CN116930023A - Fine interpretation method and device for dense sandstone phase-control classified porosity logging - Google Patents
Fine interpretation method and device for dense sandstone phase-control classified porosity logging Download PDFInfo
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Abstract
The application discloses a dense sandstone phase-control classified porosity logging fine interpretation method and device. According to the application, various factors influencing reservoir porosity calculation can be analyzed according to the geological and logging characteristics of the target block, typical logging Xiang Moshi characteristics of the regional sediment microphase are established, important parameters required by the porosity calculation, namely main mineral component skeleton logging response parameters, are determined under the constraint of rock core mineral component analysis data, sediment microphase, and on the basis, the calculation of the porosity of the tight sandstone reservoir is completed by utilizing conventional logging data. The method and the device for explaining the dense sandstone phase-control classified porosity logging improve the calculation accuracy of the dense sandstone reservoir porosity with complex mineral components by using conventional logging data, and provide a reliable data basis for characteristic description and reserve evaluation of the sea-phase huge thick dense sandstone reservoir.
Description
Technical Field
The application belongs to the field of development of geologic reservoirs and evaluation, and particularly relates to a method and a device for fine interpretation of tight sandstone phase-control classified porosity logging.
Background
The North American front land basin has a large amount of sea-phase huge thick compact sandstone, such as Canadian Sijia basin triad, and the deposition environment is that the coastal sea-far shore storm sand is deposited, and compact siltstone is formed through diagenesis. The reservoir is mainly characterized in that: coarse powder sandstone mainly composed of various minerals such as quartz, dolomite, feldspar, clay and the like, a small amount of superfine sandstone, good particle sorting and general rounding are adopted. The porosity of the reservoir is 2-6%, and the permeability is 0.005-0.03 mD. The characteristics of the sea-phase tight sandstone reservoir are obviously different from those of land-phase sedimentary tight sandstone, the sea-phase tight sandstone reservoir comprises multi-gyratory combined huge thick sand bodies, the clay content is low, no clay interlayer exists, the absolute difference of the porosities among different sand body sections is small, and the characteristic curve difference of the three porosities of the logging is not obvious; rock multi-component mineral composition results in large differences in logging rock skeleton response parameters. In order to improve the evaluation precision of the porosity of the reservoir, a multi-factor optimization combination porosity fine interpretation method is required to be established, wherein the method comprises the steps of determining rock skeleton parameters by a sand layer section, evaluating rock mineral composition by a deposition microphase, calibrating conventional logging data by special logging data and the like.
Referring to related patents and documents, the application patent of a complex tight reservoir porosity calculation method (CN 111241460A) is a complex tight reservoir porosity calculation method, and a traditional acoustic wave time difference (AC) calculation porosity model is established through core homing; calculating a natural potential (delta SP) difference value, and performing argillaceous correction on the sound wave time difference (AC) and neutrons (CNL) based on e (1-delta SP); and finally, establishing a complex tight reservoir porosity interpretation model, and carrying out porosity POR calculation on the complex tight reservoir. The method is used for performing argillaceous correction on the high-gamma dense sandstone, so that the porosity is calculated, and the method is not suitable for calculating the porosity of the dense sandstone reservoir under the complex condition of rock mineral components.
The application patent of the method for predicting the porosity and the permeability of the tight sandstone based on the analysis of the reservoir quality main control factors (CN 106841001A) is a method for predicting the porosity and the permeability of the tight sandstone based on the analysis of the reservoir quality main control factors, and the method quantitatively evaluates the tight sandstone through the diagenetic effect; and selecting multiple linear stepwise regression as a data analysis method, and establishing a multiple linear regression model through reservoir quality development main control factor analysis to realize porosity and permeability prediction. The method uses up to 7-8 needed variables, and the accumulated error is increased due to the introduction of multiple variables, so that the method is not suitable for the calculation of the porosity of the sea-phase huge-thickness compact siltstone reservoir with extremely low porosity (2-6%).
The application discloses a porosity interpretation method (CN 107327294A) based on a variable skeleton parameter condition of a tight oil reservoir, which is characterized in that firstly, the influence of the oil content in the pores of the tight oil reservoir on the logging parameters is ignored, and a porosity logging interpretation model based on the variable skeleton parameter condition of the tight oil reservoir is established for the characteristics of clay mineral content, rock components (mainly comprising sandy, grey matter and white cloud matter), porosity and corresponding natural gamma logging relative value parameters (delta GR), sonic time difference (delta t), density (ρb) and neutron (ΦN) response characteristics in the core analysis of the tight oil reservoir. The method is characterized in that a multiple regression porosity model of reservoir natural gamma logging relative value, acoustic time difference, density and neutron logging parameters is established by using a multiple linear regression analysis method to calculate the porosity, and the method is not applicable to the situation that the porosity of dense sandstone in the local area is extremely low.
The literature "determining the porosity of a tight reservoir based on petrophysical phase classification-taking the eastern region of the threger gas field as an example" aims at the problems of small void space, complex void type structure and logging response of the tight reservoir in the eastern region of the threger gas field of the Erdos basin. Establishing different types of petrophysical reservoir porosity parameter interpretation models by using various logging, core and gas testing data of the tight reservoir; the classification model has relatively concentrated distribution trend and better linear relation, and the calculation accuracy of the porosity parameter of the tight reservoir is obviously improved and enhanced. The field application effect shows that the compact reservoir classification modeling technology is used for solving the problem of non-uniformity and non-linearity into the problem of relatively uniformity and linearity, and an effective method is provided for accurately establishing a compact reservoir parameter model. The method mainly comprises the steps of calculating the porosity by subdividing petrophysical phases and establishing density-porosity calculation models under different petrophysical phases, and cannot solve the problem of calculation of the porosity under the conditions of more rock minerals and unfixed skeleton density.
The literature is based on element capture spectrum well logging and aims at the characteristics of complex composition and structure, poor physical conditions and strong heterogeneity of reservoir rock of a river group in the northeast China and the difficulty of limiting conventional well logging parameter interpretation model technology in application, and a set of well logging evaluation method based on element capture spectrum well logging (ECS) is established. The relative percentage content of main rock-making elements in the stratum is obtained through a spectrum decomposition and oxide closure model, and various oxide contents of the stratum are quantitatively solved by using methods such as cluster analysis, factor analysis and the like, and the porosity is calculated on the basis. The method for calculating the porosity of the rock with various mineral components by using ECS element logging data is provided, and the method is not applicable to the condition that only a small amount of ECS logging exists and the porosity is calculated by mainly relying on conventional logging data on the premise that the ECS logging data is required to be collected. The literature ' nuclear ridge regression method ' explains the porosity of a tight sandstone reservoir ' the literature proposes a nuclear ridge regression algorithm (KRR) based on nonlinear transformation of raw data aiming at the problem that the porosity of the tight sandstone reservoir is mostly nonlinear. The principle of the kernel ridge regression method based on the kernel function is firstly applied to the explanation of the porosity of the compact sandstone, and the sample curve is selected and the kernel ridge regression parameters are optimized. The root mean square error of the predicted porosity and the true porosity of the Jilin red post oilfield H90 block is 1.413, the result is compared with the results of methods such as unitary regression, multiple regression, BP neural network, support vector machine and the like, and the application shows that the kernel ridge regression algorithm based on the kernel function has higher accuracy in the explanation of the porosity of the tight sandstone reservoir. The method is a multi-element nonlinear regression algorithm meeting least square, is similar to a neural network and a stepwise regression method, the calculation effect finally depends on the representativeness of sample data, and uncertainty of the calculation result can be caused by difficulty in sample selection, so that the method is not suitable for the calculation of the dense sandstone porosity in the local area.
In summary, although there are many methods for calculating the porosity of tight sandstone reservoirs, the study of well logging interpretation parameter calculation for reservoirs that are relatively homogeneous, have very low porosity and complex mineral composition for sea-phase, very thick tight sandstone is still blank. Therefore, on the basis of fully considering factors such as reservoir deposit characteristics, mineral components and the like, the response parameters of the mineral skeleton of the sandstone rock components under different deposit microphases are determined, the reservoir porosity is interpreted by combining multiple factors, and the fine interpretation method for the dense sandstone phase control classification porosity logging is invented, so that the accuracy of the porosity interpretation is necessary.
Disclosure of Invention
Therefore, the embodiment of the application provides a fine interpretation scheme for determining the mineral skeleton logging response parameters of sandstone rock components under different sedimentary microphases on the basis of fully considering factors such as reservoir sedimentary characteristics, mineral components and the like, and combining various factors to interpret the porosity of the reservoir so as to improve the porosity interpretation precision.
In a first aspect, an embodiment of the present application provides a fine interpretation method for dense sandstone phase-control classified porosity logging, the method comprising:
step S1, according to target block rock core analysis data, rock core pictures and logging curves, establishing typical logging Xiang Moshi corresponding to each sedimentary microphase so as to identify different sedimentary microphases of a reservoir;
s2, normalizing and correcting a target block logging curve;
s3, analyzing rock mineral components of each sedimentary microphase of the core of the target block to establish a rock mineral component model corresponding to each sedimentary microphase in the core, wherein the rock mineral component model comprises rock main mineral components and the content of the rock main mineral components in each sedimentary microphase;
s4, under the constraint of a rock mineral component model, carrying out mineral component content calibration on ECS element logging data by utilizing the content of each mineral component analyzed by a rock core;
s5, determining mineral skeleton logging response parameters corresponding to each deposition microposity based on the calibrated mineral component content and a conventional logging curve comprehensive response equation;
and S6, calculating the porosities of different sedimentary microphase reservoirs according to the mineral framework logging response parameters corresponding to each sedimentary microphase and the corrected logging curves.
In some possible embodiments, the log comprises a natural gamma log, a density log, a neutron log, or an acoustic log.
In some possible embodiments, step S4 specifically includes:
obtaining the content of main elements of the stratum by adopting an ECS logging instrument;
establishing a relation between the content of the ECS logging main elements and the content of the rock mineral components according to the rock mineral component model and the content of the ECS logging main elements;
and calculating the stratum rock mineral component content based on the relation.
In some possible embodiments, step S5 specifically includes:
for each sedimentary microphase, calculating a mineral framework logging response parameter GR corresponding to the sedimentary microphase according to the following conventional logging curve comprehensive response equation mai 、DEN mai 、GNL mai 、DT mai 、U mai ：
Wherein GR, DEN, GNL, DT, U on the left side of the equation represents natural gamma, density, neutron, acoustic time difference and volume light surface absorption cross section obtained from the log, n represents the number of kinds of mineral components, V i For the content of the component of the i mineral after calibration, GR f 、DEN f 、GNL f 、DT f 、U f Is the well-logging response value of the fluid,to measure the resulting porosity.
In some possible embodiments, the step S6 specifically includes:
for each sedimentary microphase, substituting the corrected log into the left GR, DEN, GNL, DT, U of the general log comprehensive response equation, and calculating the reservoir porosity of the sedimentary microphase.
In a second aspect, an embodiment of the present application further provides an electronic device, including:
a memory storing executable instructions;
a processor executing the executable instructions in the memory to implement the method as described above.
In a third aspect, embodiments of the present application also provide a computer readable storage medium storing a computer program which, when executed by a processor, implements a method as described above.
In a fourth aspect, an embodiment of the present application further provides a tight sandstone phase-control classification porosity logging fine interpretation device, the device including:
the reservoir deposit microphase identification module is used for establishing typical well logging Xiang Moshi corresponding to each deposit microphase according to the target block core analysis data, the core photo and the well logging curve so as to identify different deposit microphases of the reservoir;
the logging curve standardization module is used for standardizing and correcting the logging curve of the target block;
the rock mineral component model building module is used for analyzing rock mineral components of each deposition microphase of the core of the target block so as to build a rock mineral component model corresponding to each deposition microphase in the core, wherein the rock mineral component model comprises rock main mineral components and the content of the rock main mineral components in each deposition microphase;
the ECS calibration module is used for calibrating the mineral component content of ECS element logging data by utilizing the content of each mineral component analyzed by the core under the constraint of the rock mineral component model;
the mineral framework logging response parameter determining module is used for determining mineral framework logging response parameters corresponding to each deposition micro-phase based on the calibrated mineral component content and a conventional logging curve comprehensive response equation;
and the reservoir porosity calculation module is used for calculating the porosities of different sedimentary microphase reservoirs according to the mineral framework logging response parameters corresponding to each sedimentary microphase and the corrected logging curve.
In some possible embodiments, the ECS calibration module is specifically configured to:
obtaining the content of main elements of the stratum by adopting an ECS logging instrument;
establishing a relation between the content of the ECS logging main elements and the content of the rock mineral components according to the rock mineral component model and the content of the ECS logging main elements;
and calculating the stratum rock mineral component content based on the relation.
In some possible embodiments, the mineral framework logging response parameter determination module is specifically configured to:
for each sedimentary microphase, calculating a mineral framework logging response parameter GR corresponding to the sedimentary microphase according to the following conventional logging curve comprehensive response equation mai 、DEN mai 、GNL mai 、DT mai 、U mai ：
Wherein GR, DEN, GNL, DT, U on the left side of the equation represents natural gamma, density, neutron, acoustic time difference and volume light surface absorption cross section obtained from the log, n represents the number of kinds of mineral components, V i For the content of the component of the i mineral after calibration, GR f 、DEN f 、GNL f 、DT f 、U f Is the well-logging response value of the fluid,to measure the resulting porosity.
According to the fine interpretation scheme of the tight sandstone phase control classification porosity logging, the method comprises the steps of tight sandstone logging sediment microphase division, logging data standardization and correction, evaluation of mineral components of different sediment microphase rocks, calculation of the content of ECS logging mineral components under the constraint of rock core analysis mineral component content, determination of main mineral framework logging response parameters under different sediment microphase, and calculation of reservoir porosity under the condition of determining the mineral framework logging response parameters of different sediment microphase sand bodies. According to the geological and logging characteristics of the target block, various factors affecting reservoir porosity calculation are analyzed, typical logging Xiang Moshi characteristics of the regional sediment microphase are established, important parameters required by the porosity calculation, namely main mineral component skeleton logging response parameters, are determined under the constraint of rock core mineral component analysis data, sediment microphase, and on the basis, the calculation of the porosity of the tight sandstone reservoir is completed by utilizing conventional logging data. The method for analyzing the dense sandstone phase-control classified porosity logging improves the calculation accuracy of the dense sandstone reservoir porosity with complex mineral components by using conventional logging data, and provides a reliable data basis for characteristic description and reserve evaluation of the sea-phase huge-thickness dense sandstone reservoir.
Additional features and advantages of the application will be set forth in the detailed description which follows.
Drawings
The foregoing and other objects, features and advantages of the application will be apparent from the following more particular descriptions of exemplary embodiments of the application as illustrated in the accompanying drawings wherein like reference numbers generally represent like parts throughout the exemplary embodiments of the application.
Fig. 1 shows a flow chart of a fine interpretation method of tight sandstone phase-controlled classified porosity logging, according to an embodiment of the present application.
FIG. 2 illustrates logging features and identification of different sedimentary microphases according to an exemplary embodiment of the application.
FIG. 3 illustrates a log normalization schematic according to an exemplary embodiment of the present application.
Fig. 4 shows a schematic diagram of the differences in rock mineral composition of different sedimentary microphases according to an exemplary embodiment of the application.
Fig. 5 shows a schematic diagram of core analysis of the main mineral components and ECS logging interpretation of the mineral components according to an exemplary embodiment of the application.
FIG. 6 illustrates a reservoir porosity schematic obtained in accordance with an exemplary embodiment of the present application.
Detailed Description
Preferred embodiments of the present application will be described in more detail below. While the preferred embodiments of the present application are described below, it should be understood that the present application may be embodied in various forms and should not be limited to the embodiments set forth herein.
Example 1
Fig. 1 shows a flow chart of a fine interpretation method of tight sandstone phase-controlled classified porosity logging, according to an embodiment of the present application. As shown, the method includes steps S1-S6.
Step S1, according to the target block core analysis data, the core photo and the logging curve, a typical logging Xiang Moshi corresponding to each sedimentary microphase is established to identify different sedimentary microphases of the reservoir.
Different sedimentary microphases can be identified based on the analysis of the core analysis data, the core photographs and the correlation analysis of the logging curve characteristics, and typical logging Xiang Moshi of different sedimentary microphases are established by summarizing typical logging patterns corresponding to the different sedimentary microphases.
The log of the reservoir may be compared to the typical log patterns corresponding to the different depositional microphases established above to identify the different depositional microphases of the reservoir.
The deposited microphase may be a sand dam, beach sand, beach room, etc.
And S2, normalizing and correcting the target block logging curve.
The log may be corrected to eliminate systematic errors due to measurements and borehole environmental effects. The log may include a natural gamma log, a density log, a neutron log, a sonic log, and the like.
And S3, analyzing the rock mineral components of each sedimentary microphase of the core of the target block to establish a rock mineral component model corresponding to each sedimentary microphase in the core, wherein the rock mineral component model comprises the rock main mineral components and the content of the rock main mineral components in each sedimentary microphase.
The characteristics of rock mineral components of different sedimentary microphases can be evaluated, and a rock mineral component model is established.
The mineral components of dense sandstones typically include quartz, dolomite, feldspar, clay minerals, and the like.
And S4, under the constraint of a rock mineral component model, calibrating the mineral component content of ECS element logging data by utilizing the content of each mineral component analyzed by the core.
And calculating the content of the mineral components of the reservoir by using the core analysis mineral components and content data calibration element ECS logging data, so that the calculated result is basically consistent with the actual mineral components of the reservoir.
Specifically, ECS logging instruments can be used to obtain the content of the main elements of the stratum; establishing a relation between the content of the ECS logging main elements and the content of the rock mineral components according to the rock mineral component model and the content of the ECS logging main elements; and calculating the stratum rock mineral component content based on the relation.
And S5, determining the mineral skeleton logging response parameters corresponding to each deposition microposity based on the calibrated mineral component content and a conventional logging curve comprehensive response equation.
Because the ECS logging data and the conventional logging data have the characteristics of identical data sampling rate, consistent continuity and good mutual matching, the mineral component content calibrated by the element ECS logging data and the conventional logging data are solved in a combined way, and skeleton logging response values of all minerals are respectively solved for different sedimentary microphase sand bodies.
Specifically, for each sedimentary microphase, the mineral framework logging response parameter GR corresponding to the sedimentary microphase can be calculated according to the following conventional logging curve comprehensive response equation mai 、DEN mai 、GNL mai 、DT mai 、U mai ：
Wherein GR, DEN, GNL, DT, U on the left side of the equation represents natural gamma, density, neutron, acoustic time difference and volume light surface absorption cross section obtained from the log, n represents the number of kinds of mineral components, V i For the content of the component of the i mineral after calibration, GR f 、DEN f 、GNL f 、DT f 、U f Is the well-logging response value of the fluid,to measure the resulting porosity.
And S6, calculating the porosities of different sedimentary microphase reservoirs according to the mineral framework logging response parameters corresponding to each sedimentary microphase and the corrected logging curves.
Based on determining the main mineral skeleton logging response parameters corresponding to different sedimentary microphase sand bodies, the porosity of the reservoir can be calculated based on a multi-mineral model optimization method by applying corrected conventional logging data.
For each sedimentary microphase, the corrected log may be substituted into the left GR, DEN, GNL, DT, U of the general log comprehensive response equation to calculate the reservoir porosity for that sedimentary microphase
In the above embodiment, the reservoir porosity is finely calculated under the condition of determining the mineral skeleton logging response parameters of different sedimentary microphase sand bodies by compact sandstone logging sedimentary microphase division, logging data standardization and correction, evaluation of mineral components of different sedimentary microphase rocks, calculation of the content of ECS logging mineral components under the constraint of the content of rock core analysis mineral components, and determination of the main mineral skeleton logging response parameters under different sedimentary microphase. According to the embodiment, various factors influencing the calculation of the porosity of the reservoir can be analyzed according to the geology and logging characteristics of the target block, typical logging Xiang Moshi characteristics of the regional sediment microphases are established, important parameters required by the calculation of the porosity, namely main mineral component skeleton logging response parameters, are determined under the constraint of core mineral component analysis data, sediment microphases and the like, and on the basis, the calculation of the porosity of the tight sandstone reservoir is completed by using conventional logging data. Therefore, the calculation accuracy of the porosity of the tight sandstone reservoir with complex mineral components by using conventional logging data is improved, and a reliable data base is provided for characteristic description and reserve evaluation of the sea-phase huge-thickness tight sandstone reservoir.
Example 2
Reservoir porosity calculations were performed on a sea-phase, huge-thickness, tight sandstone reservoir in canada according to exemplary embodiments of the present application, as follows.
(1) The geological, rock core and logging data of a target block are collected, the data show that the reservoir of the block is compact, lithology is mainly siltstone, argillaceous dolomite siltstone, argillaceous siltstone and the like, the deposition environment is mainly shore-front shore phase, the porosity distribution is mainly 2-6%, the permeability is mainly distributed in the range of 0.005-0.03mD, the rock mineral components are complex, the content of minerals with more than 5% is as much as 5, the minerals with the content of quartz, dolomite, plagioclase, potash feldspar and clay minerals are mainly quartz, calcite, siderite, white iron ore and the like, and the reservoir thickness is extremely thick and is as high as 120-200 m. The local deposition environment is sea phase deposition, the main deposition phase zone is shore phase, and three deposition microphase types are developed among sand dams, beach sand and beach. Based on the correlation analysis of the rock core analysis data and the logging curve characteristics, each sedimentary microphase characteristic is shown in fig. 2, a typical logging Xiang Tezheng mode of different sedimentary microphase is summarized, and logging sedimentary microphase identification is carried out in a local area.
(2) In order to eliminate systematic errors caused by measurement, a stable deposited marking layer is selected, and curves such as natural Gamma (GR), density (RHOB), neutrons (NPHI), sound waves (DT) and the like of a logging curve are subjected to standardized treatment, and the standardized curves are used as input for calculating parameter multi-mineral models such as rock mineral components, porosity and the like. Fig. 3 is a left diagram showing a natural gamma curve before normalization, and a right diagram showing a natural gamma curve after normalization.
(3) And (5) evaluating the characteristics of the mineral components of the different sedimentary microphase rocks by analyzing the XRF mineral component analysis data of the rock core. The results show that: the quartz content of the microphase rock mineral components of the sand dam is more than 50%, and the clay content is less than 10%; the quartz content of the micro-phase rock mineral components among beaches is less than 50%, the argillaceous content is more than 20%, and the different deposition micro-phase mineral components have obvious differences. The left diagram in fig. 4 is a schematic diagram of mineral composition of microphase of sand dam, and the right diagram is a schematic diagram of mineral composition of microphase between beach.
(4) And (3) taking the rock core analysis of the mineral component content as a constraint, and carrying out mineral component content calculation (shown in figure 5) by using ECS element logging data, wherein the main mineral component content of the logging calculation is basically consistent with the actual rock core analysis of the stratum mineral component content.
(5) Determining the skeleton logging response parameters of each mineral component (quartz, dolomite, feldspar and clay minerals) of the sand bodies with different deposition microphases. Because the characteristic difference of the logging curves of the deposited microphase sand bodies is small, but the relative value difference of the porosity is large, the skeleton logging response parameters of the minerals need to be respectively obtained for different deposited microphase sand bodies. The element ECS logging data can be used for calculating the combination solution of the mineral component content and the conventional logging data, determining the logging response parameters of each main mineral skeleton by dividing the deposition microphase, taking the sand dam deposition microphase as an example, and table 1 shows the logging response values of each main mineral skeleton of the sand dam deposition microphase stratum.
TABLE 1 logging response parameters for each mineral framework (Sand dams)
| Curve | UNIT | Quartz | Feldspar | Dolomite (Dolomite) | Illite (Italian stone) |
| RHOB | G/C3 | 2.75 | 2.66 | 2.95 | 2.7 |
| NPHI | V/V | -0.02 | 0 | 0.02 | 0.2 |
| DT | US/FT | 53 | 55 | 43 | 80 |
| U(PE) | B/C3 | 5.04 | 8 | 9.6 | 12 |
| GR | GAPI | 85 | 135 | 80 | 200 |
(6) And after determining the main mineral skeleton logging response parameters under different sedimentary microphases, calculating the porosity of the reservoir in the local area, the content of each mineral component and other parameters by adopting a multi-mineral model optimization method by using conventional logging data. Comparing the mineral skeleton logging response parameters, namely adopting a skeleton logging response parameter processing result of a unified sand dam microphase and a processing result of a mineral skeleton logging parameter of a phased classification (shown in figure 6), and as can be seen from a 5 th curve, the mineral skeleton logging response parameter processing result PHIE of a single sand dam microphase is basically consistent with the core analysis porosity (CPHIE) in a sand dam microphase interval, and the difference in a beach microphase interval is larger; when the porosity of the 7 th curve is calculated by adopting the phase control mineral framework logging response parameters, the logging interpretation Porosity (PHIE) is basically consistent with the actual core analysis porosity (CPHIE) of the reservoir.
Example 3
An electronic device according to an embodiment of the application includes a memory and a processor.
The memory is for storing non-transitory computer readable instructions. In particular, the memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and/or non-volatile memory. The volatile memory may include, for example, random Access Memory (RAM) and/or cache memory (cache), and the like. The non-volatile memory may include, for example, read Only Memory (ROM), hard disk, flash memory, and the like.
The processor may be a Central Processing Unit (CPU) or other form of processing unit having data processing and/or instruction execution capabilities, and may control other components in the electronic device to perform the desired functions. In one embodiment of the application, the processor is configured to execute the computer readable instructions stored in the memory to perform the tight sandstone phase-controlled classified porosity logging fine interpretation method described above.
It should be understood by those skilled in the art that, in order to solve the technical problem of how to obtain a good user experience effect, the present embodiment may also include well-known structures such as a communication bus, an interface, and the like, and these well-known structures are also included in the protection scope of the present application.
The detailed description of the present embodiment may refer to the corresponding description in the foregoing embodiments, and will not be repeated herein.
Example 4
The present embodiment provides a computer readable storage medium storing a computer program which when executed by a processor implements the above-described fine interpretation method of tight sandstone phase-controlled classified porosity logging.
A computer-readable storage medium according to an embodiment of the present application has stored thereon non-transitory computer-readable instructions. When executed by a processor, perform all or part of the steps of the methods of embodiments of the application described above.
The computer-readable storage medium described above includes, but is not limited to: optical storage media (e.g., CD-ROM and DVD), magneto-optical storage media (e.g., MO), magnetic storage media (e.g., magnetic tape or removable hard disk), media with built-in rewritable non-volatile memory (e.g., memory card), and media with built-in ROM (e.g., ROM cartridge).
For additional details and advantages of this embodiment, see the description above.
Example 5
The embodiment provides a dense sandstone phase control classification porosity logging fine interpretation device which is characterized by comprising a reservoir deposit microphase identification module, a logging curve standardization module, a rock mineral component model building module, an ECS calibration module, a mineral framework logging response parameter determination module and a reservoir porosity calculation module.
The reservoir deposit microphase identification module is configured to establish a typical log Xiang Moshi corresponding to each deposit microphase according to the target block core analysis data, the core photo, and the log, so as to identify different deposit microphases of the reservoir.
The log standardization module is used for standardizing and correcting the target block log.
The rock mineral component model building module is used for analyzing the rock mineral components of each deposition microphase of the core of the target block so as to build a rock mineral component model corresponding to each deposition microphase in the core, wherein the rock mineral component model comprises the rock main mineral components and the content of the rock main mineral components in each deposition microphase.
And the ECS calibration module is used for calibrating the mineral component content of the ECS element logging data by utilizing the content of each mineral component analyzed by the core under the constraint of the rock mineral component model.
The mineral framework logging response parameter determination module is used for determining mineral framework logging response parameters corresponding to each deposition microposity based on the calibrated mineral component content and a conventional logging curve comprehensive response equation.
The reservoir porosity calculation module is used for calculating the porosities of different sedimentary microphase reservoirs according to the mineral framework logging response parameters corresponding to each sedimentary microphase and the corrected logging curves.
Optionally, the ECS calibration module is specifically configured to:
obtaining the content of main elements of the stratum by adopting an ECS logging instrument;
establishing a relation between the content of the ECS logging main elements and the content of the rock mineral components according to the rock mineral component model and the content of the ECS logging main elements;
and calculating the stratum rock mineral component content based on the relation.
Optionally, the mineral framework logging response parameter determination module is specifically configured to:
for each sedimentary microphase, calculating a mineral framework logging response parameter GR corresponding to the sedimentary microphase according to the following conventional logging curve comprehensive response equation mai 、DEN mai 、GNL mai 、DT mai 、U mai ：
Wherein GR, DEN, GNL, DT, U on the left side of the equation represents natural gamma, density, neutron, acoustic time difference and volume light surface absorption cross section obtained from the log, n represents the number of kinds of mineral components, V i For the content of the component of the i mineral after calibration, GR f 、DEN f 、GNL f 、DT f 、U f Is the well-logging response value of the fluid,to measure the resulting porosity.
For additional details and advantages of this embodiment, see the description above.
The foregoing description of embodiments of the application has been presented for purposes of illustration and description, and is not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the various embodiments described.
Claims (10)
1. A method for fine interpretation of tight sandstone phase-controlled classified porosity log, the method comprising:
step S1, according to target block rock core analysis data, rock core pictures and logging curves, establishing typical logging Xiang Moshi corresponding to each sedimentary microphase so as to identify different sedimentary microphases of a reservoir;
s2, normalizing and correcting a target block logging curve;
s3, analyzing rock mineral components of each sedimentary microphase of the core of the target block to establish a rock mineral component model corresponding to each sedimentary microphase in the core, wherein the rock mineral component model comprises rock main mineral components and the content of the rock main mineral components in each sedimentary microphase;
s4, under the constraint of a rock mineral component model, carrying out mineral component content calibration on ECS element logging data by utilizing the content of each mineral component analyzed by a rock core;
s5, determining mineral skeleton logging response parameters corresponding to each deposition microposity based on the calibrated mineral component content and a conventional logging curve comprehensive response equation;
and S6, calculating the porosities of different sedimentary microphase reservoirs according to the mineral framework logging response parameters corresponding to each sedimentary microphase and the corrected logging curves.
2. The method of claim 1, wherein the log comprises a natural gamma log, a density log, a neutron log, or an acoustic log.
3. The method according to claim 1, wherein step S4 specifically comprises:
obtaining the content of main elements of the stratum by adopting an ECS logging instrument;
establishing a relation between the content of the ECS logging main elements and the content of the rock mineral components according to the rock mineral component model and the content of the ECS logging main elements;
and calculating the stratum rock mineral component content based on the relation.
4. The method according to claim 1, wherein step S5 specifically comprises:
for each sedimentary microphase, calculating a mineral framework logging response parameter GR corresponding to the sedimentary microphase according to the following conventional logging curve comprehensive response equation mai 、DEN mai 、GNL mai 、DT mai 、U mai ：
Wherein GR, DEN, GNL, DT, U on the left side of the equation represents natural gamma, density, neutron, acoustic time difference and volume light surface absorption cross section obtained from the log, n represents the number of kinds of mineral components, V i For the content of the component of the i mineral after calibration, GR f 、DEN f 、GNL f 、DT f 、U f Is the well-logging response value of the fluid,to measure the resulting porosity.
5. The method according to claim 4, wherein the step S6 specifically includes:
for each sedimentary microphase, substituting the corrected log into the left GR, DEN, GNL, DT, U of the general log comprehensive response equation, and calculating the reservoir porosity of the sedimentary microphase.
6. An electronic device, the electronic device comprising:
a memory storing executable instructions;
a processor executing the executable instructions in the memory to implement the tight sandstone phase-controlled classified porosity logging fine interpretation method of any of claims 1-5.
7. A computer readable storage medium, characterized in that the computer readable storage medium stores a computer program which, when executed by a processor, implements the fine interpretation method of tight sandstone phase-controlled classified porosity logging of any of claims 1 to 5.
8. A tight sandstone phase-controlled classified porosity logging fine interpretation device, characterized in that the device comprises:
the reservoir deposit microphase identification module is used for establishing typical well logging Xiang Moshi corresponding to each deposit microphase according to the target block core analysis data, the core photo and the well logging curve so as to identify different deposit microphases of the reservoir;
the logging curve standardization module is used for standardizing and correcting the logging curve of the target block;
the rock mineral component model building module is used for analyzing rock mineral components of each deposition microphase of the core of the target block so as to build a rock mineral component model corresponding to each deposition microphase in the core, wherein the rock mineral component model comprises rock main mineral components and the content of the rock main mineral components in each deposition microphase;
the ECS calibration module is used for calibrating the mineral component content of ECS element logging data by utilizing the content of each mineral component analyzed by the core under the constraint of the rock mineral component model;
the mineral framework logging response parameter determining module is used for determining mineral framework logging response parameters corresponding to each deposition micro-phase based on the calibrated mineral component content and a conventional logging curve comprehensive response equation;
and the reservoir porosity calculation module is used for calculating the porosities of different sedimentary microphase reservoirs according to the mineral framework logging response parameters corresponding to each sedimentary microphase and the corrected logging curve.
9. The apparatus of claim 8, wherein the ECS calibration module is specifically configured to:
obtaining the content of main elements of the stratum by adopting an ECS logging instrument;
establishing a relation between the content of the ECS logging main elements and the content of the rock mineral components according to the rock mineral component model and the content of the ECS logging main elements;
and calculating the stratum rock mineral component content based on the relation.
10. The apparatus of claim 8, wherein the mineral framework logging response parameter determination module is specifically configured to:
for each sedimentary microphase, calculating a mineral framework logging response parameter GR corresponding to the sedimentary microphase according to the following conventional logging curve comprehensive response equation mai 、DEN mai 、GNL mai 、DT mai 、U mai ：
Wherein GR, DEN, GNL, DT, U on the left side of the equation represents natural gamma, density, neutron, acoustic time difference and volume light surface absorption cross section obtained from the log, n represents the number of kinds of mineral components, V i For the content of the component of the i mineral after calibration, GR f 、DEN f 、GNL f 、DT f 、U f Is the well-logging response value of the fluid,to measure the resulting porosity.
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