A complex lithology identification method based on well logging composite parameters

Abstract

The present invention relates to the technical field of well logging lithology identification, and in particular to a complex lithology identification method based on well logging composite parameters. The method comprises: coring the formation through drilling, depth homing the core according to physical property analysis data and reading the logging curve value; constructing a neutron density envelope area model, and amplifying the response of the compensated neutron and compensated density to the reservoir; calculating the envelope area CDEN through the envelope area model; analyzing the characteristics of the logging curve and introducing the particle size index GI; using the particle size index GI as the horizontal coordinate and the neutron density envelope area CDEN as the vertical coordinate, and using the core sample data as a chart; establishing an equation group of two boundary lines, and realizing automatic lithology identification according to the equation group. The complex lithology identification method based on well logging composite parameters provided by the present invention utilizes the characteristics of five conventional logging curves for lithology identification, comprehensively integrates the response differences of the five curves to different lithologies, and effectively improves the accuracy of lithology identification.

Description

Technical Field

[0001] The present invention relates to the technical field of well logging lithology identification, and in particular to a complex lithology identification method based on well logging composite parameters. Background Art

[0002] For sandy conglomerate formations, fine siltstone and conglomerate are deposited alternately. The content and particle size of the gravel will affect the response characteristics of the logging curve. The logging curve is affected by lithology far more than by physical properties and oil and gas properties. Therefore, without distinguishing lithology, it is impossible to effectively separate sandy conglomerate with a high gravel content from fine siltstone with a low gravel content. It will be impossible to establish an interpretation model standard, and the calculated reservoir parameters will not be able to well reflect the reservoir characteristics and meet the requirements of identification accuracy. Summary of the Invention

[0003] (1) Technical issues to be resolved

[0004] The present invention provides a complex lithology identification method based on well logging composite parameters to overcome the problems in the prior art of difficulty in identifying oil and water in sandstone conglomerate reservoirs and low efficiency in lithology identification.

[0005] (2) Technical solution

[0006] In order to solve the above problems, the present invention provides a complex lithology identification method based on well logging composite parameters, comprising:

[0007] Step S1: Drilling and coring the formation, homing the cores to depth based on physical property analysis data, and reading the logging curve values;

[0008] The logging curve values ​​include: natural gamma ray (GR), deep lateral direction (RD), acoustic wave time difference (AC), compensated neutron (CN) and compensated density (DEN), so that the core depth matches the logging depth;

[0009] Step S2: Based on the variation patterns of the compensated neutron CN and compensated density DEN curves in the conglomerate formation, a neutron density envelope area model is constructed, and the responses of the compensated neutron and compensated density to the reservoir are amplified; and the envelope area CDEN is calculated using the envelope area model;

[0010] Specifically include:

[0011] Adjust the maximum and minimum scale values ​​of the neutron density curve to maximize the difference in envelope area between different lithologies. Calculate the envelope area of ​​compensated neutrons and compensated density using the following formula:

[0012]

[0013] Where CDEN is the envelope area of ​​compensated neutron and compensated density, CN is the logging curve value of compensated neutron, and CN maxand CN min are the maximum and minimum scale values ​​of the compensated neutron curve, DEN is the logging curve value of the compensated density, and DEN is the max and DEN min They are the maximum and minimum scale values ​​of the compensation density curve respectively;

[0014] Step S3: Analyze the characteristics of the well logging curve and introduce the granularity index GI, which specifically includes:

[0015] Analyzing the characteristics of the logging curve, as the particle size changes from coarse to fine, the resistivity gradually decreases, the natural gamma gradually increases, and the acoustic time difference gradually increases. The particle size index GI is introduced, and the formula is as follows:

[0016]

[0017] Where GI is the grain size index, RD is the deep lateral resistivity, GR is the natural gamma, and AC is the acoustic transit time;

[0018] Step S4: Using the particle size index GI as the horizontal coordinate and the neutron density envelope area CDEN as the vertical coordinate, the core sample data is used to make a chart;

[0019] Step S5: Establish an equation group for the two boundary lines in the drawing plate of step S4, and realize automatic lithology identification based on the equation group.

[0020] Preferably, step S4 includes:

[0021] A scatter plot was established with GI as the horizontal coordinate and CDEN as the vertical coordinate. All the core sample data were projected into the coordinate system according to lithology. From the upper left to the lower right corner, the lithology was divided into three categories according to the grain size, namely mudstone, fine siltstone and conglomerate.

[0022] Preferably, step S5 specifically includes:

[0023] The equations for the two boundary lines in the figure include: y1=4.66x1+12; y2=4.66x2-25.

[0024] (3) Beneficial effects

[0025] The complex lithology identification method based on composite logging parameters provided by the present invention utilizes the characteristics of five conventional logging curves, namely natural gamma, acoustic wave time difference, resistivity, compensated neutron and compensated density, for lithology identification. By integrating the response differences of the five curves to different lithologies, the accuracy of lithology identification is effectively improved compared with the existing technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 Schematic diagram of the process of a complex lithology identification method based on composite logging parameters according to an embodiment of the present invention;

[0027] Figure 2 This is a schematic diagram of a lithology identification model according to an embodiment of the present invention;

[0028] Figure 3 This is a comparison chart of the core lithology and identified lithology of Well X. DETAILED DESCRIPTION

[0029] The following is a further description of the method for identifying lithology of sandstone and conglomerate formations described in the present invention in conjunction with specific embodiments, in order to help professionals in this field have a more complete, accurate and in-depth understanding of the inventive concept and technical solution of the present invention; it should be stated that the descriptions in the specific embodiments are exemplary and do not mean to limit the scope of protection of the present invention. The scope of rights of the present invention shall be subject to the defined claims. The method described in this patent is used to identify the lithology of the Moriqingyi 59 block, such as Figure 1 As shown, the complex lithology identification method based on well logging composite parameters includes:

[0030] Step S1: Core sampling from several key wells in the block is performed, and the logging curve values ​​at the corresponding depths are read. The logging curve values ​​include: natural gamma ray (GR), deep lateral direction (RD), acoustic wave travel time (AC), compensated neutron (CN), and compensated density (DEN), so that the core depth matches the logging depth.

[0031] In this step, the core porosity and permeability analysis data are used to locate the core, and the logging curve values ​​and lithology after the location are read.

[0032] Step S2: Based on the variation patterns of the compensated neutron CN and compensated density DEN curves in the conglomerate formation, a neutron density envelope area model is constructed, and the responses of the compensated neutron and compensated density to the reservoir are amplified; and the envelope area CDEN is calculated using the envelope area model;

[0033] The maximum and minimum scale values ​​of the neutron density curve were adjusted to maximize the difference in the envelope area of ​​different lithologies, and the dimensions were normalized. The scale range of CN was determined to be 0-50, and the scale range of DEN was determined to be 1.0-2.65 g / cm 3 , the formula for calculating CDEN is as follows:

[0034]

[0035] Where: CDEN is the envelope area of ​​compensated neutrons and compensated density, and CN is the logging curve value of compensated neutrons.

[0036] Step S3: Analyze the logging curve characteristics, introduce the granularity index GI, and use the read GR, RD, and AC curve values ​​to calculate the granularity index GI. The formula is as follows:

[0037]

[0038] Where GI is the grain size index, RD is the deep lateral resistivity, GR is the natural gamma ray, and AC is the acoustic transit time.

[0039] Step S4: Using the particle size index GI as the horizontal coordinate and the neutron density envelope area CDEN as the vertical coordinate, the core sample data is used to make a chart;

[0040] Figure 2 is the distribution diagram of different lithologies in the coordinate system. Figure 2 As can be seen from the upper left to the lower right corner, the rock types are divided into three categories according to the particle size, namely mudstone, fine siltstone and conglomerate.

[0041] Step S5: Establish an equation group for the two boundary lines in the drawing plate of step S4, and realize automatic lithology identification based on the equation group.

[0042] The equations for the two boundary lines in the figure are: y1 = 4.66x1 + 12; y2 = 4.66x2 - 25. Here, y1 represents the linear boundary between mudstone and fine siltstone, and y2 represents the linear boundary between fine siltstone and conglomerate.

[0043] According to this set of equations, the lithology can be automatically identified, thus maximizing the accuracy of lithology identification.

[0044] The present invention utilizes the characteristics of five conventional logging curves, namely natural gamma, acoustic wave transit time, resistivity, compensated neutron and compensated density, to identify lithology. By integrating the changing characteristics of the five conventional logging curves and amplifying the corresponding differences in the logging curves caused by changes in lithologic particle size, the present invention can effectively identify conglomerate and ordinary sandstone. Figure 3 This is a comparison chart of the lithology identified by the present invention and the lithology of the core sample, which effectively improves the accuracy of lithology identification compared with the existing technology.

[0045] The present invention establishes a composite parameter lithology identification chart based on the logging curve, which can better separate sandstone with a higher gravel content from fine siltstone with a lower gravel content, and then establishes physical property and oil and gas content models based on lithology, thereby improving the evaluation of reservoir effectiveness.

[0046] The above embodiments are only used to illustrate the present invention, and are not intended to limit the present invention. Ordinary technicians in the relevant technical field may make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, all equivalent technical solutions also fall within the scope of the present invention. The scope of patent protection of the present invention should be defined by the claims.

Claims

1. A complex lithology identification method based on well logging composite parameters, characterized in that: include: Step S1: Drilling and coring the formation, homing the cores to depth based on physical property analysis data, and reading the logging curve values; The logging curve values ​​include: natural gamma ray (GR), deep lateral direction (RD), acoustic wave time difference (AC), compensated neutron (CN) and compensated density (DEN), so that the core depth matches the logging depth; Step S2: Based on the variation patterns of the compensated neutron CN and compensated density DEN curves in the conglomerate formation, a neutron density envelope area model is constructed, and the responses of the compensated neutron and compensated density to the reservoir are amplified; and the envelope area CDEN is calculated using the envelope area model; Specifically, it includes: adjusting the maximum and minimum scale values ​​of the neutron density curve to maximize the difference in the envelope area of ​​different lithologies, and calculating the envelope area of ​​compensated neutrons and compensated density. The formula is as follows: ； Where CDEN is the envelope area of ​​compensated neutron and compensated density, CN is the logging curve value of compensated neutron, and CN max and CN min are the maximum and minimum scale values ​​of the compensated neutron curve, DEN is the logging curve value of the compensated density, and DEN is the max and DEN min They are the maximum and minimum scale values ​​of the compensation density curve respectively; Step S3: Analyze the characteristics of the logging curve and introduce the granularity index GI, which specifically includes: analyzing the characteristics of the logging curve, as the granularity changes from coarse to fine, the resistivity gradually decreases, the natural gamma gradually increases, and the acoustic time difference gradually increases, the granularity index GI is introduced, The formula is as follows: ； Where GI is the grain size index, RD is the deep lateral resistivity, GR is the natural gamma, and AC is the acoustic transit time; Step S4: Using the particle size index GI as the horizontal coordinate and the neutron density envelope area CDEN as the vertical coordinate, the core sample data is used to make a chart; Step S5: Establish an equation group for the two boundary lines in the drawing plate of step S4, and realize automatic lithology identification based on the equation group.

2. The complex lithology identification method based on well logging composite parameters according to claim 1, characterized in that: Step S4 includes: A scatter plot was established with GI as the horizontal coordinate and CDEN as the vertical coordinate. All the core sample data were projected into the coordinate system according to lithology. From the upper left to the lower right corner, the lithology was divided into three categories according to the grain size, namely mudstone, fine siltstone and conglomerate.

3. The complex lithology identification method based on well logging composite parameters according to claim 2, characterized in that: Step S5 specifically includes: The equations for the two boundary lines in the figure include: y1=4.66x1+12; y2=4.66x2-25, where y1 represents the linear boundary line between mudstone and fine siltstone; y2 represents the linear boundary line between fine siltstone and conglomerate.

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