Buried hill formation logging evaluation method based on elemental analysis

Abstract

The application discloses a buried hill stratum logging evaluation method based on element analysis, which comprises the following steps: calculating mineral components of a to-be-evaluated stratum based on element logging data and core analysis data; identifying lithology based on the mineral components; establishing a corresponding relationship between rocks and element measurement through rock mechanics analysis and the mineral components; performing reservoir space analysis based on the corresponding relationship and a result of the lithology identification; determining a dominant lithofacies based on a result of the reservoir space analysis; and identifying fluid properties based on the dominant lithofacies and logging, drilling, oil testing and the core analysis data. The application can clearly determine buried hill reservoir forming potential, and provide a geological-geophysical theoretical basis for base rock reservoir prediction and technical research and development.

Description

Technical Field

[0001] This invention relates to a well logging evaluation technology for buried hill reservoirs, belonging to the field of exploration-well logging evaluation technology. Background Technology

[0002] Exploration has revealed well-developed buried hills in some areas, but the development mechanism of these buried hill reservoirs remains unclear, and the identification of fluid properties is difficult. These areas have undergone 350 million years of peneplaining, and whether the crystalline bedrock has developed well into reservoirs and whether oil and gas can accumulate and form reservoirs is a theoretical challenge. Summary of the Invention

[0003] In view of the technical defects and drawbacks existing in the prior art, the embodiments of the present invention provide a well logging evaluation method for buried hill formations based on elemental analysis to overcome or at least partially solve the above problems.

[0004] One embodiment of the present invention provides a well logging evaluation method for buried hill formations based on elemental analysis, including:

[0005] The mineral composition of the formation to be evaluated was calculated based on elemental logging data and core analysis data.

[0006] Lithological identification based on the aforementioned mineral composition;

[0007] The correspondence between rock and elemental measurements was established through rock mechanics analysis and the mineral composition.

[0008] Based on the correspondence and the results of the lithology identification, reservoir space analysis is performed;

[0009] The dominant lithofacies were determined based on the results of the reservoir space analysis.

[0010] Based on the dominant lithofacies and well logging, well logging, oil testing and core analysis data, fluid properties are identified.

[0011] One aspect of this invention is to provide an application of the above-mentioned evaluation method in fluid identification of buried hill reservoirs.

[0012] Another aspect of the present invention provides a fluid identification method for buried hill reservoirs, wherein the above-mentioned evaluation method is used.

[0013] This invention addresses the challenges of complex and variable lithology in buried hill formations, unclear reservoir development mechanisms, and difficulties in fluid property identification. It proposes a buried hill evaluation technique based on elemental analysis. This technique evaluates buried hill formations at the mineral level, quantitatively identifies lithology, determines dominant lithologies, and then comprehensively applies oil testing and core analysis data to accurately and quantitatively identify fluid properties. This clarifies the reservoir potential of buried hills and provides a geological and geophysical theoretical foundation for bedrock reservoir prediction and technology development.

[0014] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures described in the written description, claims, and drawings.

[0015] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0016] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0017] Figure 1 This is a technical block diagram of the buried hill formation logging evaluation method based on elemental analysis as described in the embodiments of the present invention;

[0018] Figure 2 To quantitatively identify lithological plates using thin-section compensated neutron and density;

[0019] Figure 3 Lithology plates for identifying thin-section natural gamma and compensated density;

[0020] Figure 4 Quantitative identification of lithology using neutron density;

[0021] Figure 5 A lithology plate for quantitative identification of gamma density;

[0022] Figure 6 Quantitative identification of lithological plates using M and N values;

[0023] Figure 7 A schematic diagram illustrating the typical characteristics of different lithologies in Chad buried hills;

[0024] Figure 8 Lithological plates for iron-potassium element identification;

[0025] Figure 9 A lithological plate for identifying silicon-iron elements;

[0026] Figure 10 Example of a cross-section diagram for silicon;

[0027] Figure 11 Example of a cross-section chart for potassium;

[0028] Figure 12 An example diagram illustrating the use of gas cross-plots to identify fluid properties;

[0029] Figure 13 Cross-plot of deep lateral resistivity and wave impedance of stratified oil testing wells;

[0030] Figure 14 Cross-plot of long-term felsic mass and wave impedance of stratified oil testing wells;

[0031] Figure 15 To verify the cross plot of lateral resistivity and wave impedance at well depth;

[0032] Figure 16 To verify the cross plot of well length, mass, and wave impedance;

[0033] Figure 17 The graph shows the net total ratio sensitivity analysis curve.

[0034] Figure 18 The graph shows the relationship between porosity and cumulative energy loss.

[0035] Figure 19 This is a graph showing the relationship between permeability and cumulative energy storage loss.

[0036] Figure 20 This is a cross-plot of porosity and resistivity of the test oil layer.

[0037] Figure 21 Cross-plot of lateral resistivity at varying depths in the tested oil layer;

[0038] Figure 22 This is a cross-sectional diagram of porosity and acoustic wave intensity in the test oil layer.

[0039] Figure 23 This is a cross-plot of density and acoustic waves in the test oil layer. Detailed Implementation

[0040] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0041] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of apparatuses and methods consistent with some aspects of the invention as detailed in the appended claims.

[0042] When the inventors were researching the above problems, they found that the characteristics of buried hill strata are: complex lithological variations, with metamorphic rocks, igneous rocks and sedimentary rocks all developed; diverse reservoir types, which can be pores, fractures or caves, or a combination of all three; large variations in physical properties, with strong vertical and planar heterogeneity.

[0043] To effectively evaluate buried hill formations, the best approach is to conduct research at the mineral level, starting with their genetic mechanisms. Elemental logging data provides abundant information and tools. Based on this, the inventors proposed a buried hill evaluation technique based on elemental analysis, which evaluates buried hill formations at the mineral level and thus accurately identifies fluid properties.

[0044] Therefore, embodiments of the present invention provide a method for evaluating buried hill formations using well logging based on elemental analysis, such as... Figure 1 As shown, it includes the following steps:

[0045] Step A: Calculate the mineral composition of the formation to be evaluated based on elemental logging data and core analysis data.

[0046] The formation to be evaluated is, for example, a buried hill formation. The mineral components, based on their color and chemical composition, can be broadly divided into light-colored and dark-colored mineral series. The light-colored mineral series mainly includes colorless or light-colored minerals containing elements such as Si, K, and Na. This mineral series exhibits low density and low neutrons in conventional well logging responses. The light-colored mineral series is dominated by quartz and feldspar, and is also known as felsic rocks. The dark-colored mineral series contains elements such as Fe, Mg, Ca, and Ti, and can also be called ferromagnesian rocks. In conventional well logging responses, it exhibits high density, high neutrons, low natural gamma, and a high photoelectric absorption cross-section index.

[0047] Specifically, elemental analysis can be performed first based on elemental logging data, that is, based on the magnitude of the measured element values, it can be determined which element combinations are characteristic of the formation to be evaluated; then, based on the results of elemental analysis and core analysis data in core analysis data, mineral composition can be calculated.

[0048] For example, since the variations in mineral composition among different lithologies are essentially variations in elemental content, elemental content data from ECS logging can be used to distinguish between different lithologies, such as... Figure 8 and Figure 9 As shown, iron, potassium, and silicon are effective elements in identifying buried hill lithology, with potassium and iron showing the best identification results. Furthermore, at specific calibrations, the potassium, silicon, and iron element curves combined exhibit "positive difference" or "negative difference" results consistent with the neutron and density curves. Figure 7 As shown, the typical characteristics of different lithologies in Chad buried hills were summarized using core thin sections, conventional logging curves, and ECS logging curves.

[0049] Step B: Lithological identification based on the mineral composition.

[0050] Specifically, a quantitative standard for identifying lithology can be established first through well logging and the calculated mineral composition; then, the lithology can be identified based on the quantitative standard.

[0051] For example, based on core analysis data, through various cross-plots ( Figures 2-6 To accurately and effectively identify lithology, well logging responses sensitive to lithological changes are selected. Combined with elemental analysis of mineral composition, quantitative standards for lithological identification are established. These quantitative standards are shown in Table 1.

[0052] Table 1

[0053]

[0054] Step C involves performing reservoir space analysis based on the results of rock mechanics analysis and the lithology identification.

[0055] The reservoir space is mainly composed of cracks, dissolution pores, and cavities.

[0056] First, the measured elements are matched in quantity according to the form of minerals (such as quartz SiO2) to establish an effective correspondence between rock minerals and elements in well logging.

[0057] The purpose of rock mechanics analysis is mainly to see which rock types are more brittle and more prone to crack formation.

[0058] Specifically, based on the quantitative identification of lithology and the establishment of rock mineral-element logging responses, systematic petrological and mechanical analyses of samples can be conducted. Experiments show that massive, fine-grained, homogeneous rocks have high uniaxial compressive strength and brittleness: granulite > acidic rock (monzogranite) > mixed granite > mixed gneiss (banded migmatite) > intermediate rock (syenite); rocks with high felsic mineral content and low dark mineral content have higher elastic modulus, lower Poisson's ratio, higher rigidity, and are more prone to fracture: mixed granite (alkali feldspar migmatite) > acidic rock (monzogranite) > mixed gneiss > granulite. The uniaxial compressive strength of the biotite-hornblende-plagioclase granulite is 153 MPa, exceeding the range achievable by regional stress; despite its high brittleness parameters, the rock does not exhibit well-developed fractures.

[0059] For example, felsic rocks have a high elastic modulus, low tensile and shear strength, and are brittle and prone to fractures. Dissolution along the fractures can further expand the reservoir space. Therefore, the reservoir space is mainly composed of fractures, and also contains some pores and caverns.

[0060] Based on the fracture development and rock type, the reservoir space analysis criteria obtained in this step are shown in Table 2:

[0061] Table 2

[0062]

[0063] Step D: Determine the dominant lithofacies based on the results of the reservoir space analysis.

[0064] Specifically, a quantitative standard for identifying dominant lithofacies can be established first based on the results of the reservoir space analysis; then, the dominant lithofacies can be determined based on the quantitative standard.

[0065] For example, based on reservoir space analysis, the dominant lithofacies were identified. Experimental results showed that the brittleness ranking of buried hill rocks, i.e., the dominant lithofacies, was as follows: mixed granites, acidic rocks, mixed gneiss, intermediate rocks, gneiss, and basic rocks. Mixed granites and granites were identified as the dominant lithofacies.

[0066] The potential for buried hill hydrocarbon accumulation was clearly identified. Light-colored minerals (quartz and feldspar) in the bedrock exhibit high elastic modulus and low tensile-shear strength, making them brittle and prone to fracture formation. Dissolution along these fractures further expands the reservoir space, revealing the "two-layer, two-porosity medium" reservoir development mechanism of the bedrock weathering crust + fracture section, thus clarifying the potential for buried hill hydrocarbon accumulation. Through systematic investigation of bedrock outcrops and petrological and mechanical analysis of samples, it was found that light-colored minerals (quartz and feldspar) in the bedrock exhibit high elastic modulus and low tensile-shear strength, making them brittle and prone to fracture formation. Dissolution along these fractures further expands the reservoir space, revealing the "two-layer, two-porosity medium" reservoir development mechanism of the bedrock weathering crust + fracture section, thus clarifying the potential for buried hill hydrocarbon accumulation.

[0067] Specifically, logging responses sensitive to lithological reactions can be selected, and cross-plots of iron-potassium and iron-silicon elements in different lithologies can be generated (e.g., ...). Figure 10 and Figure 11 As shown in the figure, logging response models for two types of rocks, felsic and ferromagnesian, were created, and methods and standards for quantitatively identifying lithofacies were established.

[0068] The correspondences obtained in this step are shown in Table 3:

[0069] Table 3

[0070]

[0071] Table 3 shows the final quantitative methods and standards for identifying lithofacies. Rock ECS (elemental capture logging) classification is based on mechanical properties; felsic rocks are brittle, easily fractured, and are the dominant lithofacies. This example only includes mixed granites and acidic intrusive rocks (mainly granites).

[0072] Step E: Identify fluid properties based on the dominant lithofacies and well logging, well logging, oil testing and core analysis data.

[0073] Specifically, multi-parameter reservoir evaluation standards and quantitative oil layer discrimination standards can be established first based on the dominant lithofacies and well logging, core testing, and core analysis data. Then, based on these standards, buried hill formation evaluation can be carried out at the mineral level to correctly identify fluid properties. For example, after identifying the reservoir, identifying fluid properties by combining well logging, core sampling, and core testing data can include the following steps:

[0074] 1. Identify fluid properties using data from well logging, coring, and oil testing.

[0075] When encountering an oil-bearing layer: the logging data response characteristics reflect fracture development and good reservoir properties; cuttings logging, core sampling, and wellbore core sampling show good oil and gas indications (level medium or above); gas logging values ​​are high and relatively active.

[0076] When drilling encounters a water-bearing layer: the logging data response characteristics reflect fracture development and good reservoir properties; the oil and gas indications from cuttings logging, core sampling, and wellbore coring are poor (level medium or below) or non-existent; the gas logging values ​​are low and inactive.

[0077] When encountering dry formations: the response characteristics of logging data reflect poor reservoir properties; the oil and gas indication levels of drilling cores and wellbore cores are low or non-existent.

[0078] 2. Quantitatively identify oil layers using gas logging data

[0079] Based on well testing data, cross-plotting of total hydrocarbons and peak-base ratios from well logging gas logging can help identify the fluid properties of the reservoir. For example... Figure 12 As shown, the total hydrocarbon values ​​measured by gas analysis for oil-bearing reservoirs are generally greater than 1500 ppm, and the peak-to-base ratio is greater than 1.5. Conversely, the total hydrocarbon values ​​and peak-to-base ratio measured by gas analysis for water-bearing reservoirs are generally lower than those for oil-bearing reservoirs. Furthermore, gas analysis is significantly affected by the specific gravity of the drilling mud; excessively high mud density often results in lower gas analysis values ​​for oil-bearing reservoirs. Therefore, the determination of fluid properties requires a comprehensive assessment considering various factors.

[0080] 3. Relationship between oil layer and long-term properties, wave impedance and resistivity

[0081] A total of 27 wells were tested in the Chad buried hill, of which 12 yielded industrial oil flow. However, most of these wells were long-section tests, with only 3 wells undergoing stratified testing: Baobab C-3, Baobab C-5, and Raphia S-11. The oil-producing sections of all three wells are located in the upper part of the buried hill, while the bottom of the buried hill consists of either dry or water-bearing layers. Three tests confirmed that the oil-producing section of Baobab C-3 was 1439-1530.0m, with the layer below 1530m being dry. Five tests confirmed that the oil-producing section of Baobab C-5 was 1306.9-1470.66m, with the layer below 1470.66m also being dry. Two tests confirmed that the oil-producing section of Raphia S-11 was 1412.00-1474.87m, with the layer between 1515.1-1602.89m being water-bearing. Through repeated study and comparison of oil testing and logging data, it was found that the lithology of the oil-bearing section is mainly acidic intrusive rock, mixed granite, and mixed gneiss. Cross-plotting the resistivity, acoustic impedance, and felsic content of the oil-bearing section revealed the following characteristics: felsic content greater than 65%, apparent acoustic impedance Z less than 15.5, and resistivity less than 10000 ohm·m. Figure 13 and Figure 14 As shown. The oil-bearing interval characteristics obtained were used to verify the oil-bearing interval interpretation results of the already interpreted wells Mimosa E-1, Raphia S-10, and Lanea E-2 in different buried hill zones, as shown. Figure 15 and Figure 16 As shown.

[0082] 4. Determination of the lower limit of the oil layer

[0083] Based on the qualitative identification of oil-bearing layers, and using data from coring, oil testing, and production testing, combined with drilling and logging data, a quantitative classification standard for oil-bearing layers can be established through calibration logging.

[0084] (1) Lower limit of physical properties

[0085] The net-total ratio sensitivity analysis curve determined that an inflection point occurred when the lower limit of porosity was 3%. When the cumulative energy loss curve showed an energy loss of 5%, the lower limit of porosity was 3%, and the lower limit of permeability was 0.1 md. Figures 17 to 19 As shown.

[0086] (2) Electrical limits

[0087] Based on the oil testing results of 27 wells, combined with well logging curves and well logging interpretation results, the electrical limits of the Chad buried hill were determined. Figures 20 to 23 ), resistivity RT>RS, Rt <10000 ohm.m, DT> 50μm / ft, DEN < 2.67g / cm 3 .

[0088] Based on the above research findings, the criteria for oil reservoir identification and classification are determined as follows:

[0089] The total hydrocarbon value measured by gas analysis is generally greater than 1500 ppm, with a peak-to-base ratio greater than 1.5; the content of long-chain minerals is greater than 65%, and the apparent impedance Z is less than 15.5; the porosity is greater than 3%, and the permeability is greater than 0.1 md; the resistivity RT > RS, and Rt <10000 ohm.m, DT> 50μm / ft, DEN < 2.67g / cm 3 Among these, the following conditions must be met: porosity greater than 5%, deep lateral resistivity less than 3000 ohm·m, acoustic transit time greater than 55 μm / ft, and compensation density less than 2.6 g / cm³. 3 When the content of long-term minerals is greater than 70%, it is defined as an oil layer (Class I oil layer); when the porosity is 3%–5%, the deep lateral resistivity is 3000–1000 ohm·m, the acoustic transit time is 50–55 μm / ft, and the compensated density is less than 2.6–2.67 g / cm³, it is considered an oil layer. 3 When the content of felsic substances is 65% to 70%, it is defined as a poor oil layer (Class II oil layer).

[0090] Using this oil-bearing layer classification standard, well logging interpretation was performed on 20 wells in Chad buried hills. Among them, Baobab buried hill zone: Class I oil layer 448.7m / 41 layers, Class II oil layer 292.8m / 51 layers; Lanea buried hill zone: Class I oil layer 102m / 20 layers, Class II oil layer 184.5m / 32 layers; Mimosa buried hill zone: Class I oil layer 99.9m / 14 layers, Class II oil layer 120.4m / 25 layers; Raphia-Phoenix buried hill zone: Class I oil layer 172.5m / 26 layers, Class II oil layer 182.1m / 40 layers.

[0091] This invention addresses the challenges of complex and variable lithology in buried hill formations, unclear reservoir development mechanisms, and difficulties in fluid property identification. It proposes a buried hill evaluation technique based on elemental analysis. This technique evaluates buried hill formations at the mineral level, quantitatively identifies lithology, determines dominant lithologies, and then comprehensively applies oil testing and core analysis data to accurately and quantitatively identify fluid properties. This clarifies the reservoir potential of buried hills and provides a geological and geophysical theoretical foundation for bedrock reservoir prediction and technology development.

[0092] This embodiment can be applied to the field of oil and gas exploration and logging engineering technology and equipment. It establishes a set of qualitative and quantitative interpretation standards for buried hill lithology, as well as multi-parameter reservoir evaluation standards and oil layer quantitative discrimination standards, which can be widely used in the formation evaluation and fluid identification of buried hill reservoirs.

[0093] This embodiment addresses the challenge of identifying bedrock oil reservoirs by establishing, for the first time, a well logging correspondence between rocks and minerals (elements). By optimizing well logging curves sensitive to lithology, well logging response models were created for two types of rocks: felsic and ferromagnesian. A method and standard for quantitative lithology identification were established. Based on elemental analysis, felsic rocks were identified as the dominant facies for reservoir development, with reservoir spaces primarily consisting of fractures, dissolution pores, and cavities. By comprehensively applying well logging, well logging, oil testing, and core analysis data, a multi-parameter reservoir evaluation standard and a quantitative oil reservoir identification standard were established, which are then used for bottom layer evaluation and fluid identification.

[0094] To further verify the above-mentioned technical effects, the inventors used the established lithological qualitative and quantitative interpretation standards and conducted verification experiments using core samples from 331 blocks in 12 wells. The experimental results showed that the lithological identification accuracy rate was 92%. Using the established oil layer classification standards, well logging interpretation was performed on 20 wells in the Chad buried hill, with an oil and gas layer identification accuracy rate of 85%. This demonstrates strong application and promotion value.

[0095] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0096] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A well logging evaluation method for buried hill formations based on elemental analysis, characterized in that, include: The mineral composition of the formation to be evaluated was calculated based on elemental logging data and core analysis data. Lithological identification based on the aforementioned mineral composition; The correspondence between rock and elemental measurements was established through rock mechanics analysis and the mineral composition. Based on the correspondence and the results of the lithology identification, reservoir space analysis is performed; The dominant lithofacies were determined based on the results of the reservoir space analysis. Based on the dominant lithofacies and well logging, well logging, oil testing and core analysis data, fluid properties are identified; The calculation of the mineral composition includes: Elemental analysis was performed on the formation to be evaluated based on the elemental logging data. The mineral composition is calculated based on the results of the elemental analysis and the core analysis data in the core analysis data. The lithological identification includes: A quantitative standard for determining lithology based on well logging is established using the calculated mineral composition. The lithological identification is performed based on the quantitative standards. Establishing the correspondence includes: Select logging responses that are sensitive to lithological reactions; Elemental cross-plots were created for different lithologies based on the logging response. Based on the intersection chart, well logging response models for two types of rocks, felsic and ferromagnesian, were created; The correspondence is determined based on the well logging response model; The dominant lithofacies include: Based on the results of the reservoir space analysis, a quantitative standard for identifying dominant lithofacies was established. The dominant lithofacies are determined based on the aforementioned quantitative criteria; The identification of fluid properties includes: Based on the dominant lithofacies and the logging, well logging, oil testing and core analysis data, a multi-parameter reservoir evaluation standard and an oil layer quantitative discrimination standard are established. Fluid properties are identified based on the aforementioned multi-parameter reservoir evaluation criteria and oil layer quantitative discrimination criteria; The storage space is a crack, pore, or cavern in felsic rock; The dominant lithofacies are mixed granite and granite.

2. An application of the method of claim 1 in fluid identification of buried hill reservoirs.

3. A method for fluid identification in buried hill reservoirs, characterized in that, Use the method described in claim 1.

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