A four-dimensional logging evaluation method, device, equipment and medium for complex offshore reservoirs

Abstract

This invention relates to the field of offshore oil and gas field exploration and development, and discloses a method, apparatus, equipment, and medium for four-dimensional logging evaluation of complex offshore reservoirs. It can acquire logging data and multiple core samples from near-wellbore complex reservoirs of a target single well in an offshore oil and gas field, with the number of core samples not exceeding a set threshold. Based on the logging data of the near-wellbore complex reservoir and each core sample, the permeability, acoustic reflection coefficient of the near-wellbore complex reservoir, and the acoustic reflection coefficient of the distant-wellbore complex reservoir are determined. Based on the permeability, acoustic reflection coefficient of the near-wellbore complex reservoir, and the acoustic reflection coefficient of the distant-wellbore complex reservoir, a comprehensive sweet spot characterization parameter for the near-wellbore and distant-wellbore complex reservoirs is determined. Based on this comprehensive sweet spot characterization parameter, it is determined whether the near-wellbore and distant-wellbore complex reservoirs are sweet spot reservoirs. This invention can effectively realize four-dimensional logging evaluation of complex reservoirs under conditions of limited data at sea, improving the accuracy of logging evaluation of complex reservoirs.

Description

Technical Field

[0001] This invention relates to the field of offshore oil and gas field exploration and development, and in particular to a four-dimensional logging evaluation method, device, equipment and medium for complex offshore reservoirs. Background Technology

[0002] As offshore oil and gas exploration and development continues to expand into deep water / ultra-deep water and deep / ultra-deep layers, deep water / ultra-deep water and deep / ultra-deep layers are gradually becoming important directions for future offshore oil and gas exploration and development.

[0003] Compared to shallow water and shallow reservoirs at sea, deep / ultra-deep water and deep / ultra-deep reservoirs, with increasing water and burial depth, exhibit increasingly complex reservoir characteristics. On the one hand, they contain numerous low-permeability and fractured reservoirs, among other complex formations. On the other hand, drilling and logging conditions become increasingly challenging, making it more difficult to obtain core samples and high-quality logging data. Under these circumstances, the accuracy of logging evaluation techniques for complex reservoirs in offshore oil and gas fields is relatively low. Summary of the Invention

[0004] This invention provides a four-dimensional logging evaluation method, apparatus, equipment, and medium for complex offshore reservoirs, which addresses the shortcomings of related technologies in terms of low logging evaluation accuracy for complex offshore oil and gas reservoirs, and improves the logging evaluation accuracy for complex offshore oil and gas reservoirs.

[0005] In a first aspect, the present invention provides a four-dimensional logging evaluation method for complex offshore reservoirs, comprising: Acquire logging data and multiple core samples from the near-wellbore complex reservoir of a target single well in an offshore oil and gas field, wherein the number of core samples does not exceed a set threshold. Based on the logging data of the near-well complex reservoir and each core sample, the permeability, acoustic reflection coefficient of the near-well complex reservoir and the acoustic reflection coefficient of the far-well complex reservoir are determined; wherein, the far-well complex reservoir is the complex reservoir within the far-well range of the target single well. Based on the permeability and acoustic reflection coefficient of the near-well complex reservoir and the acoustic reflection coefficient of the far-well complex reservoir, the comprehensive sweet spot characterization parameters of the near-well complex reservoir and the far-well complex reservoir are determined. Based on the comprehensive sweet spot characterization parameters of the near-wellbore complex reservoir and the far-wellbore complex reservoir, it is determined whether the near-wellbore complex reservoir and the far-wellbore complex reservoir are sweet spot reservoirs.

[0006] Optionally, determining the permeability, acoustic reflection coefficient, and acoustic reflection coefficient of the near-wellbore complex reservoir and the far-wellbore complex reservoir based on the logging data of the near-wellbore complex reservoir and each of the core samples includes: The permeability of the near-well complex reservoir is determined based on the logging data of the near-well complex reservoir and each of the core samples. Based on the logging data of the near-well complex reservoir, the acoustic reflection coefficients of the near-well complex reservoir and the far-well complex reservoir are determined.

[0007] Optionally, determining the permeability of the near-wellbore complex reservoir based on the logging data and each core sample includes: Each of the core samples was subjected to computed tomography (CT) scans and physical property analysis experiments to determine the CT scan images and porosity / permeability data of each core sample. Numerical transformation and three-dimensional printing are performed on the CT scan images and porosity data of each core sample to obtain multiple three-dimensional physical models, the number of which is greater than the set threshold. Physical property analysis and permeation experiments were performed on each of the three-dimensional physical models to obtain the permeability of each of the three-dimensional physical models; Based on the porosity and permeability data of each core sample and the permeability of each three-dimensional physical model, a relationship between permeability and logging permeability is established. Based on the aforementioned relationship and the logging data of the near-wellbore complex reservoir, the permeability of the near-wellbore complex reservoir is determined.

[0008] Optionally, determining the acoustic reflection coefficients of the near-wellbore complex reservoir and the far-wellbore complex reservoir based on the logging data of the near-wellbore complex reservoir includes: The acoustic reflection coefficient of the near-well complex reservoir is extracted from the logging data of the near-well complex reservoir; Based on the acoustic reflection coefficient of the near-well complex reservoir and the established mapping relationship between the near-well acoustic reflection coefficient and the far-well acoustic reflection coefficient, the acoustic reflection coefficient of the far-well complex reservoir is determined.

[0009] Optionally, determining the comprehensive sweet spot characterization parameters of the near-wellbore complex reservoir and the far-wellbore complex reservoir based on the permeability, acoustic reflection coefficient, and acoustic reflection coefficient of the near-wellbore complex reservoir includes: The permeability, acoustic reflection coefficient of the near-well complex reservoir, and the acoustic reflection coefficient of the far-well complex reservoir are input into the created sweet spot characterization parameter determination model to perform sweet spot calculation, thereby obtaining the comprehensive sweet spot characterization parameters of the near-well complex reservoir and the far-well complex reservoir. The model for determining the dessert characterization parameters is as follows: ; in, G h Let mD be the dessert characterization parameter.R The radial distance from the target well is expressed in meters (m). is the acoustic reflection coefficient of complex reservoirs near the wellbore, which is dimensionless; is the acoustic reflection coefficient of complex reservoirs in distant wells, which is dimensionless; denoted as mD, representing the permeability of a complex reservoir near the wellbore.

[0010] Optionally, determining whether the near-wellbore complex reservoir and the far-wellbore complex reservoir are sweet spot reservoirs based on the comprehensive sweet spot characterization parameters of the near-wellbore complex reservoir and the far-wellbore complex reservoir includes: Determine whether the comprehensive dessert characterization parameter is greater than the set dessert threshold; If the comprehensive sweet spot characterization parameter is greater than the sweet spot threshold, then the near-well complex reservoir and the far-well complex reservoir are determined to be sweet spot reservoirs. If the comprehensive sweet spot characterization parameter is not greater than the sweet spot threshold, then the near-well complex reservoir and the far-well complex reservoir are determined to be non-sweet spot reservoirs.

[0011] Optionally, the logging data of the near-wellbore complex reservoir includes array acoustic logging data; After acquiring logging data and multiple core samples from the near-wellbore complex reservoir of the target well in the offshore oil and gas field, the method further includes: Based on the array acoustic logging data, two-dimensional imaging is performed on multiple directional planes in the far-well range of the target single well to obtain two-dimensional far-well plane imaging of each directional plane; By performing continuous imaging of the near-wellbore and far-wellbore planes for each of the two-dimensional far-wellbore planes, continuous imaging of each target direction plane is obtained; Interpolation imaging is performed in continuous imaging of each target orientation plane to obtain a three-dimensional image of the far-well range.

[0012] Secondly, the present invention provides a four-dimensional logging evaluation device for complex offshore reservoirs, applicable to the four-dimensional logging evaluation method for complex offshore reservoirs described in the first aspect above or any corresponding embodiment, the device comprising: The acquisition unit is used to acquire logging data of near-wellbore complex reservoirs of a target single well in an offshore oil and gas field and multiple core samples, wherein the number of core samples is not greater than a set threshold. The first determining unit is used to determine the permeability, acoustic reflection coefficient, and acoustic reflection coefficient of the near-well complex reservoir and the far-well complex reservoir based on the logging data of the near-well complex reservoir and each core sample; wherein, the far-well complex reservoir is a complex reservoir within the far-well range of the target single well. The second determining unit is used to determine the comprehensive sweet spot characterization parameters of the near-well complex reservoir and the far-well complex reservoir based on the permeability and acoustic reflection coefficient of the near-well complex reservoir and the acoustic reflection coefficient of the far-well complex reservoir. The judgment unit is used to determine whether the near-well complex reservoir and the far-well complex reservoir are sweet spot reservoirs based on the comprehensive sweet spot characterization parameters of the near-well complex reservoir and the far-well complex reservoir.

[0013] Thirdly, the present invention provides a computer device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the four-dimensional logging evaluation method for complex marine reservoirs described in the first aspect or any corresponding embodiment.

[0014] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute the four-dimensional logging evaluation method for complex marine reservoirs described in the first aspect or any corresponding embodiment thereof.

[0015] This invention provides a four-dimensional logging evaluation method, apparatus, equipment, and medium for complex offshore reservoirs. It can acquire logging data and multiple core samples from near-wellbore complex reservoirs in a target well in an offshore oil and gas field, with the number of core samples not exceeding a set threshold. Based on the logging data of the near-wellbore complex reservoir and each core sample, the permeability and acoustic reflection coefficient of the near-wellbore complex reservoir and the acoustic reflection coefficient of the distant-wellbore complex reservoir are determined. Based on the permeability, acoustic reflection coefficient, and acoustic reflection coefficient of the near-wellbore complex reservoir and the distant-wellbore complex reservoir, the comprehensive sweet spot characterization parameters of the near-wellbore and distant-wellbore complex reservoirs are determined. Based on the comprehensive sweet spot characterization parameters of the near-wellbore and distant-wellbore complex reservoirs, it is determined whether the near-wellbore complex reservoir and the distant-wellbore complex reservoir are sweet spot reservoirs. This invention builds upon existing well logging evaluation methods that focus on the "near-well" and "static" two-dimensional aspects. Based on rock physics response excitation and well logging response laws, it further explores information on the "far-well (e.g., within 50m of the well)" and "dynamic (dynamic permeability, productivity)" aspects. This enables near-well and far-well cross-scale coupling, performs high-definition imaging of the far-well, and achieves static-dynamic multi-state matching based on seepage mechanisms and laws and high-precision reservoir parameters, forming a four-dimensional well logging evaluation method of "near-well—static—far-well—dynamic".

[0016] This invention can fully utilize existing logging information to comprehensively characterize complex reservoirs in deep / ultra-deep water and deep / ultra-deep waters under conditions of limited data. It can not only accurately characterize complex reservoirs under limited data conditions at sea, but also save testing costs during the exploration phase of deep / ultra-deep water and deep / ultra-deep waters. Furthermore, it can guide the number of wells and optimize well deployment during the development phase, thereby significantly improving the economic benefits of exploration and development of complex oil and gas reservoirs in deep / ultra-deep water and deep / ultra-deep waters.

[0017] The present invention, by adopting the above technical solution, also has the following advantages: Avoiding the need to drill large amounts of core samples and conduct a series of experiments can effectively save costs and improve the economic benefits of exploring and developing complex oil and gas reservoirs in deep / ultra-deep water and deep / ultra-deep areas, making it highly economical.

[0018] While ensuring accurate evaluation of complex reservoirs in deep / ultra-deep water and deep / ultra-deep offshore areas, this method provides an effective, simple, and practical new logging evaluation approach. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in this invention or related technologies, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 A flowchart of a four-dimensional logging evaluation method for complex offshore reservoirs provided in this embodiment of the invention; Figure 2 A method provided by an embodiment of the present invention A oilfield A1 Core photographs of complex reservoirs in wells; Figure 3 A method provided by an embodiment of the present invention A oilfield A1 CT scan digital core image of complex reservoir walls in wells; Figure 4 A method based on the embodiments of the present invention is provided. A oilfield A1 A 3D printed physical model of a complex reservoir well obtained from a CT scan of the well wall. Figure 5 This invention provides a numerical simulation-based 3D printing method. A Physical model diagrams of different types of reservoir spaces in an oilfield; Figure 6 A method based on the embodiments of the present invention is provided.A oilfield A1 Experimental diagram of oil-water phase permeation of a 3D printed physical model of a complex reservoir wall CT scan; Figure 7 This invention provides a three-dimensional printed physical model of the oil-water phase permeation experiment, which shows the storage space occupied by the oil phase after the water phase is driven out. Figure 8 A method provided by an embodiment of the present invention A oilfield A1 Well logging processing and comprehensive interpretation results diagram; Figure 9 A method provided by an embodiment of the present invention A oilfield A1 Two-dimensional imaging image within a 50-meter range of the well; Figure 10 A method provided by an embodiment of the present invention A oilfield A1 Three-dimensional imaging image within a 50-meter range of the well; Figure 11 A technical roadmap for a four-dimensional logging evaluation method for complex offshore reservoirs provided in this embodiment of the invention; Figure 12 This is a schematic diagram of the structure of a four-dimensional logging evaluation device for complex offshore reservoirs provided in an embodiment of the present invention; Figure 13 This is a schematic diagram of the structure of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0022] It should be noted that the relevant technologies are greatly affected by factors such as the number of core samples taken from a single well in deep / ultra-deep water and deep / ultra-deep reservoirs, the reservoir storage space, and the distribution of geological bodies far from the well, making high-precision well logging evaluation extremely difficult. This, in turn, affects the decision-making and development and production effectiveness of deep / ultra-deep water and deep / ultra-deep reservoir exploration operations. Therefore, how to improve the scale and accuracy of well logging evaluation for a series of complex reservoirs, such as low-permeability reservoirs and fractured reservoirs under conditions of limited data at sea, is a key issue in well logging evaluation in the deep / ultra-deep water and deep / ultra-deep water fields. The well logging evaluation methods in the relevant technologies mainly start from the core, focusing on the high-precision evaluation of near-wellbore (within 1m of the well) and static (porosity, static permeability, saturation, etc.) reservoir parameters. However, given the limited core samples, large well spacing, and diverse well types, on the one hand, the lack of core analysis data leads to large errors in the calculation of static reservoir parameters; on the other hand, it only characterizes reservoir information within a 1m radius of the well, which is a small scale that limits its ability to comprehensively characterize complex reservoir information in deep / ultra-deep water and deep / ultra-deep offshore areas.

[0023] The following is combined with Figures 1-11 This invention describes a four-dimensional logging evaluation method for complex offshore reservoirs.

[0024] like Figure 1 As shown in the figure, this embodiment proposes a first four-dimensional logging evaluation method for complex offshore reservoirs, which may include the following steps: S101. Obtain logging data and multiple core samples from the near-wellbore complex reservoir of the target single well in the offshore oil and gas field. The number of core samples shall not exceed the set threshold.

[0025] In this embodiment, the target well is a specific well in an offshore oil and gas field. Specifically, this embodiment can use the well containing the complex reservoir to be studied in the offshore oil and gas field as the target well.

[0026] Among them, the near-well complex reservoir refers to the complex reservoir in the target single well.

[0027] Specifically, in this embodiment, a small number of core samples or wall core samples can be collected in complex reservoirs near the wellbore.

[0028] It should be noted that this embodiment can be used to measure a series of logging data in a complex reservoir in a well in an offshore oil and gas field, including natural gamma ray, spontaneous potential, deep resistivity, medium resistivity, shallow resistivity, bulk density, neutron porosity, array acoustic wave, and electrical imaging. This embodiment can also be used to drill a small number of full-diameter core or wall core samples, such as... Figure 2 The image shown is from A oilfield A1 Core samples obtained from drilling in complex reservoirs.

[0029] S102. Based on the logging data of the near-well complex reservoir and each core sample, determine the permeability, acoustic reflection coefficient of the near-well complex reservoir and the acoustic reflection coefficient of the far-well complex reservoir; wherein, the far-well complex reservoir is the complex reservoir within the far-well range of the target single well.

[0030] The far-well range of the target well is defined as the radial distance from the target well that exceeds a minimum threshold but does not exceed a maximum threshold. For example, the minimum threshold can be 1 meter, and the maximum threshold can be 50 meters, 70 meters, or 90 meters.

[0031] Specifically, a distant-well complex reservoir refers to a complex reservoir within the distance of a distant well.

[0032] Specifically, this embodiment can perform physical property analysis experiments and computed tomography (CT) scans based on well logging data of near-wellbore complex reservoirs and each core sample to obtain the permeability, acoustic reflection coefficient of near-wellbore complex reservoirs, and acoustic reflection coefficient of far-wellbore complex reservoirs within the target well's far-wellbore range. The permeability of the near-wellbore complex reservoir can be either liquid phase permeability or gas phase permeability.

[0033] Optionally, step S102 includes: The permeability of the near-wellbore complex reservoir was determined based on logging data and core samples from each reservoir. Based on logging data of complex reservoirs near the well, the acoustic reflection coefficients of complex reservoirs near the well and complex reservoirs far from the well are determined.

[0034] Optionally, the permeability of the near-wellbore complex reservoir is determined based on logging data and each core sample, including: Each core sample was subjected to computed tomography (CT) scans and physical property analysis experiments to determine the CT scan images and porosity-permeability data of each core sample. Numerical transformation and 3D printing were performed on the CT scan images and porosity data of each core sample to obtain multiple 3D physical models, with the number of 3D physical models exceeding a set threshold. Physical property analysis and permeation experiments were performed on each three-dimensional physical model to obtain the permeability of each three-dimensional physical model; Based on the porosity and permeability data of each core sample and the permeability of each three-dimensional physical model, a relationship between permeability and logging permeability is established. Based on the relationship and logging data of complex reservoirs near the wellbore, the permeability of complex reservoirs near the wellbore is determined.

[0035] Specifically, this embodiment can conduct CT scanning and physical property analysis experiments on full-diameter core or wall core samples at a certain depth in the near-wellbore complex reservoir of the target single well. Through experimental analysis, the CT scan image, porosity, and permeability of each full-diameter core or wall core sample can be obtained, i.e., CT scan digital core model and core analysis porosity. φ c Permeability analysis of core samples K c Among them, digital core models can be like... Figure 3 As shown.

[0036] Specifically, this embodiment can use a digital core model obtained from a CT scan of a full-diameter core or wall-core sample to create a physical model through 3D printing. Then, the 3D-printed physical model is subjected to CT scanning again to obtain microstructural parameters such as pores and throats. By comparing the microstructural parameters of the digital core model obtained from the CT scan of the full-diameter core or wall-core sample with those of the 3D-printed physical model, the relative error between the two is ensured to be less than 8%, thereby guaranteeing the accuracy of the 3D physical model. For example, Figure 4 The 3D printing physical model shown is based on A oilfield A1 The physical model obtained after 3D printing of the digital core model from CT scans of complex reservoir walls in wells, and... Figure 2 middle A oilfield A1 The relative error between the microstructural parameters such as pores and throats in the digital core model obtained by CT scanning of core samples from complex reservoir walls is 4.2%.

[0037] It should be noted that the digital core model of the complex reservoir in the offshore oil and gas field obtained by CT scanning in this embodiment can be used to change the different configurations and combinations of pores and throats through numerical simulation. Then, 3D printing can be used to obtain a series of 3D printed physical models of the complex reservoir, making up for the lack of full-diameter core or wall core samples of the complex reservoir. For example, Figure 5 The physical model diagram shown is based on numerical simulation and 3D printing. A Physical model diagram of different types of reservoir space in an oil field.

[0038] Specifically, this embodiment allows for the analysis of physical properties and experiments on oil-water or gas-water interpenetration of these 3D printed physical models. This enables the generation of abundant experimental data on physical properties and oil-water or gas-water interpenetration, even with the drilling of small quantities of full-diameter core or wall core samples in the offshore oil and gas field. For example... Figure 6 As shown, it is based on A oilfield A1 3D printed model of oil-water phase permeation experiment based on CT scan of the well core of a complex reservoir. Figure 7 It refers to the storage space occupied by the oil phase after the oil phase is driven out of the water phase during the oil-water phase permeation experiment of the 3D printed model.

[0039] Optionally, the determination of the acoustic reflection coefficients of near-wellbore complex reservoirs and far-wellbore complex reservoirs based on logging data from near-wellbore complex reservoirs includes: The acoustic reflection coefficient of complex near-wellbore reservoirs is extracted from logging data. Based on the acoustic reflection coefficient of complex reservoirs near the wellbore and the established mapping relationship between the acoustic reflection coefficients of near and far wells, the acoustic reflection coefficient of complex reservoirs far from the wellbore is determined.

[0040] Specifically, this embodiment utilizes logging data such as natural gamma ray, deep resistivity, shallow resistivity, bulk density, neutron porosity, and P-wave transit time collected by logging instruments to calculate the rock mineral composition and porosity of complex reservoirs, i.e., logging porosity and logging permeability, as shown in equations (1) and (2) below, respectively. The relative error between the logging-calculated porosity and the experimentally measured core analysis porosity is less than 8%, and the relative error between the logging-calculated permeability and the experimentally measured core analysis permeability is less than 50%. ----------Formula (1); --------------------------Formula (2); in, φ l For well logging porosity, % GR For natural gamma, API; R t The resistivity is Ω·m. R xo The resistivity of the rinsing band is Ω·m; DEN Bulk density, g / cm³ 3 ; NPHI Neutron porosity, % DT The longitudinal wave time difference is expressed in μs / ft. K l Let mD represent the well logging permeability.

[0041] See Figure 8 , Figure 8 yes A oilfield A1 Well logging processing and comprehensive interpretation results diagram. Figure 8The first track shows the formation depth; the second track shows the natural gamma ray and wellbore diameter, indicating the lithological characteristics of the formation; the third track shows the deep and shallow resistivity logging curves, depicting the electrical characteristics of the formation; the fourth track shows the bulk density, neutron porosity, and sonic transit time, reflecting the physical properties of the formation; the fifth track shows the static map of electrical imaging logging data; the sixth track shows different types of fractures obtained after processing based on electrical imaging logging; the seventh track shows the porosity calculated from logging; the eighth track shows the permeability from core analysis and the permeability calculated from logging; and the ninth track shows the content of dolomite, sandstone, limestone, and mudstone calculated from logging.

[0042] In other four-dimensional logging evaluation methods for complex offshore reservoirs proposed in this embodiment, the logging data for near-wellbore complex reservoirs includes array sonic logging data. In this case, after step S101 above, the method may further include: Two-dimensional imaging of multiple directional planes in the far-well range of the target single well is performed based on array acoustic logging data to obtain two-dimensional far-well plane imaging of each directional plane. Continuous imaging of near-wellbore and far-wellbore planes is performed for each two-dimensional far-wellbore plane image to obtain continuous imaging of the plane in each target direction; Interpolation imaging is performed in continuous imaging of each target orientation plane to obtain a three-dimensional image of the far-well range.

[0043] Specifically, this embodiment utilizes array acoustic logging data acquired by logging instruments. Considering the impact of various types of noise generated by the complex geological conditions and wellbore environment of the oil and gas field on reflected shear waves, and based on the generation mechanism, response characteristics, and identification methods of different types of noise, it maximizes the amplification of weak reflected wave signals, improves the far-field detection imaging range and imaging accuracy of dipole shear waves, and achieves high-definition two-dimensional imaging of the distant well. See [link to documentation]. Figure 9 , Figure 9 yes A oilfield A1 Two-dimensional imaging image within a 50-meter range of the well.

[0044] Subsequently, based on array acoustic and electrical imaging logging data acquired by the logging instrument, an intelligent algorithm was used to establish a mapping relationship between the near-wellbore and far-wellbore acoustic reflection coefficients in the array acoustic logging data, achieving cross-scale coupling between different types of logging data from near to far wells. Combining the results of two-dimensional high-definition imaging, continuous imaging of the wellbore near and far from the wellbore can be achieved on a two-dimensional plane. Then, discretized random modeling and three-dimensional imaging tracking methods are used to achieve interpolation between different two-dimensional planes. Based on the two-dimensional planes within a 360-degree range of the wellbore and the interpolation results, three-dimensional imaging of the wellbore is then achieved. (See also...) Figure 10 , Figure 10 yes A oilfield A1 Three-dimensional imaging image of the well within a 50-meter range.

[0045] S103. Based on the permeability and acoustic reflection coefficient of the near-well complex reservoir and the acoustic reflection coefficient of the far-well complex reservoir, determine the comprehensive sweet spot characterization parameters of the near-well complex reservoir and the far-well complex reservoir.

[0046] Specifically, in this embodiment, the permeability of the near-well complex reservoir and the acoustic reflection coefficient of the far-well complex reservoir can be used to calculate the comprehensive sweet spot characterization parameters of the near-well complex reservoir and the far-well complex reservoir.

[0047] Optionally, step S103 includes: The permeability, acoustic reflection coefficient of the near-well complex reservoir, and the acoustic reflection coefficient of the far-well complex reservoir are input into the created sweet spot characterization parameter determination model to calculate the sweet spot, and the comprehensive sweet spot characterization parameters of the near-well complex reservoir and the far-well complex reservoir are obtained. The model for determining the dessert characterization parameters is as follows: ; in, G h Let mD be the dessert characterization parameter. R The radial distance from the target well is expressed in meters (m). is the acoustic reflection coefficient of complex reservoirs near the wellbore, which is dimensionless; is the acoustic reflection coefficient of complex reservoirs in distant wells, which is dimensionless; denoted as mD, representing the permeability of a complex reservoir near the wellbore.

[0048] It should be noted that, compared with the permeability models of related technologies that only consider near-wellbore permeability, the sweet spot characterization parameter determination model of this embodiment considers not only near-wellbore permeability but also far-wellbore permeability, and combines near-wellbore and far-wellbore permeability to obtain the comprehensive radial permeability of complex offshore reservoirs at a certain depth, which can more objectively characterize sweet spot reservoirs.

[0049] Specifically, this embodiment can be based on the oil phase permeability or gas phase permeability obtained from oil-water or gas-water phase permeability experiments. K h and the well logging permeability calculated from well logging. K l Establish oil phase permeability or gas phase permeability K h With well logging permeability K l The functional relationship between them is shown in equation (3).

[0050] -------------Form (3); In the formula: Kh Where D is the oil phase permeability or gas phase permeability; K l Let mD represent the well logging permeability.

[0051] According to equation (3), the oil phase permeability or gas phase permeability of the complex reservoir in the entire well section of a well in the oil and gas field can be calculated. K h .

[0052] Specifically, A Oil phase permeability or gas phase permeability of oilfield K h With well logging permeability K l The functional relationship between them is shown in equation (4): -------------Form (4); Specifically, this embodiment can combine the near-wellbore acoustic reflection coefficient, the far-wellbore acoustic reflection coefficient, and the obtained oil phase permeability or gas phase permeability to calculate the sweet spot characterization parameters of complex reservoirs within a 50-meter range in a well of this oil and gas field. G h To establish the relationship between them, see the dessert characterization parameter determination model.

[0053] S104. Based on the comprehensive sweet spot characterization parameters of near-wellbore complex reservoirs and far-wellbore complex reservoirs, determine whether near-wellbore complex reservoirs and far-wellbore complex reservoirs are sweet spot reservoirs.

[0054] Specifically, this embodiment can determine whether a near-wellbore complex reservoir or a far-wellbore complex reservoir is a sweet spot reservoir based on the comprehensive sweet spot characterization parameters of near-wellbore complex reservoirs and far-wellbore complex reservoirs.

[0055] Optionally, step S104 includes: Determine whether the overall dessert characterization parameters are greater than the set dessert threshold; If the comprehensive sweet spot characterization parameters are greater than the sweet spot threshold, then the near-well complex reservoir and the far-well complex reservoir are identified as sweet spot reservoirs. If the comprehensive sweet spot characterization parameter is not greater than the sweet spot threshold, then the near-well complex reservoir and the far-well complex reservoir are identified as non-sweet spot reservoirs.

[0056] Specifically, the dessert threshold can be 10mD. At this point, when the calculated... G h When the value is greater than 10mD, it indicates that the corresponding reservoir is a sweet spot reservoir.

[0057] It should be noted that this embodiment can use logging software such as Geolog, Techlog, CIFLog, and EGPS to process and interpret parameters such as rock and mineral composition, logging porosity, logging permeability, and sonic reflection coefficient of complex reservoirs. This embodiment can accurately evaluate a range of complex reservoirs, including low-permeability reservoirs and fractured reservoirs, in deep / ultra-deep water and deep / ultra-deep areas under conditions of limited data at sea.

[0058] The four-dimensional logging evaluation method for complex offshore reservoirs proposed in this embodiment can acquire logging data and multiple core samples from near-wellbore complex reservoirs in a target single well in an offshore oil and gas field, with the number of core samples not exceeding a set threshold. Based on the logging data of the near-wellbore complex reservoir and each core sample, the permeability, acoustic reflection coefficient of the near-wellbore complex reservoir, and the acoustic reflection coefficient of the far-wellbore complex reservoir are determined. Based on the permeability, acoustic reflection coefficient of the near-wellbore complex reservoir, and the acoustic reflection coefficient of the far-wellbore complex reservoir, the comprehensive sweet spot characterization parameters of the near-wellbore and far-wellbore complex reservoirs are determined. Based on the comprehensive sweet spot characterization parameters of the near-wellbore and far-wellbore complex reservoirs, it is determined whether the near-wellbore and far-wellbore complex reservoirs are sweet spot reservoirs. Figure 11 As shown, this embodiment, based on the existing logging evaluation methods that focus on the "near-well" and "static" two-dimensional aspects, further explores the "far-well (within 50m of the well)" and "dynamic (dynamic permeability, production capacity)" information according to rock physical response excitation and logging response laws. This achieves near-well-far-well cross-scale coupling, executes high-definition imaging of the far-well, and realizes static-dynamic multi-state matching based on seepage mechanisms and laws and high-precision reservoir parameters, forming a "near-well-static-far-well-dynamic" four-dimensional logging evaluation method. Under the condition of limited data in deep / ultra-deepwater and deep / ultra-deep offshore reservoirs, fully utilizing existing logging information to comprehensively characterize complex reservoirs in these areas not only allows for accurate characterization of complex reservoirs under limited data conditions but also saves testing costs during the exploration phase. Furthermore, it guides the number of wells and optimizes well deployment during the development phase, thereby significantly improving the economic benefits of exploring and developing complex oil and gas reservoirs in deep / ultra-deepwater and deep / ultra-deep offshore reservoirs.

[0059] Specifically, this embodiment adopts the above technical solution and also has the following advantages: 1. This embodiment avoids drilling a large number of core samples and conducting a series of experiments, which can effectively save costs and improve the economic benefits of exploration and development of complex oil and gas reservoirs in deep water / ultra-deep water and deep / ultra-deep areas at sea, and has strong economic advantages.

[0060] 2. This embodiment provides an effective, simple and practical new logging evaluation method while ensuring accurate evaluation of complex reservoirs in deep / ultra-deep water and deep / ultra-deep water areas at sea.

[0061] like Figure 12 As shown, this embodiment proposes a four-dimensional logging evaluation device for complex offshore reservoirs, applicable to any of the aforementioned four-dimensional logging evaluation methods for complex offshore reservoirs. The device includes: The acquisition unit 101 is used to acquire logging data of near-wellbore complex reservoirs of a target single well in an offshore oil and gas field and multiple core samples. The number of core samples shall not exceed a set threshold. The first determining unit 102 is used to determine the permeability, acoustic reflection coefficient, and acoustic reflection coefficient of the near-well complex reservoir and the far-well complex reservoir based on the logging data of the near-well complex reservoir and each core sample; wherein, the far-well complex reservoir is the complex reservoir within the far-well range of the target single well. The second determining unit 103 is used to determine the comprehensive sweet spot characterization parameters of the near-well complex reservoir and the far-well complex reservoir based on the permeability of the near-well complex reservoir and the acoustic reflection coefficient of the far-well complex reservoir. The judgment unit 104 is used to determine whether the near-well complex reservoir and the far-well complex reservoir are sweet spot reservoirs based on the comprehensive sweet spot characterization parameters of the near-well complex reservoir and the far-well complex reservoir.

[0062] It should be noted that the processing procedures of the acquisition unit 101, the first determination unit 102, the second determination unit 103, and the judgment unit 104, and the beneficial effects thereof, can be referred to respectively. Figure 1 Steps S101 to S104 in the process will not be described again.

[0063] Optionally, the first determining unit 102 is also used for: The permeability of the near-wellbore complex reservoir was determined based on logging data and core samples from each reservoir. Based on logging data of complex reservoirs near the well, the acoustic reflection coefficients of complex reservoirs near the well and complex reservoirs far from the well are determined.

[0064] Optionally, the first determining unit 102 is also used for: Each core sample was subjected to computed tomography (CT) scans and physical property analysis experiments to determine the CT scan images and porosity-permeability data of each core sample. Numerical transformation and 3D printing were performed on the CT scan images and porosity data of each core sample to obtain multiple 3D physical models, with the number of 3D physical models exceeding a set threshold. Physical property analysis and permeation experiments were performed on each three-dimensional physical model to obtain the permeability of each three-dimensional physical model; Based on the porosity and permeability data of each core sample and the permeability of each three-dimensional physical model, a relationship between permeability and logging permeability is established. Based on the relationship and logging data of complex reservoirs near the wellbore, the permeability of complex reservoirs near the wellbore is determined.

[0065] Optionally, the first determining unit 102 is also used for: The acoustic reflection coefficient of complex near-wellbore reservoirs is extracted from logging data. Based on the acoustic reflection coefficient of complex reservoirs near the wellbore and the established mapping relationship between the acoustic reflection coefficients of near and far wells, the acoustic reflection coefficient of complex reservoirs far from the wellbore is determined.

[0066] Optionally, the second determining unit 103 is also used for: The permeability, acoustic reflection coefficient of the near-well complex reservoir, and the acoustic reflection coefficient of the far-well complex reservoir are input into the created sweet spot characterization parameter determination model to calculate the sweet spot, and the comprehensive sweet spot characterization parameters of the near-well complex reservoir and the far-well complex reservoir are obtained. The model for determining the dessert characterization parameters is as follows: ; in, G h Let mD be the dessert characterization parameter. R The radial distance from the target well is expressed in meters (m). is the acoustic reflection coefficient of complex reservoirs near the wellbore, which is dimensionless; is the acoustic reflection coefficient of complex reservoirs in distant wells, which is dimensionless; denoted as mD, representing the permeability of a complex reservoir near the wellbore.

[0067] Optionally, the judgment unit 104 is also used for: Determine whether the overall dessert characterization parameters are greater than the set dessert threshold; If the comprehensive sweet spot characterization parameters are greater than the sweet spot threshold, then the near-well complex reservoir and the far-well complex reservoir are identified as sweet spot reservoirs. If the comprehensive sweet spot characterization parameter is not greater than the sweet spot threshold, then the near-well complex reservoir and the far-well complex reservoir are identified as non-sweet spot reservoirs.

[0068] Optionally, the logging data for near-wellbore complex reservoirs may include array acoustic logging data; The device also includes an imaging unit, which is used for: After acquiring logging data of the near-wellbore complex reservoir and multiple core samples of the target single well in the offshore oil and gas field, two-dimensional imaging of multiple directional planes in the far-wellbore range of the target single well is performed based on array sonic logging data to obtain two-dimensional far-wellbore plane imaging of each directional plane. Continuous imaging of near-wellbore and far-wellbore planes is performed for each two-dimensional far-wellbore plane image to obtain continuous imaging of the plane in each target direction; Interpolation imaging is performed in continuous imaging of each target orientation plane to obtain a three-dimensional image of the far-well range.

[0069] The four-dimensional logging evaluation device for complex offshore reservoirs proposed in this embodiment can acquire logging data and multiple core samples of a target well in an offshore oil and gas field for near-wellbore complex reservoirs, with the number of core samples not exceeding a set threshold. Based on the logging data of the near-wellbore complex reservoir and each core sample, the permeability and acoustic reflection coefficient of the near-wellbore complex reservoir, and the acoustic reflection coefficient of the far-wellbore complex reservoir within the target well's distance range are determined. Based on the permeability and acoustic reflection coefficient of the near-wellbore complex reservoir and the acoustic reflection coefficient of the far-wellbore complex reservoir, sweet spot characterization parameters of the far-wellbore complex reservoir are determined. Based on the sweet spot characterization parameters of the far-wellbore complex reservoir, it is determined whether the far-wellbore complex reservoir is a sweet spot reservoir. This embodiment, based on the existing well logging evaluation methods that focus on the "near-well" and "static" two-dimensional aspects, further explores the information of "far-well (e.g., within 50m of the well)" and "dynamic (dynamic permeability, production capacity)" according to the rock physics response excitation and well logging response laws. It achieves near-well-far-well cross-scale coupling, performs high-definition imaging of the far-well, and realizes static-dynamic multi-state matching based on seepage mechanism and laws and high-precision reservoir parameters, forming a four-dimensional well logging evaluation method of "near-well-static-far-well-dynamic".

[0070] This embodiment can fully utilize existing logging information to comprehensively characterize complex reservoirs in deep / ultra-deep water and deep / ultra-deep waters under conditions of limited data. It can not only accurately characterize complex reservoirs under limited data conditions at sea, but also save testing costs during the exploration phase of deep / ultra-deep water and deep / ultra-deep waters. It can also guide the number of wells and optimize well deployment during the development phase, thereby significantly improving the economic benefits of exploration and development of complex oil and gas reservoirs in deep / ultra-deep water and deep / ultra-deep waters.

[0071] In this embodiment, the four-dimensional logging evaluation device for complex offshore reservoirs is presented in the form of functional units. Here, a unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0072] This invention also provides a computer device having the above-described features. Figure 12 The image shows a four-dimensional logging evaluation device for complex offshore reservoirs.

[0073] Please see Figure 13The present invention provides a schematic diagram of the structure of a computer device according to an optional embodiment. The computer device includes one or more processors 10, a memory 20, and interfaces for connecting the various components, including high-speed interfaces and low-speed interfaces. The various components are interconnected via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on an external input / output device (such as a display device coupled to the interface). In some optional embodiments, multiple processors and / or multiple buses can be used with multiple memories, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 13 Take a processor 10 as an example.

[0074] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GDA), or any combination thereof.

[0075] The memory 20 stores instructions executable by at least one processor 10 to cause at least one processor 10 to perform the method shown in the above embodiments.

[0076] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function. The data storage area may store data created based on the use of the computer device. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, which can be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0077] Memory 20 may include volatile memory, such as random access memory. Memory may also include non-volatile memory, such as flash memory, hard disk, or solid-state drive. Memory 20 may also include combinations of the above types of memory.

[0078] The computer device also includes a communication interface 30 for communicating with other devices or communication networks.

[0079] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code, which, when accessed and executed by the computer, processor, or hardware, implements the methods shown in the above embodiments.

[0080] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A four-dimensional logging evaluation method for complex offshore reservoirs, characterized in that, include: Acquire logging data and multiple core samples from the near-wellbore complex reservoir of a target single well in an offshore oil and gas field, wherein the number of core samples does not exceed a set threshold. Based on the logging data of the near-well complex reservoir and each core sample, the permeability, acoustic reflection coefficient of the near-well complex reservoir and the acoustic reflection coefficient of the far-well complex reservoir are determined; wherein, the far-well complex reservoir is the complex reservoir within the far-well range of the target single well. Based on the permeability and acoustic reflection coefficient of the near-well complex reservoir and the acoustic reflection coefficient of the far-well complex reservoir, the comprehensive sweet spot characterization parameters of the near-well complex reservoir and the far-well complex reservoir are determined. Based on the comprehensive sweet spot characterization parameters of the near-wellbore complex reservoir and the far-wellbore complex reservoir, it is determined whether the near-wellbore complex reservoir and the far-wellbore complex reservoir are sweet spot reservoirs.

2. The method according to claim 1, characterized in that, The step of determining the permeability, acoustic reflection coefficient, and acoustic reflection coefficient of the near-wellbore complex reservoir and the far-wellbore complex reservoir based on the logging data of the near-wellbore complex reservoir and each of the core samples includes: The permeability of the near-well complex reservoir is determined based on the logging data of the near-well complex reservoir and each of the core samples. Based on the logging data of the near-well complex reservoir, the acoustic reflection coefficients of the near-well complex reservoir and the far-well complex reservoir are determined.

3. The method according to claim 2, characterized in that, The step of determining the permeability of the near-wellbore complex reservoir based on logging data and each core sample includes: Each of the core samples was subjected to computed tomography (CT) scans and physical property analysis experiments to determine the CT scan images and porosity / permeability data of each core sample. Numerical transformation and three-dimensional printing are performed on the CT scan images and porosity data of each core sample to obtain multiple three-dimensional physical models, the number of which is greater than the set threshold. Physical property analysis and permeation experiments were performed on each of the three-dimensional physical models to obtain the permeability of each of the three-dimensional physical models; Based on the porosity and permeability data of each core sample and the permeability of each three-dimensional physical model, a relationship between permeability and logging permeability is established. Based on the aforementioned relationship and the logging data of the near-wellbore complex reservoir, the permeability of the near-wellbore complex reservoir is determined.

4. The method according to claim 2, characterized in that, The step of determining the acoustic reflection coefficients of the near-wellbore complex reservoir and the far-wellbore complex reservoir based on the logging data of the near-wellbore complex reservoir includes: The acoustic reflection coefficient of the near-well complex reservoir is extracted from the logging data of the near-well complex reservoir; Based on the acoustic reflection coefficient of the near-well complex reservoir and the established mapping relationship between the near-well acoustic reflection coefficient and the far-well acoustic reflection coefficient, the acoustic reflection coefficient of the far-well complex reservoir is determined.

5. The method according to claim 1, characterized in that, The comprehensive sweet spot characterization parameters for the near-wellbore complex reservoir and the far-wellbore complex reservoir are determined based on the permeability, acoustic reflection coefficient, and acoustic reflection coefficient of the near-wellbore complex reservoir, including: The permeability, acoustic reflection coefficient of the near-well complex reservoir, and the acoustic reflection coefficient of the far-well complex reservoir are input into the created sweet spot characterization parameter determination model to perform sweet spot calculation, thereby obtaining the comprehensive sweet spot characterization parameters of the near-well complex reservoir and the far-well complex reservoir. The model for determining the dessert characterization parameters is as follows: ； in, G h Let mD be the dessert characterization parameter. R The radial distance from the target well is expressed in meters (m). is the acoustic reflection coefficient of complex reservoirs near the wellbore, which is dimensionless; is the acoustic reflection coefficient of complex reservoirs in distant wells, which is dimensionless; denoted as mD, representing the permeability of a complex reservoir near the wellbore.

6. The method according to claim 1, characterized in that, The step of determining whether the near-wellbore complex reservoir and the far-wellbore complex reservoir are sweet spot reservoirs based on the comprehensive sweet spot characterization parameters of the near-wellbore complex reservoir and the far-wellbore complex reservoir includes: Determine whether the comprehensive dessert characterization parameter is greater than the set dessert threshold; If the comprehensive sweet spot characterization parameter is greater than the sweet spot threshold, then the near-well complex reservoir and the far-well complex reservoir are determined to be sweet spot reservoirs. If the comprehensive sweet spot characterization parameter is not greater than the sweet spot threshold, then the near-well complex reservoir and the far-well complex reservoir are determined to be non-sweet spot reservoirs.

7. The method according to claim 1, characterized in that, The logging data for the near-well complex reservoir includes array acoustic logging data; After acquiring logging data and multiple core samples from the near-wellbore complex reservoir of the target well in the offshore oil and gas field, the method further includes: Based on the array acoustic logging data, two-dimensional imaging is performed on multiple directional planes in the far-well range of the target single well to obtain two-dimensional far-well plane imaging of each directional plane; By performing continuous imaging of the near-wellbore and far-wellbore planes for each of the two-dimensional far-wellbore planes, continuous imaging of each target direction plane is obtained; Interpolation imaging is performed in continuous imaging of each target orientation plane to obtain a three-dimensional image of the far-well range.

8. A four-dimensional logging evaluation device for complex offshore reservoirs, characterized in that, The apparatus used in the four-dimensional logging evaluation method for complex offshore reservoirs according to any one of claims 1 to 7 comprises: The acquisition unit is used to acquire logging data of near-wellbore complex reservoirs of a target single well in an offshore oil and gas field and multiple core samples, wherein the number of core samples is not greater than a set threshold. The first determining unit is used to determine the permeability, acoustic reflection coefficient, and acoustic reflection coefficient of the near-well complex reservoir and the far-well complex reservoir based on the logging data of the near-well complex reservoir and each core sample; wherein, the far-well complex reservoir is a complex reservoir within the far-well range of the target single well. The second determining unit is used to determine the comprehensive sweet spot characterization parameters of the near-well complex reservoir and the far-well complex reservoir based on the permeability and acoustic reflection coefficient of the near-well complex reservoir and the acoustic reflection coefficient of the far-well complex reservoir. The judgment unit is used to determine whether the near-well complex reservoir and the far-well complex reservoir are sweet spot reservoirs based on the comprehensive sweet spot characterization parameters of the near-well complex reservoir and the far-well complex reservoir.

9. A computer device, characterized in that, include: The system includes a memory and a processor, which are interconnected. The memory stores computer instructions, and the processor executes the computer instructions to perform the four-dimensional logging evaluation method for complex offshore reservoirs as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the four-dimensional logging evaluation method for complex offshore reservoirs as described in any one of claims 1 to 7.

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