Identification and treatment of reliable geochemical data contaminated by mud cuttings

Abstract

This invention discloses a method for identifying and processing reliable geochemical data of mud-contaminated rock cuttings, relating to the field of geochemical testing technology for rock cuttings samples. The method includes: F1, correction of contamination data identification; F2, determination of contamination error; and F3, for wells without pyrolysis spectra, recursively estimating the contamination amount using the increasing contamination pattern, summarizing two correction modes. The technical solution of this invention is applicable to rock cuttings samples contaminated by solid powdered additives in water-based mud. It requires samples from surrounding uncontaminated wells for comparison to determine whether the target well sample is contaminated, core / well wall core samples as standard samples, and pyrolysis spectra from some wells. By using this technology to identify and correct the contaminated sample, the TOC and S2 parameters are regressed to the core region, eliminating errors in organic matter abundance evaluation. Simultaneously, using this technology to identify and correct the contaminated sample's hydrogen index is also regressed to the core region, eliminating optimization effects in organic matter type evaluation.

Description

Technical Field

[0001] This invention relates to the field of geochemical testing technology for rock cuttings samples, specifically to a method for identifying and processing reliable geochemical data of mud-contaminated rock cuttings. Background Technology

[0002] Drilling mud is a circulating fluid added during drilling to control formation pressure, prevent wellbore collapse, lubricate and cool the drill bit. It can be divided into water-based mud and oil-based mud drilling fluid. Oil-based mud uses oil as the continuous phase and usually selects mineral oil with low aromatic hydrocarbon content. These mineral oils will greatly affect the geochemical parameters of drilling cuttings, mainly the organic matter abundance (TOC) and the S1 and S2 indices in rock pyrolysis analysis. Currently, there are many reports on the organic geochemical parameter characteristics of oil-based mud contaminated samples and the technology of Soxhlet extraction to remove oil-based mud contamination.

[0003] Traditionally, it was believed that organic geochemical data from rock cuttings samples obtained using water-based drilling fluids were not contaminated, as these fluids generally did not contain added organic components. However, for high-temperature, ultra-deep drilling, 2–3% sulfonated bitumen is often added during actual drilling to construct a non-dispersible polymer system. This system can form a high-quality mud cake and inhibit rock cuttings dispersion. The TOC, S1, and S2 values ​​of rock cuttings contaminated with sulfonated bitumen are not significantly different from those of normal, uncontaminated samples. Therefore, the impact of organic additives in water-based drilling mud on organic geochemical indicators has long been overlooked in past studies. However, sulfonated bitumen has a significant effect on improving the hydrogen index (TOC to S2 ratio, abbreviated as HI) of rock cuttings. When using the Tmax-HI chart to determine the type of organic matter, Type III organic matter samples are often classified as Type I, exhibiting an "optimization effect" that greatly misleads oil and gas exploration. Therefore, it is essential to identify and process the geochemical testing techniques for rock cuttings contaminated with sulfonated bitumen. Summary of the Invention

[0004] The purpose of this invention is to provide a method for identifying and processing reliable geochemical data of mud-contaminated rock cuttings, so as to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for identifying and processing reliable geochemical data of mud-contaminated rock cuttings, comprising the following steps:

[0006] F1, Contamination Data Identification Level Correction;

[0007] F2. Determine the contamination error;

[0008] F3. For wells with "three-no samples" (i.e., no experimental separation peak, no pyrolysis spectrum, and no core / rock core data), the pollution data processing results of known wells are used to obtain the incremental amount of pollution at the drilling depth, and two correction modes are summarized: "continuous pollution growth" and "pollution anomaly point".

[0009] F4. For wells without pyrolysis spectra, and by using the increasing pollution pattern to extrapolate the pollution amount, two correction modes are summarized.

[0010] Preferably, the amount of data at different well locations is inconsistent, making it impossible to correct contamination using a single step. Therefore, it is necessary to summarize the information from well locations with a large amount of data and reasonably deduce the amount of contamination at well locations with a small amount of data using mathematical formulas. In step F1, the process of contamination data identification and correction includes:

[0011] F11. Identification of mud-contaminated samples: Plot the data of the target sample and the data of uncontaminated samples from adjacent wells onto the S2-TOC cross plot. If these sample points fall on the same trend line, it indicates that the target sample is not contaminated by mud additives. If the target sample deviates significantly from the trend line of the uncontaminated samples, it indicates that the target sample is contaminated by mud additives. Two modes are marked: one is that points at continuous depths deviate from the trend line, and the other is that a few points deviate from the trend line.

[0012] F12. Determine the composition of peak S2: Due to oil-based mud contamination, a double-peak phenomenon will appear in the pyrolysis spectrum, namely the contaminated peak S2-1 and the true peak S2-2. Using manual integration software, integrate the double peak composed of S2-1 and S2-2 and the single peak of S2-2 separately, and calculate their corresponding integration areas, denoted as the total peak area W. 总 The peak area of ​​S2-1 is W 污染 The peak area of ​​S2-2 is W 真实 The relationship between the three is as follows:

[0013] W 总 =W 污染 +W 真实 ;

[0014] F13, TOC and S2 Correction: Using the peak area obtained in step F12 and the original contamination value of S2, adjust W... 真实 With W 总 The ratio of S2 to S2 multiplied by the original value of S2 gives the corrected value of S2, denoted as S2. 污染样品 and S2 真实 Using TOC pollution values ​​and W 污染 Further correct the TOC, denoted as TOC. 污染样品 and TOC 真实 The calculation relationship between them is as follows:

[0015] S2真实 =S2 污染样品 ×W 真实 / W 总 ;

[0016] TOC 真实 =TOC 污染样品 -0.083×W 污染 ;

[0017] F14, S1 Correction: This process is basically the same as the S2 correction process in step F13, the difference is that S1 and S2 are used first. 真实 The intersection diagram determines the contaminated well and the depth of contamination in S1, and then S1 is corrected according to the methods in steps S12 and F13.

[0018] Preferably, in the statistical analysis of contamination errors in different wells, in step F2, the S2 in step F13 is... 真实 and TOC 真实 Calculate the average values ​​separately, then subtract the S2 values ​​from the core and wall core values. 岩心 / 壁心 and TOC 岩心 / 壁心 The average values ​​are used to obtain the contamination errors of the corrected S2 and TOC methods, denoted as ΔS2 and ΔTOC respectively, for subsequent verification of the method reliability:

[0019] ΔS2=S2 真实平均 -S2 岩心 / 壁心平均 ;

[0020] ΔTOC=TOC 真实平均 -TOC 岩心 / 壁心平均 .

[0021] Preferably, different treatment methods are used for different contamination patterns. In step F3, the variation law of the pyrolysis spectrum S2-1 is summarized and divided into two patterns: pattern one is that the contamination increases with increasing depth, and pattern two is that abnormally high points appear at certain depths. For these two patterns, combined with the identification results of the TOC & S2 intersection plot in step F11, wells with continuous contamination at certain depths are corrected using pattern one, while wells with high contamination values ​​at certain depths are corrected using pattern two. The correction methods for the two patterns are as follows:

[0022] Mode 1: Assuming the initial depth is D1, the depth of the contaminated point is D2, the initial contamination ratio of the unknown well is set as A, the contamination increase per meter is ΔA, and the total contamination ratio is A. 总

[0023] A 总 =A + ΔA × (D2 - D1);

[0024] S'2 真实 =S2 污染样品 ×(1-A 总 );

[0025] TOC' 真实 =TOC' 污染样品 -0.083×A 总 ;

[0026] Mode 2: Pollution correction for outliers, assuming an average pollution percentage of A. 平均 ;

[0027] S'2 某深度真实 =S2 某污染深度 ×(1-A 平均 );

[0028] TOC' 某深度真实 =TOC' 某污染深度 -0.083×A 平均 .

[0029] Preferably, the error interval is used to check the correction results. In step F3, there are two error checks: the first method is to use the error calculation formula mentioned above. If the error value at this point is within the range of all pyrolysis spectrum correction errors, it is considered qualified. The second method is to use mode one to calibrate the contaminated well with the existing pyrolysis spectrum and use the intersection of TOC and S2 to determine whether their trends are similar.

[0030] Among them, the error range is determined by using the error values ​​of the correction results from steps F12, F13, and F14 for all wells in the statistical study area to determine the upper and lower limits of the error. The interval is denoted as:

[0031] S2 error range: (S2min and S2max);

[0032] TOC error range: (ΔTOCmin, ΔTOCmax).

[0033] Compared with the prior art, the beneficial effects of the present invention are:

[0034] In the method for identifying and processing reliable geochemical data of mud-contaminated rock cuttings of the present invention, the TOC and S2 parameters of the contaminated samples identified and corrected by this technology are returned to the core area, eliminating the error in the evaluation of organic matter abundance. The TOC and S2 data of the contaminated rock cuttings samples from well B21-1 are compared with the core data from wells B15-3, B21-6d, B21-1S, and B21-1. The results show that the core data from well B21-1 and the uncontaminated samples from wells B15-3, B21-6d, and B21-1S fall on the same trend line. The rock cuttings samples from well B21-1 contaminated by oil-based mud deviate from the trend line of the uncontaminated samples. Their S2 and TOC are significantly higher and much higher than those of the core samples from the same well, indicating that the TOC and S2 indicators of the rock cuttings samples from this well are overestimated. The calibrated data show that the TOC and S2 of the contaminated rock cuttings samples from well B21-1 have returned to the range of the core samples from this well.

[0035] Simultaneously, the hydrogen index (HI) of the contaminated samples identified and corrected using this technology was regressed to the core region, eliminating the "optimization effect" in organic matter type evaluation. The Tmax-HI chart can effectively classify organic matter types. Comparison of contaminated cuttings samples from well B21-1 with core data from wells B15-3, B21-6d, B21-1S, and B21-1 shows that the core data from well B21-1 and wells B15-3, B21-6d, and B21-1S were not contaminated. The contaminated samples fell within the Type III and Type II2 ranges, indicating that the actual organic matter type in well BD21 was predominantly humic. The rock cuttings samples from well B21-1 contaminated with oil-based mud mainly fell within the Type II1 organic matter range, indicating that the organic matter type was predominantly saprophytic. This deviated from the distribution range of uncontaminated samples and well core samples from the well. The calibrated data showed that the TOC and S2 of the contaminated rock cuttings samples from well B21-1 had returned to the range of the well core samples, effectively eliminating the "optimization effect" of organic matter type. Attached Figure Description

[0036] Figure 1 A comparison of uncorrected TOC and S2 data of cuttings from well B21-1 with uncontaminated samples from surrounding wells, illustrating the method for identifying and processing reliable geochemical data of mud-contaminated cuttings according to the present invention.

[0037] Figure 2 A comparison of the corrected TOC and S2 data of rock cuttings from well B21-1 with uncontaminated samples from surrounding wells, based on the method for identifying and processing reliable geochemical data of mud-contaminated rock cuttings according to the present invention.

[0038] Figure 3 A comparison of the organic matter types of uncorrected cuttings data from well B21-1 and surrounding uncontaminated well samples, used in the method for identifying and processing reliable geochemical data of mud-contaminated cuttings according to the present invention.

[0039] Figure 4 A comparison of the organic matter types of well B21-1 after calibration and surrounding uncontaminated well samples, which is the basis for the method of identifying and processing reliable geochemical data of mud-contaminated rock cuttings according to the present invention.

[0040] Figure 5 The software integral S2 original total peak W of the method for identifying and processing reliable geochemical data of mud-contaminated rock cuttings of the present invention 总 And the true peak W of S2 真实 picture;

[0041] Figure 6 The pyrolysis spectrum of well B21-4, which is the basis for the method of identifying and processing reliable geochemical data of mud-contaminated rock cuttings of the present invention, shows the contamination increasing with depth.

[0042] Figure 7 Comparison of well B21 corrected using the pollution progression formula and pyrolysis spectrum correction, which is the basis of the method for identifying and processing reliable geochemical data of mud-contaminated rock cuttings according to the present invention.

[0043] Figure 8 Correction map of some abnormal high value points in well B19-3, which is the method for identifying and processing reliable geochemical data of mud-contaminated rock cuttings according to the present invention;

[0044] Figure 9 This is a flowchart of the method for identifying and processing reliable geochemical data of mud-contaminated rock cuttings according to the present invention. Detailed Implementation

[0045] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0046] Please see Figure 1 This invention provides a technical solution: a method for identifying and processing reliable geochemical data of mud-contaminated rock cuttings, comprising the following steps:

[0047] Step 1: Pollution data identification and correction;

[0048] 11) Identification of mud-contaminated samples: Plot the data of the target sample and the data of uncontaminated samples from adjacent wells onto the S2-TOC cross plot. If these sample points fall on the same trend line, it indicates that the target sample is not contaminated by mud additives. If the target sample deviates significantly from the trend line of the uncontaminated sample, it indicates that the target sample is contaminated by mud additives. Two modes are marked: one is that points at a continuous depth of a well deviate from the trend line, and the other is that individual points deviate from the trend line.

[0049] 12) Determine the composition of peak S2: Due to oil-based mud contamination, a double-peak phenomenon will appear on the pyrolysis spectrum, namely the contaminated peak S2-1 and the true peak S2-2. Use software to manually integrate, integrating the double peak composed of S2-1 and S2-2 and the single peak of S2-2 respectively, and calculate their corresponding integration areas. Figure 5 The total peak area is denoted as W. 总 The peak area of ​​S2-1 is W 污染 The peak area of ​​S2-2 is W 真实 The relationship between the three is as follows:

[0050] W 总 =W 污染 +W 真实 ;

[0051] 13) TOC and S2 calibration: Using the peak area obtained in step F12 and the original contamination value of S2, calibrate W... 真实 With W 总 The ratio of S2 to S2 multiplied by the original value of S2 gives the corrected value of S2, denoted as S2. 污染样品 and S2 真实 Using TOC pollution values ​​and W 污染 Further calibrate TOC, denoted as TOC. 污染样品 and TOC 真实 The calculation relationship between them is as follows:

[0052] S2 真实 =S2 污染样品 ×W 真实 / W 总 ;

[0053] TOC 真实 =TOC 污染样品 -0.083×W 污染 ;

[0054] 14) S1 Correction: This process is basically the same as the S2 correction process in step F13, the difference being that S1 and S2 are first used. 真实 The intersection diagram determines the contaminated well and the depth of contamination in S1, and then S1 is corrected according to the methods in steps S12 and F13.

[0055] Step 2: Determine the contamination error: Refer to S2 in step F13. 真实 and TOC 真实 Calculate the average values ​​separately, then subtract the S2 values ​​from the core and wall core values. 岩心 / 壁心 and TOC 岩心 / 壁心 The average values ​​are used to obtain the contamination errors of the calibration S2 and TOC methods, denoted as ΔS2 and ΔTOC respectively, for subsequent verification of method reliability:

[0056] ΔS2=S2 真实平均 -S2 岩心 / 壁心平均 ;

[0057] ΔTOC=TOC 真实平均 -TOC 岩心 / 壁心平均 .

[0058] Step 3: For wells with "three-no samples" (i.e., no experimental separation peak, no pyrolysis spectrum, and no core / wall core data), the pollution data processing results of known wells are used to derive the pollution increment at drilling depth. Two correction modes are summarized: "continuous pollution growth" and "pollution anomaly point". The pollution amount is then recursively extrapolated using the pollution increment law. Figure 6 Two correction modes were summarized: based on the variation law of the pyrolysis spectrum S2-1, two modes were identified. Mode 1 states that the contamination increases with increasing depth. Figure 7 Mode 2 involves abnormally high points occurring at specific depths. Figure 8 For these two modes, and based on the identification results of the TOC & S2 intersection plot in step F11, wells with continuous contamination at consecutive depths are corrected using Mode 1, while wells with high contamination values ​​at individual depths are corrected using Mode 2. The correction methods for the two modes are as follows:

[0059] Mode 1: Assuming the initial depth is D1, the depth of the contaminated point is D2, the initial contamination ratio of the unknown well is set as A, the contamination increase per meter is ΔA, and the total contamination ratio is A. 总

[0060] A 总 =A + ΔA × (D2 - D1);

[0061] S'2 真实 =S2 污染样品 ×(1-A 总 );

[0062] TOC' 真实 =TOC' 污染样品 -0.083×A 总 ;

[0063] Mode 2: Pollution correction for outliers, assuming an average pollution percentage of A. 平均 ;

[0064] S'2 某深度真实 =S2 某污染深度 ×(1-A 平均 );

[0065] TOC' 某深度真实 =TOC' 某污染深度 -0.083×A 平均 ;

[0066] There are two methods for error verification: Method 1 uses the error calculation formula mentioned earlier; if the error value at that point is within the range of all pyrolysis spectrum correction errors, it is considered acceptable. Method 2 uses Mode 1 to calibrate contaminated wells in existing pyrolysis spectra and uses the intersection plots of TOC and S2 to determine whether their trends are similar. Figure 7 );

[0067] Among them, the error range is determined by using the error values ​​of the correction results from steps F12, F13, and F14 for all wells in the statistical study area to determine the upper and lower limits of the error. The interval is denoted as:

[0068] S2 error range: (S2min and S2max);

[0069] TOC error range: (ΔTOCmin, ΔTOCmax).

[0070] This technical solution is applicable to rock cuttings samples contaminated by solid powdered additives in water-based drilling mud. First, it requires samples from surrounding uncontaminated wells as a comparison to determine whether the target well sample is contaminated. Second, it requires core / well wall core samples as standard samples. Third, it requires pyrolysis spectra of some wells.

[0071] In the scheme of the method for identifying and processing reliable geochemical data of drilling cuttings contaminated by drilling mud of the present invention, the measured organic geochemical data of well B21-1 and surrounding wells in the deep water area of ​​the Qiongdongnan Basin are used as the basis. Well B21-1 has clearly added organic drilling mud additives such as sulfonated asphalt to the water-based drilling fluid, and the cuttings samples are contaminated, but the core samples are not contaminated, reflecting the true geochemical characteristics of this stratum, which can be used to correct the contaminated cuttings samples. B15-3, B21-6d and B21-1S have clearly not added organic additives to the water-based drilling mud, and all samples are not contaminated.

[0072] The TOC and S2 parameters of the contaminated samples identified and corrected using this technique were regressed to the core region, eliminating errors in the organic matter abundance assessment. The TOC and S2 data of the contaminated cuttings samples from well B21-1 were compared with core data from wells B15-3, B21-6d, B21-1S, and B21-1. Figure 1The results showed that the core samples from well B21-1 and the uncontaminated samples from wells B15-3, B21-6d, and B21-1S fell along the same trend line. However, the cuttings samples from well B21-1 contaminated with oil-based mud deviated from the trend line of the uncontaminated samples, with significantly higher S2 and TOC values, far exceeding those of the well core samples. This indicates that the TOC and S2 values ​​of the cuttings samples from this well were overestimated. The calibrated data showed (…). Figure 2 The TOC and S2 of the contaminated cuttings samples from well B21-1 have returned to the range of the core samples from that well.

[0073] Simultaneously, the hydrogen index (HI) of the contaminated samples identified and corrected using this technology was regressed to the core region, eliminating the "optimization effect" in organic matter type evaluation: the Tmax-HI chart can effectively classify organic matter types, and the contaminated cuttings samples from well B21-1 were compared with the core data from wells B15-3, B21-6d, B21-1S, and B21-1. Figure 3 The results showed that the core samples from well B21-1 and the uncontaminated samples from wells B15-3, B21-6d, and B21-1S fell within the Type III and Type II2 ranges, indicating that the actual organic matter type of well BD21 was predominantly humic. The rock cuttings samples from well B21-1 contaminated with oil-based mud mainly fell within the Type II1 organic matter range, indicating a predominantly saprophytic organic matter type, deviating from the distribution range of the uncontaminated samples and the well core samples. The calibrated data showed (…). Figure 4 The TOC and S2 of the contaminated cuttings samples from well B21-1 have returned to the range of the core samples from that well, effectively eliminating the "optimization effect" of organic matter.

[0074] Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for identifying and processing reliable geochemical data from drilling cuttings contaminated with mud, characterized in that, The method comprises the following steps: F1, pollution data identification correction, the process comprises: F11, identification of mud pollution samples: the data of the target sample and the data of the adjacent drilling samples without pollution are plotted on the S2-TOC crossplot, if the sample points fall on the same trend line, it indicates that the target sample is not polluted by mud additives, if the target sample deviates from the trend line of the samples without pollution, it indicates that the target sample is polluted by mud additives, two modes are marked, one is that the points of a certain well deviate from the trend line in continuous depth, and the other is that individual points deviate from the trend line; F12, determine the S2 peak composition: due to oil-based mud pollution, the pyrolysis spectrum will present double peak phenomenon, namely pollution peak S2-1 and real peak S2-2, using software manual integration, the double peak composed of S2-1 and S2-2 and S2-2 single peak are integrated respectively, the corresponding integral area is calculated, which is recorded as total peak area W 总 , peak S2-1 area W 污染 , S2-2 peak area W 真实 , the relationship among the three is: W 总 = W 污染 + W 真实 ; F13, TOC and S2 calibration: Using the peak area obtained in step F12 and the original contamination value of S2, calibrate W... 真实 With W 总 The ratio of S2 to S2 multiplied by the original value of S2 gives the corrected value of S2, denoted as S2. 污染样品 and S2 真实 Using TOC pollution values ​​and W 污染 Further calibrate TOC, denoted as TOC. 污染样品 and TOC 真实 The calculation relationship between them is as follows: S2 真实 = S2 污染样品 x W 真实 / W 总 TOC 真实 = TOC 污染样品 - 0.083 x W 污染 ; F14, S1 correction: the process is basically the same as the S2 correction process in step F13, the difference is that the wells with S1 pollution and the pollution depth are first determined by using the intersection of S1 and S2, and then the method in steps S12 and F13 is used to correct S1; F2, determine the pollution error; F3, for the wells with no experimental separation peak, no pyrolysis spectrum and no sidewall / core data, the pollution increment with the increase of drilling depth is obtained by using the pollution data processing results of known wells, and two correction modes of continuous pollution growth and pollution abnormal point are summarized, mode one is that the pollution increases with the increase of depth, and mode two is that an abnormal high point appears at individual depth, for the two modes, combined with the identification results of the S2-TOC crossplot in step F11, the wells with continuous depth pollution use mode one correction, and the wells with high pollution value at individual depth use mode two correction, the correction methods of the two modes are as follows: Mode one: assuming the starting depth is D1, the pollution point depth is D2, setting the unknown well starting pollution ratio as A, the pollution ratio per meter growth is ΔA, and the total pollution ratio is A 总 A 总 = A + ΔA x (D2 - D1) S'2 真实 = S2 污染样品 x (1 - A 总 ) TOC 真实 = TOC 污染样品 - 0.083 x A 总 ; Mode two: Pollution correction for abnormal points, assuming that the average proportion of pollution is A 平均 S'2 某深度真实 = S2 某污染深度 x (1 - A 平均 ) TOC 某深度真实 = TOC 某污染深度 - 0.083 x A 平均 .

2. The method for identifying and processing reliable geochemical data of mud contaminated cuttings according to claim 1, characterized in that, In step F2, S2 真实 and TOC 真实 are averaged, and the average of S2 岩心 / 壁心 and TOC 岩心 / 壁心 of the core and wall core are subtracted to obtain the contamination error of the calibration S2 and TOC method, denoted as ΔS2 and ΔTOC, respectively, to test the reliability of the method: AS2 = S2 真实平均 - S2 岩心 / 壁心平均 ΔTOC = TOC 真实平均 -TOC 岩心 / 壁心平均 .

3. The method for identifying and processing reliable geochemical data of mud contaminated cuttings according to claim 2, characterized in that, In step F3, there are two methods for error checking: one is to use the error calculation formula in the foregoing text, i.e. ΔS2=S2 真实平均 -S2 岩心 / 壁心平均 and ΔTOC=TOC 真实平均 -TOC 岩心 / 壁心平均 If the calculated error value is within the range of the correction error of all pyrograms, it is considered to be qualified. The second method is to use the first mode to calibrate the existing pyrograms of contaminated wells, and to use the TOC and S2 intersection map to determine whether the trend is close. Wherein, the error range is determined, the error value of the correction results of all wells in the statistical research area by using steps F12, F13 and F14 is used to determine the upper and lower limits of the error, and the interval is recorded as S2 error interval: (S2min, S2max) TOC error interval: (ΔTOCmin, ΔTOCmax).

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