CN108090653B - Reservoir type identification method and device for reservoir - Google Patents
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Abstract
The embodiment of the application discloses a reservoir type identification method and device for a reservoir, wherein the method comprises the steps of obtaining the thickness of the reservoir, a gas logging total hydrocarbon value, a gas logging base value, a gas logging abnormal total hydrocarbon value and the thickness of a gas logging abnormal section at each sampling point of the reservoir according to gas logging actual measurement data of the reservoir to be identified; calculating the gas logging abnormal amplitude of the reservoir to be identified according to the thickness of the reservoir to be identified, the gas logging base value, the gas logging abnormal total hydrocarbon value and the thickness of the gas logging abnormal section; calculating the gas logging morphological variation coefficient of the reservoir to be identified according to the gas logging total hydrocarbon value at each sampling point of the reservoir; and determining the oil reservoir type of the reservoir to be identified according to the gas logging abnormal amplitude and the gas logging morphological variation coefficient of the reservoir to be identified and a preset classification rule. By utilizing the method and the device, the accuracy and the efficiency of reservoir type identification can be improved.
Description
Technical Field
The invention relates to the technical field of energy exploration, in particular to a reservoir type identification method and device for a reservoir.
Background
With the improvement of exploration degree, the types of oil and gas reservoirs are more and more complex and changeable, different exploration methods and development technologies are generally adopted for different types of oil and gas reservoirs, and the process is complex. How to quickly determine the type of the oil and gas reservoir becomes an important technical problem facing oil and gas exploration at present.
The traditional oil reservoir type identification mainly adopts methods such as gas logging and geochemical interpretation plates, the existing gas logging and geochemical interpretation plates are single in calculation method, and the calculation is mainly carried out by using gas logging component values and geochemical pyrolysis component values. For example, the interpretation and evaluation of the gas logging hydrocarbon reservoir mainly play an important role according to the height of the displayed value of the total hydrocarbon, the shape of the curve of the total hydrocarbon and the composition of the hydrocarbon components, particularly the composition of the hydrocarbon components. The one-point area evaluation method has high recognition rate on oil layers and water layers of single fluid, but has poor application effect on complex oil and gas layers (such as low permeability oil and gas layers). For example, gas logging of reservoirs with non-uniform fluid properties (such as oil and gas reservoirs, oil-water layers and the like) in the longitudinal direction is complex and variable, in the prior art, a method for comprehensive evaluation by artificially combining multi-parameter characteristics is mostly adopted, the evaluation process is complex, and the influence of human factors is large, so that the efficiency and the accuracy of reservoir type identification are influenced.
Therefore, there is a need in the art for a more accurate and efficient method for determining reservoir types.
Disclosure of Invention
The embodiment of the application aims to provide a reservoir type identification method and device for a reservoir, which can improve the accuracy and efficiency of reservoir type identification.
The method and the device for identifying the reservoir type of the reservoir are realized by the following steps:
a reservoir type identification method for a reservoir, comprising:
acquiring the thickness of the reservoir, the gas logging total hydrocarbon value at each sampling point of the reservoir, the gas logging basic value, the gas logging abnormal total hydrocarbon value and the thickness of a gas logging abnormal section according to gas logging actual measurement data of the reservoir to be identified;
calculating the gas logging abnormal amplitude of the reservoir to be identified according to the thickness of the reservoir to be identified, the gas logging base value, the gas logging abnormal total hydrocarbon value and the thickness of the gas logging abnormal section;
calculating the gas logging morphological variation coefficient of the reservoir to be identified according to the gas logging total hydrocarbon value at each sampling point of the reservoir;
and determining the oil reservoir type of the reservoir to be identified according to the gas logging abnormal amplitude and the gas logging morphological variation coefficient of the reservoir to be identified and a preset classification rule.
According to the reservoir type identification method of the reservoir, the gas logging abnormity amplitude of the reservoir to be identified is calculated according to the thickness of the reservoir to be identified, the gas logging basic value, the gas logging abnormity all-hydrocarbon value and the thickness of the gas logging abnormity section, and the method comprises the following steps:
acquiring gas logging abnormal total hydrocarbon values of the top depth and the bottom depth of the gas logging abnormal section according to the gas logging abnormal total hydrocarbon values;
calculating the geometric mean value of the gas logging abnormal total hydrocarbon values at the top depth and the bottom depth of the gas logging abnormal section to obtain a gas logging abnormal total hydrocarbon mean value;
and calculating the gas measurement abnormal amplitude of the reservoir to be identified according to the thickness of the reservoir to be identified, the gas measurement base value, the thickness of the gas measurement abnormal section and the gas measurement abnormal total hydrocarbon mean value.
According to the reservoir type identification method of the reservoir, the gas logging abnormity amplitude of the reservoir to be identified is calculated according to the thickness of the reservoir to be identified, the gas logging basic value, the thickness of the gas logging abnormity section and the gas logging abnormity total hydrocarbon average value, and the method comprises the following steps:
calculating the abnormal amplitude of the gas logging of the reservoir to be identified according to the following formula:
wherein QYD represents abnormal amplitude of gasometry,
The method comprises the steps of representing the average value of all hydrocarbons of gas logging abnormity, HQ representing the thickness of a gas logging abnormity section, TJ representing a gas logging basic value and HS representing the thickness of a reservoir to be identified.
According to the reservoir type identification method of the reservoir, the gas logging morphological variation coefficient of the reservoir to be identified is calculated according to the gas logging total hydrocarbon values of the sampling points of the reservoir, and the method comprises the following steps:
calculating the geometric mean value of the gas measurement total hydrocarbon values at each sampling point of the reservoir to obtain the gas measurement total hydrocarbon mean value;
and calculating the gas logging morphological variation coefficient of the reservoir to be identified according to the gas logging total hydrocarbon mean value and the gas logging total hydrocarbon value at each sampling point of the reservoir.
According to the reservoir type identification method for the reservoir, the gas logging morphological variation coefficient of the reservoir to be identified is calculated according to the gas logging total hydrocarbon mean value and the gas logging total hydrocarbon values at each sampling point of the reservoir, and the method comprises the following steps:
calculating the gas logging morphological variation coefficient of the reservoir to be identified according to the following formula:
QB represents the abnormal coefficient of gas-measuring form,
Representing the gas measurement total hydrocarbon value at each sampling point of the reservoir,
The average value of the total hydrocarbon of the gas logging is shown, and n represents the number of sampling points.
According to the reservoir type identification method of the reservoir, the reservoir type of the reservoir to be identified comprises an oil and gas migration channel, a gas reservoir, an oil and gas reservoir, a damaged ancient reservoir and a non-oil and gas migration channel.
The method for identifying the oil reservoir type of the reservoir stratum according to the embodiment of the application, which is used for determining the oil reservoir type of the reservoir stratum to be identified according to the gas logging abnormal amplitude and the gas logging morphological variation coefficient of the reservoir stratum to be identified and the preset classification rule, comprises the following steps:
counting the gas logging abnormal amplitude, the gas logging morphological variation coefficient and the oil reservoir type of the sample region, and drawing an oil reservoir type identification chart;
determining an oil reservoir type identification limit according to the oil reservoir type identification chart;
and determining the oil reservoir type of the reservoir section to be identified according to the gas logging abnormal amplitude of the reservoir section to be identified and the numerical value magnitude relation judgment result of the gas logging morphological variation coefficient relative to the oil reservoir type identification limit.
On the other hand, the embodiment of the present application further provides a reservoir type identification device for a reservoir, including:
the data acquisition module is used for acquiring the thickness of the reservoir, the gas logging total hydrocarbon value, the gas logging basic value, the gas logging abnormal total hydrocarbon value and the thickness of the gas logging abnormal section at each sampling point of the reservoir according to the gas logging actual measurement data of the reservoir to be identified;
the parameter calculation module is used for calculating the gas logging abnormal amplitude of the reservoir to be identified according to the thickness of the reservoir to be identified, the gas logging base value, the gas logging abnormal total hydrocarbon value and the thickness of the gas logging abnormal section, and calculating the gas logging morphological variation coefficient of the reservoir to be identified according to the gas logging total hydrocarbon value at each sampling point of the reservoir;
and the identification module is used for determining the oil reservoir type of the reservoir to be identified according to the gas logging abnormal amplitude and the gas logging morphological variation coefficient of the reservoir to be identified and a preset classification rule.
The oil reservoir type recognition device of the reservoir stratum of the embodiment of the application, the recognition module comprises:
and the oil reservoir type determining unit is used for determining the oil reservoir type of the reservoir to be identified according to the gas logging abnormal amplitude and the gas logging morphological variation coefficient of the reservoir to be identified and preset classification rules, wherein the oil reservoir type of the reservoir to be identified comprises an oil and gas migration channel, a gas reservoir, an oil and gas reservoir, a damaged ancient oil reservoir and a non-oil and gas migration channel.
The reservoir type identification device of the reservoir comprises a processor and a memory for storing processor executable instructions, wherein the instructions are executed by the processor to realize the following steps:
acquiring the thickness of the reservoir, the gas logging total hydrocarbon value at each sampling point of the reservoir, the gas logging basic value, the gas logging abnormal total hydrocarbon value and the thickness of a gas logging abnormal section according to gas logging actual measurement data of the reservoir to be identified;
calculating the gas logging abnormal amplitude of the reservoir to be identified according to the thickness of the reservoir to be identified, the gas logging base value, the gas logging abnormal total hydrocarbon value and the thickness of the gas logging abnormal section;
calculating the gas logging morphological variation coefficient of the reservoir to be identified according to the gas logging total hydrocarbon value at each sampling point of the reservoir to be identified;
and determining the oil reservoir type of the reservoir to be identified according to the gas logging abnormal amplitude and the gas logging morphological variation coefficient of the reservoir to be identified and a preset classification rule.
According to the method and the device for identifying the reservoir type of the reservoir, gas logging actual measurement data of the reservoir to be identified can be obtained, and the gas logging abnormal amplitude and the gas logging morphological variation coefficient of the reservoir to be identified can be calculated according to the gas logging actual measurement data, so that the gas logging abnormal value, the longitudinal fullness and the gas logging curvilinear morphological variation characteristics of the reservoir can be comprehensively represented by the gas logging abnormal amplitude and the gas logging morphological variation coefficient. By utilizing the method and the device, the accuracy and the efficiency of reservoir type identification can be improved.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only some embodiments described in the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without any creative effort. In the drawings:
FIG. 1 is a schematic flow chart diagram illustrating an embodiment of a reservoir type identification method for a reservoir provided herein;
FIG. 2 is a schematic illustration of a reservoir type identification plate in one embodiment provided herein;
fig. 3 is a schematic block diagram of an embodiment of a reservoir type identification apparatus for a reservoir provided in this specification.
Detailed Description
In order to make those skilled in the art better understand the technical solutions in the present specification, the technical solutions in one or more embodiments of the present specification will be clearly and completely described below with reference to the drawings in one or more embodiments of the present specification, and it is obvious that the described embodiments are only a part of the embodiments of the specification, and not all embodiments. All other embodiments obtained by a person skilled in the art based on one or more embodiments of the present specification without making any creative effort shall fall within the protection scope of the embodiments of the present specification.
Generally, the reservoir types are explained and evaluated based on gas logging, and the comprehensive analysis is mainly based on factors such as the display value of gas-logging total hydrocarbons, the curve form of the total hydrocarbons, the composition of hydrocarbon components and the like, but the application effect on some complex hydrocarbon reservoirs is poor, such as complicated and variable reservoir types of basins and more low-permeability hydrocarbon reservoirs. For example, if the hydrocarbon anomaly is high for some reservoirs but the hydrocarbon fullness is low in the longitudinal direction, then the reservoir belongs to an unsaturated reservoir. In addition, the type of the oil and gas layer is further determined by considering the morphological characteristics of the total hydrocarbon curve, so that the determination process of the type of the oil and gas layer is complex and low in efficiency. In the prior art, a method for carrying out comprehensive evaluation by artificially combining multi-parameter characteristics is mostly adopted, but the evaluation process is complex, and the influence of human factors is large, so that the efficiency and the accuracy of oil reservoir type identification are influenced.
The embodiment of the specification provides a reservoir type identification method for a reservoir, which utilizes the abnormal gas logging amplitude to quantitatively represent the height of the abnormal gas logging display value of the reservoir and the vertical plumpness of the abnormal gas logging (namely the vertical filling degree of oil and gas in the oil and gas reservoir), and utilizes the abnormal gas logging form variation coefficient to quantitatively represent the change characteristic of the gas logging curve form. And then, quantitatively representing the characteristics of the oil-gas reservoir by comprehensively judging the abnormal gas logging amplitude and the change characteristics of the abnormal gas logging morphological variation coefficient, thereby improving the accuracy and efficiency of determining the oil reservoir type.
Fig. 1 is a schematic flow chart of an embodiment of a method for identifying reservoir types of a reservoir provided in the present specification. Although the present specification provides the method steps or apparatus structures as shown in the following examples or figures, more or less steps or modules may be included in the method or apparatus structures based on conventional or non-inventive efforts. In the case of steps or structures which do not logically have the necessary cause and effect relationship, the execution order of the steps or the block structure of the apparatus is not limited to the execution order or the block structure shown in the embodiments or the drawings of the present specification. When the described method or module structure is applied to a device, a server or an end product in practice, the method or module structure according to the embodiment or the figures may be executed sequentially or in parallel (for example, in a parallel processor or multi-thread processing environment, or even in an implementation environment including distributed processing and server clustering).
In a specific embodiment, as shown in fig. 1, in an embodiment of a method for identifying reservoir types of a reservoir provided in the present specification, the method may include:
s2, acquiring the thickness of the reservoir, the gas logging total hydrocarbon value, the gas logging basic value, the gas logging abnormal total hydrocarbon value and the thickness of the gas logging abnormal section at each sampling point of the reservoir according to the gas logging actual measurement data of the reservoir to be identified;
s4, calculating the gas logging abnormal amplitude of the reservoir to be identified according to the thickness of the reservoir to be identified, the gas logging base value, the gas logging abnormal total hydrocarbon value and the thickness of the gas logging abnormal section;
s6, calculating a gas logging morphological variation coefficient of the reservoir to be identified according to the gas logging total hydrocarbon value at each sampling point of the reservoir to be identified;
and S8, determining the oil reservoir type of the reservoir to be identified according to the gas logging abnormal amplitude and the gas logging morphological variation coefficient of the reservoir to be identified and preset classification rules.
In this embodiment, for a certain reservoir to be identified, the gas logging actual measurement data of the reservoir to be identified may be obtained by using a gas logging technology. In an embodiment of the present specification, the thickness HS of the reservoir to be identified, the gas logging base value TJ, the thickness HQ of the abnormal gas logging section of the reservoir to be identified, and the abnormal total hydrocarbon value TG of the reservoir to be identified may be selected from the gas logging actual measurement data of the reservoir to be identifiedDifferent from each otherAnd measuring the total hydrocarbon value TG of the gas at each sampling point of the reservoir to be identifiedRen。
In one embodiment of the present description, the reservoir thickness HS, the gas-measured base value TJ, and the gas-measured abnormal total hydrocarbon value TG may be identified according to the reservoir thickness HS, the gas-measured base value TJ, and the gas-measured abnormal total hydrocarbon value TGDifferent from each otherAnd calculating the gas measurement abnormal amplitude of the reservoir to be identified by the thickness HQ of the gas measurement abnormal section. In another embodiment of the present specification, the gas logging morphological variation coefficient of the reservoir to be identified may be calculated according to the gas logging total hydrocarbon value at any point of the reservoir to be identified. One or more of the present specificationIn one embodiment, the gas logging anomaly amplitude and the gas logging morphological variation coefficient may be calculated by numerical simulation, a construction function, and the like.
In the above examples of the present specification, the abnormal total hydrocarbon value TG is measured by comprehensively considering the gasDifferent from each otherThe gas measurement abnormal amplitude is calculated according to the gas measurement basic value TJ, the reservoir thickness HS and the thickness HQ of the gas measurement abnormal section, so that the gas measurement abnormal amplitude can be used for comprehensively representing the height of the gas measurement abnormal value and the distribution characteristics of the gas measurement abnormal in the longitudinal direction, namely the height of the gas measurement abnormal value and the plumpness of the gas measurement abnormal in the longitudinal direction can be comprehensively represented through the gas measurement abnormal amplitude. In addition, the variation coefficient may be generally used to represent the size of data dispersion degree, and in this embodiment, the reservoir gas logging morphological variation coefficient is calculated and determined by analyzing the gas logging total hydrocarbon value data in the reservoir, so that the reservoir gas logging total hydrocarbon curve morphological characteristics are quantitatively represented by using the gas logging morphological variation coefficient. And then, determining the reservoir type of the reservoir to be identified according to the gas logging abnormal amplitude and the gas logging morphological variation coefficient of the reservoir to be identified and a preset classification rule, thereby improving the efficiency and the accuracy of reservoir type identification.
In one embodiment of the present specification, the gas logging abnormal total hydrocarbon values at the top depth and the bottom depth of the gas logging abnormal section may be obtained according to the gas logging abnormal total hydrocarbon values, the geometric mean value of the gas logging abnormal total hydrocarbon values at the top depth and the bottom depth of the gas logging abnormal section may be calculated, and the geometric mean value may be determined as the gas logging abnormal total hydrocarbon mean value. And then, calculating the gas logging abnormity amplitude of the reservoir to be identified according to the thickness of the reservoir to be identified, the gas logging base value, the thickness of the gas logging abnormal section and the gas logging abnormal total hydrocarbon average value. The gas logging abnormal total hydrocarbon mean value is used for replacing the gas logging abnormal total hydrocarbon value measured at each point to calculate the gas logging abnormal amplitude, and the efficiency of reservoir oil deposit type identification can be further improved. In one or more embodiments of the present description, the gas logging anomaly amplitude of the reservoir to be identified may be calculated according to the following formula:
wherein QYD represents the abnormal amplitude of gasometry,
The method comprises the steps of representing the average value of abnormal total hydrocarbons of gas logging, HQ representing the thickness of the abnormal section of the reservoir to be identified, TJ representing the gas logging base value and HS representing the thickness of the reservoir to be identified. In this embodiment, the gas logging anomaly amplitude is calculated by the above formula, so that the accuracy of determining the gas logging anomaly amplitude can be further improved, and the accuracy of identifying the reservoir oil deposit type can be further improved.
In another embodiment of the present specification, the gas logging total hydrocarbon value and the number of sampling points in each sampling point in the reservoir to be identified may be obtained according to the gas logging actual measurement data, a geometric average value of the gas logging total hydrocarbon values of each sampling point in the reservoir to be identified is calculated, and the geometric average value is determined as the gas logging total hydrocarbon average value. And then, calculating the gas logging morphological variation coefficient of the reservoir to be identified according to the gas logging total hydrocarbon average value, the gas logging total hydrocarbon value of each sampling point and the number of the sampling points. In one or more embodiments of the present description, the gas logging morphological variation coefficient of the reservoir to be identified may be calculated according to the following formula:
QB represents the abnormal coefficient of gas-measuring form,
A gas measurement total hydrocarbon value representing a sampling point in the reservoir to be identified,
The expression-average value of the total hydrocarbon measured by gas and n the number of sampling points. By utilizing the embodiment of the specification, the accuracy of determining the gas logging morphological variation coefficient can be further improved, so that the accuracy of identifying the reservoir oil deposit type is further improved.
In other embodiments of the present disclosure, the gas sensing anomaly amplitude and the gas sensing morphological anomaly coefficient may be calculated according to other functional forms, which is not limited herein.
In an embodiment of the present specification, the reservoir type of the reservoir to be identified may be determined according to a preset classification rule according to the gas logging anomaly amplitude and the gas logging morphological variation coefficient of the reservoir to be identified, where the reservoir type of the reservoir to be identified includes an oil and gas migration channel, a gas reservoir, an oil and gas reservoir, a damaged ancient oil reservoir, and a non-oil and gas migration channel.
In an embodiment of the present specification, in a process of determining a reservoir type according to a preset classification rule, a region of the determined reservoir type adjacent to a region to be identified, or a region of the determined reservoir type close to a formation property of the region to be identified may be used as a sample region, and a gas logging anomaly amplitude, a gas logging morphological variation curve and a corresponding sample reservoir type of the sample region are counted to obtain a numerical distribution characteristic of the sample region. And determining the identification limit of the reservoir type according to the numerical distribution characteristics of the sample region. And then, determining the oil reservoir type of the reservoir section to be identified according to the judgment result of the numerical value size relationship between the gas measurement abnormal amplitude and the gas measurement morphological variation coefficient of the reservoir section to be identified and the identification limit.
In one or more embodiments of the present description, an oil reservoir type identification chart may be drawn according to the gas logging anomaly amplitude, the gas logging morphological variation coefficient, and the sample reservoir oil reservoir type of the statistical sample region; and determining an identification limit according to the oil reservoir type identification chart. For example, a parameter chart can be established by taking the gas logging abnormal amplitude and the gas logging morphological variation coefficient as horizontal and vertical coordinates, the gas logging abnormal amplitude, the gas logging morphological variation coefficient and the oil reservoir type data of the sample area are obtained, and the identification limit is determined by dividing according to different oil reservoir types. According to the scheme provided by the embodiment of the specification, the oil reservoir type is identified through the oil reservoir type identification chart, and the efficiency and the accuracy of identifying the oil reservoir type of the reservoir are further improved.
In order to make the solution in the embodiment provided in the present specification clearer, the present specification also provides a specific example of an actual region to be measured to which the above-described solution is applied. In one embodiment of the present disclosure, as shown in table 1 and fig. 2, table 1 shows statistical gas logging anomaly amplitude and gas logging morphological variation coefficient of a certain sample region, and corresponding reservoir type and oil testing data; FIG. 2 shows an identification chart built from the data of Table 1, with different marker points marking the reservoir type and identification boundaries determined from different divisions of the corresponding reservoir type.
The recognition limit determined from the recognition plate is as follows:
oil and gas migration channel: QYD <10, QB >0.593, or 2< QYD <10, QB < 0.593;
gas reservoir: QYD >72, QB > 0.73;
oil and gas reservoir: 72> QYD >20 or 72< QYD, QB < 0.72;
and (3) destroying the ancient oil reservoir: 10< QYD < 20;
non-oil migration channel: QYD <2, QB < 0.593;
aiming at a certain area to be identified, selecting a new well on site, calculating by using the scheme provided by the embodiment of the specification to obtain QYD and QB values of a designed perforation section of the well, wherein the QB values are 189 and 0.98 respectively, and the interval is found in a gas reservoir range in a map plate in a punctuation mode, so that the interval is predicted to be a gas reservoir; the actual oil test data is 53.2 multiplied by 10 of daily produced gas4m3The method is consistent with the prediction result, which shows that the identification method of the invention is more accurate and can be effectively applied to actual energy exploration.
TABLE 1 statistical table of gas test data, oil test data and oil reservoir identification
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. For details, reference may be made to the description of the related embodiments of the related processing, and details are not repeated herein.
The foregoing description has been directed to specific embodiments of this disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
According to the oil reservoir type identification method for the reservoir, which is provided by one or more embodiments of the specification, the gas logging actual measurement data of the reservoir to be identified can be obtained, and the gas logging abnormal amplitude and the gas logging morphological variation coefficient of the reservoir to be identified are calculated according to the gas logging actual measurement data, so that the gas logging abnormal value size, the longitudinal fullness and the gas logging curve morphological variation characteristics of the reservoir can be comprehensively represented by using the gas logging abnormal amplitude and the gas logging morphological variation coefficient. By utilizing the method and the device, the accuracy and the efficiency of reservoir type identification can be improved.
Based on the reservoir type identification method for the reservoir, one or more embodiments of the specification further provide a reservoir type identification device for the reservoir. The apparatus may include systems, software (applications), modules, components, servers, etc. that utilize the methods described in the embodiments of the present specification in conjunction with hardware implementations as necessary. Based on the same innovative conception, embodiments of the present specification provide an apparatus as described in the following embodiments. Since the implementation scheme of the apparatus for solving the problem is similar to that of the method, the specific implementation of the apparatus in the embodiment of the present specification may refer to the implementation of the foregoing method, and repeated details are not repeated. As used hereinafter, the term "unit" or "module" may be a combination of software and/or hardware that implements a predetermined function. Although the means described in the embodiments below are preferably implemented in software, an implementation in hardware, or a combination of software and hardware is also possible and contemplated. Specifically, fig. 3 is a schematic block diagram of an embodiment of a reservoir type identification apparatus for a reservoir provided in this specification, and as shown in fig. 3, the apparatus may include:
the data acquisition module 102 may acquire the thickness of the reservoir, the gas logging total hydrocarbon value at each sampling point of the reservoir, the gas logging base value, the gas logging abnormal total hydrocarbon value, and the thickness of the gas logging abnormal section according to the gas logging actual measurement data of the reservoir to be identified;
the parameter calculation module 104 can calculate the gas logging abnormal amplitude of the reservoir to be identified according to the thickness of the reservoir to be identified, the gas logging base value, the gas logging abnormal total hydrocarbon value and the thickness of the gas logging abnormal section, and calculate the gas logging morphological variation coefficient of the reservoir to be identified according to the gas logging total hydrocarbon value at each sampling point of the reservoir;
the identification module 106 may determine the oil reservoir type of the reservoir to be identified according to a preset classification rule according to the gas logging abnormal amplitude and the gas logging morphological variation coefficient of the reservoir to be identified.
Of course, in other embodiments of the apparatus, the identification module 106 may include a reservoir type identification unit, as described with reference to the previous method embodiments. The oil reservoir type identification unit can be used for determining the oil reservoir type of the reservoir to be identified according to the gas logging abnormal amplitude and the gas logging morphological variation coefficient of the reservoir to be identified and preset classification rules, wherein the oil reservoir type of the reservoir to be identified comprises an oil and gas migration channel, a gas reservoir, an oil and gas reservoir, a damaged ancient oil reservoir and a non-oil and gas migration channel.
It should be noted that the above-described apparatus may also include other embodiments according to the description of the method embodiment. The specific implementation manner may refer to the description of the related method embodiment, and is not described in detail herein.
According to the oil reservoir type identification device for the reservoir, which is provided by one or more embodiments of the specification, the gas logging actual measurement data of the reservoir to be identified can be acquired, and the gas logging abnormal amplitude and the gas logging morphological variation coefficient of the reservoir to be identified are calculated according to the gas logging actual measurement data, so that the gas logging abnormal value size, the longitudinal fullness and the gas logging curve morphological variation characteristic of the reservoir can be comprehensively represented by using the gas logging abnormal amplitude and the gas logging morphological variation coefficient. By utilizing the method and the device, the accuracy and the efficiency of reservoir type identification can be improved.
The method or apparatus provided by the present specification and described in the foregoing embodiments may implement service logic through a computer program and record the service logic on a storage medium, where the storage medium may be read and executed by a computer, so as to implement the effect of the solution described in the embodiments of the present specification. Accordingly, the present specification also provides a reservoir type identification device for a reservoir, comprising a processor and a memory storing processor-executable instructions, which when executed by the processor, implement steps comprising:
acquiring the thickness of the reservoir, the gas logging total hydrocarbon value at each sampling point of the reservoir, the gas logging basic value, the gas logging abnormal total hydrocarbon value and the thickness of a gas logging abnormal section according to gas logging actual measurement data of the reservoir to be identified;
calculating the gas logging abnormal amplitude of the reservoir to be identified according to the thickness of the reservoir to be identified, the gas logging base value, the gas logging abnormal total hydrocarbon value and the thickness of the gas logging abnormal section;
calculating the gas logging morphological variation coefficient of the reservoir to be identified according to the gas logging total hydrocarbon value at each sampling point of the reservoir to be identified;
and determining the oil reservoir type of the reservoir to be identified according to the gas logging abnormal amplitude and the gas logging morphological variation coefficient of the reservoir to be identified and a preset classification rule.
The storage medium may include a physical device for storing information, and typically, the information is digitized and then stored using an electrical, magnetic, or optical media. The storage medium may include: devices that store information using electrical energy, such as various types of memory, e.g., RAM, ROM, etc.; devices that store information using magnetic energy, such as hard disks, floppy disks, tapes, core memories, bubble memories, and usb disks; devices that store information optically, such as CDs or DVDs. Of course, there are other ways of storing media that can be read, such as quantum memory, graphene memory, and so forth.
It should be noted that the above-described apparatus may also include other embodiments according to the description of the method embodiment. The specific implementation manner may refer to the description of the related method embodiment, and is not described in detail herein.
The oil reservoir type identification device of the reservoir in the embodiment can calculate the gas logging abnormal amplitude and the gas logging morphological variation coefficient of the reservoir to be identified according to the gas logging actual measurement data by acquiring the gas logging actual measurement data of the reservoir to be identified, so that the gas logging abnormal amplitude and the gas logging morphological variation coefficient are utilized to comprehensively represent the size of the reservoir gas logging abnormal value, the longitudinal fullness and the gas logging curvilinear morphological variation characteristic. By utilizing the method and the device, the accuracy and the efficiency of reservoir type identification can be improved.
It should be noted that, the apparatus described above in this specification may also include other implementation manners according to the description of the related method embodiment, and specific implementation manners may refer to the description of the method embodiment, which is not described in detail herein. 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 hardware + program class, storage medium + program embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and for the relevant points, refer to the partial description of the method embodiment.
The foregoing description has been directed to specific embodiments of this disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
The apparatuses, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or implemented 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 vehicle-mounted human-computer interaction device, a cellular telephone, a camera phone, a smart phone, 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.
For convenience of description, the above devices are described as being divided into various modules by functions, and are described separately. Of course, when implementing one or more of the present description, the functions of each module may be implemented in one or more software and/or hardware, or a module implementing the same function may be implemented by a combination of multiple sub-modules or sub-units, etc. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
Those skilled in the art will also appreciate that, in addition to implementing the controller as pure computer readable program code, the same functionality can be implemented by logically programming method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Such a controller may therefore be considered as a hardware component, and the means included therein for performing the various functions may also be considered as a structure within the hardware component. Or even means for performing the functions may be regarded as being both a software module for performing the method and a structure within a hardware component.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method or apparatus that comprises the element.
As will be appreciated by one skilled in the art, one or more embodiments of the present description may be provided as a method, apparatus or computer program product. Accordingly, one or more embodiments of the present description may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, one or more embodiments of the present description may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
One or more embodiments of the present 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. One or more embodiments of the present specification can 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.
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. In the description of the specification, reference to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the specification. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (6)
1. A reservoir type identification method for a reservoir is characterized by comprising the following steps:
acquiring the thickness of the reservoir, the gas logging total hydrocarbon value at each sampling point of the reservoir, the gas logging basic value, the gas logging abnormal total hydrocarbon value and the thickness of a gas logging abnormal section according to gas logging actual measurement data of the reservoir to be identified;
calculating the abnormal gas logging amplitude of the reservoir to be identified according to the thickness of the reservoir to be identified, the gas logging base value, the abnormal gas logging total hydrocarbon value and the thickness of the abnormal gas logging section, and the method comprises the following steps:
wherein QYD represents abnormal amplitude of gasometry,
Representing the average value of abnormal total hydrocarbons of gas logging, HQ representing the thickness of an abnormal section of gas logging, TJ representing the base value of gas logging, and HS representing the thickness of a reservoir to be identified; the average value of the gas logging abnormal total hydrocarbon is the geometric average value of the gas logging abnormal total hydrocarbon values at the top depth and the bottom depth of the gas logging abnormal section;
calculating the gas logging morphological variation coefficient of the reservoir to be identified according to the gas logging total hydrocarbon value at each sampling point of the reservoir, including;
QB represents the abnormal coefficient of gas-measuring form,
Representing the gas measurement total hydrocarbon value at each sampling point of the reservoir,
Representing the average value of the total hydrocarbon of gas logging, and n representing the number of sampling points; the gas measurement total hydrocarbon mean value is a geometric mean value of gas measurement total hydrocarbon values at each sampling point of the reservoir to be identified;
and determining the oil reservoir type of the reservoir to be identified according to the gas logging abnormal amplitude and the gas logging morphological variation coefficient of the reservoir to be identified and a preset classification rule.
2. The reservoir type identification method for the reservoir according to claim 1, wherein the reservoir type of the reservoir to be identified comprises an oil and gas migration channel, a gas reservoir, a hydrocarbon reservoir, a damaged ancient reservoir and a non-oil and gas migration channel.
3. The reservoir type identification method for the reservoir according to claim 1, wherein the determining the reservoir type of the reservoir to be identified according to the gas logging abnormal amplitude and the gas logging morphological variation coefficient of the reservoir to be identified and according to a preset classification rule comprises:
counting the gas logging abnormal amplitude, the gas logging morphological variation coefficient and the oil reservoir type of the sample region, and drawing an oil reservoir type identification chart;
determining an oil reservoir type identification limit according to the oil reservoir type identification chart;
and determining the oil reservoir type of the reservoir section to be identified according to the gas logging abnormal amplitude of the reservoir section to be identified and the numerical value magnitude relation judgment result of the gas logging morphological variation coefficient relative to the oil reservoir type identification limit.
4. An apparatus for identifying a reservoir type of a reservoir, comprising:
the data acquisition module is used for acquiring the thickness of the reservoir, the gas logging total hydrocarbon value, the gas logging basic value, the gas logging abnormal total hydrocarbon value and the thickness of the gas logging abnormal section at each sampling point of the reservoir according to the gas logging actual measurement data of the reservoir to be identified;
the parameter calculation module is used for calculating the gas logging abnormal amplitude of the reservoir to be identified according to the thickness of the reservoir to be identified, the gas logging base value, the gas logging abnormal total hydrocarbon value and the thickness of the gas logging abnormal section, and comprises the following steps:
wherein QYD represents abnormal amplitude of gasometry,
Representing the average value of abnormal total hydrocarbons of gas logging, HQ representing the thickness of an abnormal section of gas logging, TJ representing the base value of gas logging, and HS representing the thickness of a reservoir to be identified; the average value of the gas logging abnormal total hydrocarbon is the geometric average value of the gas logging abnormal total hydrocarbon values at the top depth and the bottom depth of the gas logging abnormal section;
and is also used for calculating the gas logging morphological variation coefficient of the reservoir to be identified according to the gas logging total hydrocarbon value at each sampling point of the reservoir, including;
QB represents the abnormal coefficient of gas-measuring form,
Representing the gas measurement total hydrocarbon value at each sampling point of the reservoir,
Representing the average value of the total hydrocarbon of gas logging, and n representing the number of sampling points; the gas measurement total hydrocarbon mean value is a geometric mean value of gas measurement total hydrocarbon values at each sampling point of the reservoir to be identified;
and the identification module is used for determining the oil reservoir type of the reservoir to be identified according to the gas logging abnormal amplitude and the gas logging morphological variation coefficient of the reservoir to be identified and a preset classification rule.
5. The reservoir type identification device of the reservoir according to claim 4, wherein the identification module comprises:
and the oil reservoir type determining unit is used for determining the oil reservoir type of the reservoir to be identified according to the gas logging abnormal amplitude and the gas logging morphological variation coefficient of the reservoir to be identified and preset classification rules, wherein the oil reservoir type of the reservoir to be identified comprises an oil and gas migration channel, a gas reservoir, an oil and gas reservoir, a damaged ancient oil reservoir and a non-oil and gas migration channel.
6. An apparatus for reservoir type identification of a reservoir, comprising a processor and a memory for storing processor-executable instructions, which when executed by the processor, implement steps comprising:
acquiring the thickness of the reservoir, the gas logging total hydrocarbon value at each sampling point of the reservoir, the gas logging basic value, the gas logging abnormal total hydrocarbon value and the thickness of a gas logging abnormal section according to gas logging actual measurement data of the reservoir to be identified;
calculating the abnormal gas logging amplitude of the reservoir to be identified according to the thickness of the reservoir to be identified, the gas logging base value, the abnormal gas logging total hydrocarbon value and the thickness of the abnormal gas logging section, and the method comprises the following steps:
wherein QYD represents abnormal amplitude of gasometry,
Representing the average value of abnormal total hydrocarbons of gas logging, HQ representing the thickness of an abnormal section of gas logging, TJ representing the base value of gas logging, and HS representing the thickness of a reservoir to be identified; the average value of the gas logging abnormal total hydrocarbon is the geometric average value of the gas logging abnormal total hydrocarbon values at the top depth and the bottom depth of the gas logging abnormal section;
calculating the gas logging morphological variation coefficient of the reservoir to be identified according to the gas logging total hydrocarbon value at each sampling point of the reservoir to be identified, including;
QB represents the abnormal coefficient of gas-measuring form,
Representing the gas measurement total hydrocarbon value at each sampling point of the reservoir,
Representing the average value of the total hydrocarbon of gas logging, and n representing the number of sampling points; the gas measurement total hydrocarbon mean value is a geometric mean value of gas measurement total hydrocarbon values at each sampling point of the reservoir to be identified;
and determining the oil reservoir type of the reservoir to be identified according to the gas logging abnormal amplitude and the gas logging morphological variation coefficient of the reservoir to be identified and a preset classification rule.
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