A low-resistance oil layer dessert identification method based on nuclear magnetic logging

Abstract

The application discloses a low-resistance oil layer sweet spot identification method based on nuclear magnetic logging, which comprises the following steps: determining the bound water saturation and the bound mineralization degree of a research area according to the specific conditions of the research area, discussing the influence of the above-mentioned content on the oil layer resistivity, and analyzing the formation mechanism of the low-resistance oil layer; determining the movable porosity of the low-resistance oil layer of the research area, and then analyzing the relationship between the movable porosity and the initial daily production of a single well, and demarcating the threshold value of the movable porosity; discussing the relationship between the movable porosity and seismic attributes, and performing nonlinear analysis on the movable porosity and sensitive seismic attributes through a neural network algorithm to realize the sweet spot identification of the low-resistance oil layer. The method considers the influence of the bound water saturation and the mineralization degree on the oil layer resistivity, and on this basis, analyzes the movable porosity development characteristics of the low-resistance oil layer through nuclear magnetic logging, and then analyzes the relationship between the movable porosity and the seismic attributes, so that the sweet spot identification of the low-resistance oil layer is realized.

Description

Technical Field

[0001] This invention relates to a method for identifying sweet spots in low-resistivity oil reservoirs based on nuclear magnetic resonance logging, belonging to the field of petroleum exploration and development. Background Technology

[0002] Recent research on low-resistivity oil reservoirs has mainly focused on two aspects: first, the influencing factors and mechanisms of low-resistivity oil reservoir formation; and second, the qualitative and quantitative identification of low-resistivity oil reservoirs in individual wells through well logging interpretation. Currently, there is very little research on the identification of planar sweet spots in low-resistivity oil reservoirs. The proven reserves of low-resistivity oil reservoirs in the C oilfield of the Bohai Bay Basin are large, but the productivity of individual wells varies greatly in different areas. Therefore, it is urgent to analyze the influencing factors and, based on this analysis, identify high-quality reservoirs and formulate corresponding development strategies.

[0003] Currently, low-resistivity oil reservoirs are primarily identified qualitatively and quantitatively through well logging interpretation on a single well basis. Qualitative identification methods mainly include cross-plotting and overlay methods. The cross-plotting method involves creating a cross-plot of two types of well logging data. The resulting graph shows the values ​​and ranges of the desired parameters, allowing for intuitive identification of low-resistivity oil reservoirs. Commonly used cross-plots include resistivity versus sonic transit time (SLT) plots and natural gamma ray versus deep induction resistivity (DIRT) plots. Overlay methods mainly include two types: one is the deep lateral resistivity versus sonic transit time (SLT) overlay method, which utilizes the fact that when the reservoir contains oil and gas, the SLT curve and the DIRT curve will show a certain amplitude difference, allowing for the qualitative classification of low-resistivity oil reservoirs; the other is the dual-porosity overlay method, where the calculated water-bearing porosity differs from the formation porosity obtained from porosity logging in low-resistivity oil reservoirs, thus identifying the low-resistivity oil reservoir. Quantitative identification methods mainly include the three-water model, which posits that the conductive path of rocks consists of three parallel components: free-flowing water, micropore water, and clay-bound water. Micropore water tends to act independently within this conductive path. In terms of seepage characteristics, micropore water is consistent with clay-bound water and cannot be produced. However, in terms of conductivity, micropore water is consistent with free water, unlike clay-bound water. This model has shown good effectiveness in the quantitative identification of low-resistivity oil layers. Research on the planar distribution prediction of low-resistivity oil layers is limited, primarily focusing on predicting the oil-bearing range and thickness using seismic spectral decomposition methods.

[0004] The main drawbacks of the existing technology are twofold: firstly, the influencing factors of low resistivity oil layers vary in different oil fields, and there is a lack of research on low resistivity oil layers based on the formation mechanism of the study area; secondly, previous studies lacked the definition of resistivity threshold and quantitative characterization of planar distribution, which restricted the high-precision prediction of sweet spots in low resistivity oil layers. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a method for identifying sweet spots in low-resistivity oil reservoirs based on nuclear magnetic resonance (NMR) logging. This method considers the influence of bound water saturation and salinity on reservoir resistivity. Based on this, it analyzes the development characteristics of movable porosity in low-resistivity oil reservoirs through NMR logging, and further analyzes the relationship between movable porosity and seismic properties to achieve sweet spot identification of low-resistivity oil reservoirs.

[0006] The technical solution provided by this invention to solve the above-mentioned technical problems is: a method for identifying sweet spots in low-resistivity oil reservoirs based on nuclear magnetic resonance logging, comprising the following steps:

[0007] Step S10: Based on the specific conditions of the study area, determine the bound water saturation and bound salinity of the study area, explore the influence of the above on the oil layer resistivity, and analyze the formation mechanism of low-resistivity oil layers.

[0008] Step S20: Based on the analysis results of step S10, determine the movable porosity of the low-resistivity oil layer in the study area, and then analyze the relationship between movable porosity and the initial daily production of a single well, and define the threshold of movable porosity.

[0009] Step S30: Based on the analysis results of step S20, explore the relationship between movable porosity and seismic properties. Use a neural network algorithm to perform nonlinear analysis on movable porosity and sensitive seismic properties to identify sweet spots in low-resistivity oil layers.

[0010] A further technical solution is that the low-resistivity oil layer formation mechanism in step S10 is characterized by high bound water saturation and high bound water salinity.

[0011] A further technical solution is that, in step S20, nuclear magnetic resonance logging is used to determine the movable porosity of the low-resistivity oil layer.

[0012] A further technical solution is that the threshold value of movable porosity in step S20 is 6.0%.

[0013] A further technical solution is that the sensitive seismic attribute in step S30 is a 20Hz frequency-division amplitude attribute.

[0014] A further technical solution is that, in step S30, a nonlinear relationship between movable porosity and 20Hz frequency-division amplitude seismic properties is established using a neural network algorithm.

[0015] The present invention has the following beneficial effects:

[0016] (1) Based on the analysis of the genesis of low-resistivity oil layers in the study area, this method selects the movable porosity parameter, establishes its relationship with the productivity of low-resistivity oil layers, and eliminates the interference of bound water salinity on productivity analysis.

[0017] (2) Through the correlation analysis between the initial daily production of a single well and the movable porosity, 6% was defined as the movable porosity threshold, which laid the foundation for the classification of low-resistivity high-quality reservoirs.

[0018] (3) A nonlinear relationship between movable porosity and 20Hz frequency-division amplitude seismic properties was established using a neural network algorithm to achieve high-precision identification of sweet spots in low-resistivity oil layers, and to compare the measured and predicted movable porosity R. 2 The value of 0.72 indicates that the method has good generalizability. Attached Figure Description

[0019] Figure 1 For flowcharts;

[0020] Figure 2 Example diagrams showing the distribution characteristics of bound fluids in oil reservoirs with different resistivity;

[0021] Figure 3 Examples of bound water salinity characteristics in oil reservoirs with different resistivity;

[0022] Figure 4 This is a diagram showing the porosity distribution characteristics of a low-resistivity oil layer.

[0023] Figure 5 This is an example of the correlation between daily production and movable porosity in the early stage of a low-resistivity oil layer.

[0024] Figure 6 This is an example of movable porosity inversion based on seismic attribute constraints.

[0025] Figure 7 Example image for identifying sweet spots in low-resistivity oil layers;

[0026] Figure 8 This is an example diagram illustrating the correlation between measured and predicted movable porosity. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0028] This invention discloses a method for identifying sweet spots in low-resistivity oil reservoirs based on nuclear magnetic resonance (NMR) logging. It considers the influence of bound water saturation and salinity on reservoir resistivity. Based on this, it analyzes the development characteristics of movable porosity in low-resistivity oil reservoirs using NMR logging, and further analyzes the relationship between movable porosity and seismic properties to identify sweet spots in low-resistivity oil reservoirs. The specific process is as follows: Figure 1 As shown.

[0029] (1) Based on the specific conditions of the study area, the bound water saturation and salinity were measured, the influence of the above on the oil layer resistivity was explored, and the formation mechanism of low-resistivity oil layer was analyzed.

[0030] (2) Based on the analysis results of step (1), nuclear magnetic logging is used to measure the movable porosity of the low-resistivity oil layer, and then the relationship between movable porosity and the initial daily production of a single well is analyzed to determine the movable porosity threshold.

[0031] (3) Based on the analysis results of step (2), explore the relationship between movable porosity and seismic properties. Use a neural network algorithm to perform nonlinear analysis on movable porosity and sensitive seismic properties to identify the sweet spot of low-resistivity oil layers.

[0032] Figure 2 The data represents the distribution characteristics of bound fluids within the pores of oil layers with different resistivity, used to reflect the saturation of bound water in the oil layer. The analysis shows that the bound water saturation of the low-resistivity oil layer in the study area is relatively high. The pores of the conventional oil layer are mainly occupied by mobile fluids, and the high bound water saturation is one of the main factors leading to the low-resistivity oil layer.

[0033] Figure 3 The study investigated the bound water salinity characteristics of oil layers with different resistivity. The analysis showed that the bound water salinity ranged from 40,000 to 70,000 mg / L in low-resistivity oil layers and from 10,000 to 40,000 mg / L in conventional oil layers. High bound water salinity is one of the main factors leading to low-resistivity oil layers.

[0034] Figure 4 The analysis shows that the pore space is occupied by clay-bound water, capillary-bound water, movable water and movable hydrocarbons. Among them, clay-bound water and capillary-bound water in the pores are not movable, while movable water and movable hydrocarbons are movable.

[0035] Figure 5 This is an example diagram illustrating the correlation between daily production and movable porosity in the initial stage of a low-resistivity oil reservoir. According to... Figures 2-4 Analysis shows that the resistivity of the oil reservoir is mainly affected by the saturation of bound water and salinity. Among these, salinity has a relatively small impact on the productivity of low-resistivity oil reservoirs. Clay-bound water and capillary-bound water occupy pore space; therefore, the productivity of low-resistivity oil reservoirs is mainly affected by mobile water and mobile hydrocarbons. Based on the above research, the correlation between initial daily production and mobile porosity was analyzed. The results show that the Rresistivity of both in the study area... 2 The value is 0.86. The movable porosity corresponding to an initial daily production of 50 cubic meters is 6.0%. The movable porosity of 6.0% is set as the threshold.

[0036] Figure 6This is an example of movable porosity inversion based on seismic attribute constraints. The 20Hz frequency-division amplitude attribute and the movable porosity interpreted by nuclear magnetic resonance logging are selected for analysis. The nonlinear relationship between the two is established through a neural network algorithm to complete the planar and profile prediction of movable porosity inversion in the study area.

[0037] Figure 7 This is an example of low-resistivity oil layer sweet spot identification, based on Figures 5-6 The analysis results show that by combining the movable porosity threshold with movable porosity inversion based on seismic attribute constraints, high-precision prediction of low-resistivity high-quality reservoirs in the study area can be achieved.

[0038] Figure 8 This is an example diagram illustrating the correlation between measured and predicted movable porosity in the study area. The results show that the R values ​​of both are... 2 The value is 0.72, indicating that this method has good generalizability.

[0039] This invention addresses the complex formation of low-resistivity oil reservoirs in the C oilfield. It analyzes the development characteristics of movable porosity in these reservoirs using nuclear magnetic resonance logging. Based on this analysis, it establishes a correlation between initial daily production and movable porosity in a single well, defines a movable porosity threshold, and further analyzes the relationship between movable porosity and seismic attributes. A neural network algorithm is used to perform nonlinear analysis on movable porosity and sensitive seismic attributes, enabling the identification of sweet spots in low-resistivity oil reservoirs. Results show that the movable porosity inversion prediction based on seismic attributes matches the actual drilling conditions well, providing a reference for subsequent well location adjustments and facilitating the efficient development of low-resistivity oil reservoirs in the C oilfield.

[0040] Based on the genetic analysis of low-resistivity oil reservoirs in the study area, this method optimizes the movable porosity parameter to characterize the productivity of low-resistivity oil reservoirs, thus eliminating the interference of other factors on productivity analysis.

[0041] By analyzing the correlation between the initial daily production of a single well and the movable porosity, a movable porosity threshold was determined, laying the foundation for the classification of low-resistivity, high-quality reservoirs.

[0042] By establishing a nonlinear relationship between movable porosity and sensitive seismic properties through neural network algorithms, high-precision identification of sweet spots in low-resistivity oil layers can be achieved.

[0043] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the implementation of the present invention. Those skilled in the art can make other variations or modifications based on the above description. It is neither necessary nor possible to exhaustively describe all embodiments here. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the claims of the present invention.

Claims

1. A method for identifying sweet spots in low-resistivity oil reservoirs based on nuclear magnetic resonance logging, characterized in that, Includes the following steps: Step S10: Based on the specific conditions of the study area, determine the bound water saturation and bound salinity of the study area, explore the influence of bound water saturation and bound salinity on the oil layer resistivity, and analyze the formation mechanism of low resistivity oil layers. Step S20: Based on the analysis results of step S10, determine the movable porosity of the low-resistivity oil layer in the study area, and then analyze the relationship between movable porosity and the initial daily production of a single well, and define the threshold of movable porosity. Step S30: Based on the analysis results of step S20, explore the relationship between movable porosity and seismic properties. Use a neural network algorithm to perform nonlinear analysis on movable porosity and sensitive seismic properties to identify sweet spots in low-resistivity oil layers.

2. The method for identifying low-resistivity oil reservoir sweet spots based on nuclear magnetic resonance logging according to claim 1, characterized in that, The formation mechanism of the low-resistivity oil layer in step S10 is that the bound water saturation is high and the bound water salinity is high.

3. The method for identifying low-resistivity oil reservoir sweet spots based on nuclear magnetic resonance logging according to claim 1, characterized in that, In step S20, nuclear magnetic resonance logging is used to determine the movable porosity of the low-resistivity oil layer.

4. The method for identifying low-resistivity oil reservoir sweet spots based on nuclear magnetic resonance logging according to claim 1, characterized in that, The threshold value for movable porosity in step S20 is 6.0%.

5. The method for identifying low-resistivity oil reservoir sweet spots based on nuclear magnetic resonance logging according to claim 1, characterized in that, In step S30, the sensitive seismic attribute is the 20Hz frequency-division amplitude attribute.

6. The method for identifying low-resistivity oil reservoir sweet spots based on nuclear magnetic resonance logging according to claim 5, characterized in that, In step S30, a nonlinear relationship between movable porosity and 20Hz frequency-division amplitude seismic properties is established using a neural network algorithm.

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