Reservoir evaluation parameter determination method and device, storage medium and electronic equipment

Abstract

The invention relates to the field of oil-gas exploration, and discloses a reservoir evaluation parameter determination method and device, a storage medium and electronic equipment. The method comprises the steps that according to the while-drilling gamma of the horizontal well, the logging element content and a preset logging curve, a passing trajectory tracking operation is executed, so that a passing horizon and a horizontal well stratum model are determined; determining a horizontal well reference curve according to a preset logging curve, the element curve and the horizontal well stratum model; according to the reference curve of the horizontal well, the preferable actual measurement well logging curve of the pilot hole well corresponding to the horizontal well passing horizon and the element logging data of the horizontal well, the actual measurement well logging curve of the horizontal well and the logging element content of the horizontal well are corrected, and the corrected well logging curve and the corrected logging element content are obtained; generating an evaluation parameter determination model according to the corrected logging curve and logging element content and preset modeling data; and determining evaluation parameters of the horizontal well reservoir according to the evaluation parameter determination model. And the accuracy of determining the reservoir evaluation parameters is higher.

Description

Technical Field

[0001] The present application relates to the technical field of oil and gas exploration, and in particular to a method, device, storage medium and electronic device for determining reservoir evaluation parameters. Background Art

[0002] The purpose of the background technology description provided here is to give an overall background of the present application. The statements in this section merely provide background related to the present application and do not necessarily constitute prior art.

[0003] At present, for reservoirs with complex mineral components, lithologic scanning logging and element capture spectrum logging are the most effective logging evaluation methods. Such logging data have been used at home and abroad to evaluate the complex mineral components and their contents in unconventional reservoirs. However, due to the high cost, most evaluation wells and development wells do not have such new logging data in addition to conventional logging data.

[0004] Conventional logging methods are used to calculate mineral components. Usually, a univariate or multivariate linear regression model is established based on core experimental data. The accuracy of the model often does not meet production needs. As early as the 1980s, optimization methods were used to quantitatively evaluate the mineral components of complex lithologies such as volcanic rocks, carbonate rocks, and complex clastic rocks. This type of method can make full use of various logging response information, establish a response equation group based on the logging response values ​​of different mineral skeletons, and solve the formation mineral components through optimization methods. However, for shale reservoirs, the mineral components calculated directly using optimization methods are often less accurate, especially the total organic carbon content, carbonate rock mineral content, and iron mineral content are greatly different from core analysis. This is because shale reservoirs, especially terrestrial and marine-continental transitional reservoirs, have variable sedimentary environments and lithologies, and the mineral logging response is greatly different from conventional sandstone and mudstone. For shale reservoirs, a method for calculating clay content, organic matter content and pyrite content using well logging curves was published in a Chinese patent document (publication number CN106600436B, application number CN201611062597.8), and then quartz, feldspar, calcite, dolomite content and porosity were calculated using four curves to establish an optimization model. The patent document does not explain how to specifically calculate the volume content of clay, organic matter, etc.; in addition, the accuracy of the calculation results of the response equation set established using four curves and four mineral contents is questionable.

[0005] Some scholars also calculated the total organic carbon (TOC) and clay content by the △lgR method and deuranium gamma, and corrected the three-porosity curve, and then established a response equation group through the corrected three-porosity curve, and solved the carbonate minerals and felsic minerals, and then divided the carbonate minerals into calcite and dolomite according to the density curve, and divided the felsic minerals into feldspar and quartz according to the uranium element curve. However, this method ignores the effect of pore fluid saturation on the logging curve. In addition, this method only uses the three-porosity curve to establish the response equation group, and ignores the information contained in other logging curves.

[0006] In addition to using logging data to evaluate reservoir parameters, some scholars have used logging elements to establish multivariate fitting models for evaluating reservoir parameters. However, this method has three defects: it requires a large number of core experiment annotations and requires the experimental data to match the curve; the multivariate fitting model of element logging lacks physical meaning and is usually less accurate for complex reservoirs; element logging is easily affected by drilling conditions, and it is difficult to accurately evaluate reservoir porosity and gas content using only element content.

[0007] For horizontal wells, there are often few logging data, usually only gamma, sonic and resistivity logging data. The simplified logging model of the pilot well is used or reservoir parameter evaluation is carried out only based on element logging, resulting in low evaluation accuracy. The evaluation results are very different from the actual conditions of the pilot well and the formation, which causes great interference to the subsequent sweet spot evaluation and fracturing section selection and design. Summary of the invention

[0008] In view of the above problems, this application proposes a method, device, storage medium and electronic device for determining reservoir evaluation parameters. This application establishes a method system for determining horizontal well reservoir parameters by joint inversion of logging and well data, specifically:

[0009] First, the horizontal well trajectory is tracked based on the gamma while drilling, logging element combination, logging data, lithology, and seismic well profile to determine the traversing layer and trajectory;

[0010] Then, according to the horizontal well data, three types of methods are used to establish the reservoir parameter evaluation model: 1) When only gamma while drilling is available, the interpretation results of the pilot well are forward modeled according to the trajectory to obtain the reservoir parameters; 2) When gamma while drilling and element logging are available, the preferred logging elements are corrected, and the reservoir porosity obtained by forward modeling is used as a constraint to establish a mineral-element conversion model, and the optimization method is used to solve the mineral components; 3) When gamma while drilling, element logging and logging data are available, a linear equation group is jointly established based on the logging curve and the corrected elements, and the constrained optimization method is used to invert the mineral components and reservoir porosity;

[0011] Finally, based on the calculated mineral composition and reservoir porosity, parameters such as rock brittleness, saturation, and gas content can be further calculated, and reservoir sweet spots can be divided.

[0012] The reservoir parameters determined by the method disclosed in this application are more accurate and more consistent with actual formation conditions.

[0013] In a first aspect of the present application, a method for determining reservoir evaluation parameters is provided, the method comprising:

[0014] Perform trajectory tracking operations on the horizontal well according to the horizontal well's gamma while drilling, logging element content and preset logging curves to determine the horizontal well's penetration layer and horizontal well formation model;

[0015] Determine a horizontal well reference curve according to the preset well logging curve, the element curve of the pilot well corresponding to the stratum through which the horizontal well passes, and the horizontal well formation model;

[0016] According to the reference curve of the horizontal well, the preferred measured logging curve of the pilot well corresponding to the stratum through which the horizontal well passes, and the element logging data of the horizontal well, the measured logging curve of the horizontal well and the logging element content of the horizontal well are corrected to obtain a corrected logging curve and a corrected logging element content;

[0017] Generate an evaluation parameter determination model according to the corrected logging curve, the corrected logging element content and preset modeling data;

[0018] The evaluation parameters of the horizontal well reservoir are determined according to the evaluation parameter determination model.

[0019] Further, the determining of the horizontal well reference curve according to the preset logging curve, the element curve of the pilot well corresponding to the stratum through which the horizontal well passes, and the horizontal well formation model comprises:

[0020] A forward modeling operation is performed according to the preset logging curve, the element curve and the horizontal well formation model to determine the horizontal well reference curve.

[0021] Furthermore, the preset modeling data includes:

[0022] Core analysis data and element content of pilot well, conventional logging data, preferably mineral-sensitive logging curves and element content.

[0023] Further, after determining the evaluation parameters of the horizontal well reservoir according to the response model, the method further includes:

[0024] Reservoir sweet spots are divided according to the evaluation parameters.

[0025] Furthermore, the step of generating an evaluation parameter determination model according to the corrected well logging curve, the corrected logging element content and preset modeling data includes:

[0026] generating a rock physical response model according to the corrected well logging curve;

[0027] Generate a mineral-element conversion model according to the corrected logging element content;

[0028] The evaluation parameter determination model is generated according to the rock physical response model, the mineral-element conversion model and the preset modeling data.

[0029] Furthermore, the preset logging curve includes:

[0030] The horizontal well passes through a formation corresponding to the well logging curve of the pilot well or the well logging curve of the vertical well adjacent to the horizontal well.

[0031] Furthermore, the evaluation parameter determination model includes:

[0032]

[0033] Where B = [D T G T ] T , A=[C T (F 0) T ] T , D=[GR CNL DEN AC...] T ; G=[CaSi ... K] T ;

[0034]

[0035]

[0036] V=[V1 V2 ... V i ... V m ]

[0037] Among them, i, m, and f represent different types of minerals. Ca i represents the content of Ca element in the i-th mineral, φ represents the porosity, and V represents the volume content of different types of minerals.

[0038] A second aspect of the present application provides a reservoir evaluation parameter determination device, the device comprising:

[0039] The first determination module is used to perform a trajectory tracking operation on the horizontal well according to the gamma while drilling, the logging element content and the preset logging curve of the horizontal well, so as to determine the horizontal well penetration layer and the horizontal well formation model;

[0040] A second determination module is used to determine a horizontal well reference curve according to the preset logging curve, an element curve of a pilot well corresponding to the stratum through which the horizontal well passes, and the horizontal well formation model;

[0041] A correction module, for correcting the measured logging curve of the horizontal well and the logging element content of the horizontal well according to the reference curve of the horizontal well, the preferred measured logging curve of the pilot well corresponding to the stratum through which the horizontal well passes, and the element logging data of the horizontal well, to obtain a corrected logging curve and a corrected logging element content;

[0042] A generation module, used for generating an evaluation parameter determination model according to the corrected logging curve, the corrected logging element content and preset modeling data;

[0043] An evaluation parameter determination module is used to determine the evaluation parameters of the horizontal well reservoir according to the evaluation parameter determination model.

[0044] According to a third aspect of the present application, a computer-readable storage medium is provided, wherein a computer program stored in the computer-readable storage medium can be executed by one or more processors to implement the steps of the method described above.

[0045] The fourth aspect of the present application provides an electronic device, including a memory and one or more processors, wherein the memory stores a computer program, the memory and the one or more processors are communicatively connected to each other, and when the computer program is executed by the one or more processors, the steps of the method described above are implemented.

[0046] Compared with the prior art, the technical solution of the present application has the following advantages or beneficial effects:

[0047] The present application discloses a method for determining horizontal well reservoir parameters by joint inversion of logging and mud logging data. First, the horizontal well trajectory is tracked according to the horizontal well gamma while drilling, mud logging element combination and logging curve to determine the horizontal well penetration layer and formation model; then, the typical mud logging element content is corrected with reference to the elements obtained by element logging or element logging and lithology scanning logging of the pilot well; a multi-source multi-parameter linear equation group is established according to the gamma while drilling, logging and element logging, and the mineral composition and reservoir porosity are inverted by the constrained optimization method; the formation brittleness, saturation and gas content and other parameters are calculated according to the mineral composition and reservoir porosity. The information from multiple sources in the horizontal well is comprehensively considered, a response equation group is established, and the reservoir parameters are obtained by joint inversion, which is more accurate than the reservoir parameter solution using conventional logging curves alone, and the calculation accuracy of horizontal well reservoir parameters is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are merely embodiments of the present application. For ordinary technicians in the relevant field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0049] It should also be noted that, for the convenience of description, only the parts related to the present disclosure are shown in the drawings. The drawings constituting a part of the specification of this application are used to provide a further understanding of the present application. The illustrative embodiments and their descriptions in this application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0050] Figure 1 A flow chart of a method for determining reservoir evaluation parameters provided in an embodiment of the present application;

[0051] Figure 2 A schematic diagram of a typical well horizontal section travel trajectory and formation model provided in an embodiment of the present application;

[0052] Figure 3 A schematic diagram of a pilot well logging element provided in an embodiment of the present application;

[0053] Figure 4 A schematic diagram of a horizontal well logging element provided in an embodiment of the present application;

[0054] Figure 5 A schematic diagram of an element correction coefficient provided in an embodiment of the present application;

[0055] Figure 6 A schematic diagram of calculation results of mineral composition, total organic carbon, reservoir porosity, and gas content of a horizontal well provided in an embodiment of the present application;

[0056] Figure 7 A schematic diagram of the structure of a reservoir evaluation parameter determination device provided in an embodiment of the present application;

[0057] Figure 8 A connection block diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0058] The following will describe the implementation methods of the present application in detail with reference to the accompanying drawings and embodiments, so that the implementation process of how the present application applies technical means to solve technical problems and achieve corresponding technical effects can be fully understood and implemented accordingly. The embodiments of the present application and the various features in the embodiments can be combined with each other without conflict, and the technical solutions formed are all within the protection scope of the present application.

[0059] It should be clear that the embodiments described below are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by technicians in the relevant field without making creative work are within the scope of protection of the present application.

[0060] Embodiment 1

[0061] This embodiment provides a method for determining reservoir evaluation parameters. Figure 1 A flow chart of a method for determining reservoir evaluation parameters provided in an embodiment of the present application is shown in FIG. Figure 1 As shown, the method disclosed in this embodiment includes the following steps:

[0062] Step 110: Perform a trajectory tracking operation on the horizontal well according to the while-drilling gamma, logging element content, and logging curve of the horizontal well to determine the horizontal well's penetration layer and the horizontal well's formation model.

[0063] Optionally, the trajectory of the horizontal well is tracked based on the gamma while drilling, logging element content and logging curves of the horizontal well to determine the horizontal well penetration layer and formation model.

[0064] Step 120: determining a horizontal well reference curve according to the preset logging curve, the element curve of the pilot well corresponding to the stratum through which the horizontal well passes, and the horizontal well formation model.

[0065] In some embodiments, determining the horizontal well reference curve according to the preset logging curve, the element curve of the pilot well corresponding to the stratum through which the horizontal well passes, and the horizontal well formation model includes:

[0066] A forward modeling operation is performed according to the preset logging curve, the element curve and the horizontal well formation model to determine the horizontal well reference curve.

[0067] Optionally, a horizontal well reference curve is obtained by forward modeling based on conventional logging curves of the pilot well, element curves and the horizontal well formation model.

[0068] Step 130: According to the reference curve of the horizontal well, the measured logging curve of the pilot well corresponding to the horizontal well, and the element logging data of the horizontal well, the measured logging curve of the horizontal well and the logging element content of the horizontal well are corrected to obtain the corrected logging curve and the corrected logging element content.

[0069] Optionally, the logging curve and the logging element content are corrected based on the reference curve of the horizontal well and the preferred measured logging curve and element logging data.

[0070] Step 140: Generate an evaluation parameter determination model according to the corrected logging curve, the corrected logging element content and preset modeling data.

[0071] In some embodiments, the preset modeling data includes:

[0072] Core analysis data and element content of pilot well, conventional logging data, preferably mineral-sensitive logging curves and element content.

[0073] In some embodiments, generating an evaluation parameter determination model according to the corrected well logging curve, the corrected logging element content and preset modeling data includes:

[0074] generating a rock physical response model according to the corrected well logging curve;

[0075] Generate a mineral-element conversion model according to the corrected logging element content;

[0076] The evaluation parameter determination model is generated according to the rock physical response model, the mineral-element conversion model and the preset modeling data.

[0077] Optionally, a joint rock physics response model is established based on the corrected while-drilling gamma, logging curves, and logging elements, and an evaluation parameter determination model (also referred to as a response equation group) is established with the core analysis data of the pilot well as a constraint.

[0078] Step 150: Determine the evaluation parameters of the horizontal well reservoir according to the evaluation parameter determination model.

[0079] In some embodiments, the evaluation parameters include:

[0080] Mineral composition, reservoir porosity.

[0081] Optionally, a model is determined based on the evaluation parameters and solved using an optimization algorithm to obtain the mineral composition and reservoir porosity of the reservoir.

[0082] In some embodiments, after determining the evaluation parameters of the horizontal well reservoir according to the response model, the method further includes:

[0083] Reservoir sweet spots are divided according to the evaluation parameters.

[0084] Furthermore, after obtaining the mineral composition and reservoir porosity, geological engineering parameters such as formation saturation, gas content, and rock brittleness are calculated based on the mineral composition and reservoir porosity, and reservoir sweet spot division is carried out to provide a basis for the subsequent fracturing scheme design.

[0085] The reservoir parameters determined by the method disclosed in the present application are more accurate and more in line with the actual formation conditions; further, according to the calculated mineral components and reservoir porosity, rock brittleness, saturation, gas content and other parameters can be further calculated, and reservoir sweet spot division can also be carried out. Specifically, the following steps are included: according to the horizontal well's gamma while drilling, logging element content and preset logging curve, the horizontal well is tracked to determine the horizontal well's traversal layer and the horizontal well's formation model; according to the preset logging curve, element curve and the horizontal well's formation model, the horizontal well reference curve is determined; according to the horizontal well reference curve and the preferred measured logging curve of the pilot well corresponding to the horizontal well's traversal layer and the element logging data of the horizontal well, the measured logging curve of the horizontal well and the logging element content of the horizontal well are corrected to obtain the corrected logging curve and the corrected logging element content; according to the corrected logging curve, the corrected logging element content and the preset modeling data, an evaluation parameter determination model is generated; according to the evaluation parameter determination model, the evaluation parameters of the horizontal well reservoir are determined. By comprehensively considering information from various sources in horizontal wells, a group of response equations is established, and reservoir parameters are obtained through joint inversion. This method has higher accuracy than simply using conventional logging curves to solve reservoir parameters, and improves the calculation accuracy of horizontal well reservoir parameters.

[0086] Embodiment 2

[0087] This embodiment is a specific example disclosed in the present application. In this embodiment, a horizontal well of the Longmaxi Formation is taken as an example, and the reservoir of the horizontal well is evaluated and analyzed by the reservoir evaluation parameter determination method disclosed in the present application.

[0088] The first step is to perform trajectory tracking operations on the horizontal well according to the horizontal well's gamma while drilling, logging element content and logging curves to determine the horizontal well's penetration layer and horizontal well formation model.

[0089] Optionally, the trajectory of the horizontal well is tracked based on the gamma while drilling, logging element content and logging curves of the horizontal well to determine the horizontal well penetration layer and formation model.

[0090] In some embodiments, the preset logging curve includes:

[0091] The horizontal well corresponds to the well logging curve of the pilot well or the well logging curve of the vertical well adjacent to the horizontal well.

[0092] For example, in this step, the formation model is established using the logging curves of the pilot well corresponding to the horizontal well or the vertical well adjacent to the horizontal well, and typical layer characteristics are established based on the logging curves and elements of different formations in the pilot well. For example, the formation characteristics are established using the gamma, acoustic wave, density, neutron and resistivity curves and element content of the pilot well or the adjacent vertical well, such as the layer with high ash content is characterized by low gamma, low acoustic wave, high density, low neutron, high resistivity, high calcium, low silicon, and low aluminum; the layer with high clay content is characterized by high gamma, high acoustic wave, medium-low density, high neutron, low resistivity, and high aluminum. Furthermore, according to the corresponding characteristics of the horizontal well logging curve and elements, the formation model is adjusted and the trajectory tracking is carried out to determine the traversing layer and the formation model.

[0093] Optionally, the horizontal section trajectory and stratum model can be referenced Figure 2 , Figure 2 A schematic diagram of a typical well horizontal section travel trajectory and formation model provided in an embodiment of the present application.

[0094] The second step is to determine the horizontal well reference curve according to the preset logging curve, the element curve of the pilot well corresponding to the stratum through which the horizontal well passes, and the horizontal well formation model.

[0095] In some embodiments, determining the horizontal well reference curve according to the preset logging curve, the element curve of the pilot well corresponding to the stratum through which the horizontal well passes, and the horizontal well formation model includes:

[0096] A forward modeling operation is performed according to the preset logging curve, the element curve and the horizontal well formation model to determine the horizontal well reference curve.

[0097] Optionally, a horizontal well reference curve is obtained by forward modeling based on the well logging curve, element curve and horizontal well formation model of the pilot well corresponding to the horizontal well.

[0098] For example, in this step, a horizontal well reference curve is obtained by forward modeling based on conventional well logging curves of the pilot well, element curves and the horizontal well formation model.

[0099] Optionally, horizontal well reference curves can be referenced Figure 2 , Figure 2GR_REF in FIG. 1 is the horizontal well gamma curve forward modeled according to the formation model in this embodiment.

[0100] The third step is to correct the measured logging curve of the horizontal well and the logging element content of the horizontal well according to the reference curve of the horizontal well, the preferred measured logging curve of the pilot well corresponding to the horizontal well, and the element logging data of the horizontal well to obtain the corrected logging curve and the corrected logging element content.

[0101] Optionally, the logging curve and the logging element content are corrected based on the reference curve of the horizontal well and the preferred measured logging curve and element logging data.

[0102] Among them, the pilot well logging elements can be referred to Figure 3 , Figure 3 A schematic diagram of a pilot well logging element provided in an embodiment of the present application (in Figure 3 In each layer, the elements from top to bottom are: K, FE, MG, CA, AL, SI); horizontal well logging elements can refer to Figure 4 , Figure 4 A schematic diagram of a horizontal well logging element provided in an embodiment of the present application (in Figure 4 In each layer, the elements from top to bottom are: K, FE, MG, CA, AL, SI); the correction coefficient can be referred to Figure 5 , Figure 5 A schematic diagram of an element correction coefficient provided in an embodiment of the present application.

[0103] For example, in this step, based on the core analysis data, elements and conventional logging data of the pilot well, mineral-sensitive logging curves and elements are selected as modeling data. For the pilot well, the relationship between core mineral composition, reservoir porosity and logging curves and elements is studied, and natural gamma (GR), acoustic wave (AC), density (DEN), neutron logging curve (CNL) and calcium (CA), magnesium (MG), silicon (SI), aluminum (AL), iron (FE), potassium (K) are selected as the main mineral control parameters to participate in subsequent modeling.

[0104] In some embodiments, the preset modeling data includes:

[0105] Core analysis data and element content of pilot well, conventional logging data, preferably mineral-sensitive logging curves and element content.

[0106] Furthermore, the measured logging curve of the horizontal well and the logging element content of the horizontal well are corrected according to the reference curve of the horizontal well, the measured logging curve of the preferred pilot well, and the element logging data of the horizontal well. Usually, due to different measurement methods, there are certain differences between the logging data of the horizontal well section and the logging data of the pilot well section at the same layer (see Table 1 for details), and there are also certain differences between the logging elements and the logging elements (see Table 2 for details). Therefore, it is necessary to carry out curve and element content correction before modeling. The horizontal section correction coefficient of the example well can be seen in Table 3.

[0107] Table 1 Comparison of conventional curves of pilot wells and horizontal wells

[0108]

[0109] Table 2 Comparison of pilot well elements and horizontal well elements

[0110]

[0111]

[0112] Table 3 Correction coefficients of horizontal well logging curves and logging element contents

[0113] Logging / Elements GR AC DEN CNL SI AL CA MG FE K Correction factor 1 +20us / m 1 1.15 0.5 0.6 1 1.6 0.9 1

[0114] The fourth step is to generate an evaluation parameter determination model based on the corrected logging curve, the corrected logging element content and preset modeling data.

[0115] In some embodiments, generating an evaluation parameter determination model according to the corrected well logging curve, the corrected logging element content and preset modeling data includes:

[0116] generating a rock physical response model according to the corrected well logging curve;

[0117] Generate a mineral-element conversion model according to the corrected logging element content;

[0118] The evaluation parameter determination model is generated according to the rock physical response model, the mineral-element conversion model and the preset modeling data.

[0119] Optionally, a joint rock physics response model is established based on the corrected while-drilling gamma, logging curves, and logging elements, and an evaluation parameter determination model (also referred to as a response equation group) is established with the core analysis data of the pilot well as a constraint.

[0120] For example, first, the rock physical response equation F1 is established based on the corrected logging curve:

[0121]

[0122] Right now:

[0123] Where φ is the porosity,

[0124] D=[GR CNL DEN AC...] T

[0125]

[0126] V=[V1 V2 ... V i ... V m ]

[0127] Among them, 1, 2, i, m, and f represent different types of minerals, V1, V2, V i 、V m Indicates the volume of different types of minerals.

[0128] Then, the mineral-element conversion equation F2 is established according to the corrected logging element content:

[0129]

[0130] Right now:

[0131] in,

[0132] G=[CaSi...K] T

[0133]

[0134] Finally, by combining the above two equations F1 and F2, we can get the final response equation:

[0135]

[0136] Can be simplified to:

[0137] Among them, Ca1, Ca2…Ca m Indicates the content of Ca in different mineral categories, Si1, Si2…Si m Indicates the content of Si in different mineral categories (and so on).

[0138] B=[D T G T ] T ,A=[C T (F 0) T ] T ,

[0139] Taking the core analysis data of the pilot well as the constraint, the constraint conditions can be expressed by the following set of constraint equations:

[0140]

[0141] Among them, P and Q are the coefficient matrices of inequality constraints; L and U are the upper and lower bounds of the vector to be solved.

[0142] As an example, the matrix parameters are explained below using a stratum containing four minerals: illite, quartz, calcite, and dolomite (see Table 4):

[0143] Table 4 Contents of illite, quartz, calcite and dolomite in the strata

[0144]

[0145]

[0146] It can be understood that, based on the above content table, the aforementioned parameter matrices C and F can be expressed as:

[0147]

[0148]

[0149] It should be noted that the inequality constraints and upper and lower bounds can be determined based on regional information or experimental analysis data, and can be determined based on current actual needs.

[0150] Step 5: Determine the evaluation parameters of the horizontal well reservoir according to the evaluation parameter determination model.

[0151] In some embodiments, the evaluation parameters include:

[0152] Mineral composition, reservoir porosity.

[0153] Optionally, an optimization algorithm is used to solve the set of constraint equations to obtain the mineral composition and reservoir porosity.

[0154] For example, in this step, the optimization algorithm is used to solve the constraint equations to obtain the mineral composition and reservoir porosity. The constraint equations established in the previous step can be transformed into a convex optimization problem, namely:

[0155]

[0156]

[0157] As an example, the interior point convex optimization algorithm can be used to solve the above equation, and the optimal solution of the equation, namely the mineral composition and porosity, can be obtained.

[0158] In some embodiments, after determining the evaluation parameters of the horizontal well reservoir according to the response model, the method further includes:

[0159] Reservoir sweet spots are divided according to the evaluation parameters.

[0160] For example, in this step, geological engineering parameters such as formation saturation, gas content, and rock brittleness are calculated based on mineral composition and reservoir porosity, and reservoir sweet spots are divided to provide a basis for the design of fracturing schemes.

[0161] Optionally, the formation saturation of the example well can be determined by the following formula:

[0162] S w =K1-K2*TOC-K3*V clay

[0163] Among them, K1, K2, and K3 are all constants.

[0164] Preferably, the formation saturation is determined by the following formula:

[0165] S w =57-4.5*TOC-22.2*V clay

[0166] Alternatively, the adsorbed gas content can be determined by the following formula:

[0167] G ads =K4*TOC+K5

[0168] Among them, K4 and K5 are constants.

[0169] Preferably, the adsorbed gas content is determined by the following formula:

[0170] G ads =0.378*TOC+0.4

[0171] Alternatively, the free gas content can be determined by the following formula:

[0172]

[0173] Alternatively, rock brittleness can be determined by the following formula:

[0174]

[0175] In this embodiment, the calculation results of the reservoir parameters of the well in this embodiment can be seen Figure 6 , Figure 6A schematic diagram of the calculation results of mineral composition, total organic carbon, reservoir porosity and gas content of a horizontal well provided in an embodiment of the present application.

[0176] Furthermore, after obtaining the mineral composition and reservoir porosity, geological engineering parameters such as formation saturation, gas content, and rock brittleness are calculated based on the mineral composition and reservoir porosity, and reservoir sweet spot division is carried out to provide a basis for the subsequent fracturing scheme design.

[0177] The reservoir parameters determined by the method disclosed in the present application are more accurate and more in line with the actual formation conditions; further, according to the calculated mineral components and reservoir porosity, rock brittleness, saturation, gas content and other parameters can be further calculated, and reservoir sweet spot division can also be carried out. Specifically, the following steps are included: according to the horizontal well's gamma while drilling, logging element content and preset logging curve, the horizontal well is tracked to determine the horizontal well's traversal layer and the horizontal well's formation model; according to the preset logging curve, element curve and the horizontal well's formation model, the horizontal well reference curve is determined; according to the horizontal well reference curve and the preferred measured logging curve of the pilot well corresponding to the horizontal well's traversal layer and the element logging data of the horizontal well, the measured logging curve of the horizontal well and the logging element content of the horizontal well are corrected to obtain the corrected logging curve and the corrected logging element content; according to the corrected logging curve, the corrected logging element content and the preset modeling data, an evaluation parameter determination model is generated; according to the evaluation parameter determination model, the evaluation parameters of the horizontal well reservoir are determined. By comprehensively considering information from various sources in horizontal wells, a group of response equations is established, and reservoir parameters are obtained through joint inversion. This method has higher accuracy than simply using conventional logging curves to solve reservoir parameters, and improves the calculation accuracy of horizontal well reservoir parameters.

[0178] Embodiment 3

[0179] This embodiment provides a reservoir evaluation parameter determination device. This device embodiment can be used to execute the method embodiment of this application. For details not disclosed in this device embodiment, please refer to the method embodiment of this application. Figure 7 A schematic diagram of a reservoir evaluation parameter determination device provided in an embodiment of the present application is shown in FIG. Figure 7 As shown, the device 700 disclosed in this embodiment includes:

[0180] The first determination module 701 is used to perform a trajectory tracking operation on the horizontal well according to the gamma while drilling, the logging element content and the preset logging curve of the horizontal well, so as to determine the horizontal well's penetration layer and the horizontal well's formation model;

[0181] The second determination module 702 is used to determine the horizontal well reference curve according to the preset logging curve, the element curve of the pilot well corresponding to the stratum through which the horizontal well passes, and the horizontal well formation model;

[0182] The correction module 703 is used to correct the measured logging curve of the horizontal well and the logging element content of the horizontal well according to the reference curve of the horizontal well, the preferred measured logging curve of the pilot well corresponding to the stratum through which the horizontal well passes, and the element logging data of the horizontal well, so as to obtain a corrected logging curve and a corrected logging element content;

[0183] A generation module 704 is used to generate an evaluation parameter determination model according to the corrected logging curve, the corrected logging element content and preset modeling data;

[0184] The evaluation parameter determination module 705 is used to determine the evaluation parameters of the horizontal well reservoir according to the evaluation parameter determination model.

[0185] In some embodiments, the second determination module 702 performs a forward modeling operation based on the preset logging curve, the element curve of the pilot well corresponding to the horizontal well traversal layer, and the horizontal well formation model to determine the horizontal well reference curve.

[0186] In some embodiments, the preset modeling data includes:

[0187] Core analysis data and element content of pilot well, conventional logging data, preferably mineral-sensitive logging curves and element content.

[0188] In some embodiments, a sweet spot partitioning module is further included, which is used to partition the reservoir sweet spots according to the evaluation parameters after determining the evaluation parameters of the horizontal well reservoir according to the response model.

[0189] In some embodiments, the generating module 704 includes: a first generating unit, a second generating unit, and a third generating unit; wherein,

[0190] A first generating unit, configured to generate a rock physical response model according to the corrected well logging curve;

[0191] A second generating unit, used for generating a mineral-element conversion model according to the corrected logging element content;

[0192] The third generating unit is used to generate the evaluation parameter determination model according to the rock physical response model, the mineral-element conversion model and the preset modeling data.

[0193] In some embodiments, the preset logging curve includes:

[0194] The horizontal well passes through a formation corresponding to the well logging curve of the pilot well or the well logging curve of the vertical well adjacent to the horizontal well.

[0195] In some embodiments, the evaluation parameter determination model includes:

[0196]

[0197] Where B = [D T G T ] T , A=[C T (F 0) T ] T , D=[GR CNL DEN AC...] T ; G=[CaSi ... K] T ;

[0198]

[0199]

[0200] V=[V1 V2 ... V i ... V m ]

[0201] Among them, i, m, and f represent different types of minerals. Ca i represents the content of Ca element in the i-th mineral, φ represents the porosity, and V represents the volume content of different types of minerals.

[0202] It can be understood by those skilled in the art that Figure 7 The structure shown in the figure does not constitute a limitation on the device of the embodiment of the present application, and may include more or fewer modules / units than shown in the figure, or a combination of certain modules / units, or different module / unit arrangements.

[0203] It should be understood by those skilled in the art that the above modules or steps of the present application can be implemented by a general-purpose computing device, and they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices. Optionally, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order than that here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation.

[0204] The device provided in this embodiment includes: a first determination module, which is used to perform a trajectory tracking operation on the horizontal well according to the horizontal well's gamma while drilling, logging element content and preset logging curve to determine the horizontal well's traversal layer and the horizontal well formation model; a second determination module, which is used to determine the horizontal well reference curve according to the preset logging curve, the element curve of the pilot well corresponding to the horizontal well's traversal layer and the horizontal well formation model; a correction module, which is used to correct the measured logging curve of the horizontal well and the logging element content of the horizontal well according to the reference curve of the horizontal well and the preferred measured logging curve of the pilot well corresponding to the horizontal well's traversal layer and the element logging data of the horizontal well to obtain the corrected logging curve and the corrected logging element content; a generation module, which is used to generate an evaluation parameter determination model according to the corrected logging curve, the corrected logging element content and preset modeling data; an evaluation parameter determination module, which is used to determine the evaluation parameters of the horizontal well reservoir according to the evaluation parameter determination model. By comprehensively considering information from various sources in horizontal wells, a group of response equations is established, and reservoir parameters are obtained through joint inversion. This method has higher accuracy than simply using conventional logging curves to solve reservoir parameters, and improves the calculation accuracy of horizontal well reservoir parameters.

[0205] Embodiment 4

[0206] This embodiment provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, all or part of the steps in the above method embodiment can be implemented.

[0207] Among them, the computer-readable storage medium may also include computer programs, data files, data structures, etc. alone, or include a combination thereof. The computer-readable storage medium or computer program may be specifically designed and understood by a technician in the field of computer software, or the computer-readable storage medium may be known and available to a technician in the field of computer software. Examples of computer-readable storage media include: magnetic media, such as hard disks, floppy disks, and tapes; optical media, such as CDROM disks and DVDs; magneto-optical media, such as optical disks; and hardware devices, specifically configured to store and execute computer programs, such as read-only memory (ROM), random access memory (RAM), flash memory; or servers, app application stores, etc. Examples of computer programs include machine code (e.g., code generated by a compiler) and files containing high-level code, which can be executed by a computer using an interpreter. The described hardware device may be configured to be used as one or more software modules to perform the operations and methods described above, and vice versa. In addition, the computer-readable storage medium may be distributed in a networked computer system, and program code or computer programs may be stored and executed in a decentralized manner.

[0208] Embodiment 5

[0209] This embodiment provides a computer program product. The computer program product includes a computer program or instructions, and when the computer program or instructions are executed by a processor, all or part of the steps of the method in the above method embodiment are implemented, and this embodiment will not be repeated here.

[0210] Further, the computer program product may include one or more computer executable components configured to perform the embodiment when the program is run; the computer program product may also include a computer program tangibly contained on a readable medium, the computer program including program code for performing any method in the embodiments of the present disclosure. In such an embodiment, the computer program can be downloaded and installed from a network through a communication part, and / or installed from a removable medium.

[0211] Embodiment 6

[0212] This embodiment provides an electronic device. Figure 8 A connection block diagram of an electronic device provided in an embodiment of the present application, such as Figure 8 As shown, the electronic device 800 may include: one or more processors 801 , a memory 802 , a multimedia component 803 , an input / output (I / O) interface 804 , and a communication component 805 .

[0213] The one or more processors 801 are used to execute all or part of the steps in the above method embodiment. The memory 802 is used to store various types of data, which may include instructions of any application or method in the electronic device, and data related to the application.

[0214] The memory 802 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0215] One or more processors 801 can be an application specific integrated circuit (ASIC), a digital signal processor (DSP), a digital signal processing device (DSPD), a programmable logic device (PLD), a field programmable gate array (FPGA), a controller, a microcontroller, a microprocessor or other electronic components, and are used to execute all or part of the steps in the aforementioned method embodiments.

[0216] The multimedia component 803 may include a screen and an audio component, the screen may be a touch screen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone, and the microphone is used to receive external audio signals. The received audio signal may be further stored in a memory or sent via a communication component. The audio component also includes at least one speaker for outputting audio signals.

[0217] The I / O interface 804 provides an interface between one or more processors 801 and other interface modules, which may be a keyboard, a mouse, buttons, etc. These buttons may be virtual buttons or physical buttons.

[0218] The communication component 805 is used for wired or wireless communication between the electronic device 800 and other devices. Wired communication includes communication through a network port, a serial port, etc.; wireless communication includes: Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, 4G, 5G, or one or a combination of them. Therefore, the corresponding communication component 805 may include: a Wi-Fi module, a Bluetooth module, and an NFC module.

[0219] In summary, the present application provides a method, device, computer-readable storage medium and electronic device for determining reservoir evaluation parameters. The method first tracks the trajectory of horizontal wells based on the gamma while drilling, logging element combination and logging curve of horizontal wells to determine the horizontal well penetration layer and formation model; then, the elements obtained by element logging or element logging and lithology scanning logging of pilot wells are referred to to correct the content of typical logging elements; a multi-source multi-parameter linear equation group is established based on gamma while drilling, logging and element logging, and the mineral composition and reservoir porosity are inverted using the constrained optimization method; and parameters such as formation brittleness, saturation and gas content are calculated based on the mineral composition and reservoir porosity. The method comprehensively considers the information from multiple sources in the horizontal well, establishes a response equation group, and jointly inverts to obtain reservoir parameters, which is more accurate than solving the reservoir parameters using conventional logging curves alone, and improves the calculation accuracy of horizontal well reservoir parameters.

[0220] In addition, it should be understood that the method or device disclosed in the embodiments provided in the present application can also be implemented in other ways. The method or device embodiments described above are merely schematic. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the methods and devices according to the multiple embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a computer program segment or a part of a computer program, and the module, computer program segment or a part of a computer program contains one or more computer programs for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings, and can actually be executed in parallel. They can also be executed in the opposite order sometimes, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart can be implemented with a dedicated hardware-based system that performs the specified function or action, or can be implemented with a combination of dedicated hardware and computer programs.

[0221] In this application, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements that are inherent to such process, method, article, or apparatus. In the absence of further restrictions, the elements defined by the sentence "including one..." do not exclude the existence of other identical elements in the process, method, device or equipment including the elements; if there is a description of "first", "second", etc., it is only for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the order of the indicated technical features; in the description of this application, unless otherwise specified, the terms "multiple" and "multiple" mean at least two; if there is a description of a server, it should be noted that the server can be an independent physical server or terminal, or a server cluster composed of multiple physical servers, or a cloud server that can provide basic cloud computing services such as cloud servers, cloud databases, cloud storage and CDN; if there is a description of a smart terminal or mobile device in this application, it should be noted that the smart terminal or mobile device can be a mobile phone, a tablet computer, a smart watch, a netbook, a wearable electronic device, a personal digital assistant (PDA), an augmented reality technology device (AR), a virtual reality device (VR), a smart TV, a smart speaker, a personal computer (PC), a tablet computer, a smart phone, a smart TV, a smart speaker, a smart phone ... Computer, referred to as PC), etc., but not limited to this, the present application does not specifically limit the specific form of the smart terminal or mobile device.

[0222] Finally, it should be noted that, in the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "an example" or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0223] Although the embodiments of the present application have been shown and described above, it is to be understood that the above embodiments are exemplary, and the contents described are only embodiments adopted for the convenience of understanding the present application, and are not intended to limit the present application. Any technician in the technical field to which the present application belongs can make any modifications and changes in the form and details of the implementation without departing from the spirit and scope disclosed in the present application, but the scope of protection of the present application shall still be subject to the scope defined in the attached claims.

Claims

1. A method for determining reservoir evaluation parameters, characterized in that: The method comprises: Perform trajectory tracking operations on the horizontal well according to the horizontal well's gamma while drilling, logging element content and preset logging curves to determine the horizontal well's penetration layer and horizontal well formation model; Determine a horizontal well reference curve according to the preset well logging curve, the element curve of the pilot well corresponding to the stratum through which the horizontal well passes, and the horizontal well formation model; According to the reference curve of the horizontal well, the preferred measured logging curve of the pilot well corresponding to the stratum through which the horizontal well passes, and the element logging data of the horizontal well, the measured logging curve of the horizontal well and the logging element content of the horizontal well are corrected to obtain a corrected logging curve and a corrected logging element content; Generate an evaluation parameter determination model according to the corrected logging curve, the corrected logging element content and preset modeling data; The evaluation parameters of the horizontal well reservoir are determined according to the evaluation parameter determination model.

2. The reservoir evaluation parameter determination method according to claim 1, characterized in that: The determining of the horizontal well reference curve according to the preset logging curve, the element curve of the pilot well corresponding to the stratum through which the horizontal well passes, and the horizontal well formation model comprises: A forward modeling operation is performed according to the preset logging curve, the element curve and the horizontal well formation model to determine the horizontal well reference curve.

3. The reservoir evaluation parameter determination method according to claim 1, characterized in that: The preset modeling data includes: Core analysis data and element content of pilot well, conventional logging data, preferably mineral-sensitive logging curves and element content.

4. The reservoir evaluation parameter determination method according to claim 1, characterized in that: After determining the evaluation parameters of the horizontal well reservoir according to the response model, the method further includes: Reservoir sweet spots are divided according to the evaluation parameters.

5. The reservoir evaluation parameter determination method according to claim 1, characterized in that: The step of generating an evaluation parameter determination model according to the corrected well logging curve, the corrected logging element content and preset modeling data includes: generating a rock physical response model according to the corrected well logging curve; Generate a mineral-element conversion model according to the corrected logging element content; The evaluation parameter determination model is generated according to the rock physical response model, the mineral-element conversion model and the preset modeling data.

6. The reservoir evaluation parameter determination method according to claim 1, characterized in that: The preset logging curve includes: The horizontal well passes through a formation corresponding to the well logging curve of the pilot well or the well logging curve of the vertical well adjacent to the horizontal well.

7. The method for determining reservoir evaluation parameters according to any one of claims 1 to 6, characterized in that: The evaluation parameter determination model includes: Where B = [D T G T ] T , A=[C T (F 0) T ] T , D=[GR CNL DEN AC...] T ; G = [CaSi...K] T ; V=[V1 V2...V i ...V m ] Among them, i, m, and f represent different types of minerals. Ca i represents the content of Ca element in the i-th mineral, φ represents the porosity, and V represents the volume content of different types of minerals.

8. A reservoir evaluation parameter determination device, characterized in that: include: The first determination module is used to perform a trajectory tracking operation on the horizontal well according to the gamma while drilling, the logging element content and the preset logging curve of the horizontal well, so as to determine the horizontal well penetration layer and the horizontal well formation model; A second determination module is used to determine a horizontal well reference curve according to the preset logging curve, an element curve of a pilot well corresponding to the stratum through which the horizontal well passes, and the horizontal well formation model; A correction module, for correcting the measured logging curve of the horizontal well and the logging element content of the horizontal well according to the reference curve of the horizontal well, the preferred measured logging curve of the pilot well corresponding to the stratum through which the horizontal well passes, and the element logging data of the horizontal well, to obtain a corrected logging curve and a corrected logging element content; A generation module, used for generating an evaluation parameter determination model according to the corrected logging curve, the corrected logging element content and preset modeling data; An evaluation parameter determination module is used to determine the evaluation parameters of the horizontal well reservoir according to the evaluation parameter determination model.

9. A computer-readable storage medium, characterized in that: The computer program stored in the computer-readable storage medium, when executed by one or more processors, implements the steps of the method according to any one of claims 1 to 7.

10. An electronic device, characterized in that: The method comprises a memory and one or more processors, wherein a computer program is stored in the memory, and the memory and the one or more processors are communicatively connected with each other, and when the computer program is executed by the one or more processors, the steps of the method as claimed in any one of claims 1 to 7 are executed.

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