Shale classification method and device

Abstract

The invention belongs to the technical field of oil exploration, and particularly relates to a shale classification method and device. The method comprises the following specific steps: determining a sensitive curve according to an actually measured organic carbon value, determining a coring standard well shale organic matter content classification result, and calculating a correlation coefficient by utilizing actually measured organic carbon data and each sensitive logging curve subjected to normalization processing so as to calculate a weight coefficient; then, sensitive parameter multiple regression is used for hedging and weakening adverse effects of unary regression on a single sensitive curve due to complex factors such as well diameters, heavy minerals and special lithology, a shale characteristic indication curve is reconstructed, and a threshold value is obtained according to a standard well shale classification result; generating a single well characteristic indication curve which is not cored or is not completely cored, and classifying the organic shale of the well in combination with a threshold value. The method greatly improves the recognition precision, is easy to popularize, can quickly evaluate the longitudinal strata series and transverse distribution of the organic shale in the basin, and meets the requirements of shale oil resource evaluation of the lower chalk system broken lake basin.

Description

Technical Field

[0001] The invention belongs to the technical field of petroleum exploration, and in particular relates to a shale classification method and device. Background Art

[0002] The domestic market demand for oil and gas resources is growing year by year. Conventional oil and gas exploration has entered the middle and late stages, and the newly-proven oil reserves are declining year by year. In order to reduce the external dependence on oil and improve the domestic oil self-sufficiency, a new strategic pattern of unconventional oil and gas exploration and development has been formed in China, and shale oil has become an important oil replacement resource. Shale oil has the characteristics of integrated source and reservoir and continuous distribution, and its storage medium is organic-rich shale with moderate maturity. There is a certain similarity between organic-poor mudstone and organic-rich shale in logging response characteristics. How to identify and delineate the distribution of organic-rich shale has become the top priority in evaluating the resource potential of a certain region.

[0003] Zhu Yongfei and Xian Benzhong (2016) evaluated the shale oil exploration potential of the Lower Cretaceous Tengger Formation in the Al Sag of the Erlian Basin based on the principles of sequence stratigraphy combined with sedimentology theory; in 2020, the Exploration Division of PetroChina Huabei Oilfield Branch conducted a preliminary evaluation of the scale of tight oil resources in the Lower Cretaceous Alshan Formation in the Errennur Sag of the Erlian Basin using cores, thin sections, microscopic observations, oil and gas geochemical experimental parameters and other data; Han Jiabing and Liu Rong (2019) conducted a study on the geochemical hydrocarbon generation indicators of oil shale in the Lower Cretaceous Bagmaode oil shale mining area on the northeastern edge of the Yin'e Basin, and clarified the main layers and system domains where oil shale is developed. The above research results have important guiding significance for the search for shale oil in the Lower Cretaceous sedimentary strata, but they are more inclined to macro-guidance and are not applicable to the specific identification and prediction of mud and shale at the micro level.

[0004] The Chinese invention patent application with application publication number CN110685676A discloses a method for quantitatively identifying high-quality shale sections, which includes: qualitatively identifying high-quality shale sections based on the correlation between the distribution of organic carbon content (TOC) and uranium (U), thorium (Th), potassium (K) and natural gamma curve (GR), selecting valid data based on the consistency of logging data analyzed by representative drilling or outcrop profiles and the validity of experimental test TOC data, using the valid data to make scatter plots of TOC and U, TOC and Th, TOC and K, and TOC and Th / U, respectively, and establishing a quantitative plate for identifying high-quality shale sections based on the distribution range of high-quality shale sections in the scatter plots. Due to the high cost of spectral logging, there are few wells with U, Th, and K logging data. Although high-quality shale sections can be found through radioactive characteristics and discrimination marks, there is a disadvantage that it cannot be widely promoted and used.

[0005] The Chinese invention patent application with application publication number CN110568150A discloses a method and device for identifying oil shale. The method is based on the fusion of conventional wave impedance at different depths at a preset position and wave impedance reconstructed using acoustic waves, density, and resistance, and identifies the rock layer corresponding to the position and depth where the fused wave impedance value is greater than the first preset wave impedance value as oil shale. Since oil shale has the logging response characteristics of high acoustic wave time difference and low density, it will form a low value of conventional wave impedance. The participation of parameters such as density in the reconstruction of wave impedance cannot guarantee that the fused wave impedance value of oil shale is higher than that of sandstone and mudstone. The uncertainty of the calculation result reduces the recognition accuracy.

[0006] The above research results have low accuracy and narrow breadth in delineating favorable shale areas during the geological evaluation of the Lower Cretaceous. Summary of the invention

[0007] The object of the present invention is to provide a shale classification method and device to solve the problems of lack of universality in the geological evaluation process of the Lower Cretaceous under existing technical conditions and insufficient accuracy and breadth of shale classification.

[0008] In order to solve the above technical problems, the present invention provides a shale classification method, comprising the following steps:

[0009] 1) For the standard wells with cores in the study area, select the well logging curves that are sensitive to the organic carbon value, where the well logging data changes when the organic carbon value changes; and determine the shale classification results of the standard wells, where the shale classification results are the classification results of different organic matter contents;

[0010] 2) normalizing the determined sensitivity curves, calculating the correlation coefficient between each sensitivity curve and the measured organic carbon value, and using the correlation coefficient to calculate the weight coefficient of each sensitivity curve; constructing a regression equation for calculating the shale indicator characteristic value, the regression equation is used to reflect the regression relationship between the shale indicator characteristic value and each sensitivity curve, and the regression coefficient of the sensitivity curve is its weight coefficient; determining the characteristic value threshold for distinguishing different categories according to the regression equation and the shale classification result obtained in step 1);

[0011] 3) For a single well that has not been cored or has incomplete coring, the characteristic value of each point on each section of the single well is calculated based on the sensitivity curve and regression equation of the well, and then the organic shale of the well is classified in combination with the characteristic value threshold obtained in step 2).

[0012] Its beneficial effects are as follows: in view of the problem that the existing technology is not accurate enough and wide enough to delineate favorable shale areas in the process of geological evaluation of the Lower Cretaceous under the existing technical conditions, the present invention calculates the correlation coefficient and weight coefficient according to the measured organic carbon value after normalization based on the acquired sensitivity curve to generate a regression equation for the shale indication characteristic value, and determines the characteristic value threshold according to this equation and the shale classification result; for single wells that are not cored or incompletely cored, the shale with different organic matter contents in this well is classified according to the regression equation and the characteristic value threshold. This method greatly improves the recognition accuracy, has a smooth recognition process, is quick to operate, and is easy to promote. It can quickly evaluate the vertical strata and lateral distribution of organic shale in the basin, meet the needs of shale oil resource evaluation in the fault lake basin of the Lower Cretaceous, and can guide and be applied to shale oil exploration and well site deployment in the fault basin.

[0013] Furthermore, the sensitivity curves determined in step 1) include a natural gamma curve, a compensated neutron curve, a density curve and an acoustic time difference curve, and the regression equation in step 2) is as follows:

[0014] T shale =k GR γ+k DT Δt+k DEN ρ+k CN Φ N +k

[0015] Among them, γ, Δt, ρ and Φ N are the natural gamma, acoustic time difference, density and compensated neutron curve values ​​respectively; k GR is the weight coefficient of natural gamma, k DT is the weight coefficient of the acoustic time difference, k DEN is the density weight coefficient, k CN is the weight coefficient for compensating neutrons; k is an empirical constant; T shale Indicates characteristic values ​​for shale.

[0016] The beneficial effects are as follows: it is helpful to obtain shale indication characteristic values, so as to calculate the threshold value of organic shale classification, which is helpful to improve the recognition accuracy of shale, improve the recognition fluency, improve the recognition efficiency, and can quickly evaluate the vertical stratification and lateral distribution of organic shale in the basin.

[0017] Furthermore, the calculation formula of the weight coefficient is:

[0018]

[0019] Among them, k x is the weight coefficient of the xth sensitive curve; r x is the correlation coefficient between the xth sensing curve and the measured organic carbon value; r GR 、r DT 、|rDEN | and r CN They are the correlation coefficient of natural gamma, the correlation coefficient of acoustic time difference, the absolute value of the correlation coefficient of density and the correlation coefficient of compensated neutrons.

[0020] Its beneficial effects are: the degree to which each sensitive curve is affected by the organic carbon value is obtained to highlight the measurement value of organic shale, which is conducive to improving the identification accuracy of shale, improving the identification fluency, and improving the identification efficiency. It can quickly evaluate the vertical stratification and lateral distribution of organic shale in the basin.

[0021] Furthermore, the function used to calculate the correlation coefficient in step 2) is a correl function or a pearson function.

[0022] Furthermore, the regression equation in step 2) is used to reflect the regression relationship between the characteristic value of the shale index and each normalized sensitivity curve, and the normalization formula is:

[0023]

[0024] Among them, x i is the curve value of the i-th data, max{x i}、min{x i} respectively correspond to the maximum and minimum values ​​of the curve, y i is the corresponding curve value after normalization.

[0025] The beneficial effects are: completing the linear transformation of the original data of sensitive parameters to facilitate the calculation of their correlation coefficients, improving the recognition accuracy of shale, improving the recognition fluency, and improving the recognition efficiency.

[0026] Furthermore, the classification results of organic shales include three categories, namely organic-poor mud shale, relatively organic-rich shale and organic-rich shale.

[0027] The beneficial effects are as follows: since the shale oil storage medium is organic-rich shale with moderate maturity, it is conducive to evaluating the vertical stratification and lateral distribution of organic shale in the basin, meeting the needs of shale oil resource evaluation in the Lower Cretaceous fault lake basin, and can guide and be applied to shale oil exploration and well deployment in fault basins.

[0028] In order to solve the above technical problems, the present invention also provides a shale classification device, including a memory and a processor, wherein the processor is used to execute computer program instructions stored in the memory to implement the shale classification method introduced above.

[0029] The beneficial effect is that the device ensures the effective and reliable execution of a shale classification method. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1is a flow chart of the method for identifying organic-rich shale of the present invention;

[0031] Figure 2 This is the comprehensive histogram of the target layer of Well 1# of the present invention;

[0032] Figure 3-1 This is the high-level core calibration sensitivity logging curve diagram of Well 1# of the present invention;

[0033] Figure 3-2 This is the sensitive logging curve diagram of the core calibration of the lake invasion area of ​​Well 1# of the present invention;

[0034] Figure 4-1 This is the result diagram of the comprehensive evaluation of shale in the high-level region of Well 1# of the present invention;

[0035] Figure 4-2 This is the result map of the comprehensive evaluation of shale in the lake invasion area of ​​Well 1# of the present invention. DETAILED DESCRIPTION

[0036] The basic concept of the present invention is as follows: the present invention determines a sensitive curve according to the measured organic carbon value, determines the result of the classification of shale organic matter content in the coring standard well, calculates the correlation coefficient using the measured organic carbon data and each sensitive logging curve after normalization, and calculates the weight coefficient; then uses the sensitive parameter multivariate regression to hedge and weaken the adverse effects of the single sensitive curve due to complex factors such as well diameter, heavy minerals, special lithology, etc. on the univariate regression, reconstructs the shale characteristic indicator curve and obtains the threshold value according to the shale classification result of the standard well; generates the characteristic indicator curve of a single well that has not been cored or has been incompletely cored, and classifies the organic shale of this well in combination with the threshold value.

[0037] The present invention will be described in detail below with reference to the accompanying drawings and method embodiments.

[0038] Method Example:

[0039] A shale classification method of the present invention has a flow chart as shown in Figure 1 As shown, the specific implementation steps include:

[0040] Step 1: Prepare the data of the standard well.

[0041] Obtain the logging data and core analysis data of the wells drilled in the target area and complete the classification and sorting.

[0042] Step 2: Establish a comprehensive histogram of the target layer of the standard well, such as Figure 2 shown.

[0043] In this example, the No. 1 well located in the structurally stable area of ​​the near-basin zone of the lake basin was selected, and the evaluation strata were complete with coring and drilling. The sequence interface was identified based on the cycle style and inflection point of the No. 1 well logging curve combined with the natural gamma curve (GR) spectrum attribute trend analysis curve. The well section 3463-3660m is a complete third-level cycle, and the lake invasion well section 3660-3593m and the high-level well section 3593-3463m each have 1 core. Among them, the generation of the GR spectrum attribute trend analysis curve is realized by the maximum entropy spectrum analysis method.

[0044] Step three: Select the sensitivity curve and determine the sensitivity curve involved in the calculation based on the measured organic carbon content.

[0045] 1) Select the natural gamma ray curve (GR) and compensated neutron curve (CN) that are sensitive to the main mineral components of shale, clay minerals, and select the density curve (DEN), acoustic time difference curve (DT) and deep induction resistivity curve (ILD) that are sensitive to TOC content; each curve is superimposed with the natural gamma ray curve (GR) and TOC content discrete data to generate a combined channel, such as Figure 3-1 and Figure 3-2 shown.

[0046] 2) According to the classification results of organic-poor shale, relatively organic-rich shale and organic-rich shale determined by core analysis and TOC content indicators in the shale coring section, fine-tune the sensitivity curve scale in the combined channel to move it left and right, observe the relative relationship between the sensitivity curve and the GR curve, and make the sensitivity curve and the GR curve in each channel overlap in the organic-poor shale section; compare and analyze the amplitude difference of each sensitivity curve caused by the change of amplitude according to the size of the organic carbon value, and determine the sensitivity curve involved in the calculation based on the qualitative reflection of the amplitude difference.

[0047] Among them, the amplitude value of the deep induction resistivity curve (ILD) in the 1# well has poor sensitivity to the TOC value, so it is not involved in shale prediction (for example in the embodiment), and the other curves all have visible amplitude changes as the measured organic carbon value changes.

[0048] Step 4: normalize each sensitivity curve, and perform correlation analysis between each sensitivity curve value and the measured organic carbon data point to obtain the correlation coefficient (r).

[0049] 1) The selected sensitive logging curves are normalized, deviations are standardized one by one, and dimensionless transformation of original data such as acoustic time difference, density, compensated neutron and natural gamma is completed. The specific calculation formula is as follows:

[0050]

[0051] Where: x iis the curve value of the i-th data, max{x i}、min{x i} respectively correspond to the maximum and minimum values ​​of the curve, y i is the corresponding curve value after normalization.

[0052] 2) The measured organic carbon data points were correlated with the corresponding sensitivity curve values ​​to obtain the correlation coefficients of natural gamma r GR =0.779, correlation coefficient of acoustic time difference r DT =0.648, density correlation coefficient r DEN =-0.530, correlation coefficient of compensated neutron r CN =0.631. The correlation coefficient calculation is not limited to the correl and pearson functions.

[0053] Step 5: Calculate the normalized weight coefficients of each sensitivity curve and reconstruct the mathematical model of the shale characteristic indicator curve.

[0054] 1) The weight coefficient of each sensitivity curve is calculated using the correlation coefficient weighted factor comparison method. The calculation formula is as follows:

[0055]

[0056]

[0057]

[0058]

[0059] The weight coefficient k of natural gamma is obtained respectively GR =0.301, weight coefficient k of acoustic time difference DT =0.250, density weight coefficient k DEN =-0.205, weight coefficient k for neutron compensation CN =0.244.

[0060] 2) Reconstruct the shale characteristic indicator curve according to the weight coefficient to obtain the multivariate regression equation reflecting the relationship between natural gamma, acoustic wave time difference, density and compensated neutron of shale quality:

[0061] T shale =0.301γ+0.250Δt-0.205ρ+0.244Φ N +0.317

[0062] Among them: γ, Δt, ρ, Φ N are the normalized curve values ​​of natural gamma, acoustic time difference, density and compensated neutron, 0.317 is the empirical constant, T shaleIndicates characteristic values ​​for shale.

[0063] Step six: classify organic-poor mud shale, relatively organic-rich shale, and organic-rich shale according to step three, and combine the regression equation in step five to obtain the characteristic value threshold for well 1# used to distinguish different types of shale; use the obtained characteristic value threshold and regression equation to classify different types of shale in single wells that have not been cored or have been incompletely cored in the evaluation area.

[0064] 1) The generated shale characteristic indicator curve is loaded into Well 1#. Under the limitation of the lake invasion well section 3660-3593m and the high position well section 3593-3463m, the T values ​​corresponding to the organic-rich shale section of the core calibration scale are read respectively. shale The minimum value a, read the T corresponding to the organic-rich shale section of the core calibration scale shale The minimum value b, a and b are used as thresholds to determine the baseline, and the organic-poor shale, relatively organic-rich shale, and organic-rich shale are segmented according to the threshold baseline. Figure 4-1 As shown in Figure 1, the identified 1# high-level organic-rich shale totals 38.25 m and 39 layers of m; Figure 4-2 As shown, the organic-rich shale in the lake invasion area of ​​Well 1# is identified to be 27.5 m in total and 17 layers.

[0065] 2) According to the regression equation obtained in step 5, the shale characteristic indicator curve of the single well without coring or incomplete coring in the evaluation area is obtained, and the organic-poor mud shale, relatively organic-rich shale, and organic-rich shale are segmented according to the threshold value obtained in step 1).

[0066] The present invention is first limited to the third-level sequence stratigraphic unit to ensure the avoidance of threshold systematic errors caused by changes in shale mineral composition, organic matter abundance, type, etc. due to changes in the sedimentary environment.

[0067] The present invention is limited to the third-level sequence stratigraphic unit, and uses the correlation coefficient between the measured organic carbon data and each sensitive logging curve to calculate the weight to highlight the measurement value of organic-rich shale. Then, the sensitive parameter multivariate regression is used to hedge and weaken the adverse effects of the single sensitive curve due to complex factors such as well diameter, heavy minerals, special lithology, etc. in the univariate regression. The shale characteristic indicator curves are reconstructed in turn and the organic-rich shale is accurately identified according to the threshold value, which greatly improves the recognition accuracy, has a smooth recognition process, is quick to operate, and is easy to promote. The vertical strata and lateral distribution of organic-rich shale in the basin can be quickly evaluated, meeting the needs of shale oil resource evaluation in the Lower Cretaceous fault lake basin, and can guide and be applied to shale oil exploration and well site deployment in the fault basin.

[0068] Device Example:

[0069] A shale classification device of the present invention includes a memory and a processor, wherein the processor is used to execute computer program instructions stored in the memory to implement a shale classification method described in the method embodiment of the present invention. The processor can be a processing device such as a programmable logic device FPGA. The memory can be various memories that use electrical energy to store information, such as RAM, ROM, etc., and can also be other types of memories.

Claims

1. A shale classification method, It is characterized in that The steps include: 1) For the standard wells with cores in the study area, select the well logging curves that are sensitive to the organic carbon value, where the well logging data changes when the organic carbon value changes; and determine the shale classification results of the standard wells, where the shale classification results are the classification results of different organic matter contents; 2) normalizing the determined sensitivity curves, calculating the correlation coefficient between each sensitivity curve and the measured organic carbon value, and using the correlation coefficient to calculate the weight coefficient of each sensitivity curve; constructing a regression equation for calculating the shale indicator characteristic value, the regression equation is used to reflect the regression relationship between the shale indicator characteristic value and each sensitivity curve, and the regression coefficient of the sensitivity curve is its weight coefficient; determining the characteristic value threshold for distinguishing different categories according to the regression equation and the shale classification result obtained in step 1); 3) For a single well that has not been cored or has incomplete coring, the characteristic value of each point on each section of the single well is calculated based on the sensitivity curve and regression equation of the well, and then the organic shale of the well is classified in combination with the characteristic value threshold obtained in step 2).

2. The shale classification method according to claim 1, It is characterized in that The sensitive curves determined in step 1) include a natural gamma curve, a compensated neutron curve, a density curve and an acoustic time difference curve. The regression equation in step 2) is as follows: T shale =k GR c+k DT Δt+k DEN p+k CN F N +k Among them, γ, Δt, ρ and Φ N are the natural gamma, acoustic time difference, density and compensated neutron curve values ​​respectively; k GR is the weight coefficient of natural gamma, k DT is the weight coefficient of the acoustic time difference, DEN is the density weight coefficient, CN is the weight coefficient for compensating neutrons; k is an empirical constant; T shale Indicates characteristic values ​​for shale.

3. The shale classification method according to claim 2, It is characterized in that The calculation formula of the weight coefficient is: Among them, k x is the weight coefficient of the xth sensitive curve; r x is the correlation coefficient between the xth sensing curve and the measured organic carbon value; r GR 、r DT 、|r DEN | and r CN They are the correlation coefficient of natural gamma, the correlation coefficient of acoustic time difference, the absolute value of the correlation coefficient of density and the correlation coefficient of compensated neutrons.

4. The shale classification method according to claim 1, It is characterized in that The function used to calculate the correlation coefficient in step 2) is the correl function or the pearson function.

5. The shale classification method according to claim 1, It is characterized in that The regression equation in step 2) is used to reflect the regression relationship between the characteristic value of the shale index and each normalized sensitivity curve, and the normalization formula is: Among them, x i is the curve value of the i-th data, max{x i }、min{x i } respectively correspond to the maximum and minimum values ​​of the curve, y i is the corresponding curve value after normalization.

6. The shale classification method according to claim 1, It is characterized in that The classification results of organic shales include three categories, namely organic-poor mud shale, relatively organic-rich shale and organic-rich shale.

7. A shale classification device, It is characterized in that The method comprises a memory and a processor, wherein the processor is used to execute computer program instructions stored in the memory to implement the shale classification method according to any one of claims 1 to 6.

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