A method for evaluating reservoir pore structure using three-pore curve
By utilizing neutron, density, and sonic logging data to construct reservoir physical property factors, the problems of high cost and limited parameters in determining reservoir pore structure have been solved, enabling rapid and effective evaluation of reservoir pore structure and reducing exploration and development costs.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- CHINA NAT PETROLEUM CORP
- Filing Date
- 2022-01-27
- Publication Date
- 2026-05-12
AI Technical Summary
Existing technologies for determining reservoir pore structure suffer from problems such as high core experiment costs, significant human factors, and limited pore structure parameters in well logging data. In particular, nuclear magnetic resonance logging is expensive and cannot effectively and quickly evaluate reservoir pore structure.
By utilizing conventional neutron, density, and sonic logging data, reservoir physical property factors are constructed through three porosities, and the relationship between reservoir pore structure evaluation parameters is established to achieve rapid and accurate quantitative evaluation.
In the absence of core and nuclear magnetic resonance logging, the reservoir pore structure can be quickly and effectively determined, providing basic parameters for reservoir identification and production prediction, saving oil testing and production testing costs, and providing more pore structure parameters to support reservoir evaluation.
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Figure CN116556924B_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of petroleum geological exploration technology, specifically to a method for evaluating reservoir pore structure using a three-pore curve. Background Technology
[0002] Currently, there are two main approaches to determining reservoir pore structure. One approach involves qualitatively or quantitatively studying pore structure through core experiments or capillary pressure measurements of rock capillary pressure curves; directly observing pore structure parameters such as porosity, throats, and voids using thin-section casting; visually identifying pore type, throat type, and determining pore-throat radius using scanning electron microscopy; or obtaining pore structure, filling material distribution, and particle surface structure using CT scanning. The other approach uses well logging data to evaluate pore structure, or uses resistivity logging data to study pore structure (based on physical models and Archie formulas); or uses nuclear magnetic resonance logging to study rock pore structure.
[0003] The advantage of laboratory experimental methods is that they can obtain a large number of parameters, but they are limited by the number of core samples, are expensive, or are greatly affected by human factors, and cannot obtain continuous parameters. Pore structure parameters obtained from resistivity data in well logging are continuous, but the calculated pore structure parameters are relatively few. Comparative analysis of the T2 distribution and rock capillary pressure curves in nuclear magnetic resonance (NMR) logging data can obtain characteristic pore structure parameters, but the disadvantage is that NMR logging is expensive. Summary of the Invention
[0004] To address the problems existing in the prior art, this invention provides a method for evaluating reservoir porosity using a three-porosity curve. This method enables the rapid and accurate quantitative evaluation of reservoir pore structure using conventional neutron, density, and sonic logging data, even without core experiments or NMR logging data. Based on a porosity and permeability model of the study area, reservoir physical property factors are constructed using the three-porosity curve. A relationship is established between these physical property factors and pore structure evaluation parameters, thus providing fundamental evaluation parameters for effective reservoir identification and production prediction.
[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for evaluating reservoir pore structure using a three-pore curve, the specific steps of which are as follows:
[0006] S1 obtains neutron porosity, density porosity, and acoustic porosity based on the neutron porosity interpretation model, density porosity interpretation model, and acoustic porosity interpretation model, and uses neutron porosity, density porosity, acoustic porosity, and regional coefficient to obtain reservoir property factors;
[0007] S2 establishes experimental relationships between reservoir physical property factors and characteristic parameters of mercury intrusion porosimetry experiments;
[0008] S3 acquires neutron, density, and acoustic data of the well with the pore structure of the reservoir to be evaluated, and obtains the reservoir physical property factors of the well with the pore structure of the reservoir to be evaluated.
[0009] S4 substitutes the reservoir physical property factors into the experimental relationship of step S2 to obtain pore structure parameters, which are used to evaluate the pore structure of the well.
[0010] Furthermore, in step S1, the well is divided into layers, and neutron, density, acoustic wave and porosity data of each layer are obtained. Regression curves of neutron, density and acoustic wave values with porosity are established respectively, which are the neutron porosity interpretation model, density porosity interpretation model and acoustic wave porosity interpretation model.
[0011] Furthermore, in step S1, wells in the study area that simultaneously possess neutron, density, and acoustic porosity logging data and core physical property analysis data are selected. Regression curves of neutron, density, and acoustic porosity with porosity are established according to geological stratification, which are the interpretation models for neutron porosity, density porosity, and acoustic porosity.
[0012] Furthermore, in step S1, the formula for calculating the reservoir physical property factor is as follows:
[0013]
[0014] Where a and b are regional coefficients; φ AC φ DEN φ CNL These are sound waves, density, and neutron porosity, respectively.
[0015] Furthermore, in step S1, the regional coefficient is obtained by physical property experiments, and the regional coefficient is determined by the region where the selected well is located.
[0016] Furthermore, in step S2, the characteristic parameters of the mercury intrusion porosimetry experiment include the mercury intrusion porosimetry experiment pressure parameter, the pore throat size parameter, and the pore throat sorting parameter.
[0017] Furthermore, in step S2, the pressure parameters of the mercury intrusion test include the median pressure and the displacement pressure; the pore throat size parameters include the median pore throat radius and the average pore throat radius; and the pore throat sorting parameters include the sorting coefficient and the mean value.
[0018] Furthermore, in step S2, the relationship between the median pore throat radius and the reservoir property factor is as follows:
[0019] Median pore throat radius = 0.0086 * e 0.4526porgeo R 2 =0.7195 (5)
[0020] The relationship between the average pore throat radius and reservoir physical property factors is as follows:
[0021] Average pore throat radius = 0.0006 * e 0.5673porgeo R 2 =0.8390 (6)
[0022] Among them, porgeo is the reservoir property factor.
[0023] Furthermore, in step S2, the relationship between median pressure and reservoir property factors is as follows:
[0024] Median pressure = -20.77ln(porgeo) + 56.234 R 2 =0.6183 (7)
[0025] The relationship between displacement pressure and reservoir physical property factors is as follows:
[0026] Discharge pressure = 0.6463 * (0.1 * porgeo) -6.259 R 2 =0.7968 (8)
[0027] Among them, porgeo is the reservoir property factor.
[0028] Furthermore, in step S2, the relationship between the sorting coefficient and the reservoir physical property factor is as follows:
[0029] Sorting coefficient = 5E-08 * porogeo 6.419 R 2 =0.8056 (9)
[0030] The relationship between the mean and reservoir property factors is as follows:
[0031] Mean = 1E-07*porgeo 5.9746 R 2 =0.7645 (10)
[0032] Among them, porgeo is the reservoir property factor.
[0033] Compared with the prior art, the present invention has at least the following beneficial effects:
[0034] This invention provides a method for evaluating reservoir porosity using three-porosity curves. This method utilizes well logging data with neutron, density, and acoustic porosity parameters, based on a constructed regional porosity model, to obtain estimated reservoir pore structure parameters even without core experiments or nuclear magnetic resonance logging. This allows for faster and more effective determination of reservoir pore structure characteristics, providing an effective means of evaluating pore structure in areas with only conventional logging data. Furthermore, compared to conventional resistivity-based methods for estimating pore structure (pore throat radius, tortuosity), this invention provides more pore structure parameters (median pore throat radius, average pore throat radius, median pressure, displacement pressure, mercury injection sorting coefficient, average mercury injection value, etc.). These parameters provide more fundamental parameters for evaluating reservoir permeability, fluid distribution, oil and gas production capacity prediction, oil and water movement in the reservoir, waterflooding efficiency, and recovery rate, effectively guiding subsequent exploration and development. In particular, it significantly reduces testing and production costs for production capacity prediction. Attached Figure Description
[0035] Figure 1 A flowchart of a method for determining the pore structure of sandstone using conventional three-porosity curves in well logging;
[0036] Figure 2 A comparison chart of pore structure parameters calculated using conventional three-pore calculations and pore structure parameters calculated using NMR.
[0037] Figure 3 For acoustic porosity model;
[0038] Figure 4 This is a median porosity model;
[0039] Figure 5 For density porosity model;
[0040] Figure 6 For physical properties and core Comparison chart;
[0041] Figure 7 A comparison diagram of physical property factors and mercury porosimeter throat radius;
[0042] Figure 8 This is a comparison chart of physical property factors and mercury intrusion pressure;
[0043] Figure 9 This is a graph showing the median pore throat radius versus physical property factors.
[0044] Figure 10 A graph showing the average pore throat radius versus physical property factors;
[0045] Figure 11 This is a plot of median pressure versus physical property factors.
[0046] Figure 12 A graph showing the relationship between displacement pressure and physical property factors;
[0047] Figure 13 A graph showing the mercury porosimetry sorting coefficient and physical property factors;
[0048] Figure 14 Mean mercury porosimetry and physical property factor plot. Detailed Implementation
[0049] The present invention will be further described below with reference to the accompanying drawings and specific embodiments.
[0050] See Figure 1 This invention provides a method for evaluating the pore structure of sandstone using conventional three-porosity logging curves, comprising the following steps:
[0051] Step (101): Select wells in the study area that simultaneously possess neutron, density, and acoustic porosity logging data and core physical property analysis data. Establish regression curves for neutron, density, and acoustic porosity with porosity according to geological stratification (curve stability principle). These are the neutron porosity interpretation model, density porosity interpretation model, and acoustic porosity interpretation model (e.g., ...). Figures 3-5 The model has regional characteristics:
[0052] (1) Acoustic porosity interpretation model:
[0053] φ AC =0.3005×AC-56.349 R 2 =0.8348 (1)
[0054] Where AC represents the acoustic logging value; φ AC To calculate acoustic porosity.
[0055] (2) Neutron porosity interpretation model:
[0056] φ CNL =0.6992×CNL-2.303 R 2 =0.7882 (2)
[0057] Where CNL is the neutron logging value; φ CNL To calculate neutron porosity.
[0058] (3) Density porosity interpretation model:
[0059] φ DEN = -64.682 × DEN - 171.16 R 2 =0.8446 (3)
[0060] Where DEN is the density logging value; φ DEN To calculate density porosity.
[0061] Step (102): Obtain neutron porosity, density porosity, and acoustic porosity from the neutron porosity interpretation model, density porosity interpretation model, and acoustic porosity interpretation model. Take the b-th power of the ratio of acoustic porosity to neutron porosity as the effective porosity of the reservoir, multiply it by the acoustic porosity representing the total porosity, and add a regional coefficient to obtain the reservoir property factor porogeo.
[0062] The proportions of the three pore types were determined by utilizing the characteristics of the interstitial material within the sandstone pores. In practical research, 100% pure sandstone is rare; that is, sandstone without any mud is practically nonexistent. The presence of mud is a factor that cannot be ignored in sandstone research. The presence of structural mud has a small impact on porosity, while the presence of dispersed mud acts as an internal lining to fill the pores, altering the pore structure and size. The presence of mud has a far greater impact on neutrons than on acoustic waves and density. Therefore, the reservoir physical property factors established using the three pore types are as follows:
[0063] Reservoir physical property factors:
[0064]
[0065] Where a and b are regional coefficients obtained through physical property experiments, and the regional coefficients are determined by the location of the selected well. In this invention, a = 1 and b = 0.5; φ AC φ DEN φ CNL These are sound waves, density, and neutron porosity, respectively.
[0066] Comparison of reservoir physical property factors obtained from three-porosity calculations with laboratory-measured pore structure characteristic parameters, and comparison of reservoir physical property factors (porgeo) with core analysis. There is a good correlation, see Figure 6 The reservoir property factor Progeo showed good correlations with the mean pore throat radius, median pore throat radius, and mean pore throat radius obtained from mercury intrusion porosimetry experiments. The correlation coefficient with the median pore throat radius was 0.839. (See...) Figure 7 The reservoir property factor Progeo showed good correlation with the median pressure and displacement pressure in mercury intrusion porosimetry experiments, especially with the displacement pressure, where the correlation coefficient was 0.8443. (See...) Figure 8 It can be seen that the reservoir property factor established by the three-pore structure in this invention can better indicate the changes in reservoir porosity.
[0067] Step (103) establishes experimental relationships between reservoir physical property factors and mercury injection experimental pressure parameters (median pressure, displacement pressure), pore throat size parameters (median pore throat radius, average pore throat radius), and pore throat sorting parameters (sorting coefficient, mean). Using these experimental relationships, the estimated values of pore structure parameters are obtained.
[0068] like Figure 9-14As shown, characteristic parameters (pressure parameters, pore throat size, sorting, etc.) of mercury intrusion porosimetry experiments on sandstones with different pore sizes in the study area were selected. Relationships between the calculated reservoir physical property factors and the mercury intrusion porosimetry pore structure parameters were established sequentially, yielding estimated values for various parameters of the sandstone pore structure calculated using a three-pore method.
[0069] Median pore throat radius:
[0070] Median pore throat radius = 0.0086 * e 0.4526porgeo R 2 =0.7195 (5)
[0071] Average pore throat radius:
[0072] Average pore throat radius = 0.0006 * e 0.5673porgeo R 2 =0.8390 (6)
[0073] Median pressure:
[0074] Median pressure = -20.77ln(porgeo) + 56.234 R 2 =0.6183 (7)
[0075] Discharge pressure:
[0076] Discharge pressure = 0.6463 * (0.1 * porgeo) -6.259 R 2 =0.7968 (8)
[0077] Sorting coefficient:
[0078] Sorting coefficient = 5E-08 * porogeo 6.419 R 2 =0.8056 (9)
[0079] Mean:
[0080] Mean = 1E-07*porgeo 5.9746 R 2 =0.7645 (10)
[0081] Figure 6-12 This demonstrates the relationship between data obtained from actual core experiments and physical property factors, showing a high correlation (R). 2 Therefore, by constructing physical property factors, we can evaluate the pore structure parameters required by the oil field, and then use the obtained pore structure parameters to predict the oil field's production capacity, guide the scale of fracturing, and so on.
[0082] In actual data processing, the above methods are programmed to modularize the processing, and the specific processing results are as follows: Figure 2As shown in the figure, the reservoir property factor porgeo calculated using this method is basically consistent with the pore structure coefficient determined by nuclear magnetic resonance logging, verifying the reliability of this method in assessing pore structure. This invention is simple, easy to operate, and highly reproducible. The calculation results meet the needs of oilfield operations and have good universality and promotional value.
[0083] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.
[0084] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.
Claims
1. A method for evaluating reservoir pore structure using a three-pore curve, characterized in that, The specific steps are as follows: S1 obtains neutron porosity, density porosity, and acoustic porosity based on the neutron porosity interpretation model, density porosity interpretation model, and acoustic porosity interpretation model, and uses neutron porosity, density porosity, acoustic porosity, and regional coefficient to obtain reservoir property factors; S2 establishes experimental relationships between reservoir physical property factors and characteristic parameters of mercury intrusion porosimetry experiments; S3 acquires neutron, density, and acoustic data of the well with the pore structure of the reservoir to be evaluated, and obtains the reservoir physical property factors of the well with the pore structure of the reservoir to be evaluated. S4 substitutes the reservoir physical property factors into the experimental relationship of step S2 to obtain the pore structure parameters, which are used to evaluate the pore structure of the well. In step S1, the formula for calculating reservoir physical property factors is as follows: Where a and b are regional coefficients; , , These are sound waves, density, and neutron porosity, respectively.
2. The method for evaluating reservoir pore structure using a three-pore curve according to claim 1, characterized in that, In step S1, the well is divided into layers, and neutron, density, acoustic wave and porosity data of each layer are obtained. Regression curves of neutron, density and acoustic wave values with porosity are established respectively, which are the neutron porosity interpretation model, density porosity interpretation model and acoustic wave porosity interpretation model.
3. The method for evaluating reservoir pore structure using a three-pore curve according to claim 2, characterized in that, In step S1, wells in the study area that simultaneously possess neutron, density, and acoustic porosity logging data and core physical property analysis data are selected. Regression curves of neutron, density, and acoustic porosity are established according to geological stratification, which are the interpretation models for neutron porosity, density porosity, and acoustic porosity.
4. The method for evaluating reservoir pore structure using a three-pore curve according to claim 1, characterized in that, In step S1, the regional coefficient is obtained by physical property experiments and is determined by the region where the selected well is located.
5. The method for evaluating reservoir pore structure using a three-pore curve according to claim 1, characterized in that, In step S2, the characteristic parameters of the mercury intrusion porosimetry experiment include the mercury intrusion porosimetry pressure parameter, the pore throat size parameter, and the pore throat sorting parameter.
6. The method for evaluating reservoir pore structure using a three-pore curve according to claim 5, characterized in that, In step S2, the pressure parameters of the mercury intrusion test include the median pressure and the displacement pressure; the pore throat size parameters include the median pore throat radius and the average pore throat radius; and the pore throat sorting parameters include the sorting coefficient and the mean value.
7. The method for evaluating reservoir pore structure using a three-pore curve according to claim 6, characterized in that, In step S2, the relationship between the median pore throat radius and the reservoir property factor is as follows: Median pore throat radius = 0.0086 * e 0.4526porgeo , =0.7195 The relationship between the average pore throat radius and reservoir physical property factors is as follows: Average throat radius = 0.0006 * e 0.5673porgeo , =0.8390 Among them, porgeo is the reservoir property factor.
8. The method for evaluating reservoir pore structure using a three-pore curve according to claim 6, characterized in that, In step S2, the relationship between median pressure and reservoir property factors is as follows: Median pressure = -20.77ln(porgeo) + 56.234 =0.6183 The relationship between displacement pressure and reservoir physical property factors is as follows: Discharge pressure = 0.6463 * (0.1 * porgeo) -6.259 , =0.7968 Among them, porgeo is the reservoir property factor.
9. A method for evaluating reservoir pore structure using a three-pore curve according to claim 6, characterized in that, In step S2, the relationship between the sorting coefficient and the reservoir physical property factor is as follows: Sorting coefficient = 5E-08 * porogeo 6.419 , =0.8056 The relationship between the mean and reservoir property factors is as follows: Mean = 1E-07 * porno 5.9746 , =0.7645 Among them, porgeo is the reservoir property factor.