A method for constructing classification curves of low-permeability gas layers in marine areas by integrating static and dynamic data

Abstract

This invention relates to the field of oil and gas field exploration and development technology, and particularly to a method for constructing classification curves for low-permeability gas reservoirs in the sea using integrated static and dynamic data. The method includes: Step S1, preprocessing logging data and core data from the entire well section; Step S2, based on Step S1, delineating target sections with low-permeability gas reservoirs, selecting the main influencing parameters related to reservoir static layers and production capacity, and extracting curves for each main influencing parameter of the target section; Step S3, based on Step S2, normalizing the extracted curves for each main influencing parameter, and constructing reservoir classification curves according to the correlation between each main influencing parameter curve and production capacity; Step S4, based on Step S3, verifying the accuracy of the reservoir classification curves using test production capacity data from different dynamic layers of the reservoir within the target section. Using this reservoir classification curve for the classification of low-permeability gas reservoirs in the sea has high accuracy and operability, and can be used to accurately predict production capacity.

Description

Technical Field

[0001] This invention relates to the field of oil and gas field exploration and development technology, and in particular to a method for constructing classification curves of low-permeability gas layers in the sea area by integrating static and dynamic data. Background Technology

[0002] The East China Sea shelf basin's oil and gas resources are primarily natural gas. In exploration of deep, low-permeability gas reservoirs, formations with similar reservoir properties exhibit vastly different development capacities. Some formations have natural production capacities exceeding 300,000 cubic meters per day, while others require reservoir stimulation to achieve industrial production. Still others have capacities below 10,000 cubic meters per day, failing to reach industrial production standards even after fracturing. Since production capacity directly impacts development policy formulation and economic performance evaluation, simple reservoir classification methods are no longer sufficient to accurately assess reservoir properties, let alone predict production capacity.

[0003] Current methods classify gas reservoirs based on reservoir properties, primarily porosity and permeability, into broad categories such as low-porosity and low-permeability, ultra-low-porosity and ultra-low-permeability, and extra-low-porosity and extra-low-permeability. This classification fails to consider differences in rock pore structure or adequately link it to productivity factors. This qualitative classification method frequently leads to errors when facing increasingly complex geological conditions, failing to accurately guide regional exploration. Therefore, establishing a new comprehensive classification and evaluation method for deep-sea low-permeability gas reservoirs is urgently needed. Summary of the Invention

[0004] In view of the above-mentioned deficiencies of the prior art, the technical problem to be solved by the present invention is to provide a method for constructing a classification curve of low-permeability gas reservoirs in the sea area by integrating static and dynamic data, which can establish a reservoir classification curve that can accurately predict production capacity.

[0005] To solve the above-mentioned technical problems, the present invention adopts the following technical solution:

[0006] This invention provides a method for constructing classification curves of low-permeability gas reservoirs in the sea using integrated static and dynamic data, comprising the following steps: Step S1, preprocessing logging data and core data for the entire well section; Step S2, based on Step S1, delineating target sections with low-permeability gas reservoirs, selecting the main influencing parameters related to reservoir static layers and production capacity, and extracting the curves of each main influencing parameter for the target section; Step S3, based on Step S2, normalizing the extracted curves of each main influencing parameter, and constructing reservoir classification curves according to the correlation between each main influencing parameter curve and production capacity; Step S4, based on Step S3, verifying the accuracy of the reservoir classification curves according to the test production capacity data of different dynamic layers of reservoirs within the target section.

[0007] Preferably, in step S1, the preprocessing includes quality inspection, preprocessing and standardization of well logging curve data, as well as quality inspection and depth repositioning of core data.

[0008] Preferably, in step S2, the selection of the main influencing parameters related to reservoir static layer productivity includes: lithological and lithofacies analysis: selecting the median rock grain size Md and clay content SH, where Md is positively correlated with productivity and SH is negatively correlated with productivity; extraction of reservoir macroscopic parameters: selecting porosity φ and permeability K, where both φ and K are positively correlated with productivity; extraction of reservoir microscopic parameters: selecting mercury injection displacement pressure Pd and average pore throat radius of the reservoir. Among them, Pd is negatively correlated with production capacity. Positively correlated with production capacity.

[0009] Preferably, in step S3, the normalization process includes: reading the maximum value Md of the extracted target layer's median rock grain size curve. max and minimum value Md min According to the formula Normalization was performed to obtain the normalized median rock grain size ΔMd curve; the maximum value SH of the extracted target layer's clay content SH curve was read. max and minimum value SH min According to the formula Normalization was performed to obtain the normalized clay content ΔSH curve; the maximum value φ of the porosity φ curve of the extracted target layer was read. max and minimum value φ min According to the formula Perform normalization to obtain the normalized porosity Δφ curve; read the maximum value K of the extracted permeability K curve for the target layer. max and minimum value K min According to the formula Normalization is performed to obtain the normalized permeability ΔK curve; the maximum value Pd of the mercury injection displacement pressure Pd curve of the extracted target layer is read. max and minimum value Pd min According to the formula Normalization was performed to obtain the normalized mercury injection displacement pressure ΔPd curve; the average pore throat radius of the reservoir in the extracted target layer was then read. maximum value of the curve and minimum value According to the formula After normalization, the normalized average pore throat radius of the reservoir is obtained. Curve; Reservoir classification curve is: Furthermore, the value of reservoir classification curve C satisfies: C∈[-2,4].

[0010] Preferably, in step S4, the low-permeability gas reservoirs in the sea area are divided into the following three categories according to the value of the reservoir classification curve C: Class I gas reservoirs with a test production capacity Q greater than 100,000 cubic meters / day: C∈[2,4]; Class II gas reservoirs with a test production capacity Q of 50,000 cubic meters / day to 100,000 cubic meters / day: C∈[0,2]; and Class III gas reservoirs with a test production capacity Q less than 50,000 cubic meters / day: C∈[-2,0].

[0011] Compared with the prior art, the present invention has significant progress:

[0012] The method for constructing classification curves of low-permeability gas reservoirs in the ocean based on integrated static and dynamic data of the present invention, based on well logging curve data and core experimental data, and guided by geological laws, integrates static and dynamic reservoir layer data to obtain a reservoir classification curve whose accuracy has been verified by test production data. Using this reservoir classification curve for the classification of low-permeability gas reservoirs in the ocean has high accuracy and operability, and can be further used to accurately predict production capacity, thereby solving the problem in the prior art that the coarse reservoir classification affects the prediction of high-quality gas reservoirs. Attached Figure Description

[0013] Figure 1 This is a flowchart illustrating the method for constructing a classification curve of a low-permeability gas layer in the sea area using integrated static and dynamic data, according to an embodiment of the present invention.

[0014] Figure 2 This is an analysis diagram showing the matching of the extracted curves of major influencing parameters with experimental analysis data in the method for constructing classification curves of low-permeability gas layers in the sea area using integrated static and dynamic data in this embodiment of the invention.

[0015] Figure 3 This is a diagram showing the relationship between reservoir classification curve C and test production capacity Q in the method for constructing a classification curve of a low-permeability gas layer in the sea area using integrated static and dynamic data according to an embodiment of the present invention. Detailed Implementation

[0016] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. These embodiments are for illustrative purposes only and are not intended to limit the scope of the invention.

[0017] like Figures 1 to 3 As shown, this invention provides an embodiment of a method for constructing classification curves of low-permeability gas layers in marine areas using integrated static and dynamic data.

[0018] See Figure 1 The method for constructing a classification curve of low-permeability gas layer in the sea area using integrated static and dynamic data in this embodiment includes the following steps.

[0019] Step S1: Preprocess the logging data and core data of the entire well section.

[0020] Preferably, in step S1, the preprocessing of logging data and core data for the entire well section includes quality inspection, preprocessing and standardization of logging curve data, as well as quality inspection and depth repositioning of core data.

[0021] The quality inspection, preprocessing, and standardization of well logging curve data are as follows: Well logging curve data quality inspection is performed, checking for consistency in names and units; multiple segments of the same type of well logging curve obtained from multiple measurements are spliced ​​together, invalid values ​​are removed, and redundant curve versions are deleted; a set of stable mudstone approximately 20m thick at the top of the target gas layer is selected as a standard section, and well logging curve data from each well in the block are read within this standard section. A frequency histogram is plotted, and the difference between the average distribution of each well logging curve value and the average value is used as a correction value. The correction values ​​of each well logging curve are applied to the entire well section to achieve regional standardization. The standardized well logging curves are checked; a good normal distribution trend indicates successful standardization.

[0022] The quality inspection and depth calibration of core data specifically involves: conducting a core data quality inspection; firstly, calibrating the core depth to the corresponding logging depth based on the drill pipe depth and logging depth adjustment; then, selecting logging curves with good correlation and matching them with the core data for fine-tuning the depth. It should be noted that the entire batch of core data should be comprehensively determined, not individually calibrated for each sample point. Due to the sampling and representativeness of the samples, a normal distribution is sufficient; complete consistency is not required.

[0023] Step S2: Based on step S1, delineate the target section with low permeability gas reservoir, select the main influencing parameters related to reservoir static layer and production capacity, and extract the curves of each main influencing parameter of the target section.

[0024] Preferably, in step S2, the selection of the main influencing parameters related to reservoir static level and production capacity includes lithological and lithofacies analysis, extraction of reservoir macroscopic parameters and extraction of reservoir microscopic parameters, as detailed below.

[0025] Lithological and lithofacies analysis: Lithology refers to the comprehensive characteristics of sedimentary environments and the sedimentary rocks formed within those environments. Lithology is closely related to lithology, and the most important indicators reflecting lithology are the median grain size (Md) and the clay content (SH). Therefore, the median grain size (Md) and the clay content (SH) are selected as the main influencing parameters related to reservoir static strata and productivity. Among them, Md is positively correlated with productivity, and SH is negatively correlated with productivity.

[0026] Extraction of macroscopic reservoir parameters: The two most important macroscopic parameters of the reservoir are porosity φ, which reflects the rock reservoir space, and permeability K, which reflects the fluid flow capacity. Therefore, porosity φ and permeability K are selected as the main influencing parameters related to productivity in the static reservoir layer. Both φ and K are positively correlated with productivity.

[0027] Reservoir micro-parameter extraction: Reservoir micro-parameters are usually determined by mercury intrusion porosimetry (MIP), among which the most sensitive parameters are the mercury displacement pressure Pd and the average pore throat radius of the reservoir. Therefore, the mercury injection displacement pressure Pd and the average pore throat radius of the reservoir are selected. These are the main parameters related to reservoir static levels and productivity. Among them, Pd is negatively correlated with productivity. Positively correlated with production capacity.

[0028] Therefore, by analyzing various parameters of the reservoir's static layers, six sensitive curves most closely related to productivity were selected, namely the six main influencing parameter curves mentioned above. Based on the data preprocessed in step S1, the six main influencing parameter curves for the target layer were extracted, namely: median rock grain size (Md) curve, clay content (SH) curve, porosity (φ) curve, permeability (K) curve, mercury injection displacement pressure (Pd) curve, and average pore throat radius of the reservoir. curve.

[0029] Those skilled in the art will know that the parameter models of the static reservoir layer can be established based on the principle of "core calibration logging", as follows.

[0030] The median Md curve of rock grain size can be obtained based on the correlation between the well logging GR (natural gamma) curve and the grain size analysis data of the core sample.

[0031] The SH curve of clay content can be calculated using the variation law of the SP (spontaneous potential) curve in well logging.

[0032] The porosity φ curve was calculated by intersecting density-neutron curves and correcting it with the clay content SH curve.

[0033] The permeability K curve is obtained by establishing a power function or exponential correlation between porosity φ and permeability K based on core physical property analysis data.

[0034] Mercury injection displacement pressure Pd curve and average pore throat radius of reservoir The curves were determined through mercury intrusion porosimetry (MIP) experiments. Specifically, the mercury intrusion displacement pressure Pd curve was obtained by reading experimental data, and the average pore throat radius of the reservoir was also considered. The curve is obtained by calculating using the following formula:

[0035]

[0036] In the formula, r i Δs is the throat radius of the interval, in μm; i is the mercury increment corresponding to the throat radius of the interval, in %; n is the number of pore throat intervals.

[0037] Correlation analysis between microscopic parameters obtained by mercury intrusion porosimetry and conventional logging curves yields reservoir microscopic parameter curves.

[0038] The curves of the main influencing parameters constructed from the well logging data must be well matched with the experimental analysis data, such as... Figure 2 As shown.

[0039] Step S3: Based on step S2, normalize the extracted curves of each major influencing parameter, and construct reservoir classification curves according to the correlation between each major influencing parameter curve and production capacity.

[0040] Since the six main influencing parameter curves extracted in step S2 are not on the same dimension and have different trends, but the six parameters are intrinsically related, the curves are first normalized to ensure they are within the same scale, matching, comparable, and operable. Preferably, in step S3, the normalization of the extracted main influencing parameter curves is performed using a normalization formula to normalize the six main influencing parameter curves, generating six basic curves.

[0041] The median rock grain size (Md) curve, clay content (SH) curve, and porosity (φ) curve were normalized using the same method, as shown below.

[0042] Read the maximum value Md of the median rock grain size curve of the extracted target layer. max and minimum value Md min According to the formula After normalization, the normalized median rock grain size ΔMd curve is obtained.

[0043] Read the maximum value SH of the SH curve of the extracted target layer. max and minimum value SH min According to the formula Normalization was performed to obtain the normalized mud content ΔSH curve.

[0044] Read the maximum value φ of the porosity φ curve of the extracted target layer. max and minimum value φ min According to the formula After normalization, the normalized porosity Δφ curve is obtained.

[0045] Permeability K curve, mercury injection displacement pressure Pd curve, and average pore throat radius of the reservoir The curves are all logarithmically normalized, as shown below.

[0046] Read the maximum value K of the permeability K curve of the extracted target layer. max and minimum value K min According to the formula After normalization, the normalized permeability ΔK curve is obtained.

[0047] Read the maximum value Pd of the mercury displacement pressure Pd curve of the extracted target layer. max and minimum value Pd min According to the formula After normalization, the normalized mercury displacement pressure ΔPd curve is obtained.

[0048] Read the average pore throat radius of the reservoir in the target segment. maximum value of the curve and minimum value According to the formula After normalization, the normalized average pore throat radius of the reservoir is obtained. curve.

[0049] Based on the correlation between the curves of each major influencing parameter and production capacity, and using the normalized median rock grain size ΔMd curve, the normalized clay content ΔSH curve, the normalized porosity Δφ curve, the normalized permeability ΔK curve, the normalized mercury injection displacement pressure ΔPd curve, and the normalized reservoir average pore throat radius... The reservoir classification curve is constructed as follows: The reservoir classification curve C has a dimensionless value and satisfies the following condition: C∈[-2,4].

[0050] Step S4: Based on step S3, verify the accuracy of the reservoir classification curve using the test production capacity data of different dynamic reservoir layers within the target section. The test production capacity Q is finite data of a specific layer type. Different low-permeability gas-bearing layers within the target section of the block are selected as test layers, and the test production capacity data of the test layers are used as verification data.

[0051] According to the industrial standards for offshore gas field production capacity, deep, low-permeability gas reservoirs in the sea are classified into three categories, denoted as I, II, and III. (See also...) Figure 3 Preferably, in step S4, the low-permeability gas reservoirs in the sea area are classified into the following three categories according to the reservoir classification curve C value:

[0052] For Class I gas reservoirs with a test capacity Q greater than 100,000 cubic meters per day: C∈[2,4]. The relationship between C and Q for Class I gas reservoirs is exponential.

[0053] The tested production capacity Q is 50,000-100,000 cubic meters / day in a Class II gas reservoir: C∈[0,2]. The relationship between C and Q in a Class II gas reservoir is linear.

[0054] For Class III gas reservoirs with a test capacity Q less than 50,000 cubic meters per day: C∈[-2,0]. The relationship between C and Q for Class III gas reservoirs is logarithmic.

[0055] Using test production data of different low-permeability gas-bearing sections within the target block as validation data, the accuracy of reservoir classification curve C was verified. The validation results show that the distribution range of reservoir classification curve C is consistent with the gas layer type. The value of reservoir classification curve C for the test section is highly consistent with the test production capacity Q data of the test section, and the C value and Q value are highly correlated. The higher the C value, the larger the test production capacity Q value of the section. There is a good positive correlation between the two. Therefore, using reservoir classification curve C to classify low-permeability gas layers in the sea has high accuracy and operability, and can be further used to accurately predict production capacity.

[0056] By using the reservoir classification curve C, low-permeability gas-bearing sections in different blocks can be classified and their production capacity predicted. There is no need to test the production capacity of the corresponding blocks. Only steps S1 to S3 of this embodiment are needed to obtain the reservoir classification curve C values ​​for different low-permeability gas-bearing sections in the corresponding blocks. Then, according to step S4, the corresponding low-permeability gas-bearing sections are classified based on the range of the C values: if C∈[2,4], it is classified as a Class I gas layer, with a production capacity greater than 100,000 cubic meters per day, possessing natural industrial production capacity; if C∈[0,2], it is classified as a Class II gas layer, with a production capacity of 50,000 to 100,000 cubic meters per day, possessing industrial production capacity after reservoir modification measures; if C∈[-2,0], it is classified as a Class III gas layer, with a production capacity less than 50,000 cubic meters per day, lacking economic development value.

[0057] In a specific embodiment, taking the reservoir classification and evaluation of the Y gas field in the Xihu Depression of the East China Sea as an example, the southern part of the Y gas field mainly exhibits a single-fault structure controlled by the main fault, while the northern part is controlled by multiple feather-like faults derived from the main fault, forming a step-transform fault structure that gradually descends southward. The Pinghu Formation of the Eocene develops two favorable reservoir-seal assemblages: the upper and lower Pinghu Formation. The upper Pinghu Formation has a significant source supply in the western part, developing tidal deltaic sedimentary facies with good sand body continuity. The lower Pinghu Formation features alternating paleogeographic uplifts and depressions, developing source and tidal deltaic sedimentary facies in the northwest and west, with a large stratigraphic thickness, and low-permeability gas layers are mainly developed in favorable areas. To better perform reservoir classification and evaluation, the method of constructing a marine low-permeability gas layer classification curve using comprehensive static and dynamic data in this embodiment is adopted, and the work is carried out sequentially according to steps S1 to S4 above. In step S4, the accuracy of the reservoir classification curve C is verified using the test production data of different low-permeability gas layers within the target stratigraphic segment of the block as verification data. The verification results are as follows.

[0058] Well Y1 contains gravelly sandstone in the H3 layer. The rock has a high median Md grain size, good reservoir properties, and a microstructure showing that it is mainly composed of medium and large throats. The tested natural production capacity reaches 480,000 cubic meters per day, and it is classified as a Class I gas layer.

[0059] The H4 layer of well Y2 is composed of fine sandstone with moderate physical properties, mainly consisting of medium-fine throats. The initial test production was 12,000 cubic meters per day, and the subsequent fracturing test production was 60,000 cubic meters per day, meeting industrial standards and being classified as a Class II gas layer.

[0060] The H5 layer of well Y5 is composed of fine sandstone with high mud content and poor physical properties. The throat is fine to very fine. Tests showed trace amounts of gas, and the production after fracturing was 0.5 million cubic meters per day, which did not meet industrial standards. It was classified as a Class III gas layer.

[0061] The C value of the reservoir classification curve of the test layer matches the test productivity results of the layer 100%.

[0062] In summary, the method for constructing a classification curve for low-permeability gas reservoirs in the sea area based on integrated static and dynamic data in this embodiment, guided by geological principles, combines static and dynamic reservoir level data to obtain a reservoir classification curve C whose accuracy has been verified by test production data. Using this reservoir classification curve C for the classification of low-permeability gas reservoirs in the sea area has high accuracy and operability, and can be further used to accurately predict production capacity, thereby solving the problem in the prior art where the coarse reservoir classification affects the prediction of high-quality gas reservoirs.

[0063] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and substitutions can be made without departing from the technical principles of the present invention, and these improvements and substitutions should also be considered within the scope of protection of the present invention.

Claims

1. A method for constructing classification curves of low-permeability gas layers in marine areas by integrating static and dynamic data, characterized in that, Includes the following steps: Step S1: Preprocess the logging data and core data for the entire well section; Step S2: Based on step S1, delineate the target segment with low-permeability gas reservoirs, select the main influencing parameters related to reservoir static layer productivity, and extract the curves of each main influencing parameter of the target segment. The selection of the main influencing parameters related to reservoir static layer productivity includes: Lithological and lithofacies analysis: median rock grain size was selected. and mud content ,in, Positively correlated with production capacity Negatively correlated with production capacity; Extraction of macroscopic parameters of reservoir: selection of porosity and penetration rate , and All are positively correlated with production capacity; Reservoir micro-parameter extraction: Selecting mercury injection displacement pressure and reservoir average pore throat radius ,in, Negatively correlated with production capacity Positively correlated with production capacity; Step S3: Based on step S2, normalize the extracted curves of each major influencing parameter. Based on the correlation between each major influencing parameter curve and production capacity, construct a reservoir classification curve. The reservoir classification curve is as follows: And reservoir classification curve The numerical values ​​satisfy: , This is the normalized median rock grain size curve. The normalized porosity curve is shown below. This is the normalized permeability curve. This is the normalized reservoir average pore throat radius curve. This is the normalized clay content curve. The normalized mercury displacement pressure curve; Step S4: Based on step S3, verify the accuracy of the reservoir classification curve according to the test production capacity data of different reservoir dynamic layers within the target segment.

2. The method for constructing classification curves of low-permeability gas layers in marine areas based on integrated static and dynamic data according to claim 1, characterized in that, In step S1, the preprocessing includes quality inspection, preprocessing and standardization of well logging curve data, as well as quality inspection and depth repositioning of core data.

3. The method for constructing classification curves of low-permeability gas layers in marine areas based on integrated static and dynamic data according to claim 1, characterized in that, In step S3, the normalization process includes: Read the median rock grain size of the extracted target layer. maximum value of the curve and minimum value According to the formula Normalization was performed to obtain the normalized median rock grain size. curve; Read the clay content of the extracted target layer maximum value of the curve and minimum value According to the formula Normalization was performed to obtain the normalized clay content. curve; Read the porosity of the extracted target layer maximum value of the curve and minimum value According to the formula Normalization is performed to obtain the normalized porosity. curve; Read the permeability of the extracted target layer. maximum value of the curve and minimum value According to the formula Perform normalization to obtain the normalized penetration rate. curve; Read the mercury impingement displacement pressure of the extracted target layer maximum value of the curve and minimum value According to the formula After normalization, the normalized mercury displacement pressure is obtained. curve; Read the average pore throat radius of the reservoir in the target segment. maximum value of the curve and minimum value According to the formula After normalization, the normalized average pore throat radius of the reservoir is obtained. curve.

4. The method for constructing classification curves of low-permeability gas layers in marine areas based on integrated static and dynamic data according to claim 3, characterized in that, In step S4, according to the reservoir classification curve The numerical values ​​classify low-permeability gas layers in the ocean into the following three categories: Testing capacity Class I gas-bearing layers with a capacity of more than 100,000 cubic meters per day: ; Testing capacity Class II gas-bearing layers with a capacity of 50,000 to 100,000 cubic meters per day: ; Testing capacity Class III gas-bearing layers with a capacity of less than 50,000 cubic meters per day: .

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