WO2019128685A1 - 一种元素俘获能谱测井的伽马能谱解谱方法及装置 - Google Patents

一种元素俘获能谱测井的伽马能谱解谱方法及装置 Download PDF

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WO2019128685A1
WO2019128685A1 PCT/CN2018/120083 CN2018120083W WO2019128685A1 WO 2019128685 A1 WO2019128685 A1 WO 2019128685A1 CN 2018120083 W CN2018120083 W CN 2018120083W WO 2019128685 A1 WO2019128685 A1 WO 2019128685A1
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energy spectrum
gamma
spectrum
capture
data
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French (fr)
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武宏亮
李宁
冯周
兰长林
吕韬
王克文
冯庆付
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Petrochina Co Ltd
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Petrochina Co Ltd
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Priority to CN201880051139.5A priority patent/CN111032997B/zh
Priority to US16/470,539 priority patent/US11313223B2/en
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    • E—FIXED CONSTRUCTIONS
    • E21—EARTH OR ROCK DRILLING; MINING
    • E21B—EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
    • E21B49/00—Testing the nature of borehole walls; Formation testing; Methods or apparatus for obtaining samples of soil or well fluids, specially adapted to earth drilling or wells
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V5/00—Prospecting or detecting by the use of ionising radiation, e.g. of natural or induced radioactivity
    • G01V5/04—Prospecting or detecting by the use of ionising radiation, e.g. of natural or induced radioactivity specially adapted for well-logging
    • G01V5/08—Prospecting or detecting by the use of ionising radiation, e.g. of natural or induced radioactivity specially adapted for well-logging using primary nuclear radiation sources or X-rays
    • G01V5/10—Prospecting or detecting by the use of ionising radiation, e.g. of natural or induced radioactivity specially adapted for well-logging using primary nuclear radiation sources or X-rays using neutron sources
    • G01V5/101—Prospecting or detecting by the use of ionising radiation, e.g. of natural or induced radioactivity specially adapted for well-logging using primary nuclear radiation sources or X-rays using neutron sources and detecting the secondary Y-rays produced in the surrounding layers of the bore hole
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
    • Y02A90/30—Assessment of water resources

Definitions

  • the invention relates to a gamma energy spectrum de-spectral method and device for element capture energy spectrum logging, and belongs to the field of logging technology in the field of oil and gas exploration.
  • the Elemental Capture Spectroscopy (ECS) logging tool is a new formation element logging tool introduced by Schlumberger to the Chinese market.
  • the instrument uses the principle of nuclear reaction between neutron and formation nuclei to obtain gamma.
  • Energy spectrum the gamma energy spectrum contains information on the main rock-forming elements Si, Ca, Fe, Al, S, Ti, H, Gd and other elements in the stratum, providing a new way for the fine evaluation of complex reservoir lithology.
  • the core of the element capture spectrum logging interpretation is to use the standard spectrum of each element to calibrate the original measurement spectrum of the formation, and obtain the yield of various elements by de-spreading.
  • the present invention aims to provide a gamma spectroscopy spectrum decomposing method and apparatus for element capture spectroscopy logging.
  • the method is a method for de-spreading the original measurement spectrum obtained by the element capture energy spectrum logging instrument, and has the advantages of high precision and strong operability.
  • the present invention provides a gamma spectroscopy spectroscopy method for elemental capture spectroscopy logging, which comprises the following steps:
  • Step 1 Acquire and analyze the study area data, which includes at least elemental capture gamma spectroscopy log data (also referred to as measuring gamma energy spectrum, measurement spectrum, measuring mixed gamma energy spectrum, total element in the present invention) Gamma spectrum measurement spectrum, etc.) and total rock oxide analysis data to determine the main element types of the block;
  • Step 2 Preprocessing the elemental capture gamma spectroscopy log data, the preprocessing including energy window selection, energy spectrum smoothing filtering, normalization processing, and inelastic scattering information subtraction, to obtain pre-processed element capture Gamma spectrum
  • Step 3 Construct a main element group and an auxiliary element group composed of different elements to clarify the order of decomposing the different elements;
  • Step 4 According to the main element group and the auxiliary element group obtained in step 3, in the element capture gamma energy spectrum after the pretreatment in step 2, the main element is first decomposed by the least square method, and then the pre-processed
  • the total element gamma energy spectrum ie, the element capture gamma energy spectrum after the pretreatment in step 2) deducts the contribution of all the main elements, and then uses the least squares method to decompose the auxiliary elements, thereby obtaining the relative production of each element. amount;
  • Step 5 According to the relative yield of each element obtained in step 4, combined with the elemental capture standard gamma spectroscopy (also referred to as a single element standard spectrum in the present invention) of the normalized unit cell, the element capture gamma The reconstruction of the horse energy spectrum, and the reconstructed gamma energy spectrum is compared with the measured gamma energy spectrum to determine whether the deconvolution result is reliable.
  • elemental capture standard gamma spectroscopy also referred to as a single element standard spectrum in the present invention
  • the acquired and analyzed research area data further includes one or more of conventional logging data, logging data, and geological data.
  • the combination preferably, in step 1 of the above dissolving method, the acquired and analyzed research area data further includes one or more of conventional logging data, logging data, and geological data.
  • the main element type of the study area is determined by analyzing the whole rock oxide analysis data of the study area, and optionally, A combination of one or more of conventional logging data, mud logging data, and geological data yields the weight percent of rock of different types of oxides, and the major element types of the block are determined based on the weight percent of each oxide.
  • the geological data mainly includes: core sheets and/or geological reports.
  • the total rock oxide analysis data is necessary to determine the main element types in the study area, and can refer to one or more of the conventional logging data, logging data and geological data.
  • step 2 pre-processes the gamma energy spectrum, which can eliminate the influence of factors such as the wellbore environment and the inherent characteristics of the instrument during the measurement process, and improve the accuracy of the spectrum.
  • the gamma ray energy detected by elemental capture spectroscopy logs is generally concentrated at 0-10 MeV, corresponding to a site address of 0-255, for a total of 256 channels.
  • the low-energy section is susceptible to high counts due to factors such as the Compton platform effect and instrument noise, ie, a large number of interfering signals should be rejected before de-spreading.
  • the energy spectrum is often very low, and it has little effect on the final solution in the process of de-spreading. It should also be eliminated before the spectrum is resolved. If you do not completely remove the data with adverse effects, it will affect the spectrum; however, if you remove too much data will remove the useful information, it will also affect the spectrum. Therefore, choosing the appropriate energy range is a key issue that must be solved before the spectrum is resolved.
  • the selected energy window range is 30-210 channels.
  • the basic principle of selecting the spectral energy window proposed by the present invention is that the energy corresponding to the all-energy peak of the element is not removed.
  • the inventor of the present invention finds the characteristic peak position of various elements involved in the de-spectralization by querying the nuclear data published by the IAEA, and determines the energy window range of the de-spectral according to the energy information corresponding to the peak position.
  • the peak positions of each element are ranked as 1.38, 1.808, 1.924, 1.951, 2.073, 2.092, 2.223, 2.282, 2.379, 2.828, 3.033, 3.22, 3.539, 3.587, 3.693, 3.981, 3.419, 4.437, 3.693, 3.981, 4.419, 4.733, 4.737. , 4.869, 4.933, 5.42, 5.9, 5.92, 6.018, 6.11, 6.36, 6.379, 6.395, 6.418, 6.42, 6.76, 7.414, 7.631, 7.646, 7.724, 7.769, 7.79, 8.153 (MeV).
  • the energy range of the energy window is set to 1.2 MeV to 8.4 MeV, and the corresponding energy window range is 30-210 channels, as shown in FIG.
  • the spectral smoothing filtering method is a Savitzky-Golay filter
  • the selected energy window is selected by the Savitzky-Golay five-point filtering method.
  • the elements in the range capture the gamma spectroscopy log data for filtering.
  • the formula of the Savitzky-Golay five-point filtering method is as shown in Equation 1:
  • y i represents the count on the address
  • y i-1 represents the count on the previous address of the address
  • y i-2 represents the first two addresses of the address
  • the count, y i+1 indicates the count on the next address of the address
  • y i+2 indicates the count on the last two addresses of the address.
  • the Savitzky-Golay filter used in the present invention uses a polynomial fit to measure the data of the gamma energy spectrum within the window length of the filter, and obtains the filtered result, that is, the value corresponding to the filter polynomial at the filter point.
  • the Savitzky-Golay five-point filtering method proposed by the present invention can eliminate burrs.
  • the essence of the filtering method of the present invention is to perform weighted averaging on the measured data, so that each data fluctuation in the energy spectrum data is averaged, and the average has a very small influence on the data trend, and the average value of the fluctuation is zero, which can effectively eliminate The impact of statistical fluctuations.
  • the normalization processing adopts a method: element capture gamma ray spectrometry after energy window range selection and energy spectrum smoothing filtering Well data, the sum of the 181 (by-channel) energy spectrum data is 10, and the normalized element capture gamma energy spectrum is obtained.
  • the formula used is as shown in Equation 2:
  • N Gkj is the count of the elemental capture gamma energy spectrum corresponding to the j depth point in the kth channel
  • N kj is the element after the energy window range selection and the energy spectrum smoothing filter corresponding to the j depth point Capture the gamma energy spectrum at the kth track
  • j is the depth point below the formation.
  • the normalization process is to accumulate the energy spectrum counts of the entire energy segment, and then divide each count by the sum of the counts corresponding to all the channel addresses in the energy window, so that the normalized The data is between 0 and 10, and the sum of the energy spectrum data of the entire energy segment is 10.
  • the normalization process normalizes the gamma energy spectrum data in the filtered energy window range, and obtains the normalized gamma energy spectrum data, so that the normalized captured elements are obtained.
  • the qualitative calculation value can accurately reflect the change of the whole well section, so the normalization treatment can guarantee the method of qualitative calculation and separation of elements, and can also accurately reflect the formation change within the energy window.
  • the inelastic scattering information is deducted by selecting 30-54 channels, 55 for the normalized element capturing gamma ray spectrum.
  • the counts of the -75 and 76-210 three-segment sites were deducted using 0.9, 0.7, and 0.8 as deduction coefficients for inelastic information deduction.
  • the method for subtracting inelastic scattering information in the measurement spectrum is a segmentation subtraction method, and different subtraction coefficients are used in different energy segments according to the contribution of different elements to the total count of different energy segments. Eliminate the effects of inelastic scatter information in the measurement spectrum.
  • the main element group constructed includes: elements such as Si, Ca, S, H, Cl, Ti, Fe, Na, Ba, and Gd.
  • the auxiliary element group constructed includes: elements such as Mg, K, Cr, Ni, I, Tb, and Al.
  • elements such as Mg, K, Cr, Ni, I, Tb, and Al.
  • the difference between the spectral shape and the characteristic peak position of the single element standard gamma energy spectrum is the core and key of the element capture spectrum spectrum decomposing spectrum, but in fact, the spectral shape and the characteristic peak position distribution exist between different elements. Certain similarities and overlaps. As shown in Fig.
  • the element group proposed and constructed by the present invention is established on the basis of considering the mutual influence of different elemental unit standard gamma energy spectra; the main element group mainly includes the elements constituting the rock skeleton part; the auxiliary element group is constructed to eliminate or assist The auxiliary elements Mg and K are mainly used to analyze and judge the yield of S.
  • auxiliary elements Mg, Cr and Ni are used to analyze and judge the yield of Si, Ca and Fe elements, and the auxiliary element Cr is established.
  • Ni, I, Tb and Al are used to analyze and judge the yield of H, Gd and Ti, and thus improve the accuracy of the spectrum.
  • step 4 of the above dissolving method the de-split using the least squares method is performed according to the algorithm shown in the following formula 3 to obtain the relative yield of each element:
  • y j is the relative yield of the jth element (ie, the contribution of the element to the total count)
  • a ij is the elemental capture standard gamma spectroscopy of the elementized element of the jth element
  • the count of i track, c i is the count of the element capture gamma energy spectrum after preprocessing in the i-th track, ⁇ i is the correction coefficient, ⁇ i ⁇ 0.1.
  • the present invention deducts the contribution of the main element from the pre-processed total element gamma energy spectrum by decomposing the main element group by using the least squares formula described above, and then using the least squares formula to the auxiliary element group
  • the spectrum is de-dissolved again, and the yield information of each element in the main and auxiliary element groups is gradually obtained, thereby effectively improving the accuracy of the spectrum.
  • the elemental capture standard gamma spectroscopy of the normalized unit cell is obtained by the following method:
  • the element captures the standard gamma spectrum, and the sum of the 256-channel (channel-by-channel) energy spectrum data is 10.
  • the formula used is as shown in Equation 4:
  • N Gkj is the count of the elemental capture standard gamma energy spectrum of the normalized unit cell corresponding to the j depth point in the kth channel
  • N kj is the unit cell of the normalization process corresponding to the j depth point
  • the element captures the standard gamma spectrum at the kth track and j is the depth point below the formation.
  • the reconstruction of the element capture gamma energy spectrum is calculated according to the following formula 5: the count of the reconstructed gamma energy spectrum in the i-th track (ie, All the despread elements are counted in the i-th track), and after each track is counted, the reconstructed gamma energy spectrum is drawn:
  • X i is the count of the reconstructed gamma spectrum in the i-th track
  • y j is the relative yield of the j-th element (calculated from Equation 3)
  • a ij is the normalized processing of the j-th element
  • the elemental element of the single element captures the count of the standard gamma energy spectrum in the i-th channel (calculated from Equation 4)
  • ⁇ i is the correction coefficient, ⁇ i ⁇ 0.1.
  • step 5 of the above dissolving method comparing the reconstructed gamma energy spectrum with the measured gamma energy spectrum is performed according to Equation 6:
  • c i is the measurement of the gamma energy spectrum in the i-th track
  • X i is the count of the reconstructed gamma energy spectrum in the i-th track
  • ⁇ is the relative error
  • the de-spectral result is considered to be reliable; when the relative error is greater than 5%, the de-split result is considered to be unreliable.
  • the reconstruction method of the gamma energy spectrum is a reference stripping analysis method, and it is considered that measuring the mixed gamma energy spectrum is a linear combination of single elements, and the unit element standard obtained when determining the content of the formation element Based on the spectral database, the reconstruction is based on the results of known capture and inelastic scattering elements, and then the actual measured gamma energy spectrum is compared with the reconstructed gamma energy spectrum to determine whether the deconvolution result is reliable or not. Resolution accuracy.
  • the invention also provides a gamma spectroscopy spectroscopy apparatus for element capture spectroscopy logging, comprising:
  • the data collection and main element type determining module is configured to acquire and analyze the research area data, and the data includes at least an element capture gamma ray spectrum log data and a whole rock oxide analysis data, and determine a main element type of the block;
  • An element capture gamma spectroscopy log data preprocessing module for preprocessing elemental capture gamma spectroscopy log data, the preprocessing including energy window selection, energy spectrum smoothing filtering, normalization processing, and non- The elastic scattering information is deducted to obtain the elemental capture gamma energy spectrum after pretreatment;
  • the main element group and the auxiliary element group building module are used to construct a main element group and an auxiliary element group composed of different elements to clarify the order of decomposing the different elements;
  • a least squares demodulation module based on element group is used to decompose the main element in the pre-processed element capture gamma energy spectrum according to the main element group and the auxiliary element group, and then first decompose the main element by least square method After processing, the total element gamma energy spectrum (ie, the element capture gamma energy spectrum after pretreatment) deducts the contribution of all the main elements, and then uses the least squares method to decompose the auxiliary elements, thereby obtaining the relative production of each element. amount;
  • the gamma energy spectrum reconstruction and error control module is used for reconstructing the elemental capture gamma energy spectrum according to the relative yield of each element and the element capture standard gamma energy spectrum of the normalized unit cell.
  • the reconstructed gamma energy spectrum is compared with the measured gamma energy spectrum to determine whether the deconvolution result is reliable.
  • the acquired and analyzed research area data further includes one or more of conventional logging data, logging data, and geological data. combination.
  • the main element type of the study area is determined by analyzing the whole rock oxide analysis data of the study area, and optionally, conventionally The combination of one or more of logging data, logging data and geological data, the weight percentage of rocks occupied by different kinds of oxides is obtained, and the main element types of the block are determined according to the weight percentage of each oxide.
  • the selected energy window range is 30-210 channels.
  • the spectral smoothing filtering method is performed by using a Savitzky-Golay filter and using a Savitzky-Golay five-point filtering method.
  • the elements in the energy window range capture the gamma spectroscopy log data for filtering.
  • the formula of the Savitzky-Golay five-point filtering method is as shown in Equation 1:
  • y i represents the count on the address
  • y i-1 represents the count on the previous address of the address
  • y i-2 represents the first two addresses of the address
  • the count, y i+1 indicates the count on the next address of the address
  • y i+2 indicates the count on the last two addresses of the address.
  • the normalization processing method is: element capturing gamma after energy window range selection and energy spectrum smoothing filtering.
  • the spectrum data of the well spectrum is such that the sum of the 181 (by-channel) energy spectrum data is 10, and the normalized element capture gamma spectrum is obtained.
  • the formula used is as shown in the following formula 2:
  • N Gkj is the count of the elemental capture gamma energy spectrum corresponding to the j depth point in the kth channel
  • N kj is the element after the energy window range selection and the energy spectrum smoothing filter corresponding to the j depth point Capture the gamma energy spectrum at the kth track
  • j is the depth point below the formation.
  • the inelastic scatter information subtraction method is: selecting the normalized element capture gamma ray spectrum 30-54 The counts of the three sections of the road, 55-75 and 76-210 are deducted by 0.9, 0.7 and 0.8 as the deduction coefficient for inelastic information deduction.
  • the main element group constructed includes: elements such as Si, Ca, S, H, Cl, Ti, Fe, Na, Ba, and Gd.
  • the auxiliary element group constructed includes one or more elements of elements such as Mg, K, Cr, Ni, I, Tb, and Al.
  • the deconvolution by the least squares method is performed according to the algorithm shown in the following formula 3 to obtain the relative yield of each element:
  • y j is the relative yield of the jth element (ie, the contribution of the element to the total count)
  • a ij is the elemental capture standard gamma spectroscopy of the elementized element of the jth element
  • the count of i track, c i is the count of the element capture gamma energy spectrum after preprocessing in the i-th track, ⁇ i is the correction coefficient, ⁇ i ⁇ 0.1.
  • the element capture standard gamma spectrum of the normalized unit cell is obtained by:
  • N Gkj is the count of the elemental capture standard gamma energy spectrum of the normalized unit cell corresponding to the j depth point in the kth channel
  • N kj is the unit cell of the normalization process corresponding to the j depth point
  • the element captures the standard gamma spectrum at the kth track and j is the depth point below the formation.
  • the reconstruction of the element capture gamma energy spectrum is calculated according to the following formula 5: the count of the reconstructed gamma energy spectrum in the i-th track ( That is, all the despread elements are counted in the i-th track. After the counts of the tracks are obtained, the reconstructed gamma energy spectrum is drawn:
  • X i is the count of the reconstructed gamma spectrum in the i-th track
  • y j is the relative yield of the j-th element (calculated from Equation 3)
  • a ij is the normalized processing of the j-th element
  • the elemental element of the single element captures the count of the standard gamma energy spectrum in the i-th channel (calculated from Equation 4)
  • ⁇ i is the correction coefficient, ⁇ i ⁇ 0.1.
  • comparing the reconstructed gamma energy spectrum with the measured gamma energy spectrum is performed according to Equation 6:
  • c i is the measurement of the gamma energy spectrum in the i-th track
  • X i is the count of the reconstructed gamma energy spectrum in the i-th track
  • ⁇ is the relative error
  • the de-spectral result is considered to be reliable; when the relative error is greater than 5%, the de-split result is considered to be unreliable.
  • the invention has carried out in-depth study on the processing method of element capture gamma ray spectrum logging data, and provides a high-precision and operative solution of gamma spectroscopy spectrum obtained by element capture spectrum logging instrument.
  • Spectral methods and devices provide excellent technical means for accurately performing the spectrum of element capture energy spectrum.
  • the invention firstly preprocesses the gamma energy spectrum data acquired by the element capture energy spectrum logging instrument, and eliminates the influence of factors such as the wellbore environment and the inherent characteristics of the instrument in the measurement process. Then, for the first time, considering the interaction between different elements, the idea of neutron capture gamma spectroscopy based on element group and the specific implementation method are proposed. The theory between different elements' spectral shape, characteristic peak and background is adopted.
  • the main element group and the auxiliary element group are constructed according to the degree of mutual influence between elements, and the order of the dissociation of different elements is clearly defined for the first time.
  • the neutron capture gamma spectroscopy spectrum decomposing method based on element group is implemented, and the energy yield of each element obtained by the least squares method is theoretically reconstructed by the theory.
  • the horse energy spectrum is compared with the reconstructed gamma energy spectrum for error control, which effectively improves the spectral accuracy.
  • Field application of oilfield shows that the de-spectral method and device of the invention have good universality for different types of reservoirs, and provide important reference and reference for neutron gamma spectroscopy data processing of other types of instruments.
  • Figure 1 is a schematic diagram of the selection of the demodulation energy window.
  • Figure 3 shows the de-spectrum effect of each element in the DAS3 well 2403.1956m.
  • Figure 4 is a comparison of the reconstructed gamma ray spectrum and the measured gamma ray spectrum of the DAS3 well 2403.1956m.
  • Figure 5 is a comparison of the elemental capture gamma spectroscopy and the measured gamma ray spectrum after pretreatment of the DAS3 well 2403.1956m.
  • Fig. 6 is a comparison diagram of the main element group reconstructed gamma energy spectrum and the measured gamma energy spectrum of the DAS3 well 2403.1956m.
  • Example 7 is a schematic diagram of a gamma ray spectrum decomposing apparatus for element trapping energy spectrum logging provided in Example 2.
  • This embodiment provides a gamma spectroscopy spectroscopy method for elemental capture spectroscopy logging, which may include the following steps:
  • the elemental capture energy spectrum logging tool is used to log the 3230-3270m reservoir section of the DAS3 well in Daqing Oilfield, and the total gamma energy spectrum of the stratigraphic elements at each depth point is obtained, that is, the element capture gamma ray spectrum logging data.
  • the depth points correspond to a spectrum.
  • the measured spectra of the depth points of the DAS3 well reservoir section 3230-3270m are preprocessed respectively, and the pre-processed element capture gamma energy spectrum corresponding to each depth point is obtained respectively, and the pretreatment includes the selection of the energy window and the energy spectrum. Smoothing filtering, normalization processing, and inelastic scattering information subtraction.
  • the selected energy window range is 30-210 channels, as shown in Figure 1.
  • the spectral smoothing filtering method is to use Savitzky-Golay filter, and the Savitzky-Golay five-point filtering method is used to filter the measurement spectrum in the selected energy window.
  • the formula of Savitzky-Golay five-point filtering method is as follows: Show:
  • y i represents the count on the address
  • y i-1 represents the count on the previous address of the address
  • y i-2 represents the first two addresses of the address
  • the count, y i+1 indicates the count on the next address of the address
  • y i+2 indicates the count on the last two addresses of the address.
  • the normalization processing method is: the measurement spectrum after the energy window range selection and the energy spectrum smoothing filtering is performed, so that the sum of the 181 (channel-by-channel) energy spectrum data is 10, and the normalized measurement spectrum is obtained.
  • the formula used is as shown in Equation 2 below:
  • N Gkj is the count of the normalized processed measurement spectrum corresponding to the j depth point in the kth channel
  • N kj is the energy window range selection corresponding to the j depth point
  • the measurement spectrum after the energy spectrum smoothing filtering is at the kth
  • the count of the track, j is the depth point below the formation.
  • the method of deducting the inelastic scattering information is to select the counts of the three-stage sites of 30-54, 55-75, and 76-210 for the normalized measurement spectrum with 0.9, 0.7, and 0.8 as the deduction coefficients, respectively. Inelastic information deduction.
  • the main element types of the DAS3 well reservoir section 3230m-3270m are Si, Ca, Al, Fe, S, K, Na, Mg, Ti, Gd, H and Ba.
  • the main element groups constructed according to this are: Si, Ca, S, Ti, Fe, Na, Gd; considering the influence of the instrument background, the Tb element is added when constructing the auxiliary element group, so the auxiliary element group constructed is: Mg, K, Al and Tb.
  • the main elements are firstly used by the least square method (ie: Si, Ca, S, Ti, Fe, Na, H, Ba, and Gd) are de-spreaded, and then the total elemental gamma spectrum (ie, the element-captured gamma spectrum after pretreatment in step 2) is deducted from the contribution of all the main elements.
  • the auxiliary elements ie, Mg, K, Al, and Tb
  • y j is the relative yield of the jth element (ie, the contribution of the element to the total count)
  • a ij is the elemental capture standard gamma spectroscopy of the elementized element of the jth element
  • the count of i track, c i is the count of the element capture gamma energy spectrum after preprocessing in the i-th track, ⁇ i is the correction coefficient, ⁇ i ⁇ 0.1.
  • the elemental capture gamma energy spectrum is reconstructed, and the reconstructed gamma energy spectrum is reconstructed. Compare with the measured gamma spectrum to determine if the deconvolution results are reliable.
  • the element capture standard gamma spectroscopy of the normalized unit cell is obtained by the element capture standard gamma spectrum of the single element, and 256 (channel by channel) energy spectrum data is obtained.
  • the sum is 10, and the formula used is as shown in Equation 4:
  • N Gkj is the count of the elemental capture standard gamma energy spectrum of the normalized unit cell corresponding to the j depth point in the kth channel
  • N kj is the unit cell of the normalization process corresponding to the j depth point
  • the element captures the standard gamma spectrum at the kth track and j is the depth point below the formation.
  • the elemental capture standard gamma spectroscopy of the single element can also be normalized by using the following formula 4-1:
  • the reconstruction of the elemental capture gamma energy spectrum is calculated according to the following formula 5: the count of the reconstructed gamma energy spectrum in the i-th track (that is, the count of all the demodulated elements in the i-th track), and the count of each track is obtained, and the weight is drawn.
  • Structured gamma ray spectrum :
  • X i is the count of the reconstructed gamma spectrum in the i-th track
  • y j is the relative yield of the j-th element (calculated from Equation 3)
  • a ij is the normalized processing of the j-th element
  • the element of the single element captures the count of the standard gamma energy spectrum in the i-th track (calculated from Equation 4 or Equation 4-1)
  • ⁇ i is the correction coefficient, ⁇ i ⁇ 0.1.
  • c i is the measurement of the gamma energy spectrum in the i-th track
  • X i is the count of the reconstructed gamma energy spectrum in the i-th track
  • ⁇ is the relative error
  • the de-spectral result is considered to be reliable; when the relative error is greater than 5%, the de-split result is considered to be unreliable.
  • the reconstructed gamma energy spectrum and the measured gamma energy spectrum (Fig. 4)
  • the pre-processed element capture gamma energy spectrum and the measured gamma energy spectrum (Fig. 5)
  • the main element group reconstructs the gamma energy spectrum (that is, calculates the count of all the main elements in the i-th channel according to Equation 5, obtains the count of each track, and reconstructs the gamma energy spectrum) and measures the gamma energy.
  • the spectrum (Fig. 6) is compared. As can be seen from Fig. 4-6, the deciphering result of this embodiment is reliable, and the deconvolution method is accurate and effective.
  • This embodiment provides a gamma spectroscopy spectrum spectroscopy apparatus for element capture spectroscopy logging, as shown in FIG. 7, which includes:
  • the data collection and main element type determining module 101 is configured to acquire and analyze the research area data, where the data includes at least an element capture gamma ray spectrum log data and a whole rock oxide analysis data, and determine a main element type of the block;
  • the main element group and the auxiliary element group building module 103 are configured to construct a main element group and an auxiliary element group composed of different elements to clarify the deciphering sequence of the different elements;
  • An element group-based least squares demodulation module 104 is configured to de-separate the main element from the pre-processed element-capture gamma energy spectrum according to the main element group and the auxiliary element group, and then de-spread the main element by least squares method, and then After the pre-processed total element gamma energy spectrum (ie, the element capture gamma energy spectrum after pretreatment), the contribution of all the main elements is deducted, and then the auxiliary elements are decomposed by the least squares method to obtain the relative of each element. Yield
  • the gamma energy spectrum reconstruction and error control module 105 is configured to perform element capture gamma energy spectrum reconstruction according to the relative yield of each element and the element capture standard gamma energy spectrum of the normalized unit cell. And the reconstructed gamma energy spectrum is compared with the measured gamma energy spectrum to determine whether the deconvolution result is reliable.

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Abstract

一种元素俘获能谱测井的伽马能谱解谱方法及装置，解谱方法首先对元素俘获能谱测井仪获取的数据进行预处理，然后构建主元素群和辅助元素群，进而在主、辅助元素群构建的基础上利用最小二乘法进行解谱，最后根据最小二乘法解谱获得的各元素产额进行能谱重构，将测量的伽马能谱与重构的伽马能谱对比进行误差控制，相应地，解谱装置包括数据采集及主要元素类型确定模块（101）、元素俘获伽马能谱测井数据预处理模块（102）、主元素群和辅助元素群构建模块（103）、基于元素群的最小二乘法解谱模块（104）以及伽马能谱重构及误差控制模块（105）。元素俘获能谱测井的伽马能谱解谱方法及装置均具有高精度、可操作性强的优点，对不同类型储层均具有较好的普适性，同时为其他类型仪器的中子伽马能谱数据的处理提供了重要的参考和借鉴意义。

Description

一种元素俘获能谱测井的伽马能谱解谱方法及装置 技术领域
本发明涉及一种元素俘获能谱测井的伽马能谱解谱方法及装置，属于油气勘探领域中的测井技术领域。
背景技术
元素俘获能谱(Elemental Capture Spectroscopy，简称ECS)测井仪是由斯伦贝谢公司向中国市场推出的新型地层元素测井仪器，该仪器利用中子与地层原子核碰撞发生核反应的原理得到伽马能谱，该伽马能谱包含了地层中主要造岩元素Si、Ca、Fe、Al、S、Ti、H、Gd等元素的信息，为复杂储层岩性精细评价提供了一种新途径。元素俘获能谱测井解释的核心是利用各元素标准谱对地层原始测量谱进行刻度，通过解谱获得各种元素的产额。在元素俘获能谱仪器获取的测量谱中，既包含了俘获伽马信息又包含了非弹性散射信息，而对油田现场解释评价最重要的是俘获伽马信息，因此在对测量谱进行解谱处理时必须考虑非弹性散射信息对解谱精度的影响。与此同时，不同地层元素之间的相互影响也制约着中子俘获伽马能谱的解谱精度。
以往学者虽然对元素俘获能谱测井的解谱方法开展了研究，但主要侧重于数值模拟和理论阐述上，没有针对实际井下数据提供有效的技术方法，因此未能在实际生产中进行应用。
发明内容
为解决上述技术问题，本发明的目的在于一种元素俘获能谱测井的伽马能谱解谱方法及装置。该方法是针对元素俘获能谱测井仪器得到的原始测量谱进行解谱的方法，具有高精度、可操作性强等优点。
为了实现上述目的，本发明提供了一种元素俘获能谱测井的伽马能谱解谱方法，其包括以下步骤：
步骤1：获取、分析研究区资料，所述资料至少包括元素俘获伽马能谱测井数据(在本发明中也称为测量伽马能谱、测量谱、测量混合伽马能谱、总元素伽马能谱测量谱等)和全岩氧化物分析数据，确定该区块主要元素类型；
步骤2：对元素俘获伽马能谱测井数据进行预处理，所述预处理包括能窗的选择、能谱平滑滤波、归一化处理以及非弹性散射信息扣除，得到预处理后的元素俘获伽马 能谱；
步骤3：构建由不同元素组成的主元素群和辅助元素群，以明确不同元素的解谱先后顺序；
步骤4：根据步骤3得到的主元素群和辅助元素群，在经步骤2预处理后的元素俘获伽马能谱中，利用最小二乘法先对主元素进行解谱，然后从预处理后的总元素伽马能谱(即经步骤2预处理后的元素俘获伽马能谱)中扣除全部主元素所占贡献，再利用最小二乘法对辅助元素进行解谱，从而得到各元素的相对产额；
步骤5：根据步骤4得到的各元素的相对产额，结合归一化处理后的单元素的元素俘获标准伽马能谱(在本发明中也称为单元素标准谱)，进行元素俘获伽马能谱的重构，并将重构的伽马能谱与测量伽马能谱进行对比，确定解谱结果是否可靠。
根据本发明的具体实施方式，优选地，在上述解谱方法的步骤1中，所获取、分析的研究区资料还包括常规测井数据、录井数据和地质资料等中的一种或几种的组合。
根据本发明的具体实施方式，优选地，在上述解谱方法的步骤1中，研究区主要元素类型是通过以下方式确定的：通过分析研究区的全岩氧化物分析数据、以及可选择地，常规测井数据、录井数据和地质资料中的一种或几种的组合，得出不同种类氧化物所占岩石的重量百分比，依据各氧化物重量百分比确定该区块主要元素类型。其中，所述地质资料主要包括：岩心薄片和/或地质报告等。全岩氧化物分析数据是确定研究区主要元素类型所必需的资料，同时可以参考常规测井数据、录井数据和地质资料中的一种或几种资料。
在本发明的解谱方法中，步骤2对伽马能谱进行预处理，能够消除仪器在测量过程中受井眼环境、仪器固有特性等因素的影响，提高解谱的精度。
在能窗的选择方面，元素俘获能谱测井探测到的伽马射线能量一般集中在0-10MeV，对应道址为0-255，总共256道。低能区段容易受到康普顿平台效应以及仪器的噪声等因素影响而产生很高的计数，即大量干扰信号，应在解谱之前予以剔除。而在高能区段，能谱的计数往往非常低，在解谱的过程中对最终的求解结果影响不大，也应在解谱之前予以剔除。如果没有完全去掉带有不利影响的数据，会对解谱产生影响；但是，如果去掉过多的数据会去掉有用信息，也会对解谱产生影响。因此，选取适当的能量范围是解谱前必须解决的关键问题。
根据本发明的具体实施方式，优选地，在上述解谱方法的步骤2中，所选择的能窗范围是30-210道。本发明提出的选取解谱能窗的基本原则是不去掉元素全能峰所对应的能量。本发明的发明人通过对国际原子能机构公布的核数据进行查询，查出解谱涉及的各种元素的特征峰位，根据峰位所对应的能量信息，确定解谱的能窗范围。各元素特征峰位按能量从低到高排序为1.38，1.808，1.924，1.951，2.073，2.092，2.223，2.282，2.379，2.828，3.033，3.22，3.539，3.587，3.691，3.981，4.419，4.733，4.737，4.869，4.933，5.42，5.9，5.92，6.018，6.11，6.36，6.379，6.395，6.418，6.42，6.76，7.414，7.631，7.646，7.724，7.769，7.79，8.153(MeV)。根据这些数据将能窗的能量范围取为1.2MeV～8.4MeV，其对应的能窗范围是30-210道，如图1所示。
根据本发明的具体实施方式，优选地，在上述解谱方法的步骤2中，能谱平滑滤波的方式是采用Savitzky-Golay滤波器，并用Savitzky-Golay五点滤波法，对所选择的能窗范围内的元素俘获伽马能谱测井数据进行滤波，Savitzky-Golay五点滤波法的公式如下式1所示：
Figure PCTCN2018120083-appb-000001
其中，
Figure PCTCN2018120083-appb-000002
表示某一道址上滤波后的计数，y i表示该道址上的计数，y i-1表示该道址的前一道址上的计数，y i-2表示该道址的前二道址上的计数，y i+1表示该道址的后一道址上的计数，y i+2表示该道址的后二道址上的计数。
元素俘获伽马能谱测井仪器的测量过程中，存在固有的统计涨落，能谱平滑滤波能够消除测量伽马能谱中存在的假峰、弱峰等问题。本发明采用的Savitzky-Golay滤波器，在滤波的窗长内，利用多项式拟合测量伽马能谱的数据，得到滤波后的结果，即拟合多项式在滤波点对应的值。通过对原始测量伽马能谱的分析，本发明提出的Savitzky-Golay五点滤波法可以消除毛刺。本发明的滤波方法的实质是对测量数据做加权平均，使得能谱数据中每个数据波动都做了平均，这种平均对数据趋势影响非常小，而波动的平均值为零，可有效消除统计涨落的影响。
根据本发明的具体实施方式，优选地，在上述解谱方法的步骤2中，归一化处理采用的方法是：对经过能窗范围选择、能谱平滑滤波之后的元素俘获伽马能谱测井数据，使181道(逐道)的能谱数据之和为10，得到归一化处理后的元素俘获伽马能谱，所采用的公式如下式2所示：
Figure PCTCN2018120083-appb-000003
其中，N Gkj为j深度点对应的归一化处理后的元素俘获伽马能谱在第k道的计数，N kj为j深度点对应的经过能窗范围选择、能谱平滑滤波之后的元素俘获伽马能谱在第k道的计数，j为地层下的深度点。
在本发明的具体实施方式中，归一化处理是将整个能量段的能谱计数累加，再将每道计数除以能窗范围内所有道址对应的计数之和，从而使归一化后的数据在0～10之间，整个能量段的能谱数据之和为10。在本发明中，归一化处理是对滤波后的能窗范围内的伽马能谱数据进行归一化，获取归一化后的伽马能谱数据，使归一化处理后的俘获元素定性计算值可以准确反映整个井段的变化，因此归一化处理可以为元素定性计算和分离的方法提供保证，也可以准确地反应能窗范围内的地层变化。
根据本发明的具体实施方式，优选地，在上述解谱方法的步骤2中，非弹性散射信息扣除的方法是：对归一化处理后的元素俘获伽马能谱选取30-54道、55-75道和76-210道三段道址的计数分别用0.9、0.7和0.8作为扣除系数进行非弹性信息扣除。由于元素俘获能谱测井获取的伽马能谱中既包含了俘获伽马信息又包含了非弹性散射信息，而对油田现场解释评价最重要的是俘获伽马信息，因此在对测量谱进行剥谱处理时必须扣除非弹性散射信息对总元素伽马能谱测量谱的贡献，可以有效提高后期解谱的精度。在本发明的具体实施方式中，对测量谱中非弹性散射信息扣除的方法采用的是分段扣除方法，根据不同元素对不同能量段总计数的贡献，在不同能量段分别采用不同的扣除系数消除测量谱中非弹性散射信息的影响。
根据本发明的具体实施方式，优选地，在上述解谱方法的步骤3中，所构建的主元素群包括：Si、Ca、S、H、Cl、Ti、Fe、Na、Ba和Gd等元素中的一种或几种元素，这些元素的贡献占总元素伽马能谱的80％以上；所构建的辅助元素群包括：Mg、K、Cr、Ni、I、Tb和Al等元素中的一种或几种元素。单元素标准伽马能谱在谱形、特征峰位的差异性是元素俘获能谱测井解谱的核心和关键，但实际上，不同元素之间在谱形及特征峰位分布上会存在一定的相似性和重叠。如图2所示，Na元素和Mg元素在谱形上存在一定的相似性，在特征能峰所在道址上存在重叠，因此，在实际解谱过程中如果不考虑元素之间的相互影响因素，则会降低解谱的精度，从而影响油气 储层评价的准确性。本发明提出和构建的元素群是在考虑不同元素单元素标准伽马能谱相互影响的基础上建立的；主元素群主要包括构成岩石骨架部分的元素；辅助元素群的构建是为了消除或者辅助提高其它元素的解谱精度而建立的，辅助元素Mg和K主要用于分析判断S的产额，辅助元素Mg、Cr、Ni用于分析判断Si、Ca和Fe元素的产额，辅助元素Cr、Ni、I、Tb和Al用于分析判断H、Gd和Ti素的产额，进而提高解谱的精度。
根据本发明的具体实施方式，优选地，在上述解谱方法的步骤4中，利用最小二乘法进行解谱是按照如下式3所示的算法进行的，以得到各元素的相对产额：
Figure PCTCN2018120083-appb-000004
其中，y j为第j种元素的相对产额(即该元素对总计数的贡献)，a ij为第j种元素的归一化处理后的单元素的元素俘获标准伽马能谱在第i道的计数，c i为预处理后的元素俘获伽马能谱在第i道的计数，ε i为校正系数，ε i≤0.1。
本发明利用上述最小二乘法公式在对主元素群进行解谱后，从预处理后的总元素伽马能谱中对主元素所占贡献进行扣除，再利用上述最小二乘法公式对辅助元素群进行再次解谱，逐步得到主、辅元素群中的各元素产额信息，从而有效提高解谱精度。
根据本发明的具体实施方式，优选地，在上述解谱方法的步骤5中，所述归一化处理后的单元素的元素俘获标准伽马能谱是通过以下方法得到的：对于单元素的元素俘获标准伽马能谱，使256道(逐道)的能谱数据之和为10，所采用的公式如下式4所示：
Figure PCTCN2018120083-appb-000005
其中，N Gkj为j深度点对应的归一化处理后的单元素的元素俘获标准伽马能谱在第k道的计数，N kj为j深度点对应的归一化处理前的单元素的元素俘获标准伽马能谱在第k道的计数，j为地层下的深度点。
根据本发明的具体实施方式，优选地，在上述解谱方法的步骤5中，元素俘获伽马能谱的重构是按照下式5计算重构伽马能谱在第i道的计数(即所有解谱元素在第i道的计数)，得到各道的计数后，绘制重构伽马能谱：
Figure PCTCN2018120083-appb-000006
其中，X i为重构伽马能谱在第i道的计数，y j为第j种元素的相对产额(由式3计算得到)，a ij为第j种元素的归一化处理后的单元素的元素俘获标准伽马能谱在第i道的计数(由式4计算得到)，ε i为校正系数，ε i≤0.1。
根据本发明的具体实施方式，优选地，在上述解谱方法的步骤5中，将重构的伽马能谱与测量伽马能谱进行对比是按照式6进行：
︱C i－X i︱《ε        (式6)
c i为测量伽马能谱在第i道的计数，X i为重构的伽马能谱在第i道的计数，ε为相对误差；
当相对误差小于或等于5％时，认为解谱结果是可靠的；当相对误差大于5％，则认为解谱结果不可靠。
在本发明的具体实施方式中，伽马能谱的重构方式是参考剥谱分析方法，认为测量混合伽马能谱是单元素的线性组合，在确定地层元素含量时以获取的单元素标准谱数据库为基础，根据已知俘获和非弹性散射元素产额结果进行重构，然后将实际测量的伽马能谱与重构伽马能谱进行对比，确定解谱结果可靠与否，进而控制解谱精度。
本发明还提供了一种元素俘获能谱测井的伽马能谱解谱装置，其包括：
数据采集及主要元素类型确定模块，用于获取、分析研究区资料，所述资料至少包括元素俘获伽马能谱测井数据和全岩氧化物分析数据，确定该区块主要元素类型；
元素俘获伽马能谱测井数据预处理模块，用于对元素俘获伽马能谱测井数据进行预处理，所述预处理包括能窗的选择、能谱平滑滤波、归一化处理以及非弹性散射信息扣除，得到预处理后的元素俘获伽马能谱；
主元素群和辅助元素群构建模块，用于构建由不同元素组成的主元素群和辅助元素群，以明确不同元素的解谱先后顺序；
基于元素群的最小二乘法解谱模块，用于根据主元素群和辅助元素群，在预处理后的元素俘获伽马能谱中，利用最小二乘法先对主元素进行解谱，然后从预处理后的总元素伽马能谱(即预处理后的元素俘获伽马能谱)中扣除全部主元素所占贡献，再利用最小二乘法对辅助元素进行解谱，从而得到各元素的相对产额；
伽马能谱重构及误差控制模块，用于根据各元素的相对产额，结合归一化处理后的单元素的元素俘获标准伽马能谱，进行元素俘获伽马能谱的重构，并将重构的伽马 能谱与测量伽马能谱进行对比，确定解谱结果是否可靠。
在上述的装置中，优选地，在数据采集及主要元素类型确定模块中，所获取、分析的研究区资料还包括常规测井数据、录井数据和地质资料等中的一种或几种的组合。
在上述的装置中，优选地，在数据采集及主要元素类型确定模块中，研究区主要元素类型是通过以下方式确定的：通过分析研究区的全岩氧化物分析数据、以及可选择地，常规测井数据、录井数据和地质资料等中的一种或几种的组合，得出不同种类氧化物所占岩石的重量百分比，依据各氧化物重量百分比从而确定该区块主要元素类型。
在上述的装置中，优选地，在元素俘获伽马能谱测井数据预处理模块中，所选择的能窗范围是30-210道。
在上述的装置中，优选地，在元素俘获伽马能谱测井数据预处理模块中，能谱平滑滤波的方式是采用Savitzky-Golay滤波器，并用Savitzky-Golay五点滤波法，对所选择的能窗范围内的元素俘获伽马能谱测井数据进行滤波，Savitzky-Golay五点滤波法的公式如下式1所示：
Figure PCTCN2018120083-appb-000007
其中，
Figure PCTCN2018120083-appb-000008
表示某一道址上滤波后的计数，y i表示该道址上的计数，y i-1表示该道址的前一道址上的计数，y i-2表示该道址的前二道址上的计数，y i+1表示该道址的后一道址上的计数，y i+2表示该道址的后二道址上的计数。
在上述的装置中，优选地，在元素俘获伽马能谱测井数据预处理模块中，归一化处理采用的方法是：对经过能窗范围选择、能谱平滑滤波之后的元素俘获伽马能谱测井数据，使181道(逐道)的能谱数据之和为10，得到归一化处理后的元素俘获伽马能谱，所采用的公式如下式2所示：
Figure PCTCN2018120083-appb-000009
其中，N Gkj为j深度点对应的归一化处理后的元素俘获伽马能谱在第k道的计数，N kj为j深度点对应的经过能窗范围选择、能谱平滑滤波之后的元素俘获伽马能谱在第k道的计数，j为地层下的深度点。
在上述的装置中，优选地，在元素俘获伽马能谱测井数据预处理模块中，非弹性散射信息扣除的方法是：对归一化处理后的元素俘获伽马能谱选取30-54道、55-75道和76-210道三段道址的计数分别用0.9、0.7和0.8作为扣除系数进行非弹性信息扣除。
在上述的装置中，优选地，在主元素群和辅助元素群构建模块中，所构建的主元素群包括：Si、Ca、S、H、Cl、Ti、Fe、Na、Ba和Gd等元素中的一种或几种元素；所构建的辅助元素群包括：Mg、K、Cr、Ni、I、Tb和Al等元素中的一种或几种元素。
在上述的装置中，优选地，在基于元素群的最小二乘法解谱模块中，利用最小二乘法进行解谱是按照如下式3所示的算法进行的，以得到各元素的相对产额：
Figure PCTCN2018120083-appb-000010
其中，y j为第j种元素的相对产额(即该元素对总计数的贡献)，a ij为第j种元素的归一化处理后的单元素的元素俘获标准伽马能谱在第i道的计数，c i为预处理后的元素俘获伽马能谱在第i道的计数，ε i为校正系数，ε i≤0.1。
在上述的装置中，优选地，在伽马能谱重构及误差控制模块中，所述归一化处理后的单元素的元素俘获标准伽马能谱是通过以下方法得到的：对单元素的元素俘获标准伽马能谱，使256道(逐道)的能谱数据之和为10，所采用的公式如下式4所示：
Figure PCTCN2018120083-appb-000011
其中，N Gkj为j深度点对应的归一化处理后的单元素的元素俘获标准伽马能谱在第k道的计数，N kj为j深度点对应的归一化处理前的单元素的元素俘获标准伽马能谱在第k道的计数，j为地层下的深度点。
在上述的装置中，优选地，在伽马能谱重构及误差控制模块中，元素俘获伽马能谱的重构是按照下式5计算重构伽马能谱在第i道的计数(即所有解谱元素在第i道的计数)，得到各道的计数后，绘制重构伽马能谱：
Figure PCTCN2018120083-appb-000012
其中，X i为重构伽马能谱在第i道的计数，y j为第j种元素的相对产额(由式3 计算得到)，a ij为第j种元素的归一化处理后的单元素的元素俘获标准伽马能谱在第i道的计数(由式4计算得到)，ε i为校正系数，ε i≤0.1。
在上述的装置中，优选地，在伽马能谱重构及误差控制模块中，将重构的伽马能谱与测量伽马能谱进行对比是按照式6进行：
︱C i－X i︱《ε         (式6)
c i为测量伽马能谱在第i道的计数，X i为重构的伽马能谱在第i道的计数，ε为相对误差；
当相对误差小于或等于5％时，认为解谱结果是可靠的；当相对误差大于5％，则认为解谱结果不可靠。
本发明对元素俘获伽马能谱测井资料的处理方法进行了深入研究之后，提供了一种高精度的、具有可操作性的针对元素俘获能谱测井仪器得到的伽马能谱的解谱方法及装置，为准确开展元素俘获能谱的解谱提供了优秀的技术手段。本发明首先对元素俘获能谱测井仪器获取的伽马能谱数据进行预处理，消除了仪器在测量过程中受井眼环境、仪器固有特性等因素影响。然后首次在考虑不同元素间相互影响的基础上提出了基于元素群的中子俘获伽马能谱解谱思想和具体实现方法；通过对不同元素谱形、特征峰道及本底之间的理论分析和数值计算，按照元素间相互影响程度构建了主元素群和辅助元素群，并首次明确了不同元素解谱的先后顺序。之后首次基于最小二乘法实现了基于元素群的中子俘获伽马能谱解谱的方法，并根据最小二乘法解谱获得的各元素产额通过理论进行能谱重构，将实际测量的伽马能谱与重构伽马能谱对比进行误差控制，有效提高了解谱精度。油田现场应用表明，本发明的解谱方法及装置对不同类型储层均具有较好的普适性，同时为其他类型仪器的中子伽马能谱数据处理提供了重要的参考和借鉴意义。
附图说明
图1为解谱能窗的选择示意图。
图2为Na元素和Mg元素单元素的元素俘获标准伽马能谱对比图。
图3为DAS3井2403.1956m的各元素解谱效果。
图4为DAS3井2403.1956m的重构伽马能谱和测量伽马能谱对比图。
图5为DAS3井2403.1956m的预处理后的元素俘获伽马能谱和测量伽马能谱对比图。
图6为DAS3井2403.1956m的主元素群重构伽马能谱和测量伽马能谱对比图。
图7为实施例2提供的元素俘获能谱测井的伽马能谱解谱装置的示意图。
具体实施方式
为了对本发明的技术特征、目的和有益效果有更加清楚的理解，结合油田现场实际应用实例，现对本发明的技术方案进行以下详细说明，但不能理解为对本发明的可实施范围的限定。
实施例1
本实施例提供了一种元素俘获能谱测井的伽马能谱解谱方法，其可以包括以下步骤：
1.总元素伽马能谱测量谱的获取以及研究区主要元素分布种类的确定
利用元素俘获能谱测井仪器对对大庆油田DAS3井储层段3230-3270m进行测井，获得各深度点的地层元素总的伽马能谱，即元素俘获伽马能谱测井数据，每个深度点对应一张谱图。获取、分析大庆油田DAS3井的全岩氧化物分析实验结果、录井数据，发现在储层段3230m-3270m主要包括Si、Ca、Al、Fe、S、K、Na、Mg、Ti、Gd、H和Ba元素，这些元素的氧化物占岩石重量百分含量的98.9％。
2.对元素俘获伽马能谱数据进行预处理
对DAS3井储层段3230-3270m各深度点的测量谱分别进行预处理，分别得到各深度点对应的预处理后的元素俘获伽马能谱，所述预处理包括能窗的选择、能谱平滑滤波、归一化处理以及非弹性散射信息扣除。
其中，所选择的能窗范围是30-210道，如图1所示。
能谱平滑滤波的方式是采用Savitzky-Golay滤波器，并用Savitzky-Golay五点滤波法，对所选择的能窗范围内的测量谱进行滤波，Savitzky-Golay五点滤波法的公式如下式1所示：
Figure PCTCN2018120083-appb-000013
其中，
Figure PCTCN2018120083-appb-000014
表示某一道址上滤波后的计数，y i表示该道址上的计数，y i-1表示该道址的前一道址上的计数，y i-2表示该道址的前二道址上的计数，y i+1表示该道址的后一道址上的计数，y i+2表示该道址的后二道址上的计数。
归一化处理采用的方法是：对经过能窗范围选择、能谱平滑滤波之后的测量谱，使181道(逐道)的能谱数据之和为10，得到归一化处理后的测量谱，所采用的公 式如下式2所示：
Figure PCTCN2018120083-appb-000015
其中，N Gkj为j深度点对应的归一化处理后的测量谱在第k道的计数，N kj为j深度点对应的经过能窗范围选择、能谱平滑滤波之后的测量谱在第k道的计数，j为地层下的深度点。
非弹性散射信息扣除的方法是：对归一化处理后的测量谱选取30-54道、55-75道和76-210道三段道址的计数分别用0.9、0.7和0.8作为扣除系数进行非弹性信息扣除。
3.构建主元素群和辅助元素群
根据步骤1知道，DAS3井储层段3230m-3270m的主要元素种类为Si、Ca、Al、Fe、S、K、Na、Mg、Ti、Gd、H和Ba。据此构建的主元素群为：Si、Ca、S、Ti、Fe、Na、Gd；考虑到仪器本底的影响，在构建辅助元素群时增加了Tb元素，因此构建的辅助元素群为：Mg、K、Al和Tb。
4.基于元素群的最小二乘法解谱
根据步骤3得到的主元素群和辅助元素群，在经步骤2预处理后的元素俘获伽马能谱中，利用最小二乘法先对主元素(即：Si、Ca、S、Ti、Fe、Na、H、Ba、和Gd)进行解谱，然后从预处理后的总元素伽马能谱(即经步骤2预处理后的元素俘获伽马能谱)中扣除全部主元素所占贡献，再利用最小二乘法对辅助元素(即：Mg、K、Al和Tb)进行解谱，从而得到各元素的相对产额；
利用最小二乘法进行解谱是按照如下式3所示的算法进行的，以得到各元素的相对产额：
Figure PCTCN2018120083-appb-000016
其中，y j为第j种元素的相对产额(即该元素对总计数的贡献)，a ij为第j种元素的归一化处理后的单元素的元素俘获标准伽马能谱在第i道的计数，c i为预处理后的元素俘获伽马能谱在第i道的计数，ε i为校正系数，ε i≤0.1。
以DAS3井2403.1956m深度点获取的元素俘获伽马能谱为例，解谱得到的各元素的相对产额如图3所示。由于Td为本底产额，没有参考价值，故在图3中没有展 示。
5.重构伽马能谱并控制误差
据步骤4得到的各元素的相对产额，结合归一化处理后的单元素的元素俘获标准伽马能谱，进行元素俘获伽马能谱的重构，并将重构的伽马能谱与测量伽马能谱进行对比，确定解谱结果是否可靠。
其中，所述归一化处理后的单元素的元素俘获标准伽马能谱是通过以下方法得到的：对于单元素的元素俘获标准伽马能谱，使256道(逐道)的能谱数据之和为10，所采用的公式如下式4所示：
Figure PCTCN2018120083-appb-000017
其中，N Gkj为j深度点对应的归一化处理后的单元素的元素俘获标准伽马能谱在第k道的计数，N kj为j深度点对应的归一化处理前的单元素的元素俘获标准伽马能谱在第k道的计数，j为地层下的深度点。
考虑到前二道址的计数没有实际意义，因此也可以采用下式4-1对单元素的元素俘获标准伽马能谱进行归一化处理：
Figure PCTCN2018120083-appb-000018
元素俘获伽马能谱的重构是按照下式5计算重构伽马能谱在第i道的计数(即所有解谱元素在第i道的计数)，得到各道的计数后，绘制重构伽马能谱：
Figure PCTCN2018120083-appb-000019
其中，X i为重构伽马能谱在第i道的计数，y j为第j种元素的相对产额(由式3计算得到)，a ij为第j种元素的归一化处理后的单元素的元素俘获标准伽马能谱在第i道的计数(由式4或式4-1计算得到)，ε i为校正系数，ε i≤0.1。
将重构的伽马能谱与测量伽马能谱进行对比是按照式6进行：
︱C i－X i︱《ε        (式6)
c i为测量伽马能谱在第i道的计数，X i为重构的伽马能谱在第i道的计数，ε为相对误差；
当相对误差小于或等于5％时，认为解谱结果是可靠的；当相对误差大于5％，则认为解谱结果不可靠。
以DAS3井2403.1956m深度点各元素解谱结果为例，分别对重构伽马能谱和测量伽马能谱(图4)、预处理后的元素俘获伽马能谱和测量伽马能谱(图5)、主元素群重构伽马能谱(即按照式5计算全部主元素在第i道的计数，得到各道的计数后，重构的伽马能谱)和测量伽马能谱(图6)进行了对比，由图4-6可以看出，本实施例的解谱结果可靠，并且解谱方法精确、有效。
实施例2
本实施例提供了一种元素俘获能谱测井的伽马能谱解谱装置，如图7所示，其包括：
数据采集及主要元素类型确定模块101，用于获取、分析研究区资料，所述资料至少包括元素俘获伽马能谱测井数据和全岩氧化物分析数据，确定该区块主要元素类型；
元素俘获伽马能谱测井数据预处理模块102，用于对元素俘获伽马能谱测井数据进行预处理，所述预处理包括能窗的选择、能谱平滑滤波、归一化处理以及非弹性散射信息扣除，得到预处理后的元素俘获伽马能谱；
主元素群和辅助元素群构建模块103，用于构建由不同元素组成的主元素群和辅助元素群，以明确不同元素的解谱先后顺序；
基于元素群的最小二乘法解谱模块104，用于根据主元素群和辅助元素群，在预处理后的元素俘获伽马能谱中，利用最小二乘法先对主元素进行解谱，然后从预处理后的总元素伽马能谱(即预处理后的元素俘获伽马能谱)中扣除全部主元素所占贡献，再利用最小二乘法对辅助元素进行解谱，从而得到各元素的相对产额；
伽马能谱重构及误差控制模块105，用于根据各元素的相对产额，结合归一化处理后的单元素的元素俘获标准伽马能谱，进行元素俘获伽马能谱的重构，并将重构的伽马能谱与测量伽马能谱进行对比，确定解谱结果是否可靠。

Claims (12)

  1. 一种元素俘获能谱测井的伽马能谱解谱方法，其包括以下步骤：
    步骤1：获取、分析研究区资料，所述资料至少包括元素俘获伽马能谱测井数据和全岩氧化物分析数据，确定该区块主要元素类型；
    步骤2：对元素俘获伽马能谱测井数据进行预处理，所述预处理包括能窗的选择、能谱平滑滤波、归一化处理以及非弹性散射信息扣除，得到预处理后的元素俘获伽马能谱；
    步骤3：构建由不同元素组成的主元素群和辅助元素群，以明确不同元素的解谱先后顺序；
    步骤4：根据步骤3得到的主元素群和辅助元素群，在经步骤2预处理后的元素俘获伽马能谱中，利用最小二乘法先对主元素进行解谱，然后从经步骤2预处理后的元素俘获伽马能谱中扣除全部主元素所占贡献，再利用最小二乘法对辅助元素进行解谱，从而得到各元素的相对产额；
    步骤5：根据步骤4得到的各元素的相对产额，结合归一化处理后的单元素的元素俘获标准伽马能谱，进行元素俘获伽马能谱的重构，并将重构的伽马能谱与测量伽马能谱进行对比，确定解谱结果是否可靠。
  2. 根据权利要求1所述的解谱方法，其中，在步骤1中，所获取、分析的研究区资料还包括常规测井数据、录井数据和地质资料中的一种或几种的组合；研究区主要元素类型是通过以下方式确定的：通过分析研究区的全岩氧化物分析数据、以及可选择地，常规测井数据、录井数据和地质资料中的一种或几种的组合，得出不同种类氧化物所占岩石的重量百分比，依据各氧化物重量百分比确定该区块主要元素类型。
  3. 根据权利要求1所述的解谱方法，其中，在步骤2中，所选择的能窗范围是30-210道。
  4. 根据权利要求1所述的解谱方法，其中，在步骤2中，能谱平滑滤波的方式是采用Savitzky-Golay滤波器，并用Savitzky-Golay五点滤波法，对所选择的能窗范围内的元素俘获伽马能谱测井数据进行滤波，Savitzky-Golay五点滤波法的公式如下式1所示：
    Figure PCTCN2018120083-appb-100001
    其中，
    Figure PCTCN2018120083-appb-100002
    表示某一道址上滤波后的计数，y i表示该道址上的计数，y i-1表示该道址的前一道址上的计数，y i-2表示该道址的前二道址上的计数，y i+1表示该道址的后一道址上的计数，y i+2表示该道址的后二道址上的计数。
  5. 根据权利要求1所述的解谱方法，其中，在步骤2中，归一化处理采用的方法是：对经过能窗范围选择、能谱平滑滤波之后的元素俘获伽马能谱测井数据，使181道的能谱数据之和为10，得到归一化处理后的元素俘获伽马能谱，所采用的公式如下式2所示：
    Figure PCTCN2018120083-appb-100003
    其中，N Gkj为j深度点对应的归一化处理后的元素俘获伽马能谱在第k道的计数，N kj为j深度点对应的经过能窗范围选择、能谱平滑滤波之后的元素俘获伽马能谱在第k道的计数，k为道址，j为地层下的深度点。
  6. 根据权利要求1所述的解谱方法，其中，在步骤2中，非弹性散射信息扣除的方法是：对归一化处理后的元素俘获伽马能谱选取30-54道、55-75道和76-210道三段道址的计数分别用0.9、0.7和0.8作为扣除系数进行非弹性信息扣除。
  7. 根据权利要求1所述的解谱方法，其中，在步骤3中，所构建的主元素群包括：Si、Ca、S、H、Cl、Ti、Fe、Na、Ba和Gd中的一种或几种元素；所构建的辅助元素群包括：Mg、K、Cr、Ni、I、Tb和Al中的一种或几种元素。
  8. 根据权利要求1所述的解谱方法，其中，在步骤4中，利用最小二乘法进行解谱是按照如下式3所示的算法进行的，以得到各元素的相对产额：
    Figure PCTCN2018120083-appb-100004
    其中，y j为第j种元素的相对产额，a ij为第j种元素的归一化处理后的单元素的元素俘获标准伽马能谱在第i道的计数，c i为预处理后的元素俘获伽马能谱在第i道的计数，ε i为校正系数，ε i≤0.1。
  9. 根据权利要求1或8所述的解谱方法，其中，在步骤5中，所述归一化处理后的单元素的元素俘获标准伽马能谱是通过以下方法得到的：对单元素的元素俘获标准伽马能谱，使256道的能谱数据之和为10，所采用的公式如下式4所示：
    Figure PCTCN2018120083-appb-100005
    其中，N Gkj为j深度点对应的归一化处理后的单元素的元素俘获标准伽马能谱在第k道的计数，N kj为j深度点对应的归一化处理前的单元素的元素俘获标准伽马能谱在第k道的计数，k为道址，j为地层下的深度点。
  10. 根据权利要求1所述的解谱方法，其中，在步骤5中，元素俘获伽马能谱的重构是按照下式5计算重构伽马能谱在第i道的计数，得到各道的计数后，绘制重构伽马能谱：
    Figure PCTCN2018120083-appb-100006
    其中，X i为重构伽马能谱在第i道的计数，y j为第j种元素的相对产额，a ij为第j种元素的归一化处理后的单元素的元素俘获标准伽马能谱在第i道的计数，ε i为校正系数，ε i≤0.1。
  11. 根据权利要求1所述的解谱方法，其中，在步骤5中，将重构的伽马能谱与测量伽马能谱进行对比是按照式6进行：
    ︱C i－X i︱《ε  (式6)
    c i为测量伽马能谱在第i道的计数，X i为重构的伽马能谱在第i道的计数，ε为相对误差；
    当相对误差小于或等于5％时，认为解谱结果是可靠的；当相对误差大于5％，则认为解谱结果不可靠。
  12. 一种元素俘获能谱测井的伽马能谱解谱装置，其包括：
    数据采集及主要元素类型确定模块，用于获取、分析研究区资料，所述资料至少包括元素俘获伽马能谱测井数据和全岩氧化物分析数据，确定该区块主要元素类型；
    元素俘获伽马能谱测井数据预处理模块，用于对元素俘获伽马能谱测井数据进行预处理，所述预处理包括能窗的选择、能谱平滑滤波、归一化处理以及非弹性散射信息扣除，得到预处理后的元素俘获伽马能谱；
    主元素群和辅助元素群构建模块，用于构建由不同元素组成的主元素群和辅助元素群，以明确不同元素的解谱先后顺序；
    基于元素群的最小二乘法解谱模块，用于根据主元素群和辅助元素群，在预处理 后的元素俘获伽马能谱中，利用最小二乘法先对主元素进行解谱，然后从预处理后的元素俘获伽马能谱中扣除全部主元素所占贡献，再利用最小二乘法对辅助元素进行解谱，从而得到各元素的相对产额；
    伽马能谱重构及误差控制模块，用于根据各元素的相对产额，结合归一化处理后的单元素的元素俘获标准伽马能谱，进行元素俘获伽马能谱的重构，并将重构的伽马能谱与测量伽马能谱进行对比，确定解谱结果是否可靠。
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