Calculation method of organic carbon, storage medium and equipment

Abstract

The invention provides an organic carbon calculation method, a storage medium and equipment. The method comprises the following steps: carrying out comparative analysis on rock debris element data under water-based and oil-based drilling fluid conditions of a pilot well, dividing data sets of unaffected element types into a shale data set and a limestone data set, defining a GR ratio and a Ca ratio, and removing limestone to obtain a shale data set of the pilot well; arranging the shale data set of the pilot well, carrying out fitting, and establishing an organic carbon calculation model; the target horizontal well rock debris is analyzed according to the steps, the shale data set of the target horizontal well is calculated by applying the established organic carbon calculation model, and the obtained result is the organic carbon corresponding to the shale of the horizontal section. Reliable information of the stratum is rapidly obtained while drilling, a basis is provided for trajectory optimization, decision making while drilling, scheme making and the like, and drilling construction is guided.

Description

Technical Field

[0001] The invention belongs to the technical field of oil and gas exploration data processing, and in particular relates to an organic carbon calculation method and storage medium and equipment. Background Art

[0002] Continental shale oil and gas is a hot spot in current exploration and development. Taking the Jurassic continental shale formation in the Sichuan Basin as an example, pilot wells are often constructed first to obtain data, and then the target window is optimized for horizontal well construction. Organic carbon is an important evaluation parameter. The organic carbon content is positively correlated with the oil and gas content in the formation. Under water-based drilling fluid conditions, the rock pyrolysis logging technology can be used to obtain the organic carbon content of the formation while drilling. However, horizontal wells generally use oil-based drilling fluids, and the returned cuttings are mixed with oil, which makes it impossible to use rock pyrolysis logging technology to measure organic carbon. At the same time, continental shale formations are generally intercalated with thin layers of limestone, and the elemental data of limestone and shale are quite different. It is difficult to directly refer to the multivariate regression calculation method of organic carbon established for marine shale formations, and it is impossible to obtain the key parameters for evaluating the formation. Therefore, it is necessary to carry out research and application of new methods.

[0003] At present, the methods mentioned in domestic and foreign literature are mainly based on vertical well conditions, including multivariate fitting method, volume density method, volume model method, natural gamma spectroscopy method, ΔlogR method and other methods. In the actual development of shale gas work areas, the deployment is basically to exploit in the form of horizontal wells. Affected by comprehensive geological and engineering factors, logging construction is difficult and data is difficult to obtain. At present, horizontal wells generally use standard logging + natural gamma spectroscopy. Some wells collect compensation density under good borehole conditions. The overall logging quality is generally slightly worse than that of vertical well sections. For this type of logging data, most of the models established under vertical well conditions are limited or cannot be directly applied. For example, in the ΔlogR method, in addition to being affected by pyrite, the resistivity in horizontal wells is greatly affected by interfaces or gray interlayers, and abnormally high resistance will appear locally. At this time, the measured value is not a reflection of the gas content of the reservoir; in the energy spectrum method, there will be many abnormally high gamma well sections in the horizontal wells drilled at the No. 3 peak at the bottom of Longmaxi. At this time, the high gamma characteristics do not correspond to the size of the organic matter-rich reservoir. There is no literature on how to establish a method for calculating organic matter in horizontal wells. Therefore, it is necessary to optimize the organic matter calculation model suitable for horizontal wells.

[0004] Chinese patent CN107703561A provides a logging method for calculating the organic carbon content in shale gas horizontal wells. It is based on experimental analysis and logging data, analyzes the factors affecting the organic carbon in shale gas, and based on limited logging data conditions, forms an organic carbon content calculation method based on horizontal well logging data, solves the problem of quantitative calculation of organic carbon in shale gas horizontal wells, and has high calculation accuracy.

[0005] Chinese patent CN111580179A provides a method for determining organic carbon content. The method can first obtain the well logging data corresponding to the preset well logging data type of the work area to be tested and the corresponding measured organic carbon content data as the first sample data, and then predict the organic carbon content based on the pre-constructed support vector machine model and the ΔlogR model. Then, the prediction results based on the two models and the measured organic carbon content in the first sample data are used as the second sample data to further train the decision tree model, so as to construct an integrated prediction model for organic carbon content. The well logging data corresponding to the preset well logging data type of the work area to be tested is input into the above-constructed integrated prediction model for organic carbon content, and the prediction result of organic carbon content can be obtained.

[0006] In the prior art, it is not considered that oil-based drilling fluid is generally used in the construction process of horizontal wells, which makes it impossible to apply logging technologies such as rock pyrolysis, and it is impossible to obtain this parameter, which affects the rapid analysis while drilling, and affects the formulation of construction plans, trajectory adjustment, and reservoir evaluation while drilling during the drilling process. The present invention selects the types of elements that are not affected by oil-based drilling fluid, uses rock cuttings gamma spectroscopy logging technology to evaluate the quality of element logging, and eliminates the data of thin-layer limestone sections. The machine learning algorithm is selected to establish a method for calculating the organic carbon of continental formations under oil-based drilling fluid conditions using element logging data, and quickly obtains reliable information about the formation while drilling, providing a basis for trajectory optimization, decision-making while drilling, and plan formulation, and guiding drilling construction. Summary of the invention

[0007] In order to solve the above problems in the prior art, the present invention proposes a method for calculating organic carbon, a storage medium and a device.

[0008] In a first aspect, the present invention provides a method for calculating organic carbon, comprising the following steps:

[0009] S1: Conduct comparative analysis of cuttings element data under water-based and oil-based drilling fluid conditions in the pilot well, and define the data set of unaffected element types as {XRF valid};

[0010] S2: the data set of unaffected element types obtained in step S1 of the pilot well data is divided into a shale data set and a limestone data set, and the GR ratio and the Ca ratio are defined to remove the limestone and obtain the shale data set of the pilot well;

[0011] S3: sorting the shale data set of the pilot well obtained in step S2 to obtain a pilot well shale effective data set {XRF effective} and a pilot well shale effective data set {organic carbon};

[0012] S4: Fitting the pilot well data set obtained in step S3 to establish an organic carbon calculation model;

[0013] S5: Analyze the horizontal well cuttings according to steps S1 to S3 to obtain a shale data set of the horizontal well;

[0014] S6: Apply the organic carbon calculation model established in step S4 to calculate the shale data set of the horizontal well in step S5, and the result obtained is the organic carbon corresponding to the shale in the horizontal section.

[0015] As a specific implementation of the present invention, in step S1, the cuttings element data under the oil-based drilling fluid conditions include cuttings under the oil-based drilling fluid conditions of at least 5 same-layer positions; the cuttings element data under the water-based drilling fluid conditions are cuttings under the water-based drilling fluid conditions of the pilot well.

[0016] As a specific implementation of the present invention, in the step S1, the unaffected element types are element types with a relative error of element content of less than 10%.

[0017] As a specific implementation of the present invention, in step S2, the pilot well data includes pilot well gamma, element, organic carbon, and lithology data.

[0018] As a specific implementation of the present invention, in step S2, the GR ratio is defined as shale gamma / limestone gamma, and the Ca ratio is defined as limestone Ca / shale Ca.

[0019] As a specific implementation of the present invention, in step S3, the shale data includes gamma, organic carbon and elements; wherein the element data is defined as a shale valid data set {XRF valid}, and the organic carbon data is defined as a shale valid data set {organic carbon}; the number of shale data is ≥30.

[0020] As a specific implementation of the present invention, in step S4, the fitting is performed using a support vector machine (SVM) algorithm; establishing the calculation model also includes using a CV cross-validation method for verification, and determining the final tuning parameters according to the correlation coefficient;

[0021] As a specific implementation of the present invention, in step S4, the established organic carbon calculation model is:

[0022] SVM_TOC_Fit = ksvm (shale valid data set {XRF valid}, y = shale valid data set {organic carbon}, kpar = sigma, C, epsilon).

[0023] In a second aspect, the present invention provides a computer-readable storage medium, wherein a computer program stored in the computer-readable storage medium can be executed by one or more processors to implement the method described in the first aspect.

[0024] In a third aspect, the present invention provides an electronic device comprising a memory and one or more processors, wherein the memory stores a computer program, and the memory and the one or more processors are communicatively connected to each other, and when the computer program is executed by the one or more processors, the method described in the first aspect is performed.

[0025] Compared with the prior art, the present invention has the following beneficial effects:

[0026] 1. The organic carbon calculation model of the present invention eliminates the influence of oil-based drilling fluid and thin limestone, and the calculation results are more reliable. Elements that are not affected by oil-based drilling fluid are used to participate in the calculation, and thin limestone is eliminated to avoid a large change in element content. The SVM machine learning algorithm is used, and the calculation results are more accurate.

[0027] 2. The organic carbon calculation method of the present invention has strong timeliness. The method is carried out while drilling, so it can be analyzed in real time during the drilling process. There is no need to wait until the drilling is completed to carry out comprehensive logging or send samples to the laboratory for analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 It is a comparison chart of the organic carbon calculation results in the embodiment of the present invention and the organic carbon calculation results in the prior art. DETAILED DESCRIPTION

[0029] The present invention will be further described below in conjunction with specific embodiments, but they do not constitute any limitation to the present invention.

[0030] The organic carbon calculation method of the present invention comprises the following steps:

[0031] S1: Carry out comparative analysis of element data under water-based and oil-based drilling fluid conditions, and select the unaffected element types. Use the same element logging instrument to analyze the cuttings of the pilot well under water-based drilling fluid conditions, and take at least 5 cuttings of the same layer under oil-based drilling fluid conditions for comparative analysis. If the relative error of element content is less than 10%, it is considered that it is not affected by the oil-based drilling fluid and can be used in subsequent calculations or applications. The unaffected element set is defined as {XRF valid}.

[0032] S2: The unaffected element set obtained according to step S1 of the pilot well data is divided into a shale data set and a limestone data set, and the data sets are saved as a shale data set and a limestone data set respectively according to the lithology of shale and limestone, and the GR ratio is defined as shale gamma / limestone gamma, and the Ca ratio is defined as limestone Ca / shale Ca; the well section whose GR ratio and Ca ratio are both greater than or equal to the GR ratio and Ca ratio of the corresponding layer of the pilot well is determined to be limestone, and is eliminated to obtain the shale data set of the pilot well.

[0033] S3: Sort the shale data set of the pilot well obtained in step S2, and only retain three types of data: gamma, organic carbon and element, where the element data type = {XRF valid}. The sorted data set is saved as a shale valid data set, where the element data is defined as a shale valid data set {XRF valid}, and the organic carbon data is defined as a shale valid data set {organic carbon}. The number of data samples is not less than 30.

[0034] S4: Fitting of the organic carbon calculation model is carried out for the effective shale data set obtained in step S3. The support vector machine (SVM) algorithm is used for fitting. The data needs to be standardized before calculation, and the CV cross-validation method is used for verification. This process can be implemented using commercial software or programming. For example, taking the open source software R language programming as an example, the caret package is loaded, the train function is called for model training, x is the element data involved in the fitting, y is the organic carbon to be fitted, the kernel function uses the svmRadial function, and the preProc function is used for data standardization. The standardization parameters are specified as center and scale, and cv is specified as the verification method.

[0035] train(

[0036] x=shale effective data set {XRF effective},

[0037] y = shale effective data set {organic carbon},

[0038] method="svmRadial",

[0039] preProcess=c("center","scale"),

[0040] trControl=trainControl(method="cv") )

[0042] After training, the correlation coefficient of the model and the key adjustment parameters epsilon, C, and sigma are obtained. The epsilon, C, and sigma corresponding to the correlation coefficient greater than or equal to 0.8 and the highest absolute value are selected as the final parameters of the model. If the highest correlation coefficient is lower than 0.8, it indicates that this method is not applicable and other algorithms need to be used. The established organic carbon calculation model is:

[0043] SVM_TOC_Fit = ksvm (shale effective data set {XRF effective}, y = shale effective data set {organic carbon}, kpar = sigma, C, epsilon)

[0044] S5: Gamma spectroscopy analysis of cuttings from horizontal well drilling is first performed to obtain cuttings gamma data, and then element logging analysis is performed using the same sample. Comparison is performed between the pilot well and the horizontal well, and shale is determined based on the GR ratio and Ca ratio. When the GR ratio and Ca ratio are both greater than or equal to the GR ratio and Ca ratio of the corresponding layer of the pilot well, it is determined to be limestone, and the data of this section needs to be eliminated and does not participate in the calculation. The element data of other well sections are sorted into horizontal well shale data sets according to the element types in {XRF valid}.

[0045] S6: Apply the organic carbon calculation model established in step 4 to carry out calculations. The R language code is: Predict(SVM_TOC_Fit, horizontal well shale data set). The result obtained is the organic carbon corresponding to the horizontal section shale.

[0046] Example

[0047] This example provides a method for calculating organic carbon, which is applied to the L work area in the Sichuan Basin. The specific details are as follows:

[0048] S1: The drilling target of L1 well is mainly Jurassic continental shale oil and gas. The well type is a horizontal well. The pilot well uses water-based drilling fluid, and the side drilling horizontal well uses oil-based drilling fluid. By comparison, the P and Co elements become smaller, the Cl, Pb, and Ti elements become larger, and the relative errors of the contents are greater than 10%; while the relative errors of the contents of Mg, Al, Si, Ca, Fe, K, S, etc. are less than 10%. Therefore, the unaffected element set is {Mg, Al, Si, Ca, Fe, K, S}.

[0049] S2: The data set of unaffected element types obtained from the pilot well data in step S1 is divided into a shale data set and a limestone data set, and the GR ratio = shale gamma / limestone gamma = 178 / 125 = 1.42 and the Ca ratio = limestone Ca / shale Ca = 29 / 13 = 1.42 are calculated and determined. When the GR ratio and the Ca ratio are both greater than 1.42, it is determined to be a limestone data set and is removed. The remaining well sections are shale data sets.

[0050] S3: sorting the shale data set of the pilot well obtained in step S2, retaining only the three types of data: gamma, organic carbon and elements, and obtaining the pilot well shale effective data set {XRF effective} and the pilot well shale effective data set {organic carbon}.

[0051] S4: The shale effective data set obtained in step S3 is used to fit the organic carbon calculation model. The open source software R language is used for programming. The caret package is loaded and the train function is called for model training. x is the element data involved in the fitting, y is the organic carbon to be fitted, and the kernel function uses the svmRadial function. The preProc function is used to standardize the data. The standardization parameters are specified as center and scale, and cv is specified as the verification method.

[0052] train(

[0053] x=shale effective data set {XRF effective},

[0054] y = shale effective data set {organic carbon},

[0055] method="svmRadial",

[0056] preProcess=c("center","scale"),

[0057] trControl=trainControl(method="cv") )

[0059] The highest correlation coefficient is 0.89, epsilon = 0.1, C = 256, sigma = 0.113, and the organic carbon calculation model of the well is established:

[0060] SVM_TOC_Fit = ksvm (shale effective data set {XRF effective}, y = shale effective data set {organic carbon}, kpar = sigma, C, epsilon)

[0061] S5: During the construction of the horizontal well, rock cuttings gamma spectroscopy and elemental analysis were carried out simultaneously (the rock cuttings gamma spectroscopy and elemental analysis data table is omitted). The thin-layer limestone section was eliminated based on the GR ratio (1.42) and Ca ratio (1.42). The GR ratio and Ca ratio of the well sections 3062.00-3066.00m and 3074.00-3079.00m were both greater than 1.42, which were limestone sections and were eliminated. The elemental data of the shale sections 3052.00-3062.00m, 3066.00-3074.00m, and 3079.00-3105.00m were sorted out to obtain a shale valid data set containing only Mg, Al, Si, Ca, Fe, K, and S elements {XRF valid}

[0062] S6: Carry out calculations using the calculation model obtained in step S4 to obtain the organic carbon in the formation, and guide the horizontal well construction of the well according to the change in the content of organic carbon, as shown in Table 1:

[0063] Table 1 Changes of organic carbon content with depth

[0064]

[0065] After the well depth of 3092m, the organic carbon increased significantly by 0-0.06↑1.356%, indicating that it has entered the high-quality shale layer. The window was immediately increased and the drilling was switched to the horizontal section. The total hydrocarbon in the gas test also increased significantly, indicating that the calculation results of organic carbon are relatively reliable. Figure 1 The black-filled sections in columns 8 and 9 are thin-layer limestone sections determined by GR ratio and Ca ratio, and no organic carbon calculation is performed.

[0066] Comparative Example

[0067] This comparative example provides an existing evaluation while drilling method, which is applied to the project in Example 1. The specific details are as follows:

[0068] The existing evaluation method does not consider the influence of oil-based drilling fluid, does not select elements, and does not exclude thin limestone sections. If the limestone is not excluded, the well depth of 3062-3066m is affected by the limestone, and the calculated organic carbon is 1.792%, which will be misjudged as a high-quality reservoir. The actual formation is a thin layer of limestone, and the total hydrocarbon in the gas test has no obvious change. The calculated results are inconsistent with the actual situation. Figure 1 This is shown as the curve in the third column from the bottom.

[0069] In summary, the organic carbon calculation model of the present invention eliminates the influence of oil-based drilling fluid and thin limestone, and the calculation results are more reliable. Elements that are not affected by oil-based drilling fluid are used to participate in the calculation, and thin limestone is eliminated to avoid a large change in element content. The SVM machine learning algorithm is used, and the calculation results are more accurate.

[0070] In one embodiment, a computer-readable storage medium is provided. The computer-readable storage medium stores a computer program that can be executed by one or more processors to implement the method described in any of the above embodiments.

[0071] In one embodiment, an electronic device is provided, including a memory and one or more processors, wherein a computer program is stored in the memory, and the memory and the one or more processors are communicatively connected to each other. When the computer program is executed by the one or more processors, the method described in any of the above embodiments is performed.

[0072] In addition, it should be understood that the method or system disclosed in the embodiments provided in the present application can also be implemented in other ways. The method or system embodiments described above are merely schematic. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the methods and devices according to the multiple embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a computer program segment or a part of a computer program, and the module, computer program segment or a part of a computer program contains one or more computer programs for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings, and can actually be executed in parallel. They can also be executed in the opposite order sometimes, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart can be implemented with a dedicated hardware-based system that performs the specified function or action, or can be implemented with a combination of dedicated hardware and computer programs.

[0073] In the present application, the terms "comprises", "includes" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "including one..." do not exclude the existence of other identical elements in the process, method, device or device including the elements; if there is a description of "first", "second", etc., it is only for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features or implicitly indicating the order of the indicated technical features. In addition, in the description of the present application, unless otherwise specified, the terms "multiple" and "multiple" mean at least two.

[0074] Finally, it should be noted that, in the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "an example" or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0075] Although the embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary, and the contents described are only embodiments adopted for the convenience of understanding the present application, and are not intended to limit the present application. Any technician in the technical field to which the present application belongs can make any modifications and changes in the form and details of the implementation without departing from the spirit and scope disclosed in the present application, but the scope of protection of the present application shall still be based on the scope defined in the attached claims.

Claims

1. A method for calculating organic carbon, characterized in that: The following steps are involved: S1: Conduct comparative analysis of cuttings element data under water-based and oil-based drilling fluid conditions in the pilot well, and define the data set of unaffected element types as {XRF valid}; S2: dividing the data set of unaffected element types obtained in step S1 of the pilot well data into a shale data set and a limestone data set, and defining the GR ratio and the Ca ratio to remove the limestone to obtain the shale data set of the pilot well; S3: sorting the shale data set of the pilot well obtained in step S2 to obtain a pilot well shale effective data set {XRF effective} and a pilot well shale effective data set {organic carbon}; S4: Fitting the pilot well data set obtained in step S3 to establish an organic carbon calculation model; S5: Analyze the horizontal well cuttings according to steps S1 to S3 to obtain a shale data set of the horizontal well; S6: Apply the organic carbon calculation model established in step S4 to calculate the shale data set of the horizontal well in step S5, and the result obtained is the organic carbon corresponding to the shale in the horizontal section.

2. The calculation method according to claim 1, characterized in that: In the step S1, the cuttings element data under the oil-based drilling fluid condition includes cuttings under the oil-based drilling fluid condition of at least 5 same-layer positions; the cuttings element data under the water-based drilling fluid condition is cuttings under the water-based drilling fluid condition of the pilot well.

3. The calculation method according to claim 1 or 2, characterized in that: In the step S1, the unaffected element types are element types with a relative error of element content less than 10%.

4. The calculation method according to any one of claims 1 to 3, characterized in that: In step S2, the method for removing limestone includes determining the well section whose GR ratio and Ca ratio are both greater than or equal to the GR ratio and Ca ratio of the corresponding layer of the pilot well as limestone, and removing the limestone.

5. The calculation method according to any one of claims 1 to 4, characterized in that: In step S2, the GR ratio is defined as shale gamma / limestone gamma, and the Ca ratio is defined as limestone Ca / shale Ca.

6. The calculation method according to any one of claims 1 to 5, characterized in that: In step S3, the shale data includes gamma, organic carbon and elements; the element data is defined as a shale valid data set {XRF valid}, and the organic carbon data is defined as a shale valid data set {organic carbon}; the number of shale data is ≥30.

7. The calculation method according to any one of claims 1 to 6, characterized in that: In the step S4, the fitting is performed using a support vector machine (SVM) algorithm; establishing the calculation model also includes using a CV cross-validation method for verification, and determining the final tuning parameters based on the correlation coefficient.

8. The calculation method according to any one of claims 1 to 7, characterized in that: In step S4, the established organic carbon calculation model is: SVM_TOC_Fit = ksvm (shale valid data set {XRF valid}, y = shale valid data set {organic carbon}, kpar = sigma, C, epsilon).

9. A computer-readable storage medium, characterized in that: The computer program stored in the computer-readable storage medium can be executed by one or more processors to implement the method according to any one of claims 1 to 7.

10. An electronic device, characterized in that: The method comprises a memory and one or more processors, wherein a computer program is stored in the memory, and the memory and the one or more processors are communicatively connected with each other, and when the computer program is executed by the one or more processors, the method as described in any one of claims 1 to 7 is executed.

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