Flow unit division method considering tortuosity characteristics of reservoir pore network

Abstract

The invention discloses a flow unit division method considering tortuosity characteristics of a reservoir pore network. The flow unit division method comprises the following steps: (1) carrying out observation and conventional slice and pore permeability testing on a reservoir core; (2) correcting the depth of the rock core; (3) carrying out reservoir feature analysis and reservoir preliminary classification based on causes; (5) FZIl calculation considering pore tortuosity characteristics; (4) determining cementation through a rock electricity experiment; (6) carrying out flow unit division through a clustering method; and (7) dividing a single well. The method effectively solves the problems that an existing method is only rock physical classification and does not consider different reservoir formation causes and petrology differences; in China, unconventional reservoir pore throats are complex in structure and obvious in runner network bending characteristic, and targeted correction needs to be carried out; the existing method does not consider the discrete degree of the FZI in actual data, is more suitable for new oil-gas exploration and development conditions in China, can be applied to actual production, and has wide popularization and practical values.

Description

Technical Field

[0001] The invention relates to reservoir evaluation technology in conventional and unconventional oil and gas exploration and development processes, and in particular to an experimental method for evaluating water lock damage in tight sandstone gas reservoirs. Background Art

[0002] The global demand for energy is rising, while conventional oil and gas resources are decreasing, and the contradiction between energy supply and demand is deepening. Against this background, oil and gas energy exploration around the world has entered a new stage, especially in my country, where the exploration and development of tight oil and gas reservoirs, ultra-deep layers and other unconventional and conventional oil and gas reservoirs are carried out simultaneously. Under the new exploration and development situation, oil and gas reservoirs have more obvious heterogeneous characteristics than in the past, the pore network is more complex, the flow channel is more tortuous, the flow law of oil and gas in the reservoir is more complex, and the pore structure has strong spatial heterogeneity.

[0003] Since the flow laws of fluids in reservoirs with different pore structures are different, and the control effects on the production dynamics of fluid minerals such as oil and gas are also different, it is difficult to analyze the overall flow law, and thus the concept of flow unit is proposed. Hearn et al. first proposed this concept in 1984. By dividing rock types and sedimentary phases, they determined the influence of sedimentation and diagenesis on the pore structure of reservoirs, and then distinguished 5 different flow units. Ebank et al. pointed out in 1987 that the flow unit is a rock mass that is further subdivided according to the influence of fluid flow in rocks. They proposed a method to divide the flow unit based on the changes in rock geology and physical properties, but did not propose quantitative analysis parameters, and its applicability in other areas is poor. Amaefule redefined the flow unit from the perspective of hydraulic characteristics in 1993, pointing out that the flow unit is a layer section with similar hydraulic characteristics in a given rock. The flow unit is a representative basic volume in which the physical properties of the oil layer that affect the flow of fluids are constant in the total reservoir rock volume and can be distinguished from other rock volumes. Amaefule obtained the relationship equation between reservoir flow index (FZI) and reservoir quality index (RQI) by transforming the Kozeney-Carman equation, and proposed a method to divide flow units based on FZI, which provided important guidance for subsequent research on reservoir flow units. According to the concept of flow unit proposed by Amaefule, a single reservoir can be divided into multiple flow units with different rock physical properties. Different flow units show differences in rock physical properties, large differences in flow laws, and obvious differences in porosity and permeability. Within the same flow unit, the geological parameters affecting fluid flow (seepage) are similar, but this method is a simple rock division method, which does not consider geological factors such as lithology, sedimentation and diagenesis.

[0004] Chinese scholars have conducted a lot of research on flow units and proposed different methods to divide flow units into two categories. Qiu Yinan, Wu Shenghe and others believe that flow units are "connected bodies". The connected sand bodies formed in the entire reservoir after different sedimentation, diagenesis and other processes are flow units. Affected by the conditions of isolation and crossflow bypass, the division method is to identify the sand body configuration and connected sand bodies. This research idea is mainly applicable to conventional sandstone reservoirs, and is mainly based on qualitative research. No universal quantitative division method has been proposed; Xiong Qihua and others believe that flow units are controlled by factors such as rock physical properties and pore structure. The flow units are divided by establishing pore models or quantitative parameters. This method lacks geological significance and cannot explain the geological characteristics of different flow units.

[0005] The control effect of reservoir rocks on flow is formed through the characteristics of the pore network, and the pore network of rocks is formed by the comprehensive effects of geological factors such as sedimentary phases and diagenesis. Therefore, the division of flow units requires the establishment of a quantitative method based on geological knowledge to further divide the rock physical phases and determine the hydraulic properties of different flow units. The pore throat structure of my country's tight sandstone, highly heterogeneous carbonate rock and other oil and gas reservoirs is complex, and the flow channel network has obvious bending characteristics, but the existing methods have not made corresponding corrections to this feature; at the same time, the currently commonly used FZI quantitative division method mainly uses the integer FZI with a fixed increase as the boundary standard for dividing different flow units, without considering the discrete degree of FZI in actual data, and cannot quantitatively divide similar FZIs. Therefore, the present invention focuses on the above-mentioned problems and proposes a flow unit division method that considers the tortuosity characteristics of the reservoir pore network, which is more suitable for the current status of my country's new unconventional oil and gas exploration and development, can be applied to actual production, and has wide promotion and practical value. Summary of the invention

[0006] The purpose of the present invention is to provide a flow unit division method that takes into account the tortuosity characteristics of the reservoir pore network. The method takes into account the complex pore throat structure and obvious curvature characteristics of the flow channel network of oil and gas reservoirs such as tight sandstone and highly heterogeneous carbonate rocks in my country. The currently commonly used FZI quantitative division method does not take into account the discrete degree of FZI in actual data and cannot quantitatively divide similar FZIs. A targeted innovative invention is made to establish a flow unit division index FZI that takes into account tortuosity correction. l , used to solve the problem of high tortuosity and strong heterogeneity in my country's unconventional oil and gas reservoirs; clustering analysis algorithm is integrated to cluster FZI1, and then the flow unit type is determined, which can better reflect the difference information and geological significance of different flow unit types. This solution can well adapt to the unconventional reservoir conditions with high tortuosity characteristics under the new oil and gas exploration and development situation in my country.

[0007] In order to achieve the above object, the technical solution adopted by the present invention is:

[0008] 1. A flow unit division method considering the tortuosity characteristics of the reservoir pore network, comprising the following steps:

[0009] (1) Depth correction and observation of reservoir cores, and uniform sampling of reservoir sections;

[0010] (2) Prepare conventional rock and cast thin sections and conduct conventional tests such as physical properties and mercury injection;

[0011] (3) Carry out characteristic analysis of the reservoir and make a preliminary classification based on the differences in rock lithology, sedimentary environment, pore structure, pore genesis, etc., which will serve as the basis for the subsequent division of rock physical flow units;

[0012] (4) Conduct rock electrical test to determine the reservoir cementation index:

[0013] The same reservoir section needs to be sampled more than twice, and the reservoir cementation index is calculated using the Archie formula.

[0014]

[0015] a—lithology coefficient related to lithology; m—cementation index; R w —Resistivity of formation water, Ω.m; Ro—Resistivity of rock 100% saturated with formation water, Ω.m; — is porosity, %; F—formation factor.

[0016] If more than two samples cannot be obtained, calculate m by taking the value of a based on experience;

[0017] (5) The rock physical flow unit division parameter FZI is modified by Archie formula, Kozeny–Carmen formula, etc. to obtain FZI l The mathematical model is constructed, and the cementation index obtained from the rock electrical experiment is substituted into it to obtain the FZI value of the reservoir rock.

[0018] Kozeny–Carmen formula:

[0019]

[0020] Where k is the permeability in μm 2 , fg is the shape factor, l is the tortuosity, SV gr is the specific surface area of ​​the grains, in μm -1 ; is the effective porosity, %

[0021] Tortuosity model:

[0022]

[0023] The FZI calculation model is modified by combining formulas (1)-(3) to obtain the reservoir quality index, porosity and new FZI l The relationship model between:

[0024]

[0025] (6) Using the sum of squares of deviations clustering method, FZI l The specific principle of clustering is: first, divide n samples into one class each, at this time W = 0. Then merge two classes each time, because each time a class is reduced, the sum of squared deviations will increase. At this time, the two classes that make W WW increase the least should be selected for merging until all samples are merged into one class (Formula 4, 5):

[0026]

[0027] After clustering is completed, if there are overlapping areas between different classes, the boundary can be divided by taking the median of the overlapping area or the average value of the overlapping area data;

[0028] (7) Based on the preliminary classification, fine-scale reservoir flow units are divided.

[0029] According to the flow unit division method considering the tortuosity characteristics of the reservoir pore network provided by the present invention, the reservoir core is depth-corrected and observed, and the reservoir section is uniformly sampled and conventional physical property tests are performed to complete the reservoir characteristic analysis, and the reservoirs with obvious genetic differences are preliminarily classified, thus giving the flow unit geological significance; by substituting the reservoir cementation index determined by the rock electrical experimental test into the new FZI l In the calculation method, a new flow index was obtained. Compared with the existing FZI classification method, the new calculation method takes into account the high tortuosity characteristics of my country's unconventional reservoirs and is more suitable for the current exploration and development of unconventional reservoirs in my country. l Classification is performed and boundary values ​​are determined. Based on the preliminary classification, fine flow units are divided. Compared with the previous incremental boundary determination method, the method provided by the present invention can better reflect the development characteristics of different flow units in the reservoir. The present invention can achieve fine division of flow units based on the consideration of both geological characteristics and hydraulic characteristics of high tortuosity reservoirs. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 It is a schematic diagram of the technical process implemented by the present invention.

[0031] Figure 2There are two reservoir types that are initially classified, namely, insect debris beach reservoir and algae debris beach reservoir.

[0032] Figure 3 The FZI of the worm beach reservoir is analyzed by using the sum of squares of deviation clustering method. l And the classification results of RQI.

[0033] Figure 4 The FZI of algal shoal reservoirs was analyzed by using the sum of squares of deviation clustering method. l And the classification results of RQI.

[0034] Figure 5 It is the porosity and permeability distribution relationship of the flow unit of the worm beach reservoir.

[0035] Figure 6 It is the porosity and permeability distribution relationship of the flow unit of the algae shoal reservoir.

[0036] Figure 7 It is the geological characteristic of the flow unit of the worm debris beach reservoir.

[0037] Figure 8 It is the geological characteristic of the flow unit of the algae shoal reservoir. DETAILED DESCRIPTION

[0038] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. 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.

[0039] Taking the carbonate reservoir data of an oil field in central my country as an example, the specific implementation steps of the present invention are described:

[0040] S1: Systematically observe the lithology, structure, and hydrocarbon content of the reservoir core, and based on the identification of the reservoir, uniformly sample the reservoir section. The sample is preferably a plunger sample to facilitate later testing, but irregular samples can also be used.

[0041] The higher the density, the more accurate the classification result;

[0042] S2: For the samples obtained, conventional rock and cast thin sections are made and conventional tests such as physical properties and mercury injection are carried out. The test methods are based on technical specifications such as "SY / T 5368-2016 Rock Thin Section Identification", "SY / T 6385-2016 Determination of Rock Porosity and Permeability under Overburden Pressure" and "SY / T5346-2005 Determination of Rock Capillary Pressure Curve";

[0043] S3: Based on the theories of sedimentary petrology and development geology, the core data such as cast thin sections and mercury injection are comprehensively analyzed, and statistical analysis is used to carry out characteristic studies such as reservoir petrology and pore structure characteristics. Based on the differences in reservoir rock lithology, sedimentary environment, diagenesis, pore structure, pore genesis and other characteristics, a preliminary classification is carried out as the basis for the later division of rock physical flow units;

[0044] Figure 2 Two reservoir types were initially classified and divided, one is the worm debris beach reservoir and the other is the algae debris beach reservoir. The main pore types developed in the two reservoirs are body cavity pores and cast film pores, which are obviously different.

[0045] S4: Conduct rock electrical test to determine the reservoir cementation index. The specific test method is based on "SY / T 5385-2007 Laboratory Measurement and Calculation Method of Rock Resistivity Parameters":

[0046] The same reservoir section needs to be sampled more than twice, and the reservoir cementation index is calculated using the Archie formula.

[0047]

[0048] a—lithology coefficient related to lithology; m—cementation index; R w —Resistivity of formation water, Ω.m; Ro—Resistivity of rock 100% saturated with formation water, Ω.m; — is porosity, %; F—formation factor.

[0049] If more than two samples cannot be obtained, calculate m by taking the value of a based on experience;

[0050] S5: Based on FZI l The mathematical model calculates the RQI and FZI of all samples l data:

[0051] The FZI parameter of rock physical flow unit is modified by Archie formula, Kozeny–Carmen formula, etc. l The mathematical model is constructed, and the cementation index obtained from the rock electrical experiment is substituted into it to obtain the FZI value of the reservoir rock.

[0052] Kozeny–Carmen formula:

[0053]

[0054] Where k is the permeability in μm 2 , fg is the shape factor, l is the tortuosity, SV gr is the specific surface area of ​​the grains, in μm -1 ; is the effective porosity, %

[0055] Tortuosity model:

[0056]

[0057] The FZI calculation model is modified by combining formulas (1)-(3) to obtain the reservoir quality index, porosity and new FZI l The relationship model between:

[0058]

[0059] S6: FZI is analyzed by using the sum of squares of deviation clustering method. l and RQI for classification.

[0060] The specific principle of clustering is: first, divide n samples into one class each, at this time W = 0. Then merge two classes each time, because each time a class is reduced, the sum of squared deviations will increase. At this time, the two classes that make the smallest increase in W WW should be selected for merging, until all samples are merged into one class (Formula 4, 5).

[0061]

[0062] Figure 3 and Figure 4 In order to use the sum of squares of deviation clustering method to classify the FZI of insect debris beach reservoirs and algae debris beach reservoirs, l And the classification results of RQI.

[0063] After clustering is completed, if there are overlapping areas between different classes, the boundary can be divided by taking the median of the overlapping area or the average value of the overlapping area data.

[0064] S7: Divide the reservoir flow units into fine units based on the preliminary classification.

[0065] Figure 5 and Figure 6 The porosity and permeability distribution relationship of the flow units divided for insect debris beach reservoirs and algae debris beach reservoirs respectively.

[0066] Figure 7 and Figure 8 These are the geological characteristics of the flow units of insect debris beach reservoirs and algae debris beach reservoirs, respectively.

Claims

1. A flow unit division method considering the tortuosity characteristics of the reservoir pore network, characterized in that: The following steps are involved: (1) Observe the reservoir core and uniformly sample the reservoir section, make conventional rock and cast thin sections, and conduct conventional tests such as physical properties and mercury injection; (2) Depth correction of the core; (3) Analyze reservoir characteristics and make preliminary classifications of reservoirs with obvious genetic differences; (4) Conduct rock electrical experimental tests to determine the reservoir cementation index; (5) Substituting the cementation index into the FZI considering the pore tortuosity characteristics l Calculate the corresponding value in (6) Using clustering method, we can analyze the FZI l Classify with RQI and determine the cut-off value; (7) Based on the preliminary classification, fine-scale reservoir flow units are divided.

2. A flow unit division method considering the tortuosity characteristics of the reservoir pore network according to claim 1, characterized in that: The step (3) is to conduct a rock electrical test to determine the reservoir cementation index: For the same reservoir, sampling should be carried out more than twice, and the reservoir cementation index should be calculated by Archie's formula: a—lithology coefficient related to lithology; m—cementation index; R w —Resistivity of formation water, Ω.m; Ro—Resistivity of rock 100% saturated with formation water, Ω.m; — is porosity, %; F—formation factor; If more than two samples cannot be obtained, calculate m by taking the value of a based on experience.

3. A flow unit division method considering the tortuosity characteristics of the reservoir pore network according to claim 1, characterized in that: The step (4): The reservoir characteristics are analyzed and a preliminary classification is made based on the differences in rock lithology, sedimentary environment, pore structure, pore genesis, etc., which serves as the basis for the subsequent division of rock physical flow units.

4. A flow unit division method considering the tortuosity characteristics of the reservoir pore network according to claim 1, characterized in that: The step (5): The FZI parameter of rock physical flow unit is modified by Archie formula, Kozeny–Carmen formula, etc. l The mathematical model is used to obtain the FZI of the reservoir rock by substituting the cementation index obtained from the rock electrical experiment. l ; Kozeny–Carmen formula: Where k is the permeability in μm 2 , fg is the shape factor, l is the tortuosity, SV gr is the specific surface area of ​​the grains, in μm -1 ; is the effective porosity, % Tortuosity model: The FZI calculation model is modified by combining formulas (1)-(3) to obtain the reservoir quality index, porosity and new FZI l The relationship model between:

5. A flow unit division method considering the tortuosity characteristics of the reservoir pore network according to claim 1, characterized in that: The step (6): The FZI is clustered by using clustering methods such as the sum of squares of deviations clustering or K-means clustering. l The RQI is used for classification. Taking the clustering of the sum of squared deviations as an example, the specific principle is: first, n samples are divided into one category each, at this time W = 0, and then two of the categories are merged each time. Because the sum of squared deviations of one category will increase each time it is reduced, the two categories that make the increase of W the smallest should be selected for merging until all samples are merged into one category (Formula 4, 5): After clustering is completed, if there are overlapping areas between different classes, the boundary can be divided by taking the median of the overlapping area or the average value of the overlapping area data.

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