CN111025409A - Flooded layer evaluation method and device and storage medium - Google Patents
Flooded layer evaluation method and device and storage medium Download PDFInfo
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Abstract
The embodiment of the specification provides a flooded layer evaluation method, a flooded layer evaluation device and a storage medium. The method comprises the following steps: acquiring an actually measured logging curve of a target well group, and preprocessing the actually measured logging curve to obtain a standardized logging curve; wherein the target well group comprises a target well and an old well; determining the shale content, the reservoir porosity and the formation water mineralization before flooding of the target well group according to the standardized logging curve; reconstructing a natural potential curve of the target well before flooding according to the argillaceous content of the old well and the water mineralization of the stratum before flooding; inputting the standardized logging curve, the shale content curve and the reservoir porosity curve of the target well into a reconstruction model to obtain a resistivity curve before the target well is reconstructed by flooding; and constructing a comprehensive flooding index to evaluate the flooding layer of the target well according to the reconstructed natural potential curve of the target well before flooding and the reconstructed resistivity curve of the target well before flooding, so that the accuracy of evaluating the flooding layer is improved.
Description
Technical Field
The embodiment of the specification relates to the field of geophysical logging and machine learning, in particular to a method and a device for evaluating a water flooded layer and a storage medium.
Background
Developed oil fields in China generally enter a double-high development stage with high water content and high extraction degree, but a large amount of residual oil still exists underground, so that well patterns of many oil fields are encrypted for better performing residual oil excavation, and accurate evaluation of the flooding level of an encrypted well is an important basis for further improving the oil field recovery efficiency. After the oilfield water injection development, the physical properties of reservoir rock and the properties of formation fluid are changed, and the resistivity of reservoir formation water is changed due to the difference of the mineralization degrees of injected water and native formation water, so that the oil saturation cannot be directly calculated by an Archie's formula. Many oil fields also have the phenomena of complicated history of injected water and alternate injection of water with different mineralization degrees. The complex nature of the mixed solution of formation water makes the well logging evaluation of the water flooded layer a difficult problem.
At present, conventional logging curves are mainly used for evaluating a water flooded layer and are divided into two types: one is to evaluate the water flooded layer by calculating the resistivity of the stratum water mixed liquid through a parallel conductive model; and the other type is to reconstruct the logging curve of the original reservoir before flooding by a mathematical method to realize the evaluation of the flooding layer. The related research results are as follows:
in 2016, 04 months, in the logging technology, the application of the inversion of the formation water resistivity of mixed liquid in the evaluation of the water flooded layer [ J ] logging technology, 2016,40(2):189 and 192 ] of Luyunlong and the like (Luyunlong, Lixingli, Luhongzhi, et al) in the application of the inversion of the formation water resistivity of mixed liquid in the evaluation of the water flooded layer) is calculated through a water ion conductive model of the mixed liquid formation, so that the quantitative evaluation of the water flooded layer is realized. The article determines relevant parameters of the electric conduction model through experiments, realizes quantitative evaluation of a water flooded layer, but has the precondition that the mineralization degree of injected water is kept known and unchanged.
In 2018, in "logging technology", Shiyujiang et al (Shiyujiang, Zhou jin Yi, Zhongjibin, et al. reconstructed resistivity curve identification method for identifying a water flooded layer and application [ J ]. logging technology, 2018.) in "reconstructed resistivity curve identification method for identifying a water flooded layer and application" the resistivity of an oil layer before water flooding is reconstructed by using a neural network method, and graded evaluation of the water flooded layer is realized through the difference with the actually measured resistivity. The article realizes the grading evaluation of the water flooded layer by reconstructing the water flooded resistivity curve of the oil layer, and only reconstructing the resistivity curve does not consider the change of the formation water in the water flooded process.
Because the determination of the actual formation water mixed liquor resistivity is often influenced by various factors such as heterogeneous reservoirs, the property of injected water, the water injection history and the like, and is difficult to quantify and correct, the application range of the method for evaluating the water flooded layer from the formation mixed liquor resistivity is relatively large in limitation. With the advance of fresh water flooding, the resistance rate is firstly reduced and then increased, and the multidisciplinary evaluation is carried out only by the resistivity curve. At present, no generally applicable evaluation method with high accuracy exists for a low-permeability water flooded layer.
Disclosure of Invention
An object of the embodiments of the present disclosure is to provide a method and an apparatus for evaluating a flooded layer, and a storage medium, so as to improve accuracy of evaluation of the flooded layer.
In order to solve the above problem, embodiments of the present specification provide a method, an apparatus, and a storage medium for evaluating a flooded layer.
A flooded layer evaluation method, the method comprising: acquiring an actually measured logging curve of a target well group, and preprocessing the actually measured logging curve to obtain a standardized logging curve; wherein the target well group comprises a target well and an old well; determining the shale content, the reservoir porosity and the formation water mineralization before flooding of the target well group according to the standardized logging curve; reconstructing a natural potential curve of the target well before flooding according to the argillaceous content of the old well and the water mineralization of the stratum before flooding; inputting the standardized logging curve, the shale content curve and the reservoir porosity curve of the target well into a reconstruction model to obtain a resistivity curve before the target well is reconstructed by flooding; and evaluating the flooding layer of the target well according to the reconstructed natural potential curve of the target well before flooding and the reconstructed resistivity curve of the target well before flooding.
A flooded layer evaluation apparatus, the apparatus comprising: the acquisition module is used for acquiring an actually measured logging curve of a target well group, and preprocessing the actually measured logging curve to obtain a standardized logging curve; wherein the target well group comprises a target well and an old well; the determining module is used for determining the shale content, the reservoir porosity and the reservoir water mineralization before flooding of the target well group according to the standardized logging curve; the first reconstruction module is used for reconstructing a natural potential curve of the target well before flooding according to the argillaceous content of the old well and the water mineralization of the stratum before flooding; the second reconstruction module is used for inputting the standardized logging curve, the shale content curve and the reservoir porosity curve of the target well into the reconstruction model to obtain a reconstructed resistivity curve of the target well before flooding; and the evaluation module is used for evaluating the water flooded layer of the target well according to the reconstructed natural potential curve of the target well before water flooding and the reconstructed resistivity curve of the target well before water flooding.
A computer readable storage medium having computer program instructions stored thereon that when executed implement: acquiring an actually measured logging curve of a target well group, and preprocessing the actually measured logging curve to obtain a standardized logging curve; wherein the target well group comprises a target well and an old well; determining the shale content, the reservoir porosity and the formation water mineralization before flooding of the target well group according to the standardized logging curve; reconstructing a natural potential curve of the target well before flooding according to the argillaceous content of the old well and the water mineralization of the stratum before flooding; inputting the standardized logging curve, the shale content curve and the reservoir porosity curve of the target well into a reconstruction model to obtain a resistivity curve before the target well is reconstructed by flooding; and evaluating the flooding layer of the target well according to the reconstructed natural potential curve of the target well before flooding and the reconstructed resistivity curve of the target well before flooding.
According to the technical scheme provided by the embodiment of the specification, the embodiment of the specification can acquire the actually measured logging curve of the target well group, and preprocess the actually measured logging curve to obtain the standardized logging curve; wherein the target well group comprises a target well and an old well; determining the shale content, the reservoir porosity and the formation water mineralization before flooding of the target well group according to the standardized logging curve; reconstructing a natural potential curve of the target well before flooding according to the argillaceous content of the old well and the water mineralization of the stratum before flooding; inputting the standardized logging curve, the shale content curve and the reservoir porosity curve of the target well into a reconstruction model to obtain a resistivity curve before the target well is reconstructed by flooding; and evaluating the flooding layer of the target well according to the reconstructed natural potential curve of the target well before flooding and the reconstructed resistivity curve of the target well before flooding. According to the embodiment of the specification, the natural potential curve before the target well is flooded and the resistivity curve before the target well is flooded can be reconstructed through reservoir parameters such as the shale content, the porosity and the like determined by the logging curve, the flooded layer of the target well is evaluated according to the reconstructed natural potential curve before the target well is flooded and the reconstructed resistivity curve before the target well is flooded, the problem of multi-resolution of the resistivity recognition flooded layer is solved, and the accuracy of evaluation of the flooded layer is improved.
Drawings
In order to more clearly illustrate the embodiments of the present specification or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments described in the specification, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a flowchart of a flooded layer evaluation method according to an embodiment of the present disclosure;
FIG. 2 is a graph showing the relationship between the resistivity of a NaCl solution and its concentration and temperature in examples of the present invention;
FIG. 3 is a diagram illustrating the relationship between the mud content and the natural potential coefficient of an old well in the embodiment of the present disclosure;
FIG. 4 is a flow chart of reconstructing pre-flooding resistivity in accordance with an embodiment of the present disclosure;
FIG. 5 is a schematic diagram illustrating evaluation results of a well flooding layer of an X well zone according to an embodiment of the present disclosure;
FIG. 6 is a schematic diagram showing the relationship between the comprehensive flooding index CFI and the water production rate of a well in the X well;
FIG. 7 is a schematic diagram of evaluation results of a flooded layer of a well in an X-well area through a comprehensive flooding index CFI;
fig. 8 is a functional block diagram of a flooded layer evaluation apparatus according to an embodiment of the present disclosure.
Detailed Description
The technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the drawings in the embodiments of the present disclosure, and it is obvious that the described embodiments are only a part of the embodiments of the present disclosure, and not all of the embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present specification without any creative effort shall fall within the protection scope of the present specification.
Fig. 1 is a flowchart of a method for evaluating a flooded layer according to an embodiment of the present disclosure. In the embodiment of the present specification, a main body for executing the flooding layer evaluation method may be an electronic device having a logical operation function, and the electronic device may be a server or a client. The client can be a desktop computer, a tablet computer, a notebook computer, a workstation and the like. Of course, the client is not limited to the electronic device with certain entities, and may also be software running in the electronic device, or may also be program software formed by program development. The program software may be run in the electronic device described above.
As shown in fig. 1, the flooded layer evaluation method may include the following steps.
S110: acquiring an actually measured logging curve of a target well group, and preprocessing the actually measured logging curve to obtain a standardized logging curve; wherein the target well group comprises a target well and an old well.
In some embodiments, the target well group may include a target well and an old well. The target well may be an encrypted well obtained by re-encrypting the well pattern on the basis of an old well.
In some embodiments, the measured log may be a log obtained from actual logging. The measured well log may include sonic moveout curves, natural gamma curves, natural potential curves, resistivity curves, and the like. In the embodiment of the present specification, the actual measurement logging curves of the target well and the old well may be obtained respectively, and the actual measurement logging curves of the target well and the old well may be preprocessed to obtain the standardized logging curves. Wherein the normalized curves may include a normalized sonic time difference curve, a normalized natural gamma curve, and a normalized resistivity curve. Specifically, the histogram method may be adopted to perform standardization on the logging curve, and delete abnormal values of the logging curve caused by borehole diameter expansion or abnormal measurement process, thereby obtaining a standardized logging curve. The histogram method may be an analysis method for grouping and sorting the collected data and drawing the data into a frequency distribution histogram to describe the quality distribution state.
S120: and determining the shale content, the reservoir porosity and the formation water mineralization before flooding of the target well group according to the standardized logging curve.
In the examples of this specification, the argillaceous content may be the percentage of the volume of fine silt, clay and water contained in the rock, which is a very fine particle, to the volume of the rock. The shale content can be obtained by calculation according to logging curves such as a natural gamma curve, a natural potential curve and the like.
In the embodiment of the present specification, taking the shale content of the target well group calculated according to the natural gamma curve as an example, the shale content may be calculated by using a standardized natural gamma curve, and the formula is as follows:
wherein, VshIs the mud content; GR is the natural gamma curve, API; GRminIs the natural gamma curve minimum, API; GRmaxThe natural gamma curve maximum, API.
In the embodiments of the present description, the reservoir porosity, also called reservoir porosity, is a parameter for measuring the volume of pores contained in the rock of the oil and gas reservoir, and the reservoir porosity can reflect the capacity of the rock to store fluids. Factors that affect reservoir porosity may include, among others, differences in particle size, arrangement, particle shape, degree and type of cementation, clay content, and the like. The reservoir porosity may be calculated by different methods, such as by sonic time difference, densitometry, compensated neutron calculations, and the like.
In some embodiments, taking the example of reservoir porosity calculation by sonic time-difference method, a normalized sonic time-difference curve may be used to calculate reservoir porosity, as follows:
wherein Phie is the reservoir porosity; Δ tmaThe acoustic time difference of the sandstone framework can be 180 mu s/m; Δ tfThe acoustic time difference of the pore fluid can be 620 mu s/m; Δ tshThe acoustic time difference is the sound wave time difference of a pure mudstone section, and is mu s/m; delta t is a sound wave time difference logging curve, mu s/m; vshIs the argillaceous content.
In the examples of this specification, the formation water mineralization is an inherent feature of formation water and is the sum of the contents of various mineral elements, the size of which is related to the formation reservoir environment and the source of rock debris particle deposits, the common element being Ca2+、Mg2+、Na+、K+、HCO3-、Cl-And the like. The formation water mineralization before flooding can be determined through the existing well logging curve, formation water analysis data and oil field injection and production historical data. Specifically, the water salinity of the original formation before flooding can be determined by using a water analysis data method, a natural potential curve inverse algorithm or a visual formation water resistivity method.
In some embodiments, the formation water salinity of the target well group before flooding and during the low water period can be obtained by collecting the formation water analysis data of the target well group before flooding and during the low water period and verifying the data with the formation water resistivity method based on the visual formation water resistivity inverse algorithm. Specifically, the apparent formation water resistivity of the target well group may be determined according to the following formula:
wherein R iswaOmega · m, depending on the formation water resistivity; rtThe resistivity of a water layer in a reservoir of the target well group is omega m; RLLD is a deep induction resistivity curve of a water layer in a reservoir of a target well group, omega.m;
porosity of the aqueous layer; and m is a cementation index. In some embodiments, m may take on a value of 2.
In some embodiments, the pre-flooded formation water mineralization of the target well group may be determined from the apparent formation water resistivity. As shown in FIG. 2, the resistivity R of the formation water can be determined according to the relationship between the resistivity of the NaCl solution and the concentration and temperature thereofwaWhich translates into formation water mineralization at a temperature in the formation.
S130: and reconstructing a natural potential curve of the target well before flooding according to the argillaceous content of the old well and the water mineralization of the stratum before flooding.
In some embodiments, a natural potential coefficient may be determined according to the shale content of the old well, and a natural potential curve of the target well before flooding may be reconstructed according to the natural potential coefficient and the water salinity of the formation before flooding.
Specifically, as shown in fig. 3, a corresponding relationship between the shale content of the old well and the natural potential coefficient may be established in advance, and the natural potential coefficient may be determined according to the corresponding relationship shown in fig. 3.
In some embodiments, the pre-flooding natural potential curve of the target well may be reconstructed according to the following equation:
wherein SP _0 is a reconstructed pre-flooding natural potential curve of the target well, namely mV; SSP1The reconstructed natural potential curve abnormal amplitude, mV; SPmaxTo the eyesMarking the natural potential value of the mudstone section of the well, namely mV; rmf1The target well mud filtrate resistivity, Ω · m; rwThe resistivity of original formation water before flooding of a target well is omega.m, and can be obtained according to the salinity of the formation water before flooding; k is the natural potential coefficient K.
S140: and inputting the standardized logging curve, the shale content curve and the reservoir porosity curve of the target well into a reconstruction model to obtain a reconstructed resistivity curve of the target well before flooding.
In the embodiment of the specification, the mapping relation among the parameters in the old well can be extracted through a machine learning method, and the resistivity curve of the target well before water flooding is reconstructed according to the mapping relation. The parameters may include, among others, well logs, shale content, reservoir porosity, etc. By the method for reconstructing the resistivity curve of the target well before water logging, mapping between parameters can be more definite, and the reconstructed resistivity curve of the target well before water logging is more accurate.
In some embodiments, a reconstruction model may be constructed first, and then a resistivity curve of the target well before flooding may be reconstructed according to the reconstruction model. In some embodiments, the reconstructed model may be obtained by: and taking the standard well logging curve, the shale content curve and the porosity curve of the old well as a training set, training a pre-constructed model according to the training set, and taking the trained model as a reconstruction model.
When any machine learning problem needs to be solved, a proper algorithm needs to be selected, no machine learning model can solve all the problems, different machine learning algorithms are dependent on the size of data, the quality of the data, the data structure and specific characteristics of the problem to be solved, and each machine learning algorithm has respective advantages and disadvantages. Therefore, different algorithms can be selected to construct the model according to needs. The algorithm may include a support vector machine algorithm, a gradient boosting regression tree algorithm, an integration algorithm, a regression algorithm, a bayesian algorithm, and the like.
In the embodiment of the present specification, taking the case that the pre-constructed model is constructed based on a gradient lifting regression tree algorithm as an example, the pre-constructed model is trained by using the training set, the trained model is used as a reconstruction model, and a normalized logging curve, a shale content curve and a reservoir porosity curve of the target well are input into the reconstruction model to obtain a resistivity curve before flooding of the reconstructed target well. In particular, this can be seen in fig. 4.
S141: a training set is input.
In some embodiments, a normalized log of an old well, such as a normalized natural gamma curve, a normalized sonic moveout curve, a normalized resistivity curve, and a mudness content curve and a porosity curve, may be used as a training set
n-1, 2,3. Wherein, a standardized natural gamma curve, a standardized sound wave time difference curve, a mud content curve and a porosity curve can be used as x in a training set, a standardized resistivity curve is used as y in the training set, and a mapping function f (x to y) is established for establishing x to yi)。
In some embodiments, the intervals represented by the curves in the training set may be the same intervals as the intervals represented by the resistivity curves before flooding of the target well to be reconstructed. Furthermore, due to the existence of the calcareous interlayer, the flooding condition becomes extremely complex, for example, the logging curve is influenced, and is distorted, so that the evaluation of the flooding layer is difficult. Therefore, the individual curves of the calcareous interbed of the old well were not trained as a training set.
S142: and adjusting the iteration times and the step length.
In some embodiments, a constant f may be initialized first0(x) Construction of h (x)i)＝yi-f(xi) Constantly changing f (x)i) Let f (x)i) The predicted value of (a) gradually approaches y. And (3) making the loss function become smaller through iteration, and realizing negative gradient fitting, wherein the loss function is as follows:
L(y,ft(x))＝L(y,ft-1(x)+ht(x))
wherein the content of the first and second substances,ht(x) Representing residual errors, wherein the value of the negative gradient of the loss function equivalent to the current model is smaller, and the fitting effect is better; f. oft(x) Representing the strong learner that results from the t-th iteration. The strong learning function can be expressed as:
fk(x)＝fk-1(x)+vhk(x)
where k denotes the number of iterations and v denotes the step size.
S143: and (5) checking the training effect.
In some embodiments, the training effect may be determined according to the size of the residual. If the training effect is not reasonable, the step can enter S142 to continuously adjust the iteration times and the step length; if the training result is reasonable, the trained model can be used as a reconstruction model.
S144: target well parameters are input.
In some embodiments, the target well parameters may include a normalized log of the target well, a shale content curve, a reservoir porosity curve, and the like. Correspondingly, the parameters such as the standardized logging curve, the shale content curve and the reservoir porosity curve of the target well can be input into the reconstruction model.
S145: and outputting the reconstructed resistivity curve of the target well before flooding.
S150: and evaluating the flooding layer of the target well according to the reconstructed natural potential curve of the target well before flooding and the reconstructed resistivity curve of the target well before flooding.
In the embodiment of the present description, the difference comparison between the actually measured well log and the reconstructed well log may be performed, the difference between the actually measured well log and the reconstructed well log is converted into a flooding index and a flooding process, a comprehensive flooding index is calculated according to the flooding index and the flooding process, and finally, a flooding layer of the target well may be evaluated according to the comprehensive flooding index. Specifically, the actual measurement logging curve may be preprocessed to obtain a standardized logging curve, such as a standardized acoustic time difference curve, a standardized natural gamma curve, and a standardized resistivity curve. And then, the method is realized by utilizing a standardized sound wave time difference curve, a standardized natural gamma curve, a standardized resistivity curve and other parameters to obtain a reconstructed natural potential curve of the target well before water flooding and a reconstructed resistivity curve of the target well before water flooding. And converting the difference between the reconstructed curve and the actually measured logging curve into a flooding index and a flooding process.
In some embodiments, a flooding index may be determined from the measured resistivity curve of the target well and the reconstructed resistivity curve of the target well before flooding, and the formula is as follows:
wherein, IwRepresenting a flooding index; rt represents the measured resistivity curve of the target well, Ω · m; rt _0 represents the reconstructed resistivity curve, Ω · m.
In some embodiments, the flooding process may be determined according to the measured natural potential curve of the target well and the reconstructed natural potential curve of the target well before flooding, and the formula is as follows:
wherein, InRepresenting a flooding process; Δ SP represents the abnormal amplitude, mV, of the measured natural potential curve of the target well; Δ SP _0 is the abnormal amplitude, mV, of the reconstructed natural potential curve.
In some embodiments, a composite flooding indicator may be determined based on the flooding index and the flooding schedule. The formula is as follows:
CFI＝Iw+aIn
wherein, CFI represents a comprehensive flooding index; and a represents a water injection mode coefficient, wherein a is 0 when produced water is reinjected, and a is 1 when fresh water is injected.
In some embodiments, the evaluation criteria for evaluating the flooding layer of the target well as shown in table 1 may be obtained by performing flooding identification on the target well according to the comprehensive flooding index and dividing the flooding level of the target well.
TABLE 1
In order to clearly illustrate the beneficial effects of the embodiments of the present specification, flooded layer evaluations are performed on different areas by the flooded layer evaluation method provided by the embodiments of the present specification, which is described below with reference to fig. 5 to 7.
As shown in fig. 5, fig. 5 is a diagram of a well in an X-well area, where a logging curve is reconstructed by using a flooding layer evaluation method provided in an embodiment of the present specification, and a flooding layer is identified and a flooding level evaluation result is obtained. Fig. 6 is a schematic diagram showing a relationship between a comprehensive flooding index CFI and a production water yield obtained by a flooding layer evaluation method provided in an embodiment of the present specification, when a well logging curve reconstruction process is performed on a well in an X well zone, and it can be seen from fig. 6 that a good linear relationship exists between the comprehensive flooding index CFI and an actual water yield. Fig. 7 is a schematic diagram of the results of quantitative evaluation of a flooded layer by a comprehensive flooded index CFI obtained by the flooded layer evaluation method provided in the embodiment of the present specification after a well log reconstruction processing is performed on a certain well of an X well zone, and the evaluation coincidence rate reaches 97%.
The embodiment of the specification can obtain an actually measured logging curve of a target well group, and preprocess the actually measured logging curve to obtain a standardized logging curve; wherein the target well group comprises a target well and an old well; determining the shale content, the reservoir porosity and the formation water mineralization before flooding of the target well group according to the standardized logging curve; reconstructing a natural potential curve of the target well before flooding according to the argillaceous content of the old well and the water mineralization of the stratum before flooding; inputting the standardized logging curve, the shale content curve and the reservoir porosity curve of the target well into a reconstruction model to obtain a resistivity curve before the target well is reconstructed by flooding; and evaluating the flooding layer of the target well according to the reconstructed natural potential curve of the target well before flooding and the reconstructed resistivity curve of the target well before flooding. According to the embodiment of the specification, a natural potential curve before the target well is flooded and a resistivity curve before the target well is flooded can be reconstructed through reservoir parameters such as the shale content and the porosity determined by the logging curve, then the difference between the reconstructed curve and the actually-measured logging curve is converted into a flooding index and a flooding process, a comprehensive flooding index is constructed to evaluate a flooding layer of the target well, the problem of multi-resolution of the resistivity recognition flooding layer is solved, and the accuracy of flooding layer evaluation is improved.
Embodiments of the present specification further provide a computer-readable storage medium of a flooded layer evaluation method, where the computer-readable storage medium stores computer program instructions, and when the computer program instructions are executed, the computer program instructions implement: acquiring an actually measured logging curve of a target well group, and preprocessing the actually measured logging curve to obtain a standardized logging curve; wherein the target well group comprises a target well and an old well; determining the shale content, the reservoir porosity and the formation water mineralization before flooding of the target well group according to the standardized logging curve; reconstructing a natural potential curve of the target well before flooding according to the argillaceous content of the old well and the water mineralization of the stratum before flooding; inputting the standardized logging curve, the shale content curve and the reservoir porosity curve of the target well into a reconstruction model to obtain a resistivity curve before the target well is reconstructed by flooding; and evaluating the flooding layer of the target well according to the reconstructed natural potential curve of the target well before flooding and the reconstructed resistivity curve of the target well before flooding.
In the present embodiment, the storage medium includes, but is not limited to, a Random Access Memory (RAM), a Read-Only Memory (ROM), a Cache (Cache), a Hard disk (HDD), or a Memory Card (Memory Card). The memory may be used for storing the computer program and/or the module, and the memory may mainly include a storage program area and a storage data area, where the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, a text conversion function, etc.), and the like; the storage data area may store data (such as audio data, text message data, etc.) created according to the use of the user terminal, etc. In addition, the memory may include high speed random access memory, and may also include non-volatile memory. In this embodiment, the functions and effects specifically realized by the program instructions stored in the computer-readable storage medium can be explained by comparing with other embodiments, and are not described herein again.
Referring to fig. 8, an embodiment of the present disclosure further provides a device for evaluating a flooded layer, where the device may specifically include the following structural modules.
An obtaining module 810, configured to obtain an actually measured logging curve of a target well group, and perform preprocessing on the actually measured logging curve to obtain a standardized logging curve; wherein the target well group comprises a target well and an old well;
a determining module 820, configured to determine a shale content, a reservoir porosity, and a reservoir water mineralization before flooding of the target well group according to the standardized well logging curve;
a first reconstruction module 830, configured to reconstruct a natural potential curve of the target well before flooding according to the argillaceous content of the old well and the salinity of the formation water before flooding;
the second reconstruction module 840 is used for inputting the standardized logging curve, the shale content curve and the reservoir porosity curve of the target well into the reconstruction model to obtain a reconstructed resistivity curve of the target well before flooding;
and the evaluation module 850 is used for evaluating the water flooded layer of the target well according to the reconstructed natural potential curve of the target well before water flooding and the reconstructed resistivity curve of the target well before water flooding.
In some embodiments, the first reconstruction module 830 comprises: the determining submodule is used for determining a natural potential coefficient according to the shale content of the old well; and the reconstruction submodule is used for reconstructing a natural potential curve of the target well before water flooding according to the natural potential coefficient and the water mineralization degree of the stratum before water flooding.
In some embodiments, the evaluation module 850 includes: the first determining submodule is used for determining a flooding index according to the measured resistivity curve of the target well and the reconstructed resistivity curve of the target well before flooding; the second determining submodule determines a flooding process according to the actually measured natural potential curve of the target well and the reconstructed natural potential curve of the target well before flooding; the third determining submodule is used for determining a comprehensive flooding index according to the flooding index and the flooding process; and the evaluation submodule is used for carrying out flooding identification on the target well according to the comprehensive flooding index and dividing the flooding level of the target well.
It should be noted that, in the present specification, each embodiment is described in a progressive manner, and the same or similar parts in each embodiment may be referred to each other, and each embodiment focuses on differences from other embodiments. In particular, as for the apparatus embodiment and the apparatus embodiment, since they are substantially similar to the method embodiment, the description is relatively simple, and reference may be made to some descriptions of the method embodiment for relevant points.
After reading this specification, persons skilled in the art will appreciate that any combination of some or all of the embodiments set forth herein, without inventive faculty, is within the scope of the disclosure and protection of this specification.
In the 90 s of the 20 th century, improvements in a technology could clearly distinguish between improvements in hardware (e.g., improvements in circuit structures such as diodes, transistors, switches, etc.) and improvements in software (improvements in process flow). However, as technology advances, many of today's process flow improvements have been seen as direct improvements in hardware circuit architecture. Designers almost always obtain the corresponding hardware circuit structure by programming an improved method flow into the hardware circuit. Thus, it cannot be said that an improvement in the process flow cannot be realized by hardware physical modules. For example, a Programmable Logic Device (PLD), such as a Field Programmable Gate Array (FPGA), is an integrated circuit whose Logic functions are determined by programming the Device by a user. A digital system is "integrated" on a PLD by the designer's own programming without requiring the chip manufacturer to design and fabricate application-specific integrated circuit chips. Furthermore, nowadays, instead of manually making an integrated Circuit chip, such Programming is often implemented by "logic compiler" software, which is similar to a software compiler used in program development and writing, but the original code before compiling is also written by a specific Programming Language, which is called Hardware Description Language (HDL), and HDL is not only one but many, such as abel (advanced Boolean Expression Language), ahdl (alternate Language Description Language), traffic, pl (core unified Programming Language), HDCal, JHDL (Java Hardware Description Language), langue, Lola, HDL, laspam, hardward Description Language (vhr Description Language), vhjhdul, vhr Description Language, and vhr-Language, which are currently used in most common. It will also be apparent to those skilled in the art that hardware circuitry that implements the logical method flows can be readily obtained by merely slightly programming the method flows into an integrated circuit using the hardware description languages described above.
The systems, devices, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. One typical implementation device is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.
From the above description of the embodiments, it is clear to those skilled in the art that the present specification can be implemented by software plus a necessary general hardware platform. Based on such understanding, the technical solutions of the present specification may be essentially or partially implemented in the form of software products, which may be stored in a storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., and include instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments of the present specification.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
The description is operational with numerous general purpose or special purpose computing system environments or configurations. For example: personal computers, server computers, hand-held or portable devices, tablet-type devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like.
This description may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
While the specification has been described with examples, those skilled in the art will appreciate that there are numerous variations and permutations of the specification that do not depart from the spirit of the specification, and it is intended that the appended claims include such variations and modifications that do not depart from the spirit of the specification.
Claims (10)
1. A flooded layer evaluation method, characterized by comprising:
acquiring an actually measured logging curve of a target well group, and preprocessing the actually measured logging curve to obtain a standardized logging curve; wherein the target well group comprises a target well and an old well;
determining the shale content, the reservoir porosity and the formation water mineralization before flooding of the target well group according to the standardized logging curve;
reconstructing a natural potential curve of the target well before flooding according to the argillaceous content of the old well and the water mineralization of the stratum before flooding;
inputting the standardized logging curve, the shale content curve and the reservoir porosity curve of the target well into a reconstruction model to obtain a resistivity curve before the target well is reconstructed by flooding;
and evaluating the flooding layer of the target well according to the reconstructed natural potential curve of the target well before flooding and the reconstructed resistivity curve of the target well before flooding.
2. The method of claim 1, wherein the well log comprises sonic moveout, natural gamma, and resistivity curves.
3. The method of claim 1, wherein the shale content of the target well group is determined according to the following formula:
wherein, VshIs the mud content, GR is the natural gamma curve, GRminIs the natural gamma curve minimum, GRmaxIs the natural gamma curve maximum.
4. The method of claim 1, wherein the reservoir porosity for the target set of wells is determined according to the formula:
wherein Phie is the porosity of the reservoir, delta tmaAcoustic time difference, Δ t, for sandstone frameworksfAcoustic time difference, Δ t, for pore fluidsshIs the acoustic time difference of a pure mudstone section, and delta t is an acoustic time difference logging curve VshIs the argillaceous content.
5. The method of claim 1, wherein the pre-flooded formation water mineralization of the target group of wells is determined according to the following method:
determining apparent formation water resistivity for the target well group according to the following formula:
wherein R iswaIn view of formation water resistivity, RtThe resistivity of the water layer in the reservoir of the target well group is shown, and the RLLD is a deep induction resistivity curve of the water layer in the reservoir of the target well group
Porosity of the water layer, and m is a cementation index;
and determining the formation water mineralization degree of the target well group before flooding according to the apparent formation water resistivity.
6. The method of claim 1, wherein reconstructing the pre-flooded natural potential curve of the target well from the muddiness content and the pre-flooded formation water salinity of the old well comprises:
determining a natural potential coefficient according to the shale content of the old well;
and reconstructing a natural potential curve of the target well before flooding according to the natural potential coefficient and the water mineralization of the stratum before flooding.
7. The method of claim 1, wherein the reconstructed model is obtained according to the following method:
taking a standardized well logging curve, a shale content curve and a porosity curve of an old well as a training set;
and training a pre-constructed model according to the training set, and taking the trained model as a reconstruction model.
8. The method of claim 1, wherein evaluating the flooding zone of the target well based on the reconstructed pre-flooding natural potential curve and the reconstructed pre-flooding resistivity curve of the target well comprises:
determining a flooding index according to the measured resistivity curve of the target well and the reconstructed resistivity curve of the target well before flooding;
determining a flooding process according to the actually measured natural potential curve of the target well and the reconstructed natural potential curve of the target well before flooding;
determining a comprehensive flooding index according to the flooding index and the flooding process;
and performing flooding identification on the target well according to the comprehensive flooding index, and dividing the flooding level of the target well.
9. A flooded layer evaluation apparatus, comprising:
the acquisition module is used for acquiring an actually measured logging curve of a target well group, and preprocessing the actually measured logging curve to obtain a standardized logging curve; wherein the target well group comprises a target well and an old well;
the determining module is used for determining the shale content, the reservoir porosity and the reservoir water mineralization before flooding of the target well group according to the standardized logging curve;
the first reconstruction module is used for reconstructing a natural potential curve of the target well before flooding according to the argillaceous content of the old well and the water mineralization of the stratum before flooding;
the second reconstruction module is used for inputting the standardized logging curve, the shale content curve and the reservoir porosity curve of the target well into the reconstruction model to obtain a reconstructed resistivity curve of the target well before flooding;
and the evaluation module is used for evaluating the water flooded layer of the target well according to the reconstructed natural potential curve of the target well before water flooding and the reconstructed resistivity curve of the target well before water flooding.
10. A computer readable storage medium having computer program instructions stored thereon that when executed implement: acquiring an actually measured logging curve of a target well group, and preprocessing the actually measured logging curve to obtain a standardized logging curve; wherein the target well group comprises a target well and an old well; determining the shale content, the reservoir porosity and the formation water mineralization before flooding of the target well group according to the standardized logging curve; reconstructing a natural potential curve of the target well before flooding according to the argillaceous content of the old well and the water mineralization of the stratum before flooding; inputting the standardized logging curve, the shale content curve and the reservoir porosity curve of the target well into a reconstruction model to obtain a resistivity curve before the target well is reconstructed by flooding; and evaluating the flooding layer of the target well according to the reconstructed natural potential curve of the target well before flooding and the reconstructed resistivity curve of the target well before flooding.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112360443A (en) * | 2020-08-19 | 2021-02-12 | 中国石油天然气股份有限公司 | Flooding level evaluation method based on rock resistance change rate and phase permeation coupling |
| CN112983411A (en) * | 2021-03-09 | 2021-06-18 | 中国石油大学(华东) | Method for estimating mixed liquor resistivity by using inspection well data |
| CN117332301A (en) * | 2023-10-17 | 2024-01-02 | 大庆油田有限责任公司 | Flooding layer interpretation method for reservoir classification evaluation |
Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1423135A (en) * | 2001-12-04 | 2003-06-11 | 曲贤才 | Radioactive isotope logging method for determining water logging degree of oil rock |
| US20100165789A1 (en) * | 2008-12-29 | 2010-07-01 | Schlumberger Technology Corporation | Method for determination of the oil formation water-flooding area pattern and size in the wellbore zone |
| CN103225500A (en) * | 2013-05-02 | 2013-07-31 | 中国石油大学(华东) | Novel water flooding layer logging evaluation method applying three parameters self-consistent iterative algorithm |
| CN104806231A (en) * | 2014-01-27 | 2015-07-29 | 中国石油化工股份有限公司 | Quantitative evaluation method of heavy oil steam stimulation water flooded layer |
| CN105447762A (en) * | 2015-12-08 | 2016-03-30 | 中国石油天然气集团公司 | Calculation method for low permeability reservoir flooding information of fluid replacement |
| CN106503295A (en) * | 2016-09-22 | 2017-03-15 | 中国石油天然气股份有限公司 | The method and device of oil field Water Flooding Layer explained by a kind of utilization state spatial model |
| CN106951660A (en) * | 2017-04-05 | 2017-07-14 | 中国石油天然气股份有限公司 | A kind of marine clastics horizontal well reservoir log interpretation method and device |
| CN107644110A (en) * | 2016-07-20 | 2018-01-30 | 中国石油大学(华东) | A kind of horizontal well water flooding degree evaluation method |
| CN107741605A (en) * | 2017-08-17 | 2018-02-27 | 中国海洋石油总公司 | The method that infinitesimal electrical conduction model based on time passage seeks Water Flooding Layer relevant parameter |
| WO2018052449A1 (en) * | 2016-09-19 | 2018-03-22 | Halliburton Energy Services, Inc. | Method of detecting substance saturation in a formation |
| CN109653725A (en) * | 2018-09-13 | 2019-04-19 | 山东鼎维石油科技有限公司 | A layer water flooding degree log interpretation method is stored up based on sedimentary micro and the mixed of rock phase |
| CN109670539A (en) * | 2018-12-03 | 2019-04-23 | 中国石油化工股份有限公司 | A kind of silt particle layer detection method based on log deep learning |
| CN109886559A (en) * | 2019-01-25 | 2019-06-14 | 中国石油天然气集团有限公司 | A kind of oil field Water Flooding Layer Fine structural interpretation and remaining oil comprehensive estimation method |
| CN110017136A (en) * | 2019-03-14 | 2019-07-16 | 中国石油天然气集团有限公司 | A kind of Water Flooding Layer identification and producing water ratio prediction technique based on view water layer resistivity |
-
2019
- 2019-12-23 CN CN201911334572.2A patent/CN111025409B/en active Active
Patent Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1423135A (en) * | 2001-12-04 | 2003-06-11 | 曲贤才 | Radioactive isotope logging method for determining water logging degree of oil rock |
| US20100165789A1 (en) * | 2008-12-29 | 2010-07-01 | Schlumberger Technology Corporation | Method for determination of the oil formation water-flooding area pattern and size in the wellbore zone |
| CN103225500A (en) * | 2013-05-02 | 2013-07-31 | 中国石油大学(华东) | Novel water flooding layer logging evaluation method applying three parameters self-consistent iterative algorithm |
| CN104806231A (en) * | 2014-01-27 | 2015-07-29 | 中国石油化工股份有限公司 | Quantitative evaluation method of heavy oil steam stimulation water flooded layer |
| CN105447762A (en) * | 2015-12-08 | 2016-03-30 | 中国石油天然气集团公司 | Calculation method for low permeability reservoir flooding information of fluid replacement |
| CN107644110A (en) * | 2016-07-20 | 2018-01-30 | 中国石油大学(华东) | A kind of horizontal well water flooding degree evaluation method |
| WO2018052449A1 (en) * | 2016-09-19 | 2018-03-22 | Halliburton Energy Services, Inc. | Method of detecting substance saturation in a formation |
| CN106503295A (en) * | 2016-09-22 | 2017-03-15 | 中国石油天然气股份有限公司 | The method and device of oil field Water Flooding Layer explained by a kind of utilization state spatial model |
| CN106951660A (en) * | 2017-04-05 | 2017-07-14 | 中国石油天然气股份有限公司 | A kind of marine clastics horizontal well reservoir log interpretation method and device |
| CN107741605A (en) * | 2017-08-17 | 2018-02-27 | 中国海洋石油总公司 | The method that infinitesimal electrical conduction model based on time passage seeks Water Flooding Layer relevant parameter |
| CN109653725A (en) * | 2018-09-13 | 2019-04-19 | 山东鼎维石油科技有限公司 | A layer water flooding degree log interpretation method is stored up based on sedimentary micro and the mixed of rock phase |
| CN109670539A (en) * | 2018-12-03 | 2019-04-23 | 中国石油化工股份有限公司 | A kind of silt particle layer detection method based on log deep learning |
| CN109886559A (en) * | 2019-01-25 | 2019-06-14 | 中国石油天然气集团有限公司 | A kind of oil field Water Flooding Layer Fine structural interpretation and remaining oil comprehensive estimation method |
| CN110017136A (en) * | 2019-03-14 | 2019-07-16 | 中国石油天然气集团有限公司 | A kind of Water Flooding Layer identification and producing water ratio prediction technique based on view water layer resistivity |
Non-Patent Citations (4)
| Title |
|---|
| 李桢 等: ""水淹层测井解释方法综述"", 《工程地球物理学报》 * |
| 王娜 等: ""水淹层测井评测方法在鄯善油田二次开发中的应用"", 《工程地球物理学报》 * |
| 石玉江 等: ""重构电阻率曲线识别水淹层的方法及应用"", 《测井技术》 * |
| 赵军 等: ""支持向量机在水淹层测井识别中的应用"", 《物探与化探》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112360443A (en) * | 2020-08-19 | 2021-02-12 | 中国石油天然气股份有限公司 | Flooding level evaluation method based on rock resistance change rate and phase permeation coupling |
| CN112360443B (en) * | 2020-08-19 | 2023-08-22 | 中国石油天然气股份有限公司 | Water flooding level evaluation method based on rock resistivity change rate and phase-seepage coupling |
| CN112983411A (en) * | 2021-03-09 | 2021-06-18 | 中国石油大学(华东) | Method for estimating mixed liquor resistivity by using inspection well data |
| CN117332301A (en) * | 2023-10-17 | 2024-01-02 | 大庆油田有限责任公司 | Flooding layer interpretation method for reservoir classification evaluation |
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