, so as to facilitate the next step of imaging processing.

[0023] The step five is specifically as follows: after performing wave field separation on the acoustic wave data, the data needs to be enhanced. After superposition, the intensity of the reflected wave is enhanced relative to the intensity of other wave components, thereby improving the imaging quality in the subsequent migration processing.

[0024] The present invention has the following advantages and positive effects:

[0025] The present invention takes into account actual field applications, conducts direct-push logging in horizontal wells, performs acoustic remote detection imaging processing and interpretation based on the characteristics of fracture-cavity reservoirs, finely processes the acoustic remote detection data of fracture-cavity reservoirs, identifies hidden oil and gas reservoirs outside the well, and guides production. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 This is a schematic diagram of the fk filtering principle of the acoustic wave long-range detection technology provided by the present invention.

[0027] Figure 2 A schematic diagram of the common center point superposition principle in the acoustic wave long-range detection technology provided by the present invention. Figure 3 Schematic diagram of the inclination superposition principle of the acoustic wave long-range detection technology provided by the present invention.

[0028] Figure 4 This is a flow chart of imaging processing of the acoustic wave long-range detection technology provided by the present invention.

[0029] Figure 5 This is a diagram of the processing results of the acoustic wave long-range detection technology and imaging technology provided by the present invention. DETAILED DESCRIPTION

[0030] like Figure 4 As shown, the present invention proposes a method for identifying and evaluating horizontal well far-well fractures using acoustic wave far-reaching imaging technology, and the working process is as follows:

[0031] Step 1: Obtain the original sound wave data from the scene and import the data into the data processing software to facilitate the subsequent data preprocessing;

[0032] Step 2: Preprocess the data to ensure the accuracy and consistency of the data, optimize the quality of the acoustic remote detection logging data, and improve the accuracy of the logging interpretation;

[0033] Step 3: Filter the preprocessed data to remove burr interference that is irrelevant to the formation properties and reduce interference factors of automatic correction to achieve the effect of improving the signal-to-noise ratio;

[0034] Step 4: Perform wave field separation, including linear prediction, FK filtering, median filtering, etc. In the actual process of acoustic logging, due to the different propagation paths, the travel time difference between the wellbore direct wave and the reflected wave outside the well is large. Therefore, the linear prediction wave field separation method is proposed based on this premise. Its basic idea is: using the known wellbore direct wave time difference, formula (1) uses the least squares method to estimate the wellbore direct wave amplitude on each receiver in the well, and formula (2) subtracts the wellbore direct wave of the corresponding receiver from the actual logging waveform, and the remaining part is the reflected wave.

[0035]

[0036] In the process of image processing, it is usually necessary to moderately reduce the noise of the data before further processing such as edge detection. Median filtering is a nonlinear signal processing technology based on the neighborhood operation method of sorting statistics theory. Its processing method is to replace the value of a certain point in a digital image or digital sequence with the median of all point values ​​in the neighborhood of the point, and use it as the output, discarding the original value, thereby eliminating isolated noise points and improving the signal-to-noise ratio of the signal.

[0037] For the common receiver array of logging data, the arrival time of the direct wave in the well is linearly related to the depth, while the arrival time of the reflected wave is hyperbolic to the depth. Based on this feature, combined with the median filtering technology, the reflected shear wave can be effectively extracted from the original waveform data while suppressing the influence of the direct wave.

[0038] Step 5: Perform imaging processing on the processed data, including common center point stacking, dip stacking, etc.

[0039] The common center point superposition principle is as follows Figure 2 As shown. For the reflection interface parallel to the well axis, the reflection point position of each set of data in the common center point gather is the same. For the inclined reflection interface, the reflection point can be corrected to the same self-excited and self-receiving point through dynamic correction, so that the subsequent stacking processing has physical meaning. Although each data in the common center point gather is less than the gather data in seismic processing due to the small number of receivers in the logging instrument, the actual processing results show that the use of the common center point gather can still effectively enhance the intensity of the reflected wave and meet the expected processing requirements.

[0040] For high-angle reflectors, when the background noise is large, the inclination of the reflector can be roughly estimated first, and then the inclination stacking can be performed according to the principle of coherent stacking to enhance the amplitude and energy of the reflected wave. The inclination value used for stacking is an approximate value and can be estimated by different methods. If there is conventional inclination logging data at the logging site, we can directly use the inclination value measured on site. If conventional inclination logging is not performed on site and only dipole shear wave far-detection imaging logging data is available, you can experiment with different inclination values ​​and select an inclination value that can achieve the best imaging effect.

[0041]

[0042] like Figure 3As shown, given the inclination value α, according to the reflection wave at the nth channel in the receiver array, the distance from the intersection of the reflection interface and the well axis to the transmitter is calculated using formula (5), and then the arrival time of the reflection wave at the mth channel different from the nth channel is calculated according to formulas (6) and (7), and the mth channel waveform is superimposed on the nth channel waveform. If the time shift between the two waveforms is within a quarter of the main period of the waveform, the energy of the nth channel reflection waveform is enhanced based on the coherent superposition principle. Therefore, the allowable error range of the estimated inclination value α depends on the wavelength and the distance from the intersection of the reflection interface and the well axis to the transmitter.

[0043] Step 6: Perform offset imaging on the processing result to determine whether the imaging result is clear. If it is not clear, adjust the previous parameters until the imaging result is clear.

[0044] Step 7: De-noise the imaging result to obtain the final imaging result.

[0045] Step 8: Evaluate the far-well fracture-cavity bodies in horizontal well imaging.

[0046] The step four is specifically as follows: according to the significant characteristics of the reflected wave, which is short duration, small amplitude, and easy to be mixed with the mode wave, it is necessary to perform wave field separation on the pre-processed data to extract the reflected wave, and then separate the upgoing wave and the downgoing wave, so as to facilitate the next step of imaging processing.

[0047] The step five is specifically as follows: after performing wave field separation on the acoustic wave data, the data needs to be enhanced. After superposition, the intensity of the reflected wave is enhanced relative to the intensity of other wave components, thereby improving the imaging quality in the subsequent migration processing.

[0048] As can be seen from the results, the acoustic wave remote detection imaging result obtained by processing the acoustic wave remote detection technology data in the horizontal well shows that the imaging results have a good display of the horizontal well far hole fracture body. However, due to the existence of noise and Stoneley waves, the evaluation of the fracture body of the horizontal well far hole will still be hindered, and the evaluation of the actual fracture body still requires certain experience to be accurately evaluated. There is a high-angle fracture of about 1,000 meters in the well section (7920-8010), and the imaging result is good. It can be seen that this method is more accurate in evaluating the cementing quality. The good consistency and correspondence shown in the field application examples prove the feasibility of this method and its broad application prospects.

Claims

1. A method for identifying and evaluating horizontal well far-well fractures using acoustic wave far-reaching imaging technology, comprising the following steps: Step 1: Obtain raw sound wave data from the site and import the data into data processing software; Step 2: Preprocess the data to ensure the accuracy and consistency of the data, optimize the quality of the acoustic remote detection logging data, and improve the accuracy of the logging interpretation; Step 3: Filter the preprocessed data to remove burr interference that is irrelevant to the formation properties and reduce interference factors of automatic correction to achieve the effect of improving the signal-to-noise ratio; Step 4: Perform wave field separation, including linear prediction, FK filtering, median filtering, etc. In the actual process of acoustic logging, due to the different propagation paths, the travel time difference between the wellbore direct wave and the reflected wave outside the well is large. Therefore, the linear prediction wave field separation method is proposed based on this premise. Its basic idea is: using the known wellbore direct wave time difference, formula (1) uses the least squares method to estimate the wellbore direct wave amplitude on each receiver in the well, and formula (2) subtracts the wellbore direct wave of the corresponding receiver from the actual logging waveform, and the remaining part is the reflected wave. In the process of image processing, it is usually necessary to moderately reduce the noise of the data before further processing such as edge detection. Median filtering is a nonlinear signal processing technology based on the neighborhood operation method of sorting statistics theory. Its processing method is to replace the value of a certain point in a digital image or digital sequence with the median of all point values ​​in the neighborhood of the point, and use it as the output, discarding the original value, thereby eliminating isolated noise points and improving the signal-to-noise ratio of the signal. For the common receiver array of well logging data, the arrival time of the direct wave in the well has a linear relationship with the depth, while the arrival time of the reflected wave has a hyperbolic relationship with the depth. Based on this feature and combined with the median filtering technology, the reflected shear wave can be effectively extracted from the original waveform data while suppressing the influence of the direct wave. Step 5: Perform imaging processing on the processed data, including common center point stacking, dip stacking, etc. The principle of common center point stacking is shown in Figure 2. For the reflection interface parallel to the well axis, the reflection point position of each set of data in the common center point gather is the same. For the inclined reflection interface, the reflection point can be corrected to the same self-excited and self-receiving point through dynamic correction, so that the subsequent stacking processing has physical meaning. Although each data in the common center point gather is less than the gather data in seismic processing due to the small number of receivers in the logging instrument, the actual processing results show that the use of common center point gathers can still effectively enhance the intensity of the reflected wave and meet the expected processing requirements. For high-angle reflectors, when the background noise is large, the inclination of the reflector can be roughly estimated first, and then the inclination stacking can be performed according to the principle of coherent stacking to enhance the amplitude and energy of the reflected wave. The inclination value used for stacking is an approximate value and can be estimated by different methods. If there is conventional inclination logging data at the logging site, we can directly use the inclination value measured on site. If conventional inclination logging is not performed on site and only dipole shear wave far-detection imaging logging data is available, you can experiment with different inclination values ​​and select an inclination value that can achieve the best imaging effect. As shown in Figure 3, given the inclination value α, according to the reflection wave at the nth channel in the receiver array, the distance from the intersection of the reflection interface and the well axis to the transmitter is calculated using formula (5), and then the arrival time of the reflection wave at the mth channel at a different position from the nth channel is calculated according to formulas (6) and (7), and the mth channel waveform is superimposed on the nth channel waveform. If the time shift between the two waveforms is within a quarter of the main cycle of the waveform, based on the principle of coherent superposition, the energy of the nth channel reflection waveform is enhanced. Therefore, the allowable error range of the estimated inclination value α depends on the wavelength and the distance from the intersection of the reflection interface and the well axis to the transmitter. Step 6: Perform offset imaging on the processing result to determine whether the imaging result is clear. If it is not clear, adjust the previous parameters until the imaging result is clear. Step 7: De-noise the imaging result to obtain the final imaging result. Step 8: Evaluate the far-well fracture-cavity bodies in horizontal well imaging.

2. According to the method for identifying and evaluating long-distance fractures in horizontal wells using acoustic wave long-distance detection imaging technology as described in claim 1, the specific step four is: according to the significant characteristics of the reflected wave, which is short duration, small amplitude, and easy to be mixed with the mode wave, it is necessary to perform wave field separation on the pre-processed data to extract the reflected wave therefrom, and then separate the upgoing wave and the downgoing wave, so as to facilitate the next step of imaging processing.

3. According to the method for identifying and evaluating long-distance fractures in horizontal wells using acoustic wave long-distance detection imaging technology described in claim 1, the specific step five is: after the acoustic wave data is subjected to wave field separation, the data needs to be enhanced, and after superposition, the intensity of the reflected wave is enhanced relative to the intensity of other wave components, thereby improving the imaging quality in the subsequent migration processing.

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