A method for identifying a fractured-vug reservoir type

Abstract

The application provides a method and device for identifying a fractured-vug reservoir type, a computer readable storage medium and an electronic device. The method comprises determining a lithology type of a reservoir in a study area and a reservoir type of the reservoir; analyzing reservoir porosity of the study area; obtaining conventional well logging curves of the study area, and determining sensitive well logging curves and sensitive well logging response characteristics that can be used to identify the reservoir type of the study area based on the conventional well logging curves; establishing identification criteria for different types of reservoirs based on the porosity of different types of reservoirs, the difference in the overlapping amplitude of the sensitive well logging curves and the values of the sensitive well logging response characteristics; and identifying and dividing the reservoir type of the study area by using the identification criteria for different types of reservoirs. The identification criteria for the reservoir type established by the application can accurately and intuitively identify different types of reservoirs, and can greatly improve the accuracy of well logging technology in identifying fractured-vug reservoirs, thereby guiding the exploration and development of oilfields.

Description

Technical Field

[0001] This invention relates to the field of oil and gas exploration well detection technology, and in particular to a method, apparatus, computer-readable storage medium, and electronic device for identifying fractured reservoir types in tight sandstone. Background Technology

[0002] Using natural gamma or spontaneous potential logging curves is a common method for reservoir identification in well logging technology. For conventional reservoirs with simple lithology, the reservoir exhibits low natural gamma values ​​and negative spontaneous potential anomalies in the logging response, resulting in good identification performance. However, for tight sandstone fractured reservoirs with fractures and pores, there are three types of reservoirs: porous, fracture-porosity, and fracture-pore. Using only natural gamma or spontaneous potential curves cannot effectively distinguish between these three reservoir types, leading to drawbacks such as easy misjudgment and low identification accuracy, ultimately failing to achieve satisfactory identification precision.

[0003] In a certain area of ​​the Ordos Basin in China, fractured reservoirs are characterized by several challenges. First, mudstone has poor reservoir properties, and only sandstone offers the potential for effective reservoir development. Second, these reservoirs are controlled by strike-slip faults and, based on the degree of formation fracturing, are classified into three reservoir types: porous, fracture-porous, and fracture-void. The more developed the fractures and pores, the greater the reservoir porosity. Third, while core, drilling, and lost circulation data indicate differences in logging response characteristics among different reservoir types, the logging sensitivity curves, identification methods, and identification criteria remain unclear. Therefore, the identification process for fractured reservoir types should include four parts: lithological identification, porosity calculation, selection of optimal logging sensitivity curves, and reservoir identification methods. Currently, most existing techniques rely solely on a single logging curve for reservoir type identification, which is difficult and inaccurate. How to optimize sensitive curves and identification methods based on lithology identification, porosity calculation, and logging response, and establish a logging standard suitable for identifying fractured reservoirs, has always been a challenge in the exploration and development of this type of reservoir. Summary of the Invention

[0004] To address the aforementioned problems, embodiments of the present invention provide a method, apparatus, computer-readable storage medium, and electronic device for identifying fractured reservoir types.

[0005] In a first aspect, embodiments of the present invention provide a method for identifying fractured reservoir types, including:

[0006] S100, determine the lithological type and reservoir type of the reservoir in the study area; wherein the reservoir type includes at least one of the following: porous, fracture-porous, and fracture-cavity.

[0007] S200, analyzes the reservoir porosity in the study area;

[0008] S300, acquire conventional logging curves for the study area, and determine sensitive logging curves and sensitive logging response characteristics that can be used to identify reservoir types in the study area based on the conventional logging curves;

[0009] S400 establishes identification criteria for different types of reservoirs based on the porosity of different types of reservoirs, the difference in overlap amplitude of sensitive logging curves, and the values ​​of sensitive logging response characteristics.

[0010] S500 uses different reservoir identification standards to identify and classify reservoir types in the study area.

[0011] According to an embodiment of the present invention, step S100 includes:

[0012] Based on core observation and thin section analysis results, the lithological type of the reservoir in the study area was determined using natural gamma logging curves.

[0013] According to an embodiment of the present invention, the above-mentioned method of determining the lithological type of the reservoir in the study area using natural gamma logging curves includes:

[0014] When the natural gamma ray (GR) value is less than the preset first threshold and the mud content (SH) value is less than the preset second threshold, the well logging lithology of the study area is determined to be sandstone.

[0015] When the natural gamma ray (GR) value is greater than the preset first threshold and the mud content (SH) value is greater than the preset second threshold, the well logging lithology of the study area is determined to be mudstone.

[0016] According to an embodiment of the present invention, step S100 includes:

[0017] Based on the drilling time variation, well leakage size, trench rise, and imaging logging data calibration in the study area, the degree of fracture and cavity development in the study area is determined, and the reservoir type of the oil reservoir in the study area is identified.

[0018] According to an embodiment of the present invention, step S200 includes:

[0019] Acquire the sonic transit time logging curves of the study area, and use the sonic transit time logging curves to calculate reservoir porosity.

[0020] According to an embodiment of the present invention, step S300 includes:

[0021] Obtain the natural gamma logging curves, eight-lateral resistivity logging curves, and sonic transit time logging curves of the reservoir;

[0022] By creating an eight-lateral resistivity-sonic transit time cross plot and a natural gamma-ray-sonic transit time cross plot, the sensitive logging curves are determined to be the eight-lateral resistivity logging curve and the sonic transit time logging curve. The sensitive logging response characteristics are determined to be the natural gamma logging response characteristics, the eight-lateral resistivity logging response characteristics, and the sonic transit time logging response characteristics.

[0023] According to an embodiment of the present invention, step S400 includes:

[0024] The eight-lateral resistivity logging curves and the sonic transit time logging curves are overlaid at a specified scale. Based on the difference in overlap amplitude between the eight-lateral resistivity logging curves and the sonic transit time logging curves of different reservoir types, porosity, and the values ​​of natural gamma, eight-lateral resistivity, and sonic transit time, identification criteria for different reservoir types are established.

[0025] In a second aspect, the present invention also provides an apparatus, characterized in that it comprises:

[0026] The type determination module is used to determine the lithological type and reservoir type of the reservoir in the study area; wherein the reservoir type includes at least one of the following types: porous, fracture-porous, and fracture-cavity.

[0027] The porosity analysis module is used to analyze the reservoir porosity in the study area.

[0028] The parameter selection module is used to acquire conventional logging curves of the study area and, based on the conventional logging curves, determine sensitive logging curves and sensitive logging response characteristics that can be used to identify reservoir types in the study area.

[0029] The standard establishment module is used to establish identification standards for different types of reservoirs based on the porosity, the difference in overlap amplitude of sensitive logging curves, and the values ​​of sensitive logging response characteristics of different types of reservoirs.

[0030] The identification and classification module is used to identify and classify reservoir types in the study area using different reservoir identification standards.

[0031] Thirdly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a method for identifying fractured reservoir types as described in the first aspect.

[0032] Fourthly, embodiments of the present invention provide an electronic device comprising:

[0033] processor;

[0034] Memory used to store the processor's executable instructions;

[0035] The processor is configured to execute the instructions to implement a method for identifying fractured reservoir types as described in the first aspect above.

[0036] Compared with the prior art, the above-mentioned technical solution of the present invention has the following beneficial effects:

[0037] The embodiments of this invention establish a set of identification standards suitable for different types of reservoirs in fractured tight sandstone reservoirs based on three aspects: lithology identification, porosity calculation, and well logging response. This avoids the drawbacks of existing technologies that rely solely on a small number of well logging curves to identify reservoir types in fractured reservoirs, which are prone to misjudgment and have low accuracy. This invention fully considers the influencing factors such as lithology, physical properties, and fractures that affect the identification of fractured reservoir types, significantly improving the accuracy of well logging technology in identifying different types of reservoirs in fractured reservoirs. This method has a wide range of applications in the field. It is highly operable for identifying reservoir types in different lithologies such as sandstone and limestone, and for different well types such as vertical and horizontal wells, greatly enhancing its guiding role in oil and gas field exploration and development. Attached Figure Description

[0038] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0039] Figure 1 This is a flowchart of the method for identifying fractured reservoir types provided in Embodiment 1 of the present invention;

[0040] Figure 2 This is a plate for identifying RT-AC reservoir types in a fractured body reservoir in a certain location, as described in Embodiment 2 of the present invention.

[0041] Figure 3 This is a diagram illustrating the GR-AC reservoir type identification of a fractured body reservoir in a certain location, as described in Embodiment 2 of the present invention.

[0042] Figure 4 This is an illustration of the identification effect of different types of reservoirs in a fractured reservoir in a certain area, based on Embodiment 2 of the present invention.

[0043] Figure 5 This is a schematic diagram of the composition of the electronic device provided in Embodiment 5 of the present invention. Detailed Implementation

[0044] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0045] Example 1

[0046] like Figure 1 As shown, the method for identifying fractured reservoir types provided in Embodiment 1 of the present invention mainly includes the following steps.

[0047] 1. Determine the lithological type of the fractured reservoir.

[0048] Based on core observations and thin section analysis, the lithology of fractured sandstone reservoirs is determined to mainly consist of sandstone and mudstone. Mudstone lacks the capacity to store oil and gas and is therefore a non-reservoir; the effective reservoirs are all developed within the sandstone. Due to the presence of radioactive minerals, mudstone exhibits high gamma-ray logging response characteristics, while sandstone, lacking radioactive minerals, exhibits relatively low gamma-ray logging response characteristics. Based on core observations and thin section identification data, natural gamma-ray curves are used to distinguish between sandstone and mudstone.

[0049] 2. Determine the reservoir type of fractured reservoirs.

[0050] Based on the changes in drilling time, well leakage size, trench rise, and calibration of imaging logging data, the degree of fracture and pore development is determined, and fractured reservoirs are identified as having three reservoir types: porous, fracture-porous, and fracture-cavity.

[0051] 3. Calculate the reservoir porosity curve

[0052] The sonic transit time curve is less affected by wellbore collapse. Using the sonic transit time logging curve, the sonic transit time curve can be converted into a formation porosity curve φ using the following formula. s :

[0053]

[0054] In the formula:

[0055] φ s Acoustic porosity, %;

[0056] Δt: Sonic transit time logging value of the target layer, in μs / ft;

[0057] Δt mac : Sonic transit time of the stratigraphic rock skeleton, in μs / ft;

[0058] Δt f : Sonic transit time of formation fluid, in μs / ft.

[0059] 4. Determine the sensitive logging curves for identifying reservoir types.

[0060] Conventional logging curves such as natural gamma, eight-lateral resistivity, and sonic transit time were read from typical porosity, fracture-porosity, and fracture-cavity reservoirs. Resistivity-sonic transit time and natural gamma-sonic transit time cross plots were created to determine that the eight-lateral resistivity and sonic transit time curves are sensitive curves for identifying fractured reservoir types. The logging response characteristics of eight-lateral resistivity, sonic transit time, and natural gamma for the three types of reservoirs (porosity, fracture-porosity, and fracture-cavity) were also determined.

[0061] 5. Determine the identification methods and standards for different types of reservoirs.

[0062] To address the limitation that resistivity-acoustic transit time and natural gamma-acoustic transit time intersection maps cannot completely distinguish between fracture-pore and fracture-void reservoirs, eight lateral resistivity and acoustic transit time curves are overlaid at a certain scale. Based on the difference in overlap amplitude of eight lateral resistivity and acoustic transit time and porosity of different reservoir types, identification criteria for three types of reservoirs—porosity, fracture-pore, and fracture-void—are established to identify and classify reservoir types in the study area.

[0063] This invention, based on lithology identification, porosity calculation, and logging response, optimizes sensitive curves and identification methods to establish a set of logging standards suitable for identifying fractured reservoir types. First, sandstone is identified as the lithology type of the developed reservoir. Based on core observation and thin section identification data, natural gamma curves are used to distinguish between sandstone and mudstone. Based on drilling time variations, wellbore leakage, trench rise, and imaging logging data calibration, fractured reservoirs are identified as having three reservoir types: porous, fracture-porous, and fracture-void. Resistivity-sonic transit time and natural gamma-sonic transit time cross plots are created, and the eight lateral resistivity and sonic transit time curves are determined to be sensitive curves for identifying fractured reservoir types. The logging response characteristics of three types of reservoirs were defined. Since sonic transit time curves are less affected by wellbore collapse, porosity was calculated for three reservoir types: porosity-type, fracture-porosity-type, and fracture-void-type. The eight lateral resistivity and sonic logging curves were overlaid at a certain scale. Based on the eight lateral resistivity, the difference in sonic transit time overlap amplitude, porosity, and logging response characteristics of different reservoir types, identification standards for porosity-type, fracture-porosity-type, and fracture-void-type reservoirs were established to identify and classify reservoir types in the study area. This invention avoids the drawbacks of easy misjudgment and low accuracy in identifying reservoir types in fractured reservoirs using only a few logging curves. The established reservoir type identification standards can accurately and intuitively identify different types of reservoirs such as porosity-type, fracture-porosity-type, and fracture-void-type, significantly improving the accuracy of logging technology in fractured reservoir identification, thereby guiding oilfield exploration and development.

[0064] Example 2

[0065] like Figure 2-4 As shown, the present invention will be further described using the JH-X well in a certain sand formation as an example:

[0066] 1. Determine the lithological type of the fractured reservoir.

[0067] Based on core observations and thin section analysis, the lithology of fractured sandstone reservoirs is determined to mainly consist of sandstone and mudstone / shale. Mudstone lacks the capacity to store oil and gas and is therefore a non-reservoir; the effective reservoirs are all developed within the sandstone. Due to the presence of radioactive minerals, mudstone exhibits high gamma-ray logging response characteristics, while sandstone, lacking radioactive minerals, exhibits relatively low gamma-ray logging response characteristics. Based on core observations and thin section identification data, natural gamma-ray curves are used to distinguish between sandstone and mudstone.

[0068] Using natural gamma curves to distinguish between sandstone and shale is a basic principle and conventional method in well logging, and the specific methods will not be elaborated in this patent. Based on the well logging response characteristics of different types of lithology in a study area of ​​the Ordos Basin, well logging identification criteria for sandstone and shale were determined.

[0069] The criteria for distinguishing between sandstone and mudstone logging are as follows:

[0070] When the natural gamma ray GR value is less than 100 API and the mud content SH value is less than 30%, the well logging lithology is identified as sandstone.

[0071] When the natural gamma ray (GR) value is greater than 100 API and the mud content (SH) value is greater than 30%, the well logging lithology is identified as mudstone.

[0072] 2. Determine the reservoir type of fractured reservoirs.

[0073] Based on the drilling time variation, well leakage size, and trench rise of 25 horizontal wells in a sand formation in a study area of ​​the Ordos Basin, as well as the calibration of two imaging logging data, the degree of fracture and pore development was determined, and the fractured reservoir was identified as having three reservoir types: porous, fracture-porous, and fracture-cavity.

[0074] The determination of the porosity type, fracture-porosity type, and fracture-void type is based on the following criteria:

[0075] Porous reservoirs: with constant drilling time, there was no well leakage or change in the trench surface, and no fractures or pores were observed in the imaging logging images;

[0076] Fractured-porosity reservoir: Drilling time decreased, well leakage occurred, mud loss was less than 20 cubic meters, the trench surface rose slightly, imaging logging images showed well-developed fractures, but no pores were seen;

[0077] Fractured-void reservoirs: Drilling time variation greater than 20 min / m, some wells have venting, mud loss greater than 20 cubic meters or loss of return occurs, overflow is common, the trench level rises significantly, and imaging logging images show dense development of fractures and pores.

[0078] 3. Calculate the reservoir porosity curve

[0079] The sonic transit time curve is less affected by wellbore collapse. Using the sonic transit time logging curve, the sonic transit time curve can be converted into a formation porosity curve φ using the following formula. s :

[0080]

[0081] In the formula:

[0082] φ s Acoustic porosity, %;

[0083] Δt: Sonic transit time logging value of the target layer, in μs / ft;

[0084] Δt mac : Sonic transit time of the stratigraphic rock skeleton, in μs / ft;

[0085] Δtf : Sonic transit time of formation fluid, in μs / ft.

[0086] Taking a sandstone formation in a study area of ​​the Ordos Basin as an example, the acoustic transit time Δt of the stratigraphic rock skeleton mac The value is 182 μs / m, and the formation fluid acoustic transit time Δt f The value is 620 μs / m. Substituting this into the above formula, the acoustic transit time curve Δt of the Chang 8 formation can be converted into the acoustic porosity curve φ. s .

[0087] 4. Determine the sensitive logging curves for identifying reservoir types.

[0088] Natural gamma, lateral resistivity, and sonic transit time (RTT) logs were collected from 49 typical porosity, fracture-porosity, and fracture-vuggy reservoirs in 25 horizontal wells in a study area of ​​the Ordos Basin. Resistivity-RTT and natural gamma-RTT cross plots were then created (see appendix). Figure 2 Appendix Figure 3 The study determined that the eight lateral resistivity and sonic transit time curves are sensitive curves for identifying fractured reservoir types, and identified the eight lateral resistivity, sonic transit time, and natural gamma logging response characteristics of three types of reservoirs: porosity, fracture-porosity, and fracture-cavity.

[0089] The logging response characteristics of pore-type, fracture-pore-type, and fracture-cavity-type logging are as follows:

[0090] Porous reservoirs: natural gamma ray (GR) value less than 100 API, lateral resistivity greater than 50 ohm / mm; acoustic transit time (AC) value greater than 200 μs / m and less than 240 μs / m;

[0091] Fracture-pore type reservoirs: natural gamma ray (GR) value less than 100 API, lateral resistivity greater than 30 ohm / mm and less than 50 ohm / mm; acoustic transit time (AC) value greater than 235 μs / m and less than 280 μs / m;

[0092] Fracture-porosity reservoirs: natural gamma ray (GR) value less than 100 API, lateral resistivity less than 40 ohm mm; acoustic transit time (AC) value greater than 250 μs / m.

[0093] Based on the analysis of the logging response characteristics of different reservoir types mentioned above, this range of values ​​can effectively distinguish porosity reservoirs. However, there is some overlap between fracture-porosity reservoirs and fracture-vuggy reservoirs in terms of sonic transit time and eight-sided resistivity thresholds (see Appendix). Figure 2 The shaded area cannot completely distinguish between the two types of reservoirs.

[0094] 5. Determine the identification methods and standards for different types of reservoirs.

[0095] To address the limitation that resistivity-acoustic transit time and natural gamma-acoustic transit time intersection maps cannot completely distinguish between fracture-pore and fracture-void reservoirs, eight lateral resistivity and acoustic transit time curves are overlaid at a certain scale. Based on the difference in overlap amplitude of eight lateral resistivity and acoustic transit time and porosity of different reservoir types, identification criteria for three types of reservoirs—porosity, fracture-pore, and fracture-void—are established to identify and classify reservoir types in the study area.

[0096] The method for identifying reservoir types by overlaying eight lateral resistivity and acoustic transit time curves is as follows:

[0097] 1) Select porosity reservoirs with a horizontal apparent thickness greater than 50m, and overlay eight lateral resistivity and acoustic transit time curves. The calibration method and range used are as follows:

[0098] The eight lateral resistivity curves use a logarithmic scale, with the left scale at 200 ohms and the right scale at 10 ohms; the acoustic transit time curves use a linear scale, with basic scale values ​​of 250 μs / m on the left scale and 150 μs / m on the right scale.

[0099] 2) If the above scale is used, the eight lateral resistivity and acoustic transit time curves do not completely overlap. The eight lateral resistivity curves need to be fixed and the left and right scale values ​​of the acoustic transit time curves need to be dynamically adjusted. The adjustment method is as follows: the left and right scales of the acoustic transit time curves are increased synchronously by 5 μs / m each time, but the difference between the left and right scales remains unchanged at 100 μs / m until the eight lateral resistivity and acoustic transit time curves overlap in the selected porosity reservoir.

[0100] 3) After the eight lateral resistivity and sonic transit time curves are overlaid, the different types of reservoirs in fractured reservoirs are comprehensively identified based on the logging response values ​​and the amplitude difference of the overlaid eight lateral resistivity and sonic transit time curves:

[0101] Porous reservoirs: The eight lateral resistivity and acoustic transit time curves basically overlap with no amplitude difference; the average porosity is less than 7%; and the natural gamma ray (GR) value is less than 100 API, the eight lateral resistivity is greater than 50 ohm mm, and the acoustic transit time (AC) value is greater than 200 μs / m and less than 240 μs / m.

[0102] Fracture-pore type reservoir: The eight lateral resistivity and acoustic transit time curves show a moderate amplitude difference; the average porosity is greater than 7% and less than 10%; at the same time, the natural gamma GR value is less than 100 API, the eight lateral resistivity is greater than 30 ohm mm and less than 50 ohm mm, and the acoustic transit time AC value is greater than 235 μs / m and less than 280 μs / m.

[0103] Fracture-porosity reservoirs: The eight lateral resistivity and acoustic transit time curves show a large amplitude difference; the average porosity is greater than 10%; and the natural gamma ray (GR) value is less than 100 API, the eight lateral resistivity is less than 40 ohm mm, and the acoustic transit time (AC) value is greater than 250 μs / m.

[0104] If there is a large amplitude difference between the eight lateral resistivity and acoustic transit time curves, but the natural gamma is greater than 100 API and the mud content SH value is greater than 30%, the layer is identified as mudstone and is not a reservoir.

[0105] Select Figure 4 The diagram shown illustrates the identification results of different reservoir types in a fractured reservoir in a study area of ​​the Ordos Basin. The 1900-2420m section represents the horizontal well interval of the fractured reservoir to be identified. Figure 4 The sixth channel is the AC-LL8 curve overlap channel, and the seventh channel is the reservoir type identification conclusion.

[0106] Layer 16 is sandstone with a thickness of 120m. Its logging response characteristics indicate a porosity reservoir with an average porosity of 6.5% and poor fracture development. Layer 16 is selected as the AC-LL8 curve overlap layer. Based on a comprehensive analysis of lithology, reservoir porosity, and curve overlap difference, the reservoir type classification is as follows:

[0107] Lithological identification of layers 11 and 14: sandstone. The AC-LL8 curves show a large difference in amplitude when they overlap. The calculated average porosity is 13.3% and 11% respectively, which are identified as fracture-vuggy reservoirs.

[0108] The lithology of layers 10, 12, 15, and 19 is sandstone. The AC-LL8 curves overlap with a moderate amplitude difference. The calculated average porosity is 7.8%, 8.7%, 9.7%, and 7.6%, respectively, all of which are identified as fracture-porosity reservoirs.

[0109] The lithological identification of layers 13 and 17 is sandstone. The AC-LL8 curves overlap with almost no amplitude difference. The calculated average porosity is 5.6% and 6.4%, respectively, which are identified as porous reservoirs.

[0110] The lithology of the 18th layer is mudstone, which is not a reservoir.

[0111] The results of identifying different types of fractured reservoirs are consistent with core observation and imaging logging results, which fully demonstrates the feasibility and effectiveness of the identification method and provides a technical basis for the exploration and development of this type of oil and gas reservoir.

[0112] Example 3

[0113] The following are embodiments of the apparatus of the present invention, which can be used to execute embodiments of the method of the present invention. For details not disclosed in the embodiments of the apparatus of the present invention, please refer to the embodiments of the method of the present invention.

[0114] This embodiment provides a device for identifying fractured reservoir types, characterized in that it includes:

[0115] The type determination module is used to determine the lithological type and reservoir type of the reservoir in the study area; wherein the reservoir type includes at least one of the following types: porous, fracture-porous, and fracture-cavity.

[0116] The porosity analysis module is used to analyze the reservoir porosity in the study area.

[0117] The parameter selection module is used to acquire conventional logging curves of the study area and, based on the conventional logging curves, determine sensitive logging curves and sensitive logging response characteristics that can be used to identify reservoir types in the study area.

[0118] The standard establishment module is used to establish identification standards for different types of reservoirs based on the porosity, the difference in overlap amplitude of sensitive logging curves, and the values ​​of sensitive logging response characteristics of different types of reservoirs.

[0119] The identification and classification module is used to identify and classify reservoir types in the study area using different reservoir identification standards.

[0120] Example 4

[0121] This embodiment provides a computer-readable medium having a computer program stored thereon. When executed by a processor, the program implements the various steps of a method for identifying fractured reservoir types as described in the above embodiment.

[0122] It should be noted that all or part of the processes in the methods of the above embodiments of the present invention can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. Of course, there are other readable storage media, such as quantum memories, graphene memories, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0123] Example 5

[0124] Figure 5 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present invention. Figure 5 As shown, at the hardware level, this electronic device includes a processor, and optionally also includes an internal bus, a network interface, and memory. The memory may include main memory, such as high-speed random-access memory (RAM), or it may include non-volatile memory, such as at least one disk drive. Of course, this electronic device may also include other hardware required for other business operations.

[0125] The processor, network interface, and memory can be interconnected via an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. This bus can be categorized as an address bus, data bus, control bus, etc. For ease of illustration, only line segments are used in the diagram, but this does not imply that there is only one bus or one type of bus.

[0126] A memory is used to store programs. Specifically, the program may include program code, which includes computer operation instructions. The memory may include main memory and non-volatile memory, and provides instructions and data to the processor. The processor reads the corresponding computer program from the non-volatile memory into main memory and then runs it. The processor executes the program stored in the memory to perform all the steps in the aforementioned method for identifying fractured reservoir types.

[0127] The communication bus mentioned above can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc. For ease of illustration, only one thick line is used to represent it in the diagram, but this does not mean that there is only one bus or one type of bus. The communication interface is used for communication between the above electronic devices and other devices.

[0128] A bus, including hardware, software, or both, is used to couple the aforementioned components together. For example, a bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Microchannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses, or combinations of two or more of these. Where appropriate, a bus may include one or more buses. Although specific buses are described and illustrated in embodiments of the invention, the invention contemplates any suitable bus or interconnect.

[0129] The memory may include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0130] The memory may include a large-capacity storage device for data or instructions. For example, and not limitingly, the memory may include a hard disk drive (HDD), a floppy disk drive, flash memory, an optical disk drive, a magneto-optical disk drive, magnetic tape, or a Universal Serial Bus (USB) drive, or a combination of two or more of these. Where suitable, the memory may include removable or non-removable (or fixed) media. In a particular embodiment, the memory is a non-volatile solid-state memory. In a particular embodiment, the memory includes a read-only memory (ROM). Where suitable, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically rewritable ROM (EAROM), or flash memory, or a combination of two or more of these.

[0131] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0132] It should be noted that those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this invention. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0133] The apparatus, device, system, module, or unit described in the above embodiments can be implemented by a computer chip or entity, or by a product with a certain function. A typical implementation device is a computer. Specifically, the computer can be, for example, a personal computer, a laptop computer, an in-vehicle human-machine interaction device, a cellular phone, 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 any combination of these devices.

[0134] While this invention provides the method operation steps as described in the embodiments or flowcharts, more or fewer operation steps may be included based on conventional or non-inventive means. The order of steps listed in the embodiments is merely one possible execution order among many and does not represent the only execution order. In actual devices or terminal products, the methods shown in the embodiments or drawings can be executed sequentially or in parallel (e.g., in a parallel processor or multi-threaded processing environment, or even a distributed data processing environment).

[0135] This 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 block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can 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, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0136] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0137] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1The steps of the function specified in one or more boxes.

[0138] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0139] The various embodiments in this specification are described in a related manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments of apparatus, electronic devices, and readable storage media are basically similar to the method embodiments, so the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0140] The above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention are included within the scope of protection of the present invention.

Claims

1. A method for identifying fractured reservoir types, characterized in that, Includes the following steps: S100, determine the lithological type and reservoir type of the reservoir in the study area; wherein the reservoir type includes at least one of the following: porous, fracture-porous, and fracture-cavity. S200, analyzes the reservoir porosity in the study area; S300: Obtain the natural gamma ray logging curve, eight-lateral resistivity logging curve, and sonic transit time logging curve of the reservoir. By creating the eight-lateral resistivity-sonic transit time cross plot and the natural gamma ray-sonic transit time cross plot, determine the eight-lateral resistivity logging curve and the sonic transit time logging curve, and determine the natural gamma ray logging response characteristics, the eight-lateral resistivity logging response characteristics, and the sonic transit time logging response characteristics. S400 overlays the eight-lateral resistivity logging curves and the sonic transit time logging curves at a specified scale. Based on the difference in overlap amplitude between the eight-lateral resistivity logging curves and the sonic transit time logging curves of different reservoir types, porosity, and the values ​​of natural gamma, eight-lateral resistivity and sonic transit time, identification criteria for different reservoir types are established. S500 uses different reservoir identification standards to identify and classify reservoir types in the study area.

2. The method for identifying fractured reservoir types as described in claim 1, characterized in that, Step S100 includes: Based on core observation and thin section analysis results, the lithological type of the reservoir in the study area was determined using natural gamma logging curves.

3. The method for identifying fractured reservoir types as described in claim 2, characterized in that, The method of determining the lithological type of the reservoir in the study area using natural gamma logging curves includes: When the natural gamma ray (GR) value is less than the preset first threshold and the mud content (SH) value is less than the preset second threshold, the well logging lithology of the study area is determined to be sandstone. When the natural gamma ray (GR) value is greater than the preset first threshold and the mud content (SH) value is greater than the preset second threshold, the well logging lithology of the study area is determined to be mudstone.

4. The method for identifying fractured reservoir types as described in claim 1, characterized in that, Step S100 includes: Based on the drilling time variation, well leakage size, trench rise, and imaging logging data calibration in the study area, the degree of fracture and cavity development in the study area is determined, and the reservoir type of the oil reservoir in the study area is identified.

5. The method for identifying fractured reservoir types as described in claim 1, characterized in that, Step S200 includes: Acquire the sonic transit time logging curves of the study area, and use the sonic transit time logging curves to calculate reservoir porosity.

6. A device for identifying fractured reservoir types, characterized in that, include: The type determination module is used to determine the lithological type and reservoir type of the reservoir in the study area; wherein the reservoir type includes at least one of the following types: porous, fracture-porous, and fracture-cavity. The porosity analysis module is used to analyze the reservoir porosity in the study area. The parameter selection module is used to acquire the natural gamma ray logging curve, eight-lateral resistivity logging curve, and sonic transit time logging curve of the reservoir. By creating the eight-lateral resistivity-sonic transit time cross plot and the natural gamma ray-sonic transit time cross plot, the eight-lateral resistivity logging curve and the sonic transit time logging curve are determined, as well as the natural gamma ray logging response characteristics, the eight-lateral resistivity logging response characteristics, and the sonic transit time logging response characteristics. The standard establishment module is used to overlay the eight-lateral resistivity logging curve and the sonic transit time logging curve at a specified scale. Based on the overlap amplitude difference, porosity, and the values ​​of natural gamma, eight-lateral resistivity and sonic transit time of the eight-lateral resistivity logging curve and the sonic transit time logging curve of different reservoir types, identification standards for different reservoir types are established. The identification and classification module is used to identify and classify reservoir types in the study area using different reservoir identification standards.

7. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements a method for identifying fractured reservoir types as described in any one of claims 1 to 5.

8. An electronic device comprising: processor; Memory used to store the processor's executable instructions; The processor is configured to execute the instructions to implement a method for identifying fractured reservoir types as described in any one of claims 1 to 5.

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