Evaluation method for tight beach-bar sand reservoir

Abstract

The present invention discloses a method for evaluating tight beach-bar sand reservoirs. Based on mercury injection experimental data analysis, the pore fractal dimension of a single sample point is calculated. After classifying the target strata as bar sand and beach sand reservoirs, the method uses the correlation coefficients between the experimentally calculated pore fractal dimension of the single sample point and various logging parameters, selecting the logging parameters with the largest correlation coefficients for regression fitting to establish pore fractal dimension logging prediction models for both bar sand and beach sand. The method also predicts the pore fractal dimension values ​​vertically and horizontally for individual wells. The method also establishes a tight beach-bar sand reservoir evaluation standard based on the correlation between the development performance of the produced blocks and the pore fractal dimension, and classifies the tight beach-bar sand according to the reservoir evaluation standard. The method has high prediction accuracy, is simple and easy to operate, and provides comprehensive characterization.

Description

Technical Field

[0001] The present invention relates to the field of unconventional oil development, and in particular to a method for evaluating tight beach-bar sand reservoirs. Background Art

[0002] Tight oil, a new hotspot in global unconventional oil and gas exploration and development after shale gas, is hailed as "black gold" by the oil industry. Tight oil refers to oil accumulations that occur in an adsorbed or free state within source rocks, or in reservoir rocks such as tight sandstones and tight carbonates that interbed or are adjacent to source rocks. Tight oil generally has no natural production capacity and requires large-scale fracturing to generate industrial production capacity.

[0003] Beach-bar sandstones are a very important oil and gas reservoir type in faulted lake basins. Because their scale and development are less extensive than those of fluvial and deltaic sedimentary systems, lacustrine beach-bar sedimentary systems have been relatively understudied. However, with increasing exploration, beach-bar sandstones have gradually become a key area of ​​oil and gas exploration in continental faulted basins in eastern my country and are receiving increasing attention. For example, in the Shengli Oilfield, tight oil reserves reach 2.7 million tons, representing a viable alternative energy source for conventional oil and gas reservoirs. Tight beach-bar sandstones hold 1.7 million tons of reserves, accounting for 63% of the total tight oil reserves and making them the most predominant tight oil reservoir type. However, their reserve development remains challenging, resulting in a low production rate. Selecting the best from the worst in tight reservoirs is a key issue in improving tight oil reserve production. Based on the experience of developing tight oil reservoirs in other regions, analyzing the microscopic pore structure of tight reservoirs can reveal the fundamental reasons for their difficulty and low production rate. In the quantitative evaluation of microscopic pore structure, numerous scholars have demonstrated through various experiments and methods that sedimentary rock pore structure exhibits fractal characteristics, and fractal theory is an effective means of describing the complexity and heterogeneity of an object. The pore fractal dimension is one of the comprehensive parameters used to quantitatively describe pore throat size, heterogeneity, and connectivity. However, current calculations of pore fractal dimension are mostly based on single-point analysis from experimental tests. Due to the strong heterogeneity of tight beach-bar sandstone reservoirs, continuous systematic coring and experimental analysis are expensive and difficult to perform. The practice of taking the average value of a limited number of samples to represent the entire sandstone body fails to reflect subtle differences, and the resulting conclusions are likely to mislead development decisions. Currently, there is no effective method for predicting the vertical and horizontal continuity of the fractal dimension of tight beach-bar sandstone reservoirs in a single well.

[0004] Currently, reservoir evaluation methods using pore structure parameters often employ a multi-parameter evaluation approach. These include the displacement pressure parameter (PT), maximum pore throat radius (rmax), median capillary pressure (Pc50), and average pore throat radius (r50), which represent pore size; pore throat sorting coefficient (Sp), pore throat skewness (Skp), pore throat kurtosis (Kp), pore throat distribution peak number (N), peak value (X), peak position (Rv), and homogeneity coefficient (α), which represent pore heterogeneity; and mercury removal efficiency (We), minimum unsaturated pore throat volume percentage (Smin), tortuosity (L), pore structure coefficient (φ), pore throat coordination number (coordination number), pore tortuosity (tortuosity), apparent pore throat volume ratio (VR), and structural uniformity (α·We), which represent pore throat connectivity. Given the large number of parameters and the complex methods for obtaining them, it is difficult to develop a statistical reservoir evaluation scheme. Summary of the Invention

[0005] The present invention aims to solve at least one of the technical problems existing in the prior art and to provide a method for evaluating tight beach-bar sand reservoirs, which can improve prediction accuracy and is simple and easy to operate.

[0006] An embodiment of the present invention provides a method for evaluating a tight beach-bar sand reservoir, the method mainly comprising the following steps:

[0007] A method for evaluating a tight beach-bar sand reservoir comprises the following steps:

[0008] The pore fractal dimension of a single tight beach-bar sand reservoir sample point was calculated using mercury injection experimental data and the wetting phase model.

[0009] Based on the classification of target reservoirs, bar sand and beach sand, the characteristics of bar sand and beach sand logging parameters were studied, the correlation between pore fractal dimension and various logging parameters was analyzed, and the parameters with high correlation were selected. The parameters with high correlation included natural gamma ray, lithologic density, resistivity, microelectrode curve and acoustic wave transit time.

[0010] The steps specifically include: studying the logging parameter characteristics of dam sand and beach sand, projecting the normalized logging parameters onto a radar map, and analyzing the correlation between the pore fractal dimension and each logging parameter;

[0011] After normalizing the logging parameters in the step, regression fitting is performed to respectively establish fractal dimension logging prediction models for tight dam sand and tight beach sand, wherein the fractal dimension logging prediction model for tight dam sand is:

[0012] D 坝砂 =2.90+0.13GR-0.02DEN-0.44RT-0.14K,

[0013] The fractal dimension logging prediction model for tight beach sand is:

[0014] D 滩砂 =2.95+0.06GR-0.30AC-0.39RT-0.01K,

[0015] Among them, D 坝砂 、D 滩砂 is the fractal dimension, GR is the natural gamma value, and DEN is the lithologic density value (unit: g / cm2). 3 ; RT is the resistivity value, unit is Ω·m; K is the absolute value of the microelectrode curve difference, unit is Ω·m; AC is the acoustic wave time difference, unit is μs / ft;

[0016] The step is to calculate the pore fractal dimension value of the single well in the vertical direction by weighted average of the small layers to obtain the plane pore fractal dimension value corresponding to the well. Specifically, it includes: determining the average pore fractal dimension value of the tight beach-bar sand reservoir at the same layer of the single well, calculating the fractal dimensions of the bar sand and beach sand in the section respectively, and then obtaining the pore fractal dimension value of the corresponding plane by weighted average. The formula for calculating the average pore fractal dimension is:

[0017]

[0018] Among them, D 坝 is the fractal dimension of dam sand, H 坝 is the thickness of dam sand, D 滩 is the fractal dimension of beach sand, H 滩 is the thickness of the beach sand, H is the total thickness of the dam sand and beach sand, and D is the fractal dimension value on the plane corresponding to the well;

[0019] After normalizing the logging parameters, fractal dimension logging prediction models of tight bar sand and tight beach sand are established by regression fitting respectively;

[0020] The vertical pore fractal dimension value of a single well is predicted based on the established fractal dimension well logging prediction model of the tight bar sand and tight beach sand;

[0021] According to the pore fractal dimension value in the vertical direction of the single well, the pore fractal dimension value is calculated by weighted average of small layers to obtain the plane pore fractal dimension value corresponding to the well;

[0022] The development effect of the produced blocks was selected, and the correlation between it and the pore fractal dimension was analyzed to establish an evaluation standard for tight beach-bar sand reservoirs based on the pore fractal dimension.

[0023] According to the evaluation standard of tight beach-bar sand reservoirs based on pore fractal dimension, tight beach-bar sand reservoirs are classified and the development and producing sequence is determined.

[0024] Furthermore, the step of calculating the pore fractal dimension of a single tight beach-bar sand reservoir sample point using mercury injection experimental data and a wetting phase model specifically includes: obtaining the fractal geometry formula of the reservoir capillary pressure curve based on the relationship between capillary pressure and wetting phase saturation:

[0025]

[0026] Where V is the saturation of the wetting phase during mercury injection, P min is the capillary pressure corresponding to the maximum pore throat radius, in MPa, P c is the capillary pressure, in MPa. Combined with the Laplace equation, the formula is transformed into:

[0027]

[0028] r is the pore radius, in μm; taking the logarithm of both sides, we get

[0029] lgV=lg(1-SHg)=(3-D)lgr-(3-D)lgrmax,

[0030] Where SHg is the cumulative mercury saturation;

[0031] By plotting the fractal curves of lg(1-SHg) and lgr and fitting the slope K of the curve, the fractal dimension of the pore throat can be obtained according to D=3-K. During the calculation process, the fractal dimension appears in two stages. At this time, the total fractal dimension D of the entire pore throat needs to be obtained by weighted average of the porosity of each pore space. The formula is:

[0032]

[0033] Where, represents the porosity corresponding to the first section of pores, and D1 represents the fractal dimension corresponding to the first section of pores; It represents the porosity corresponding to the second section of pores, and D2 represents the fractal dimension corresponding to the second section of pores.

[0034] The embodiments of the present invention have the following beneficial effects: well logging prediction models for the total fractal dimensions of tight bar sand and beach sand are established respectively, forming a method for predicting the vertical and planar continuity of a single well based on the fractal dimension of a tight beach-bar sand reservoir, thereby improving prediction accuracy; and an evaluation standard for tight beach-bar sand reservoirs based on pore fractal dimension is established. Compared with previous methods for evaluating tight reservoirs based on multiple pore structure parameters, this standard is simpler to operate and provides comprehensive and integrated characterization. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The present invention will be further described below with reference to the accompanying drawings and embodiments;

[0036] Figure 1This is a flow chart of a method for evaluating tight beach-bar sand reservoirs according to an embodiment of the present invention;

[0037] Figure 2 for Figure 1 An example diagram of mercury injection experimental data and pore fractal dimension calculation using the wetting phase model in the embodiment;

[0038] Figure 3 for Figure 1 Normalized logging parameter radar analysis diagram of different fractal dimensions D of dam sand and beach sand in the embodiment;

[0039] Figure 4 for Figure 1 The vertical predicted distribution diagram of the pore fractal dimension of a single well in the embodiment;

[0040] Figure 5 for Figure 1 The pore fractal dimension plane prediction distribution diagram in the embodiment;

[0041] Figure 6 for Figure 1 Analysis diagram of total fractal dimension and daily oil production in the embodiment. DETAILED DESCRIPTION

[0042] This section will describe in detail the specific embodiments of the present invention. The preferred embodiments of the present invention are shown in the accompanying drawings. The purpose of the accompanying drawings is to supplement the description of the text part of the specification with graphics, so that people can intuitively and vividly understand each technical feature and the overall technical solution of the present invention, but it should not be understood as a limitation on the scope of protection of the present invention.

[0043] Reference Figure 1 As shown, the present invention provides a well logging prediction method for the pore fractal dimension of a tight beach-bar sand reservoir, comprising the following steps:

[0044] Step 110 : Calculate the pore fractal dimension of a single tight beach-bar sand reservoir sample point using mercury injection experimental data and a wetting phase model.

[0045] In the embodiment of the present invention, based on the relationship between capillary pressure and wet phase saturation, the fractal geometry formula for obtaining the reservoir capillary pressure curve is:

[0046] Where V is the wetting phase saturation during mercury injection, Pmin is the capillary pressure corresponding to the maximum pore throat radius, in MPa, and Pc is the capillary pressure, in MPa. Combined with the Laplace equation, the formula is:

[0047]

[0048] r is the pore radius, in μm; taking the logarithm of both sides, we get:

[0049] lgV=lg(1-SHg)=(3-D)lgr-(3-D)lgrmax,

[0050] Where SHg is the cumulative mercury saturation.

[0051] like Figure 2 As shown in Figure 1, by plotting the fractal curves of lg(1-SHg) and lgr and fitting the slope K of the curve, the fractal dimension of the pore throat can be obtained according to D=3-K. During the calculation process, the fractal dimension appears in two stages. At this time, the total fractal dimension D of the entire pore throat needs to be obtained by weighted average of the porosity of each pore space. The formula is:

[0052]

[0053] In the formula, φ1 represents the porosity corresponding to the first section of pores, D1 represents the fractal dimension corresponding to the first section of pores; φ2 represents the porosity corresponding to the second section of pores, D2 represents the fractal dimension corresponding to the second section of pores. As a specific example of the embodiment of the present invention, refer to Figure 2 According to the formula D=3-K, the fractal dimension D1 corresponding to the first section of pores is 2.2975, and the fractal dimension D2 corresponding to the second section of pores is 2.9795. The total fractal dimension D of the sample is calculated to be 2.4339.

[0054] Step 120 , based on the classification of the target layer bar sand and beach sand reservoirs, study the logging parameter characteristics of the bar sand and beach sand, analyze the correlation between the pore fractal dimension and each logging parameter, and select the parameters with higher correlation.

[0055] It is understandable that conventional logging parameters mainly include natural gamma, acoustic transit time, density, resistivity, neutron and microelectrode curves, such as Figure 3 The four samples shown in the figure (a) and (b) are bar sand samples, with logging curves showing a pentagonal shape, while the beach sand sample shows a compass shape. As the fractal dimension (D) increases, the gamma value increases, the acoustic transit time decreases, the density increases, the resistivity decreases, the neutron value decreases, and the microelectrode difference decreases. Overall, the total fractal dimension is positively correlated with the gamma and density values, and negatively correlated with the acoustic transit time, resistivity, neutron value, and the absolute value of the microelectrode difference. Correlations between the logging values ​​and the total fractal dimension (D) for bar sand and beach sand show correlation coefficients exceeding 0.8 for the bar sand and gamma, density, resistivity, and absolute value of the microelectrode difference for the beach sand, and correlation coefficients exceeding 0.8 for the beach sand.

[0056] Step 130 : normalize the logging parameters and then perform regression fitting to establish fractal dimension logging prediction models for dam sand and beach sand, respectively.

[0057] In the embodiment of the present invention, the optimized logging parameters are normalized to a range of 0 to 1 to ensure that the distribution range of various logging parameters remains consistent. The standard normalization formula is:

[0058]

[0059] Among them, the logging sample data is x, x max =max{x},x min =min{x}, the normalized data of the logging samples is X.

[0060] Then, the fractal dimension and the optimized logging parameters were regressed and fitted to establish the pore fractal dimension logging prediction models of dam sand and beach sand respectively:

[0061] The fractal dimension logging prediction model for tight dam sand is:

[0062] D 坝砂 =2.90+0.13GR-0.02DEN-0.44RT-0.14K

[0063] The fractal dimension logging prediction model of tight beach sand is:

[0064] D 滩砂 =2.95+0.06GR-0.30AC-0.39RT-0.01K

[0065] Among them, D 坝砂 、D 滩砂 is the fractal dimension; GR is the natural gamma value, API; DEN is the lithologic density value, g / cm3; RT is the resistivity value, Ω·m; K is the absolute value of the microelectrode curve difference, Ω·m; AC is the acoustic time difference value, μs / ft.

[0066] Step 140 : Predicting the vertical pore fractal dimension value of a single well based on the established fractal dimension well logging prediction model for dam sand and beach sand.

[0067] like Figure 4 As shown, in the embodiment of the present invention, the vertical fractal dimensions D of the dam sand and the beach sand are predicted respectively.

[0068] Step 150 , based on the vertical pore fractal dimension values ​​of a single well, the pore fractal dimension values ​​are calculated by weighted average of small layers to obtain the planar pore fractal dimension value corresponding to the well.

[0069] To determine the average pore fractal dimension of a tight beach-bar sand reservoir at the same level in a single well, it is necessary to calculate the fractal dimensions of the bar sand and beach sand within the section separately. Based on the vertical prediction results of the fractal dimension D, the fractal dimensions D of the bar sand and beach sand are weighted averaged to obtain the pore fractal dimension value of the corresponding plane. The formula for calculating the average pore fractal dimension is:

[0070]

[0071] Among them, D 坝 is the fractal dimension of dam sand, H 坝 is the thickness of dam sand, D 滩 is the fractal dimension of beach sand, H 滩 is the thickness of the beach sand, H is the total thickness of the dam sand and beach sand, and D is the fractal dimension value on the plane corresponding to the well.

[0072] Figure 5 It is the fractal dimension D plane distribution prediction map;

[0073] Step 160 : Select the development effect of the produced blocks, analyze the correlation between the development effect and the pore fractal dimension, and establish a tight beach-bar sand reservoir evaluation standard based on the pore fractal dimension.

[0074] In the embodiment of the present invention, the correlation between the daily oil production of a single well and the pore fractal dimension is analyzed, such as Figure 6 As shown in the figure, when the daily oil production is greater than 10 t / d, the fractal dimension D is less than 2.36, indicating a Class I reservoir; when the daily oil production is between 8 and 10 t / d, the fractal dimension D is between 2.36 and 2.43, indicating a Class II reservoir; when the daily oil production is between 6 and 8 t / d, the fractal dimension D is between 2.43 and 2.51, indicating a Class III reservoir; when the daily oil production is between 4 and 6 t / d, the fractal dimension D is between 2.51 and 2.59, indicating a Class IV reservoir; when the daily oil production is between 2 and 4 t / d, the fractal dimension D is between 2.59 and 2.66, indicating a Class V reservoir; and when the daily oil production is less than 2 t / d, the fractal dimension D is greater than 2.66, indicating a Class VI reservoir. From Class I to Class VI, the tight beach-bar sand reservoirs gradually deteriorate.

[0075] Step 170 : Classify the tight beach-bar sand reservoirs according to the tight beach-bar sand reservoir evaluation standard based on the pore fractal dimension, and determine the development and producing sequence.

[0076] like Figure 5 As shown in the figure, according to the evaluation criteria for tight beach-bar sand reservoirs based on pore fractal dimension, tight beach-bar sand plane reservoirs are classified from Class IV to Class I, with increasing reservoir producing priority.

[0077] The embodiments of the present invention are described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the above embodiments. Various changes can be made within the scope of knowledge possessed by ordinary technicians in the technical field without departing from the scope of the present invention.

Claims

1. A method for evaluating tight beach-bar sand reservoirs, characterized in that: The method comprises the following steps: calculating the pore fractal dimension of a single tight beach-bar sand reservoir sample point using mercury injection experimental data and a wetting phase model; studying the logging parameter characteristics of the bar sand and beach sand based on the classification of the target layer bar sand and beach sand reservoir, analyzing the correlation between the pore fractal dimension and each logging parameter, and selecting parameters with a high correlation, wherein the parameters with a high correlation include natural gamma, lithologic density, resistivity, microelectrode curve and acoustic wave time difference; the steps specifically comprise: studying the logging parameter characteristics of the bar sand and beach sand, projecting the normalized logging parameters onto a radar map, and analyzing the correlation between the pore fractal dimension and each logging parameter; and establishing, after normalizing the logging parameters, regression fitting the fractal dimension logging prediction models of the tight bar sand and the tight beach sand, respectively, wherein the tight bar sand fractal dimension logging prediction model is: D 坝砂 =2.90+0.13GR-0.02DEN-0.44RT-0.14K, the tight beach sand fractal dimension logging prediction model is: D 滩砂 =2.95+0.06GR-0.30AC-0.39RT-0.01K, where D 坝砂 、D 滩砂 is the fractal dimension, GR is the natural gamma value, and DEN is the lithologic density value (unit: g / cm2). 3 ; RT is the resistivity value, in Ω·m; K is the absolute value of the microelectrode curve difference, in Ω·m; AC is the acoustic time difference, in μs / ft; according to the steps, the pore fractal dimension value in the vertical direction of the single well is calculated, and the pore fractal dimension value is calculated by weighted average of small layers to obtain the plane pore fractal dimension value corresponding to the well, specifically including: determining the average pore fractal dimension value of the tight beach-bar sand reservoir in the same layer of the single well, calculating the fractal dimensions of the bar sand and beach sand in the section respectively, and then obtaining the pore fractal dimension value of the corresponding plane by weighted average; the average pore fractal dimension calculation formula is: , where D 坝 is the fractal dimension of dam sand, H 坝 is the thickness of dam sand, D 滩 is the fractal dimension of beach sand, H 滩 is the thickness of the beach sand, H is the total thickness of the bar sand and beach sand, D is the fractal dimension value on the plane corresponding to the well, n is the number of bar sand bodies, and m is the number of beach sand bodies; the pore fractal dimension value in the vertical direction of a single well is predicted based on the established fractal dimension logging prediction model for tight bar sand and tight beach sand; based on the pore fractal dimension value in the vertical direction of the single well, the pore fractal dimension value is calculated by weighted average of each small layer to obtain the pore fractal dimension value in the plane corresponding to the well; parameters representing the development effect of the produced block are selected, and the correlation between them and the pore fractal dimension is analyzed to establish an evaluation standard for tight beach-bar sand reservoirs based on the pore fractal dimension; based on the evaluation standard for tight beach-bar sand reservoirs based on the pore fractal dimension, tight beach-bar sand reservoirs are classified to determine the development and producing order.

2. The method for evaluating tight beach-bar sand reservoirs according to claim 1, wherein: The step uses mercury injection experimental data and a wetting phase model to calculate the pore fractal dimension of a single tight beach-bar sand reservoir sample point, specifically including: according to the relationship between capillary pressure and wetting phase saturation, obtaining the fractal geometry formula of the reservoir capillary pressure curve as follows: , where V is the saturation of the wetting phase during mercury injection, P min is the capillary pressure corresponding to the maximum pore throat radius, in MPa, P c is the capillary pressure, in MPa. Combined with the Laplace equation, the formula is transformed into: , r is the pore radius, in μm; take the logarithm of both sides and we get lgV=lg(1-SHg)=(3-D)lgr-(3-D)lgr max , Where SHg is the cumulative mercury saturation. By plotting the fractal curves of lg(1-SHg) and lgr and fitting the slope K of the curve, the fractal dimension of the pore throat can be calculated according to D=3-K. During the calculation process, the fractal dimension appears in two stages. In this case, the total fractal dimension D of the entire pore throat needs to be obtained by weighted average of the porosity of each pore space. The formula is: , where φ1 represents the porosity corresponding to the first section of pores, and D1 represents the fractal dimension corresponding to the first section of pores; φ2 represents the porosity corresponding to the second section of pores, and D2 represents the fractal dimension corresponding to the second section of pores.

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