Reservoir fluid property identification method and device, storage medium and equipment

Abstract

The invention discloses a reservoir fluid property identification method and device, a storage medium and equipment. The method comprises the steps of obtaining an echo data parameter sequence according to multi-echo interval nuclear magnetic resonance logging data of a to-be-logged well in a target area; performing nuclear magnetic resonance two-dimensional inversion on the echo data parameter acquisition sequence to obtain a diffusion coefficient-transverse relaxation time nuclear magnetic resonance spectrum; according to a fluid distribution interval calibration result of the diffusion coefficient-transverse relaxation time nuclear magnetic resonance spectrum of the target area standard well, determining a fluid signal distribution interval in the diffusion coefficient-transverse relaxation time nuclear magnetic resonance spectrum of the to-be-measured well; based on the fluid signal amplitudes in the signal distribution intervals of the different types of fluids, determining the volumes of the different types of fluids, and establishing fluid evaluation indexes; and explaining the fluid property of each reservoir of the to-be-logged well according to the volume and fluid evaluation index of each reservoir fluid of the to-be-logged well. The fluid property of the reservoir can be accurately and rapidly predicted, and the mining efficiency is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of navigation, in particular to a reservoir fluid property identification method, device, storage medium and equipment. BACKGROUND

[0002] The current oil and gas exploration and development objects are increasingly complex, and the geological conditions of many reservoirs are complex, with large lithology changes and diverse pore structures, such as tight sandstone and conglomerate reservoirs, shale oil and gas reservoirs, and deep complex lithology reservoirs. The rock mineral composition of these reservoirs is complex, and the pore structure is complex. In complex reservoirs, various fluids such as oil, gas and water often coexist, and the boundaries between them are blurred, so it is difficult to accurately identify various fluid types. Quantitative identification of complex reservoir fluid properties is of great significance for improving exploration and development efficiency, improving economic benefits, coping with reservoir complexity and promoting technological progress.

[0003] For the identification of complex reservoir fluid properties, the method based on resistivity logging is generally used for identification, but due to the covering of the electrical properties of the fluid in the complex reservoir by the complex lithology and pore structure characteristics, the traditional oil and gas layer identification method based on resistivity logging cannot accurately identify the fluid properties of the complex reservoir. The current method for identifying the type of fluid in the reservoir is based on two-dimensional nuclear magnetic resonance technology of longitudinal relaxation-transverse relaxation (T1-T2) for identification and analysis.

[0004] The Chinese patent with publication number CN114000864 discloses a method for identifying shale oil reservoir fluid composition and relative volume based on two-dimensional nuclear magnetic resonance logging data, which includes constructing a T1-T2 two-dimensional nuclear magnetic learning sample library; splicing each T1-T2 two-dimensional data in the learning sample library to form a large two-dimensional matrix; initializing the source number to obtain an initialized source matrix and an initialized coefficient matrix; performing principal component analysis on the initialized coefficient matrix to determine the best source number; setting the source number of the non-negative matrix decomposition to the best source number to obtain the source matrix and the coefficient matrix; determining the T1 and T2 coordinates corresponding to the centroid of each type of fluid; dividing the fluid types according to the distance of the pixel points in each T1-T2 graph data in the database from the Euclidean distance of the centroid of each type of fluid; determining the total signal intensity of each type of fluid according to the fluid type division result, and determining the relative volume of each type of fluid according to the signal intensity ratio.

[0005] The Chinese patent with publication number CN108049866A discloses a two-dimensional nuclear magnetic resonance logging method for quantitative evaluation of tight gas reservoirs, which includes obtaining two-dimensional nuclear magnetic resonance logging information; selecting a T1-T2 cross method to identify natural gas in a tight reservoir; and establishing a reservoir parameter model. This method is also a two-dimensional nuclear magnetic resonance logging method for quantitative evaluation of tight gas reservoirs based on longitudinal relaxation-transverse relaxation (T1-T2). SUMMARY

[0006] The method of identifying and analyzing reservoir fluids based on two-dimensional nuclear magnetic resonance (NMR) technology with longitudinal relaxation-transverse relaxation (T1-T2) is not suitable for identifying complex reservoir fluid properties due to the complex signal relationship between oil, gas and water in the T1-T2 spectrum. The accuracy of the analysis results is not high, and a large number of core two-dimensional NMR experiments are required to calibrate the location range of different fluid types, resulting in low work efficiency and making it unsuitable for field application.

[0007] In view of the above problems, the present invention is proposed to provide a method and apparatus for quantitatively identifying reservoir fluid properties that overcomes or at least partially solves the above problems.

[0008] In a first aspect, embodiments of the present invention provide a method for identifying reservoir fluid properties, comprising:

[0009] Based on the multi-echo interval nuclear magnetic resonance logging data of the wells to be logged in the target area, the echo data parameter sequence is obtained;

[0010] Two-dimensional nuclear magnetic resonance inversion was performed on the echo data parameter acquisition sequence to obtain the nuclear magnetic resonance spectrum of diffusion coefficient-lateral relaxation time;

[0011] Based on the calibration results of the distribution range of different types of fluids in the NMR spectrum of diffusion coefficient-lateral relaxation time of standard wells in the target area, the signal distribution range of different types of fluids in the NMR spectrum of diffusion coefficient-lateral relaxation time of the well to be logged is determined.

[0012] Based on the fluid signal amplitude within the signal distribution range of different types of fluids, the volume of different types of fluids is determined, and a fluid evaluation index is established.

[0013] Based on the volume of fluids in each reservoir of the well to be logged and the fluid evaluation index, interpret the fluid properties of each reservoir.

[0014] In some optional embodiments, an echo data parameter sequence is obtained based on multi-echo interval nuclear magnetic resonance logging data of the well to be logged in the target area, including:

[0015] Multi-echo interval nuclear magnetic resonance logging data of the well to be logged in the target area;

[0016] The echo data volumes in the nuclear magnetic resonance logging data are merged according to the order of the echo intervals. Each echo data volume includes at least one of the echo signal and echo noise, and the number of echo data volumes is not less than 3.

[0017] The acquisition parameters of the echo data volume are formed into an echo data parameter sequence according to the merging order. The acquisition parameters include at least the number of echoes, waiting time, echo interval, and magnetic field gradient.

[0018] In some optional embodiments, based on the calibration results of the distribution intervals of different types of fluids in the diffusion coefficient-lateral relaxation time NMR spectrum of the standard well in the target area, the signal distribution intervals of different types of fluids in the diffusion coefficient-lateral relaxation NMR spectrum of the well to be logged are determined, including:

[0019] Based on the core analysis and oil testing results of the standard wells in the target area, different types of fluid signals in the diffusion coefficient-lateral relaxation nuclear magnetic resonance spectrum of the standard wells in the target area are divided to obtain the distribution range calibration results of different types of fluids.

[0020] Based on the calibration results, the signal distribution ranges of different types of fluids in the diffusion coefficient-lateral relaxation nuclear magnetic resonance spectrum of the well to be logged are divided.

[0021] In some optional embodiments, the volume of different types of fluids is determined based on the fluid signal amplitude within the signal distribution range of different types of fluids, including:

[0022] For each type of fluid;

[0023] The fluid signal and non-fluid signal are determined from the signal distribution range of the fluid, the non-fluid signal is eliminated, and the fluid signal is preprocessed.

[0024] The amplitude of the preprocessed fluid signal is determined, and the amplitudes of the fluid signals are accumulated to obtain the fluid volume.

[0025] In some optional embodiments, determining the amplitude of the preprocessed fluid signal and accumulating the fluid signal amplitudes to obtain the fluid volume includes:

[0026] After determining the preprocessed fluid signal, the data points of the fluid signal are extracted from the diffusion coefficient-lateral relaxation nuclear magnetic resonance spectrum of the well to be logged;

[0027] The amplitude of each data point of the fluid signal is calculated and the amplitudes of the data points are accumulated. The result of the accumulated amplitudes of the data points is used as the volume of the fluid.

[0028] In some optional embodiments, a fluid evaluation index is established, including:

[0029] For each type of fluid;

[0030] A fluid evaluation index is established based on the fluid volume and the total amplitude of all fluid signals;

[0031] The fluid evaluation index is expressed by the following formula: in V is the fluid evaluation index for the i-th fluid. i Let A be the volume of the i-th fluid. 总This represents the total amplitude of all fluid signals.

[0032] In some optional embodiments, the fluid properties of each reservoir in the well to be logged are interpreted based on the volume of fluid in each reservoir and the fluid evaluation index, including:

[0033] For each reservoir in the well to be logged, if the volume of fluid in the reservoir is greater than the preset fluid volume and / or the fluid evaluation index is greater than the preset fluid evaluation index, the reservoir is interpreted as the corresponding fluid reservoir.

[0034] In some optional embodiments, the above method further includes;

[0035] If the volume of multiple fluids in a reservoir is greater than a preset fluid volume and / or the fluid evaluation index is greater than a preset fluid evaluation index, the reservoir is interpreted as having multiple fluids in the same layer.

[0036] Secondly, embodiments of the present invention also provide a reservoir fluid property identification device, comprising:

[0037] The two-dimensional nuclear magnetic resonance (NMR) spectrum acquisition module is used to obtain the echo data parameter sequence based on the multi-echo interval NMR logging data of the well to be logged in the target area; and to perform two-dimensional NMR inversion on the echo data parameter acquisition sequence to obtain the diffusion coefficient-lateral relaxation NMR spectrum.

[0038] The signal distribution interval determination module is used to determine the signal distribution interval of different types of fluids in the diffusion coefficient-lateral relaxation nuclear magnetic resonance spectrum of the well to be tested, based on the calibration results of different types of fluids in the diffusion coefficient-lateral relaxation nuclear magnetic resonance spectrum of the standard well in the target area.

[0039] The reservoir fluid identification module is used to determine the volume of different types of fluids based on the fluid signal amplitude within the signal distribution range of different types of fluids, and to establish a fluid evaluation index; based on the volume of fluids in each reservoir of the well to be logged and the fluid evaluation index, the fluid properties of each reservoir of the well to be logged are interpreted.

[0040] This invention also provides a computer storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described reservoir fluid property identification method.

[0041] This invention also provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the above-described reservoir fluid property identification method.

[0042] The beneficial effects of the above-described technical solutions provided in the embodiments of the present invention include at least the following:

[0043] The reservoir fluid property identification method provided in this invention obtains an echo data parameter sequence based on multi-echo interval nuclear magnetic resonance (NMR) logging data from wells to be logged in the target area. Acquiring NMR logging data from the wells to be logged results in a more sensitive logging response signal, providing reliable measurement results in complex formations. Two-dimensional NMR inversion is performed on the acquired echo data parameter sequence to obtain a D-T2 NMR spectrum. The signal positions of oil, gas, and water in the D-T2 two-dimensional NMR spectrum are more clearly defined, accurately reflecting the properties of complex reservoir fluids with limited core experiments and well testing data calibration. This improves on-site exploration efficiency and effectively guides reservoir fluid identification work.

[0044] Based on the calibration results of the D-T2 nuclear magnetic resonance (NMR) spectra of standard wells in the target area, the signal distribution range of different types of fluids in the D-T2 NMR spectra of the well to be logged is determined. In the target exploration area, the standard wells already have core analysis and oil testing results, and the calibration results of different fluid types can be divided on the standard wells. Therefore, the D-T2 spectra of the well to be logged in the target area can be calibrated based on the calibration results of the D-T2 NMR spectra of the standard wells, without the need for a large number of core analyses and oil testing experiments on the well to be logged, saving workload and facilitating its application in the exploration field.

[0045] Based on the fluid signal amplitude within the signal distribution range of different fluid types, the volume of different fluid types is determined, and a fluid evaluation index is established. Based on the fluid volume and fluid evaluation index of each reservoir in the well to be logged, the fluid properties of each reservoir are interpreted. The fluid volume and fluid evaluation index reflect the fluid's occupancy and distribution characteristics in the reservoir, and these characteristics are closely related to the fluid's physical properties and the reservoir's pore structure. By measuring the fluid volume distribution in different pores, we can further understand the reservoir's pore structure and fluid distribution patterns, thereby more accurately interpreting the fluid types in the reservoir.

[0046] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings.

[0047] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0048] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0049] Figure 1This is a flowchart of the reservoir fluid property identification method in an embodiment of the present invention;

[0050] Figure 2 This is an example diagram showing the distribution range of different types of fluid signals in an embodiment of the present invention;

[0051] Figure 3 This is an example diagram showing the distribution range of different types of fluid signals in an embodiment of the present invention;

[0052] Figure 4 This is an example of using D-T2 nuclear magnetic resonance spectroscopy to identify complex reservoir fluid properties in an embodiment of the present invention;

[0053] Figure 5 This is a schematic diagram of the reservoir fluid property identification device in an embodiment of the present invention. Detailed Implementation

[0054] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0055] To address the problems of inaccurate and inefficient identification of reservoir fluids in existing technologies, this invention provides a method for identifying reservoir fluid properties.

[0056] This invention provides a method for identifying reservoir fluid properties, the process of which is as follows: Figure 1 As shown, it includes the following steps:

[0057] S101: Obtain the echo data parameter sequence based on the multi-echo interval nuclear magnetic resonance logging data of the well to be logged in the target area.

[0058] S102: Perform two-dimensional nuclear magnetic resonance inversion on the echo data parameter acquisition sequence to obtain the nuclear magnetic resonance spectrum of diffusion coefficient-lateral relaxation time.

[0059] S103: Based on the calibration results of the distribution range of different types of fluids in the nuclear magnetic resonance spectrum of diffusion coefficient-lateral relaxation time of the standard well in the target area, determine the signal distribution range of different types of fluids in the nuclear magnetic resonance spectrum of diffusion coefficient-lateral relaxation time of the well to be tested.

[0060] S104: Based on the fluid signal amplitude within the signal distribution range of different types of fluids, determine the volume of different types of fluids and establish a fluid evaluation index.

[0061] S105: Based on the volume of fluid in each reservoir of the well to be logged and the fluid evaluation index, interpret the fluid properties of each reservoir in the well to be logged.

[0062] Steps S101-S105 identify different types of fluid signals in the reservoir based on the NMR spectrum of diffusion coefficient-lateral relaxation time. Based on the amplitude of these signals, the volume of different fluid types in the reservoir is determined, and a fluid evaluation index is established. Then, based on the volume of fluids in each reservoir and the fluid evaluation index, the fluid properties of each reservoir in the well to be logged are interpreted. The NMR spectrum of diffusion coefficient-lateral relaxation time allows for more precise segmentation of the signal distribution range of different types of fluids in complex reservoirs, providing data support for subsequent interpretation of reservoir fluid properties. The interpretation and evaluation of fluid properties in each reservoir based on the volume of fluids in each reservoir and the fluid evaluation index guides the field oil and gas reservoir development work.

[0063] Optionally, in step S101 above, the echo data parameter sequence is obtained based on the multi-echo interval nuclear magnetic resonance logging data of the well to be logged in the target area, including:

[0064] The data is collected from the target area using multi-echo interval nuclear magnetic resonance logging data. The echo data volumes in the nuclear magnetic resonance logging data are merged according to the echo interval order. Each echo data volume includes at least one of echo signal and echo noise, and the number of echo data volumes is not less than 3. The acquisition parameters of the echo data volumes are formed into an echo data parameter sequence according to the merging order. The acquisition parameters include at least the number of echoes, waiting time, echo interval, and magnetic field gradient.

[0065] Multiple echo data volumes of the acquired multi-echo interval nuclear magnetic resonance logging data are organized and merged according to the echo interval order to form an echo data volume set. Each data volume includes at least one of the echo interval and echo noise. Each data volume includes at least the following acquisition parameters: number of echoes NE, waiting time TW, echo interval TE, and magnetic field gradient G. Then, the acquisition parameters of each data volume in the data volume set are formed into a parameter acquisition sequence according to the merging order. The parameter acquisition sequence is used in the two-dimensional nuclear magnetic resonance inversion in step S102 to perform diffusion coefficient-lateral relaxation time inversion processing to obtain the diffusion coefficient-lateral relaxation time nuclear magnetic resonance spectrum, i.e., the D-T2 nuclear magnetic resonance spectrum.

[0066] In step S101, a larger number of echo data bodies can improve the accuracy of subsequent identification. For example, three or more data bodies with different echo intervals can be used. Alternatively, if there are fewer than three echo data bodies with different echo intervals, echo data bodies with the same echo interval but different waiting times can be added to the echo data body set to form an echo parameter sequence for D-T2 two-dimensional nuclear magnetic resonance processing and interpretation. In short, to obtain richer logging response signals, there should be at least three echo data bodies. If there are too few echo data bodies, it will affect the integrity of fluid signal acquisition, resulting in inaccurate acquisition results and affecting the accuracy of identification.

[0067] In step S102 above, the parameter acquisition sequence is used for diffusion coefficient-lateral relaxation time inversion processing to obtain the D-T2 spectrum. This spectrum shows more obvious and accurate response characteristics of different types of fluids, thus providing clearer fluid signals and locations, leading to more accurate measurement results. During D-T2 two-dimensional nuclear magnetic resonance inversion processing, the regularization factor value can be adjusted according to the quality of the nuclear magnetic resonance logging data. When the nuclear magnetic resonance logging data quality is good and the signal-to-noise ratio is high, the regularization factor value is generally 10 or smaller; conversely, the regularization factor value needs to be increased to obtain better processing results. By adjusting the regularization factor, better inversion results can be obtained.

[0068] Optionally, in step S103 above, based on the calibration results of the distribution intervals of different types of fluids in the NMR spectrum of the diffusion coefficient-lateral relaxation time of the standard well in the target area, the signal distribution intervals of different types of fluids in the NMR spectrum of the diffusion coefficient-lateral relaxation time of the well to be tested are determined, including:

[0069] Based on the core analysis and oil testing results of the standard wells in the target area, the different types of fluid signals in the diffusion coefficient-lateral relaxation nuclear magnetic resonance spectrum of the standard wells in the target area are divided to obtain the distribution interval calibration results of different types of fluids; based on the calibration results, the signal distribution intervals of different types of fluids in the diffusion coefficient-lateral relaxation nuclear magnetic resonance spectrum of the well to be tested are divided.

[0070] After obtaining the diffusion coefficient-lateral relaxation NMR spectrum according to steps S101-S102, the fluid interval boundaries on the spectrum represent the approximate location where the fluid signal theoretically appears. Further detailed and accurate calibration should be performed, combining core analysis and oil testing results from standard wells, to obtain more precise fluid signal intervals. By combining the core analysis and oil testing results from standard wells, different types of fluid signals in the diffusion coefficient-lateral relaxation NMR spectrum of the target area are divided, resulting in calibration results for the distribution intervals of different fluid types. Using the calibration results of the target well as the standard, the D-T2 spectrum of the well to be logged can be calibrated to obtain the signal distribution intervals of different types of fluids in the well to be logged. This method eliminates the need for extensive core analysis and oil testing of the well to be logged, reducing the time required for fluid signal distribution area identification. It allows for rapid and accurate calibration on-site, improving work efficiency.

[0071] See the example diagram of the distribution range of different types of fluid signals in the D-T2 two-dimensional nuclear magnetic resonance spectrum of the well to be logged. Figure 2 and Figure 3 As shown, Figure 2 The fluid signal distribution range of the D-T2 two-dimensional nuclear magnetic resonance spectrum at a reservoir depth of 2674.3 m is shown. Figure 3 The fluid signal distribution range of the D-T2 two-dimensional nuclear magnetic resonance spectrum at a reservoir depth of 2658 m is shown. Figure 2 and Figure 3 In the diagram, the horizontal axis represents the lateral relaxation time T2, and the vertical axis represents the diffusion coefficient D. The green diagonal line OIL represents the theoretical oil line, the blue horizontal line WATER represents the theoretical water line, and the red horizontal line GAS represents the theoretical gas line. The yellow boxes delineate the signal distribution intervals for different types of fluids, specifically: CBW for clay-bound water, BVI for capillary-bound fluid, FFI for movable water, OIL for movable oil, and GAS for movable gas. The bound fluid signal interval is T2 < 50 ms. When T2 > 50 ms, it is the free fluid signal interval, where the movable gas signal interval is D > 5000 cm² / s (1e-7), the movable water signal interval is 5000 cm² / s (1e-7) > D > 200 cm² / s (1e-7), and the movable oil signal interval is 200 cm² / s (1e-7) > D > 4 cm² / s (1e-7). Figure 2 and Figure 3 The TPHI indicator represents the total pore signal range, while the EPHI indicator represents the effective pore signal range. This allows for a more accurate determination of the signal distribution range for different types of fluids.

[0072] Optionally, in step S103 above, determining the volume of different types of fluids based on the fluid signal amplitude within the signal distribution range of different types of fluids includes:

[0073] For each type of fluid, fluid signals and non-fluid signals are identified from the signal distribution range of the fluid. Non-fluid signals are eliminated, and the fluid signals are preprocessed. The amplitude of the preprocessed fluid signals is determined, and the amplitudes of the fluid signals are accumulated to obtain the fluid volume.

[0074] Optionally, the amplitude of the preprocessed fluid signal is determined, and the fluid signal amplitudes are accumulated to obtain the fluid volume, including:

[0075] After determining the preprocessed fluid signal, data points of the fluid signal are extracted from the diffusion coefficient-lateral relaxation nuclear magnetic resonance spectrum of the well to be tested; the amplitude of each data point of the fluid signal is calculated and the amplitudes of the data points are accumulated, and the result of the amplitude accumulation of the data points is taken as the volume of the fluid.

[0076] After determining the distribution ranges of different types of fluid signals in the D-T2 NMR spectrum, within each fluid signal distribution range, fluid and non-fluid signals can be identified first. Then, non-fluid signals are removed to avoid interference with the fluid signals. Within the fluid signal distribution range, fluid is only generated at locations with pores; therefore, not every location within the range contains a fluid signal. The presence of non-fluid signals affects the accuracy of the identification results, so it is necessary to distinguish between fluid and non-fluid signals and process them separately. After removing fluid signals, preprocessing is performed on the fluid signals. Preprocessing can involve filtering or gain adjustment. The purpose of preprocessing is to improve signal quality, reduce noise, and retain useful information.

[0077] After preprocessing the fluid signal, data points of the fluid signal are obtained from the D-T2 nuclear magnetic resonance spectrum. By calculating the amplitude of each data point and accumulating the amplitudes, the volume of each fluid can be obtained. For example, it can be... Figure 2 The sum of the amplitudes of fluid signals within the bound fluid distribution range in the D-T2 NMR spectrum shown is taken as the bound fluid volume. The sum of the amplitudes of fluid signals within the movable water distribution range is taken as the movable water volume. The sum of the amplitudes of fluid signals within the movable oil distribution range is taken as the movable oil volume. The sum of the amplitudes of fluid signals within the movable gas boundary range is taken as the movable gas volume. Based on this, a "D-T2 two-dimensional NMR quantitative fluid identification" software module can be developed on a geological analysis software development platform, such as the Geolog software platform. In this module, the distribution ranges of the above oil, gas, and water signals are used as input parameters to process and interpret the D-T2 two-dimensional NMR spectrum data, obtaining the bound fluid volume, movable water volume, movable oil volume, and movable gas volume, respectively.

[0078] Optionally, in step S104 above, establishing a fluid evaluation index includes:

[0079] For each type of fluid, a fluid evaluation index is established based on the fluid volume and the total amplitude of all fluid signals.

[0080] The fluid evaluation index is expressed by the following formula: in V is the fluid evaluation index for the i-th fluid. i Let A be the volume of the i-th fluid. 总 This represents the total amplitude of all fluid signals.

[0081] By establishing a fluid evaluation index based on the fluid volume and the sum of all fluid signal amplitudes, we can better study the distribution characteristics of each type of fluid in the reservoir. This allows for the accurate identification of various fluid properties in the reservoir based on these characteristics. For example, the ratio of movable oil volume to the sum of all fluid signal amplitudes can be used as the movable oil evaluation index to evaluate whether the reservoir contains movable oil. Fluid evaluation indices for other types of fluids can be established accordingly based on the movable oil index, which will not be elaborated here.

[0082] Optionally, in step S104 above, the fluid properties of each reservoir in the well to be logged are interpreted based on the volume and fluid evaluation index of the fluid in each reservoir, including:

[0083] For each reservoir in the well to be logged, if the volume of fluid in the reservoir is greater than the preset fluid volume and / or the fluid evaluation index is greater than the preset fluid evaluation index, the reservoir is interpreted as the corresponding fluid reservoir.

[0084] If the volume of multiple fluids in a reservoir is greater than a preset fluid volume and / or the fluid evaluation index is greater than a preset fluid evaluation index, the reservoir is interpreted as having multiple fluids in the same layer.

[0085] Because mud water intruding into the formation from the wellbore is always present within the detection range of nuclear magnetic resonance logging tools, the identification and evaluation of fluid properties in complex reservoirs requires comprehensive consideration of various information, such as the calculated volume of different types of fluids and fluid evaluation indices, to provide a reasonable interpretation. For example, the reservoir can be interpreted as an oil-bearing layer when the following conditions exist:

[0086] 1. When there is movable oil in the reservoir, the volume of movable oil is greater than the preset movable oil volume, and the movable oil evaluation index is greater than the preset movable oil evaluation index.

[0087] 2. When there is movable oil in the reservoir, and the volume of movable oil is greater than the preset movable oil volume or the movable oil evaluation index is greater than the preset movable oil evaluation index.

[0088] Correspondingly, the reservoir fluid properties can be interpreted in the same way for other types of fluids.

[0089] The following conditions can be used to explain whether a reservoir is an oil-water co-element:

[0090] 1. When there is mobile water and mobile oil in the reservoir, the volume of mobile water is greater than the preset volume of mobile water and the volume of mobile oil is greater than the preset volume of mobile oil, and the mobile water evaluation index is greater than the preset mobile water evaluation index and the mobile oil evaluation index is greater than the preset mobile oil evaluation index.

[0091] 2. When there is mobile water and mobile oil in the reservoir, the volume of mobile water is greater than the preset volume of mobile water and the volume of mobile oil is greater than the preset volume of mobile oil, or the mobile water evaluation index is greater than the preset mobile water evaluation index and the mobile oil evaluation index is greater than the preset mobile oil evaluation index.

[0092] Correspondingly, the same method can be used to interpret the properties of reservoir fluids for many other types of fluids.

[0093] In addition, the reservoir can also be interpreted as an oil-water co-element under the following circumstances:

[0094] When there is mobile water and mobile oil in the reservoir, the mobile water volume is greater than the preset mobile water volume or the mobile water evaluation index is greater than the preset mobile water evaluation index, and the mobile oil volume is greater than the preset mobile oil volume or the mobile oil evaluation index is greater than the preset mobile oil evaluation index.

[0095] Identifying the fluid properties of complex reservoirs requires not only comprehensive consideration of fluid volume and fluid evaluation index, but also sometimes the integration of formation temperature, formation pressure, and fluid analysis data in the target area for reasonable interpretation. In short, the identification of reservoir fluids should be based on specific circumstances to accurately identify the fluid properties of the reservoir.

[0096] Figure 4An example of using D-T2 nuclear magnetic resonance spectroscopy to identify the properties of complex reservoir fluids. The first track in the figure is the lithology logging curve track, including natural gamma ray (GR), spontaneous potential (SP), and caliper CAL logging curves; the second track is the reservoir depth track; the third track is the resistivity logging curve track, with a resolution of 2FT, including five array induction logging curves AT10, AT20, AT30, AT60, and AT90 at depths of 10IN, 20IN, 30IN, 60IN, and 90IN respectively; the fourth track is the transverse relaxation time distribution spectrum T2_DIST; the fifth track is the diffusion coefficient distribution spectrum DC_DIST; the sixth track is the fluid volume curve and movable oil evaluation index track calculated from the D-T2 two-dimensional nuclear magnetic resonance spectrum, including four fluid volume curves (BVIT2D for capillary-bound fluid, FFIT2D for free water, OILT2D for movable oil, and GAST2D for movable gas) and the movable oil evaluation index SOT2D curve; the seventh track is the logging interpretation conclusion, with red indicating oil layers, blue indicating water layers, and red-blue oblique intersection indicating oil-water co-containment. Taking the identification of oil and water layers in a reservoir as an example, Figure 4 The sixth line indicates that reservoir 70 can be divided into upper and lower sections. The upper section and the upper part of the lower section show clear movable oil signals, with a movable oil evaluation index of 0.2, exceeding the preset movable oil evaluation index of 0.05. The lower part of the lower section of reservoir 70 shows very weak movable oil signals, with a movable oil evaluation index less than the preset movable oil evaluation index of 0.05. Reservoir 71 shows weak movable oil signals but strong movable water signals, with a movable water evaluation index greater than the preset movable water evaluation index of 0.03 and a movable oil evaluation index less than the preset movable oil evaluation index of 0.05. Therefore, the upper part of reservoir 70 is interpreted as an oil layer, and the lower part as a co-existing oil-water layer; reservoir 71 is interpreted as a water layer. The fluid evaluation index should be determined based on the specific circumstances, and no specific value is limited here. Figure 4 Although layers 70 and 71 also contain movable gas, the volume of movable gas in the yellow area is very small. Since underground oil or water layers may contain a certain amount of dissolved gas, when the volume of movable gas is very small relative to the volume of movable oil or water, it can be ignored. Subsequent oil testing verified that the well logging interpretation conclusion was correct.

[0097] Based on the same inventive concept, embodiments of the present invention also provide a reservoir fluid property identification device, which can be installed in a device with computing power, and the structure of the device is as follows. Figure 5 As shown, it includes:

[0098] The two-dimensional nuclear magnetic resonance (NMR) spectrum acquisition module 11 is used to obtain the echo data parameter sequence based on the multi-echo interval NMR logging data of the well to be logged in the target area; and to perform two-dimensional NMR inversion on the echo data parameter acquisition sequence to obtain the diffusion coefficient-lateral relaxation NMR spectrum.

[0099] The signal distribution interval determination module 12 is used to determine the signal distribution interval of different types of fluids in the diffusion coefficient-lateral relaxation nuclear magnetic resonance spectrum of the well to be tested, based on the calibration results of different types of fluids in the diffusion coefficient-lateral relaxation nuclear magnetic resonance spectrum of the standard well in the target area.

[0100] The reservoir fluid identification module 13 is used to determine the volume of different types of fluids based on the fluid signal amplitude within the signal distribution range of different types of fluids, and to establish a fluid evaluation index; based on the volume of fluids in each reservoir of the well to be logged and the fluid evaluation index, the fluid properties of each reservoir of the well to be logged are interpreted.

[0101] Regarding the reservoir fluid property identification device in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated here.

[0102] This invention also provides a computer storage medium storing computer-executable instructions, which, when executed by a processor, provide the aforementioned reservoir fluid property identification method.

[0103] This invention also provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the above-described reservoir fluid property identification method.

[0104] Unless otherwise specifically stated, terms such as processing, calculation, operation, determination, display, etc., may refer to the actions and / or processes of one or more processing or computing systems or similar devices that represent the manipulation and conversion of data representing physical (e.g., electronic) quantities within the registers or memory of the processing system into other data similarly representing physical quantities within the memory, registers, or other such information storage, transmission, or display devices of the processing system. Information and signals can be represented using any of a variety of different techniques and methods. For example, data, instructions, commands, information, signals, bits, symbols, and chips mentioned throughout the above description can be represented by voltage, current, electromagnetic waves, magnetic fields or particles, light fields or particles, or any combination thereof.

[0105] It should be understood that the specific order or hierarchy of steps in the disclosed process is an example of an exemplary method. Based on design preferences, it should be understood that the specific order or hierarchy of steps in the process may be rearranged without departing from the scope of this disclosure. The appended method claims provide elements of various steps in an exemplary order and are not intended to limit the scope to the specific order or hierarchy described.

[0106] In the detailed description above, various features are combined together in a single embodiment to simplify this disclosure. This approach to disclosure should not be construed as reflecting an intention that embodiments of the claimed subject matter require more features than are explicitly stated in each claim. Rather, as reflected in the appended claims, the invention is presented with fewer features than all of the features in a single disclosed embodiment. Therefore, the appended claims are hereby explicitly incorporated into the detailed description, with each claim representing a separate preferred embodiment of the invention.

[0107] Those skilled in the art will also understand that the various illustrative logic blocks, modules, circuits, and algorithm steps described in conjunction with the embodiments herein can be implemented as electronic hardware, computer software, or a combination thereof. To clearly illustrate the interchangeability between hardware and software, the various illustrative components, blocks, modules, circuits, and steps described above are generally described in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the specific application and the design constraints imposed on the overall system. Those skilled in the art can implement the described functionality in alternative ways for each specific application; however, such implementation decisions should not be construed as departing from the scope of this disclosure.

[0108] The steps of the methods or algorithms described in conjunction with the embodiments herein can be directly embodied in hardware, software modules executed by a processor, or a combination thereof. The software modules can reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disks, removable disks, CD-ROMs, or any other form of storage medium well known in the art. An exemplary storage medium is connected to the processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and storage medium can reside in an ASIC. The ASIC can reside in a user terminal. Alternatively, the processor and storage medium can exist as discrete components in the user terminal.

[0109] For software implementation, the techniques described in this application can be implemented using modules (e.g., procedures, functions, etc.) that perform the functions described in this application. This software code can be stored in memory units and executed by a processor. The memory units can be implemented within the processor or outside the processor; in the latter case, they are communicatively coupled to the processor via various means, as is well known in the art.

[0110] The foregoing description includes examples of one or more embodiments. It is certainly impossible to describe all possible combinations of components or methods in order to describe the above embodiments, but those skilled in the art will recognize that further combinations and arrangements of the various embodiments are possible. Therefore, the embodiments described herein are intended to cover all such changes, modifications, and variations that fall within the scope of the appended claims. Furthermore, the term "comprising" as used in the specification or claims is interpreted in a manner similar to the term "including," as interpreted when used as a conjunction in the claims. Additionally, the use of any term "or" in the specification of the claims is intended to mean "non-exclusive or."

Claims

1. A method of reservoir fluid property identification, characterized by, The method comprises the following steps: According to the multi-echo interval NMR logging data of the target well in the target area, a sequence of echo data parameters is obtained; A two-dimensional NMR inversion is performed on the sequence of echo data parameters to obtain a NMR spectrum of diffusion coefficient-transverse relaxation time; According to the distribution interval calibration results of different types of fluids in the NMR spectrum of diffusion coefficient-transverse relaxation time of the standard well in the target area, the signal distribution interval of different types of fluids in the NMR spectrum of diffusion coefficient-transverse relaxation time of the well to be measured is determined; Based on the fluid signal amplitude in the signal distribution interval of different types of fluids, the volume of different types of fluids is determined, and a fluid evaluation index is established; According to the volume and fluid evaluation index of each reservoir fluid of the well to be measured, the fluid properties of each reservoir of the well to be measured are interpreted.

2. The method of claim 1, wherein, The method comprises the following steps: The multi-echo interval NMR logging data of the well to be measured in the target area is collected; The echo data bodies in the NMR logging data are merged in the order of echo interval, the echo data body including at least one of echo signal and echo noise, and the number of echo data bodies is not less than 3; The acquisition parameters of the echo data bodies are formed into a sequence of echo data parameters in the order of merging, the acquisition parameters including at least echo number, waiting time, echo interval and magnetic field gradient.

3. The method of claim 1, wherein, The method comprises the following steps: According to the core analysis and oil testing results of the standard well in the target area, the different types of fluid signals in the NMR spectrum of diffusion coefficient-transverse relaxation time of the standard well in the target area are divided to obtain the distribution interval calibration results of different types of fluids; According to the calibration results, the signal distribution interval of different types of fluids in the NMR spectrum of diffusion coefficient-transverse relaxation time of the well to be measured is divided.

4. The method of claim 1, wherein, The method comprises the following steps: For each type of fluid; The fluid signal and non-fluid signal in the signal distribution interval of the fluid are determined, the non-fluid signal is removed, and the fluid signal is preprocessed; The amplitude of the preprocessed fluid signal is determined, and the amplitudes of the fluid signals are accumulated to obtain the volume of the fluid.

5. The method of claim 4, wherein, The method comprises the following steps: After the preprocessed fluid signal is determined, the data points of the fluid signal are extracted from the NMR spectrum of diffusion coefficient-transverse relaxation time of the well to be measured; The amplitude of each data point of the fluid signal is calculated, and the amplitudes of the data points are accumulated, and the accumulation result of the amplitudes of the data points is taken as the volume of the fluid.

6. The method of claim 1, wherein, The method comprises the following steps: For each type of fluid; According to the volume of the fluid and the total amplitude of all fluid signals, a fluid evaluation index is established; The fluid evaluation index is expressed by the following expression: wherein is the fluid evaluation index for the i-th fluid, V i is the volume of the i-th fluid, A 总 is the total amplitude of all fluid signals.

7. The method of claim 1, wherein, The method comprises the following steps: For each reservoir of the well to be measured, if the volume of the fluid in the reservoir is greater than a preset fluid volume and / or the fluid evaluation index is greater than a preset fluid evaluation index, the reservoir is interpreted as a corresponding fluid reservoir.

8. The method of claim 7, wherein, Further comprising: If the volume of multiple fluids in a reservoir is greater than a preset fluid volume and / or the fluid evaluation index is greater than a preset fluid evaluation index, the reservoir is interpreted as a multiple fluid layer.

9. A reservoir fluid property identification apparatus, characterized by, Further comprising: A two-dimensional nuclear magnetic spectrum acquisition module is configured to obtain an echo data parameter sequence according to multi-echo interval nuclear magnetic resonance logging data of the well to be measured in a target area; The echo data parameter acquisition sequence is subjected to nuclear magnetic resonance two-dimensional inversion to obtain a diffusion coefficient-transverse relaxation nuclear magnetic resonance spectrum; A signal distribution interval determination module is configured to determine a signal distribution interval of different types of fluids in the diffusion coefficient-transverse relaxation nuclear magnetic resonance spectrum of the well to be measured according to a calibration result of the diffusion coefficient-transverse relaxation nuclear magnetic resonance spectrum of the standard well in the target area; A reservoir fluid identification module is configured to determine the volume of different types of fluids based on the fluid signal amplitude in the signal distribution interval of the different types of fluids, and establish a fluid evaluation index; and interpret the fluid properties of each reservoir of the well to be measured according to the volume of the fluid in each reservoir of the well to be measured and the fluid evaluation index.

10. A computer storage medium, characterized in that, The computer storage medium stores computer executable instructions, and the computer executable instructions are executed by the processor to implement the reservoir fluid property identification method of any one of claims 1-8.

11. A computer device, comprising: Further comprising: A memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor executes the program to implement the reservoir fluid property identification method of any one of claims 1-8.

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