A method, device and electronic equipment for determining productivity of complex conglomerate reservoir

Abstract

The present invention discloses a method, device and electronic equipment for determining the productivity of a complex conglomerate reservoir, characterized in that the method comprises: obtaining array sensing radial invasion characteristic parameters of the reservoir; using the array sensing radial invasion characteristic parameters to characterize the oil content of the reservoir; and solving the problem that the existing productivity determination method for complex conglomerate reservoirs does not take into account the influence of different lithologies on the resistivity curve value, thereby failing to indicate the oil content well.

Description

Technical Field

[0001] The present disclosure relates to a method for predicting reservoir productivity in the technical field of oil and gas field development. Background Art

[0002] Reservoir capacity prediction is a technology for comprehensive evaluation of reservoir production capacity. Generally speaking, capacity is a comprehensive indicator of the dynamic characteristics of oil and gas reservoirs. It is a dynamic balance achieved in the process of mutual constraints between the production potential of oil and gas reservoirs and various influencing factors. The results of reservoir capacity prediction can test the results of oil and gas exploration and provide the most basic basis for oil and gas field development. Reservoir capacity prediction technology is a comprehensive evaluation technology for the comprehensive liquid production capacity of reservoirs, a core technology for evaluating the development value of single wells, and provides a basis for oil testing for oil and gas field development.

[0003] Many scholars have explored the use of logging curves for capacity prediction. In summary, the envelope area of ​​logging curves or multiple logging curves is used to establish a relationship with test oil and test production data. Zeng Jingbo, "Capacity determination method based on low permeability reservoir quality evaluation", Journal of Yangtze University (Natural Science Edition), Vol. 14, No. 7, 2017. On the basis of reservoir quality evaluation and combined with development needs, a capacity determination method for low permeability reservoirs is proposed to establish a relationship between a comprehensive evaluation index and a meter of liquid production index. Zhang Jun, "A method and system for constructing logging parameters that reflect reservoir capacity", patent number 201810273202.1, date October 11, 2019. He uses logging parameters such as natural gamma, natural potential, and logging interpretation porosity data, and the envelope area between curves to construct a method that can reflect reservoir capacity parameters. Zhang Songyang, Fan Yiren, Huang Guoqian, et al. (Application of conventional logging characteristic ratio method in the study of productivity evaluation of Daniudi gas field [J]. Logging Technology, 2006, 30(5): 420-424.) The natural gamma ratio, well diameter ratio, deep-shallow resistivity ratio and other parameters were weighted according to regional experience to form a comprehensive index, which was multiplied by the gas layer thickness and established a relationship with the productivity, that is, the comprehensive ratio was used to divide the productivity level. Wei Ruling (Research on logging productivity evaluation technology of deep conglomerate reservoirs [D]. Dongying: China University of Petroleum, 2010.) Nuclear magnetic logging was used to evaluate the reservoir pore structure, and a comprehensive classification index was constructed by integrating multiple pore structure parameters to classify the reservoir productivity and evaluate the reservoir productivity.

[0004] These methods have achieved good results in capacity prediction in reservoirs with similar lithology. However, complex conglomerate reservoirs such as the Moriqing area have alluvial fan sedimentation, fast lithology transition and large particle size variation, fine sandstone, siltstone and conglomerate intersect and deposit. The resistivity values ​​reflected by different lithologies vary greatly, and the high and low resistivity are well correlated with lithology, which masks the contribution of fluid to resistivity. Therefore, for complex conglomerate reservoirs, the influence of lithology on resistivity is greater than that of oil content, and the high and low resistivity values ​​cannot reflect the change of oil content, making it difficult to identify the properties of fluids, resulting in deviations in reservoir capacity prediction results. Summary of the invention

[0005] In view of this, the present disclosure provides a method, device and electronic equipment for determining the production capacity of complex conglomerate reservoirs, which solves the problem that the existing production capacity determination methods for complex conglomerate reservoirs do not take into account the influence of different lithologies on the resistivity curve values, and thus cannot well indicate the oil content.

[0006] In a first aspect, a method for determining the productivity of a complex conglomerate reservoir is characterized by comprising:

[0007] Obtain array sensing radial invasion characteristic parameters of the reservoir;

[0008] The array is used to sense radial invasion characteristic parameters to characterize the oil content of the reservoir.

[0009] Furthermore, the array-induced radial intrusion characteristic parameter is calculated by the following formula:

[0010]

[0011] Wherein: AIR is the array sensing radial intrusion characteristic parameter, M2RX is the array sensing detection depth 120in resistivity, M2R9 is the array sensing detection depth 90in resistivity, M2R6 is the array sensing detection depth 60in resistivity, and M2R3 is the array sensing detection depth 30in resistivity.

[0012] Further, obtaining the physical quality factor parameters of the reservoir;

[0013] The physical property quality factor parameter is used to characterize the physical property of the reservoir.

[0014] Furthermore, the method for obtaining the physical property quality factor parameter is to calculate it through the following formula:

[0015]

[0016] Where: POSU is the physical quality factor, PERM is the permeability, and POR is the porosity.

[0017] Furthermore, the permeability is calculated by the following formula:

[0018] lgPERM=[a+b×lg(POR)+k×lg(XMD)]×10 -3

[0019] Where: XMD is the median particle size, POR is the porosity, and a, b, and k are coefficients.

[0020] Furthermore, the porosity is calculated by the following formula:

[0021]

[0022] Where: DEN is the measured value of the well logging bulk density, DM is the skeleton value of the bulk density, DF is the fluid value of the bulk density, SH is the shale content, and DSH is the density of the mudstone.

[0023] Furthermore, the mud content is calculated by the following formula:

[0024]

[0025] Where: GR index is the relative value of natural gamma, and C is the empirical coefficient of the formation.

[0026] Furthermore, the method further comprises:

[0027] Establishing the intersection diagram of the product of the array sensing radial invasion parameter and the physical property quality factor with the oil production per meter and the water production per meter, respectively, to obtain the capacity prediction oil production and capacity prediction water production interpretation diagrams;

[0028] The explanatory diagram is used to establish the daily oil production equation and the daily water production equation.

[0029] In a second aspect, the production capacity determination device for a complex sandstone reservoir is characterized by comprising:

[0030] The first establishing unit is used to establish the intersection diagram of the product of the array sensing radial invasion parameter and the physical property quality factor with the oil production per meter and the water production per meter, so as to obtain the capacity prediction oil production and capacity prediction water production interpretation diagrams respectively;

[0031] The second establishing unit is used to establish the daily oil production equation and the daily water production equation using the explanation chart.

[0032] In a third aspect, the electronic device is characterized by comprising:

[0033] processor;

[0034] a memory for storing processor-executable instructions;

[0035] The processor is configured to call the instructions stored in the memory to execute the productivity determination method for a complex sandstone reservoir as described in the first aspect.

[0036] The present invention has the following beneficial effects:

[0037] The present invention discloses an array sensing radial invasion characteristic parameter AIR for evaluating the oil content of a complex conglomerate reservoir, because the influence of lithology on resistivity is greater than that of oil content, to characterize the oil content of the reservoir; in terms of evaluating physical properties, the reservoir parameters such as porosity and permeability are calculated, and according to core test analysis, the key factor affecting permeability is the size of the particle size, and the permeability interpretation model is optimized by using the particle size, and then the physical property quality factor parameter is quoted to characterize the physical property; oil test data are collected, and an intersection diagram of the array sensing radial invasion parameter, the physical property quality factor parameter, and the oil production per meter and the water production is established, and then the production capacity equation is obtained to realize the production capacity prediction of complex conglomerate; the problem that the existing production capacity determination method for complex conglomerate reservoirs does not take into account the influence of different lithologies on the resistivity curve value, and thus cannot well indicate the oil content is solved. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The above and other objects, features and advantages of the present disclosure will become more apparent through the following description of the embodiments of the present disclosure with reference to the accompanying drawings, in which:

[0039] Figure 1 is a flow chart of a method for determining production capacity according to an embodiment of the present disclosure;

[0040] Figure 2 is the AIR-POSU logging interpretation plate of the present disclosure;

[0041] Figure 3 is a production capacity prediction oil production interpretation chart of an embodiment of the present disclosure;

[0042] Figure 4 is an explanatory chart of production capacity prediction and water production in an embodiment of the present disclosure;

[0043] Figure 5 is a production capacity prediction result diagram of an embodiment of the present disclosure;

[0044] Figure 6 is a block diagram of an electronic device 800 shown in an embodiment of the present disclosure;

[0045] Figure 7 It is a block diagram of an electronic device 1900 shown in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0046] The present disclosure is described below based on embodiments, but it is worth noting that the present disclosure is not limited to these embodiments. In the detailed description of the present disclosure below, some specific details are described in detail. However, for the parts that are not described in detail, those skilled in the art can also fully understand the present disclosure.

[0047] In addition, those skilled in the art should understand that the drawings are only provided to illustrate the purpose, features and advantages of the present disclosure, and the drawings are not actually drawn to scale.

[0048] At the same time, unless the context clearly requires otherwise, the words "include", "comprises" and similar words throughout the specification and claims should be interpreted as inclusive rather than exclusive or exhaustive; that is, the meaning is "including but not limited to".

[0049] The inventive concept of the disclosed method is: select the array sensing radial invasion characteristic parameter AIR to characterize the reservoir production capacity and oil content, select the physical property quality factor POSU as the physical property sensitive parameter, combine the oil test data, establish the AIR-POSU logging interpretation chart to identify the reservoir fluid properties, obtain the production capacity equation, and achieve the purpose of predicting the complex sandstone conglomerate production capacity.

[0050] To realize the inventive concept, the embodiments of the present disclosure adopt the following technical solutions. Figure 1 : is a flow chart of a method for determining production capacity according to an embodiment of the present disclosure; the principles and steps of the method for determining production capacity are as follows:

[0051] Step S101: logging a complex sandstone reservoir to obtain logging curves, which include lithology curves (natural gamma curves and natural potential curves), array induction logging curves and volume density curves, wherein the volume density curve and natural gamma curve are used to calculate reservoir porosity.

[0052] Step S102: Since the sedimentary phase of the complex sandstone reservoir is alluvial fan deposition, the lithology transition is fast and the grain size changes greatly. Fine sandstone, siltstone and conglomerate are deposited alternately, and the resistivity values ​​reflected by different lithologies vary greatly, which masks the contribution of fluid to resistivity. Therefore, the resistivity is highly correlated with lithology, cannot reflect the change of oil content, and is difficult to identify the properties of fluid.

[0053] The array induction logging curve has 6 resistivity curves of different detection depths, namely M2RX (R120), M3R9 (R90), M2R6 (R60), M3R3 (R30), M2R2 (R20), and M2R1 (R10).

[0054] Through research, it is found that array induction logging curves have different invasion characteristics in oil layers and water layers, specifically:

[0055] (1) Typical oil layers

[0056] Generally, the low-intrusion characteristics are shown as high resistivity of M2RX (120in) and low resistivity of M2R1 (10in). The radial characteristics of array induction resistivity are R120>R90>R 60>R 30>R 20>R10.

[0057] (2) Mud intrusion into deeper oil layers

[0058] The radial intrusion characteristics of the array induction resistivity are that the resistivity of 120in, 90in, and 60in is positively different from that of 30in, and negatively different from that of 20in and 10in, that is, R120>R90>R60>R30 <R20<R10;

[0059] (3) Typical water layers

[0060] Generally, it is characterized by high-intrusion features with high resistivity of M2R1 (10in) and low resistivity of M2RX (120in). The radial characteristics of array induction resistivity are R10>R20>R30>R60>R90>R120.

[0061] (4) Typical oil-water layer

[0062] The logging response is that the resistivity is lower than the regional oil layer value and higher than the regional water layer value. The array sensing resistivity gradually decreases from shallow to deep. The higher the formation water mineralization, the greater the negative difference will be. The 10in and 20in resistivity are greatly affected by mud invasion, formation water mineralization and wellbore.

[0063] According to the above characteristics, the array sensing radial invasion characteristic parameter AIR can be introduced to well reflect the oil content of the reservoir. AIR is obtained by the following formula:

[0064]

[0065] Among them: AIR is the array sensing radial intrusion characteristic parameter, M2RX is the array sensing detection depth 120in resistivity, M2R9 is the array sensing detection depth 90in resistivity, M2R6 is the array sensing detection depth 60in resistivity, M2R3 is the array sensing detection depth 30in resistivity.

[0066] Combined with the oil test data, the AIR-POSU logging interpretation chart is established to identify the reservoir fluid properties, including the oil layer, oil-water layer and water layer of the oil test conclusion.

[0067] Among them, for high-yield oil layers, the array sensing radial characteristic parameter AIR>1.6, and the physical property quality factor POSU>0.9; for low-yield oil layers, AIR>1.35, POSU>0.8; for high-yield oil and water layers, AIR>1.0-1.35, POSU>1.4; for low-yield oil and water layers, AIR>1.0-1.5, POSU>0.8-1.4; for water layers, AIR<0.9, POSU>0.8.

[0068] Step S103: Physical properties are another important parameter related to production capacity. Porosity and permeability are parameters that characterize physical properties. Since the porosity calculated by volume density has the smallest error with the porosity obtained by core analysis, porosity can be calculated by volume density. However, the permeability of complex gravel is not easy to calculate accurately. According to core test analysis, permeability is positively correlated with the particle size of the rock, and the correlation is good. Based on this feature, the median particle size parameter is referenced to establish a permeability model based on rock particle size. Then, the physical property quality factor POSU is established based on porosity and permeability. That is, the physical property quality factor POSU characterizes the seepage capacity of unit pores and has a good correlation with production capacity.

[0069] The formula for calculating the physical quality factor POSU is as follows:

[0070]

[0071] Where: POSU is the physical quality factor, PERM is the permeability, and POR is the porosity.

[0072] Among them, PERM is the permeability, and its calculation formula is as follows:

[0073] lgPERM=[a+b×lg(POR)+k×lg(XMD)]×10 -3

[0074] Where: XMD is the median particle size (which can be obtained from known rock cuttings particle size test data), POR is the porosity, k is the coefficient (the coefficient is related to the lithology, where siltstone is 1 and conglomerate is 1.586), a and b are coefficients, with values ​​of 0.25 and 0.71.

[0075] The porosity calculation formula is as follows:

[0076]

[0077] Where: DEN is the measured value of the well logging bulk density, DM and DF are the skeleton value and fluid value of the bulk density, respectively, which are 2.68 g / cm 3 , 1.0g / cm 3 , SH is the mud content, DSH is the density of mudstone, and the parameter data of this formula can be obtained from the logging volume density curve.

[0078] SH is the mud content, which can be obtained through the natural gamma curve. The specific calculation formula is:

[0079]

[0080] Where: GR index is the relative value of natural gamma, C is the empirical coefficient of the formation, and the formula is 3.7.

[0081] By calculating the shale content, the correction for the effect of porosity is completed.

[0082] Step S104: using the oil test data, collect basic data such as oil test data and effective thickness data; specifically collect single-layer oil test data, including daily oil production, daily water production, perforation section, and reservoir thickness (difference between perforation top and bottom depth).

[0083] Step S105: Calculate the production capacity parameters, select the perforation section data, the daily oil production of the perforation layer divided by the reservoir thickness is the oil production per meter, and the daily water production of the perforation layer divided by the reservoir thickness is the water production per meter.

[0084] The intersection plots of the product of array sensing radial invasion parameters and physical quality factors with oil production per meter and water production per meter were established to determine the capacity prediction oil production and water production interpretation charts, respectively, and fit the daily oil production equation and daily water production equation to realize the capacity prediction of complex sandstone conglomerate reservoirs.

[0085] Step S106: compile a program and connect it to the interpretation software to achieve production capacity prediction of complex lithology.

[0086] Specifically, a well in a certain oil field is taken as an example to further illustrate the disclosed method. Figure 5 is a production capacity prediction result diagram of an embodiment of the present disclosure, such as Figure 5 As shown in the figure, tracks 1-5 are the original curves of logging and mud recording measurements, tracks 6-9 are the production capacity prediction process and results curves, among which the first track is the lithology curve, including the natural gamma curve and the natural potential curve, the second track is the depth track, the third track is the geological mud logging lithology and display track, the fourth track is the array sensing logging curve of the detection depth of 120in, 90in, 60in, and 30in, the fifth track is the volume density curve, the sixth track is the array sensing radial invasion characteristic parameter AIR, the seventh track is the physical property quality factor curve, the eighth track is the production capacity prediction curve, including the predicted daily oil production and daily water production, and the ninth track is the production capacity conclusion.

[0087] From the logging curve analysis, the array induction resistivity of the logging interpretation layer 21-23 is 21-114Ω·m, and the lithology is conglomerate; the array induction resistivity of the layer 24-25 is 5.4-9.1Ω·m, and the lithology is siltstone. The resistivity of the two layers differs by 5-10 times.

[0088] The physical quality factor analysis shows that the physical properties of conglomerate are 6.5 and 5.8 respectively, indicating that the physical properties of conglomerate are slightly better than those of siltstone. The calculated array sensing characteristic parameter AIR shows that the physical properties of conglomerate are 5.3 and siltstone are 4.8-2.4 respectively. The geological logging shows that the physical properties of conglomerate are 6.5 and 5.8 respectively (see Figure 2 ).

[0089] In the cross-plot, the X-axis is the product of the array sensing radial invasion parameter and the physical quality factor, the Y-axis is the daily oil production, and the data point is the daily oil production per meter of the key well test section. The functional relationship of xy is calculated to establish the production capacity prediction and oil production interpretation chart (see Figure 3 ), the fitted daily oil production equation is Y = 1.8961X 1.6032 , the correlation coefficient is 0.8403.

[0090] Similarly, the capacity prediction and water production interpretation chart (see Figure 4 ), daily water production equation Y = 0.2069X -3.9376 , the correlation coefficient is 0.7323.

[0091] Comprehensive analysis shows that the 21-23 layers of sandstone are oil layers, the 24th layer of siltstone is an oil layer, and the 25th layer of siltstone is an oil-water layer. The production capacity is predicted to be 10.5 tons of oil per day for the 21-23 layers, 7 tons of oil per day for the 24th layer, 10 tons of oil per day for the 25th layer, and 15m of water per day. 3 It is recommended to test the oil on layers 21-24.

[0092] The test results showed that the 21-24 layers produced 15.4 tons of oil. Three months later, the 25th layer was opened up again, with a daily oil production of 22.8 tons and a daily water production of 13.8m 3 .

[0093] By utilizing the above production capacity, the influence of lithology on resistivity value is eliminated, thus avoiding the loss of low-resistance oil and gas layers.

[0094] Those skilled in the art will appreciate that, in the above method of specific implementation, the order in which the steps are written does not imply a strict execution order and does not constitute any limitation on the implementation process. The specific execution order of the steps should be determined by their functions and possible internal logic.

[0095] In addition, the present disclosure also provides a corresponding production capacity determination device and electronic equipment for complex conglomerate reservoirs, both of which can be used to implement the production capacity determination method for complex conglomerate reservoirs provided by the present disclosure.

[0096] The execution subject of the productivity determination method for complex conglomerate reservoirs proposed in the present disclosure may be a productivity determination device for complex conglomerate reservoirs. For example, the determination method may be executed by a terminal device or a server or other processing device, wherein the terminal device may be a user equipment (UE), a mobile device, a user terminal, a terminal, a cellular phone, a cordless phone, a personal digital assistant (PDA), a handheld device, a computing device, a vehicle-mounted device, a wearable device, etc. In some possible implementations, the determination method may be implemented by a processor calling a computer-readable instruction stored in a memory. "

[0097] The functions or modules included in the device provided in the embodiments of the present disclosure can be used to execute the method described in the above method embodiments. The specific implementation thereof can refer to the description of the above method embodiments, and will not be repeated here for the sake of brevity.

[0098] The embodiment of the present disclosure also provides a computer-readable storage medium, on which computer program instructions are stored, and when the computer program instructions are executed by a processor, the above method is implemented. The computer-readable storage medium may be a non-volatile computer-readable storage medium.

[0099] Figure 6 8 is a block diagram of an electronic device 800 according to an exemplary embodiment. For example, the electronic device 800 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, or the like.

[0100] Reference Figure 6 , the electronic device 800 may include one or more of the following components: a processing component 802 , a memory 804 , a power component 806 , a multimedia component 808 , an audio component 810 , an input / output (I / O) interface 812 , a sensor component 814 , and a communication component 816 .

[0101] The processing component 802 generally controls the overall operation of the electronic device 800, such as operations associated with display, phone calls, data communications, camera operations, and recording operations. The processing component 802 may include one or more processors 820 to execute instructions to complete all or part of the steps of the above-mentioned method. In addition, the processing component 802 may include one or more modules to facilitate the interaction between the processing component 802 and other components. For example, the processing component 802 may include a multimedia module to facilitate the interaction between the multimedia component 808 and the processing component 802.

[0102] The memory 804 is configured to store various types of data to support operations on the electronic device 800. Examples of such data include instructions for any application or method operating on the electronic device 800, contact data, phone book data, messages, pictures, videos, etc. The memory 804 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0103] The power supply component 806 provides power to the various components of the electronic device 800. The power supply component 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to the electronic device 800.

[0104] The multimedia component 808 includes a screen that provides an output interface between the electronic device 800 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundaries of the touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 808 includes a front camera and / or a rear camera. When the electronic device 800 is in an operating mode, such as a shooting mode or a video mode, the front camera and / or the rear camera may receive external multimedia data. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.

[0105] The audio component 810 is configured to output and / or input audio signals. For example, the audio component 810 includes a microphone (MIC), and when the electronic device 800 is in an operating mode, such as a call mode, a recording mode, and a voice recognition mode, the microphone is configured to receive an external audio signal. The received audio signal can be further stored in the memory 804 or sent via the communication component 816. In some embodiments, the audio component 810 also includes a speaker for outputting audio signals.

[0106] I / O interface 812 provides an interface between processing component 802 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include but are not limited to: home button, volume button, start button, and lock button.

[0107] The sensor assembly 814 includes one or more sensors for providing various aspects of status assessment for the electronic device 800. For example, the sensor assembly 814 can detect the open / closed state of the electronic device 800, the relative positioning of the components, such as the display and keypad of the electronic device 800, and the sensor assembly 814 can also detect the position change of the electronic device 800 or a component of the electronic device 800, the presence or absence of contact between the user and the electronic device 800, the orientation or acceleration / deceleration of the electronic device 800, and the temperature change of the electronic device 800. The sensor assembly 814 may include a proximity sensor configured to detect the presence of a nearby object without any physical contact. The sensor assembly 814 may also include an optical sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 814 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0108] The communication component 816 is configured to facilitate wired or wireless communication between the electronic device 800 and other devices. The electronic device 800 can access a wireless network based on a communication standard, such as WiFi, 2G or 3G, or a combination thereof. In an exemplary embodiment, the communication component 816 receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 816 also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.

[0109] In an exemplary embodiment, the electronic device 800 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above methods.

[0110] In an exemplary embodiment, a non-volatile computer-readable storage medium is also provided, such as a memory 804 including computer program instructions, which can be executed by a processor 820 of an electronic device 800 to perform the above method.

[0111] Figure 7 1 is a block diagram of an electronic device 1900 according to an exemplary embodiment. For example, the electronic device 1900 may be provided as a server. Figure 4, the electronic device 1900 includes a processing component 1922, which further includes one or more processors, and a memory resource represented by a memory 1932 for storing instructions executable by the processing component 1922, such as an application. The application stored in the memory 1932 may include one or more modules, each corresponding to a set of instructions. In addition, the processing component 1922 is configured to execute instructions to perform the above method.

[0112] The electronic device 1900 may also include a power supply component 1926 configured to perform power management of the electronic device 1900, a wired or wireless network interface 1950 configured to connect the electronic device 1900 to a network, and an input / output (I / O) interface 1958. The electronic device 1900 may operate based on an operating system stored in the memory 1932, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™ or the like.

[0113] In an exemplary embodiment, a non-volatile computer-readable storage medium is also provided, such as a memory 1932 including computer program instructions, which can be executed by the processing component 1922 of the electronic device 1900 to perform the above method.

[0114] The present disclosure may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0115] A computer-readable storage medium may be a tangible device that can hold and store instructions used by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples of computer-readable storage media (a non-exhaustive list) include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination of the foregoing. As used herein, a computer-readable storage medium is not to be interpreted as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through a wire.

[0116] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0117] The computer program instructions for performing the operation of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages, such as Smalltalk, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. Computer-readable program instructions may be executed completely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be customized by utilizing the state information of the computer-readable program instructions, and the electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present disclosure.

[0118] Various aspects of the present disclosure are described herein with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present disclosure. It should be understood that each box in the flowchart and / or block diagram and the combination of each box in the flowchart and / or block diagram can be implemented by computer-readable program instructions.

[0119] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine, so that when these instructions are executed by the processor of the computer or other programmable data processing device, a device that implements the functions / actions specified in one or more boxes in the flowchart and / or block diagram is generated. These computer-readable program instructions can also be stored in a computer-readable storage medium, and these instructions cause the computer, programmable data processing device, and / or other equipment to work in a specific manner, so that the computer-readable medium storing the instructions includes a manufactured product, which includes instructions for implementing various aspects of the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0120] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device so that a series of operating steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to implement the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0121] The flow chart and block diagram in the accompanying drawings show the possible architecture, function and operation of the system, method and computer program product according to multiple embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a part of a module, program segment or instruction, and a part of the module, program segment or instruction includes one or more executable instructions for realizing the specified logical function. In some alternative implementations, the function marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two continuous square boxes can actually be executed substantially in parallel, and they can sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs the specified function or action, or can be implemented with a combination of special hardware and computer instructions.

[0122] The above-described embodiments are only embodiments of the present disclosure, and the descriptions thereof are relatively specific and detailed, but they cannot be construed as limiting the scope of the present disclosure. It should be noted that, for a person of ordinary skill in the art, without departing from the concept of the present disclosure, several variations, equivalent substitutions, improvements, etc. may be made, all of which fall within the scope of protection of the present disclosure. Therefore, the scope of protection of the present disclosure shall be subject to the attached claims.

Claims

1. A method for determining the productivity of a complex conglomerate reservoir, characterized in that: include: Obtain array sensing radial invasion characteristic parameters of the reservoir; Using the array to sense radial invasion characteristic parameters to characterize the oil content of the reservoir; The array-induced radial intrusion characteristic parameter is calculated by the following formula: Where: AIR is the array sensing radial invasion characteristic parameter, M2RX is the array sensing detection depth 120in resistivity, M2R9 is the array sensing detection depth 90in resistivity, M2R6 is the array sensing detection depth 60in resistivity, M2R3 is the array sensing detection depth 30in resistivity; Obtaining physical quality factor parameters of the reservoir; Characterizing the physical properties of the reservoir using the physical property quality factor parameter; The method for obtaining the physical quality factor parameter is to calculate it through the following formula: Where: POSU is the physical quality factor, PERM is the permeability, and POR is the porosity; The permeability is calculated by the following formula: lg PERM=[a+b×lg(POR)+k×lg(XMD)]×10 -3 Where: XMD is the median grain size, POR is the porosity, a and b are coefficients, which are 0.25 and 0.71 respectively, and k is a coefficient related to the lithology, where siltstone is 1 and conglomerate is 1.586; The porosity is calculated by the following formula: Where: DEN is the measured value of the well logging bulk density, DM is the skeleton value of the bulk density, DF is the fluid value of the bulk density, SH is the shale content, and DSH is the density of the mudstone; The mud content is calculated by the following formula: Where: GR index is the relative value of natural gamma, C is the empirical coefficient of the formation; Establishing the intersection diagram of the product of the array sensing radial invasion parameter and the physical property quality factor with the oil production per meter and the water production per meter, respectively, to obtain the capacity prediction oil production and capacity prediction water production interpretation diagrams; The explanatory diagram is used to establish the daily oil production equation and the daily water production equation.

2. A production capacity determination device for complex sandstone reservoirs, characterized in that: include: The first establishment unit is used to establish the intersection diagram of the product of the array sensing radial invasion parameter and the physical quality factor with the oil production per meter and the water production per meter, so as to obtain the interpretation diagrams of the capacity prediction oil production and the capacity prediction water production respectively; The array-induced radial intrusion parameter is calculated by the following formula: Where: AIR is the array sensing radial invasion characteristic parameter, M2RX is the array sensing detection depth 120in resistivity, M2R9 is the array sensing detection depth 90in resistivity, M2R6 is the array sensing detection depth 60in resistivity, M2R3 is the array sensing detection depth 30in resistivity; The method for obtaining the physical quality factor parameter is to calculate it through the following formula: Where: POSU is the physical quality factor, PERM is the permeability, and POR is the porosity; The permeability is calculated by the following formula: lg PERM=[a+b×lg(POR)+k×lg(XMD)]×10 -3 Where: XMD is the median grain size, POR is the porosity, a and b are coefficients, which are 0.25 and 0.71 respectively, and k is a coefficient related to the lithology, where siltstone is 1 and conglomerate is 1.586; The porosity is calculated by the following formula: Where: DEN is the measured value of the well logging bulk density, DM is the skeleton value of the bulk density, DF is the fluid value of the bulk density, SH is the shale content, and DSH is the density of the mudstone; The mud content is calculated by the following formula: Where: GR index is the relative value of natural gamma, C is the empirical coefficient of the formation; The second establishing unit is used to establish the daily oil production equation and the daily water production equation using the explanation chart.

3. An electronic device, characterized in that: include: processor; a memory for storing processor-executable instructions; The processor is configured to call the instructions stored in the memory to execute the productivity determination method for a complex sandstone reservoir according to claim 1.

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