A method for characterizing the characteristics of gravel around a horizontal well in a conglomerate reservoir

Abstract

The present application belongs to the field of oil and gas field development, and particularly relates to a method for characterizing the characteristics of gravel around a horizontal well in a conglomerate reservoir. The method comprises the following steps: (1) using a conglomerate reservoir gravel characteristic identification and analysis software to process FMI imaging logging images and extract gravel parameters; (2) using a multiple linear regression analysis method to carry out sensitivity analysis of the gravel parameters and logging data, and to determine the statistical relationship between the gravel parameters and logging curves; and (3) establishing a model of the logging curves and the gravel parameters, and substituting the logging curve data to measure the gravel characteristics. The present application solves the problem of characterizing conglomerate reservoir gravel, and for the first time realizes the use of conventional logging curves to characterize the gravel parameters around a horizontal well section, thereby providing an optimization direction for multi-cluster volume fracturing technology in a horizontal well section.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of oil and gas field development, and particularly relates to a method for characterizing the characteristics of gravels around a horizontal well in a conglomerate reservoir. BACKGROUND

[0002] A conglomerate reservoir is a special kind of clastic rock oil and gas reservoir. Compared with a sandstone reservoir, due to near provenance, multiple water systems and rapidly changing sedimentary environments, the micro-pore structure of the reservoir presents a complex modal feature, the pore throat distribution is extremely uneven, the average throat radius is small, the pore throat ratio is large, the pore throat coordination number is low, and the overall presents a multi-peak fine state feature. The special pore structure leads to more complex micro-pore oil production rules under different displacement modes.

[0003] In the sandstone and conglomerate reservoirs of the conglomerate reservoir in Mahu Sag, the gravels are unevenly distributed in size and content, the lithology varies greatly, and the stress distribution is complex, which leads to the following problems in the multi-cluster volume fracturing reconstruction in the horizontal well section: the balanced initiation of each perforation cluster in the section cannot be effectively achieved, the liquid sand volume of each perforation cluster in the same reconstruction section is greatly different, the liquid supply of each cluster in the section after pressure is greatly different, and the like, which seriously affects the reservoir producing degree. Therefore, it is of great significance to develop the conglomerate reservoir gravel feature identification technology for guiding the segmentation and clustering of the conglomerate reservoir and achieving the balanced initiation of each cluster in the section.

[0004] The key technologies for developing the sandstone and conglomerate reservoirs include: (1) Seismic imaging technology. In the seismic imaging of complex target geological bodies, foreign theoretical method research started early, and a large amount of research work has been done on complex geological bodies such as salt domes and nappe structures. For example, the research on the internationally recognized Marmousi, SEG / EAGA and the like. In specific application, through the popularization of pre-stack migration imaging technology, the imaging quality of complex geological bodies such as salt domes in the North American Gulf of Mexico and the like has been obviously improved.

[0005] (2) Imaging logging technology. Imaging logging technology refers to the measurement of a large amount of physical information of a formation in the longitudinal, circumferential and radial directions by an array sensor of a downhole instrument along the well wall or rotating scanning measurement, and the two-dimensional image of the well wall or the three-dimensional image of a certain detection depth around the well hole is obtained through image processing after the information is transmitted to the ground collection system by a cable. The commonly used one is FMI (Micro-resistivity Scanning Imaging) electrical imaging logging.

[0006] At present, the research on the gravel content and distribution of the conglomerate reservoir at home and abroad is all based on FMI imaging logging images or core images, and some literatures introduce the use of seismic methods to predict the gravel distribution, but the FMI imaging logging images or core data and the like are limited and have high cost, which seriously restricts the identification of the gravel features of the conglomerate reservoir.

[0007] In order to obtain the gravel characteristics of each single well along the horizontal well section, the method tries to find the correlation between the conventional logging data and the gravel characteristics, establishes a gravel reservoir gravel characteristic characterization method based on the conventional logging, realizes the "one well one strategy", provides guidance for the gravel reservoir segmentation and clustering, optimizes the fracturing technology and pump injection program, and realizes the balanced fracturing of each cluster in the horizontal well volume fracturing section. SUMMARY

[0008] In view of the deficiencies in the prior art, the present application provides a gravel reservoir horizontal well gravel characteristic characterization method. The method processes the logging image, converts the electrical imaging image into continuous gravel information, and then extracts the gravel parameters to quantitatively characterize the gravel parameters. The sensitivity analysis is combined with the logging image interpretation data to establish a model of the logging curve and the gravel equivalent radius and the gravel line density per unit length. The gravel equivalent radius and the gravel line density per unit length are used as the gravel reservoir gravel characteristic markers to realize the purpose of effectively characterizing the gravel characteristics of the gravel reservoir by using the conventional logging curve.

[0009] In order to achieve the above-mentioned purpose of the present application, the specific technical scheme adopted by the present application is as follows:

[0010] A gravel reservoir horizontal well gravel characteristic characterization method, comprising the following steps:

[0011] (1) using a gravel reservoir gravel characteristic identification and analysis software to process the FMI imaging logging image and extract gravel parameters;

[0012] (2) using a multivariate linear regression analysis method to carry out sensitivity analysis of the gravel parameters and the logging data, and to determine the statistical relationship between the gravel parameters and the logging curve; (logging data is imported into the system to generate conventional logging curves);

[0013] (3) establishing a model of the logging curve and the gravel parameters, and substituting the logging curve data to measure the gravel characteristics.

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

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

[0016] Preferably, the conventional logging curve in step (2) includes AC (acoustic time difference), CALI (caliper), CNL (neutron), DEN (density), GR (natural gamma), RI (invasion zone resistivity), RT (true resistivity), RXO (flush zone resistivity) and SP (natural potential).

[0017] The present application adopts the multiple regression analysis method in the sensitivity analysis. The change of the dependent variable is affected by several important factors, namely the gravel equivalent radius and the gravel line density are affected by multiple logging curve data, at this time, two or more than two influencing factors are needed as independent variables to explain the change of the dependent variable, which is the multiple regression, also called multiple regression. The present application adopts the multiple regression analysis method to establish the relationship between the conventional logging curve and the gravel equivalent radius and the gravel line density.

[0018] Preferably, the process of the sensitivity analysis in step (2) comprises: setting AC (acoustic time difference), CALI (caliper), CNL (neutron), DEN (density), GR (natural gamma), RI (invasion zone resistivity), RT (true resistivity), RXO (flush zone resistivity) and SP (natural potential) as having no relationship with the gravel equivalent radius and the gravel line density, presetting the significance level as 0.1, if a parameter calculates P < 0.1, the original hypothesis is rejected under the 0.1 significance level, that is, the parameter has a linear relationship with the gravel equivalent radius and the gravel line density, then it is called a sensitive factor, otherwise it is called a non-sensitive factor.

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

[0020] Preferably, the correlation relationship between the logging curve and the gravel equivalent radius and the gravel line density in step (3).

[0021] Preferably, the process of establishing the model in step (3) comprises: selecting the top three parameters with the greatest impact for linear regression analysis according to the sensitivity analysis results.

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

[0023] The method and operation process formed by the present application realize the conversion from the qualitative characterization of the gravel parameters in the FMI logging image to the quantitative characterization of the gravel parameters, and based on the FMI logging image interpretation results, the relationship between the logging data and the gravel parameters is constructed, the gravel equivalent radius and the gravel line density per unit length are taken as the characteristic marks of the glutenite reservoir, the gravel characteristics of the glutenite reservoir are effectively characterized, the gravel characterization method based on the conventional logging is formed, the gravel characterization problem of the glutenite reservoir is solved, and the horizontal section gravel parameters around the well are characterized by using the conventional logging curve for the first time, which provides an optimization direction for the multi-cluster volume fracturing technology in the horizontal section. BRIEF DESCRIPTION OF DRAWINGS

[0024] Figure 1 is the flow chart of the gravel characterization method of the horizontal well gravel around the glutenite reservoir of the present application;

[0025] Figure 2This is a processed image of the FMI imaging logging from Example 1;

[0026] Figure 3 This is the automatically extracted gravel radius and linear density map from Example 1;

[0027] Figure 4 This is an image showing the FMI gravel identification results from Example 1;

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

[0029] Figure 6 This is the logging curve and gravel linear density P-value diagram from Example 1;

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

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

[0032] Figure 9 This is a gravel parameter extraction image from a CT image in Example 2;

[0033] Figure 10 This is the P-value plot of the logging curve and gravel equivalent radius in Example 2;

[0034] Figure 11 This is the logging curve and gravel linear density P-value diagram from Example 2;

[0035] Figure 12 This is a comparison chart of FMI imaging logging and predicted gravel parameters in Example 2. Detailed Implementation

[0036] The technical solutions in the embodiments of the present invention will be further described in detail below. The described embodiments are only a part of the present invention and are used to explain the present invention, but are not intended to limit the present invention. Therefore, other embodiments obtained by other people skilled in the art without creative labor are all within the protection scope of the present invention.

[0037] Establish a quantitative calculation model for axial gravel characteristics in wellbore

[0038] The number of gravels per unit length is defined as the gravel linear density, and the formula for calculating the gravel linear density is as follows:

[0039]

[0040] The area of ​​an irregular gravel is equivalent to that of a regular circle, and the size of the gravel is defined by its equivalent radius. The equivalent radius of the gravel is calculated as follows:

[0041]

[0042] Example 1

[0043] Taking a field experiment as an example

[0044] (1) Gravel parameter extraction

[0045] The gravel parameters of the FMI imaging logging image of a single well are extracted by using the gravel reservoir gravel feature identification analysis software (see Figure 2 , Figure 3 ), and the FMI gravel identification result is shown in Figure 4 .

[0046] (2) Model establishment

[0047] The original hypothesis H0 is that the logging curve data and the gravel equivalent radius and the gravel line density have no relationship, and the relationship is completely generated by sampling, and the preset significance level is 0.1. If P < 0.1, the original hypothesis is rejected at the 0.1 significance level, that is, the logging curve and the gravel equivalent radius and the gravel line density have a correlation relationship.

[0048] Taking the Excel table as a tool, the P-value value of the FMI gravel identification equivalent radius, the gravel line density and the logging curve data is obtained by using the “data analysis” module, as shown in Figure 5 , Figure 6 .

[0049] Among them, P < 0.1 means that the logging curve and the gravel equivalent radius and the gravel line density have a correlation relationship, the coefficient of the independent variable in the regression equation is Coefficients, and the intercept is Intercept, so the model of the logging curve and the gravel equivalent radius and the line density is:

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

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

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

[0053] (4) Model verification

[0054] The FMI imaging logging gravel identification result of the same well in Example 1 is used to verify the model accuracy, and the gravel parameters are calculated according to the above formula and compared with the gravel parameters observed by the FMI logging image. As shown in Figure 7 , Figure 8The error is less than 15% (see Table 1) after averaging all measured data, which meets the engineering accuracy requirement.

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

[0056]

[0057]

[0058] Example 2

[0059] Core CT image verification

[0060] (1) Based on the conventional logging sand gravel reservoir feature representation method formed in Example 1, combined with the core CT image (see Figure 9 ), the multiple linear regression method is used to determine that the gravel equivalent radius has a greater correlation with AC, GR and RT in the logging curve (see Figure 10 ), and the gravel line density has a greater correlation with CNL, DEN and RT in the logging curve (see Figure 11 ).

[0061] (2) The coefficients of the independent variables in the regression equation are Coefficients, and the intercept is Intercept, so the model of the logging curve and the gravel equivalent radius and line density is:

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

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

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

[0065] (3) According to the above formula, the gravel parameters are calculated, and compared with the gravel parameters observed by FMI logging image, the error is less than 10% (see Figure 12 ), which proves that the method has generalizability.

[0066] The above detailed description is a specific description of one of the feasible embodiments of the present application, and this embodiment is not used to limit the patent scope of the present application. Any equivalent implementation or change without departing from the present application shall be included in the scope of the technical solutions of the present application.

Claims

1. A method of characterizing the characteristics of the formation surrounding a horizontal well in a conglomerate reservoir, characterized in that, The method comprises the following steps: (1) using the conglomerate reservoir conglomerate feature recognition analysis software to process the FMI imaging logging image, and extracting the conglomerate parameters; the conglomerate parameters are the conglomerate parameters in the axial direction of the wellbore, and the conglomerate parameters include the conglomerate equivalent radius and the conglomerate linear density; wherein the conglomerate linear density is defined as the number of conglomerates per unit length; (2) using the multiple linear regression analysis method to carry out the sensitivity analysis of the conglomerate parameters and the logging data, and determining the statistical relationship between the conglomerate parameters and the logging curves; the confirmation process of the statistical relationship includes: the establishment of the conglomerate equivalent radius and the conglomerate linear density formula; (3) establishing the model of the logging curves and the conglomerate parameters, and substituting the logging curve data to measure the conglomerate features.

2. The characterization method of claim 1, wherein, The processing in step (1) includes gray processing, blind area filling and image segmentation.

3. The characterization method of claim 1, wherein, The logging curves in step (2) include acoustic time difference, caliper, density, natural gamma, invaded zone resistivity, true resistivity, flushing zone resistivity and natural potential.

4. The characterization method of claim 1, wherein, The process of the sensitivity analysis in step (2) includes: taking the acoustic time difference, the caliper, the neutron, the density, the natural gamma, the invaded zone resistivity, the true resistivity, the flushing zone resistivity and the natural potential as having no relationship with the conglomerate equivalent radius and the conglomerate linear density, presetting the significance level as 0.1, if P<0.1 is calculated for a parameter, then the original hypothesis is rejected under the 0.1 significance level, that is, the parameter has a linear relationship with the conglomerate equivalent radius and the conglomerate linear density, and then it is called a sensitive factor, otherwise it is called a non-sensitive factor; Wherein, P is the P-value of the FMI conglomerate recognition equivalent radius, the conglomerate linear density and the logging curve data obtained by using the "data analysis" module of the Excel table.

5. The characterization method of claim 1, wherein, The logging curves and the conglomerate equivalent radius and the conglomerate linear density in step (3) have a correlation relationship.

6. The characterization method according to any one of claims 1 to 5, characterized in that, The process of establishing the model in step (3) includes: according to the sensitivity analysis result, selecting the first three parameters with the greatest influence to carry out linear regression analysis.

Images