Method and device for correcting layer thickness influence of formation resistivity logging information interpretation

Abstract

The invention relates to the technical field of open hole well logging in petroleum and natural gas exploration and development, in particular to a layer thickness influence correction method and device for stratum resistivity logging data interpretation, and the method comprises the steps: setting stratum parameters corresponding to different layer thickness intervals, carrying out forward modeling to measure the parameters, and calculating a theoretical K value and an Rd4-Rs18 value according to the parameters; optimizing the layer thickness interval to obtain an optimal layer thickness interval, and establishing a neural network model corresponding to the optimal layer thickness interval; training the neural network to obtain an interpretation model, and realizing layer thickness influence correction of the variable K value; the interpretation model can be used for interpreting three stratum parameters of a variable K value of each layer, a specific value of stratum and intrusive zone resistivity and an intrusive zone radius; the method is used for solving the problem that the stratum true resistivity value cannot be quantitatively explained due to the fact that the resistivity with the corresponding layer thickness and an invasion influence correction chart cannot be developed and the layer thickness influence correction cannot be carried out on the ultrathin layer due to the fact that the obtained apparent resistivity value of the ultrathin layer is too low and the change range is too small in the original fixed K value explaining method.

Description

Technical Field

[0001] The present invention relates to the technical field of open hole logging in oil and gas exploration and development, and particularly to a method and device for correcting the influence of layer thickness in the interpretation of formation resistivity logging data. Background Art

[0002] The influence of layer thickness is one of the most important factors among various influencing factors of resistivity logging data. Abroad, marine sedimentary oil reservoirs are mainly developed, thick layers are relatively developed, and the method for correcting the influence of layer thickness is relatively simple, which is achieved by developing a correction chart for the influence of layer thickness. The correction chart for the influence of layer thickness of the most advanced Schlumberger logging company in the world is as shown in Figure 1 shown, Figure 1 where the abscissa is the layer thickness, the ordinate is the layer thickness correction coefficient, R LLD is the apparent resistivity value of the deep dual laterolog, R LLDcor is the apparent resistivity value of the deep dual laterolog after correction for the influence of layer thickness, and R s in the curve modulus is the resistivity of the surrounding rock. It can be seen from Figure 1 that when the layer thickness is greater than 1 m, the correction accuracy for the influence of layer thickness is relatively high. Between 1.5 m and 0.6 m, the influence of layer thickness suddenly increases, resulting in a gradual increase in the error of layer thickness correction. For ultra-thin layers with a thickness of 0.2 - 0.5 m, due to the too large influence of layer thickness, no research has been conducted and it is a blank. Therefore, the correction for the influence of layer thickness is only applicable to relatively thick formations with a thickness of more than 1.5 m.

[0003] In most areas of China, continental sedimentary oil layers are developed, with thin layers and thin interbeds. The traditional fixed K-value interpretation method corrects the layer thickness by developing a set of resistivity and invasion influence correction charts for different layer thicknesses.

[0004] The basic formula in resistivity logging and interpretation is:

[0005] R a = K * U d / I 0 (1);

[0006] In the formula: U d is the main electrode potential, with the unit of millivolt (mv), I 0 is the main electrode supply current, with the unit of milliampere (mA), K is the electrode system constant, and R a is the apparent resistivity value, with the unit of ohm-meter (Ω·m). U d / I 0 can be measured, and R a and K are two variables. Since the resistivity logging and interpretation technology came into being, the traditional practice has been to move from the right end to the left end of the above formula, that is, to measure the measured U d / I 0The apparent resistivity R is obtained by multiplying the value by the electrode system constant K obtained from a homogeneous formation to fix the K value. a Regarding the value of R a as a variable, and finally through correction of various influencing factors, the true resistivity R of the formation is interpreted. t value.

[0007] For decades since the implementation of this interpretation method with a fixed K value, it has preferably solved the problem of quantitative interpretation of the true resistivity R of relatively thick formations, provided an important basis for accurately determining the oil saturation value of the formation, and finally carried out reservoir evaluation, achieving obvious geological effects. However, this interpretation method with a fixed K value has the following two problems: t Problem 1: As shown in the attached

[0008] two triple lateral resistivity logs with low invasion and invasion correction charts for a layer thickness of 5 meters and 0.8 meters, in Figure 2 and Figure 3 , where d is the well diameter, di is the diameter of the invaded zone, dn is the outer diameter of the instrument, R Figure 2 and 3 , R LL3 d is the apparent resistivity of the deep triple lateral, R LL3 s is the apparent resistivity of the shallow triple lateral, R s is the resistivity of the surrounding rock, h is the layer thickness, R m is the resistivity of the mud. It can be seen from the comparison between Figure 2 and Figure 3 that as the layer thickness gradually decreases from 5 meters to 0.8 meters, the opening degree of the correction chart gradually becomes narrower, resulting in a gradual increase in the interpretation error of the true resistivity R of the formation. Especially when the layer thickness reaches 0.6 meters, due to the narrow chart and low values, some errors in the original data may cause the intersection point to fall outside the chart, making it impossible to quantitatively interpret the value of R t . t value.

[0009] Problem 2: For ultra-thin layers less than 0.6 meters, taking a 0.2-meter ultra-thin layer as an example, due to being severely affected by the layer thickness, the value of the apparent resistivity R a is not only too low but also has a small variation range. It is impossible to develop a resistivity and invasion influence correction chart for ultra-thin layers, impossible to correct for the influence of layer thickness on ultra-thin layers, and impossible to quantitatively interpret the value of the true resistivity R of the formation t / R m (R m = 1) value.

[0010] Therefore, so far, there has been no in-depth study on the layer thickness influence law and correction method for ultra-thin layers of 0.2 - 0.6 meters in the world. Summary of the Invention

[0011] The present invention provides a method and device for correcting the influence of layer thickness in the interpretation of formation resistivity logging data, so as to solve the problem that in the original method, when quantitatively interpreting the true formation resistivity of ultra-thin layers, due to the too low apparent resistivity value and small variation range, it is impossible to develop resistivity and invasion influence correction charts for ultra-thin layers to correct the influence of layer thickness on the formation, and thus the quantitative interpretation of the true formation resistivity value cannot be carried out.

[0012] According to one aspect of the present invention, there is provided a method for correcting the influence of layer thickness in the interpretation of formation resistivity logging data, including:

[0013] Setting formation parameters corresponding to different layer thickness intervals, and performing forward simulation to obtain measurement parameter values related to the layer thickness interval, and determining the deep theoretical K value and the difference R between the apparent resistivities of the deep and shallow triple laterologs after resistivity correction according to the set formation parameters and measurement parameter values d 4 -R s 18 ;

[0014] Optimizing the set different layer thickness intervals to obtain the optimized best layer thickness interval;

[0015] Using the formation parameters corresponding to the best layer thickness interval set by the forward simulation, the obtained measurement parameter values, the deep theoretical K value and R d 4 -R s 18 , training the established neural network model to obtain an interpretation model corresponding to each best layer thickness interval;

[0016] Obtaining the measurement parameter values related to the layer thickness interval, the formation thickness corresponding to the target formation, and R determined according to the measurement parameter values d 4 -R s 18 ;

[0017] Finding the interpretation model corresponding to the best layer thickness interval closest to the formation thickness of the target formation, and inputting the measurement parameters related to the layer thickness interval and R d 4 -R s 18 corresponding to the target formation into the interpretation model to run, and obtaining the deep variable K value, invasion zone radius r i , formation resistivity ratio R t / R xo value interpretation results corresponding to the target formation.

[0018] Preferably, the method of setting formation parameters corresponding to different layer thickness intervals and performing forward simulation to obtain measurement parameter values related to the layer thickness interval includes:

[0019] Set the true formation resistivity R corresponding to different layer thickness intervals t , invasion zone resistivity R xo , invasion zone radius r i for a group of formations, input them into the forward model for forward simulation, and obtain the measurement parameter values related to the layer thickness interval corresponding to each formation.

[0020] Preferably, the measurement parameter values related to the layer thickness interval at least include: the ratio of the supply current of the first shielding electrode to the main electrode of the deep triple lateral, i.e., deep I 1 / I 0 , the ratio of the sum of the supply currents of the first and second shielding electrodes of the deep triple lateral to the supply current of the main electrode, i.e., deep I 1 +I 2 / I 0 , the potential U of the main electrode of the deep triple lateral d , the ratio of the supply current of the shielding electrode to the main electrode of the shallow triple lateral, i.e., shallow I 1 / I 0 , the potential U of the main electrode of the shallow triple lateral s .

[0021] Preferably, the method for determining the deep theoretical K value according to the set formation parameters and measurement parameter values includes:

[0022] The calculation formula for the deep theoretical K value is: deep theoretical K value = R t / U d ;

[0023] Where: R t is the true formation resistivity set during forward simulation, and U d is the potential of the main electrode of the deep triple lateral measured through forward simulation.

[0024] Preferably, the method for optimizing the set different layer thickness intervals to obtain the optimized best layer thickness interval includes:

[0025] Judge whether the average relative error of the deep theoretical K values of all formations between two adjacent layer thickness intervals is less than a predetermined percentage. If not, insert several layer thickness intervals between the two adjacent layer thickness intervals until the average relative error of the deep theoretical K values of all formations between every two adjacent inserted layer thickness intervals is less than the predetermined percentage, so as to obtain the best layer thickness interval.

[0026] Preferably, the method for determining the difference R d 4 -R s 18 of the apparent resistivity of the deep and shallow triple laterals after resistivity correction according to the measurement parameter values includes:

[0027] Among them, the method for determining R after resistivity correction includes: d 4

[0028] Determine the relationship between the deep I 1 / I 0 in the forward simulation result and the deep theoretical K value;

[0029] Substitute the deep I 1 / I 0 measured from the target formation into the relationship between the deep I 1 / I 0 and the deep theoretical K value, calculate the corresponding value, and multiply the corresponding value by the U d measured from this target formation to obtain R d 4 ;

[0030] Among them, the method for determining R after resistivity correction includes: s 18

[0031] Determine the shallow theoretical K value according to the set formation parameters and measurement parameter values. The method includes:

[0032] The calculation formula for the shallow theoretical K value is: shallow theoretical K value = R t / U s ;

[0033] In the formula: R t is the true formation resistivity set during forward simulation, and U s is the main electrode potential of the shallow triple lateral measured through forward simulation;

[0034] Determine the relationship between the shallow I 1 / I 0 in the forward simulation result and the shallow theoretical K value;

[0035] Substitute the shallow I 1 / I 0 measured from the target formation into the relationship between the shallow I 1 / I 0 and the shallow theoretical K value, calculate the corresponding value, and multiply the corresponding value by the U s measured from this target formation to obtain R s 18 .

[0036] Preferably, it further includes: the measurement parameters measured during forward simulation, and R determined according to the measurement parameters d 4 -R s ​​18 and the formation thickness are input into an interpretation model of the optimal layer thickness interval closest to the formation thickness, and the corresponding deep-variable K value and r i , R t / R xo value interpretation results are obtained;

[0037] Compare it with the deep theoretical K value of the forward simulation and the set r i , R t / R xo value to determine the relative error between the two. The relative error between the deep-variable K value and the deep theoretical K value is the relative error of the final formation true resistivity interpretation.

[0038] According to an aspect of the present invention, a layer thickness influence correction device for interpreting formation resistivity logging data is provided, including:

[0039] A forward simulation unit for setting formation parameters corresponding to different layer thickness intervals, performing forward simulation, obtaining measurement parameter values related to the layer thickness interval, and determining the deep theoretical K value and the difference R d 4 -R s 18 ;

[0040] A layer thickness interval optimization unit for optimizing the set different layer thickness intervals to obtain the optimized best layer thickness interval;

[0041] An interpretation model establishment unit for training the established neural network model by using the formation parameters corresponding to the best layer thickness interval set by the forward simulation, the obtained measurement parameter values, the deep theoretical K value, and R d 4 -R s 18 , and obtaining an interpretation model corresponding to each best layer thickness interval;

[0042] An acquisition unit for acquiring the measurement parameter values related to the layer thickness interval corresponding to the target formation, the formation thickness, and R determined according to the measurement parameter values d 4 -R s 18 ;

[0043] An interpretation result output unit for finding the interpretation model corresponding to the best layer thickness interval closest to the formation thickness of the target formation, and outputting the measurement parameters related to the layer thickness interval corresponding to the target formation and R d 4 -R s 18, run in the interpretation model to obtain the deep-variable K value corresponding to the target formation and the invasion zone radius r i 、 the formation resistivity ratio to the invasion zone R t / R xo value interpretation result.

[0044] The present invention has at least the following beneficial effects:

[0045] The present invention provides a method and device for correcting the influence of layer thickness in the interpretation of formation resistivity logging data. By performing forward simulation on a group of formations with different layer thickness intervals, parameters related to the layer thickness and the true resistivity of the formation are obtained, and the optimal layer thickness interval is determined; then, the neural network model with the optimal layer thickness interval is trained by the relevant parameters to obtain a group of interpretation models with the optimal layer thickness interval; the thickness of the target formation is determined, and finally, the interpretation result is obtained by running the interpretation model corresponding to the optimal layer thickness interval closest to the thickness of the target formation, thereby realizing accurate correction of the influence of layer thickness on ultra-thin layers, and further being able to effectively improve the interpretation accuracy of the true resistivity value of ultra-thin layer formations and solve the problem that the true resistivity value of ultra-thin layer formations cannot be quantitatively interpreted. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The drawings herein are incorporated into the specification and form a part of this specification. These drawings show embodiments consistent with the present invention and are used together with the specification to illustrate the technical solutions of the present invention.

[0047] Figure 1 Show the deep dual laterolog layer thickness influence correction chart according to the embodiment of the present invention;

[0048] Figure 2 Show the layer thickness 5m triple lateral resistivity logging low invasion correction chart according to the embodiment of the present invention;

[0049] Figure 3 Show the layer thickness 0.8m triple lateral resistivity logging low invasion correction chart according to the embodiment of the present invention;

[0050] Figure 4 Show the main current line distribution diagram of homogeneous formation according to the embodiment of the present invention;

[0051] Figure 5 Show the main current line distribution diagram of layer thickness 4.8m according to the embodiment of the present invention;

[0052] Figure 6 Show the main current line distribution diagram of layer thickness 0.2m according to the embodiment of the present invention;

[0053] Figure 7 Show the R t / R m and deep theoretical K value relationship diagram according to the embodiment of the present invention;

[0054] Figure 8 Showing the deep invasion I according to an embodiment of the present invention 1 / I 0 and R t / R m relationship diagram;

[0055] Figure 9 Showing the deep invasion I of the fourth invasion state according to an embodiment of the present invention 1 / I 0 and the relationship diagram of the deep theoretical K value;

[0056] Figure 10 Showing the shallow invasion I of the eighteenth invasion state according to an embodiment of the present invention 1 / I 0 and the relationship diagram of the shallow theoretical K value;

[0057] Figure 11 Showing the relationship diagram of the deep variable K value and the formation thickness according to an embodiment of the present invention;

[0058] Figure 12 Showing the flowchart of the layer thickness influence correction method for the interpretation of formation resistivity logging data according to an embodiment of the present invention. Detailed implementation manners

[0059] Various exemplary embodiments, features and aspects of the present invention will be described in detail below with reference to the accompanying drawings. The same reference numerals in the drawings denote elements having the same or similar functions. Although various aspects of the embodiments are shown in the drawings, the drawings do not have to be drawn to scale unless otherwise specified.

[0060] The special term "exemplary" herein means "serving as an example, embodiment or illustration". Any embodiment described as "exemplary" herein does not have to be construed as superior to or better than other embodiments.

[0061] The term "and / or" herein merely describes the associated relationship of associated objects and indicates that three relationships may exist. For example, A and / or B may represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the term "at least one" herein means any one of a plurality or any combination of at least two of a plurality. For example, including at least one of A, B, and C may represent including any one or more elements selected from the set composed of A, B, and C.

[0062] In addition, in order to better illustrate the present invention, numerous specific details are given in the following detailed implementation manners. Those skilled in the art should understand that the present invention can also be implemented without some specific details. In some instances, methods, means, elements and circuits well known to those skilled in the art are not described in detail so as to highlight the gist of the present invention.

[0063] Figure 1 Show the deep dual laterolog layer thickness influence correction chart according to an embodiment of the present invention; Figure 2 Show the layer thickness 5m triple lateral low invasion correction chart according to an embodiment of the present invention; Figure 3 Show the layer thickness 0.8m triple lateral low invasion correction chart according to an embodiment of the present invention; Figure 4 Show the main current line distribution chart of homogeneous formation according to an embodiment of the present invention; Figure 5 Show the main current line distribution chart of layer thickness 4.8m according to an embodiment of the present invention; Figure 6 Show the main current line distribution chart of layer thickness 0.2m according to an embodiment of the present invention; Figure 7 Show the R of non-invaded formation according to an embodiment of the present invention t / R m Relationship diagram with deep theoretical K value; Figure 8 Show the deep I of non-invaded formation according to an embodiment of the present invention 1 / I 0 With R t / R m Relationship diagram; Figure 9 Show the deep I of the fourth invasion state according to an embodiment of the present invention 1 / I 0 Relationship diagram with deep theoretical K value; Figure 10 Show the shallow I of the eighteenth invasion state according to an embodiment of the present invention 1 / I 0 Relationship diagram with shallow theoretical K value; Figure 11 Show the relationship diagram between deep variable K value and formation thickness according to an embodiment of the present invention; Figure 12 Show the flow chart of the layer thickness influence correction method for formation resistivity logging data interpretation according to an embodiment of the present invention. As Figure 1 - 12 Shown, a layer thickness influence correction method for formation resistivity logging data interpretation includes: Step S01: Set formation parameters corresponding to different layer thickness intervals, and perform forward modeling to obtain measurement parameter values related to the layer thickness interval, and determine the deep theoretical K value and the difference in apparent resistivity of the deep and shallow triple laterologs after resistivity correction R d 4 -R s 18 ; Step S02: Optimize the set different layer thickness intervals to obtain the optimized best layer thickness interval; Step S03: Use the formation parameters corresponding to the best layer thickness interval set by the forward modeling and the obtained measurement parameter values, deep theoretical K value and R d 4 -R s 18, training the established neural network model to obtain an interpretation model corresponding to each optimal layer thickness interval; Step S04: Obtaining the measurement parameter values related to the layer thickness interval corresponding to the target formation, the formation thickness, and R determined according to the measurement parameter values d 4 -R s 18 ; Step S05: Finding the interpretation model corresponding to the optimal layer thickness interval closest to the formation thickness of the target formation, and inputting the measurement parameters related to the layer thickness interval corresponding to the target formation and R d 4 -R s 18 , running it in this interpretation model to obtain the deep variation K value, invasion zone radius r i , formation resistivity ratio to invasion zone resistivity R t / R xo value interpretation result.

[0064] In the embodiment of the present invention, an ultra-thin layer generally refers to a formation with a layer thickness of 0.2m - 0.5m. To solve the problems of the influence of the ultra-thin layer thickness and the quantitative interpretation of the true resistivity R t , it is necessary to accurately find the main problems existing in the fixed K value interpretation method.

[0065] (1). For the first time, the main current line distribution diagram visually reveals the contradictory pair of layering ability and detection depth existing in the lateral logging method and its root cause.

[0066] In the homogeneous formation for calibrating the K value, the main current distribution is as shown in the appendix Figure 4 shown. As can be seen from the appendix Figure 4 , the main current can not only enter the formation in a flat plate shape, but also the divergence width is only 0.8 meters at a depth of 10 meters, which fully conforms to the basic principle of the triple lateral logging. This shows that the shielding electrode and the main electrode are equipotential (U 1 = U 0 ) and the shielding effect is the best, the detection depth is the deepest, and the measured main electrode potential is the largest at 688.1mv.

[0067] In the formation where the ratio of the true formation resistivity to the mud resistivity R t / R m = 30 (R m = 1), and the invasion radius r i = 0.1 meter in the non-invaded formation, the main current distribution for a layer thickness of 4.8 meters is as shown in the appendix Figure 5 shown. As can be seen from the appendix Figure 5 , the main current can also enter the formation in a flat plate shape at the beginning, but it starts to diverge at a depth of 4 meters, starts to shunt to the surrounding rock at a depth of 7 meters, and 1 / 4 of the main current still flows through at a depth of 10 meters. This shows that the shielding electrode and the main electrode are equipotential (U 1 = U0 ) It has a good shielding effect, a relatively deep detection depth, and the measured main electrode potential is 516.1 mv, which is relatively large.

[0068] The main current distribution of the ultra-thin layer with a layer thickness of 0.2 meters is as shown in the appendix Figure 6 as shown, from the appendix Figure 6 It can be seen that most of the main current diverges along the wellbore to the surrounding rock, only a small part enters the formation and immediately diverges to the surrounding rock, and there is no main current flowing at 0.3 meters. This shows that the shielding electrode and the main electrode are equipotential (U 1 = U 0 ) It has the worst shielding effect, the shallowest detection depth, and the measured main electrode potential is 114.3, which is the smallest.

[0069] Therefore, it can be concluded that for lateral logging, for each heterogeneous formation, a one-size-fits-all approach is adopted, and the shielding electrode and the main electrode are equipotential (U 1 = U 0 ) for measurement. This is the fundamental reason why the contradictory pair of layering ability and detection depth cannot be unified, and the true resistivity of the ultra-thin layer formation cannot be interpreted.

[0070] (2) Deeply reveals the fundamental problems existing in the fixed K-value interpretation method.

[0071] In a non-invaded formation with R t / R m = 30 (R m = 1), the effects of different interpretation methods are shown in Table 1:

[0072] Table 1: Data table of the results of different interpretation methods

[0073]

[0074] As can be seen from Table 1, as the formation gradually changes from a homogeneous layer to a 0.2-meter ultra-thin layer, the measured main electrode potential U d decreases from 688.1 mv to 114.3 mv, a reduction of 6.02 times. Multiplying by the fixed K-value of 0.0436 measured in the homogeneous formation, the calculated apparent resistivity R a value rapidly decreases from 30 to 4.98, also a reduction of 6.02 times. Thus, the drawbacks of the lateral logging method are all transferred to the determination of the apparent resistivity R a value by multiplying the fixed K-value. Finally, the apparent resistivity R a value of the 0.2-meter ultra-thin layer is not only too low but also has too small a variation range, making it impossible to develop a resistivity and invasion correction chart for the corresponding layer thickness and unable to quantitatively interpret the true resistivity R t value of the formation.

[0075] Thus, it deeply reveals that in the fixed-K value interpretation method of the original resistivity logging data, for each heterogeneous formation, a one-size-fits-all approach is adopted, and the fixed K value measured in the homogeneous formation is used for calculating the apparent resistivity R a value. This transfers the drawback that the logging method cannot unify the contradictory pair of layer separation ability and detection depth directly to the apparent resistivity interpretation. This is also the fundamental reason why the true resistivity R t value of ultra-thin layers cannot be quantitatively interpreted at present.

[0076] The layer thickness influence correction method for interpreting formation resistivity logging data provided by the embodiments of the present invention specifically includes the following steps:

[0077] Step S01: Set formation parameters corresponding to different layer thickness intervals, and perform forward modeling to obtain measurement parameter values related to the layer thickness intervals. Determine the deep theoretical K value and the difference in apparent resistivity of the deep and shallow triple lateral resistivity logs R d 4 -R s 18 .

[0078] In the present invention, the method of setting formation parameters corresponding to different layer thickness intervals and performing forward modeling to obtain measurement parameter values related to the layer thickness intervals includes: setting a group of formations with true resistivity R t of the invaded zone resistivity R xo , invaded zone radius r i values, inputting them into the forward model for forward modeling, and obtaining measurement parameter values related to the layer thickness intervals corresponding to each formation.

[0079] In the present invention, the measurement parameter values related to the layer thickness intervals at least include: the ratio of the supply current of the first shielding electrode to the main electrode of the deep triple lateral, that is, deep I 1 / I 0 , the ratio of the sum of the supply currents of the first and second shielding electrodes of the deep triple lateral to the supply current of the main electrode, that is, deep I 1 +I 2 / I 0 , the main electrode potential U d of the deep triple lateral, the ratio of the supply current of the shielding electrode to the main electrode of the shallow triple lateral, that is, shallow I 1 / I 0 , and the main electrode potential U s of the shallow triple lateral.

[0080] In the embodiments of the present invention, among the measurement parameters obtained by forward modeling, U d and U s are two parameters that have been measured in the original fixed-K value interpretation method, while deep I 1 / I0 、 Deep I 1 +I 2 / I 0 、 Shallow I 1 / I 0 These three parameters have never been measured or utilized.

[0081] In the present invention, the method for determining the deep theoretical K value according to the set formation parameters and measured parameter values includes: The calculation formula for the deep theoretical K value is:

[0082] Deep theoretical K value = R t / U d ;

[0083] Where: R t is the true formation resistivity set during forward simulation, and U d is the deep triple lateral main electrode potential measured through forward simulation.

[0084] In the embodiment of the present invention, the variable K value (deep variable K value) interpretation method is based on reverse thinking on the original fixed K value interpretation method. In the calculation formula of the original formation apparent resistivity R a , the K value is regarded as a variable. If the measured main electrode potential of the layer thickness is large, the K value is taken a little smaller; if the measured main electrode potential of the thin layer is small, the K value is taken a little larger. The deep variable K value is used to offset the influence of layer thickness on the measurement. The expected effect of the deep variable K value interpretation method for the electric logging data of 0.2-meter ultra-thin layers is shown in Table 1. As can be seen from Table 1, when gradually changing from a homogeneous layer to a 0.2-meter ultra-thin layer, although the value of the deep lateral main electrode potential U d rapidly drops from 688.1 mv to 114.3 mv, a decrease of 6.02 times, the calculated deep theoretical K value rapidly increases from 0.0436 to 0.2625, which is a reverse increase of 6.02 times. Thus, the drawbacks of the lateral logging method are overcome, and the contradictory relationship between the layer separation ability and the detection depth is perfectly unified in the deep variable K value interpretation method. Finally, whether it is a 4.8-meter large layer thickness or a 0.2-meter ultra-thin layer, it can make R a / R m = R t / R m = 30, that is, the true formation resistivity value, achieving the same effect as a homogeneous formation. Thus, it can be seen that as long as the deep variable K value K d 0 of each layer is calculated, corrected for various influencing factors to make it approach the deep theoretical K value, and multiplied by the measured deep triple lateral main electrode potential U d , the true formation resistivity R t value is obtained. Thus, the quantitative interpretation of the true formation resistivity R t value is transformed into the deep variable K value K d 0Accurate solution.

[0085] In the embodiment of the present invention, the resistivity effect is one of the most important influencing factors in the interpretation of electrical logging data. The ratio R of the true resistivity of the 0.2-meter ultra-thin layer invasion-free formation to the mud resistivity t / R m and the deep theoretical K value K d 0 are related as shown in the appendix Figure 7 As can be seen from the appendix Figure 7 when R t / R m gradually increases from 2 to 40, the corresponding K d 0 increases from 0.063 to 0.351, an increase of 5.57 times. There is a good proportional relationship between the two, indicating that the deep theoretical K value K d 0 is greatly affected by the resistivity and must be corrected.

[0086] Since it is impossible to develop a corresponding resistivity and invasion effect correction chart for the 0.2-meter ultra-thin layer to correct the influence on the true resistivity of the formation, and the true resistivity R t value of the formation is unknown before interpretation, in order to correct the resistivity influence on the deep variable K value K d 0 it is necessary to explore innovative ideas and methods.

[0087] The relationship between the deep I 1 / I 0 of the 0.2-meter ultra-thin layer invasion-free formation and R t / R m is as shown in the appendix Figure 8 As can be seen from Figure 8 when R t / R m increases from 2 to 40, its deep I 1 / I 0 value also increases from 15.13 to 40.55, an increase of 2.64 times. There is also a good proportional relationship between the two. In traditional lateral logging, the parameter of deep I 1 / I 0 has not been measured and studied for application, but it can be easily obtained by additional measurement. Therefore, it is proposed for the first time to use the ratio of the supply current of the first shielding electrode to the supply current of the main electrode of the deep triple lateral logging, deep I 1 / I 0 to replace the true resistivity R t / R m to correct the resistivity influence on the deep variable K value K d 0 and solve the problem of the inability to correct the resistivity influence on the deep variable K value for the 0.2-meter ultra-thin layer.

[0088] In the present invention, the method for determining the difference R between the apparent resistivities of the deep and shallow laterologs after resistivity correction according to the measured parameter values d 4 -R s 18 includes: wherein, determining R after resistivity correction d 4 includes: determining the relationship between the deep I 1 / I 0 in the forward simulation result and the deep theoretical K value; substituting the deep I 1 / I 0 measured from the target formation into the relationship between the deep I 1 / I 0 and the deep theoretical K value, calculating the corresponding value, and multiplying the corresponding value by the U d measured from the target formation to obtain R d 4 ;

[0089] wherein, the method for determining R after resistivity correction s 18 includes: determining the shallow theoretical K value according to the set formation parameters and measured parameter values, and the method includes:

[0090] The calculation formula for the shallow theoretical K value is: shallow theoretical K value = R t / U s ;

[0091] In the formula: R t is the true resistivity of the formation set during forward simulation, and U s is the main electrode potential of the shallow laterolog measured through forward simulation.

[0092] Determining the relationship between the shallow I 1 / I 0 in the forward simulation result and the shallow theoretical K value; substituting the shallow I 1 / I 0 measured from the target formation into the relationship between the shallow I 1 / I 0 and the shallow theoretical K value, calculating the corresponding value, and multiplying the corresponding value by the U s measured from the target formation to obtain R s 18 .

[0093] In an embodiment of the present invention, taking the set formation thickness as an ultra-thin layer of 0.2 m, and the deep laterolog with R t / R xo = 2, ri Based on the intrusion condition of 0.3 meters, the measured deep I is obtained through forward simulation 1 / I 0 and the theoretical deep K value. Among them, the deep I 1 / I 0 and the theoretical deep K value K d 0 The relational data table is shown in Table 2 below.

[0094] Table 2: Deep I 1 / I 0 and the theoretical deep K value K d 0 Relational data table

[0095] <![CDATA[R t / R m > 40 30 20 10 5 3 <![CDATA[R xo / R m > 20 15 10 5 2.5 1.5 <![CDATA[r i (meter)]]> 0.30 0.30 0.30 0.30 0.30 0.30 <![CDATA[Shen I 1 / I 0 > 34.49 31.60 27.47 21.09 16.35 14.03 <![CDATA[Deep theory K value K d 0 > 0.421 0.349 0.275 0.191 0.138 0.107

[0096] Draw the crossplot of deep I 1 / I 0 and the theoretical deep K value K d 0 The result is as Figure 9 shown. It can be seen that there is an obvious proportional relationship between the two, thus establishing the relationship between deep I 1 / I 0 and the theoretical deep K value K d 0 as shown in the following formula (2).

[0097] The theoretical deep K value K d 0 = 0.0465e 0.0643x (2);

[0098] Among them, x is the deep I 1 / I 0 .

[0099] Substitute the measured deep I 1 / I 0 value of the target formation into formula (2) to calculate the corresponding value K d 0 . Since this intrusion state is uniformly defined as the fourth intrusion state, it is called K d 4 . Multiply K d 4 by the U d value measured from the target formation, and the R d 4 is obtained.

[0100] In the embodiment of the present invention, with the set formation thickness of 0.2 meters for the ultra-thin layer and the shallow triple lateral R t / R xo = 0.33, ri Based on the intrusion condition of 0.175 meters, the measured shallow I is obtained through forward simulation 1 / I 0 and the calculated shallow theoretical K value K s 0 , where the shallow I 1 / I 0 and the shallow theoretical K value K s 0 The relational data table is shown in Table 3 below.

[0101] Table 3: Shallow I 1 / I 0 and the shallow theoretical K value K s 0 Relational data table

[0102] <![CDATA[R t / R m > 2 5 7 10 <![CDATA[R xo / R m > 6 15 20 30 <![CDATA[r i (meter)]]> 0.175 0.175 0.175 0.175 <![CDATA[Shallow I 1 / I 0 > 24.21 33.90 37.12 41.23 Shallow Theory K 0.088 0.146 0.183 0.232

[0103] Plot the crossplot of shallow I 1 / I 0 and the shallow theoretical K value K s 0 . The result is as Figure 10 shown, thus establishing the relationship between shallow I 1 / I 0 and the shallow theoretical K value K s 0 as shown in Equation (3) below.

[0104] Shallow theoretical K value K s 0 = 0.0219e 0.057x (3);

[0105] where x is the shallow I 1 / I 0 .

[0106] Substitute the value of shallow I 1 / I 0 measured from the target formation into Equation (3) to calculate the corresponding K s 0 value. Since this intrusion state is uniformly defined as the eighteenth intrusion state, it is called K s 18 . Multiply K s 18 by the U s value measured from the target formation to obtain R s 18 .

[0107] Among the input parameters, R d 4 -R s18 This parameter is calculated by processing the deep and shallow triple lateral logging data respectively. R d 4 -R s 18 The maximum value of the difference is 57.99, which is very large, while the maximum value of the difference between the original apparent resistivity of the deep and shallow triple lateral logs R d -R s is only 0.76, which is very small. Compared with each other, the absolute value of the maximum difference is 57.23 higher, and the relative value has increased by 76.3 times. R d 4 -R s 18 The maximum change range of the value of -R is between 57.99 and 1.94, and the absolute value of the difference has changed nearly 30 times, with a very large change range; while the original R d -R s The change range of the difference is between 0.76 and 0.04. Although the relative change of the difference has also reached 19 times, the absolute value of the difference has only changed by 0.72, and the change range is really too small.

[0108] Thus, it can be seen that using the processed R d 4 -R s 18 as the input parameter, the effect of correcting the resistivity and layer thickness on the deep-variable K value through the neural network model is surely qualitatively improved compared with the original use of R d -R s .

[0109] Step S02: Optimize the set different layer thickness intervals to obtain the optimized best layer thickness interval.

[0110] In the embodiment of the present invention, the layer thickness effect is one of the most important influencing factors in the interpretation of resistivity logging data. The present invention scientifically reveals the objective law of the layer thickness effect of thin layers and ultra-thin layers for the first time. In a non-invaded formation where the ratio of the true resistivity of the formation to the resistivity of the mud is 30, the relationship between the deep-variable K value K d 0 and the formation thickness h is as shown in the appendix Figure 11 as shown. It can be seen from the appendix Figure 11 that as the layer thickness gradually thins, its variable K value gradually increases, and there is a good inverse relationship between the two. When the layer thickness is less than 0.5 meters, there is a sudden increase in the layer thickness effect of thin layers and ultra-thin layers. For example, when comparing a layer thickness of 0.5 meters with 4.8 meters, the layer thickness difference is 4.3 meters, and the deep-variable K value only increases by 36%. However, when comparing a layer thickness of 0.2 meters with 0.3 meters, the layer thickness difference is only 0.1 meter, while the deep-variable K value increases by an average of 53%, and the maximum increase is 92.3%, nearly doubling. Thus, it can be seen that the variable K value is greatly affected by the layer thickness and must be corrected.

[0111] Since the existing electrical logging data interpretation method cannot correct the influence of the thickness of ultra-thin layers, it is necessary to explore a new method. After repeated research, it is finally determined that a neural network algorithm interpretation model with a set of different layer thickness intervals is used to correct the layer thickness.

[0112] In the present invention, the method for optimizing the set different layer thickness intervals to obtain the optimal layer thickness interval after optimization includes: determining whether the average relative error of all theoretical K values of formation depth between the two adjacent layer thickness intervals is less than a predetermined percentage. If not, several layer thickness intervals are inserted between the two adjacent layer thickness intervals until the average relative error of all theoretical K values of formation depth between each two adjacent layer thickness intervals after insertion is less than the predetermined percentage, thereby obtaining the optimal layer thickness interval.

[0113] In the embodiment of the present invention, the predetermined percentage is 10%. The optimal standard for the layer thickness interval is that the average relative error of all theoretical K values of formation depth between two adjacent layer thickness intervals must be less than 10%, so as to ensure that the error of the final layer thickness correction is controlled within 5%. If the average relative error of the theoretical K value of depth is greater than 10%, several layer thickness intervals are inserted between the two adjacent layer thickness intervals until the average relative error of all theoretical K values of formation depth between each two adjacent layer thickness intervals after insertion is less than 10%. If the sum of the average relative errors of the theoretical K values of depth between two adjacent layer thickness intervals among three adjacent layer thickness intervals is still less than 10%, one of the layer thickness intervals is deleted. The layer thickness intervals are optimized according to the principle that as the layer thickness gradually thins from thick to thin, the layer thickness intervals gradually become denser from sparse.

[0114] The initial settings of 10 different layer thickness intervals from 4.8 m to 0.2 m are respectively: 4.8, 3.6, 2.4, 1.2, 0.8, 0.6, 0.5, 0.4, 0.3, 0.2. The average relative error of the deep change K value K between each two layer thickness intervals is calculated. d 0 The average relative error and the initial selection of the layer thickness intervals are shown in Table 6.

[0115] Table 6: Initial Selection Statistical Table of Layer Thickness Intervals from 4.8 m to 0.2 m

[0116]

[0117] As can be seen from Table 6, the average relative error of the theoretical K value of depth between the two layer thickness intervals of 4.8 m and 3.6 m is 6.7%, so there is no need for thinning or densification in the middle.

[0118] Among the six layer thickness intervals of 3.6 and 2.4, 2.4 and 1.2, 1.2 and 0.8, 0.8 and 0.6, 0.6 and 0.5, although the average relative error of the deep theoretical K value is between 7.4% and 9.8%, although it does not exceed 10%, when the formation resistivity is relatively large and the invasion is relatively deep, most layers exceed 10%. Therefore, five layer thickness intervals of 3.0, 1.8, 1.0, 0.7, and 0.55 must be added in the middle.

[0119] The average relative error of the deep theoretical K value between the two layer thickness intervals of 0.5 and 0.4 m is 14.1%. Therefore, a layer thickness interval of 0.45 must be added in the middle.

[0120] The average relative error of the deep theoretical K value between the two layer thickness intervals of 0.4 m and 0.3 m is as high as 26.4%. Therefore, 2 - 3 layer thickness intervals must be added in the middle.

[0121] The average relative error of the deep theoretical K value between the two layer thickness intervals of 0.3 m and 0.2 m is as high as 53.7%. Therefore, 3 - 4 layer thickness intervals must be added in the middle.

[0122] The error between the two layer thickness intervals is too large, exceeding 10%. Multiple layer thickness intervals must be added in the middle. That is, several layer thickness intervals are inserted between the layer thickness intervals with an average relative error greater than 10% so that the relative error between the corresponding deep - variable K values of every two adjacent layer thickness intervals after insertion is less than or equal to 10%. In addition, for the two layer thickness intervals with an error less than 10% and greater than 7%, a layer thickness interval with an intermediate value can be inserted to make the calibration accuracy higher.

[0123] To deeply understand the preferred situation of the layer thickness intervals between 0.6 m and 0.2 m, 14 layer thickness intervals were measured, and the average relative error of the deep theoretical K value of all formations between every two layer thickness intervals and the preferred situation of the layer thickness intervals are shown in Table 7.

[0124] Table 7: Preferred statistical table of layer thickness intervals from 0.2 m to 0.6 m

[0125]

[0126] As can be seen from Table 7, the average relative error of the depth-varying K value between the 12 layer thickness intervals from 0.5 m to 0.2 m is less than 10%, so there is no need for encryption or thinning. Among the 3 layer thickness intervals from 0.5 m to 0.6 m, the sum of the average relative errors of the theoretical depth K values of adjacent two layer thickness intervals is still less than 10%. Therefore, the layer thickness interval of 0.55 m in the middle can be deleted. The finally determined optimal layer thickness intervals are 4.8 m, 3.6 m, 3.0 m, 2.4 m, 1.8 m, 1.2 m, 1.0 m, 0.8 m, 0.7 m, 0.6 m, 0.5 m, 0.45 m, 0.4 m, 0.375 m, 0.35 m, 0.325 m, 0.3 m, 0.28 m, 0.26 m, 0.24 m, 0.22 m, 0.2 m, a total of 22 layer thickness intervals.

[0127] Step S03: Use the formation parameters corresponding to the optimal layer thickness interval set by the forward simulation, the obtained measurement parameter values, the theoretical depth K value, and R d 4 -R s 18 , train the established neural network model to obtain an interpretation model corresponding to each optimal layer thickness interval.

[0128] In the embodiment of the present invention, different R values corresponding to the optimal layer thickness interval set during forward simulation t , R xo , r i values and the measurement parameters corresponding to the forward simulation, that is, the depth I 1 / I 0 , the depth I 1 +I 2 / I 0 , U d , the shallow I 1 / I 0 , U s and the theoretical depth K value K d 0 and R d 4 -R s 18 are used as input data and input into the corresponding neural network model to train the neural network model, so as to obtain 22 interpretation models corresponding to 22 optimal layer thickness intervals. Among them, the neural network model is modeled using the Bayesian regularization backpropagation neural network algorithm.

[0129] In the embodiment of the present invention, six measured input parameters are used to model and solve the formation depth-varying K value K d 0 , R t / R xo , r iThree parameters, and the preferred criteria for their input parameters are not only closely related to layer thickness, resistivity, and invasion, but also the values should be large, and the maximum variation range should be more than twice to ensure the calibration accuracy.

[0130] The maximum values and the maximum variation multiples of the 8 parameters measured at layer thicknesses of 0.2 m and 1.0 m are shown in Table 4, where the maximum and minimum values are respectively measured in the formation with R t / R m = 40 without invasion and in the invaded formation with R t / R m = 3 and r i = 0.3 m.

[0131] Table 4: Table of maximum values and maximum variation multiples of measured parameters for different layer thicknesses

[0132]

[0133]

[0134] It can be seen from Table 4 that: 1. The value of deep I 1 / I 0 is closely related to layer thickness, true resistivity, and invasion parameter R t / R xo and r i . For a layer thickness of 0.2 m, its maximum value is 25.8, and the maximum variation range is 2.4 times. For a layer thickness of 1.0 m, the maximum value is 11, and the maximum variation range is 1 time, meeting the preferred criteria for input parameters. Use it for calibration of the influence of layer thickness, resistivity, and invasion on the deep-variable K value, and for the calculation of the R d 4 value.

[0135] 2. The value of deep I 1 +I 2 / I 0 is closely related to layer thickness, true resistivity, and invasion parameter R t / R xo and r i . Its value gradually increases as the layer thickness thickens. For a layer thickness of 0.2 m, the maximum value is 1098.3, and the maximum variation range is 4.7 times. For a layer thickness of 1 m, the maximum value is 4737.6, and the maximum variation range is 15.4 times, meeting the preferred criteria for input parameters. Use it for calibration of the influence of layer thickness, resistivity, and invasion on the deep-variable K value and for the calculation of R d 4 . As the layer thickness gradually thickens from 0.2 m, its effect is better than that of deep I 1 / I 0 .

[0136] 3. The value of U d is related to layer thickness, true resistivity, and invasion parameter R t / Rxo , r i is closely related. The maximum value of the layer thickness is 173.7 at 0.2 m, with a maximum variation range of 4.7 times. The maximum value of the layer thickness is 877.5 at 1 m, with a maximum variation range of 17.1 times. The effect is much better than that of the apparent resistivity R calculated by the original method, meeting the preferred standard of input parameters. Use it to correct the layer thickness, resistivity, and invasion influence of the deep variable K value, and calculate the theoretical K value K of each layer depth a and K d 0 and K d 4 .

[0137] 4. The value of shallow I 1 / I 0 is well related to the layer thickness, true resistivity, and invasion parameters R t / R xo , r i . The maximum value of the layer thickness is 34.2 at 0.2 m, with a maximum variation of 2.3 times. The maximum value of the layer thickness is 39.1 at 1 m, with a maximum variation range of 2.6 times, meeting the preferred standard of input parameters. Use it to correct the layer thickness, resistivity, and invasion influence of the deep variable K value, and calculate the theoretical K value K of each layer shallow s 0 and R s 18 .

[0138] 5. The value of U s is well related to the layer thickness, true resistivity, and invasion parameters R t / R xo , r i . The maximum value of the layer thickness is 42.3 at 0.2 m, with a maximum variation of 3.3 times. The maximum value of the layer thickness is 67.4 at 1 m, with a maximum variation range of 5.0 times. The effect is much better than that of the original apparent resistivity R of the shallow triple lateral, meeting the preferred standard of input parameters. Use it to correct the layer thickness, resistivity, and invasion influence of the deep variable K value, and calculate the shallow theoretical K value K s and R s 0 and R s 18 .

[0139] 6. The value of R d 4 -R s 18 is closely related to the layer thickness, true resistivity, and invasion parameters R t / R xo , r i . The maximum value of the layer thickness is 57.99 at 0.2 m, with a maximum variation of 30 times, meeting the preferred standard of input parameters. Use it to correct the layer thickness, resistivity, and invasion influence of the deep variable K value

[0140] Step S04: Obtain the measurement parameter values related to the layer thickness interval corresponding to the target formation, the formation thickness, and R determined according to the measurement parameter values d 4 -R s 18 。

[0141] In the embodiments of the present invention, the formation thickness of the target formation needs to be interpreted by the layer-by-layer value-taking method of well logging data. For formations above 0.6 meters, conventional well logging data is used for division, and for ultra-thin formations of 0.2 - 0.5 meters, resistivity imaging logging data is used for division. The detailed requirements for the formation thickness division accuracy are clearly put forward for the first time as follows: when the layer thickness is between 4.8 meters and 1.2 meters, the accuracy should reach within 0.6 meters; when the layer thickness is between 1.2 meters and 0.8 meters, the accuracy should reach within 0.2 meters; when the layer thickness is between 0.8 meters and 0.5 meters, the accuracy should reach within 0.1 meters; when the layer thickness is between 0.5 meters and 0.4 meters, the accuracy should reach within 0.05 meters; when the layer thickness is between 0.4 meters and 0.3 meters, the accuracy should reach within 0.025 meters; when the layer thickness is between 0.3 meters and 0.2 meters, the accuracy should reach within 0.02 meters.

[0142] The measurement parameters corresponding to the target formation are deep I 1 / I 0 、deep I 1 +I 2 / I 0 、U d 、shallow I 1 / I 0 、U s respectively, and then determine the corresponding R d 4 -R s 18 。

[0143] Step S05: Find the interpretation model corresponding to the best layer thickness interval closest to the formation thickness of the target formation, and input the measurement parameters related to the layer thickness interval corresponding to the target formation and R d 4 -R s 18 into this interpretation model to run, and obtain the deep variation K value, invasion zone radius r i 、formation resistivity ratio to invasion zone resistivity R t / R xo value interpretation results of the target formation.

[0144] In the embodiment of the present invention, if the target stratum thickness is 0.36 meters, the neural network interpretation model corresponding to the optimal layer thickness interval of 0.35 meters closest to the target stratum thickness is selected, and the five parameters actually measured in the target stratum and the R calculated according to the measured parameters of the target stratum are input into the interpretation model. d 4 -R s 18 Value, run to get the interpretation result, that is, deep variable K value, r i , R t / R xo The present invention uses the Bayesian regularized back propagation neural network algorithm for modeling for the first time, and replaces the correction plate in the original fixed K value interpretation method with the neural network model to achieve accurate correction of the layer thickness effect of resistivity logging interpretation.

[0145] The present invention also includes: measuring parameters obtained during forward simulation, and determining R according to the measured parameters. d 4 -R s 18 The formation thickness is input into the interpretation model of the best layer thickness interval closest to the formation thickness, and the corresponding deep variable K value and r are obtained. i , R t / R xo The results are explained by comparing the deep theoretical K value of the forward modeling and the set r i , R t / R xo The values ​​are compared to determine the relative error between the two. The relative error between the deep variable K value and the deep theoretical K value is the relative error of the final interpretation of the true resistivity of the formation.

[0146] In the embodiment of the present invention, after the interpretation model is obtained through training, if the error of the interpretation result of the interpretation model is to be determined, it is necessary to find the formation with the set thickness during the forward simulation corresponding to the optimal layer thickness interval of the interpretation model, and to use the measurement parameters obtained by the forward simulation corresponding to the formation, that is, the deep theoretical K value K d 0 、Deep I 1 / I 0 、Deep I 1 +I 2 / I 0 , U d , shallow I 1 / I 0 , U s and calculated R d 4 -R s 18 Input the corresponding explanation model and get the explanation result after running, i.e. r i , Rt / R xo and the deep variable K value K d 0 Compare the interpretation results with the set r i and R t / R xo in the corresponding forward model respectively, and compare with the calculated deep theoretical K value to determine their relative error, which is the relative error of the final interpretation result obtained by running the final interpretation model. Among them, the relative error of the K d 0 value interpretation is the quantitative interpretation error of the true formation resistivity R t

[0147] In the embodiment of the present invention, the No. 9 electrode system is selected to measure 360 formations with layer thicknesses of 0.2 m and 0.8 m, and the variable K value method is used for interpretation. The interpretation results are shown in Table 5 below.

[0148] Table 5: Data table of interpretation results of variable K value for formations with different layer thicknesses

[0149]

[0150] As can be seen from Table 5, 1. For the 0.2 m ultra-thin layer, the average relative error of the variable K value interpretation is 8.45%, with high precision, and the number of layers and average relative error of positive and negative errors are very close. The layers with relative error less than 10% account for 99.5%, and the layers with relative error less than 20% account for 100%, with good results. The average relative error of the deep variable K value K d 0 interpretation is the average relative error of the quantitative interpretation of the true formation resistivity R t value. For the 0.2 m ultra-thin layer, due to the measured apparent resistivity R a value being not only too low but also having too small a variation range, it is impossible to develop the resistivity and invasion influence correction charts for the corresponding layer thickness, and the true formation resistivity R t value cannot be quantitatively interpreted. Only the measured apparent resistivity R a value can be used as the true formation resistivity, and its average relative error of interpretation is 50.9%. The variable K value interpretation has improved the precision by 42.5 percentage points compared with the original fixed K value interpretation, achieving a qualitative leap and solving the world problem of the inability to quantitatively interpret the true formation resistivity R t value for the 0.2 m ultra-thin layer. Therefore, it fully demonstrates the innovation, scientificity, practicality and accuracy of the first developed variable K value layer thickness influence correction and true formation resistivity R t quantitative interpretation method for 0.2 m ultra-thin layer logging data from both theoretical and practical aspects.

[0151] 2. For the formation with a layer thickness of 0.8 m, the variable K value K d 0 ​The average relative error of the interpretation is 8.18%, and the accuracy is also very high. Moreover, the number of positive and negative error layers and the average relative error are very close. The layers with a relative error less than 10% account for 100%, and the effect is also very good. The true formation resistivity R t The accuracy of the quantitative interpretation of the value is 38.8 percentage points higher than that of the original fixed K-value method. This shows that this variable K-value interpretation method is not only applicable to ultra-thin layers with a thickness of 0.2 meters, but also applicable to formations with all other layer thicknesses. Only as the layer thickness gradually thins from thick to thin, compared with the original fixed K-value interpretation method, the percentage points of the improved interpretation accuracy gradually increase from small to large.

[0152] 3. In the variable K-value interpretation method, R t / R xo and r i Two important parameters of the formation invasion conditions. The average relative errors of the ultra-thin layer with a thickness of 0.2 meters are 2.41% and 13.6% respectively, and those of the formation with a thickness of 0.8 meters are 2.32% and 12.2% respectively. The accuracy is very high and can be used quantitatively. Thus, the invasion conditions of each layer can be scientifically analyzed, providing important basic data for accurate invasion effect correction.

[0153] The technical solution of the present invention is not only applicable to lateral logging, but also applicable to the interpretation of all resistivity logging data. It is not only applicable to ultra-thin layers with a thickness of 0.2 - 0.5 meters, but also applicable to formations with all other layer thicknesses. The thinner the layer, the greater the improvement in accuracy compared with the original interpretation method. It has the advantages of high interpretation accuracy, low input cost, and high output benefit.

[0154] It can be understood that, without violating the principle logic, the above-mentioned various method embodiments mentioned in the present invention can be combined with each other to form a combined embodiment. Due to space limitations, the present invention will not elaborate further.

[0155] The execution subject of the layer thickness influence correction method for formation resistivity logging data interpretation can be a layer thickness influence correction device for formation resistivity logging data interpretation. For example, the layer thickness influence correction method for formation resistivity logging data interpretation can be executed by a terminal device, a server, or other processing devices. Among them, the terminal device can be a user equipment (UE), a mobile device, a user terminal, a terminal, a cellular phone, a cordless phone, a personal digital assistant (PDA), a handheld device, a computing device, a vehicle-mounted device, a wearable device, etc. In some possible implementation manners, the layer thickness influence correction method for formation resistivity logging data interpretation can be implemented by a processor calling computer-readable instructions stored in a memory.

[0156] Those skilled in the art can understand that in the above method of the specific implementation manner, the writing order of each step does not mean a strict execution order and does not impose any limitation on the implementation process. The specific execution order of each step should be determined according to its function and possible internal logic.

[0157] The present invention also provides a layer thickness influence correction device for interpreting formation resistivity logging data, including: a forward simulation unit, configured to set formation parameters corresponding to different layer thickness intervals, perform forward simulation, obtain measurement parameter values related to the layer thickness interval, and determine the deep theoretical K value and the difference R between the apparent resistivities of the deep and shallow triple laterologs after resistivity correction according to the set formation parameters and measurement parameter values d 4 -R s 18 ; a layer thickness interval optimization unit, configured to optimize the set different layer thickness intervals to obtain an optimized best layer thickness interval; an interpretation model establishment unit, configured to use the formation parameters corresponding to the best layer thickness interval set by the forward simulation, the obtained measurement parameter values, the deep theoretical K value, and R d 4 -R s 18 , train the established neural network model to obtain an interpretation model corresponding to each best layer thickness interval; an acquisition unit, configured to acquire the measurement parameter values related to the layer thickness interval, the formation thickness, and R determined according to the measurement parameter values corresponding to the target formation d 4 -R s 18 ; an interpretation result output unit, configured to find the interpretation model corresponding to the best layer thickness interval closest to the formation thickness of the target formation, and input the measurement parameters related to the layer thickness interval and R d 4 -R s 18 corresponding to the target formation into the interpretation model to run, and obtain the interpretation results of the deep variable K value, the invasion zone radius r i of the target formation, and the formation resistivity ratio R t / R xo value.

[0158] In some embodiments, the functions, modules, or units included in the device provided by the embodiments of the present invention can be used to execute the methods described in the above method embodiments. The specific implementation can refer to the description of the above method embodiments. For the sake of brevity, it will not be repeated here.

[0159] In the present invention, a Bayesian regularization backpropagation neural network interpretation model for establishing a set of optimal layer thickness intervals is established through variable K-value interpretation, replacing the resistivity and invasion influence correction charts with different layer thicknesses in the original fixed K-value interpretation method, avoiding the situation where due to the a R value being too low and the variation range being too small, it is impossible to develop a layer thickness influence correction chart for ultra-thin layers and quantitative interpretation of the true resistivity R t of the formation cannot be carried out. By optimizing the optimal layer thickness interval, the error of layer thickness influence correction is controlled within 5%, thus providing a reliable guarantee for solving the world problem of accurate correction of layer thickness influence of ultra-thin layers and quantitative interpretation of the true resistivity R t of the formation.

[0160] The embodiments of the present invention have been described above. The above description is exemplary and not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations are obvious to those of ordinary skill in the art in the technical field without departing from the scope and spirit of the described embodiments. The selection of the terms used herein is intended to best explain the principles of the embodiments, practical applications, or improvements to the technology in the market, or to enable other ordinary skill in the art in the technical field to understand the disclosed embodiments.

Claims

1. A method for correcting the effect of layer thickness on the interpretation of formation resistivity logging data. It is characterized in that include: Set the formation parameters corresponding to different layer thickness intervals, and perform forward simulation to obtain the measurement parameter values ​​related to the layer thickness intervals. According to the set formation parameters and the measurement parameter values, determine the deep theoretical K value and the deep and shallow three-way apparent resistivity difference R after resistivity correction. d 4 -R s 18 ; Optimizing the different set layer thickness intervals to obtain the optimal layer thickness interval after optimization; The formation parameters corresponding to the optimal layer thickness interval set by the forward simulation and the obtained measured parameter values, deep theoretical K value and R d 4 -R s 18 , train the established neural network model to obtain the explanation model corresponding to each optimal layer thickness interval; Obtain the measurement parameter value related to the layer thickness interval, the layer thickness, and the R value determined according to the measurement parameter value corresponding to the target layer. d 4 -R s 18 ; Find the interpretation model corresponding to the best layer thickness interval closest to the layer thickness of the target layer, and compare the measurement parameters related to the layer thickness interval and R d 4 -R s 18 , input into the interpretation model and run to obtain the deep-variable K value and invasion zone radius r corresponding to the target stratum i , formation resistivity R t / R xo Value explains the result.

2. The layer thickness effect correction method for formation resistivity logging data interpretation according to claim 1, It is characterized in that The method of setting formation parameters corresponding to different layer thickness intervals and performing forward simulation to obtain measurement parameter values ​​related to the layer thickness intervals includes: Set the true resistivity R of the formation corresponding to different layer thickness intervals t , Intrusion zone resistivity R xo , Intrusion zone radius r i A group of strata with different values ​​are input into the forward model for forward simulation to obtain the measured parameter values ​​related to the layer thickness interval corresponding to each stratum.

3. The layer thickness effect correction method for formation resistivity logging data interpretation according to claim 2, It is characterized in that The measured parameter values ​​related to the layer thickness interval include at least: the ratio of the power supply current of the first shielding electrode in the deep three-side direction to the power supply current of the main electrode, i.e., the deep I 1 / I 0 , the ratio of the sum of the supply currents of the first and second shielding electrodes to the supply current of the main electrode, i.e., the depth I 1 +I 2 / I 0 , deep three lateral main electrode potential U d , the ratio of the supply current of the shallow three lateral shielding electrodes to the main electrode, that is, shallow I 1 / I 0 , shallow three lateral main electrode potential U s .

4. The layer thickness effect correction method for formation resistivity logging data interpretation according to claim 3, Features: The method for determining the deep theoretical K value according to the set formation parameters and measurement parameter values ​​comprises: The calculation formula of the deep theoretical K value is: deep theoretical K value = R t / U d ; Where: R t is the true resistivity of the formation set in the forward modeling, U d is the deep three lateral main electrode potential measured by forward modeling.

5. The layer thickness effect correction method for formation resistivity logging data interpretation according to claim 2, It is characterized in that The method of optimizing the different set layer thickness intervals to obtain the optimal layer thickness interval after optimization includes: Determine whether the average relative error of all theoretical K values ​​of the formation depth between two adjacent layer thickness intervals is less than a predetermined percentage. If not, insert several layer thickness intervals between the two adjacent layer thickness intervals until the average relative error of all theoretical K values ​​of the formation depth between each two adjacent layer thickness intervals after insertion is less than a predetermined percentage, thereby obtaining the optimal layer thickness interval.

6. The layer thickness effect correction method for formation resistivity logging data interpretation according to claim 2, It is characterized in that Determine the apparent resistivity difference R of the three lateral directions in the depth after resistivity correction according to the measured parameter value d 4 -R s 18 method, include: Among them, determine the R after resistivity correction d 4 methods, including: Determine the depth I in the forward simulation results 1 / I 0 The relationship between the K value of deep theory; The depth I obtained by measuring the target formation 1 / I 0 Substitute the depth I 1 / I 0 The corresponding value is calculated from the relationship between the theoretical K value and the deep K value, and the corresponding value is multiplied by the U measured in the target formation. d , and get R d 4 ; Among them, determine the R after resistivity correction s 18 methods, including: The method for determining the shallow theoretical K value according to the set formation parameters and the measured parameter values ​​comprises: The calculation formula of the shallow theoretical K value is: shallow theoretical K value = R t / U s ; Where: R t is the true resistivity of the formation set in the forward modeling, U s is the shallow three-lateral main electrode potential measured by forward modeling; Determine the shallow I in the forward simulation results 1 / I 0 The relationship between the shallow theoretical K value and the The shallow I 1 / I 0 Substitute the shallow I 1 / I 0 The corresponding value is calculated from the relationship between the theoretical K value and the target formation measured U s , and get R s 18 .

7. The layer thickness effect correction method for formation resistivity logging data interpretation according to any one of claims 2 to 6, It is characterized in that Also includes: The measured parameters obtained during the forward simulation and the R determined based on the measured parameters are d 4 -R s 18 The formation thickness is input into the interpretation model of the best layer thickness interval closest to the formation thickness, and the corresponding deep variable K value and r are obtained. i , R t / R xo Values ​​explain the results; It is compared with the deep theoretical K value of the forward simulation and the set r i , R t / R xo The values ​​are compared to determine the relative error between the two. The relative error between the deep variable K value and the deep theoretical K value is the relative error of the final interpretation of the true resistivity of the formation.

8. A device for correcting the influence of layer thickness on the interpretation of formation resistivity logging data. It is characterized in that include: The forward simulation unit is used to set the formation parameters corresponding to different layer thickness intervals, and perform forward simulation to obtain the measurement parameter values ​​related to the layer thickness intervals. According to the set formation parameters and the measurement parameter values, the deep theoretical K value and the deep and shallow three-way apparent resistivity difference R after resistivity correction are determined. d 4 -R s 18 ; The layer thickness interval optimization unit is used to optimize the different set layer thickness intervals to obtain the optimal layer thickness interval after optimization; The interpretation model building unit is used to use the formation parameters corresponding to the optimal layer thickness interval set by the forward simulation and the obtained measured parameter values, deep theoretical K value and R d 4 -R s 18 , train the established neural network model to obtain the explanation model corresponding to each optimal layer thickness interval; an acquisition unit, for acquiring the measurement parameter value related to the layer thickness interval, the layer thickness, and the R value determined according to the measurement parameter value corresponding to the target layer; d 4 -R s 18 ; The interpretation result output unit is used to find the interpretation model corresponding to the best layer thickness interval closest to the layer thickness of the target layer, and to convert the measurement parameters related to the layer thickness interval and R corresponding to the target layer into the interpretation model. d 4 -R s 18 , input into the interpretation model and run to obtain the deep-variable K value and invasion zone radius r corresponding to the target stratum i , formation resistivity R t / R xo Value explains the result.

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