Characterization method for characteristics of gravel around horizontal well of conglomerate oil reservoir

Abstract

The invention belongs to the field of oil-gas field development, and particularly relates to a method for characterizing the characteristics of gravel around a horizontal well of a conglomerate oil reservoir. Comprising the following steps: (1) processing an FMI imaging logging image by adopting conglomerate reservoir gravel feature recognition analysis software, and extracting gravel parameters; (2) carrying out sensitivity analysis on gravel parameters and logging data by utilizing a multiple linear regression analysis method, and determining a statistical relationship between the gravel parameters and a logging curve; and (3) establishing a well logging curve and gravel parameter model, and substituting well logging curve data to measure gravel characteristics. According to the method, the problem of gravel characterization of the conglomerate oil reservoir is solved, the horizontal section well periphery gravel parameters are characterized by using the conventional logging curve for the first time, and an optimization direction is provided for a multi-cluster volume fracturing technology in a horizontal well section.

Description

Technical Field

[0001] The invention belongs to the field of oil and gas field development, and in particular relates to a method for characterizing gravel characteristics around a horizontal well in a conglomerate oil reservoir. Background Art

[0002] Conglomerate reservoir is a special clastic oil and gas reservoir. Compared with sandstone reservoir, due to the close source, multiple water systems and fast-changing sedimentary environment, the microscopic pore structure of the reservoir presents complex modal characteristics, the pore throat distribution is extremely uneven, the throat radius mean is small, the pore throat ratio is large, the pore throat coordination number is low, and the overall multi-peak and fine state is characteristic. The special pore structure makes the utilization law of microscopic pore crude oil under different displacement modes more complicated.

[0003] The gravel content and size distribution in the conglomerate reservoir of the Mahu Sag conglomerate reservoir are uneven, the lithology varies greatly, and the stress distribution is complex. As a result, in the multi-cluster volume fracturing transformation in the horizontal well section, it is impossible to effectively achieve balanced fracturing of each perforation cluster in the section. It is easy to have large differences in the amount of sand injected into each perforation cluster in the same transformation section, and large differences in the fluid supply of each cluster in the post-fracturing section, which seriously affects the reservoir production degree. Therefore, the research on gravel feature identification technology in conglomerate reservoirs is of great significance to guide the segmentation and clustering of conglomerate reservoirs and achieve balanced fracturing of each cluster in the section.

[0004] The key technologies for developing sandstone oil reservoirs include: (1) seismic imaging technology. In terms of seismic imaging of complex target geological bodies, foreign countries started early in the study of theoretical methods, and have done a lot of research on complex geological bodies such as salt domes and thrust structures, such as the research on the internationally recognized Marmousi, SEG / EAGA and other models. In terms of specific applications, through the promotion of prestack migration imaging technology, the imaging quality of complex geological bodies such as salt domes in the Gulf of Mexico in North America has been significantly improved.

[0005] (2) Imaging logging technology. Imaging logging technology refers to the use of array sensors in downhole instruments to measure along the well wall or rotate and scan to collect a large amount of physical information of the formation in the vertical, circumferential and radial directions. After the information is transmitted to the ground acquisition system by cable, it is processed through image processing to obtain a two-dimensional image of the well wall or a three-dimensional image within a certain detection depth around the wellbore. Commonly used are FMI (microresistivity scanning imaging) electrical imaging logging.

[0006] At present, the research on gravel content and distribution in conglomerate reservoirs at home and abroad is based on FMI imaging logging images or core images. Some literatures introduce the use of seismic methods to predict gravel distribution, but FMI imaging logging images or core data are limited and costly, which seriously restricts the identification of gravel characteristics in conglomerate reservoirs.

[0007] In order to obtain the gravel characteristics of each single well along the horizontal well section, the method of the present invention attempts to find the correlation between conventional logging data and gravel characteristics, establishes a gravel characteristic characterization method for gravel reservoirs based on conventional logging, implements "one well, one policy", provides guidance for the segmentation and clustering of gravel reservoirs, optimizes fracturing technology and pumping procedures, and realizes balanced fracturing of multiple clusters in the volume fracturing section of the horizontal well. Summary of the invention

[0008] In view of the deficiencies in the prior art, the present invention provides a method for characterizing gravel characteristics around horizontal wells in conglomerate reservoirs. The method of the present invention aims to establish a method for characterizing gravel characteristics around horizontal wells in conglomerate reservoirs, processes logging images, converts electrical imaging images into continuous gravel information, and then extracts gravel parameters to quantitatively characterize gravel parameters. Sensitivity analysis is performed in combination with logging image interpretation data, and a model of logging curves, gravel equivalent radius, and gravel linear density per unit length is established. The gravel equivalent radius and gravel linear density per unit length are used as gravel characteristic markers for conglomerate reservoirs, so as to achieve the purpose of effectively characterizing gravel characteristics of conglomerate reservoirs using conventional logging curves.

[0009] In order to achieve the above-mentioned purpose of the present invention, the specific technical solution adopted by the present invention is:

[0010] A method for characterizing gravel characteristics around a horizontal well in a conglomerate reservoir comprises the following steps:

[0011] (1) Using conglomerate reservoir gravel feature recognition and analysis software, the FMI imaging logging images were processed to extract gravel parameters;

[0012] (2) Using the multivariate linear regression analysis method, conduct sensitivity analysis on gravel parameters and logging data, and clarify the statistical relationship between gravel parameters and logging curves; (the logging data is imported into the system to generate conventional logging curves);

[0013] (3) Establish a model of well logging curves and gravel parameters, and substitute the well logging curve data to measure the gravel characteristics.

[0014] Preferably, the processing in step (1) includes grayscale processing, blind area filling and image segmentation.

[0015] Preferably, the gravel parameters in step (1) are gravel equivalent radius and gravel linear density.

[0016] Preferably, the conventional logging curves in step (2) include AC (acoustic time difference), CALI (wellbore diameter), CNL (neutron), DEN (density), GR (natural gamma), RI (invasion zone resistivity), RT (true resistivity), RXO (flushing zone resistivity) and SP (spontaneous potential).

[0017] The present invention adopts the multiple regression analysis method in sensitivity analysis. The change of the dependent variable is affected by several important factors, that is, the gravel equivalent radius and the gravel linear density are affected by multiple logging curve data. At this time, two or more influencing factors are needed as independent variables to explain the change of the dependent variable. This is multiple regression, also known as multiple regression. The present invention adopts the multiple regression analysis method to establish the relationship between the conventional logging curve and the gravel equivalent radius and the gravel linear density.

[0018] Preferably, the sensitivity analysis process in step (2) includes: setting AC (acoustic time difference), CALI (well diameter), CNL (neutron), DEN (density), GR (natural gamma), RI (invasion zone resistivity), RT (true resistivity), RXO (flushing zone resistivity) and SP (spontaneous potential) to have no relationship with the gravel equivalent radius and gravel linear density, and presetting the significance level to 0.1. If P<0.1 is calculated for a parameter, the null hypothesis is rejected at the significance level of 0.1, that is, the parameter has a linear relationship with the gravel equivalent radius and gravel linear density, and it is called a sensitive factor, otherwise it is called an insensitive factor.

[0019] Preferably, the confirmation process of the statistical relationship in step (2) includes: establishing the formula of gravel equivalent radius and gravel linear density.

[0020] Preferably, the logging curve in step (3) is correlated with the gravel equivalent radius and the gravel linear density.

[0021] Preferably, the process of establishing the model in step (3) includes: selecting the top three most influential parameters for linear regression analysis based on the sensitivity analysis results.

[0022] Compared with the prior art, the present invention has the following beneficial effects:

[0023] The method and operation flow formed by the present invention realize the transformation from qualitative characterization of gravel parameters by FMI logging images to quantitative characterization of gravel parameters, and based on the results of FMI logging image interpretation, construct the relationship between logging data and gravel parameters, use gravel equivalent radius and gravel line density per unit length as gravel reservoir characteristic markers, effectively characterize gravel characteristics of gravel reservoirs, form a gravel characteristic characterization method for gravel reservoirs based on conventional logging, overcome the gravel characterization problem of gravel reservoirs, and realize the first time to use conventional logging curves to characterize gravel parameters around horizontal sections, providing an optimization direction for multi-cluster volume fracturing technology in horizontal well sections. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 It is a flow chart of a method for characterizing gravel characteristics around a horizontal well in a conglomerate reservoir according to the present invention;

[0025] Figure 2is the FMI imaging logging image processing diagram in Example 1;

[0026] Figure 3 The gravel radius and line density map are automatically extracted in Example 1;

[0027] Figure 4 is the FMI gravel identification result diagram in Example 1;

[0028] Figure 5 is a graph of the well logging curve and the gravel equivalent radius P-value in Example 1;

[0029] Figure 6 is a graph of the well logging curve and the gravel line density P-value in Example 1;

[0030] Figure 7 is a comparison diagram of gravel equivalent radius identification results in Example 1;

[0031] Figure 8 is a comparison chart of the gravel linear density identification results in Example 1;

[0032] Fig. 9 is the gravel parameter extraction diagram of the CT image in Example 2;

[0033] Fig.10 is a graph of the well logging curve and the gravel equivalent radius P-value in Example 2;

[0034] Fig.11 is a graph of the well logging curve and the gravel line density P-value in Example 2;

[0035] Fig.12 This is a comparison chart of FMI imaging logging and predicted gravel parameters in Example 2. DETAILED DESCRIPTION

[0036] The technical solutions in the embodiments of the present invention are further clearly described. The described embodiments are only a part of the present invention and are used to explain the present invention but not to limit the present invention. Therefore, other embodiments obtained by other technicians in the field without creative work all belong to the protection scope of the present invention.

[0037] Establishment of a quantitative calculation model for borehole axial gravel characteristics

[0038] The number of gravels per unit length is defined as the gravel linear density. The gravel linear density calculation formula is as follows:

[0039]

[0040] The irregular gravel area is equivalent to a regular circle, and the gravel equivalent radius is used to define the size of the gravel. The gravel equivalent radius is calculated as follows:

[0041]

[0042] Example 1

[0043] Take the mine experiment as an example

[0044] (1) Gravel parameter extraction

[0045] The gravel characteristics recognition and analysis software for conglomerate reservoirs was used to extract the gravel parameters of single well FMI imaging logging images (see Figure 2 , Figure 3 ), forming the FMI gravel identification result as follows Figure 4 shown.

[0046] (2) Model establishment

[0047] Set the null hypothesis H 0 There is no relationship between the logging curve data and the gravel equivalent radius and gravel linear density. The relationship is completely generated by sampling. The preset significance level is 0.1. If P < 0.1, the null hypothesis is rejected at the 0.1 significance level, that is, there is a correlation between the logging curve and the gravel equivalent radius and gravel linear density.

[0048] Using Excel as a tool and applying its "data analysis" module, we can obtain the P-value of FMI gravel identification equivalent radius, gravel linear density and logging curve data. Figure 5 , Figure 6 shown.

[0049] Where P < 0.1 means that the logging curve is correlated with the gravel equivalent radius and gravel linear density. The coefficient of the independent variable in the regression equation is Coefficients, and the intercept is Intercept. Therefore, the model of the logging curve, gravel equivalent radius and linear density is:

[0050] Equivalent gravel radius: r = -0.884AC-1.44CNL+0.694GR+72.03

[0051] Gravel linear density: C = 0.152AC + 0.247CNL - 0.119GR + 5.64

[0052] Among them, AC, CNL, and GR represent acoustic transit time, neutron, and natural gamma, respectively.

[0053] (4) Model verification

[0054] The model accuracy was verified by using the gravel identification results of the other sections of the same well in Example 1. The gravel parameters were calculated according to the above formula and compared with the gravel parameters observed by the FMI logging image. Figure 7 , Figure 8As shown, the error after averaging all measured data is less than 15% (see Table 1), which meets the engineering accuracy requirements.

[0055] Table 1 Comparison of identification results of gravel equivalent radius and gravel linear density

[0056]

[0057]

[0058] Example 2

[0059] Core CT image verification

[0060] (1) Based on the conventional logging sandstone reservoir characterization method formed in Example 1, combined with the core CT image (see Fig. 9 ), using the multivariate linear regression method, it is clear that the gravel equivalent radius has a large correlation with AC, GR, and RT in the well logging curve (see Fig.10 ), the gravel linear density has a great correlation with CNL, DEN and RT in the well logging curve (see Fig.11 ).

[0061] (2) The coefficient of the independent variable in the regression equation is Coefficients, and the intercept is Intercept, so the model of the well logging curve, gravel equivalent radius, and linear density is obtained as follows:

[0062] r=0.173GR-0.00497RT-0.102AC+1.33

[0063] C=67.78DEN+0.0742RT+0.1298CNL-161.414

[0064] Among them, GR, RT, AC, DEN and CNL are natural gamma, true resistivity, acoustic time difference, density and neutron, respectively.

[0065] (3) The gravel parameters calculated according to the above formula were compared with the gravel parameters observed by FMI logging images, and the error was less than 10% (see Fig.12 ), which proves that this method is scalable.

[0066] The above detailed description is a specific description of one feasible embodiment of the present invention. The embodiment is not intended to limit the patent scope of the present invention. Any equivalent implementation or modification that does not deviate from the present invention should be included in the scope of the technical solution of the present invention.

Claims

1. A method for characterizing gravel characteristics around a horizontal well in a conglomerate reservoir, characterized in that: The following steps are involved: (1) Using conglomerate reservoir gravel feature recognition and analysis software, the FMI imaging logging images were processed to extract gravel parameters; (2) Using the multivariate linear regression analysis method, we conducted a sensitivity analysis of gravel parameters and logging data to clarify the statistical relationship between gravel parameters and logging curves; (3) Establish a model of well logging curves and gravel parameters, and substitute the well logging curve data to measure the gravel characteristics.

2. The characterization method according to claim 1, characterized in that The processing in step (1) includes grayscale processing, blind area filling and image segmentation.

3. The characterization method according to claim 1, characterized in that The gravel parameters in step (1) are gravel parameters in the axial direction of the wellbore.

4. The characterization method according to claim 1, characterized in that The gravel parameters in step (1) include gravel equivalent radius and gravel linear density.

5. The characterization method according to claim 1, characterized in that: The logging curve in step (2) includes acoustic wave time difference, well diameter, density, natural gamma, invasion zone resistivity, true resistivity, flushing zone resistivity and natural potential.

6. The characterization method according to claim 1, characterized in that The sensitivity analysis process in step (2) includes: setting the acoustic time difference, well diameter, neutron, density, natural gamma, invaded zone resistivity, true resistivity, flushed zone resistivity and natural potential to have no relationship with the gravel equivalent radius and gravel linear density, and presetting the significance level to 0.

1. If a parameter is calculated to have P < 0.1, the original hypothesis is rejected at the significance level of 0.1, that is, the parameter has a linear relationship with the gravel equivalent radius and gravel linear density, and it is called a sensitive factor, otherwise it is called an insensitive factor.

7. The characterization method according to claim 1, characterized in that: The confirmation process of the statistical relationship in step (2) includes: establishing the formula of gravel equivalent radius and gravel linear density.

8. The characterization method according to claim 1, characterized in that: The logging curve in step (3) is correlated with the gravel equivalent radius and the gravel linear density.

9. The characterization method according to any one of claims 1 to 8, characterized in that: The process of establishing the model in step (3) includes: selecting the top three most influential parameters for linear regression analysis based on the results of sensitivity analysis.

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