Method and device for improving longitudinal resolution of logging curve

Abstract

The invention discloses a method and device for improving the longitudinal resolution of a logging curve, and the method comprises the steps: obtaining the longitudinal resolution and the sampling interval of a to-be-processed logging curve, and determining the detection range of a logging instrument corresponding to the logging curve; dividing the stratum in the detection range into a plurality of small layers with equal thickness according to the longitudinal resolution and the sampling interval; the relative volume corresponding to each small layer is calculated according to a pre-established logging volume model; according to the relative volume and a preset constraint condition, calculating a physical parameter corresponding to each small layer by adopting an optimal objective function; and calculating according to the physical parameters of each small layer to obtain a processed logging curve.

Description

Technical Field

[0001] The present invention relates to the field of well logging technology, and in particular to a method and device for improving the vertical resolution of a well logging curve. Background Art

[0002] About 30% of the world's oil and gas is stored in thin interbeds of sandstone and mudstone, especially in unconventional oil and gas. Unconventional oil and gas has become a hot topic in China's oil and gas industry and even in the energy field. Reservoirs such as biogas, tight oil and gas, and shale oil and gas all have thin interbeds. The thickness of the thin layer is usually less than 25cm. The vertical resolution of conventional logging curves is between 40-60cm, and logging data is the highest resolution continuous data at present. How to further improve the ability of logging data to identify thin layers and then find sweet spots for oil and gas exploration and development is a problem that needs to be solved urgently.

[0003] Thin layers usually refer to thin strata, which are particularly common in sandstone and mudstone sections. They have different meanings in different fields, regions, and oil fields. The Geological Vocabulary defines thin layers as strata with a thickness of 5-60 cm. From the perspective of well logging, thin layers are relative. A stratum with a thickness less than the vertical resolution of the logging instrument is called a thin layer. Thin layers can be divided into single thin layers and thin interlayers. The interpretation and evaluation of thin interlayers is the most difficult.

[0004] Different logging instruments have different vertical resolutions. The resolution of formation microresistivity scanning imaging logging is the highest, up to 0.5cm. The resolution of conventional logging curves is between 40-60cm. For example, the resolution of natural gamma logging (GR) is 45-60cm, the resolution of compensated neutron (CNL) is 60cm, and the resolution of compensated acoustic wave (AC) is 50-60cm. It can be seen that the vertical resolution of conventional logging curves is difficult to meet the identification and division of unconventional oil and gas reservoirs. The price of formation microresistivity scanning imaging logging is very expensive, and most wells do not have such data. Therefore, how to improve the vertical resolution of conventional logging curves is the key to thin layer identification and division. Summary of the invention

[0005] The present application provides a method and device for improving the vertical resolution of a logging curve, dividing the formation within the detection range of the logging instrument into several small layers, and using a logging volume model to determine the physical parameters of each small layer, and obtaining a high-resolution logging curve based on the determined physical parameters of each small layer.

[0006] In a first aspect, the present application provides a method for improving the vertical resolution of a well logging curve, the method comprising:

[0007] Obtaining the vertical resolution and sampling interval of the well logging curve to be processed, and determining the detection range of the well logging instrument corresponding to the well logging curve;

[0008] Dividing the stratum within the detection range into a plurality of small layers of equal thickness according to the vertical resolution and the sampling interval;

[0009] The relative volume corresponding to each small layer is calculated according to the pre-established logging volume model;

[0010] According to the relative volume and the preset constraints, the physical parameters corresponding to each small layer are calculated using the optimization objective function;

[0011] The processed logging curve is calculated based on the physical parameters of each small layer.

[0012] In an exemplary embodiment, when the well logging instrument is a nuclear well logging instrument, determining the detection range of the well logging instrument corresponding to the well logging curve includes:

[0013] When the logging instrument is a nuclear logging instrument, determining the center position of a transmitter and a receiver in the logging instrument corresponding to the logging curve;

[0014] A sphere whose diameter is determined by taking the center position as the center point and whose vertical resolution value is the detection range of the logging instrument.

[0015] In an exemplary embodiment, dividing the stratum within the detection range into a plurality of small layers of equal thickness according to the vertical resolution and the sampling interval includes:

[0016] The quotient obtained by dividing the longitudinal resolution by the sampling interval is rounded to obtain a value n, where n is a positive integer greater than 1;

[0017] The strata within the detection range are divided into n small layers of equal thickness.

[0018] In an exemplary embodiment, the well logging volume model is:

[0019]

[0020] Where m(i) is the logging value at the measured depth i, i is the measured depth, z is the depth of the small layer, p(z) is the physical parameter of the small layer, VR l is the vertical resolution of the logging instrument, n is the number of small layers, w(iz,v) is the weight coefficient of the small layer at depth z, and v is the relative volume of the small layer at depth z.

[0021] In an exemplary embodiment, the relative volume is the ratio of the volume of each small layer to the total volume of the detection range.

[0022] In an exemplary embodiment, the preset constraint conditions include equality constraint conditions and range constraint conditions.

[0023] In an exemplary embodiment, the equality constraint condition is: within the detection range of the logging instrument, the sum of the products of the relative volumes of all small layers and the corresponding physical parameters is equal to the logging value.

[0024] In an exemplary embodiment, the range constraint condition includes: a logging curve numerical range constraint and a dynamic range constraint.

[0025] In an exemplary embodiment, the dynamic range constraint is to determine an upper boundary value and a lower boundary value according to the well logging volume model;

[0026] Wherein, the upper boundary value is:

[0027]

[0028] The lower boundary value is:

[0029]

[0030] Among them, Up is the upper boundary value, Low is the lower boundary value, and m j is the physical parameter of the jth layer, m j-1 is the physical parameter of the j-1th sublayer, m j+1 is the physical parameter of the j+1th sublayer, ε is the compensation value, max(m) is the maximum physical parameter among the upper boundary values ​​outside the peaks and troughs, and min(m) is the minimum physical parameter among the lower boundary values ​​outside the peaks and troughs.

[0031] In an exemplary embodiment, the optimization objective function is a loss function;

[0032] Wherein, the loss function is:

[0033]

[0034] In the above function, e i-s is the loss function, m(i) is the logging value at the measured depth i, and m s (i) is the weighted average physical parameter value, i is the measurement depth, and D is the bottom depth of the well logging curve to be processed.

[0035] In a second aspect, an embodiment of the present invention provides a device for improving the vertical resolution of a well logging curve, the device comprising: a memory and a processor; the memory is used to store a program for improving the vertical resolution of a well logging curve, the processor is used to read and execute the program for improving the vertical resolution of a well logging curve, and execute any one of the methods described in the above embodiments.

[0036] In a third aspect, an embodiment of the present invention provides a computer storage medium, in which a program for improving the vertical resolution of a logging curve is stored, and the program is configured to execute any of the methods described in the above embodiments when running.

[0037] Compared with the related art, the present application provides a method and device for improving the vertical resolution of a logging curve, the method comprising: obtaining the vertical resolution and sampling interval of the logging curve to be processed, and determining the detection range of the logging instrument corresponding to the logging curve; dividing the stratum within the detection range into a plurality of small layers of equal thickness according to the vertical resolution and the sampling interval; calculating the relative volume corresponding to each small layer according to a pre-established logging volume model; calculating the physical parameters corresponding to each small layer using an optimization objective function according to the relative volume and pre-set constraints; and obtaining a processed logging curve according to the physical parameters of each small layer. The present application divides the stratum within the detection range of the logging instrument into several small layers, and uses the logging volume model to determine the physical parameters of each small layer, and obtains a high-resolution logging curve according to the determined physical parameters of each small layer.

[0038] Other features and advantages of the present application will be described in the following description, and partly become apparent from the description, or be understood by implementing the present application. Other advantages of the present application can be realized and obtained by the schemes described in the description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The accompanying drawings are used to provide an understanding of the technical solution of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the technical solution of the present application and do not constitute a limitation on the technical solution of the present application.

[0040] Figure 1 This is a flow chart of a method for improving the vertical resolution of conventional logging curves according to an embodiment of the present application;

[0041] Figure 2 A schematic diagram of a device for improving the vertical resolution of conventional logging curves according to an embodiment of the present application;

[0042] Figure 3 A schematic diagram of the detection range of a well logging instrument in some exemplary embodiments;

[0043] Figure 4 is a schematic diagram of a volume model in some exemplary embodiments;

[0044] Figure 5 is a schematic diagram of range constraints in some exemplary embodiments;

[0045] Figure 6 is a schematic diagram of volume constraints in some exemplary embodiments;

[0046] Figure 7 is a schematic diagram of resampling in some exemplary embodiments;

[0047] Figure 8 Schematic diagram of high-resolution processing results of conventional well logging curves in some exemplary embodiments. DETAILED DESCRIPTION

[0048] The present application describes multiple embodiments, but the description is exemplary rather than restrictive, and it is obvious to those skilled in the art that there may be more embodiments and implementations within the scope of the embodiments described in the present application. Although many possible feature combinations are shown in the drawings and discussed in the specific embodiments, many other combinations of the disclosed features are also possible. Unless specifically limited, any feature or element of any embodiment may be used in combination with any other feature or element in any other embodiment, or may replace any other feature or element in any other embodiment.

[0049] The present application includes and contemplates combinations of features and elements known to those of ordinary skill in the art. The embodiments, features and elements disclosed in the present application may also be combined with any conventional features or elements to form a unique invention scheme defined by the claims. Any features or elements of any embodiment may also be combined with features or elements from other invention schemes to form another unique invention scheme defined by the claims. Therefore, it should be understood that any feature shown and / or discussed in the present application may be implemented individually or in any appropriate combination. Therefore, except for the limitations made according to the attached claims and their equivalents, the embodiments are not subject to other restrictions. In addition, various modifications and changes may be made within the scope of protection of the attached claims.

[0050] In addition, when describing representative embodiments, the specification may have presented the method and / or process as a specific sequence of steps. However, to the extent that the method or process does not rely on the specific order of the steps described herein, the method or process should not be limited to the steps of the specific order described. As will be understood by those of ordinary skill in the art, other sequences of steps are also possible. Therefore, the specific sequence of the steps set forth in the specification should not be interpreted as a limitation to the claims. In addition, the claims for the method and / or process should not be limited to the steps of performing them in the order written, and those skilled in the art can easily understand that these sequences can be changed and still remain within the spirit and scope of the embodiments of the present application.

[0051] In some technical solutions, methods for improving the resolution of conventional logging curves include: frequency matching method, vertical resolution matching method, α factor method, Walsh function method and deconvolution method, among which, the frequency matching method, vertical resolution matching method and α factor method all use curves with higher vertical resolution to improve the resolution of low-resolution curves, so that the low-resolution logging curves have both the original good detection depth and the same vertical resolution as the high-resolution logging curves. In principle, such methods require high-resolution curves for further processing; the Walsh function method and the deconvolution method convolve or spectrally analyze the logging curves through signal processing, and then reconstruct the real logging response characteristics. Such methods have strong multi-solution and high uncertainty.

[0052] Based on the problems existing in the above conventional logging curve vertical resolution method, the inventors propose a method that can overcome the uncertainty of the existing method and provide higher resolution, thereby more accurately dividing thin layers.

[0053] The embodiment of the present invention provides a method for improving the vertical resolution of a well logging curve, such as Figure 1 As shown, the method includes steps S100-S140, which are specifically as follows:

[0054] Step S100: obtaining the vertical resolution and sampling interval of the well logging curve to be processed, and determining the detection range of the well logging instrument corresponding to the well logging curve;

[0055] Step S110: dividing the stratum within the detection range into a plurality of small layers of equal thickness according to the vertical resolution and the sampling interval;

[0056] Step S120: Calculate the relative volume corresponding to each small layer according to the pre-established well logging volume model;

[0057] Step S130: Calculate the physical parameters corresponding to each small layer using an optimization objective function according to the relative volume and preset constraints;

[0058] Step S140: Calculate and obtain the processed logging curve according to the physical parameters of each sublayer.

[0059] In this embodiment, the logging value is the recorded value within the detection range of the logging instrument, and the recorded value is usually specified as the property of a certain depth point, which is the measurement point, usually the center point of the instrument detection range. Stratigraphic deposition is usually layered. If the measured stratum is thick enough and far exceeds the detection range of the logging instrument, the recorded value of the measurement point, i.e., the logging value, is consistent with the true value of the stratum; if the stratum is thin and the thickness is less than the detection range of the logging instrument, the recorded value of the measurement point, i.e., the logging value, includes the logging response of other layers adjacent to the stratum, and cannot represent the true value of the stratum.

[0060] The physical parameter corresponding to each sublayer refers to the true value corresponding to each sublayer.

[0061] In an exemplary embodiment, the vertical resolution and sampling interval of the well logging curve to be processed are obtained, and the detection range of the well logging instrument corresponding to the well logging curve is determined. The detection range is the distance that the well logging instrument can detect in all directions. For nuclear well logging series, such as natural gamma, density and neutron well logging, the detection range is centered on the center position of the transmitter and receiver in the well logging instrument corresponding to the well logging curve; the vertical resolution value is a sphere determined by the diameter. Figure 3 The schematic diagram of the detection range of the nuclear logging instrument is shown. The left and right sides represent typical layered sandstone and mudstone formations. Gray represents mudstone, white represents sandstone, and the dotted circle represents the detection range of the logging instrument. The middle of the formation is the wellbore, and the middle of the wellbore is the logging instrument. Z is the center of the detection range of the logging instrument. The logging value at point Z represents the average value of the formation logging response within the entire detection range, not the formation logging response value at point Z. VR l is the vertical resolution of the logging tool.

[0062] In an exemplary embodiment, the stratum within the detection range is divided into a plurality of small layers of equal thickness according to the vertical resolution and the sampling interval, including: rounding the quotient obtained by dividing the vertical resolution by the sampling interval to obtain a value of n, where n is a positive integer greater than 1; and dividing the stratum within the detection range into n small layers of equal thickness. For example, if the vertical resolution of the logging instrument is 50 cm and its detection range is relatively large, the sampling interval of the logging data is usually used to define the boundaries of the small layers. If the sampling interval is 12.5 cm, the stratum can be divided into 4 small layers, that is, the thickness of each small layer is 12.5 cm, and the number is 4. Figure 4 If the vertical resolution of the instrument is very high, such as the vertical resolution of formation microresistivity scanning imaging logging is 0.5 cm, then thinner and more numerous small layers can be divided.

[0063] In an exemplary embodiment, the well logging volume model assumes that the formation under study is composed of several small layers, and the volume fraction of each small layer is multiplied by the corresponding physical parameters, and finally the weighted average is performed to obtain the logging value. The radius and number of layers of the detection range are determined according to the vertical resolution and sampling interval of the logging instrument. The thickness of each small layer can be determined according to the number of layers, and then the relative volume of each small layer can be determined, so as to establish the well logging volume model. Since the logging value of a thin layer is not the true physical parameter value of the layer, but the weighted average of the true value of the layer and the upper and lower nearby formations, the problem is abstracted into the following expression:

[0064]

[0065] Where m(i) is the logging value at the measured depth i, i is the measured depth, z is the depth of the small layer, p(z) is the physical parameter of the small layer, w is the weight coefficient of the small layer at the depth z, VR l is the vertical resolution.

[0066] Since the stratum is usually a layered medium, the discretized equation (1) can be obtained as follows:

[0067]

[0068] Where m(i) is the logging value at the measured depth i, i is the measured depth, z is the depth of the small layer, p(z) is the physical parameter of the small layer, VR l is the vertical resolution of the logging instrument, n is the number of small layers, w(iz,v) is the weight coefficient of the small layer at depth z, and v is the relative volume of the small layer at depth z.

[0069] In an exemplary embodiment, the relative volume is the ratio of the volume of each small layer to the total volume of the detection range.

[0070] In an exemplary embodiment, the physical parameters corresponding to each small layer are calculated using an optimization objective function based on the relative volume and pre-set constraints.

[0071] In an exemplary embodiment, the preset constraints include equality constraints and range constraints.

[0072] In an exemplary embodiment, the equality constraint condition is: within the detection range of the logging instrument, the sum of the products of the relative volumes of all small layers and the corresponding physical parameters is equal to the logging value, that is, within a detection range, the relative volumes of all layers multiplied by the physical parameters of the layers are equal to the measured values. The specific formula is:

[0073]

[0074] Where, m is the logging value; m i is the physical parameter of the i-th layer; v i is the relative volume of the i-th layer.

[0075] In an exemplary embodiment, the range constraint is to quickly obtain the real formation parameters by limiting the curve to be processed by giving a certain range, and the range constraint conditions include: logging curve value range constraint and dynamic range constraint.

[0076] The numerical range constraints of well logging curves include numerical range constraints of common well logging curves and range constraints of the curves to be processed calculated based on other well logging curves.

[0077] Among them, the common logging curve value range constraint is to use the common range of logging curve values ​​for constraint, for example: the range of density curve is 2-3g / cm 3 , the range of the natural gamma curve is 0-150GAPI.

[0078] The range constraint of the curve to be processed calculated based on other well logging curves is the range of the curve to be processed calculated based on the well logging interpretation model.

[0079] The dynamic range constraint includes a dynamic range constraint derived based on a volume model, that is, an upper boundary value and a lower boundary value are determined according to the well logging volume model;

[0080] Wherein, the upper boundary value is:

[0081]

[0082] The lower boundary value is:

[0083]

[0084] Among them, Up is the upper boundary value, Low is the lower boundary value, and m j is the physical parameter of the jth layer, m j-1 is the physical parameter of the j-1th sublayer, m j+1 is the physical parameter of the j+1th sublayer, ε is the compensation value, max(m) is the maximum physical parameter among the upper boundary values ​​outside the peaks and troughs, and min(m) is the minimum physical parameter among the lower boundary values ​​outside the peaks and troughs.

[0085] Upper boundary: When a certain logging value is the maximum value, the true value will only be greater than or equal to the current logging value; when a certain logging value is the minimum value, the maximum value of the true value will not be greater than the minimum value. The same applies to the lower boundary.

[0086] In an exemplary embodiment, the physical parameters corresponding to each small layer are calculated using an optimization objective function according to the relative volume and preset constraints;

[0087] If the logging value m(i) and the weight coefficient w are known, the true formation parameter s(z) can be solved. This type of problem is an optimization problem, and the solution is usually an optimization method.

[0088] The optimization problem is to determine the values ​​of certain optional variables under certain constraints so that the selected objective function reaches the optimal value;

[0089] The optimization method is a method for solving optimization problems. The method adopted in this embodiment is a loss function method (Loss function).

[0090] The Loss function is a function that maps the value of a random event or its related random variable to a non-negative real number to represent the "risk" or "loss" of the random event. Its function expression is:

[0091]

[0092] In the above function, e i-s is the loss function, m(i) is the logging value at the measured depth i, and m s (i) is the weighted average physical parameter value, i is the measured depth, and D is the bottom depth of the well logging curve to be processed. When the error between the weighted average physical parameter value and the actual well logging value is the smallest, that is, e i-s The optimal solution is obtained when .

[0093] In an exemplary embodiment, after calculating the physical parameters corresponding to each sublayer, a high-resolution logging curve is obtained according to the physical parameters of each sublayer. In this embodiment, after obtaining the physical parameters of each sublayer, the high-resolution logging curve is obtained by resampling, that is, setting a suitable sampling interval and detection range to obtain a logging curve with a higher resolution than the original curve. For example: the sampling interval is the distance between adjacent acquisition data points. Usually, the sampling rate of the logging instrument is 8 points per meter, that is, the sampling interval is 0.125m. Since the assumed formation thickness is the size of the sampling interval when obtaining the weight coefficient, the highest resolution that can be achieved after resampling is the size of the sampling interval. If there are other high-resolution logging data to determine more accurate formation boundaries to obtain weight coefficients, the resolution of conventional logging curves can be increased to be consistent with the high-resolution logging data.

[0094] The beneficial effect of the implementation of the present invention is that, in view of the limitations of the current method for improving the vertical resolution of conventional logging curves, a theoretical solution is proposed to divide the formation within the detection range of the logging instrument into several small layers and use the logging volume model to improve the resolution of conventional logging curves. On this basis, the logging volume model is abstracted into a mathematical problem, a mathematical model is established, the weight coefficient is determined and the constraint conditions are set, and the physical parameters of each small layer are inverted by the method of optimizing the objective function. Further, a high-resolution logging curve is obtained by resampling, forming a method and device for improving the vertical resolution of conventional logging curves. The method for improving the vertical resolution of logging curves proposed in this application does not require other high-resolution curves as an aid, can provide higher vertical resolution, and provide technical support for fine reservoir evaluation and sweet spot optimization.

[0095] The disclosed embodiment also provides a device for improving the vertical resolution of a well logging curve, such as Figure 2As shown, it includes a memory 210 and a processor 220; the memory is used to store a program for improving the vertical resolution of the logging curve, and the processor is used to read and execute the program for improving the vertical resolution of the logging curve, and execute any one of the methods in the above embodiments.

[0096] The embodiment of the present disclosure further provides a computer storage medium, in which a program for improving the vertical resolution of a logging curve is stored, and the program is configured to execute any of the methods in the above embodiments when running.

[0097] Example 1

[0098] Taking the nuclear logging series as an example, a method for improving the vertical resolution of conventional logging curves is demonstrated. The process is as follows:

[0099] Step 1. Establish a well logging volume model;

[0100] The detection range of a logging instrument is the distance that the instrument can detect in all directions. For nuclear logging series, such as natural gamma, density and neutron logging, the detection range is a sphere, which is centered at the center of the transmitter and receiver, and the vertical resolution value is determined by the diameter.

[0101] The logging value is the recorded value within the detection range of the logging instrument. The recorded value is usually specified as the property of a certain depth point. The depth point is the measurement point, which is usually the center point of the instrument detection range. Stratigraphic deposition is usually layered. If the measured stratum is thick enough and far exceeds the detection range of the logging instrument, the recorded value of the measurement point, i.e. the logging value, is consistent with the true value of the stratum. If the stratum is thin and the thickness is less than the detection range of the logging instrument, the recorded value of the measurement point, i.e. the logging value, includes the logging response of other layers adjacent to the stratum and cannot represent the true value of the stratum.

[0102] The logging volume model is that the formation within the detection range is composed of several small layers, and the volume fraction of each small layer is multiplied by the corresponding physical parameters, and finally the weighted average is performed to obtain the logging value. The physical parameter of each small layer refers to the true value of each small layer.

[0103] According to the fact that the logging value of a thin layer is not the real physical parameter value of the layer, but the weighted average value of the real physical parameter values ​​of the layer and the upper and lower formations, the expression of the logging volume model is obtained as follows:

[0104]

[0105] Where m(i) is the logging value at the measured depth i, i is the measured depth, z is the depth of the small layer, p(z) is the physical parameter of the small layer, w is the weight coefficient of the small layer at the depth z, VR l is the vertical resolution.

[0106] Considering that the stratum is usually a layered medium, the discretization of equation (1) can give the expression of the discretized well logging volume model:

[0107]

[0108] Where m(i) is the logging value at the measured depth i; z is the depth of the small layer; p(z) is the physical parameter of the small layer; VR l is the instrument's vertical resolution; n is the number of small layers; w(iz,v) is the weight coefficient of the small layer. Figure 4 The volume model schematic diagram shown divides the strata within the detection range of the logging instrument into a certain number of small layers of a certain thickness, and the top and bottom surfaces of the small layers are parallel to each other. The certain number and thickness are related to the longitudinal resolution of the instrument. If the longitudinal resolution of the instrument is 50cm, its detection range is large, and the sampling interval of the logging data is usually used to delineate the boundaries of the small layers. The sampling interval is 12.5cm, and the strata can be divided into 4 small layers, that is, the thickness of each small layer is 12.5cm, and the number is 4. If the longitudinal resolution of the instrument is very high, such as the longitudinal resolution of the formation microresistivity scanning imaging logging is 0.5cm, then thinner and more numerous small layers can be divided.

[0109] Step 2. Determine the weight coefficient;

[0110] Determine the weight coefficient in the logging volume model, which is related to the relative volume of the small layer within the detection range. Assuming that the signal emitted by the instrument is uniform and the signal strength received in each small layer volume is the same, the weight coefficient is equal to the relative volume of the small layer within the detection range. The relative volume is the ratio of the volume of each small layer to the total volume of the detection range. For example: the vertical resolution of the density logging instrument is 50cm, and the sampling interval of conventional logging data is 12.5cm. The strata within the detection range of the instrument can be divided into 4 small layers according to thickness. Figure 4 As shown, the resolution is increased by 4 times to 12.5 cm. The weight coefficients determined according to the relative volumes of the four small layers are 0.156, 0.344, 0.344 and 0.156 from top to bottom.

[0111] Step 3. Set constraints;

[0112] Setting constraint conditions is to set certain conditions to quickly and accurately obtain the real formation parameters. Constraint conditions include equality constraints and range constraints.

[0113] The equality constraint is that within a detection range, the relative volume of all layers multiplied by the physical parameter of the layer is equal to the measured value. The specific formula is:

[0114]

[0115] Where, m is the logging value; m i is the physical parameter of the i-th layer; v i is the relative volume of the i-th layer.

[0116] The range constraint is to quickly obtain the real formation parameters by limiting the curve to be processed by giving a certain range. The specific methods include the numerical range constraint of common logging curves, the range constraint of the curve to be processed calculated based on other logging curves, and the dynamic range constraint derived based on the volume model.

[0117] The common logging curve value range constraint is to use the common range of logging curve values ​​for constraint, for example, the range of density curve is 2-3g / cm 3 , the range of the natural gamma curve is 0-150GAPI.

[0118] The range constraint of the curve to be processed calculated based on other well logging curves is the range of the curve to be processed calculated based on the well logging interpretation model. Taking the calculation of density curve by neutron well logging curve as an example, the density is different due to the different lithology and fluid properties in the pores, so the density range is set accordingly. The lithology is the same, and the density of gas (0.2g / cm 3 ) is less than the density of oil (0.8g / cm 3 , which varies depending on the viscosity of the oil) is less than the density of water (1g / cm 3 , which varies depending on the mineralization of the water); the fluid properties in the pores are the same, and the density of sandstone is (2.65g / cm 3 ) is less than the density of limestone (2.71g / cm 3 ) is less than the density of dolomite (2.87g / cm 3 ), so that the density measurement value can be constrained according to the actual formation information. Figure 5 The figure shows the range of density logging values ​​constrained by neutron logging curves. The black line represents the density logging value, the black dotted line represents the density curve calculated by neutron porosity when there are only limestone and water in the formation model, the gray line represents the density curve calculated by neutron porosity when there are only sandstone and water in the formation model, the black dot-dash line represents the density curve calculated by neutron porosity when there are only sandstone and oil in the formation model, and the gray dotted line represents the density curve calculated by neutron porosity when there are only sandstone and gas in the formation model. If the fluid properties and skeleton composition in the formation are roughly understood through comprehensive logging processing and interpretation, the size of the true density value can be constrained by various idealized density ranges.

[0119] The dynamic range constraint derived from the volume model is that the measured value should be between the upper boundary (Up) and the lower boundary (Low) derived from the volume model. The upper boundary value is:

[0120]

[0121] The lower boundary value is:

[0122]

[0123] Among them, Up is the upper boundary value, Low is the lower boundary value, and m j is the physical parameter of the jth layer, m j-1 is the physical parameter of the j-1th sublayer, m j+1 is the physical parameter of the j+1th sublayer, ε is the compensation value, max(m) is the maximum physical parameter among the upper boundary values ​​outside the peaks and troughs, and min(m) is the minimum physical parameter among the lower boundary values ​​outside the peaks and troughs.

[0124] Upper boundary: When a certain logging value is the maximum value, the true value will only be greater than or equal to the current logging value; when a certain logging value is the minimum value, the minimum maximum value of the true value will not be greater than the minimum value. The same applies to the lower boundary. Taking density as an example, the upper and lower boundaries of the density logging value can be derived according to formula 5 and formula 6 to constrain the density logging value. Figure 6 As shown, the black line is the density curve logging value, the gray line is the upper boundary, and the gray dotted line is the lower boundary.

[0125] Step 4. Invert the physical parameters of the small layer;

[0126] The physical parameters of the small layer can be inverted by combining the determined weight coefficients and constraints.

[0127] If the logging value m(i) and the weight coefficient w are known, the true formation parameter s(z) can be solved by the loss function method (Loss function).

[0128] The Loss function is a function that maps the value of a random event or its related random variables to a non-negative real number to represent the "risk" or "loss" of the random event. Its function expression is:

[0129]

[0130] In the above function, e i-s is the loss function, m(i) is the logging value at the measured depth i, and m s (i) is the weighted average physical parameter value, i is the measured depth, and D is the bottom depth of the well logging curve to be processed. When the error between the weighted average physical parameter value and the actual well logging value is the smallest, that is, e i_s The optimal solution is obtained when .

[0131] Step 5: Resample to obtain high-resolution logging curves.

[0132] Obtaining high-resolution logging curves through resampling means obtaining the real physical parameters of the formation, and then setting appropriate sampling intervals and detection ranges to obtain logging curves with higher resolution than the original curves. The sampling interval is the distance between adjacent data points. Usually, the sampling rate of logging instruments is 8 points per meter, which means that the sampling interval is 0.125m. Since the formation thickness is assumed to be the size of the sampling interval when obtaining the weight coefficient, the highest resolution that can be achieved after resampling is the size of the sampling interval. If there are other high-resolution logging data to determine more accurate formation boundaries to obtain weight coefficients, the resolution of conventional logging curves can be increased to be consistent with the high-resolution logging data. Figure 7 As shown in the figure, the measured depths of the three sampling points are at A, B and Z respectively. Compared with the conventional logging curve, the vertical resolution has been greatly improved.

[0133] like Figure 8 As shown, it is a conventional logging curve high-resolution processing result diagram of an embodiment of the present invention, including:

[0134] The first one is the natural gamma curve, the dotted line is the natural gamma curve after high-resolution processing, and the solid line is the conventional natural gamma curve. The natural gamma curve mainly represents the changes in lithology.

[0135] The second channel is the depth channel, which indicates the distance between the measured well section (i.e. the target layer) and the wellhead.

[0136] The third track is the resistivity curve. The dotted line is the deep lateral resistivity curve measured by a high-resolution logging instrument with a vertical resolution of 0.2 m. The solid line is the conventional deep lateral resistivity curve. The resistivity curve is used to interpret lithology and fluid properties.

[0137] The fourth track is the formation microresistivity scanning electrical imaging curve. The color represents the difference in rock properties. The whiter the color, the more the rock property tends to be sandstone, and vice versa.

[0138] The fifth track is the density curve. The dotted line is the density curve after high-resolution processing (DEN), and the solid line is the conventional density curve. The density curve represents the changes in lithology and physical properties.

[0139] The black box in the figure is used to show the application effect of improving the vertical resolution of conventional logging curves. Figure 8Comparison of the GR and DEN curves processed with medium and high resolution with conventional curves, curves measured by high-resolution logging instruments, and electrical imaging images shows that the resolution of the GR and DEN curves processed with high resolution has been greatly improved compared with that of conventional curves, and is basically consistent with the resolution of the deep lateral curves measured by high-resolution logging instruments. They have a good contrast with the electrical imaging images shown in the box. For some thin reservoirs (white strips in the electrical imaging images), the GR and DEN curves processed with high resolution both respond, confirming that the method of improving the vertical resolution of logging curves in this application is very effective.

[0140] It will be appreciated by those skilled in the art that all or some of the steps, systems, and functional modules / units in the methods disclosed above may be implemented as software, firmware, hardware, and appropriate combinations thereof. In hardware implementations, the division between the functional modules / units mentioned in the above description does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed by several physical components in cooperation. Some or all components may be implemented as software executed by a processor, such as a digital signal processor or a microprocessor, or implemented as hardware, or implemented as an integrated circuit, such as an application-specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include a computer storage medium (or non-transitory medium) and a communication medium (or temporary medium). As known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by a computer. In addition, it is well known to those of ordinary skill in the art that communication media typically contain computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

Claims

1. A method for improving the vertical resolution of a well logging curve, characterized in that: The method comprises: Obtaining the vertical resolution and sampling interval of the well logging curve to be processed, and determining the detection range of the well logging instrument corresponding to the well logging curve; Dividing the stratum within the detection range into a plurality of small layers of equal thickness according to the vertical resolution and the sampling interval; The relative volume corresponding to each small layer is calculated according to the pre-established logging volume model; According to the relative volume and the preset constraints, the physical parameters corresponding to each small layer are calculated using the optimization objective function; The processed logging curve is calculated based on the physical parameters of each small layer.

2. The method for improving the vertical resolution of a well logging curve according to claim 1, characterized in that: When the well logging instrument is a nuclear well logging instrument, the step of determining the detection range of the well logging instrument corresponding to the well logging curve includes: When the logging instrument is a nuclear logging instrument, determining the center position of a transmitter and a receiver in the logging instrument corresponding to the logging curve; A sphere whose diameter is determined by taking the center position as the center point and whose vertical resolution value is the detection range of the logging instrument.

3. The method for improving the vertical resolution of a well logging curve according to claim 1, characterized in that: The step of dividing the stratum within the detection range into a plurality of small layers of equal thickness according to the vertical resolution and the sampling interval comprises: The quotient obtained by dividing the longitudinal resolution by the sampling interval is rounded to obtain a value n, where n is a positive integer greater than 1; The strata within the detection range are divided into n small layers of equal thickness.

4. The method for improving the vertical resolution of a well logging curve according to claim 1, characterized in that: The well logging volume model is: Where m(i) is the logging value at the measured depth i, i is the measured depth, z is the depth of the small layer, p(z) is the physical parameter of the small layer, VR l is the vertical resolution of the logging instrument, n is the number of small layers, w(iz,v) is the weight coefficient of the small layer at depth z, and v is the relative volume of the small layer at depth z.

5. The method for improving the vertical resolution of a well logging curve according to claim 1, characterized in that: The relative volume is the ratio of the volume of each small layer to the total volume of the detection range.

6. The method for improving the vertical resolution of a well logging curve according to claim 4, characterized in that: The preset constraint conditions include equality constraint conditions and range constraint conditions.

7. The method for improving the vertical resolution of a well logging curve according to claim 6, characterized in that: The equation constraint condition is: within the detection range of the logging instrument, the sum of the products of the relative volumes of all small layers and the corresponding physical parameters is equal to the logging value.

8. The method for improving the vertical resolution of a well logging curve according to claim 6, characterized in that: The range constraint conditions include: logging curve numerical range constraint and dynamic range constraint.

9. The method for improving the vertical resolution of conventional logging curves according to claim 8, characterized in that: The dynamic range constraint is to determine an upper boundary value and a lower boundary value according to the well logging volume model; Wherein, the upper boundary value is: The lower boundary value is: Among them, Up is the upper boundary value, Low is the lower boundary value, and m j is the physical parameter of the jth layer, m j-1 is the physical parameter of the j-1th sublayer, m j+1 is the physical parameter of the j+1th sublayer, ε is the compensation value, max(m) is the maximum physical parameter among the upper boundary values ​​outside the peaks and troughs, and min(m) is the minimum physical parameter among the lower boundary values ​​outside the peaks and troughs.

10. The method for improving the vertical resolution of a well logging curve according to claim 1, characterized in that: The optimization objective function is a loss function; Wherein, the loss function is: In the above function, e i_s is the loss function, m(i) is the logging value at the measured depth i, and m s (i) is the weighted average physical parameter value, i is the measurement depth, and D is the bottom depth of the well logging curve to be processed.

11. A device for improving the vertical resolution of a well logging curve, characterized in that: The device comprises: a memory and a processor; the memory is used to store a program for improving the vertical resolution of a logging curve, and the processor is used to read and execute the program for improving the vertical resolution of a logging curve, and execute the method described in any one of claims 1-10.

12. A computer storage medium, characterized in that: The storage medium stores a program for improving the vertical resolution of a logging curve, and the program is configured to execute the method according to any one of claims 1 to 10 when running.

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