Method and system for identifying high-quality alumina rock reservoir by using logging data

Abstract

The invention discloses a method and system for identifying a high-quality bauxite rock reservoir by using logging data, and the method comprises the steps: obtaining an envelope through adjusting the left and right scales of a natural gamma logging curve and an interval transit time logging curve, and calculating the mirror image envelope area of the natural gamma logging curve and the interval transit time logging curve; the alumina rock stratum section can be qualitatively identified by using the size of the enveloping area; according to the method, the enveloping area and the content of diaspore mineral which is a main rock mineral component in the alumina rock reservoir are established, the content of diaspore, clay mineral, heavy mineral and sand can be rapidly and quantitatively calculated continuously and deeply in the longitudinal direction, meanwhile, the problem that the error of a single-parameter model is large is solved, and therefore the alumina rock stratum section is quantitatively recognized. And the high-quality alumina rock reservoir stratum is identified and divided in the alumina rock stratum in combination with the multi-parameter porosity and the lithologic homogeneity index, so that the identification accuracy of the natural gas reservoir stratum is effectively improved, and the precision requirements of logging interpretation evaluation work such as reservoir stratum classification and reserve calculation are met.

Description

Technical Field

[0001] The invention belongs to the technical field of unconventional reservoir logging evaluation, and relates to a method and a system for identifying high-quality bauxite reservoirs by using logging data. Background Art

[0002] Bauxite is a sedimentary rock rich in aluminum hydroxide. Its minerals are mainly composed of diaspore, with a content of more than 90%, but it generally contains clay minerals, sand and heavy minerals such as anatase, siderite, calcite, and pyrite. In the past, it was believed that bauxite formations had high clay content, high density, and poor physical properties. They were the capping layer of natural gas reservoirs or gas-generating layers, so they have not been taken seriously. However, with the continuous deepening of natural gas exploration and development, major discoveries and breakthroughs have been made in the new field of bauxite natural gas exploration. As a new type of unconventional natural gas reservoir, bauxite is different from general conventional reservoirs in that it has diverse sedimentary types, variable lithology, diverse mineral components, and large differences in physical properties. There is currently little systematic research on logging identification methods for this type of reservoir. The mineral components still need to be finely calibrated by logging, and there is a lack of fine logging identification methods.

[0003] Through literature research, it was found that a bauxite identification method, device, electronic device and storage medium 202110474007.7 uses a cluster analysis model to perform cluster learning processing on the preferred conventional logging sensitivity curve, and determines the conventional logging response characteristics corresponding to the bauxite according to the target clustering results, thereby automatically identifying the bauxite. This method can solve the problem that the bauxite identification method in the prior art is low in intelligence and the identification process is highly subjective. However, this technology has the following problems: 1) Since the enrichment of bauxite gas reservoirs is affected by characteristic control factors such as reservoir formation characteristics, structure, micro-paleomorphology, and reservoir, this method is affected by data statistical laws and requires a large amount of data sample learning, otherwise the accuracy cannot be guaranteed, and this method is difficult for geological researchers to understand, making manual logging interpretation difficult, and the identification results have certain limitations; 2) It is impossible to identify the quality of bauxite formations.

[0004] The prior art also proposes to use the established acoustic time difference and natural gamma aluminum-bearing rock identification chart to identify bauxite. It is believed that typical bauxite has "four highs and four lows" logging response characteristics, namely ultra-high natural gamma, high compensated neutrons, high uranium content, high thorium content, low acoustic time difference and low potassium content. Therefore, a natural gamma and acoustic time difference lithology identification chart was established; at the same time, a multi-mineral calculation model was established based on X-ray diffraction whole-rock mineral data and the corresponding logging curves, and a single-parameter linear regression model was established for the main mineral diaspore and natural gamma; on this basis, the classification and identification of bauxite reservoirs were realized in combination with physical properties and saturation evaluation. There are the following problems: 1) The chart and single parameter fitting model basically realize the qualitative and semi-quantitative identification and evaluation of the reservoir, but there is human influence on the reading of the chart data, and there are certain errors and limitations in the actual application process. The single correlation model established has a relatively simple model parameter and the accuracy cannot be guaranteed; 2) The bauxite skeleton parameters determined by the acoustic time difference in porosity calculation cannot be accurately verified by experiment, and thus the accuracy of the porosity calculation cannot be evaluated. In addition, the special logging series is more effective in identifying bauxite reservoirs. For example, nuclear magnetic resonance logging can reflect the changes in pore structure through the longitudinal continuous T2 spectrum, and then distinguish between sand and mud formations and bauxite formations; formation element logging ECS ​​can fit the content of elements such as Si, Al, Ca, Fe, S, Ti, etc. in the formation, and further obtain the mineral content. However, the special logging series is expensive, especially the measurement of old wells is extremely limited, and large-scale promotion and application have economic limitations. Summary of the invention

[0005] The purpose of the present invention is to solve the problem that the recognition accuracy of natural gas reservoirs in the prior art is low and it is difficult to meet the accuracy requirements of reservoir classification and evaluation work, and to provide a method and system for identifying high-quality bauxite reservoirs using well logging data.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] The present invention proposes a method for identifying high-quality bauxite reservoirs using well logging data, comprising the following steps:

[0008] According to the natural gamma logging curve and the sonic time difference logging curve, the envelope area of ​​the natural gamma logging curve and the sonic time difference logging curve is obtained;

[0009] The content of diaspore minerals is obtained based on the envelope area, the content of clay minerals is obtained based on the natural gamma logging value, and the content of heavy minerals is obtained based on the lithology density and the photoelectric absorption cross-section index;

[0010] The sand mineral content is obtained according to the diaspore mineral content, the clay mineral content and the heavy mineral content; the lithology is identified as bauxite according to the diaspore mineral content, the clay mineral content, the heavy mineral content and the sand mineral content;

[0011] The porosity and lithology homogeneity index of bauxite are obtained, and high-quality bauxite reservoirs are obtained by combining the porosity and lithology homogeneity index of bauxite.

[0012] Preferably, the envelope area of ​​the logging curve is obtained as follows:

[0013] ENV=GR env -AC env

[0014] Among them, the unit area of ​​the natural gamma logging curve is Unit area of ​​acoustic transit time logging curve GR is the natural gamma logging value, AC is the acoustic time difference logging value, GR L is the minimum value of the natural gamma logging curve, GR R is the maximum value of the natural gamma logging curve, AC L is the minimum value of the acoustic time difference logging curve, AC R It is the maximum value of the acoustic time difference logging curve.

[0015] Preferably, the diaspore mineral content V ALOX =a1*ENV+b1, clay mineral content V clay =a2*GR+b2, heavy mineral content V FE =a3*PE*DEN+b3;

[0016] The method for obtaining the sand mineral content is as follows:

[0017] V SI =100-V ALOX -V clay -V FE

[0018] Among them, a1 is the statistical linear regression coefficient of the main mineral diaspore content at the core measurement depth points of multiple wells and the corresponding envelope area data, b1 is the constant term of sample data regression, which represents the diaspore content when ENV is 0; a2 is the sample data regression coefficient, b2 is the constant term of sample data regression, which represents the clay mineral content when GR is 0; a3 is the sample data regression coefficient, b3 is the constant term of sample data regression, which represents the heavy mineral content when PE or DEN is 0.

[0019] Preferably, the method for identifying the lithology as bauxite is as follows:

[0020] When the mineral content of diaspore in the mineral is >50%, the mineral content of clay is <25%, the content of heavy minerals is <10%, and the mineral content of sand is <10%, the resulting lithology is bauxite.

[0021] Preferably, the porosity of bauxite is obtained as follows:

[0022] POR=aa*CNL+bb*K+cc*PE+dd

[0023] Among them, aa is the regression coefficient of core analysis porosity and CNL, bb is the regression coefficient of core analysis porosity and K, cc is the regression coefficient of core analysis porosity and PE, dd is the constant term of sample data regression, CNL is the compensated neutron logging value, PE is the photoelectric absorption cross-section index, and K is the potassium content logging value;

[0024] The lithology homogeneity index of bauxite is obtained as follows:

[0025]

[0026] Among them, ENV n is the envelope area of ​​the natural gamma and acoustic time difference logging curves at the nth depth sampling point, ENV n-1 is the envelope area of ​​the natural gamma and acoustic time difference logging curves at the n-1th depth sampling point, n is the unit depth sampling point, n = 1, 2, ... n.

[0027] Preferably, when the porosity is not less than the lower limit of Class I porosity, the bauxite reservoir quality is good; when the porosity is between the lower limit of Class I porosity and the lower limit of Class II porosity, the bauxite reservoir quality is medium; when the porosity is less than the lower limit of Class II porosity, the bauxite reservoir quality is poor.

[0028] Preferably, when the lithology homogeneity index is not less than the first lower limit, the bauxite reservoir has strong homogeneity; when the lithology homogeneity index is between the first lower limit and the second lower limit, the bauxite reservoir has medium homogeneity; when the lithology homogeneity index is less than the second lower limit, the bauxite reservoir has poor homogeneity.

[0029] The present invention proposes a system for identifying high-quality bauxite reservoirs using well logging data, comprising:

[0030] An envelope area acquisition module, which is used to acquire the envelope areas of the natural gamma logging curve and the acoustic time difference logging curve according to the natural gamma logging curve and the acoustic time difference logging curve;

[0031] A mineral content acquisition module, which is used to acquire the content of diaspore minerals according to the envelope area, acquire the content of clay minerals according to the natural gamma logging value, and acquire the content of heavy minerals according to the lithology density and the photoelectric absorption cross-section index;

[0032] A bauxite identification module, the bauxite identification module is used to obtain the sandy mineral content according to the diaspore mineral content, the clay mineral content and the heavy mineral content; and identify the lithology as bauxite according to the diaspore mineral content, the clay mineral content, the heavy mineral content and the sandy mineral content;

[0033] A bauxite reservoir analysis module is used to obtain the porosity and lithology homogeneity index of bauxite, and obtain a high-quality bauxite reservoir by combining the porosity and lithology homogeneity index of bauxite.

[0034] A computer device comprises a memory and a processor, wherein the memory stores a computer program and the processor implements the steps of a method for identifying high-quality bauxite reservoirs by using well logging data when executing the computer program.

[0035] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a method for identifying high-quality bauxite reservoirs using well logging data.

[0036] Compared with the prior art, the present invention has the following beneficial effects:

[0037] The present invention proposes a method for identifying high-quality bauxite reservoirs using logging data. The method combines the geological characteristics of the study area with the logging response law, and calculates the mirror envelope area of ​​the natural gamma logging curve and the sonic time difference logging curve by adjusting the left and right scales of the fixed study area. The bauxite layer segment can be qualitatively identified using the size of the envelope area; further establishes the relationship between the envelope area and the main rock mineral component in the bauxite reservoir, the content of diaspore minerals, other clay minerals, heavy minerals, and sandy minerals, and conventional logging curves, and establishes a corresponding logging interpretation model to achieve rapid quantitative calculation of diaspore content, clay minerals, heavy minerals, and sandy content at continuous depth in the vertical direction, while overcoming the problem of large errors in the single parameter model, and achieving accurate calculation of mineral components in the bauxite formation; thereby quantitatively identifying the bauxite layer segment. Combined with the established multi-parameter porosity calculation model and the lithology homogeneity index based on acoustic time difference and natural gamma envelope area accumulation considering the thickness of the bauxite reservoir, the lithology homogeneity index and porosity are used to identify and divide high-quality bauxite reservoirs in the bauxite formation, which effectively improves the recognition accuracy of natural gas reservoirs and meets the accuracy requirements of logging interpretation and evaluation work such as reservoir classification and reserve calculation. The identification implementation method of the present invention is simple, easy to operate, and highly reproducible, which improves the exploration and development efficiency of tight oil and gas reservoirs, avoids the misclassification and omission of natural gas reservoirs in bauxite formations, and realizes the accurate identification of effective reservoirs. In the absence of formation elements and special nuclear magnetic resonance logging projects, the present invention can quickly and effectively determine the lithology and physical parameters of the reservoir using conventional logging data such as natural gamma and porosity logging, with low cost, can improve production timeliness, save collection and gas test production costs, and has good universality and promotion value.

[0038] The present invention proposes a system for identifying high-quality bauxite reservoirs using well logging data, which obtains high-quality bauxite reservoirs by dividing the system into an envelope area acquisition module, a mineral content acquisition module, a bauxite identification module, and a bauxite reservoir analysis module. The modular concept is adopted to make each module independent of each other, which is convenient for unified management of each module. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0040] Figure 1 The present invention is a flow chart of a method for identifying high-quality bauxite reservoirs using well logging data.

[0041] Figure 2 This is a schematic diagram of identifying high-quality bauxite reservoirs using well logging data according to the present invention.

[0042] Figure 3 This is a system diagram for identifying high-quality bauxite reservoirs using well logging data according to the present invention. DETAILED DESCRIPTION

[0043] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0044] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0045] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0046] In the description of the embodiments of the present invention, it should be noted that if the terms "upper", "lower", "horizontal", "inner", etc. indicate an orientation or positional relationship based on the orientation or positional relationship shown in the drawings, or the orientation or positional relationship in which the product of the invention is usually placed when in use, it is only for the convenience of describing the present invention and simplifying the description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. In addition, the terms "first", "second", etc. are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

[0047] In addition, if the term "horizontal" appears, it does not mean that the component must be absolutely horizontal, but can be slightly tilted. For example, "horizontal" only means that its direction is more horizontal than "vertical", which does not mean that the structure must be completely horizontal, but can be slightly tilted.

[0048] In the description of the embodiments of the present invention, it is also necessary to explain that, unless otherwise clearly specified and limited, the terms "set", "install", "connect", and "connect" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal connection of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0049] The present invention is further described in detail below in conjunction with the accompanying drawings:

[0050] The present invention proposes a method for identifying high-quality bauxite reservoirs using well logging data, such as Figure 1 As shown, the following steps are included:

[0051] S1. Obtaining the envelope area of ​​the natural gamma logging curve and the sonic time difference logging curve according to the natural gamma logging curve and the sonic time difference logging curve;

[0052] The envelope area of ​​the logging curve is obtained as follows:

[0053] ENV=GR env -AC env

[0054] Among them, the unit area of ​​the natural gamma logging curve is Unit area of ​​acoustic transit time logging curve GR is the natural gamma logging value, AC is the acoustic time difference logging value, GR L is the minimum value of the natural gamma logging curve, GR R is the maximum value of the natural gamma logging curve, AC L is the minimum value of the acoustic time difference logging curve, AC R It is the maximum value of the acoustic time difference logging curve.

[0055] S2. Obtain the content of diaspore minerals according to the envelope area, obtain the content of clay minerals according to the natural gamma logging value, and obtain the content of heavy minerals according to the lithology density and the photoelectric absorption cross-section index;

[0056] Diaspore mineral content V ALOX =a1*ENV+b1, clay mineral content V clay =a2*GR+b2, heavy mineral content V FE =a3*PE*DEN+b3;

[0057] S3. Obtain the sand mineral content according to the diaspore mineral content, the clay mineral content and the heavy mineral content; and identify the lithology as bauxite according to the diaspore mineral content, the clay mineral content, the heavy mineral content and the sand mineral content;

[0058] The method for obtaining the sand mineral content is as follows:

[0059] V SI =100-V ALOX -V clay -V FE

[0060] Among them, a1 is the statistical linear regression coefficient of the main mineral diaspore content at the core measurement depth points of multiple wells and the corresponding envelope area data, b1 is the constant term of sample data regression, which represents the diaspore content when ENV is assumed to be 0; the clay mineral content in core analysis is linearly correlated with GR, a2 is the sample data regression coefficient, b2 is the constant term of sample data regression, which represents the clay mineral content when GR is assumed to be 0; the heavy mineral content is linearly correlated with the product of PE and DEN, a3 is the sample data regression coefficient, b3 is the constant term of sample data regression, which represents the heavy mineral content when PE or DEN is assumed to be 0.

[0061] The method to identify the mineral as bauxite is as follows:

[0062] When the mineral content of diaspore in the mineral is >50%, the mineral content of clay is <25%, the content of heavy minerals is <10%, and the mineral content of sand is <10%, the resulting lithology is bauxite.

[0063] S4. Obtain the porosity and lithology homogeneity index of the bauxite, and obtain a high-quality bauxite reservoir by combining the porosity and lithology homogeneity index of the bauxite.

[0064] The porosity of bauxite is obtained as follows:

[0065] POR=aa*CNL+bb*K+cc*PE+dd

[0066] Among them, aa is the regression coefficient of core analysis porosity and CNL, bb is the regression coefficient of core analysis porosity and K, cc is the regression coefficient of core analysis porosity and PE, dd is the constant term of sample data regression, CNL is the compensated neutron logging value, PE is the photoelectric absorption cross-section index, and K is the potassium content logging value;

[0067] The lithology homogeneity index of bauxite is obtained as follows:

[0068]

[0069] Among them, ENV nis the envelope area of ​​the natural gamma and acoustic time difference logging curves at the nth depth sampling point, ENV n-1 is the envelope area of ​​the natural gamma and acoustic time difference logging curves at the n-1th depth sampling point, n is the unit depth sampling point, n = 1, 2, ... n.

[0070] When the porosity is not less than the lower limit of Class I porosity, the bauxite reservoir quality is good; when the porosity is between the lower limit of Class I porosity and the lower limit of Class II porosity, the bauxite reservoir quality is medium; when the porosity is less than the lower limit of Class II porosity, the bauxite reservoir quality is poor.

[0071] When the lithology homogeneity index is not less than the first lower limit, the bauxite reservoir has strong homogeneity; when the lithology homogeneity index is between the first lower limit and the second lower limit, the bauxite reservoir has medium homogeneity; when the lithology homogeneity index is less than the second lower limit, the bauxite reservoir has poor homogeneity.

[0072] The specific steps are as follows:

[0073] Step 1: Conduct X-ray diffraction whole-rock mineral analysis experimental measurement and core analysis porosity experimental measurement on the bauxite cores in the study area to obtain experimental data on diaspore content, clay minerals, heavy minerals and sand content of the core analysis;

[0074] The X-ray diffraction whole-rock mineral analysis experimental measurement of bauxite core analysis is obtained according to the process specified in the standard "SY / T 5163-2018 X-ray Diffraction Analysis Method for Clay Minerals and Common Non-Clay Minerals in Sedimentary Rocks", and the core analysis porosity experimental measurement data is obtained according to the process specified in the standard "GB / T 29172-2012 Core Analysis Method".

[0075] Step 2, plotting the diaspore content, clay minerals, heavy minerals, sand content and porosity of the core analysis obtained from the experiment on the same graph along with the well logging curves of the corresponding wells;

[0076] The diaspore content, clay minerals, heavy minerals, sand content of the core analysis and the natural gamma GR, acoustic wave time difference AC, compensated neutron CNL, lithology density DEN and potassium K, photoelectric absorption cross-section index PE logging curves of the corresponding wells are plotted on the same graph.

[0077] The diaspore content, clay minerals, heavy minerals and sand content of the core analysis are displayed using bar graphs.

[0078] Step 3, adjusting the left and right scales of the natural gamma and acoustic time difference logging curves, so that the natural gamma and acoustic time difference logging curves basically overlap in the low natural gamma GR and low acoustic time difference AC reservoir segments, and making the intersection of the ultra-high natural gamma and low acoustic time difference segments non-empty, so as to generate an envelope;

[0079] Specifically, the left and right scales of the natural gamma GR and acoustic time difference AC logging curves are adjusted in combination with the geological characteristics and logging response laws of the study area. Both the natural gamma and acoustic time difference curves use linear scales with a scale range of [GR L , G.R. R ] and [AC L , A.C. R ], so that the natural gamma and acoustic time difference curves overlap in the low natural gamma GR and low acoustic time difference AC reservoir segments, and make the intersection of the ultra-high natural gamma and low acoustic time difference segments non-empty, generating an envelope.

[0080] Step 4, calculating the envelope area of ​​the natural gamma and acoustic time difference logging curves of the well corresponding to the core analysis;

[0081] A similar normalization method is used to eliminate the difference in physical dimension level between natural gamma and acoustic transit time logging curves, calculate the unit area of ​​natural gamma and acoustic transit time logging curves, and determine the envelope area of ​​natural gamma and acoustic transit time logging curves.

[0082] The unit area of ​​the natural gamma logging curve is:

[0083]

[0084] The unit area of ​​the acoustic time difference logging curve is:

[0085]

[0086] The envelope area of ​​the natural gamma and acoustic time-difference logging curves is:

[0087] ENV=GR env -AC env

[0088] Where, AC is the acoustic time difference logging value, μs / m; AC env , acoustic wave time difference per unit area, dimensionless; GR is the natural gamma logging value, API; GR env , natural gamma unit area, dimensionless; ENV is the envelope area of ​​natural gamma and acoustic time difference logging curves, dimensionless.

[0089] Step 5, analyzing the relationship between the content of the main mineral diaspore at the depth point of the bauxite core measurement and the corresponding envelope area, as well as the relationship between the content of other clay minerals, heavy minerals, sand and the corresponding well logging curve;

[0090] The content of diaspore, the main mineral, at the depth points measured by cores of multiple wells and the corresponding envelope areas were statistically analyzed to establish a logging interpretation model for diaspore mineral content in bauxite layers. At the same time, based on the natural gamma, lithology density, photoelectric absorption cross-section index PE and clay minerals and heavy minerals analyzed in other cores, mathematical fitting was used to establish multiple regression models for clay minerals and heavy minerals, respectively. The final sand content can be obtained by subtracting the other three minerals from the sum of all minerals.

[0091] Specifically, based on the typical logging response characteristics of high natural gamma and low acoustic time difference of bauxite reservoirs, the GR-AC envelope area is analyzed to have a good correlation with diaspore minerals. Therefore, the established diaspore mineral content model expression is:

[0092] V ALOX =a1*ENV+b1

[0093] Where V ALOX is the content of diaspore mineral, %; a1 and b1 are model parameters.

[0094] Specifically, for conventional reservoirs, the higher the clay mineral content, the higher the natural gamma. Similarly, combined with the logging response characteristics of the bauxite reservoir in the study area, it is found through analysis that clay minerals and natural gamma logging curves also have a good correlation. The clay mineral content model expression established is:

[0095] V clay =a2*GR+b2

[0096] Where V clay is the clay mineral content, %; a2 and b2 are model parameters.

[0097] Specifically, based on the influence of heavy mineral content on lithology density and photoelectric absorption cross-section index, generally, the higher the content of heavy minerals such as pyrite and anatase, the greater the lithology density and PE value. Through statistical analysis, a logging interpretation model of heavy minerals and the product of PE and DEN was established, which further improved the calculation accuracy of heavy minerals. The heavy mineral content model expression is:

[0098] V FE =a3*PE*DEN+b3

[0099] Where V FE is the heavy mineral content, %; a3 and b3 are model parameters.

[0100] Specifically, the sand mineral content model expression is:

[0101] V SI =100-V ALOX -V clay -VFE

[0102] Where V SI Is the sandy mineral content, %.

[0103] The calculated contents of the four minerals were further compared with the contents of the four minerals obtained from the core analysis in step (1) to verify the calculation accuracy.

[0104] Step 6: Combining the natural gamma and acoustic time-difference logging curve envelope areas in the prediction well, according to the above-mentioned bauxite X-ray diffraction whole-rock mineral logging interpretation model, the diaspore content, clay minerals, heavy minerals, and sand content in the well to be identified can be quickly and continuously calculated and predicted quantitatively, and bauxite and bauxite mudstone can be precisely identified according to the contents of the four minerals;

[0105] The natural gamma ray and acoustic time difference envelope area curves were compared. At the same time, the contents of diaspore, clay minerals, heavy minerals and sandy minerals were calculated through the logging interpretation model of four minerals. Bauxite and bauxite mudstone can be precisely identified based on the contents of the four minerals.

[0106] The criteria for identifying bauxite by the content of four minerals are shown in Table 1:

[0107] Table 1 Identification standards for the main mineral contents of bauxite

[0108]

[0109] Step 7: In the bauxite layer, the relationship between the core analysis porosity and the corresponding logging curve is analyzed, a bauxite layer porosity logging interpretation model is established, and the porosity of the bauxite reservoir to be identified is calculated. The quality of the bauxite reservoir can be distinguished according to the porosity;

[0110] Specifically, the porosity model expression is:

[0111] POR=aa*CNL+bb*K+cc*PE+dd

[0112] Where POR is the porosity content, %; aa, bb, cc, and dd are model parameters.

[0113] Specifically, the standard for judging the quality of bauxite reservoirs according to porosity is as follows: when the porosity is not less than the lower limit of the porosity of Class I, the bauxite reservoir quality is good; when the porosity is between the lower limit of the porosity of Class I and the lower limit of the porosity of Class II, the bauxite reservoir quality is medium; when the porosity is less than the lower limit of the porosity of Class II, the bauxite reservoir quality is poor.

[0114] Step 8: According to the envelope area of ​​the natural gamma and acoustic transit time logging curves of the bauxite layer, a lithology homogeneity index (ENVAREA) based on the accumulation of acoustic transit time (AC)-natural gamma (GR) envelope area considering the thickness of the bauxite reservoir is constructed. The lithology homogeneity index can characterize the homogeneity and thickness of the bauxite reservoir, and can be used as another discriminant condition for classifying high-quality bauxite reservoirs.

[0115] Combined with the influence of bauxite reservoir thickness on gas content in the study area, the lithology homogeneity index ENVAREA based on the accumulation of acoustic wave transit time AC-natural gamma ray GR envelope area considering the thickness of bauxite reservoir is constructed. The lithology homogeneity index can be used to characterize the homogeneity and thickness of bauxite reservoir, which can be used as another discriminant criterion for classifying high-quality bauxite reservoirs.

[0116] Specifically, the expression of lithology homogeneity index is:

[0117]

[0118] Where ENVAREA is the lithology homogeneity index, n is the sampling point per unit depth, n = 1, 2, ... n.

[0119] Specifically, the standard for distinguishing bauxite reservoirs according to the lithology homogeneity index as good (class one), medium (class two), and poor (non-reservoir) is: when the lithology homogeneity index is not less than the lower limit of class one, the bauxite reservoir has strong homogeneity; when the lithology homogeneity index is between the lower limit of class one and the lower limit of class two, the bauxite reservoir has medium homogeneity; when the lithology homogeneity index is less than the lower limit of class two, the bauxite reservoir has poor homogeneity.

[0120] Step 9: Based on steps 7 and 8, high-quality reservoirs are comprehensively identified and divided in the bauxite layer, and the well logging identification of the high-quality bauxite reservoir to be identified is completed.

[0121] The lower limit of regional reservoir porosity and the lower limit of lithology homogeneity index are used as division conditions to comprehensively identify and divide high-quality reservoirs in the bauxite layer. Specifically, it includes:

[0122] When the porosity is not less than the lower limit of the first category porosity and the lithology homogeneity index is not less than the first category lower limit, the bauxite layer is identified and classified as a first category high-quality reservoir; when the porosity is between the first category porosity lower limit and the second category porosity lower limit and the lithology homogeneity index is between the first category lower limit and the second category lower limit, the bauxite layer is identified and classified as a second category high-quality reservoir; when the porosity is less than the second category porosity lower limit and the lithology homogeneity index is less than the second category lower limit, the bauxite layer is identified as a non-high-quality reservoir.

[0123] Example

[0124] The embodiment of the present invention provides a new method for identifying high-quality bauxite reservoirs using well logging data, which specifically includes the following steps:

[0125] Step 1: X-ray diffraction whole-rock mineral analysis experimental measurement and core analysis porosity experimental measurement were carried out on the bauxite cores in the study area to obtain the diaspore content, clay mineral, heavy mineral and sand mineral content and porosity experimental data of the core analysis. The X-ray diffraction whole-rock mineral analysis experimental measurement of the bauxite core analysis was obtained in accordance with the procedures specified in the standard "SY / T5163-2018 X-ray Diffraction Analysis Method for Clay Minerals and Common Non-Clay Minerals in Sedimentary Rocks", and the core analysis porosity experimental measurement data was obtained in accordance with the procedures specified in the standard "GB / T 29172-2012 Core Analysis Method".

[0126] Step 2: Plot the diaspore content, clay minerals, heavy minerals, sand content of the core analysis and the natural gamma (GR), acoustic transit time (AC), compensated neutron (CNL), lithology density (DEN), potassium (K), and photoelectric absorption cross-section index (PE) logging curves of the corresponding wells on the same graph (e.g. Figure 2 The diaspore content of the core analysis ( Figure 2 ), clay minerals ( Figure 2 8th bar in the graph), heavy minerals ( Figure 2 The 9th bar graph in the figure), sand content ( Figure 2 The 10th bar graph in the core analysis porosity ( Figure 2 The 13th bar graph in the figure) are all displayed in bar graph.

[0127] Step 3: Combined with the geological characteristics of the study area and the logging response law, the dolomite below the bauxite layer is used as the reference lithology, and the left and right scales of the natural gamma (GR) and acoustic transit time (AC) logging curves are adjusted to make the natural gamma and acoustic transit time curves basically overlap in the low natural gamma (GR) and low acoustic transit time (AC) reservoir sections ( Figure 2 The 4th track 4056~4060m) makes the intersection of the layers with high natural gamma and acoustic time difference non-empty, generating an envelope ( Figure 2 The fourth track is 4040~4054m), and the natural gamma and acoustic time difference curves are both linearly scaled, with the scale range being [0, 500] and [150, 275].

[0128] Step 4: Use a similar normalization method to eliminate the difference in physical dimension level between the natural gamma and acoustic time difference logging curves, calculate the unit area of ​​the natural gamma and acoustic time difference logging curves, and determine the envelope area of ​​the natural gamma and acoustic time difference logging curves.

[0129] Calculate the unit area of ​​the natural gamma curve, such as Figure 2 GR on track 5 env curve:

[0130]

[0131] Where, GR is the natural gamma logging value, API; GR env , natural gamma per unit area, dimensionless.

[0132] The unit area of ​​the acoustic time difference logging curve is calculated as follows: Figure 2 AC in Lane 5 env curve:

[0133]

[0134] Where, AC is the acoustic time difference logging value, μs / m; AC env , unit area of ​​acoustic transit time, dimensionless. Calculate the envelope area of ​​natural gamma and acoustic transit time logging curves, such as Figure 2 The 6th ENV curve:

[0135] ENV=GR env -AC env

[0136] Where ENV is the envelope area of ​​natural gamma and acoustic time-difference logging curves, dimensionless.

[0137] Step five, statistically analyze the content of the main mineral diaspore at depth points measured by cores of multiple wells and the corresponding envelope area, and establish a logging interpretation model for the diaspore content of bauxite in the study area; at the same time, based on the natural gamma, lithology density, photoelectric absorption cross-section index (PE) combined with other core analyses of clay minerals and heavy minerals, mathematical fitting is used to establish multiple regression models for clay minerals and heavy minerals respectively; the final sand content can be obtained by subtracting the other three minerals from the sum of all minerals (100%).

[0138] The model expression for calculating the content of diaspore mineral is:

[0139] V ALOX =a1*ENV+b1

[0140] Where V ALOXis the diaspore mineral content, %; a1 is the statistical linear regression coefficient of the diaspore content of the main mineral at the core measurement depth points of multiple wells and the corresponding envelope area data, b1 is the constant term of the sample data regression, which represents the diaspore content when ENV is 0; by statistically analyzing the relationship between the diaspore content at the core measurement depth points of multiple wells in the study area and the corresponding envelope area, the two parameters a1 and b1 in the model were determined by mathematical fitting, among which a1=106.8, b1=7.637, and the correlation coefficient of the model reached 0.893.

[0141] The model expression for calculating clay mineral content is:

[0142] V clay =a2*GR+b2

[0143] Where V clay is the clay mineral content, %; a2 is the sample data regression coefficient, b2 is the constant term of sample data regression, which represents the clay mineral content when GR is 0; by statistically analyzing the clay mineral content at the core measurement depth points of multiple wells in the study area and the natural gamma logging curve, it is found that there is a good correlation between the two parameters a2 and b2 in the model by mathematical fitting, where a2=-0.219, b2=131.8, and the correlation coefficient of the model reaches 0.807.

[0144] The model expression for calculating heavy mineral content is:

[0145] V FE =a3*PE*DEN+b3

[0146] Where V FE is the heavy mineral content, %; a3 is the sample data regression coefficient, b3 is the constant term of sample data regression, which represents the heavy mineral content when PE or DEN is 0; by statistically analyzing the heavy mineral content at the core measurement depth points of multiple wells in the study area, it is found that there is a good correlation between the photoelectric absorption cross-section index and the lithology density. The two parameters a3 and b3 in the model are determined by mathematical fitting, where a3=1.713, b3=-7.337, and the correlation coefficient of the model reaches 0.852.

[0147] The final sand content can be obtained by subtracting the other three minerals from the sum of all minerals (100%). The model expression for calculating the sand mineral content is:

[0148] V SI =100-V ALOX -V clay -V FE

[0149] Where V SI Is the sandy mineral content, %.

[0150] Step six, combining natural gamma and sonic time difference envelope area, calculate the content of diaspore mineral, clay mineral, heavy mineral and sandy mineral according to the logging interpretation model of the above four minerals, and finely identify bauxite and bauxite mudstone according to the identification standard of the main mineral content of bauxite in Table 1.

[0151] Step seven, in the bauxite layer section, the porosity of the depth points is measured by core measurements of multiple wells, and the compensated neutron (CNL), photoelectric absorption cross-section index (PE) and potassium (K) logging curves with good correlation with the porosity are preferably selected, and a multi-parameter regression model is established by mathematical fitting to calculate the porosity of the bauxite reservoir to be identified.

[0152] The porosity calculation model expression is:

[0153] POR=aa*CNL+bb*K+cc*PE+dd

[0154] Wherein, POR is the porosity content, %; aa is the regression coefficient of core analysis porosity and CNL, bb is the regression coefficient of core analysis porosity and K, cc is the regression coefficient of core analysis porosity and PE, dd is the constant term of sample data regression. By statistically analyzing the relationship between the porosity of core measurement depth points of multiple wells in the study area and CNL, K, and PE, the four parameters aa, bb, cc, and dd in the model were determined by mathematical fitting, among which aa=0.21, bb=-3.34, cc=-1.25, and dd=-0.59, and the correlation coefficient of the model reached 0.86.

[0155] After the porosity is calculated, the quality of the bauxite reservoir is judged according to whether the reservoir is good (class one), medium (class two), or poor (non-reservoir). When the porosity is not less than the lower limit of the first class porosity, the bauxite reservoir quality is good; when the porosity is between the lower limit of the first class porosity and the lower limit of the second class porosity, the bauxite reservoir quality is medium; when the porosity is less than the lower limit of the second class porosity, the bauxite reservoir quality is poor. In this example, the lower limit of the first class porosity is 12%, and the lower limit of the second class porosity is 5%, that is, when the porosity content is above 12%, the reservoir quality is good; when the porosity content is between 5-12%, the reservoir quality is medium; when the porosity content is below 5%, the reservoir quality is poor.

[0156] Step eight, considering the influence of bauxite reservoir thickness on reservoir effectiveness and gas content in the study area, a lithology homogeneity index (ENVAREA) based on acoustic transit time (AC)-natural gamma ray (GR) envelope area accumulation considering the bauxite reservoir thickness is constructed.

[0157] The expression for calculating the lithology homogeneity index is:

[0158]

[0159] Where ENVAREA is the lithology homogeneity index, n is the sampling point per unit depth, n = 1, 2, ... n; in this example, according to the above-identified bauxite reservoir section, the effective thickness of the reservoir can be obtained, and then the unit depth of 0.125m is used as the sampling interval to calculate the lithology homogeneity index (ENVAREA) of the acoustic wave transit time (AC)-natural gamma (GR) envelope area accumulation ( Figure 2 Track 15). The homogeneity of the bauxite reservoir is determined according to the categories of good reservoir (Class I), medium (Class II), and poor (non-reservoir). When the lithology homogeneity index is not less than the lower limit of Class I, the homogeneity of the bauxite reservoir is strong; when the lithology homogeneity index is between the lower limit of Class I and the lower limit of Class II, the homogeneity of the bauxite reservoir is medium; when the lithology homogeneity index is less than the lower limit of Class II, the homogeneity of the bauxite reservoir is poor. In this example, the lower limit of the Class I lithology homogeneity index is 30, and the lower limit of the Class II lithology homogeneity index is 5, that is, when the lithology homogeneity index is above 30, the reservoir homogeneity is good; when the lithology homogeneity index is between 5-30, the reservoir homogeneity is medium; when the lithology homogeneity index is below 5, the reservoir homogeneity is poor.

[0160] Step nine, based on steps seven and eight, comprehensively identifying and dividing high-quality reservoirs in the bauxite interval, specifically, Figure 2 As shown, after the bauxite layer section is identified in step 6, on the basis of steps 7 and 8, the lower limit of the regional reservoir porosity and the lower limit of the lithology homogeneity index are used as discrimination conditions at the same time, and the reservoir is divided into good (class 1), medium (class 2), and poor (non-reservoir) as the standard. Specifically, in this example, the lower limit of the porosity of class 1 is 12%, the lower limit of the porosity of class 2 is 5%, the lower limit of the lithology homogeneity index of class 1 is 30, and the lower limit of the lithology homogeneity index of class 2 is 5, that is, when the porosity content is above 12% and the lithology homogeneity index is above 30, it is identified as a class 1 high-quality bauxite reservoir; when the porosity content is between 5-12% and the lithology homogeneity index is between 5-30, it is a class 2 high-quality reservoir; when the porosity content is below 5% and the lithology homogeneity index is below 5, it is a non-high-quality reservoir.

[0161] In the actual data processing process, a new method of using conventional logging data to identify high-quality bauxite reservoirs is implemented by writing a program. Figure 2In order to use the natural gamma and acoustic wave time difference envelope area and other conventional logging curves to calculate the content of four minerals, porosity and lithology homogeneity index and identify and divide high-quality reservoirs, the first channel is the lithology curve natural gamma (GR), photoelectric absorption cross-section index (PE) and potassium (K), the second channel is three porosity curves (AC, DEN, CNL), and the third channel is the depth channel. According to the process of steps 1 to 9, the diaspore content ( Figure 2 ), clay minerals ( Figure 2 8th bar in the graph), heavy minerals ( Figure 2 The 9th bar graph in the figure), sand content ( Figure 2 The 10th bar graph in the core analysis porosity ( Figure 2 According to the 13th bar graph in the study area, the linear scale of natural gamma (GR) [0, 500] and the linear scale of acoustic transit time (AC) [150, 275] were adjusted to make the natural gamma and acoustic transit time curves overlap in the low natural gamma (GR) and low acoustic transit time (AC) reservoir sections ( Figure 2 The fourth track is 4056~4060m), and the intersection of high natural gamma and acoustic time difference is non-empty, generating an envelope ( Figure 2Track 4 (4040-4054 ​​m); Track 5 is the calculated unit area of ​​natural gamma and acoustic transit time logging curves; Track 6 is the calculated envelope area of ​​the two; Tracks 7 to 10 are respectively the calculated accuracy comparisons of diaspore mineral content, clay mineral, heavy mineral and sand content with the corresponding core analysis of four types of rock minerals; Track 11 is the calculated mineral lithology profile; Track 12 is the lithology conclusion track; Track 13 is the calculated porosity accuracy comparison with the core analysis porosity; Track 14 is the calculated acoustic transit time (AC)-natural gamma (GR) envelope surface cumulative addition; Track 15 is the final accumulated lithology homogeneity index (ENVAREA); Track 16 is the final bauxite reservoir division conclusion track. From the actual data processing results, the calculated contents of the four minerals are basically consistent with the experimental analysis results. According to the identification standard of the main mineral content of bauxite (Table 1), the 4042.4-4044.8m and 4046.2-4054m depth sections are identified as bauxite; the calculated porosity results are also consistent with the experimental analysis. Combined with the calculated lithology homogeneity index, the 4046.2-4054m depth section is identified as a first-class high-quality reservoir with a porosity greater than 12% and a lithology homogeneity index greater than 15; the porosity of the 4042.4-4044.8m depth section is between 5-12%, and the lithology homogeneity index is between 5-15, which is identified as a second-class reservoir; the porosity of the 4038-4041.8m and 4054-5057.3m depth sections is less than 5%, and the lithology homogeneity index is less than 5, which is a non-high-quality reservoir. The feasibility and effectiveness of the method were verified, and the characteristics of the invention in identifying high-quality bauxite reservoirs with high accuracy and strong adaptability were demonstrated.

[0162] The present invention combines the geological characteristics of the study area and the logging response law, and proposes a method for identifying high-quality bauxite reservoirs using logging data. By adjusting the left and right scales of the natural gamma and sonic time difference curves of the fixed study area, the size of the envelope area can be used to qualitatively identify the aluminum-bearing rock layer; then the unit area of ​​the natural gamma and sonic time difference logging curves is calculated, and then the mirror image envelope area of ​​the two is determined; then a multivariate regression fitting model is used to establish the relationship between the envelope area and the content of diaspore minerals, the main rock mineral component in the bauxite reservoir, and other clay minerals, heavy minerals, and sandy minerals and conventional logging curves, and a corresponding logging interpretation model is established to achieve rapid vertical continuous depth determination. The content of diaspore, clay minerals, heavy minerals and sand content are calculated to quantitatively identify the bauxite layer; then based on the core analysis physical property data, a multi-parameter logging interpretation model is established using data such as compensated neutron, potassium, and photoelectric absorption cross-section index PE to calculate the porosity of the bauxite layer; then the lithology homogeneity index ENVAREA based on the acoustic wave time difference AC-natural gamma ray GR envelope surface accumulation considering the thickness of the bauxite reservoir is constructed; finally, the lithology homogeneity index and porosity are used to identify and divide high-quality bauxite reservoirs in the bauxite formation. The method of the present invention is simple, clear in thought, easy to operate, and highly reproducible, which improves the calculation accuracy of reservoir mineral components and physical property parameters. It has good universality and promotion value.

[0163] The present invention proposes a system for identifying high-quality bauxite reservoirs using well logging data, such as Figure 3 As shown, it includes an envelope area acquisition module, a mineral content acquisition module, a bauxite identification module and a bauxite reservoir analysis module;

[0164] The envelope area acquisition module is used to acquire the envelope areas of the natural gamma logging curve and the acoustic time difference logging curve according to the natural gamma logging curve and the acoustic time difference logging curve;

[0165] The mineral content acquisition module is used to obtain the content of diaspore minerals according to the envelope area, obtain the content of clay minerals according to the natural gamma logging value, and obtain the content of heavy minerals according to the lithology density and the photoelectric absorption cross-section index;

[0166] The bauxite identification module is used to obtain the sandy mineral content according to the diaspore mineral content, the clay mineral content and the heavy mineral content; and to identify the lithology as bauxite according to the diaspore mineral content, the clay mineral content, the heavy mineral content and the sandy mineral content;

[0167] The bauxite reservoir analysis module is used to obtain the porosity and lithology homogeneity index of bauxite, and obtain a high-quality bauxite reservoir by combining the porosity and lithology homogeneity index of bauxite.

[0168] The present invention combines the geological characteristics of the study area and the logging response law, and proposes a new method for identifying high-quality bauxite reservoirs using logging data. By adjusting the left and right scales of the natural gamma and acoustic time difference curves of the fixed study area, the size of the envelope area can be used to qualitatively identify the aluminum-bearing rock layer; then the unit area of ​​the natural gamma and acoustic time difference logging curves is calculated, and the mirror image envelope area of ​​the two is determined; then a multivariate regression fitting model is used to establish the relationship between the envelope area and the main rock mineral component in the bauxite reservoir - the content of diaspore minerals and other clay minerals, heavy minerals, and sandy minerals and conventional logging curves, and a corresponding logging interpretation model is established to achieve rapid quantitative calculation of vertical continuous depth - The content of diaspore, clay minerals, heavy minerals and sand content are used to quantitatively identify the bauxite layer; then based on the core analysis physical property data, a multi-parameter logging interpretation model is established using data such as compensated neutron, potassium, and photoelectric absorption cross-section index (PE) to calculate the porosity of the bauxite layer; then a lithology homogeneity index (ENVAREA) based on the acoustic time difference (AC)-natural gamma ray (GR) envelope surface accumulation is constructed considering the thickness of the bauxite reservoir; finally, the lithology homogeneity index and porosity are used to identify and divide high-quality bauxite reservoirs in the bauxite formation. The method of the present invention is simple, clear in thought, easy to operate, and highly reproducible, which improves the calculation accuracy of reservoir mineral components and physical property parameters. It has good universality and promotion value.

[0169] The terminal device provided in an embodiment of the present invention comprises: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps in the above-mentioned method embodiments are implemented. Alternatively, when the processor executes the computer program, the functions of the modules / units in the above-mentioned device embodiments are implemented.

[0170] The computer program may be divided into one or more modules / units, and the one or more modules / units are stored in the memory and executed by the processor to accomplish the present invention.

[0171] The terminal device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0172] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0173] The memory may be used to store the computer programs and / or modules, and the processor implements various functions of the terminal device by running or executing the computer programs and / or modules stored in the memory and calling the data stored in the memory.

[0174] If the module / unit integrated in the terminal device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0175] The purpose of the present invention is to improve the calculation accuracy of the main mineral components and physical property parameters of the bauxite reservoir, avoid the misclassification and omission of the natural gas reservoir in the bauxite formation, realize the accurate identification of the effective reservoir, and reduce the logging cost. Therefore, it is proposed to use the envelope area of ​​the natural gamma ray and acoustic time difference logging curves to qualitatively identify bauxite, and at the same time establish a logging interpretation model of the content of diaspore minerals and other clay minerals, heavy minerals and sand content with conventional logging curves to realize quantitative identification of bauxite; combined with the established multi-parameter porosity calculation model and the lithology homogeneity index (ENVAREA) based on the acoustic time difference (AC)-natural gamma ray (GR) envelope area accumulation considering the thickness of the bauxite reservoir, it can realize the rapid identification of high-quality bauxite reservoirs in the interpretation of old wells and new wells.

[0176] The present invention provides a method for identifying high-quality bauxite reservoirs using conventional logging data. The method combines the geological characteristics of the study area with the logging response law, and by adjusting the left and right scales of the natural gamma and acoustic time difference curves of the fixed study area, the bauxite layer section can be qualitatively identified using the size of the envelope area; at the same time, on this basis, the unit area of ​​the natural gamma and acoustic time difference logging curves is calculated, and then the mirror image envelope area of ​​the two is determined; further, a multivariate regression fitting model is used to establish the relationship between the envelope area and the main rock mineral component of the bauxite reservoir, the content of diaspore minerals, other clay minerals, heavy minerals, and sandy three minerals, and the conventional logging curve, and a corresponding logging interpretation model is established to achieve rapid quantitative calculation of the diaspore content, clay minerals, heavy minerals, and sandy content in a vertical continuous depth, while overcoming the problem that the single parameter model has a large error. The paper solves the big problem and realizes the accurate calculation of mineral components in bauxite formations; thus, the bauxite intervals are quantitatively identified; then, based on the physical property data of core analysis, a multi-parameter logging interpretation model is established using the compensated neutron, potassium, and photoelectric absorption cross-section index (PE) logging data to calculate the porosity of the bauxite interval with higher accuracy; based on the influence of the thickness of the bauxite reservoir in the study area on the gas content, a lithology homogeneity index (ENVAREA) based on the acoustic time difference (AC)-natural gamma ray (GR) envelope surface accumulation is constructed considering the thickness of the bauxite reservoir to characterize the homogeneity of the bauxite reservoir; finally, the lithology homogeneity index and porosity are used to identify and divide high-quality bauxite reservoirs in bauxite formations, which effectively improves the identification accuracy of natural gas reservoirs and meets the accuracy requirements of logging interpretation and evaluation work such as reservoir classification and reserve calculation. The identification implementation method of the present invention is simple, easy to operate, and highly reproducible, which improves the efficiency of exploration and development of tight oil and gas reservoirs, avoids the misclassification and omission of natural gas reservoirs in bauxite formations, and realizes accurate identification of effective reservoirs; in the absence of formation elements and special nuclear magnetic resonance logging projects, the present invention uses conventional logging data such as natural gamma and porosity logging to quickly and effectively determine reservoir lithology and physical property parameters, with low cost, can improve production timeliness, save collection and gas testing and production costs, and has good universality and promotion value. The present invention effectively solves the problems of lack of data on old wells in oil fields and difficulty in logging evaluation of bauxite. The proposed method is simple, easy to operate, highly reproducible, and the calculation accuracy meets the needs of on-site production. It can better serve the work of logging evaluation and gas testing and layer selection of unconventional reservoirs such as bauxite, and has good universality and promotion value.

[0177] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for identifying high-quality bauxite reservoirs using well logging data, characterized in that: The steps include: According to the natural gamma logging curve and the sonic time difference logging curve, the envelope area of ​​the natural gamma logging curve and the sonic time difference logging curve is obtained; The content of diaspore minerals is obtained based on the envelope area, the content of clay minerals is obtained based on the natural gamma logging value, and the content of heavy minerals is obtained based on the lithology density and the photoelectric absorption cross-section index; The sand mineral content is obtained according to the diaspore mineral content, the clay mineral content and the heavy mineral content; the lithology is identified as bauxite according to the diaspore mineral content, the clay mineral content, the heavy mineral content and the sand mineral content; The porosity and lithology homogeneity index of bauxite are obtained, and high-quality bauxite reservoirs are obtained by combining the porosity and lithology homogeneity index of bauxite.

2. The method for identifying high-quality bauxite reservoirs using well logging data according to claim 1, characterized in that: The envelope area of ​​the logging curve is obtained as follows: ENV=GR env -AC env Among them, the unit area of ​​the natural gamma logging curve is Unit area of ​​acoustic transit time logging curve GR is the natural gamma logging value, AC is the acoustic time difference logging value, GR L is the minimum value of the natural gamma logging curve, GR R is the maximum value of the natural gamma logging curve, AC L is the minimum value of the acoustic time difference logging curve, AC R It is the maximum value of the acoustic time difference logging curve.

3. The method for identifying high-quality bauxite reservoirs using well logging data according to claim 1, characterized in that: Diaspore mineral content V ALOX =a1*ENV+b1, clay mineral content V clay =a2*GR+b2, heavy mineral content V FE =a3*PE*DEN+b3; The method for obtaining the sand mineral content is as follows: V SI =100-V ALOX -V clay -V FE Among them, a1 is the statistical linear regression coefficient of the main mineral diaspore content at the core measurement depth points of multiple wells and the corresponding envelope area data, b1 is the constant term of sample data regression, which represents the diaspore content when ENV is 0; a2 is the sample data regression coefficient, b2 is the constant term of sample data regression, which represents the clay mineral content when GR is 0; a3 is the sample data regression coefficient, b3 is the constant term of sample data regression, which represents the heavy mineral content when PE or DEN is 0.

4. The method for identifying high-quality bauxite reservoirs using well logging data according to claim 1, characterized in that: The method to identify the lithology as bauxite is as follows: When the mineral content of diaspore in the mineral is >50%, the mineral content of clay is <25%, the content of heavy minerals is <10%, and the mineral content of sand is <10%, the resulting lithology is bauxite.

5. The method for identifying high-quality bauxite reservoirs using well logging data according to claim 1, characterized in that: The porosity of bauxite is obtained as follows: POR=aa*CNL+bb*K+cc*PE+dd Among them, aa is the regression coefficient of core analysis porosity and CNL, bb is the regression coefficient of core analysis porosity and K, cc is the regression coefficient of core analysis porosity and PE, dd is the constant term of sample data regression, CNL is the compensated neutron logging value, PE is the photoelectric absorption cross-section index, and K is the potassium content logging value; The lithology homogeneity index of bauxite is obtained as follows: Among them, ENV n is the envelope area of ​​the natural gamma and acoustic time difference logging curves at the nth depth sampling point, ENV n-1 is the envelope area of ​​the natural gamma and acoustic time difference logging curves at the n-1th depth sampling point, n is the unit depth sampling point, n = 1, 2, ... n.

6. The method for identifying high-quality bauxite reservoirs using well logging data according to claim 5, characterized in that: When the porosity is not less than the lower limit of Class I porosity, the bauxite reservoir quality is good; when the porosity is between the lower limit of Class I porosity and the lower limit of Class II porosity, the bauxite reservoir quality is medium; when the porosity is less than the lower limit of Class II porosity, the bauxite reservoir quality is poor.

7. The method for identifying high-quality bauxite reservoirs using well logging data according to claim 5, characterized in that: When the lithology homogeneity index is not less than the first lower limit, the bauxite reservoir has strong homogeneity; when the lithology homogeneity index is between the first lower limit and the second lower limit, the bauxite reservoir has medium homogeneity; when the lithology homogeneity index is less than the second lower limit, the bauxite reservoir has poor homogeneity.

8. A system for identifying high-quality bauxite reservoirs using well logging data, characterized in that: include: An envelope area acquisition module, which is used to acquire the envelope areas of the natural gamma logging curve and the acoustic time difference logging curve according to the natural gamma logging curve and the acoustic time difference logging curve; A mineral content acquisition module, wherein the mineral content acquisition module is used to acquire the content of diaspore minerals according to the envelope area, acquire the content of clay minerals according to the natural gamma logging value, and acquire the content of heavy minerals according to the lithology density and the photoelectric absorption cross-section index; A bauxite identification module, the bauxite identification module is used to obtain the sandy mineral content according to the diaspore mineral content, the clay mineral content and the heavy mineral content; and identify the lithology as bauxite according to the diaspore mineral content, the clay mineral content, the heavy mineral content and the sandy mineral content; A bauxite reservoir analysis module is used to obtain the porosity and lithology homogeneity index of bauxite, and obtain a high-quality bauxite reservoir by combining the porosity and lithology homogeneity index of bauxite.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method for identifying high-quality bauxite reservoirs using well logging data as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method for identifying high-quality bauxite reservoirs using well logging data as described in any one of claims 1 to 7 are implemented.

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