A method for predicting shale oil soluble hydrocarbons

Abstract

The present application belongs to the field of unconventional and new energy technology, and discloses a shale oil soluble hydrocarbon prediction method, which comprises the following steps: S1, analyzing and obtaining total organic carbon content sensitive well logging curve, and combining with measured geochemical data to establish a calculation model of soluble hydrocarbon and total organic carbon content sensitive well logging curve; S2, extracting well logging data in a production area, and importing the calculation model to calculate soluble hydrocarbon single well data; S3, establishing a data set of seismic attributes and soluble hydrocarbon single well data, taking soluble hydrocarbon as target data, optimizing sensitive seismic attributes through intersection of target data and seismic attributes, applying a probability neural network method to machine learning to establish a mapping relationship between target data and sensitive seismic attributes, and then calculating a soluble hydrocarbon target probability body on a three-dimensional seismic body to obtain a planar prediction result of soluble hydrocarbon. The present application realizes effective prediction of soluble hydrocarbon by using geochemical data, well logging data and seismic data, and provides accurate data support for shale oil sweet spot optimization.

Description

Technical Field

[0001] This invention belongs to the field of unconventional and new energy technologies, specifically relating to a method for predicting soluble hydrocarbons in shale oil. Background Technology

[0002] Soluble hydrocarbons (S1) are an important indicator for evaluating the sweet spot of shale oil, representing the amount of oil in the shale formation. For a long time, soluble hydrocarbon (S1) data have been obtained mainly from experimental analysis of drilling core data. However, the number of core wells is small and the cost is high. Moreover, the conclusions obtained from actual measurements in the core through data acquisition methods have little guiding significance in actual production and experimental derivation, and therefore are not applicable to generalization.

[0003] Currently, there are no commercial software methods specifically for predicting soluble hydrocarbons (S1). Researching relevant technological achievements through article and patent searches reveals that, apart from the aforementioned method of directly obtaining soluble hydrocarbon (S1) data from core samples (this is based on actual S1 data measurement, not distribution prediction), the industry typically uses curve reconstruction methods. This method uses well logging curves associated with soluble hydrocarbons (S1) and supplements seismic data to predict S1. However, this method only extracts data from a few fixed sampling points to predict soluble hydrocarbons (S1), lacking lateral variation characteristics between data points. Therefore, the prediction results lack lateral variation information from seismic data, resulting in inaccurate predictions. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention aims to provide a method for predicting soluble hydrocarbons in shale oil, thereby achieving effective prediction of soluble hydrocarbons using geochemical data, well logging data, and seismic data.

[0005] To achieve the above objectives, the technical solution adopted by this invention is as follows: A method for predicting soluble hydrocarbons in shale oil, comprising the following steps:

[0006] I. Analyze and obtain the sensitive logging curves for total organic carbon content, and establish a calculation model for the sensitive logging curves for soluble hydrocarbons and total organic carbon content based on measured geochemical data.

[0007] TOC = a(logRT - logRT) min ) / (logRT max -logRT min )+b(AC-AC min ) / (AC max -AC min Formula 1

[0008] S1 = TOC(a3D) 3 +a2D 2 +a1D+a0) Formula 2

[0009] In Equation 1, TOC is the total organic carbon content, RT is the resistivity, AC is the acoustic transit time, a is the proportionality coefficient of RT to TOC, and b is the proportionality coefficient of AC to TOC.

[0010] In Equation 2, S1 represents soluble hydrocarbons, TOC represents total organic carbon content, D represents formation depth, and a0, a1, a2, and a3 are the fitting coefficients for multiple terms.

[0011] 2. Extract well logging data from the production area and import it into Equations 1 and 2 to calculate soluble hydrocarbon single-well data;

[0012] Third, establish a dataset of seismic attributes and single-well data of soluble hydrocarbons, take soluble hydrocarbons as target data, select sensitive seismic attributes by intersecting the target data and seismic attributes, apply probabilistic neural network method to machine learning to establish the mapping relationship between target data and sensitive seismic attributes, and then calculate the soluble hydrocarbon target probability volume on the three-dimensional seismic volume to obtain the planar prediction results of soluble hydrocarbons.

[0013] As a limitation of this invention, in step three, the mapping relationship between the target data and the sensitive seismic attributes is defined as follows:

[0014] S1 目标值 =W1S1+W2S2+…+WnSn Equation 3

[0015] In Equation 3, W1…Wn are the weights corresponding to seismic attribute points highly correlated with the target data, obtained through statistical analysis of the intersection of single attribute points and target data; S1 目标值 This represents the target value for soluble hydrocarbons.

[0016] As another limitation of the present invention, the analysis and acquisition of the "total organic carbon content sensitive logging curve" in step one is as follows:

[0017] Collect and load core and well logging data and establish a cross-plot. Apply the total organic carbon content of core tests and well logging data to perform curve cross-plot analysis. Through polynomial fitting, calculate the fitting error and fitting ratio coefficient, and select the best well logging curve sensitive to total organic carbon content based on the comparison of multiple curve fittings.

[0018] As a further limitation of the present invention, the core data includes lithological components, total organic carbon content, and soluble hydrocarbon measurement results; the well logging data includes sonic transit time, density, resistivity, natural gamma, spontaneous potential, and well diameter.

[0019] As a third limitation of the present invention, the formula fitting of "Equation 1" in step one is:

[0020] After preprocessing and eliminating outliers in the resistivity curve and sonic transit time curve of the total organic carbon content sensitive logging curve, and standardizing the curve, a fitting formula can be established—Equation 1.

[0021] As a further limitation of the present invention, the formula fitting derivation of "Equation 2" in step one is as follows:

[0022] Based on core geochemical analysis data, core sample data were selected over a wide depth range to establish an intersection analysis of soluble hydrocarbon and total organic carbon content with depth. The changing pattern of the ratio of soluble hydrocarbon to total organic carbon content with increasing formation depth was statistically analyzed, and a formula for fitting the ratio to depth was established.

[0023] S1 / TOC=a3D 3 +a2D 2 +a1D+a0 Formula 4

[0024] Equation 4 can be transformed to obtain Equation 2.

[0025] As a further limitation of the present invention, in step three, the seismic attributes of the data set established with the single-well data of soluble hydrocarbons are extracted from the seismic attribute samples of the shale oil development area in the well bypass.

[0026] As a further limitation of the present invention, the extraction operation of the seismic attribute sample is as follows:

[0027] Collect and load high-resolution 3D seismic data for fine well seismic calibration; conduct high-precision seismic horizon interpretation for shale oil development areas; optimize and preprocess the seismic data using industrial interpretation software, and extract amplitude, frequency, and geometric seismic attribute samples from the 3D seismic body based on the seismic horizon interpretation results.

[0028] By adopting the above-described technical solution, the beneficial effects achieved by this invention compared to the prior art are as follows:

[0029] The formation of soluble hydrocarbons is directly related to two variables: total organic carbon (TOC) and depth. TOC represents the organic matter content in the formation, while depth is a function of temperature and pressure conditions. This invention starts with measured data from a single well, simulates the conversion rate of TOC to soluble hydrocarbons at different depths, establishes the correspondence between soluble hydrocarbons and TOC and depth, and achieves soluble hydrocarbon prediction for a single well. Then, it utilizes sensitive seismic attributes to conduct neural network prediction of the planar distribution range of soluble hydrocarbons, thus realizing the prediction of soluble hydrocarbons in shale oil.

[0030] This invention effectively improves the single-well prediction accuracy of soluble hydrocarbons, thereby enhancing the accuracy of well-logging-constrained seismic inversion technology in predicting soluble hydrocarbons in shale oil and increasing the reliability of seismic lateral inversion prediction under complex geological conditions. This invention fills a gap in geophysical methods for predicting soluble hydrocarbons and provides accurate data support for the optimal selection of sweet spot targets for shale oil. Attached Figure Description

[0031] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0032] Figure 1 This is a comprehensive map of well G108-8 in the production area. By comparing the measured TOC with the calculated TOC and the measured S1 with the calculated S1, it is confirmed that the longitudinal TOC and S1 of the single well have a high degree of agreement.

[0033] Figure 2 The figure shows the inversion results after obtaining S1 using the single-well depth fitting method. The figure shows a high degree of agreement between S1 at the well points, and the inter-well variation relationship conforms to geological laws, confirming that the lateral prediction is reliable and highly accurate. Detailed Implementation

[0034] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustrative and understanding purposes only and are not intended to limit the scope of the invention.

[0035] This embodiment proposes a method for predicting soluble hydrocarbons in shale oil. This method utilizes core, well logging, and seismic data, and through a series of calculation steps, calculates crucial data required for the comprehensive evaluation of shale oil sweet spots—soluble hydrocarbons (S1). Specifically, it includes the following steps performed sequentially:

[0036] I. Analyze and obtain the sensitive logging curves of total organic carbon content, and establish a calculation model for the sensitive logging curves of soluble hydrocarbons and total organic carbon content based on the measured geochemical data (core data).

[0037] (1) Analysis of logging curves sensitive to total organic carbon content

[0038] Collect and load core and logging data and establish a cross-plot. Specifically, the total organic carbon content of the core test and the logging data are used to perform curve cross-plot analysis. Through polynomial fitting, the fitting error and fitting ratio coefficient are calculated. Based on the comparison of multiple curve fitting, the logging curve sensitive to total organic carbon content is selected.

[0039] Core data and well logging data were collected and acquired using existing methods. Core data included measurements of lithological components, total organic carbon content, and soluble hydrocarbons; well logging data included sonic transit time, density, resistivity, natural gamma ray, spontaneous potential, and well diameter.

[0040] (2) Fitting formula for total organic carbon content

[0041] The optimized total organic carbon content sensitive logging curves were near-normalized, mainly by preprocessing the resistivity and sonic transit time curves to eliminate outliers, and then establishing a fitting formula after curve standardization.

[0042] TOC = a(logRT - logRT) min ) / (logRT max -logRT min )+b(AC-AC min ) / (AC max -AC min Formula 1

[0043] Wherein, TOC is the total organic carbon content, RT is the resistivity, AC is the acoustic transit time, a is the ratio coefficient of RT to TOC, and b is the ratio coefficient of AC to TOC.

[0044] (3) Fitting formula for ratio (S1 / TOC)

[0045] Based on core geochemical analysis data (core data), core sample data were selected over a wide depth range to establish an intersection analysis of soluble hydrocarbon and total organic carbon content with depth. The variation of the ratio of soluble hydrocarbon to total organic carbon (S1 / TOC) with increasing formation depth was statistically analyzed, and a formula for fitting the ratio to depth was established.

[0046] S1 / TOC=a3D 3 +a2D 2 +a1D+a0 Formula 4

[0047] Where S1 represents soluble hydrocarbons, TOC represents total organic carbon content, D represents formation depth, and a0, a1, a2, and a3 are the fitting coefficients for multiple terms.

[0048] (4) Derive the formula for calculating soluble hydrocarbons (S1)

[0049] By transforming the above ratio formula (Equation 4), we can obtain the formula for calculating soluble hydrocarbons:

[0050] S1 = TOC(a3D) 3 +a2D 2 +a1D+a0) Formula 2.

[0051] 2. Extract the required logging data from the logging data in the production area and import it into Equations 1 and 2 above to calculate the single-well data of soluble hydrocarbons.

[0052] III. Planar prediction of soluble hydrocarbons based on seismic data

[0053] (1) Extracting seismic attribute samples from the well-side shale oil development zone

[0054] Collect and load high-resolution 3D seismic data for fine well seismic calibration; conduct high-precision seismic horizon interpretation for shale oil development areas; optimize and preprocess seismic data using industrial interpretation software, and extract amplitude, frequency, and geometric seismic attribute samples from 3D seismic volumes based on the seismic horizon interpretation results;

[0055] Seismic data was collected and loaded using existing methods.

[0056] (2) The extracted seismic attribute samples are integrated with the soluble hydrocarbon single-well data calculated above to establish a data set.

[0057] (3) Establish the mapping relationship between target data and sensitive seismic attributes to obtain the soluble hydrocarbon plane prediction results.

[0058] Based on high-quality seismic processing attribute data, soluble hydrocarbons are used as target data. Sensitive seismic attributes are selected by intersecting the target data with seismic attributes. A probabilistic neural network method is applied to machine learning to establish the mapping relationship between the target data and sensitive seismic attributes. The mapping relationship formula is as follows:

[0059] S1 目标值 =W1S1+W2S2+…+WnSn Equation 3

[0060] Where W1…Wn are the weights corresponding to seismic attribute points highly correlated with the target data, obtained through statistical analysis of the intersection between single attribute points and target data; S1 目标值 The target value is for soluble hydrocarbons;

[0061] By utilizing the mapping relationship between soluble hydrocarbon target data and sensitive seismic attributes, a probabilistic volume of soluble hydrocarbon targets is calculated on a 3D seismic body. By extracting the layer attributes of the probabilistic volume using seismic horizons, the planar prediction results of soluble hydrocarbons can be obtained.

[0062] It should be noted that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art can still modify the technical solutions described in the above 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 predicting soluble hydrocarbons in shale oil, characterized in that: Includes the following steps: I. Analyze and obtain the total organic carbon content-sensitive logging curves, and establish a calculation model for the soluble hydrocarbon and total organic carbon content-sensitive logging curves based on measured geochemical data: TOC = a(logRT - logRT min ) / (logRT max - logRT min ) + b(AC - AC min ) / (AC max - AC min ) Equation 1 S1 = TOC(a3D 3 + a2D 2 + a1D + a0) Equation 2 In Equation 1, TOC is the total organic carbon content, RT is the resistivity, AC is the acoustic transit time, a is the proportionality coefficient of RT to TOC, and b is the proportionality coefficient of AC to TOC. In Equation 2, S1 represents soluble hydrocarbons, TOC represents total organic carbon content, D represents formation depth, and a0, a1, a2, and a3 are the fitting coefficients for multiple terms.

2. Extract well logging data from the production area and import it into Equations 1 and 2 to calculate soluble hydrocarbon single-well data; Third, establish a dataset of seismic attributes and single-well data of soluble hydrocarbons, take soluble hydrocarbons as target data, select sensitive seismic attributes by intersecting the target data and seismic attributes, apply probabilistic neural network method to machine learning to establish the mapping relationship between target data and sensitive seismic attributes, and then calculate the soluble hydrocarbon target probability volume on the three-dimensional seismic volume to obtain the planar prediction results of soluble hydrocarbons.

2. The method for predicting soluble hydrocarbons in shale oil according to claim 1, characterized in that: In step three, the formula for the mapping relationship between the target data and the sensitive seismic attributes is as follows: S1 目标值 =W1S1+W2S2+…+WnSn Equation 3 In Equation 3, W1…Wn are the weights corresponding to seismic attribute points highly correlated with the target data, obtained through statistical analysis of the intersection of single attribute points and target data; S1 目标值 This represents the target value for soluble hydrocarbons.

3. The method for predicting soluble hydrocarbons in shale oil according to claim 1 or 2, characterized in that: The analysis and acquisition of the "total organic carbon content sensitive logging curve" in step one is as follows: Collect and load core and well logging data and establish a cross-plot. Apply the total organic carbon content from core tests and well logging data to perform curve cross-plot analysis. Through polynomial fitting, calculate the fitting error and fitting ratio coefficient, and select the best well logging curve sensitive to total organic carbon content based on the comparison of multiple curve fittings.

4. The method for predicting soluble hydrocarbons in shale oil according to claim 3, characterized in that: Core data includes lithological components, total organic carbon content, and soluble hydrocarbon measurements; well logging data includes sonic transit time, density, resistivity, natural gamma, spontaneous potential, and well diameter.

5. A method for predicting soluble hydrocarbons in shale oil according to any one of claims 1-2 and 4, characterized in that: The formula fitting of "Equation 1" in step one is: After preprocessing and eliminating outliers in the resistivity curve and sonic transit time curve of the total organic carbon content sensitive logging curve, and standardizing the curve, a fitting formula can be established—Equation 1.

6. The method for predicting soluble hydrocarbons in shale oil according to claim 5, characterized in that: The formula fitting derivation of "Equation 2" in step one is as follows: Based on core geochemical analysis data, core sample data were selected over a wide depth range to establish an intersection analysis of soluble hydrocarbon and total organic carbon content with depth. The changing pattern of the ratio of soluble hydrocarbon to total organic carbon content with increasing formation depth was statistically analyzed, and a formula for fitting the ratio to depth was established. S1 / TOC = a3D 3 + a2D 2 + a1D + a0 Equation 4 Equation 4 can be transformed to obtain Equation 2.

7. A method for predicting soluble hydrocarbons in shale oil according to any one of claims 1-2, 4, and 6, characterized in that: In step three, the seismic attributes of the data set are established with the single-well data of soluble hydrocarbons, and the seismic attribute samples of the shale oil development area in the well bypass are extracted.

8. The method for predicting soluble hydrocarbons in shale oil according to claim 7, characterized in that: The extraction operation of the seismic attribute samples is as follows: Collect and load high-resolution 3D seismic data for fine well seismic calibration; conduct high-precision seismic horizon interpretation for shale oil development areas; optimize and preprocess the seismic data using industrial interpretation software, and extract amplitude, frequency, and geometric seismic attribute samples from the 3D seismic body based on the seismic horizon interpretation results.

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