A method and apparatus for improving the vertical resolution of well logging curves

Abstract

A method and apparatus for improving the vertical resolution of well logging curves, the method comprising: acquiring 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 formation within the detection range into multiple layers of equal thickness according to the vertical resolution and the sampling interval; calculating the relative volume corresponding to each layer according to a pre-established well logging volume model; calculating the physical parameters corresponding to each layer using an optimization objective function based on the relative volume and pre-set constraints; and calculating the processed well logging curve based on the physical parameters of each layer.

Description

Technical Field

[0001] This article relates to the field of well logging technology, and in particular to a method and apparatus for improving the longitudinal resolution of well logging curves. Background Technology

[0002] Approximately 30% of the world's oil and gas reserves are located in thin interbedded sandstone and mudstone layers, particularly in unconventional oil and gas. Unconventional oil and gas has become a hot topic in China's oil and gas industry and even the energy sector. Reservoirs such as biogas, tight oil and gas, and shale oil and gas all exhibit thin interbedded characteristics, with the thin layers typically less than 25 cm thick. Conventional logging curves have a vertical resolution between 40 and 60 cm, and logging data currently represents the highest resolution continuous data available. Therefore, how to further improve the ability of logging data to identify thin layers and thus find promising areas for oil and gas exploration and development is a pressing problem that needs to be solved.

[0003] Thin layers generally refer to formations with relatively thin thickness, especially common in sandstone and mudstone profiles. Their meaning varies across different fields, regions, and oilfields. The "Geological Glossary" defines a thin layer as a formation with a thickness of 5-60 cm. From a well logging perspective, "thin layer" is relative; a formation thickness less than the vertical resolution of the logging instrument is considered a thin layer. Thin layers can be further divided into single thin layers and interbedded thin layers, with interbedded thin layers being the most difficult to interpret and evaluate.

[0004] Different logging instruments have different vertical resolutions. Formation microresistivity scanning imaging logging has the highest resolution, reaching 0.5 cm. Conventional logging curves have a resolution between 40-60 cm. For example, natural gamma ray logging (GR) has a resolution of 45-60 cm, compensated neutron logging (CNL) has a resolution of 60 cm, and compensated acoustic logging (AC) has a resolution of 50-60 cm. It is evident that the vertical resolution of conventional logging curves is insufficient for the identification and segmentation of unconventional oil and gas reservoirs. Furthermore, formation microresistivity scanning imaging logging is very expensive, and most wells lack this type of data. Therefore, improving the vertical resolution of conventional logging curves is crucial for thin-layer identification and segmentation. Summary of the Invention

[0005] This application provides a method and apparatus for improving the vertical resolution of well logging curves. The method involves dividing the formation within the detection range of the well logging instrument into several smaller layers, using a well logging volume model to determine the physical parameters of each smaller layer, and processing the determined physical parameters of each smaller layer to obtain a high-resolution well logging curve.

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

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

[0008] Based on the longitudinal resolution and the sampling interval, the strata within the detection range are divided into multiple layers of equal thickness;

[0009] The relative volume corresponding to each sub-layer is calculated based on the pre-established well logging volume model;

[0010] Based on the relative volume and the pre-set constraints, the physical parameters corresponding to each sub-layer are calculated using an optimization objective function;

[0011] The processed logging curves are calculated based on the physical parameters of each sub-layer.

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

[0013] When the logging instrument is a nuclear logging instrument, determine the center positions of the transmitter and receiver in the logging instrument that correspond to the logging curve;

[0014] The detection range of the logging instrument is defined by a sphere with the central position as the center point and the vertical resolution value as the diameter.

[0015] In one exemplary embodiment, dividing the strata within the detection range into multiple layers of equal thickness according to the longitudinal resolution and the sampling interval includes:

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

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

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

[0019]

[0020] In the formula, m(i) is the logging value at a measurement depth of i, i is the measurement depth, z is the depth of the sub-layer, p(z) is the physical parameter of the sub-layer, and VR l denoted as the vertical resolution of the logging instrument, n is the number of sublayers, w(iz,v) is the weighting coefficient of the sublayer at depth z, and v is the relative volume of the sublayer at depth z.

[0021] In one exemplary embodiment, the relative volume is the ratio of the volume of each sublayer to the total volume of the detection range.

[0022] In one exemplary embodiment, the pre-set constraints include equality constraints and range constraints.

[0023] In one exemplary embodiment, the equality constraint is: within the detection range of the logging instrument, the sum of the products of the relative volumes of all sub-layers and their corresponding physical parameters equals the logging value.

[0024] In one exemplary embodiment, the range constraints include: numerical range constraints of logging curves and dynamic range constraints.

[0025] In one exemplary embodiment, the dynamic range constraint is determined by setting upper and lower boundary values ​​based on the logging volume model;

[0026] Wherein, the upper boundary value is:

[0027]

[0028] The lower boundary value is:

[0029]

[0030] Where Up is the upper boundary value, Low is the lower boundary value, and m j Let m be the physical parameters of the j-th smallest layer. j-1 Let m be the physical parameter of the (j-1)th sublayer. j+1 Let be the physical parameter of the (j+1)th sublayer, ε be the compensation value, max(m) be the largest physical parameter among the upper boundary values ​​outside the peaks and troughs, and min(m) be the smallest physical parameter among the lower boundary values ​​outside the peaks and troughs.

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

[0032] The loss function is:

[0033]

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

[0035] Secondly, embodiments of the present invention provide an apparatus for improving the vertical resolution of well logging curves, the apparatus comprising: a memory and a processor; the memory is used to store a program for improving the vertical resolution of well logging curves, and the processor is used to read and execute the program for improving the vertical resolution of well logging curves, and execute the method described in any one of the above embodiments.

[0036] Thirdly, embodiments of the present invention provide a computer storage medium storing a program for improving the vertical resolution of well logging curves, the program being configured to execute the method described in any of the above embodiments during runtime.

[0037] Compared with related technologies, this application provides a method and apparatus for improving the vertical resolution of well logging curves. The method includes: acquiring 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 formation within the detection range into multiple layers of equal thickness according to the vertical resolution and the sampling interval; calculating the relative volume corresponding to each layer according to a pre-established well logging volume model; calculating the physical parameters corresponding to each layer using an optimization objective function based on the relative volume and pre-set constraints; and calculating the processed well logging curve based on the physical parameters of each layer. This application divides the formation within the detection range of the well logging instrument into several layers, uses a well logging volume model to determine the physical parameters of each layer, and obtains a high-resolution well logging curve based on the determined physical parameters of each layer.

[0038] Other features and advantages of this application will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the application. Other advantages of this application can be realized and obtained by means of the solutions described in the description and the accompanying drawings. Attached Figure Description

[0039] The accompanying drawings are used to provide an understanding of the technical solutions of this application and constitute a part of the specification. They are used together with the embodiments of this application to explain the technical solutions of this application and do not constitute a limitation on the technical solutions of this application.

[0040] Figure 1 This is a flowchart illustrating a method for improving the vertical resolution of conventional well logging curves according to an embodiment of this application.

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

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

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

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

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

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

[0047] Figure 8 This is a schematic diagram of the high-resolution processing results of conventional well logging curves in some exemplary embodiments. Detailed Implementation

[0048] This application describes several embodiments, but these descriptions are exemplary and not restrictive, and it will be apparent to those skilled in the art that many more embodiments and implementations are possible within the scope of the embodiments described herein. Although many possible combinations of features are shown in the drawings and discussed in the detailed description, 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, or may replace, any feature or element of any other embodiment.

[0049] This application includes and contemplates combinations of features and elements known to those skilled in the art. The embodiments, features, and elements disclosed in this application may also be combined with any conventional features or elements to form a unique inventive scheme as defined by the claims. Any feature or element of any embodiment may also be combined with features or elements from other inventive schemes to form another unique inventive scheme as defined by the claims. Therefore, it should be understood that any feature shown and / or discussed in this application may be implemented individually or in any suitable combination. Therefore, the embodiments are not limited except by the limitations imposed by the appended claims and their equivalents. Furthermore, various modifications and changes may be made within the scope of the appended claims.

[0050] Furthermore, in describing representative embodiments, the specification may have presented methods and / or processes as a specific sequence of steps. However, the method or process should not be limited to the specific order of steps described herein, to the extent that it does not depend on such a specific order. As will be understood by those skilled in the art, other sequences of steps are also possible. Therefore, the specific order of steps set forth in the specification should not be construed as a limitation of the claims. Moreover, the claims concerning the method and / or process should not be limited to the steps performed in the written order, and those skilled in the art will readily understand that these orders can be varied and still remain within the spirit and scope of the embodiments of this application.

[0051] Some technical solutions for improving the resolution of conventional logging curves include: frequency matching, vertical resolution matching, alpha factor, Walsh function, and deconvolution. Among these, frequency matching, vertical resolution matching, and alpha factor all use curves with higher vertical resolution to improve the resolution of low-resolution curves, so that the low-resolution logging curve has both the original good detection depth and the same vertical resolution as the high-resolution logging curve. In principle, these methods require high-resolution curves to carry out further processing. Walsh function and deconvolution methods use signal processing to perform convolution or spectral analysis on the logging curves to reconstruct the true logging response characteristics. These methods have strong ambiguity and high uncertainty.

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

[0053] This invention provides a method for improving the vertical resolution of well logging curves, such as... Figure 1 As shown, the method includes steps S100-S140, as detailed below:

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

[0055] Step S110: Based on the longitudinal resolution and the sampling interval, divide the strata within the detection range into multiple layers of equal thickness;

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

[0057] Step S130: Based on the relative volume and the pre-set constraints, calculate the physical parameters corresponding to each sub-layer using the optimization objective function;

[0058] Step S140: Calculate the processed logging curves based on the physical parameters of each sub-layer.

[0059] In this embodiment, the logging value is the recorded value within the detection range of the logging instrument. This recorded value is usually designated as a property at a certain depth point, which is the measurement point, typically the center point of the instrument's detection range. Formation deposits are usually layered. If the measured formation is thick enough, far exceeding the detection range of the logging instrument, then the recorded value at the measurement point, i.e., the logging value, is consistent with the true value of the formation. If the formation is thin, with a thickness less than the detection range of the logging instrument, then the recorded value at the measurement point, i.e., the logging value, includes the logging responses of adjacent layers and cannot represent the true value of that layer.

[0060] The physical parameters corresponding to each sub-layer refer to the actual values ​​corresponding to each sub-layer.

[0061] In one exemplary embodiment, the longitudinal resolution and sampling interval of the logging curve to be processed are acquired, and the detection range of the logging instrument corresponding to the logging curve is determined. This detection range is the distance that the logging instrument can detect in all directions. For nuclear logging series, such as natural gamma, density, and neutron logging, the detection range is a sphere with the center point of the transmitter and receiver in the logging instrument corresponding to the logging curve as its center point; the diameter is determined by the value of the longitudinal resolution. Figure 3 The diagram shows the detection range of a nuclear logging instrument. The left and right sides represent typical layered sandstone and mudstone formations; gray represents mudstone, white represents sandstone, and the dotted line circle represents the detection range of the logging instrument. The wellbore is located in the middle of the formation, and the logging instrument is located in the middle of the wellbore. Z represents the center of the logging instrument's detection range. The logging value at point Z represents the average formation logging response across the entire detection range, not the formation logging response value at point Z itself. VR l This refers to the longitudinal resolution of the logging instrument.

[0062] In one exemplary embodiment, the formation within the detection range is divided into multiple layers of equal thickness based on the vertical resolution and sampling interval. This includes: rounding down 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 formation within the detection range into n layers of equal thickness. For example, if a well logging instrument has a vertical resolution of 50 cm and a relatively large detection range, the sampling interval of the well logging data is typically used to delineate the boundaries of the layers. If the sampling interval is 12.5 cm, the formation can be divided into four layers, each with a thickness of 12.5 cm, and there are four layers in total. Figure 4 The diagram shows a volumetric model. If the instrument has a high vertical resolution, such as 0.5 cm for formation microresistivity scanning imaging logging, it can distinguish thinner and more numerous sublayers.

[0063] In one exemplary embodiment, the well logging volume model assumes that the studied formation consists of several sub-layers. The volume fraction of each sub-layer is multiplied by its corresponding physical parameters, and finally, a weighted average is taken to obtain the well logging value. The radius and number of layers of the detection range are determined based on the vertical resolution and sampling interval of the well logging instrument. The thickness of each sub-layer can be determined based on the number of layers, and thus the relative volume of each sub-layer can be determined, thereby establishing the well logging volume model. Since the well logging value of a thin layer is not the true physical parameter value of that layer, but rather a weighted average of the true values ​​of that layer and the surrounding upper and lower strata, this problem can be abstracted into the following expression:

[0064]

[0065] In the formula, m(i) is the logging value at depth i, i is the measurement depth, z is the depth of the sublayer, p(z) is the physical parameter of the sublayer, w is the weighting coefficient of the sublayer at depth z, and VR l This refers to the vertical resolution.

[0066] Since formations are typically layered media, discretizing equation (1) yields the discretized logging volume model as follows:

[0067]

[0068] In the formula, m(i) is the logging value at a measurement depth of i, i is the measurement depth, z is the depth of the sub-layer, p(z) is the physical parameter of the sub-layer, and VR l denoted as the vertical resolution of the logging instrument, n is the number of sublayers, w(iz,v) is the weighting coefficient of the sublayer at depth z, and v is the relative volume of the sublayer at depth z.

[0069] In one exemplary embodiment, the relative volume is the ratio of the volume of each sublayer to the total volume of the detection range.

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

[0071] In one exemplary embodiment, the pre-set constraints include equality constraints and range constraints.

[0072] In one exemplary embodiment, the equality constraint is: within the detection range of the logging instrument, the sum of the products of the relative volumes of all sub-layers and their corresponding physical parameters equals the logging value; that is: within a detection range, the product of the relative volumes of all layers and the physical parameters of that layer equals the measured value. The specific formula is:

[0073]

[0074] In the formula, m is the logging value; m i v represents the physical parameters of the i-th layer; i Let be the relative volume of the i-th layer.

[0075] In one exemplary embodiment, the range constraint is to limit the curve to be processed within a certain range to quickly obtain the real formation parameters. The range constraint conditions include: the numerical range constraint of the logging curve and the dynamic range constraint.

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

[0077] Among them, the common logging curve numerical range constraint is to use the common range of logging curve values ​​for constraint, for example: the range of density curve is 2-3 g / cm³. 3 The natural gamma curve ranges from 0 to 150 GAPI.

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

[0079] Dynamic range constraints include those derived from the volumetric model. Specifically, the upper and lower boundary values ​​are determined based on the well logging volumetric model.

[0080] Wherein, the upper boundary value is:

[0081]

[0082] The lower boundary value is:

[0083]

[0084] Where Up is the upper boundary value, Low is the lower boundary value, and m j Let m be the physical parameters of the j-th smallest layer. j-1 Let m be the physical parameter of the (j-1)th sublayer. j+1 Let be the physical parameter of the (j+1)th sublayer, ε be the compensation value, max(m) be the largest physical parameter among the upper boundary values ​​outside the peaks and troughs, and min(m) be the smallest physical parameter among the lower boundary values ​​outside the peaks and troughs.

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

[0086] In one exemplary embodiment, the physical parameters corresponding to each sub-layer are calculated using an optimization objective function based on the relative volume and pre-set constraints.

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

[0088] The optimization problem refers to the problem of determining the values ​​of certain selectable variables under certain constraints so that the selected objective function reaches its optimum.

[0089] The optimization method is a method for solving optimization problems. In this embodiment, the method used is the loss function method.

[0090] The Loss function is a function that maps the values ​​of a random event or its related random variables to non-negative real numbers to represent the "risk" or "loss" of that random event. Its function expression is as follows:

[0091]

[0092] In the above function, e i-s Let m(i) be the loss function, and m(i) be the logging value at depth i. s (i) represents the weighted average physical parameter value, where i is the measurement depth and D is the bottom depth of the logging curve to be processed. The error between the weighted average physical parameter value and the actual logging value is minimized, i.e., e i-s The optimal solution is obtained when the minimum value is reached.

[0093] In one exemplary embodiment, after calculating the physical parameters corresponding to each sub-layer, a high-resolution logging curve is obtained by processing the physical parameters of each sub-layer. In this embodiment, after obtaining the physical parameters of each sub-layer, resampling is performed to obtain a high-resolution logging curve, i.e., setting an appropriate 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 data acquisition points. Typically, the sampling rate of the logging instrument is 8 points per meter, meaning the sampling interval is 0.125m. Since the formation thickness is assumed to be the size of the sampling interval when obtaining the weighting coefficients, the highest resolution achievable after resampling is the size of the sampling interval. If other high-resolution logging data is available to determine more accurate formation boundaries for obtaining the weighting coefficients, the resolution of the conventional logging curve can be improved to match that of the high-resolution logging data.

[0094] The beneficial effects of this invention lie in addressing the limitations of current methods for improving the vertical resolution of conventional well logging curves. It proposes a theoretical scheme to divide the formation within the detection range of the well logging instrument into several smaller layers and utilize a well logging volume model to improve the resolution of conventional well logging curves. Based on this, the well logging volume model is abstracted into a mathematical problem, a mathematical model is established, weighting coefficients are determined, and constraints are set. The physical parameters of each smaller layer are derived using an optimization objective function. Furthermore, high-resolution well logging curves are obtained through resampling, forming a method and apparatus for improving the vertical resolution of conventional well logging curves. The method for improving the vertical resolution of well logging curves proposed in this application does not require other high-resolution curves as auxiliary tools, and can provide higher vertical resolution, providing technical support for fine reservoir evaluation and sweet spot selection.

[0095] This disclosure also provides an apparatus for improving the vertical resolution of well logging curves, 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 well logging curves, and the processor is used to read and execute the program for improving the vertical resolution of well logging curves, and to execute the method described in any of the above embodiments.

[0096] This disclosure also provides a computer storage medium storing a program for improving the vertical resolution of well logging curves, the program being configured to execute the method described in any of the above embodiments during runtime.

[0097] Example 1

[0098] Using nuclear logging series as an example, this paper demonstrates a method to improve the vertical resolution of conventional logging curves, as shown in the following process:

[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 on the center of the transmitter and receiver, and the vertical resolution value is determined by the diameter.

[0101] Well logging values ​​are recorded values ​​within the detection range of a well logging instrument. These recorded values ​​are typically designated as properties at a specific depth point, which is the measurement point and usually the center of the instrument's detection range. Formation deposits are usually layered. If the measured formation is thick enough, far exceeding the detection range of the well logging instrument, then the recorded value at the measurement point, i.e., the well logging value, is consistent with the true value of the formation. However, if the formation is thin, with a thickness less than the detection range of the well logging instrument, then the recorded value at the measurement point, i.e., the well logging value, includes the logging responses of adjacent layers and cannot represent the true value of that layer.

[0102] The well logging volumetric model assumes that the formation within the detection range consists of several sub-layers. The volume fraction of each sub-layer is multiplied by its corresponding physical parameter, and then a weighted average is taken to obtain the well logging value. The physical parameter of each sub-layer refers to the actual value of each sub-layer.

[0103] Since the logging values ​​of a thin layer are not the actual physical parameter values ​​of that layer, but rather a weighted average of the actual physical parameter values ​​of that layer and the surrounding strata above and below, the expression for the logging volume model is obtained as follows:

[0104]

[0105] In the formula, m(i) is the logging value at depth i, i is the measurement depth, z is the depth of the sublayer, p(z) is the physical parameter of the sublayer, w is the weighting coefficient of the sublayer at depth z, and VR l This refers to the vertical resolution.

[0106] Considering that formations are usually layered media, discretizing equation (1) yields the expression for the discretized well logging volume model:

[0107]

[0108] In the formula, m(i) is the logging value at a measurement depth of i; z is the depth of the sub-layer; p(z) is the physical parameter of the sub-layer; VR l is the instrument's longitudinal resolution; n is the number of sub-layers; w(iz,v) is the weight coefficient of the sub-layer. For example... Figure 4 The illustrated volumetric model divides the formation within the logging instrument's detection range into a certain number of sub-layers of a certain thickness, with the top and bottom surfaces of each sub-layer parallel to each other. This certain number and thickness is related to the instrument's longitudinal resolution. If the instrument's longitudinal resolution is 50 cm, its detection range is relatively large. Typically, the sampling interval of the logging data is used to delineate the boundaries of the sub-layers. With a sampling interval of 12.5 cm, the formation can be divided into four sub-layers, each with a thickness of 12.5 cm, and there are four sub-layers in total. If the instrument's longitudinal resolution is very high, such as 0.5 cm for formation microresistivity scanning imaging logging, then even thinner and more numerous sub-layers can be identified.

[0109] Step 2. Determine the weighting coefficients;

[0110] Determine the weighting coefficients in the well logging volumetric model. These weighting coefficients are related to the relative volumes of the sub-layers within the detection range. Assuming the signal emitted by the instrument is uniform and the received signal intensity is the same within each sub-layer volume, the weighting coefficients are equal to the relative volumes of the sub-layers within the detection range. The relative volume is the ratio of the volume of each sub-layer to the total volume of the detection range. For example, if a density logging instrument has a vertical resolution of 50 cm and the sampling interval of conventional logging data is 12.5 cm, the formation within the instrument's detection range can be divided into four equal sub-layers based on thickness. Figure 4 As shown, this is equivalent to a 4-fold increase in resolution, reaching 12.5 cm. The weighting coefficients, determined based on the relative volumes of the four sub-layers, are 0.156, 0.344, 0.344, and 0.156 from top to bottom.

[0111] Step 3. Set constraints;

[0112] Setting constraints involves setting certain conditions to quickly and accurately obtain the actual formation parameters. Constraints include equality constraints and range constraints.

[0113] The equality constraint states that within a detection range, the relative volume of all layers multiplied by the physical parameter of that layer equals the measured value, as shown in the specific formula:

[0114]

[0115] In the formula, m is the logging value; m i v represents the physical parameters of the i-th layer; i Let be the relative volume of the i-th layer.

[0116] The range constraint is to limit the curve to be processed within a certain range to quickly obtain the real formation parameters. Specific methods include numerical range constraints of common logging curves, range constraints of the curve to be processed calculated based on other logging curves, and dynamic range constraints derived from volumetric models.

[0117] The common logging curve numerical range constraint is based on the common range of logging curve values, for example, the range of density curve is 2-3 g / cm³. 3 The natural gamma curve ranges from 0 to 150 GAPI.

[0118] The range constraint of the curve to be processed calculated based on other well logging curves is based on the range of the curve to be processed calculated by the well logging interpretation model. Taking the density curve calculated from the neutron logging curve as an example, the density will be different due to the different lithology and fluid properties in the pores, and this is used to set the density range. For the same lithology, the density of the gas is (0.2 g / cm³). 3 The density of the substance is less than that of oil (0.8 g / cm³). 3 The density of oil (which varies depending on its viscosity) is less than that of water (1 g / cm³). 3 (This varies depending on the water's salinity); the fluid properties within the pores are the same, and the sandstone density is 2.65 g / cm³. 3 The density is less than that of limestone (2.71 g / cm³). 3 The density is less than that of dolomite (2.87 g / cm³). 3 This allows for the comprehensive constraint of density measurements based on actual stratigraphic information. For example... Figure 5 The diagram 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 from neutron porosity when the formation model contains only limestone and water. The gray line represents the density curve calculated from neutron porosity when the formation model contains only sandstone and water. The black dotted-dash line represents the density curve calculated from neutron porosity when the formation model contains only sandstone and oil. The gray dotted line represents the density curve calculated from neutron porosity when the formation model contains only sandstone and gas. By comprehensively processing and interpreting logging data to gain a general understanding of the fluid properties and skeletal structure of the formation, the true density value can be constrained using various idealized density ranges.

[0119] The dynamic range constraint derived from the volume model requires that the measured value should lie 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] Where Up is the upper boundary value, Low is the lower boundary value, and m j Let m be the physical parameters of the j-th smallest layer. j-1 Let m be the physical parameter of the (j-1)th sublayer. j+1 Let be the physical parameter of the (j+1)th sublayer, ε be the compensation value, max(m) be the largest physical parameter among the upper boundary values ​​outside the peaks and troughs, and min(m) be the smallest physical parameter among the lower boundary values ​​outside the peaks and troughs.

[0124] Upper boundary: When a logging value reaches its maximum value, the actual value will only be greater than or equal to the current logging value; when a logging value reaches its minimum value, the maximum actual value will not exceed that minimum value. The lower boundary follows the same principle. Taking density as an example, the upper and lower boundaries of density logging values ​​can be derived according to formulas 5 and 6, used to constrain density logging values. Figure 6 As shown, the black line represents the density curve logging value, the gray line represents the upper boundary, and the gray dotted line represents the lower boundary.

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

[0126] The physical parameters of the sublayer can be derived by combining the determined weighting coefficients and constraints.

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

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

[0129]

[0130] In the formula, in the above function, e i-s Let m(i) be the loss function, and m(i) be the logging value at depth i. s (i) represents the weighted average physical parameter value, where i is the measurement depth and D is the bottom depth of the logging curve to be processed. The error between the weighted average physical parameter value and the actual logging value is minimized, i.e., e i_s The optimal solution is obtained when the minimum value is reached.

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

[0132] Obtaining high-resolution logging curves through resampling involves acquiring the true 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; typically, logging instruments use a sampling rate of 8 points per meter, meaning a sampling interval of 0.125m. Since the formation thickness is assumed to be the size of the sampling interval when obtaining the weighting coefficients, the highest resolution achievable after resampling is the size of the sampling interval. If other high-resolution logging data is available to determine more accurate formation boundaries for obtaining weighting coefficients, the resolution of the conventional logging curve can be improved to match that of the high-resolution logging data. Figure 7 As shown, the measured depths of the three sampling points are at A, B, and Z, respectively. Compared with conventional well logging curves, the vertical resolution has been significantly improved.

[0133] like Figure 8 The image shown is a high-resolution processing result of a conventional well logging curve according to an embodiment of the present invention, including:

[0134] The first line 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 track is the depth track, which indicates the distance between the measured well section (i.e., the target layer) and the wellhead.

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

[0137] The fourth line is the stratigraphic microresistivity scanning electrical imaging curve. The color represents the difference in lithology. The whiter the color, the more sandstone the lithology tends to be, and vice versa.

[0138] The fifth line 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 characterizes the changes in lithology and physical properties.

[0139] The black box in the image illustrates the application effect of improving the vertical resolution of conventional well logging curves. From Figure 8A comparison of the medium-to-high resolution GR and DEN curves with conventional curves, high-resolution logging instrument measurement curves, and electrical imaging images reveals that the resolution of the high-resolution GR and DEN curves is significantly improved compared to the conventional curves, and is basically consistent with the resolution of the deep lateral curves measured by high-resolution logging instruments. They also show good contrast with the areas of the electrical imaging images shown in the boxes. For some thin reservoirs (white bands in the electrical imaging images), the high-resolution GR and DEN curves all respond, confirming that the method for improving the vertical resolution of logging curves in this application is very effective.

[0140] It will be understood by those skilled in the art that all or some of the steps, systems, or apparatuses disclosed above, and their functional modules / units, can be implemented as software, firmware, hardware, or suitable combinations thereof. In hardware implementations, the division between functional modules / units mentioned above 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 collaboratively by several physical components. Some or all components may be implemented as software executed by a processor, such as a digital signal processor or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit (ASIC). Such software may be distributed on a computer-readable medium, which may include computer storage media (or non-transitory media) and communication media (or transient media). As is known to those skilled in the art, the term computer storage media 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 technologies, CD-ROM, digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and can be accessed by a computer. Furthermore, it is well known to those skilled in the art that communication media typically contain computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

Claims

1. A method for improving the vertical resolution of well logging curves, characterized in that, The method includes: Obtain the vertical resolution and sampling interval of the logging curve to be processed, and determine the detection range of the logging instrument corresponding to the logging curve; Based on the longitudinal resolution and the sampling interval, the strata within the detection range are divided into multiple layers of equal thickness; The relative volume corresponding to each sub-layer is calculated based on the pre-established well logging volume model; Based on the relative volume and the pre-set constraints, the physical parameters corresponding to each sub-layer are calculated using an optimization objective function; The processed logging curves are calculated based on the physical parameters of each sub-layer; The well logging volume model is as follows: In the formula, m(i) is the logging value at a measurement depth of i, i is the measurement depth, z is the depth of the sub-layer, p(z) is the physical parameter of the sub-layer, and VR l denoted as the vertical resolution of the logging instrument, n is the number of sublayers, w(iZ,v) is the weighting coefficient of the sublayer at depth z, and v is the relative volume of the sublayer at depth z. The relative volume is the ratio of the volume of each sublayer to the total volume of the detection range; The pre-set constraints include equality constraints; The equation constraint is: within the detection range of the logging instrument, the sum of the products of the relative volumes of all sub-layers and their corresponding physical parameters equals the logging value.

2. The method for improving the vertical resolution of well logging curves according to claim 1, characterized in that, When the logging instrument is a nuclear logging instrument, determining the detection range of the logging instrument corresponding to the logging curve includes: When the logging instrument is a nuclear logging instrument, determine the center positions of the transmitter and receiver in the logging instrument that correspond to the logging curve; The detection range of the logging instrument is defined by a sphere with the central position as the center point and the vertical resolution value as the diameter.

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

4. The method for improving the vertical resolution of well logging curves according to claim 1, characterized in that, The pre-set constraints also include range constraints.

5. The method for improving the vertical resolution of well logging curves according to claim 4, characterized in that, The range constraints include: numerical range constraints of logging curves and dynamic range constraints.

6. The method for improving the vertical resolution of well logging curves according to claim 5, characterized in that, The dynamic range constraint is determined by upper and lower boundary values ​​based on the well logging volume model. Wherein, the upper boundary value is: The lower boundary value is: Where Up is the upper boundary value, Low is the lower boundary value, and m j Let m be the physical parameters of the j-th smallest layer. j-1 Let m be the physical parameter of the (j-1)th sublayer. j+1 Let be the physical parameter of the (j+1)th sublayer, ε be the compensation value, max(m) be the largest physical parameter among the upper boundary values ​​outside the peaks and troughs, and min(m) be the smallest physical parameter among the lower boundary values ​​outside the peaks and troughs.

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

8. A device for improving the vertical resolution of well logging curves, characterized in that, The apparatus includes a memory and a processor; the memory is used to store a program for improving the vertical resolution of well logging curves, and the processor is used to read and execute the program for improving the vertical resolution of well logging curves, and to execute the method according to any one of claims 1-7.

9. A computer storage medium, characterized in that, The storage medium stores a program for improving the vertical resolution of well logging curves, the program being configured to execute the method described in any one of claims 1-7 during runtime.

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