Tight sandstone lithofacies identification method and device, electronic equipment and storage medium

Abstract

The invention relates to a tight sandstone lithofacies identification method and device, electronic equipment and a storage medium, and belongs to the technical field of geological prospecting, and the tight sandstone lithofacies identification method comprises the steps: carrying out the identification of a to-be-detected tight sandstone lithofacies based on a pre-established cross plot, and obtaining a first identification result, the first identification result comprises fine sandstone, mudstone sandstone, siltstone and mudstone; based on an electric imaging technology, further dividing an overlapping region of siltstone and mudstone in the first identification result to obtain a second identification result; on the basis of an electric imaging technology, fine sandstones in the first recognition result are further divided, third recognition results are obtained, and the third recognition results comprise low-resistance bulk fine sandstones, parallel / inclined layered fine sandstones, high-resistance bulk fine sandstones and stratified fine sandstones; and based on the first recognition result, the second recognition result and the third recognition result, obtaining a lithofacies recognition result of the to-be-detected compact sandstone. According to the invention, the accuracy of tight sandstone lithofacies identification can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of geological exploration, and in particular to a method and device for identifying lithofacies of tight sandstone, an electronic device and a storage medium. BACKGROUND

[0002] Lithofacies records rich geological information such as sedimentary environment, diagenetic history and tectonic evolution of rock formation, so lithofacies identification is crucial for finding high-quality reservoirs and favorable exploration areas. The most intuitive method for lithofacies identification is through core observation and thin section identification, and the identification result depends on the experience of experts. In addition, coring wells are costly and the number of coring wells is limited, so it is difficult to sample every well and implement lithofacies identification for the whole well section. Logging data can provide high-resolution stratigraphic continuous information, and lithofacies identification using logging information has been widely applied.

[0003] At present, the method for identifying lithofacies mainly establishes a lithofacies identification chart or model through crossplot technology or machine learning according to the logging response characteristics of lithofacies. The traditional crossplot technology generally follows the idea of "core calibration logging". First, the lithofacies of the coring well section is manually divided through geological means such as core observation and thin section identification, and then the "rock-electricity" comparison analysis is performed with logging data to clearly reflect the logging response characteristics of various lithofacies. Then, the logging parameters are extracted to establish a logging lithofacies identification chart and standard, and to realize the qualitative and quantitative analysis of the logging response characteristics of the lithofacies in the block. For simple lithology strata, the conventional logging characteristics of lithology and lithofacies are obvious, and good application results can be achieved by using the traditional chart method. However, for complex lithology strata such as carbonate rocks and channel sandstones, it is difficult to have high identification accuracy by using the lithofacies chart established according to conventional logging. Machine learning is a data-driven technology that learns rules and patterns from historical data through model training to establish a complex function mapping relationship between input data and corresponding labels for intelligent prediction and decision-making. There is a contradiction between the large amount of labeled data required by the machine learning model theory and the small sample size in the production site, making it difficult to establish a model with strong generalization ability. Therefore, there is an urgent need for a new lithofacies identification method to solve the problem of accurate identification of complex lithofacies such as tight sandstone. SUMMARY

[0004] Therefore, it is necessary to provide a method and device for identifying lithofacies of tight sandstone to solve the problem of low accuracy in identifying complex lithofacies such as tight sandstone by using crossplot technology or machine learning in the prior art.

[0005] To solve the above problems, the present application provides a method for identifying lithofacies of tight sandstone, comprising: Based on the pre-established crossplot, the lithofacies of the tight sandstone to be measured is identified to obtain a first identification result, and the first identification result includes fine sandstone, gravel sandstone, siltstone and mudstone. Further divide the overlapping area of siltstone and mudstone in the first identification result based on the electrical imaging technology to obtain a second identification result; Further divide the fine sandstone in the first identification result based on the electrical imaging technology to obtain a third identification result, and the third identification result includes low-resistance massive fine sandstone, parallel / inclined layered fine sandstone, high-resistance massive fine sandstone, and laminated fine sandstone; Obtain the lithofacies identification result of the to-be-tested tight sandstone based on the first identification result, the second identification result, and the third identification result.

[0006] In a possible implementation, the further division of the overlapping area of siltstone and mudstone in the first identification result based on the electrical imaging technology to obtain a second identification result includes: Obtain a plurality of electrical conductivity curves corresponding to the overlapping area based on the electrical imaging logging instrument; Establish an electrical conductivity frequency distribution histogram corresponding to each depth in the overlapping area for the plurality of electrical conductivity curves, and determine the content proportion of sandstone and mudstone at each depth respectively according to a preset electrical conductivity threshold; Further divide the overlapping area according to the content proportion to obtain the second identification result.

[0007] In a possible implementation, the further division of the overlapping area according to the content proportion to obtain the second identification result includes: For the content proportion of sandstone and mudstone at any target depth in the overlapping area: If the content proportion of sandstone at the target depth in the overlapping area is greater than or equal to the content proportion of mudstone, it is determined that the target depth is siltstone; Otherwise, it is determined that the target depth is mudstone; Obtain the second identification result according to the division result of siltstone and mudstone at each depth in the overlapping area.

[0008] In a possible implementation, the further division of the fine sandstone in the first identification result based on the electrical imaging technology to obtain a third identification result includes: Determine electrical conductivity matrix data of the area corresponding to the fine sandstone based on the electrical imaging logging instrument; Determine a preliminary projection image corresponding to a matrix array restructured according to the depth displacement after projection of each data point in the electrical conductivity matrix data; Obtain a curve corresponding to the relative bedding density and average electrical conductivity respectively according to the preliminary projection image; According to the curves corresponding to the relative bedded density and the average conductivity respectively, the fine sandstone is further divided to obtain the third identification result.

[0009] In a possible implementation, the obtaining of the curves corresponding to the relative bedded density and the average conductivity respectively according to the preliminary projection image includes: The preliminary projection image is cut along an axis, and one side image is flipped to obtain two half-week projection images; The target projection image is obtained according to the two half-week projection images; The plurality of conductivity data corresponding to the target projection image is arithmetically averaged to obtain the curve corresponding to the average conductivity; The curve corresponding to the relative bedded density is obtained according to the curve corresponding to the average conductivity.

[0010] In a possible implementation, the obtaining of the crossplot includes: Based on core grain size analysis and lithology description materials, logging response characteristic parameters of different lithofacies including fine sandstone, mudstone, siltstone and mudstone in the sandstone are obtained; Based on the logging response characteristic parameters, the crossplot with the relative value of the deuranium gamma as a first coordinate axis and the difference value of the neutron density apparent porosity as a second coordinate axis in the same coordinate system is determined; Correspondingly, the identification of the lithofacies of the to-be-tested tight sandstone based on the pre-established crossplot to obtain a first identification result includes: Curves corresponding to the deuranium gamma, the neutron logging response value and the density logging response value of the to-be-tested tight sandstone are obtained; Based on the curves and the crossplot, the lithofacies of the to-be-tested tight sandstone is identified to obtain the first identification result.

[0011] The application further provides a tight sandstone lithofacies identification device, which includes: A first identification module is configured to identify the lithofacies of a to-be-tested tight sandstone based on a pre-established crossplot to obtain a first identification result, and the first identification result includes fine sandstone, mudstone, siltstone and mudstone; A second identification module is configured to further divide an overlapping area of the siltstone and the mudstone in the first identification result based on an electrical imaging technology to obtain a second identification result; A third identification module is configured to further divide the fine sandstone in the first identification result based on the electrical imaging technology to obtain a third identification result, and the third identification result includes low-resistance massive fine sandstone, parallel / inclined layered fine sandstone, high-resistance massive fine sandstone and laminated fine sandstone; A fourth identification module is configured to obtain a lithofacies identification result of the tight sandstone to be measured based on the first identification result, the second identification result and the third identification result.

[0012] In a possible implementation, the first identification module is further configured to: based on core particle size analysis and lithology description materials, analyze logging response characteristic parameters of different lithofacies including fine sandstone, argillaceous sandstone, siltstone and mudstone in the sandstone; based on the logging response characteristic parameters, determine the crossplot with the relative value of the deuranium gamma ray as a first coordinate axis and the difference value of the neutron density apparent porosity as a second coordinate axis in the same coordinate system; obtain curves corresponding to the deuranium gamma ray, the neutron logging response value and the density logging response value of the tight sandstone to be measured; based on the curves and the crossplot, identify the lithofacies of the tight sandstone to be measured to obtain the first identification result.

[0013] The application further provides an electronic device including a memory and a processor. The memory is configured to store a program. The processor is coupled to the memory and is configured to execute the program stored in the memory to implement the steps of the tight sandstone lithofacies identification method in any of the above implementation modes.

[0014] The application further provides a computer readable storage medium configured to store a computer readable program or instruction, which can implement the steps of the tight sandstone lithofacies identification method in any of the above implementation modes when executed by a processor.

[0015] The application provides a tight sandstone lithofacies identification method, which first uses a crossplot for identifying sandstone lithofacies to preliminarily identify the lithofacies of the tight sandstone to be measured to obtain a first identification result. Secondly, in view of the situation that (argillaceous) siltstone and (silty) mudstone in the first identification result have an overlapping area in a part of the crossplot, which leads to low identification accuracy, (argillaceous) siltstone and (silty) mudstone are accurately identified through an electrical imaging sandstone and mudstone rapid identification technology. Finally, in view of the large difference in the rock structure of fine sandstone, the rock structure is divided into low-resistance lump-shaped, high-resistance lump-shaped, horizontal / inclined layered and laminated through the electrical imaging technology. Through the combination of the three technologies, the lithofacies of the tight sandstone to be measured can be divided into seven kinds of lithofacies, i.e., high-resistance lump-shaped fine sandstone, low-resistance lump-shaped fine sandstone, parallel / inclined layered fine sandstone, laminated fine sandstone, argillaceous sandstone, siltstone and mudstone, thereby improving the accuracy of the identification of the lithofacies of the tight sandstone to be measured. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 A method flow chart of an embodiment of the tight sandstone lithofacies identification method provided by the present application; Figure 2 A crossplot diagram for lithology identification provided by the present application; Figure 3 A schematic diagram of the sandstone and mudstone rapid identification technology based on electrical imaging provided by the present application; Figure 4 A schematic diagram of the principle of obtaining relative bedded density by electrical imaging slices provided by the present application; Figure 5 A schematic diagram of the principle of calculating relative bedded density provided by the present application; Figure 6 A schematic diagram of the rock structure phase classification technology based on electrical imaging wellbore section projection provided by the present application; Figure 7 A schematic diagram of the river sandstone lithology identification result by crossplot provided by the present application; Figure 8 A schematic diagram of the siltstone and mudstone identification result by using conventional and electrical imaging lithology identification provided by the present application; Figure 9 A schematic diagram of the river sandstone lithofacies identification effect provided by the present application; Figure 10 A structural schematic diagram of an embodiment of the tight sandstone lithofacies identification device provided by the present application; Figure 11 A structural schematic diagram of an embodiment of the electronic device provided by the present application. DETAILED DESCRIPTION

[0017] The preferred embodiments of the present application will be described in detail below with reference to the accompanying drawings, which form a part of this application. The accompanying drawings and the embodiments together illustrate the principles of the present application, but are not intended to limit the scope of the present application.

[0018] In the related art, the lithology of tight sandstone (such as river sandstone) is complex, and the rock structure is diverse. The conventional logging response characteristics of the same lithofacies may have large differences, and different lithofacies may exhibit similar response characteristics. The traditional crossplot technology or machine learning technology is limited by the above reasons, and the tight sandstone lithofacies identification effect is poor. Electrical imaging logging has great advantages in thin interbedded sandstone and mudstone and lithology grain size similar lithology identification due to its high resolution. At the same time, electrical imaging logging can directly reflect the characteristics of the formation rock structure, so the comprehensive use of the multi-modal dense data composed of “grain size analysis + core description + conventional logging + electrical imaging logging” can theoretically better divide the lithofacies.

[0019] The present application takes the channel sandstone of Lianggaoshan Formation in Sichuan Basin as the research object, and first analyzes the different logging response characteristics of (medium) fine sandstone, mud gravel sandstone, (argillaceous) siltstone and (silt) mudstone through core lithology description and lithology grain size analysis data, and establishes a lithology identification crossplot of the relative value of deuterium gamma (KTH) and the difference value of neutron density apparent porosity. Secondly, in view of the overlapping of (argillaceous) siltstone and (silt) mudstone in the partial interval of the crossplot, which leads to low identification accuracy, the (argillaceous) siltstone and (silt) mudstone are accurately identified through the electrical imaging sand-mudstone rapid identification technology. Finally, in view of the great difference of fine sandstone rock structure, the rock structure is divided into low-resistance lump, high-resistance lump, horizontal / inclined layer and laminated layer through the electrical imaging technology (specifically, the rock structure phase classification technology of electrical imaging wellbore section projection). Through the combination of the three technologies, the channel sandstone lithofacies of the target block can be divided into seven kinds of lithofacies, including high-resistance lump fine sandstone, low-resistance lump fine sandstone, parallel / inclined layer fine sandstone, laminated fine sandstone, mud gravel sandstone, siltstone and mudstone, and the specific implementation is as follows.

[0020] As shown in Figure 1 , one specific embodiment of the present application discloses a method for identifying the lithofacies of tight sandstone, which comprises steps S101, S102, S103 and S104.

[0021] Step S101 identifies the lithofacies of the measured tight sandstone based on the pre-established crossplot, and obtains a first identification result, which includes fine sandstone, mud gravel sandstone, siltstone and mudstone. Step S102 further divides the overlapping area of siltstone and mudstone in the first identification result based on the electrical imaging technology, and obtains a second identification result. Step S103 further divides the fine sandstone in the first identification result based on the electrical imaging technology, and obtains a third identification result, which includes low-resistance lump fine sandstone, parallel / inclined layer fine sandstone, high-resistance lump fine sandstone and laminated fine sandstone. Step S104 obtains the lithofacies identification result of the measured tight sandstone based on the first identification result, the second identification result and the third identification result.

[0022] In implementation, the measured tight sandstone can be channel sandstone, carbonate rock, etc., and in the present application, the measured tight sandstone is channel sandstone.

[0023] The cross plot technology is used to pre-establish a cross plot for identifying sandstone lithofacies, and the lithofacies of the to-be-detected tight sandstone is preliminarily identified to obtain a preliminary identification result of the to-be-detected tight sandstone lithofacies, that is, a first identification result. The lithofacies refers to rocks or rock combinations formed in a certain sedimentary environment, and the main body of the description is the lithological characteristics of the sediment, including particle size, structure, composition, color, etc.

[0024] According to the first identification result, the to-be-detected tight sandstone can be preliminarily divided into four lithofacies, that is, (medium) fine sandstone, mud-gravel sandstone, (argillaceous) siltstone and (siltstone) mudstone.

[0025] In a possible implementation manner, the pre-established cross plot can be obtained in the following manner: Based on core granularity analysis and lithology description materials, logging response characteristic parameters of different lithofacies including fine sandstone, mud-gravel sandstone, siltstone and mudstone in the sandstone are obtained; Based on the logging response characteristic parameters, the cross plot with the relative value of the deuranium gamma as the first coordinate axis and the difference value of the neutron density apparent porosity as the second coordinate axis in the same coordinate system is determined; Correspondingly, step S101 can include: Obtaining curves corresponding to the deuranium gamma, neutron logging response value and density logging response value of the to-be-detected tight sandstone; Based on the curves and the cross plot, the lithofacies of the to-be-detected tight sandstone is identified to obtain the first identification result.

[0026] In implementation, based on core granularity analysis and lithology description materials, considering the particle size and composition of the rock, the rock can be preliminarily divided into four lithofacies, that is, (medium) fine sandstone, mud-gravel sandstone, (argillaceous) siltstone and (siltstone) mudstone. By analyzing the logging response characteristics of different lithofacies including (medium) fine sandstone, mud-gravel sandstone, (argillaceous) siltstone and (siltstone) mudstone, two logging response characteristic parameters, that is, the relative value of the deuranium gamma and the difference value of the neutron density apparent porosity, are selected as the logging response values of the depth section corresponding to the lithology description, and a cross plot with the relative value of the deuranium gamma as the first coordinate axis and the difference value of the neutron density apparent porosity as the second coordinate axis in the same coordinate system (for example, a Cartesian coordinate system) is established, so as to identify the lithofacies of the to-be-detected tight sandstone. The mathematical expressions of the relative value of the deuranium gamma and the difference value of the neutron density apparent porosity are as follows: (1) ​​​​​​ (2) wherein, is the deuranium gamma, unit API, is the deuranium gamma minimum value, unit API; is the deuranium gamma maximum value, unit API; is the neutron logging response value, generally a decimal; is the density logging response value, unit g / cm3; is the fluid density logging value, generally 1, g / cm3; is the matrix logging response value, unit g / cm3.

[0027] In a specific implementation, the deuranium gamma of the to-be-detected tight sandstone can be measured by an instrument , the neutron logging response value , and the density logging response value corresponding curves are used, and the deuranium gamma relative value (R) and the neutron density apparent porosity difference (Δφ) of the to-be-detected tight sandstone are calculated by using the above formula, for reference is a crossplot diagram of the two parameters. Figure 2 Based on the crossplot diagram established in advance, four types of lithology, i.e., (medium) fine sandstone, gravelly sandstone, (argillaceous) siltstone, and (silty) mudstone, can be identified, and the identification standards are shown in Table 1.

[0028] Table 1: Lithology classification standards of the to-be-detected tight sandstone

[0029] Table 1: Lithology classification standards of the to-be-detected tight sandstone

[0030] In the table, and are the corresponding threshold values of the neutron density apparent porosity difference preset in advance, is the threshold value of the deuranium gamma relative value preset in advance.

[0031] Since the (argillaceous) siltstone and the (silty) mudstone in the first identification result have no obvious difference in the neutron density apparent porosity difference response, there is an overlap in the overlapping area of the siltstone and the mudstone (i.e., Δφ < 0.2), and the (argillaceous) siltstone and the (silty) mudstone are prone to misidentification. For this part of the overlapping area, the sandstone and mudstone rapid identification technology based on electrical imaging can be applied to effectively identify. and are the corresponding threshold values of the neutron density apparent porosity difference preset in advance, the deuranium gamma relative value threshold value is not applicable to the overlapping area of the siltstone and the mudstone, and the (argillaceous) siltstone and the (silty) mudstone are prone to misidentification. For this part of the overlapping area, the sandstone and mudstone rapid identification technology based on electrical imaging can be applied to effectively identify.

[0032] Based on this, in a possible implementation, step S102 can include: ​​obtaining a plurality of conductivity curves corresponding to the overlapping area based on the electrical imaging logging instrument; establishing a conductivity frequency distribution histogram corresponding to each depth in the overlapping area for the plurality of conductivity curves, and determining a content proportion of sandstone and mudstone at each depth respectively according to a preset conductivity threshold value; further dividing the overlapping area according to the content proportion to obtain the second identification result.

[0033] In implementation, the electrical imaging logging instrument has 192 polar plates, and 192 conductivity curves can be measured. The entire well circumference (360°) is covered by polar plate rotation scanning, and 360 continuous conductivity curves of the ring well circumference are obtained by combining with a data interpolation algorithm to form a complete well wall image. Because the electrical conductivity of different geological bodies in the formation near the well wall is different, the electrical imaging logging conductivity can reflect the change of the lithology of the nearby formation. The electrical imaging logging instrument has high longitudinal resolution, and the longitudinal resolution can reach 5mm, and is relatively sensitive to thin interbedded sandstone and mudstone, (argillaceous) siltstone and (silty) mudstone. The imaging mode can be divided into static images and dynamic images, and the static image data is composed of 360 conductivity curves of the ring well circumference. Generally, the conductivity value of sandstone is lower than that of mudstone, so the embodiment of the present application identifies sandstone and mudstone by statistically distributing the size of the plurality of (360 in the embodiment of the present application) conductivity curves corresponding to the overlapping area (i.e. the well circumference). The principle is as shown in the following formula: Figure 3

[0034] Figure 3 wherein, DEPTH represents the depth corresponding to the electrical imaging logging, represents the minimum value of the conductivity at a certain depth, represents the maximum value of the conductivity at a certain depth, and frequencies represents the frequency of the conductivity in a certain range (because the electrical imaging data is composed of 360 column conductivity matrix data, there are 360 conductivity values at a certain depth).

[0035] Firstly, the conductivity frequency distribution histogram corresponding to each depth in the overlapping area is established for the 360 conductivity curves, and then a conductivity threshold value is set, and it is determined that the sandstone is less than the threshold value and the mudstone is greater than the threshold value. Secondly, the content proportion of sandstone and mudstone at each depth is counted, and the statistical formula is as follows: (3) (4) wherein, represents the content proportion of sandstone corresponding to each depth; represents the content proportion of mudstone corresponding to each depth; is an indicator function, which takes 1 when the condition is true, and 0 otherwise.​ The first conductivity curve corresponds to the conductivity value of the first sandstone. The second conductivity curve corresponds to the conductivity value of the second sandstone.

[0036] According to the content proportion of the sandstone and the content proportion of the mudstone corresponding to each depth obtained by the above calculation, the above overlapping area is further divided to obtain a second identification result.

[0037] In a possible implementation, the further division of the overlapping area according to the content proportion to obtain the second identification result includes: For the content proportion of the sandstone and the mudstone at any target depth in the overlapping area: If the content proportion of the sandstone at the target depth in the overlapping area is greater than or equal to the content proportion of the mudstone, it is determined that the target depth is siltstone; Otherwise, it is determined that the target depth is mudstone. According to the division result of the siltstone and the mudstone at each depth in the overlapping area, the second identification result is obtained.

[0038] In implementation, for the content proportion of the sandstone and the mudstone at any target depth in the calculated overlapping area, there are: The calculated and are compared, if ≥ , it is determined that the depth is sandstone; otherwise, it is determined that the depth is mudstone.

[0039] The division result of the siltstone and the mudstone at each depth in the overlapping area is counted to obtain the second identification result.

[0040] It should be noted that the technology is a supplement to the cross plot technology for identifying lithology, that is, (medium) fine sandstone, mud gravel sandstone, (argillaceous) siltstone and (siltstone) mudstone are first identified by using the cross plot technology, and then the (argillaceous) siltstone and (siltstone) mudstone overlapping area (i.e. This area) is further divided into sandstone and mudstone by using the sandstone and mudstone rapid identification technology based on electrical imaging. In this overlapping area, if it is determined to be sandstone, it corresponds to (argillaceous) siltstone, and if it is determined to be mudstone, it corresponds to (siltstone) mudstone.

[0041] In a possible implementation, step S103 can include: Based on the electrical imaging logging instrument, the conductivity matrix data of the fine sandstone corresponding area is determined; According to the depth displacement after projection of each data point in the conductivity matrix data, a preliminary projection image corresponding to a matrix array restructured according to the displaced depth is determined. Based on the preliminary projection image, curves corresponding to the relative bedding density and average conductivity are obtained respectively; Based on the curves corresponding to the relative bedding density and average electrical conductivity, the fine sandstone is further divided to obtain the third identification result.

[0042] Due to the reservoir permeability ranking: (medium) fine sandstone > (muddy) siltstone > (silty) mudstone, favorable reservoirs in channel sandstones are generally composed of (medium) fine sandstone. Within (medium) fine sandstone, different rock structures significantly influence reservoir permeability. Typically, low-resistivity massive fine sandstone and parallel / dipping layered fine sandstone exhibit better reservoir permeability than high-resistivity massive fine sandstone and lamellar fine sandstone. Classifying the rock structure of (medium) fine sandstone is crucial for identifying the dominant lithofacies in channel sandstone.

[0043] The division of rock structure can be achieved using electrical imaging wellbore cross-section projection technology. The projection operation essentially changes the position of conductivity data without altering the magnitude of the conductivity values. The electrical imaging image is actually the measured conductivity matrix data for the corresponding region. By using an electrical imaging logging tool to measure the conductivity matrix data for the corresponding region of (medium) fine sandstone, and calculating the depth displacement of each data point in the electrical imaging conductivity matrix after projection, and then reconstructing the matrix array according to the displacement depth, a preliminary projected image can be obtained, such as... Figure 4 The second image from the left in the middle shows the calculation formula as follows: (5) In the formula, For the first The displacement depth of each imaging matrix data point, in meters; The radius of the wellbore at the current depth, in meters (m). The structural dip angle at the current depth; This represents the column number to which the current imaging matrix data point belongs; This is the azimuth angle of the electrical imaging logging tool at the current depth.

[0044] Based on the preliminary projection image obtained above, curves corresponding to the relative bedding density and average conductivity can be obtained respectively.

[0045] Finally, based on the curves corresponding to the relative bedding density and average electrical conductivity, the fine sandstone was further classified into four lithofacies, including low-resistivity massive fine sandstone, parallel / inclined bedding fine sandstone, high-resistivity massive fine sandstone, and lamellar fine sandstone, resulting in a third identification outcome.

[0046] In one possible implementation, the steps described above, including obtaining the curves corresponding to the relative bedding density and average conductivity based on the preliminary projection image, include: cutting the preliminary projection image along the axis and flipping one side image to obtain two half-week projection images; obtaining a target projection image according to the two half-week projection images; performing arithmetic average on a plurality of conductivity data corresponding to the target projection image to obtain a curve corresponding to the average conductivity; obtaining a curve corresponding to the relative bedding density according to the curve corresponding to the average conductivity.

[0047] In implementation, as shown in the second image from the left in FIG. 1, Figure 4 the preliminary projection image obtained above is a pair of mirror images with the axis of the wellbore as the axis of symmetry, then the image is cut into two halves along the axis, and one side image is flipped (equivalent to folding the image along the axis of symmetry) to generate two half-week projection images as shown in the third image from the left in FIG. 1. Figure 4 Then, the matrix array is filled in the reverse direction of the original stratum dip angle, and the final slice projection image, i.e., the target projection image, can be obtained as shown in the last image from the left in FIG. 1. Figure 4

[0048] Then, a plurality of (360 in the embodiment of the present application) conductivity data corresponding to the target projection image are arithmetically averaged to obtain a curve corresponding to the average conductivity. With depth as the index, three sampling points as a unit, and one sampling point as the number of steps, sliding analysis is performed, and the average conductivity values of each sampling point and the adjacent upper and lower sampling points are compared in a loop to identify and lock the data points that are simultaneously smaller or larger than the upper and lower sampling points. After excluding minor fluctuations, the data meeting the conditions are marked as wave troughs and wave crests. At the same time, a sliding window is set from top to bottom, the sliding window length can be set according to actual needs, and the number of wave crests and troughs in the sliding window is counted, i.e., the relative bedding density at the depth, as shown in FIG. 2. Figure 5 Finally, according to the curves corresponding to the relative bedding density and the average conductivity , the (medium) fine sandstone is further divided into low-resistance massive fine sandstone, parallel / inclined layered fine sandstone, high-resistance massive fine sandstone, and laminated fine sandstone. The division criteria are shown in Table 2.

[0049] Table 2: Fine sandstone facies division criteria

[0050] In the table, and are the threshold values of the relative bedding density preset in advance, is the threshold value of the average conductivity preset in advance.​​

[0051] Finally, combining cross-plotting technology, rapid identification technology for sandstone and mudstone based on electrical imaging, and structural facies classification technology for fine sandstone, channel sandstone can be divided into seven lithofacies: low-resistivity massive fine sandstone, parallel / dipping layered fine sandstone, high-resistivity massive fine sandstone, layered fine sandstone, mudstone, siltstone, and mudstone. Specifically, as follows... Figure 6 As shown, the first track represents the lithological results identified by the cross-plot: yellow represents (medium) fine sandstone, green represents mudstone / conglomerate sandstone, pink represents (muddy) siltstone, and black represents (silty) mudstone. The second track represents the updated lithological identification results for four types of sandstone and mudstone based on a rapid identification technology using electrical imaging. The third track is a static electrical imaging image. The fourth track is an electrical imaging slice image. The sixth track is the average electrical conductivity curve. The seventh track is the relative bedding density. The eighth track shows the identification results for seven lithofacies.

[0052] In practical applications, taking the channel sandstone of the Lianggaoshan Formation in a certain block of a basin as an example, a lithological identification cross-plot was first established based on the core description, grain size analysis, and conventional logging data from two wells, Fulu 2 and Fulu 101. For example... Figure 7 As shown. (Through) Figure 7 It can identify four lithofacies: (medium) fine sandstone, mudstone, (muddy) siltstone, and (silty) mudstone.

[0053] For some areas where (argillaceous) siltstone and (silty) mudstone overlap (such as... This study utilizes an electro-imaging-based rapid identification technique for sandstone and mudstone to further identify (argillaceous) siltstone and (silty) mudstone. The updated results for the four lithological identifications are as follows: Figure 8 As shown, from left to right, the following are the parameters: The first parameter is the depth curve; the second parameter is the lithology curve, CALI is the caliber curve, KTH is the uranium-reduced gamma ray curve, and GR is the natural gamma ray curve; the third parameter is the porosity curve, CNL is the neutron curve, AC is the acoustic curve, and DEN is the density curve; the fourth parameter is the resistivity curve, RD is the deep lateral curve, and RS is the shallow lateral curve; the fifth parameter is... For the relative value of uranium-depleted gamma, nd is the neutron density apparent porosity value (i.e. The sixth track is a static image obtained from electrical imaging. The seventh track is a mineral profile of sandstone and mudstone after conversion using formulas (3) and (4). The eighth track is the (muddy) siltstone and (silty) mudstone identified by the rapid identification technology for sandstone and mudstone based on electrical imaging. The ninth track is the lithological results identified by the cross plot. The tenth track is the updated lithological identification results for the four types of lithology based on the rapid identification technology for sandstone and mudstone based on electrical imaging. The eleventh track is the lithological results described in the core.

[0054] For the identified fine sandstone, further utilize the electrical imaging logging to finely divide the lithofacies (the division standard is shown in Table 3), and combine the first two technologies, finally divide the channel sandstone into seven kinds of lithofacies (specifically refer to Figure 9 the table 3).

[0055] Table 3: Fine sandstone lithofacies division standard of Lianggaoshan Formation in a certain area of a basin

[0056] Compared with the prior art, the dense sandstone lithofacies identification method provided in the embodiment firstly utilizes the crossplot for identifying the sandstone lithofacies which is established in advance to preliminarily identify the lithofacies of the to-be-measured dense sandstone, and obtains a first identification result. Secondly, in view of the case that the (argillaceous) siltstone and (silty) mudstone in the first identification result have an overlapping area in a part interval of the crossplot, which leads to low identification accuracy, the (argillaceous) siltstone and (silty) mudstone are accurately identified through the electrical imaging sand-mudstone rapid identification technology. Finally, in view of the fact that the fine sandstone has large differences in rock structure, the rock structure is divided into low-resistance lump, high-resistance lump, horizontal / inclined layer and laminated layer through the electrical imaging technology. Through the combination of the three technologies, the lithofacies of the to-be-measured dense sandstone can be divided into seven kinds of lithofacies, i.e., high-resistance lump fine sandstone, low-resistance lump fine sandstone, parallel / inclined layer fine sandstone, laminated fine sandstone, mud-gravel sandstone, siltstone and mudstone, thereby improving the accuracy of the identification of the lithofacies of the to-be-measured dense sandstone.

[0057] As shown in Figure 10 , the application further provides a dense sandstone lithofacies identification device 100, which comprises: A first identification module 1001 is configured to identify the lithofacies of the to-be-measured dense sandstone based on a pre-established crossplot, and obtain a first identification result, wherein the first identification result comprises fine sandstone, mud-gravel sandstone, siltstone and mudstone. A second identification module 1002 is configured to further divide the overlapping area of the siltstone and mudstone in the first identification result based on an electrical imaging technology, and obtain a second identification result. A third identification module 1003 is configured to further divide the fine sandstone in the first identification result based on the electrical imaging technology, and obtain a third identification result, wherein the third identification result comprises low-resistance lump fine sandstone, parallel / inclined layer fine sandstone, high-resistance lump fine sandstone and laminated fine sandstone. A fourth identification module 1004 is configured to obtain the lithofacies identification result of the to-be-measured dense sandstone based on the first identification result, the second identification result and the third identification result.

[0058] In a possible implementation, the first identification module 1001 is further configured to: Based on the core particle size analysis and lithology description material, the logging response characteristic parameters of different facies including fine sandstone, argillaceous sandstone, siltstone and mudstone in the sandstone are analyzed; Based on the logging response characteristic parameters, the crossplot with the deuterium gamma relative value as a first coordinate axis and the neutron density apparent porosity difference value as a second coordinate axis in the same coordinate system is determined; The curves corresponding to the deuterium gamma, neutron logging response values and density logging response values of the to-be-tested tight sandstone are obtained; Based on the curves and the crossplot, the facies of the to-be-tested tight sandstone is identified, and the first identification result is obtained.

[0059] Compared with the prior art, the tight sandstone facies identification device provided by the application firstly uses the crossplot for identifying the sandstone facies which is established in advance to preliminarily identify the facies of the to-be-tested tight sandstone, and obtain the first identification result. Secondly, aiming at the case that the (argillaceous) siltstone and (silty) mudstone in the first identification result have an overlapping area in the crossplot part interval, the (argillaceous) siltstone and (silty) mudstone are accurately identified through the electrical imaging sandstone and mudstone rapid identification technology. Finally, aiming at the large difference in the fine sandstone rock structure, the rock structure is divided into low-resistance lump-shaped, high-resistance lump-shaped, horizontal / inclined layered and laminated through the electrical imaging technology. Through the combination of the three technologies, the facies of the to-be-tested tight sandstone can be divided into seven kinds of facies, i.e., high-resistance lump-shaped fine sandstone, low-resistance lump-shaped fine sandstone, parallel / inclined layered fine sandstone, laminated fine sandstone, argillaceous sandstone, siltstone and mudstone, thereby improving the accuracy of the identification of the facies of the to-be-tested tight sandstone.

[0060] As shown in Figure 11 The application also correspondingly provides an electronic device. The electronic device 110 includes a processor 1110, a memory 1120 and a display 1130. Figure 11 Only part of the components of the electronic device 110 are shown, but it should be understood that all the shown components are not required to be implemented, and more or fewer components can be alternatively implemented.

[0061] The processor 1110 can be a central processing unit (CPU), a microprocessor or other data processing chip in some embodiments, used to run the program code or process data stored in the memory 1120, such as the tight sandstone facies identification method in the application.

[0062] In some embodiments, the processor 1110 can be a single server or a group of servers. The group of servers can be centralized or distributed. In some embodiments, the processor 1110 can be local or remote. In some embodiments, the processor 1110 can be implemented in a cloud platform. In an embodiment, the cloud platform can include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an on-premise cloud, a multi-cloud, or the like, or any combination thereof.

[0063] The memory 1120 can be an internal storage unit of the electronic device in some embodiments, such as a hard disk or a memory of the electronic device 110. The memory 1120 can also be an external storage device of the electronic device 110 in other embodiments, such as a plug-in hard disk, a SmartMediaCard (SMC), a Secure Digital (SD) card, a FlashCard, or the like, equipped on the electronic device 110.

[0064] Further, the memory 1120 can include both an internal storage unit and an external storage device of the electronic device 110. The memory 1120 is used to store application software and various data installed on the electronic device 110.

[0065] The display 1130 can be an LED display, a liquid crystal display, a touch liquid crystal display, an OLED (Organic Light Emitting Diode) touch, or the like in some embodiments. The display 1130 is used to display information of the electronic device 110 and to display a visualized user interface. The components 1110 to 1130 of the electronic device 110 communicate with each other through a system bus.

[0066] In an embodiment, when the processor 1110 executes the tight sandstone facies identification program in the memory 1120, the following steps can be implemented: Based on the pre-established cross plot, the facies of the tight sandstone to be measured is identified to obtain a first identification result, the first identification result including fine sandstone, gravel sandstone, siltstone, and mudstone; Based on the electrical imaging technology, the overlapping area of the siltstone and the mudstone in the first identification result is further divided to obtain a second identification result; Based on the electrical imaging technology, the fine sandstone in the first identification result is further divided to obtain a third identification result, the third identification result including low-resistance massive fine sandstone, parallel / inclined layered fine sandstone, high-resistance massive fine sandstone, and laminated fine sandstone; Based on the second identification result and the third identification result, a facies identification result of the tight sandstone to be measured is obtained.

[0067] It should be understood that, in addition to the above functions, the processor 1110 can also implement other functions when executing the tight sand facies identification program in the memory 1120. Details can be referred to the description of the corresponding method embodiments.

[0068] Further, the type of the electronic device 110 is not limited in the embodiments of the present application. The electronic device 110 can be a mobile phone, a tablet computer, a personal digital assistant (PDA), a wearable device, a laptop computer, or the like. Exemplary embodiments of the portable electronic device include, but are not limited to, a portable electronic device running an IOS, an android, a microsoft, or other operating system. The portable electronic device can also be other portable electronic devices, such as a laptop computer having a touch-sensitive surface (e.g., a touch panel), and the like. It should also be understood that, in some other embodiments of the present application, the electronic device 110 can not be a portable electronic device, but a desktop computer having a touch-sensitive surface (e.g., a touch panel).

[0069] Correspondingly, the embodiments of the present application also provide a computer readable storage medium for storing computer readable programs or instructions, which are executed by a processor to implement the steps or functions in the data method provided by the above method embodiments.

[0070] Those skilled in the art can understand that all or part of the processes of the above embodiments can be completed by a computer program instructing related hardware, and the computer program can be stored in a computer readable storage medium. The computer readable storage medium includes a magnetic disk, an optical disk, a read-only memory, a random access memory, and the like.

[0071] The above is only a preferred embodiment of the present application, but the protection scope of the present application is not limited thereto. Any changes or replacements within the technical scope disclosed by the present application can be easily conceived by those skilled in the art, which should be covered within the protection scope of the present application.

Claims

1. A method of tight sand facies identification, characterized by, The method comprises the following steps: Based on the pre-established crossplot, the lithofacies of the to-be-tested tight sandstone is identified to obtain a first identification result, the first identification result including fine sandstone, gravelly sandstone, siltstone and mudstone; Based on the electrical imaging technology, the overlapping area of the siltstone and mudstone in the first identification result is further divided to obtain a second identification result; Based on the electrical imaging technology, the fine sandstone in the first identification result is further divided to obtain a third identification result, the third identification result including low-resistance massive fine sandstone, parallel / inclined layered fine sandstone, high-resistance massive fine sandstone and laminated fine sandstone; Based on the first identification result, the second identification result and the third identification result, a lithofacies identification result of the to-be-tested tight sandstone is obtained.

2. The method of tight sandstone facies identification of claim 1, wherein, The method further comprises the following steps for further dividing the overlapping area of the siltstone and mudstone in the first identification result based on the electrical imaging technology to obtain the second identification result: Based on the electrical imaging logging instrument, a plurality of electrical conductivity curves corresponding to the overlapping area are obtained; An electrical conductivity frequency distribution histogram corresponding to each depth in the overlapping area is established based on the plurality of electrical conductivity curves, and a content proportion of sandstone and mudstone at each depth is respectively determined according to a preset electrical conductivity threshold; The overlapping area is further divided according to the content proportion to obtain the second identification result.

3. The method of tight sandstone facies identification of claim 2, wherein, The method further comprises the following steps for further dividing the overlapping area according to the content proportion to obtain the second identification result: For the content proportion of sandstone and mudstone at any target depth in the overlapping area: If the content proportion of sandstone at the target depth in the overlapping area is greater than or equal to the content proportion of mudstone, the target depth is determined to be siltstone; Otherwise, the target depth is determined to be mudstone; The second identification result is obtained according to the division result of the siltstone and mudstone at each depth in the overlapping area.

4. The method of tight sandstone lithofacies identification of claim 1, wherein, The method further comprises the following steps for further dividing the fine sandstone in the first identification result based on the electrical imaging technology to obtain the third identification result: Based on the electrical imaging logging instrument, electrical conductivity matrix data of the area corresponding to the fine sandstone are determined; According to the depth displacement of each data point in the electrical conductivity matrix data after projection, a preliminary projection image corresponding to a matrix array restructured according to the displacement is determined; According to the preliminary projection image, a curve corresponding to the relative bedding density and a curve corresponding to the average electrical conductivity are obtained; The fine sandstone is further divided according to the curve corresponding to the relative bedding density and the curve corresponding to the average electrical conductivity to obtain the third identification result.

5. The method of tight sandstone lithofacies identification of claim 4, wherein, The method further comprises the following steps for obtaining the curve corresponding to the relative bedding density and the curve corresponding to the average electrical conductivity according to the preliminary projection image: The preliminary projection image is cut along an axis, and one side of the image is flipped to obtain two half-week projection images; A target projection image is obtained according to the two half-week projection images; A plurality of electrical conductivity data corresponding to the target projection image are arithmetically averaged to obtain the curve corresponding to the average electrical conductivity; The curve corresponding to the relative bedding density is obtained according to the curve corresponding to the average electrical conductivity.

6. The method of tight sandstone lithofacies identification according to any one of claims 1-5, characterized in that, The method further comprises the following steps for obtaining the pre-established crossplot: Based on core particle size analysis and lithology description materials, logging response characteristic parameters of different facies including fine sandstone, mudstone, siltstone and mudstone in sandstone are obtained; Based on the logging response characteristic parameters, the crossplot is determined under the same coordinate system, with the relative value of the deuranium gamma as a first coordinate axis and the difference value of the neutron density apparent porosity as a second coordinate axis; Correspondingly, the facies of the to-be-tested tight sandstone is identified based on the pre-established crossplot, to obtain a first identification result, which includes: Curves corresponding to the deuranium gamma, neutron logging response value and density logging response value of the to-be-tested tight sandstone are obtained; Based on the curves and the crossplot, the facies of the to-be-tested tight sandstone is identified, to obtain the first identification result.

7. A tight sand lithofacies identification apparatus, characterized by, It includes: A first identification module is configured to identify the facies of the to-be-tested tight sandstone based on a pre-established crossplot, to obtain a first identification result, which includes fine sandstone, mudstone, siltstone and mudstone; A second identification module is configured to further divide the overlapping area of the siltstone and mudstone in the first identification result based on an electrical imaging technology, to obtain a second identification result; A third identification module is configured to further divide the fine sandstone in the first identification result based on the electrical imaging technology, to obtain a third identification result, which includes low-resistance massive fine sandstone, parallel / inclined layered fine sandstone, high-resistance massive fine sandstone and laminated fine sandstone; A fourth identification module is configured to obtain a facies identification result of the to-be-tested tight sandstone based on the first identification result, the second identification result and the third identification result.

8. The tight sand facies identification apparatus of claim 7, wherein, The first identification module is further configured to: Based on core particle size analysis and lithology description materials, logging response characteristic parameters of different facies including fine sandstone, mudstone, siltstone and mudstone in sandstone are obtained; Based on the logging response characteristic parameters, the crossplot is determined under the same coordinate system, with the relative value of the deuranium gamma as a first coordinate axis and the difference value of the neutron density apparent porosity as a second coordinate axis; Curves corresponding to the deuranium gamma, neutron logging response value and density logging response value of the to-be-tested tight sandstone are obtained; Based on the curves and the crossplot, the facies of the to-be-tested tight sandstone is identified, to obtain the first identification result.

9. An electronic device, comprising: It includes: A memory is configured to store a program; A processor coupled to the memory is configured to execute the program stored in the memory, to implement the steps in the tight sandstone facies identification method in any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that, A computer readable program or instruction is stored, which can implement the steps in the tight sandstone facies identification method in any one of claims 1 to 6 when executed by a processor.

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