Method and device for identifying extra-low resistivity oil and gas layer based on bound water saturation calculation, equipment and medium

Abstract

The application discloses a method and device for identifying extra-low-resistance oil and gas layers based on bound water saturation calculation, equipment and medium, and relates to the field of geophysical well logging. The method comprises the following steps: performing a standardization operation on logging data of a to-be-identified formation to obtain target logging data, performing a core experiment on a first target formation in the to-be-identified formation to obtain core data, and performing core depth homing based on the target logging data to obtain target core data; a first function is constructed based on a spontaneous potential corresponding to the first target formation in the target logging data; first and second empirical coefficients are determined according to the first function and the bound water saturation corresponding to the first target formation in the target core data, and a second function is constructed by using the first and second empirical coefficients; and for a second target formation in the to-be-identified formation which has not been subjected to the core experiment, the second target formation is identified as an extra-low-resistance oil and gas layer based on porosity, deep induced resistivity and the bound water saturation determined by the second function.

Description

Technical Field

[0001] This application relates to the field of geophysical logging, and in particular to a method, apparatus, equipment and medium for identifying ultra-low resistivity oil and gas reservoirs based on bound water saturation calculation. Background Technology

[0002] Low-resistivity oil and gas reservoirs are widely developed in high-porosity, high-permeability clastic rocks in major oil and gas basins in my country. Because these reservoirs exhibit low or even ultra-low resistivity, the ratio of the resistivity of the oil / gas reservoir to that of the adjacent water layer is defined as the resistivity increase factor. Generally, oil and gas reservoirs with a resistivity increase factor less than 2 are defined as low-resistivity reservoirs, and those less than 1.5 are specifically defined as ultra-low-resistivity reservoirs. Existing data indicate that high formation water salinity and high bound water saturation are the main reasons for the ultra-low resistivity of oil and gas reservoirs. Under the dual background of high formation water salinity and high bound water saturation, oil and gas reservoirs exhibit ultra-low resistivity, concave shape, and high intrusion characteristics. Current identification methods based on "resistivity magnitude, shape, and intrusion characteristics" for ultra-low-resistivity oil and gas reservoirs have significant limitations and are prone to misjudgment. Therefore, how to accurately identify ultra-low-resistivity oil and gas reservoirs is a pressing technical problem that needs to be solved. Summary of the Invention

[0003] In view of this, the purpose of this application is to provide a method, apparatus, equipment, and medium for identifying ultra-low resistivity oil and gas reservoirs based on bound water saturation calculation, which can accurately identify ultra-low resistivity oil and gas reservoirs. The specific solution is as follows:

[0004] In a first aspect, this application provides a method for identifying ultra-low resistivity oil and gas reservoirs based on bound water saturation calculation, including:

[0005] The well logging data of the pre-acquired formation to be identified is standardized to obtain target well logging data. Core experiments are performed on the first target formation in the formation to be identified to obtain core data. Core depth repositioning is performed based on the target well logging data to update the core data, thus obtaining target core data.

[0006] A first function is constructed based on the spontaneous potential corresponding to the first target formation in the target logging data; the first function is used to obtain the relative values ​​of the spontaneous potential corresponding to the first target formation at different depths.

[0007] Based on the first function and the bound water saturation corresponding to the first target stratum in the target core data, a first empirical coefficient and a second empirical coefficient are determined, and a second function is constructed using the first empirical coefficient and the second empirical coefficient; the second function is used to obtain the bound water saturation corresponding to the second target stratum at different depths; the second target stratum is the stratum in the stratum to be identified that has not yet undergone the core experiment;

[0008] The ultra-low resistivity oil and gas layer in the second target formation is identified based on porosity, deep-induced resistivity, and bound water saturation determined by the second function; wherein the ultra-low resistivity oil and gas layer is an oil and gas layer whose resistivity increase coefficient is less than a preset coefficient threshold.

[0009] Optionally, the standardization operation of the pre-acquired logging data of the formation to be identified to obtain the target logging data includes:

[0010] Obtain well logging data corresponding to the formation to be identified, and determine the standard layer from the formation to be identified;

[0011] The logging curves corresponding to the formation to be identified in the logging data are standardized, and the standardization operation is judged based on the logging curves corresponding to the standard layer after the standardization operation.

[0012] If not completed, proceed to the step of standardizing the logging curves corresponding to the formation to be identified in the logging data, until the standardization operation is completed to obtain the target logging data.

[0013] Optionally, the standardization operation of each logging curve corresponding to the formation to be identified in the logging data includes:

[0014] Obtain the pre-set standardized correction values ​​corresponding to each logging curve; the logging curves are the logging curves corresponding to the formation to be identified in the logging data;

[0015] The well logging curves are standardized based on the standardized correction values.

[0016] Optionally, constructing the first function based on the spontaneous potential corresponding to the first target formation in the target logging data includes:

[0017] Obtain the spontaneous potential curve corresponding to the first target formation in the target logging data, and determine the maximum and minimum spontaneous potential values ​​corresponding to the spontaneous potential curve;

[0018] Based on the maximum and minimum values ​​of the natural potential, a first function is constructed with the natural potential as the variable.

[0019] Optionally, the step of determining the first empirical coefficient and the second empirical coefficient based on the first function and the bound water saturation corresponding to the first target stratum in the target core data, and constructing the second function using the first empirical coefficient and the second empirical coefficient, includes:

[0020] For a first target formation at any depth, the relative value of the spontaneous potential corresponding to the first target formation is obtained based on the first function, and the medium induced resistivity corresponding to the first target formation is determined using the target logging data.

[0021] Determine the bound water saturation corresponding to the first target stratum based on the target core data;

[0022] Based on the bound water saturation, the medium induced resistivity, and the relative value of the spontaneous potential, the first empirical coefficient and the second empirical coefficient corresponding to the first target formation are determined using a pre-set function model.

[0023] A second function is constructed based on the first and second empirical coefficients, with the relative values ​​of induced resistivity and natural potential as variables.

[0024] Optionally, the construction of the second function based on the first and second empirical coefficients, using the relative values ​​of induced resistivity and natural potential as variables, includes:

[0025] Each correlation coefficient is obtained based on the first and second empirical coefficients corresponding to the first target stratum at each depth;

[0026] The largest correlation coefficient is taken as the target correlation coefficient, and the first empirical coefficient and the second empirical coefficient corresponding to the target correlation coefficient are determined as the first target empirical coefficient and the second target empirical coefficient.

[0027] Based on the first target empirical coefficient and the second target empirical coefficient, a second function is constructed using the relative values ​​of induced resistivity and natural potential as variables.

[0028] Optionally, the identification of ultra-low resistivity oil and gas layers in the second target formation based on porosity, deep inductive resistivity, and bound water saturation determined by the second function includes:

[0029] The bound water saturation corresponding to the second target formation is calculated based on the second function, and the porosity and deep inductive resistivity corresponding to the second target formation are determined using the target logging data.

[0030] The ultra-low resistivity oil and gas reservoirs in the second target formation are identified based on the bound water saturation, porosity, and deep inductive resistivity.

[0031] Secondly, this application provides a device for identifying ultra-low resistivity oil and gas reservoirs based on bound water saturation calculation, comprising:

[0032] The data acquisition module is used to standardize the well logging data of the pre-acquired formation to be identified to obtain target well logging data, perform a core experiment on the first target formation in the formation to be identified to obtain core data, and perform a core depth repositioning operation based on the target well logging data to update the core data, thereby obtaining target core data.

[0033] The first function construction module is used to construct a first function based on the spontaneous potential corresponding to the first target formation in the target logging data; the first function is used to obtain the relative values ​​of the spontaneous potential corresponding to the first target formation at different depths.

[0034] The second function construction module is used to determine a first empirical coefficient and a second empirical coefficient based on the first function and the bound water saturation corresponding to the first target stratum in the target core data, and to construct a second function using the first empirical coefficient and the second empirical coefficient; the second function is used to obtain the bound water saturation corresponding to the second target stratum at different depths; the second target stratum is a stratum in the stratum to be identified that has not yet undergone the core experiment;

[0035] The formation identification module is used to identify ultra-low resistivity oil and gas layers in the second target formation based on porosity, deep-induced resistivity, and bound water saturation determined by the second function; wherein, the ultra-low resistivity oil and gas layer is an oil and gas layer whose resistivity increase coefficient is less than a preset coefficient threshold.

[0036] Thirdly, this application provides an electronic device, comprising:

[0037] Memory, used to store computer programs;

[0038] A processor is used to execute the computer program to implement the aforementioned method for identifying ultra-low resistivity oil and gas reservoirs based on bound water saturation calculation.

[0039] Fourthly, this application provides a computer-readable storage medium for storing a computer program, wherein the computer program, when executed by a processor, implements the aforementioned method for identifying ultra-low resistivity oil and gas reservoirs based on bound water saturation calculation.

[0040] In this application, the well logging data of the pre-acquired formation to be identified is standardized to obtain target well logging data. A core experiment is performed on the first target formation in the formation to be identified to obtain core data. Based on the target well logging data, a core depth repositioning operation is performed to update the core data, resulting in target core data. A first function is constructed based on the spontaneous potential corresponding to the first target formation in the target well logging data. The first function is used to obtain the relative values ​​of the spontaneous potential corresponding to the first target formation at different depths. A first empirical coefficient and a second empirical coefficient are determined according to the first function and the bound water saturation corresponding to the first target formation in the target core data. A second function is constructed using the first empirical coefficient and the second empirical coefficient. The second function is used to obtain the bound water saturation corresponding to the second target formation at different depths. The second target formation is a formation in the formation to be identified that has not yet undergone the core experiment. Ultra-low resistivity oil and gas layers in the second target formation are identified based on porosity, deep-induced resistivity, and the bound water saturation determined by the second function. The ultra-low resistivity oil and gas layers are oil and gas layers with a resistivity increase coefficient less than a preset coefficient threshold. As can be seen from the above, this application first obtains well logging data of the formation to be identified and core data corresponding to the first target formation, and then performs core depth repositioning operation based on the standardized target well logging data to obtain the target core data; based on the spontaneous potential corresponding to the first target formation in the target well logging data and the bound water saturation corresponding to the first target formation in the target core data, a second function is constructed to obtain the bound water saturation corresponding to the second target formation at different depths; in this way, based on porosity, deep inductive resistivity and the bound water saturation determined by the second function, the ultra-low resistivity oil and gas layer in the second target formation can be identified accurately, improving the interpretation accuracy of oil and gas layers and laying the foundation for the effective identification and evaluation of ultra-low resistivity oil and gas layers. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0042] Figure 1 This is a flowchart of a method for identifying ultra-low resistivity oil and gas reservoirs based on bound water saturation calculation disclosed in this application;

[0043] Figure 2 This is a schematic diagram illustrating the changes in well logging curves before and after a specific standardized operation disclosed in this application;

[0044] Figure 3This is a schematic diagram illustrating the changes in well logging curves before and after a specific standardized operation disclosed in this application;

[0045] Figure 4 This is a schematic diagram illustrating the correspondence between core data and well logging data before and after core depth repositioning, as disclosed in this application.

[0046] Figure 5 This is a schematic diagram of a specific well logging comprehensive evaluation disclosed in this application;

[0047] Figure 6 This is a schematic diagram of a specific well logging comprehensive evaluation disclosed in this application;

[0048] Figure 7 This is a schematic diagram of a specific well logging comprehensive evaluation disclosed in this application;

[0049] Figure 8 This is a schematic diagram of the structure of an ultra-low resistivity oil and gas reservoir identification device based on bound water saturation calculation disclosed in this application;

[0050] Figure 9 This is a schematic diagram of the structure of an electronic device disclosed in this application. Detailed Implementation

[0051] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0052] Low-resistivity oil and gas reservoirs are widely developed in high-porosity, high-permeability clastic rocks in major oil and gas basins in my country. Because these reservoirs exhibit low or even ultra-low resistivity, the ratio of the resistivity of the oil / gas reservoir to that of the adjacent water layer is defined as the resistivity increase factor. Generally, oil and gas reservoirs with a resistivity increase factor less than 2 are defined as low-resistivity reservoirs, and those less than 1.5 are specifically defined as ultra-low-resistivity reservoirs. Existing data indicate that high formation water salinity and high bound water saturation are the main reasons for ultra-low resistivity in oil and gas reservoirs. Under the dual background of high formation water salinity and high bound water saturation, oil and gas reservoirs exhibit ultra-low resistivity, concave shape, and high intrusion characteristics. Existing identification methods based on "resistivity magnitude, shape, and intrusion characteristics" for ultra-low-resistivity oil and gas reservoirs have significant limitations and are prone to misidentification. Therefore, this application provides a method for identifying ultra-low-resistivity oil and gas reservoirs based on bound water saturation calculation, which can accurately identify ultra-low-resistivity oil and gas reservoirs.

[0053] See Figure 1 As shown in the figure, this invention discloses a method for identifying ultra-low resistivity oil and gas reservoirs based on bound water saturation calculation, including:

[0054] Step S11: Standardize the well logging data of the pre-acquired formation to be identified to obtain target well logging data, perform a core experiment on the first target formation in the formation to be identified to obtain core data, and perform a core depth repositioning operation based on the target well logging data to update the core data, thereby obtaining target core data.

[0055] In this embodiment, firstly, well logging data of the formation to be identified is acquired and standardized to obtain target well logging data. Specifically, this may include: acquiring well logging data corresponding to the formation to be identified and determining a standard layer from the formation to be identified; then, standardizing each well logging curve corresponding to the formation to be identified in the well logging data, and determining whether the standardization operation is complete based on the well logging curve corresponding to the standard layer after standardization; if not, the process jumps to the step of standardizing each well logging curve corresponding to the formation to be identified in the well logging data, until the standardization operation is completed to obtain the target well logging data; wherein, standardizing each well logging curve corresponding to the formation to be identified in the well logging data may include: firstly, acquiring a pre-set standardized correction amount corresponding to each well logging curve; then, standardizing each well logging curve based on the standardized correction amount; wherein, the aforementioned well logging curves are the well logging curves corresponding to the formation to be identified in the well logging data.

[0056] Understandably, during long-term exploration and development, well logging data originates from different measuring instruments and is obtained through different operating methods. This data may contain systematic errors that do not reflect changes in geological factors; therefore, standardization of the well logging data is necessary. The following example demonstrates the standardization of well logging data for a specific low-resistivity reservoir in the target area using the histogram method:

[0057] First, key wells are selected. Through the study of these key wells, suitable geological models, logging interpretation models, interpretation methods, logging calibration and standardization methods, and the conversion relationship between logging information and geological parameters are determined for the study area. Wells with favorable structural locations, good wellbore environments, complete core data, and rich logging series can be selected as key wells. Then, key layers are selected from these key wells. Based on the logging and geological characteristics of the study area and the selection requirements for standard layers, for example, within the same stratum of a certain lithology, with the same sedimentary environment and similar geophysical characteristics, 1-2 strata with stable sedimentation, moderate thickness and small variation, wide distribution, and easily identifiable lithology and logging characteristics are selected as standard layers. For example, a section of mudstone at the top of the Triassic strata in the target area can be selected as a standard layer. Further, the standard values ​​and histograms of each logging curve corresponding to the standard layer are determined. For example, Table 1 shows the standard values ​​set for each logging curve corresponding to the Triassic mudstone in the study area, where A... C represents the acoustic transit time curve, CNL represents the compensated neutron curve, DEN represents the compensated density curve, RILD represents the deep induced resistivity curve, RILM represents the medium induced resistivity curve, and RLL8 represents the eight-lateral resistivity curve. Then, the standardized correction values ​​for each logging curve corresponding to the standard layer are determined. Based on these standardized correction values, the frequency cross-plot method is used to standardize each logging curve in the target area's logging data, obtaining the actual standard values ​​for each logging curve corresponding to the standard layer. If the actual standard values ​​of each logging curve differ significantly from the set standard values, the standardization process continues until the actual standard values ​​of each logging curve approach the set standard values, completing the standardization of the logging data. The standardized correction values ​​for the acoustic transit time curve, compensated neutron curve, and compensated density curve can be additive factors, while the standardized correction values ​​for the deep induced resistivity curve, medium induced resistivity curve, and eight-lateral resistivity curve can be multiplicative factors.

[0058] Table 1

[0059]

[0060] After standardization, the rationality of the standardization can be judged by the logging curves corresponding to the standard layer. For example, for reservoirs, the sonic transit time curve, compensated neutron curve, and compensated density curve converge under the mutual capacitance scale; for large sections of mudstone, the deep induction resistivity curve, the medium induction resistivity curve, and the eight lateral resistivity curves basically overlap. Therefore, the rationality of porosity logging curve standardization can be visually verified by comparing the degree of convergence of the three porosity curves before and after standardization at the reservoir. Figure 2 The curve within the rectangular frame is shown; similarly, the reasonableness of resistivity logging curve standardization can be visually verified by comparing the degree of overlap between the three resistivity curves before and after standardization in large sections of mudstone. Figure 3 The curve within the rectangular frame is shown. Combined with... Figure 2 , Figure 3 As shown, the standardized operation was reasonable and the target logging data was obtained.

[0061] After obtaining the target logging data, a portion of the formations to be identified is selected as the first target formation. Core experiments are then performed on the first target formation to obtain core data. Based on the aforementioned target logging data, a core depth repositioning operation is performed to update the core data, thus obtaining the target core data. The core data includes, but is not limited to, porosity and bound water saturation. Bound water saturation can be determined using methods such as relative permeability, nuclear magnetic resonance (NMR), and semi-permeable diaphragm capillary pressure.

[0062] Specifically, the correlation comparison method is used to determine the shift in the core analysis repositioning depth. The core data is compared with the target logging data, and the logging depth in the target logging data is used as a benchmark to determine the value by which the core depth is raised or lowered, ensuring consistency between the core data and the target logging data. For example, Figure 4 As shown, by performing core depth repositioning operations on 48 cored wells in the Triassic strata of an oilfield, it was found that the core analysis depth corresponding to the core data was generally shallower than the logging depth corresponding to the target logging data. In some wells, the core analysis depth was deeper than the logging depth. The amount of movement in core depth repositioning was determined by comparison, and the core analysis data was adjusted based on this movement. It is understood that during core depth repositioning, curves reflecting similar properties in the core data and the target logging data can be selected for comparative analysis to determine the matching point or matching interval between the two. For example, in this embodiment, porosity curves or physical property curves can be selected for core depth repositioning. Figure 4 The acoustic time difference curve, compensated neutron curve, and compensated density curve are shown in the figure.

[0063] Step S12: Construct a first function based on the spontaneous potential corresponding to the first target formation in the target logging data; the first function is used to obtain the relative values ​​of spontaneous potential corresponding to the first target formation at different depths.

[0064] In this embodiment, constructing a first function based on the spontaneous potential (SP) of the first target formation in the target logging data may include: obtaining the SP curve of the first target formation in the target logging data, and determining the maximum and minimum SP values ​​corresponding to the SP curve; constructing a first function with SP as a variable based on the maximum and minimum SP values; the expression of the constructed first function is:

[0065] ;

[0066] in, This is a relative value of the natural potential, expressed as a decimal. It is the natural potential; This represents the minimum natural potential corresponding to the natural potential curve. The first function expression represents the maximum natural potential value corresponding to the natural potential curve; it can obtain the relative value of the natural potential corresponding to the first target stratum at any depth.

[0067] Step S13: Determine the first empirical coefficient and the second empirical coefficient based on the first function and the bound water saturation corresponding to the first target stratum in the target core data, and construct the second function using the first empirical coefficient and the second empirical coefficient; the second function is used to obtain the bound water saturation corresponding to the second target stratum at different depths; the second target stratum is the stratum in the stratum to be identified that has not yet undergone the core experiment.

[0068] In this embodiment, since the logging curves sensitive to bound water saturation in the research section of the work area are the intermediate induced resistivity curve and the spontaneous potential curve, a second function can be constructed based on the data fitting method of the sensitive curves to obtain the bound water saturation corresponding to the second target formation at different depths. Specifically, for the first target formation at any depth, the relative value of the spontaneous potential corresponding to the first target formation is obtained based on the first function, and the intermediate induced resistivity corresponding to the first target formation is determined using the target logging data; then, the bound water saturation corresponding to the first target formation is determined based on the target core data; in this way, for the first target formation at any depth, the corresponding relative value of spontaneous potential, intermediate induced resistivity, and bound water saturation can be obtained; based on the combination of multiple bound water saturation, intermediate induced resistivity, and relative values ​​of spontaneous potential, and using a pre-set function model, the first empirical coefficient and the second empirical coefficient corresponding to the first target formation are determined; then, based on the first empirical coefficient and the second empirical coefficient, the second function is constructed with the intermediate induced resistivity and the relative value of spontaneous potential as variables. The pre-set function model is as follows:

[0069] ;

[0070] in, The bound water saturation is a decimal. For medium induced resistivity, A is the first empirical coefficient; B is the second empirical coefficient.

[0071] It should be noted that the above-mentioned construction of the second function based on the first and second empirical coefficients, using the relative values ​​of induced resistivity and spontaneous potential as variables, can include: firstly, obtaining the correlation coefficients based on the first and second empirical coefficients corresponding to the first target formation at each depth; then, using the largest correlation coefficient as the target correlation coefficient, and determining the first and second empirical coefficients corresponding to the target correlation coefficient as the first target empirical coefficient and the second target empirical coefficient; and constructing the second function based on the first and second target empirical coefficients, using the relative values ​​of induced resistivity and spontaneous potential as variables. The closer the correlation coefficient R is to 1, the more accurate the bound water saturation obtained based on the second function. Thus, based on the bound water saturation obtained from core experiments, the target core data is fitted with the target well logging data to obtain the second function. For example, based on the target work area selected in step S11, the maximum correlation coefficient R is 0.939, where A is 9.22 and B is 60.675. The second function at this time is:

[0072] ;

[0073] Step S14: Identify the ultra-low resistivity oil and gas layer in the second target formation based on porosity, deep-induced resistivity, and bound water saturation determined by the second function; wherein the ultra-low resistivity oil and gas layer is an oil and gas layer whose resistivity increase coefficient is less than a preset coefficient threshold.

[0074] In this embodiment, a comprehensive interpretation standard of resistivity-bound water saturation-porosity can be established based on the deep-induction resistivity after standardized operation, combined with bound water saturation and porosity, to identify ultra-low resistivity oil and gas layers in the second target formation that have not undergone core experiments.

[0075] Specifically, firstly, the bound water saturation corresponding to the second target formation is calculated using the second function obtained in step S13, and the porosity and deep resistivity corresponding to the second target formation are determined using the target logging data; then, the ultra-low resistivity oil and gas layers in the second target formation are identified based on the bound water saturation, porosity and deep resistivity.

[0076] It should be noted that the sonic-density intersection method can be used to calculate porosity. For example, for argillaceous sandstone formations, the well logging response equations for sonic waves and density are as follows, according to the rock physics model:

[0077] ;

[0078] ;

[0079] Convert to matrix form:

[0080] ;

[0081] Solving the equation, we get:

[0082] ;

[0083] ;

[0084] ;

[0085] in, The time difference of sound waves is expressed in microseconds per foot. This represents the clay content, a decimal. Porosity, decimal; For acoustic transit time of the rock skeleton, in microseconds per foot; For acoustic wave transit time in mud, in microseconds per foot; The sonic transit time of the drilling fluid is expressed in microseconds per foot. To compensate for density, ; To compensate for the density of the rock skeleton, ; To compensate for the density of the clay, ; To compensate for the density of the drilling fluid, .

[0086] Taking the target work area in step S11 as an example, based on the deep induced resistivity after standardized operation ( ), combined with bound water saturation ( Based on parameters such as resistivity and porosity, a comprehensive interpretation standard of resistivity-bound water saturation-porosity was established to determine the interpretation conclusion of the reservoir, in order to identify strata that have not undergone core experiments. As shown in Table 2, when the porosity is not less than 15%, it is interpreted as a reservoir; when the porosity is less than 15%, it is interpreted as a dry layer; within the reservoir, when the deep inductive resistivity is greater than 0.8... At this point, the bound water saturation is generally below 40%, which is interpreted as a normal oil and gas reservoir; when the deep inductive resistivity is between 0.4... Up to 0.8 When the resistivity is between 0.4 and the bound water saturation is above 50% (generally between 50% and 70%), it is interpreted as a low-resistivity oil and gas reservoir; when the deep inductive resistivity is between 0.4... Up to 0.8 When the bound water saturation is below 50%, it is interpreted as oil and water in the same layer; when the deep induced resistivity is less than 0.4... At this point, the saturation of bound water is generally below 50%, which is interpreted as a water layer.

[0087] Table 2

[0088]

[0089] As can be seen from the above, this embodiment first acquires the well logging data of the formation to be identified and the core data corresponding to the first target formation. Then, based on the standardized target well logging data, a core depth repositioning operation is performed to obtain the target core data. The target core data is then fitted with the target well logging data. A second function is constructed based on the spontaneous potential corresponding to the first target formation in the target well logging data and the bound water saturation corresponding to the first target formation in the target core data. This function is used to obtain the bound water saturation corresponding to the second target formation at different depths. In this way, based on porosity, deep inductive resistivity, and the bound water saturation determined by the second function, the ultra-low resistivity oil and gas layer in the second target formation can be identified accurately. This improves the interpretation accuracy of the oil and gas layer and lays the foundation for the effective identification and evaluation of ultra-low resistivity oil and gas layers.

[0090] The technical solutions in this application will be described below with specific embodiments.

[0091] Example 1:

[0092] The accuracy of this application was verified by referring to the test and production results of Well K1. See [link / reference]. Figure 5 The well logging comprehensive evaluation chart of well K1 shown in the figure reveals that the natural gamma value is low and the spontaneous potential is negative in the 4187-4225 meter interval, indicating a sandstone reservoir. Combined with the mutual compatibility convergence of the three porosities and the non-uniform variation of the three resistivity curves, the comprehensive analysis suggests that it is a suspected oil and gas layer.

[0093] Using the upper Triassic mudstone as a standard layer, the well logging data was standardized. The fourth line in the figure shows the standardized resistivity curve. It can be seen that the deep, medium and shallow resistivity curves of the pure mudstone section at 4182.0-4184.0 meters basically overlap, indicating that the standardization effect is reliable.

[0094] Based on the curve characteristics of the spontaneous potential curve in the pure mudstone and pure sandstone sections in the figure, the maximum spontaneous potential is read as 85mV and the minimum as 5mV. Thus, the relative spontaneous potential (DSP) of this stratum can be determined. Based on the standardized medium induced resistivity curve value, the bound water saturation of this stratum is calculated using the second function expression established above (Channel 5). The porosity of this stratum is obtained using the acoustic-density intersection method (Channel 6).

[0095] Based on the established comprehensive interpretation standard of resistivity-bound water saturation-porosity, the induced resistivity at depths of layers 1-2 is as low as 0.45. The calculated bound water saturation is 52-57.4%, and the average porosity is 21.3%, which can be interpreted as an ultra-low resistivity oil and gas reservoir; the inductive resistivity at depth of layer 3 is 2.58. The calculated bound water saturation is 31.9%, and the average porosity is 22%, which can be interpreted as a normal oil and gas reservoir; the inductive resistivity at depth of layer 4 is 0.68. The calculated average bound water saturation was 38.5%, and the average porosity was 22.9%, which can be interpreted as oil and water co-layering; the induced resistivity at the depth of layers 5-6 was 0.26-0.31. The calculated bound water saturation was 36-48%, and the average porosity was 23.2%, which can be interpreted as a water layer. The ultra-low resistivity oil and gas layer at 4187-4196.5m was tested, and the daily production was 20.3 tons of crude oil, 4800 cubic meters of gas, and no water. The test conclusion was that it was an oil and gas layer.

[0096] Example 2:

[0097] The accuracy of this invention was verified by referring to the test and production results of well G4. See also... Figure 6 The comprehensive evaluation chart of well logging for well G4 shown in the figure reveals that the 4166-4183 meter interval has low natural gamma values ​​and negative spontaneous potential anomalies, indicating a sandstone reservoir. Combined with the convergence of the three porosities and the non-uniformity variation characteristics of the three resistivity curves, the comprehensive analysis identifies it as a suspicious oil and gas layer.

[0098] Using the upper Triassic mudstone as a standard layer, the well logging data was standardized. The fourth line in the figure is the standardized resistivity curve. It can be seen that the deep, medium and shallow resistivity curves of the pure mudstone section from 4182.2 to 4184.5 meters basically overlap, indicating that the standardization effect is reliable.

[0099] Based on the curve characteristics of the spontaneous potential curve in the pure mudstone and pure sandstone sections in the figure, the maximum spontaneous potential is read as 95mV and the minimum as -5mV. Thus, the relative spontaneous potential (DSP) of this stratum can be determined. Based on the standardized medium induced resistivity curve value, the bound water saturation of this stratum is calculated using the previously established bound water saturation function expression (Channel 5). The porosity of this stratum is obtained using the acoustic-density intersection method (Channel 6).

[0100] Based on the established comprehensive interpretation standard of resistivity-bound water saturation-porosity, the induced resistivity at depth 1 is 2.6. The calculated average bound water saturation was 35.8%, and the average porosity was 24.0%, which can be interpreted as a normal oil and gas reservoir; the deep inductive resistivity of the upper part of layer 3 was 2.7. The calculated average bound water saturation was 32.7%, and the average porosity was 22.8%, characteristics typical of normal oil and gas reservoirs; the deep inductive resistivity in the lower part of layer 3 decreased to 0.7. The calculated average bound water saturation was 53.5% and the average porosity was 18.8%, which are characteristics of a (very) low-resistivity oil and gas layer. Layer 3 was comprehensively interpreted as an oil and gas layer. The oil and gas layer at 4166-4169m was tested and produced 35.2 tons of crude oil and 6380 cubic meters of gas per day. There was no water. The test conclusion was that it was an oil and gas layer.

[0101] Example 3:

[0102] The accuracy of this invention was verified by referring to the test and production results of well K2. See also... Figure 7 The well logging comprehensive evaluation chart of well K2 shown in the figure reveals that the 4172-4199 meter section has low natural gamma values ​​and negative spontaneous potential anomalies, indicating a sandstone reservoir. Combined with the convergence of the three porosities and the non-uniformity of the three resistivity curves, the comprehensive analysis identifies it as a suspicious oil and gas layer.

[0103] Using the upper Triassic mudstone as a standard layer, the well logging data was standardized. The fourth line in the figure is the standardized resistivity curve. It can be seen that the deep, medium and shallow resistivity curves of the pure mudstone section at 4165-4175 meters basically overlap, indicating that the standardization effect is reliable.

[0104] Based on the characteristics of the spontaneous potential curves in the pure mudstone and pure sandstone sections in the figure, the maximum spontaneous potential is 65mV and the minimum is 30mV. Thus, the relative spontaneous potential (DSP) of this section can be determined. Based on the standardized medium induced resistivity curve values, the bound water saturation of this section is calculated using the previously established bound water saturation function expression (Channel 5). The porosity of this section is determined using the acoustic-density intersection method (Channel 6).

[0105] Based on the established comprehensive interpretation standard of resistivity-bound water saturation-porosity, the induced resistivity at depth of layer 1 is 1.05. The calculated average bound water saturation was 52.5%, and the average porosity was 19.2%, which can be interpreted as a low-resistivity oil and gas layer; the deep inductive resistivity in the upper part of layer 2 decreased to 0.43. The calculated average bound water saturation was 58.9%, and the average porosity was 16%, characteristics typical of ultra-low resistivity oil and gas reservoirs; the lower deep-seated resistivity was 3.6. The calculated average bound water saturation was 33.2%, and the average porosity was 20%, characteristics typical of normal oil and gas reservoirs; layer 2 is therefore interpreted as an oil and gas reservoir; the deep-induced resistivity of layer 3 is 0.26. The calculated average bound water saturation was 46.8%, and the average porosity was 22.2%, which can be interpreted as a water layer. The oil and gas layer at 4171-4183m was tested, and the daily production was 56.3 tons of crude oil, 8910 cubic meters of gas, and no water. The test conclusion was that it was an oil and gas layer.

[0106] Based on the three specific embodiments described above, Table 3 can be derived:

[0107] Table 3

[0108]

[0109] As can be seen from the above, this embodiment illustrates the method for identifying ultra-low resistivity oil and gas reservoirs based on bound water saturation calculation described in this application using three specific examples. Based on the comprehensive interpretation of resistivity, bound water saturation, and porosity, it can accurately identify ultra-low resistivity oil and gas reservoirs, improve the interpretation accuracy of oil and gas reservoirs, and lay the foundation for the effective identification and evaluation of ultra-low resistivity oil and gas reservoirs.

[0110] See Figure 8 As shown in the embodiments, this application also discloses an ultra-low resistivity oil and gas reservoir identification device based on bound water saturation calculation, comprising:

[0111] The data acquisition module 11 is used to standardize the well logging data of the pre-acquired formation to be identified to obtain target well logging data, perform a core experiment on the first target formation in the formation to be identified to obtain core data, and perform a core depth repositioning operation based on the target well logging data to update the core data, thereby obtaining target core data.

[0112] The first function construction module 12 is used to construct a first function based on the spontaneous potential corresponding to the first target formation in the target logging data; the first function is used to obtain the relative values ​​of the spontaneous potential corresponding to the first target formation at different depths.

[0113] The second function construction module 13 is used to determine a first empirical coefficient and a second empirical coefficient based on the first function and the bound water saturation corresponding to the first target stratum in the target core data, and to construct a second function using the first empirical coefficient and the second empirical coefficient; the second function is used to obtain the bound water saturation corresponding to the second target stratum at different depths; the second target stratum is a stratum in the stratum to be identified that has not yet undergone the core experiment;

[0114] The formation identification module 14 is used to identify ultra-low resistivity oil and gas layers in the second target formation based on porosity, deep-induced resistivity, and bound water saturation determined by the second function; wherein the ultra-low resistivity oil and gas layer is an oil and gas layer whose resistivity increase coefficient is less than a preset coefficient threshold.

[0115] As can be seen from the above, this application first obtains well logging data of the formation to be identified and core data corresponding to the first target formation, and then performs core depth repositioning operation based on the standardized target well logging data to obtain the target core data; based on the spontaneous potential corresponding to the first target formation in the target well logging data and the bound water saturation corresponding to the first target formation in the target core data, a second function is constructed to obtain the bound water saturation corresponding to the second target formation at different depths; in this way, based on porosity, deep inductive resistivity and the bound water saturation determined by the second function, the ultra-low resistivity oil and gas layer in the second target formation can be identified accurately, improving the interpretation accuracy of oil and gas layers and laying the foundation for the effective identification and evaluation of ultra-low resistivity oil and gas layers.

[0116] In some specific embodiments, the data acquisition module 11 includes:

[0117] The standard layer determination unit is used to acquire well logging data corresponding to the formation to be identified and to determine the standard layer from the formation to be identified.

[0118] The standardization operation submodule is used to perform standardization operations on each logging curve corresponding to the formation to be identified in the logging data, and to determine whether the standardization operation is completed based on the logging curve corresponding to the standard layer after the standardization operation.

[0119] The data acquisition unit is used to, if not completed, jump to the step of standardizing the logging curves corresponding to the formation to be identified in the logging data, until the standardization operation is completed to obtain the target logging data.

[0120] In some specific implementations, the standardized operation submodule includes:

[0121] The calibration quantity determination unit is used to obtain the pre-set standardized calibration quantity corresponding to each logging curve; the logging curves are the logging curves corresponding to the formation to be identified in the logging data;

[0122] A standardized operation unit is used to perform standardized operations on each logging curve based on the standardized correction amount.

[0123] In some specific embodiments, the first function construction module 12 includes:

[0124] The first information acquisition unit is used to acquire the spontaneous potential curve corresponding to the first target formation in the target logging data, and to determine the maximum and minimum spontaneous potential values ​​corresponding to the spontaneous potential curve.

[0125] The first function determination unit is used to construct a first function based on the maximum value of the natural potential and the minimum value of the natural potential, using the natural potential as a variable.

[0126] In some specific embodiments, the second function construction module 13 includes:

[0127] The second information acquisition unit is used to obtain the relative value of the spontaneous potential of the first target formation based on the first function for any depth of the first target formation, and to determine the medium induced resistivity of the first target formation using the target logging data.

[0128] A bound water saturation determination unit is used to determine the bound water saturation corresponding to the first target stratum based on the target core data.

[0129] An empirical coefficient determination unit is used to determine the first empirical coefficient and the second empirical coefficient corresponding to the first target formation based on the bound water saturation, the intermediate induced resistivity and the relative value of the spontaneous potential, and using a pre-set function model.

[0130] The second function determination submodule is used to construct the second function based on the first empirical coefficient and the second empirical coefficient, with the relative values ​​of the induced resistivity and the natural potential as variables.

[0131] In some specific implementations, the second function determines the sub-module, including:

[0132] The correlation coefficient determination unit is used to obtain each correlation coefficient based on the first empirical coefficient and the second empirical coefficient corresponding to the first target stratum at each depth.

[0133] The target empirical coefficient determination unit is used to take the largest correlation coefficient as the target correlation coefficient, and to determine the first empirical coefficient and the second empirical coefficient corresponding to the target correlation coefficient as the first target empirical coefficient and the second target empirical coefficient.

[0134] The second function construction unit is used to construct a second function based on the first target empirical coefficient and the second target empirical coefficient, with the relative values ​​of induced resistivity and natural potential as variables.

[0135] In some specific embodiments, the formation identification module 14 includes:

[0136] The third information acquisition unit is used to calculate the bound water saturation corresponding to the second target formation based on the second function, and to determine the porosity and deep inductive resistivity corresponding to the second target formation using target logging data.

[0137] The oil and gas reservoir identification unit is used to identify ultra-low resistivity oil and gas reservoirs in the second target formation based on the bound water saturation, the porosity, and the deep inductive resistivity.

[0138] Furthermore, embodiments of this application also disclose an electronic device, Figure 9 This is a structural diagram of an electronic device 20 according to an exemplary embodiment. The content of the diagram should not be construed as limiting the scope of this application.

[0139] Figure 9 This is a schematic diagram of the structure of an electronic device 20 provided in an embodiment of this application. Specifically, the electronic device 20 may include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. The memory 22 stores a computer program, which is loaded and executed by the processor 21 to implement the relevant steps in the ultra-low resistivity oil and gas reservoir identification method based on bound water saturation calculation disclosed in any of the foregoing embodiments. Alternatively, the electronic device 20 in this embodiment may specifically be a computer.

[0140] In this embodiment, the power supply 23 is used to provide operating voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and external devices, and the communication protocol it follows can be any communication protocol applicable to the technical solution of this application, and is not specifically limited here; the input / output interface 25 is used to acquire external input data or output data to the outside world, and its specific interface type can be selected according to specific application needs, and is not specifically limited here.

[0141] In addition, the memory 22, as a carrier for resource storage, can be a read-only memory, random access memory, disk or optical disk, etc. The resources stored thereon can include operating system 221, computer program 222, etc., and the storage method can be temporary storage or permanent storage.

[0142] The operating system 221 is used to manage and control the various hardware devices on the electronic device 20 and the computer program 222, which may be Windows Server, Netware, Unix, Linux, etc. In addition to including a computer program capable of performing the ultra-low resistivity oil and gas reservoir identification method based on bound water saturation calculation disclosed in any of the foregoing embodiments, the computer program 222 may further include computer programs capable of performing other specific tasks.

[0143] Furthermore, this application also discloses a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, it implements the aforementioned method for identifying ultra-low resistivity oil and gas reservoirs based on bound water saturation calculation. Specific steps of this method can be found in the corresponding content disclosed in the foregoing embodiments, and will not be repeated here.

[0144] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to in the method section.

[0145] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0146] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0147] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0148] The technical solutions provided in this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for identifying extra-low resistivity oil and gas layers based on bound water saturation calculation, characterized in that, The method comprises the following steps: standardizing pre-acquired well logging data of a to-be-identified formation to obtain target well logging data, performing a core experiment on a first target formation in the to-be-identified formation to obtain core data, and updating the core data based on core depth homing operation of the target well logging data to obtain target core data; constructing a first function based on spontaneous potential corresponding to the first target formation in the target well logging data; the first function is used to obtain relative values of spontaneous potential corresponding to the first target formation at different depths; determining a first empirical coefficient and a second empirical coefficient according to the first function and the irreducible water saturation corresponding to the first target formation in the target core data, and constructing a second function using the first empirical coefficient and the second empirical coefficient; the second function is used to obtain irreducible water saturation corresponding to a second target formation at different depths; the second target formation is a formation in the to-be-identified formation that has not undergone the core experiment; identifying a low-resistivity oil and gas layer in the second target formation based on porosity, deep induced resistivity, and irreducible water saturation determined by the second function; wherein the low-resistivity oil and gas layer is an oil and gas layer with a resistivity increase coefficient less than a preset coefficient threshold; wherein the determining a first empirical coefficient and a second empirical coefficient according to the first function and the irreducible water saturation corresponding to the first target formation in the target core data, and constructing a second function using the first empirical coefficient and the second empirical coefficient, comprises: for a first target formation at any depth, obtaining a relative value of spontaneous potential corresponding to the first target formation based on the first function, and determining a middle induced resistivity corresponding to the first target formation using the target well logging data; determining the irreducible water saturation corresponding to the first target formation according to the target core data; determining a first empirical coefficient and a second empirical coefficient corresponding to the first target formation based on the irreducible water saturation, the middle induced resistivity, and the relative value of spontaneous potential, and using a pre-set function model; obtaining each correlation coefficient based on the first empirical coefficient and the second empirical coefficient corresponding to each depth of the first target formation; taking the maximum correlation coefficient as a target correlation coefficient, and determining the first empirical coefficient and the second empirical coefficient corresponding to the target correlation coefficient as a first target empirical coefficient and a second target empirical coefficient; constructing a second function based on the first target empirical coefficient and the second target empirical coefficient, with middle induced resistivity and relative value of spontaneous potential as variables.

2. The method according to claim 1, wherein, The standardizing pre-acquired well logging data of a to-be-identified formation to obtain target well logging data comprises: acquiring well logging data corresponding to the to-be-identified formation, and determining a standard layer from the to-be-identified formation; standardizing each well logging curve corresponding to the to-be-identified formation in the well logging data, and determining whether the standardization operation is completed based on the well logging curve corresponding to the standard layer after the standardization operation; If not completed, jump to the step of performing a standardization operation on each logging curve corresponding to the to-be-identified formation in the logging data until the standardization operation is completed to obtain target logging data.

3. The method according to claim 2, wherein, The standardization operation on each logging curve corresponding to the to-be-identified formation in the logging data comprises: obtaining a pre-set standardization correction quantity corresponding to each logging curve; the each logging curve is each logging curve corresponding to the to-be-identified formation in the logging data; performing a standardization operation on the each logging curve based on the standardization correction quantity.

4. The method according to claim 1, wherein, The first function is constructed based on the spontaneous potential corresponding to the first target formation in the target logging data, comprising: obtaining a spontaneous potential curve corresponding to the first target formation in the target logging data, and determining a maximum spontaneous potential value and a minimum spontaneous potential value corresponding to the spontaneous potential curve; constructing a first function with the spontaneous potential as a variable based on the maximum spontaneous potential value and the minimum spontaneous potential value.

5. The method for identifying extra-low resistivity oil and gas layers based on the calculation of irreducible water saturation according to any one of claims 1 to 4, characterized in that, The second target formation is identified based on the porosity, the deep induced resistivity, and the irreducible water saturation determined by the second function, comprising: calculating the irreducible water saturation corresponding to the second target formation based on the second function, and determining the porosity and the deep induced resistivity corresponding to the second target formation by using the target logging data; identifying the ultra-low resistivity oil and gas layer in the second target formation according to the irreducible water saturation, the porosity, and the deep induced resistivity.

6. An apparatus for identifying extra-low resistivity oil and gas layers based on bound water saturation calculation, characterized in that, comprising: The data acquisition module is configured to perform a standardization operation on pre-acquired logging data of a to-be-identified formation to obtain target logging data, perform a core experiment on a first target formation in the to-be-identified formation to obtain core data, and update the core data by performing a core depth homing operation based on the target logging data to obtain target core data. The first function construction module is configured to construct a first function based on a spontaneous potential corresponding to the first target formation in the target logging data; the first function is used to obtain a relative value of the spontaneous potential of the first target formation at different depths. The second function construction module is configured to determine a first empirical coefficient and a second empirical coefficient according to the first function and an irreducible water saturation corresponding to the first target formation in the target core data, and construct a second function by using the first empirical coefficient and the second empirical coefficient; the second function is used to obtain an irreducible water saturation of a second target formation at different depths; the second target formation is a formation in the to-be-identified formation that has not undergone the core experiment. The formation identification module is configured to identify an ultra-low resistivity oil and gas layer in the second target formation based on the porosity, the deep induced resistivity, and the irreducible water saturation determined by the second function; the ultra-low resistivity oil and gas layer is an oil and gas layer with a resistivity increase coefficient less than a pre-set coefficient threshold. The second function construction module comprises: a second information obtaining unit configured to obtain, for any depth of the first target formation, a natural potential relative value corresponding to the first target formation based on the first function, and determine a middle induced resistivity corresponding to the first target formation by using the target logging data; a bound water saturation determining unit configured to determine a bound water saturation corresponding to the first target formation according to the target core data; an empirical coefficient determining unit configured to determine a first empirical coefficient and a second empirical coefficient corresponding to the first target formation based on the bound water saturation, the middle induced resistivity and the natural potential relative value, and by using a pre-set function model; a correlation coefficient determining unit configured to obtain each correlation coefficient based on the first empirical coefficient and the second empirical coefficient corresponding to each depth of the first target formation; a target empirical coefficient determining unit configured to take the maximum correlation coefficient as a target correlation coefficient, and determine the first empirical coefficient and the second empirical coefficient corresponding to the target correlation coefficient as a first target empirical coefficient and a second target empirical coefficient; a second function constructing unit configured to construct a second function based on the first target empirical coefficient and the second target empirical coefficient, and take the middle induced resistivity and the natural potential relative value as variables.

7. An electronic device, comprising: comprising: a memory configured to save a computer program; a processor configured to execute the computer program to implement the method for identifying a special low-resistivity oil and gas layer based on bound water saturation calculation according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, a computer program, which, when executed by a processor, implements the method for identifying a special low-resistivity oil and gas layer based on bound water saturation calculation according to any one of claims 1 to 5.

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