A method and system for reservoir fluid evaluation using two-dimensional nuclear magnetic resonance logging two-dimensional spectra

Abstract

The application provides a two-dimensional spectrum reservoir fluid evaluation method and system of two-dimensional nuclear magnetic resonance logging, and relates to the technical field of oil and gas exploration.The method performs blind source separation on two-dimensional nuclear magnetic resonance logging two-dimensional spectrum by setting fluid component number r, obtains two-dimensional spectrum of multiple fluid components and a mixing matrix reflecting component proportion; further judges two-dimensional spectrum characteristics obtained by blind source separation; if a certain two-dimensional spectrum still contains multiple fluid components, performs hierarchical clustering on points with non-zero amplitude in the two-dimensional spectrum to obtain two-dimensional spectrum containing only single fluid component; according to fluid relaxation characteristics and spectrum peak position, determines fluid characteristic spectrum of various fluids; and performs operation on the determined fluid characteristic spectrum and the mixing matrix to calculate volume content of each fluid respectively, so that accurate determination of fluid component number and characteristic spectrum is realized through multiple clustering, fluid evaluation can effectively identify and quantitatively evaluate reservoir fluid, and fluid identification and component evaluation are realized.

Description

Technical Field

[0001] This invention relates to the field of oil and gas exploration technology, specifically to a two-dimensional nuclear magnetic resonance logging method and system for evaluating reservoir fluids using two-dimensional spectra. Background Technology

[0002] Nuclear magnetic resonance (NMR) logging, as an important geophysical logging technique, has unique advantages in reservoir fluid assessment. Due to the limitations of one-dimensional NMR logging in fluid identification, two-dimensional (2D) NMR logging has been developed. 2D NMR logging includes two observation modes: T2-T1 and T2-D. Compared to one-dimensional T2 NMR logging, 2D NMR logging can provide more information and more effectively identify different types of fluids, playing a crucial role in oilfield exploration and development.

[0003] Currently, there are two main methods for fluid identification in two-dimensional nuclear magnetic resonance logging:

[0004] (1) Constructing a fluid identification chart based on nuclear magnetic resonance experiments. This method measures the two-dimensional nuclear magnetic resonance response under different conditions, such as saturation, centrifugation, and drying, to obtain the two-dimensional spectral characteristics of components such as bound water, bound oil, mobile water, and mobile oil, forming a fluid identification chart. This chart is then applied to the two-dimensional spectrum of two-dimensional nuclear magnetic resonance logging to calculate the volume content of various fluid components.

[0005] (2) Determining fluid components based on blind source separation algorithm. Each fluid component in the formation has its own two-dimensional spectral response characteristics. The two-dimensional spectrum of two-dimensional nuclear magnetic resonance logging can be regarded as a mixture of two-dimensional spectra of multiple fluid components, and how to separate them is a challenge. First, the number of fluid components is obtained using methods such as Akaike Information Criterion (AIC) and Principal Component Analysis (PCA). Then, the two-dimensional spectral characteristics and mixing coefficients of different fluid components are obtained using the blind source separation algorithm. Based on the two-dimensional spectral characteristics of different fluid components, the specific fluid type is determined, and the volume content of different fluid components is calculated.

[0006] However, in the two two-dimensional nuclear magnetic resonance (NMR) logging fluid identification methods mentioned above, the first method, which uses NMR experiments to construct fluid identification charts, suffers from poor performance in logging applications because NMR experiments are typically high-frequency instruments while NMR logging uses low-frequency instruments. Furthermore, for different study areas, core samples and fluids from those areas must be selected for experiments, and the constructed fluid identification charts are not applicable to other study areas. In the second method, the fluid identification method based on blind source separation algorithms requires pre-determining the number of fluid components using other methods. With a fixed number of components, the calculation results are inaccurate when the number of formation fluid components exceeds the set number. Moreover, accurate separation is difficult when the volume content of two fluid components is linearly correlated at different depths.

[0007] In current tight oil and gas reservoir exploration, two-dimensional nuclear magnetic resonance (NMR) logging signals suffer from low signal-to-noise ratios, complex fluid compositions, and overlapping two-dimensional spectra of different fluid components, making it difficult to determine fluid boundaries, separate components, and calculate the content of each component accurately. Therefore, there is an urgent need to develop a two-dimensional NMR logging fluid identification and quantitative evaluation method that can accurately delineate all fluid components. Summary of the Invention

[0008] In view of this, the present invention provides a two-dimensional nuclear magnetic resonance (NMR) logging method and system for evaluating reservoir fluid content using two-dimensional spectra. This method is used to evaluate the content of multiple fluids in a reservoir. By deriving the equations for the two-dimensional NMR logging spectrum and component "sources," a relatively large number of fluid components is set. Blind source separation is performed using the non-negative matrix factorization (NMF) algorithm to obtain two-dimensional spectra of multiple "sources" and a mixing matrix. Then, using a hierarchical clustering algorithm based on maximum distance (Complete), the number of fluid components is automatically determined based on the fluid's relaxation characteristics and spectral peak positions. Characteristic spectra of components such as clay-bound water, capillary-bound water, and mobile water are identified. Through multiple clustering operations, the accurate determination of the number of fluid components and their characteristic spectra is achieved. Finally, these characteristic spectra are calculated with the mixing matrix to calculate the volume content of each fluid, thus realizing reservoir fluid evaluation.

[0009] This invention is achieved using the following technical solution:

[0010] In a first aspect, the present invention provides a two-dimensional nuclear magnetic resonance logging method for evaluating reservoir fluids using two-dimensional spectra, the method comprising the following steps:

[0011] By setting the fluid component number r, blind source separation is performed on the two-dimensional spectrum of two-dimensional nuclear magnetic resonance logging to obtain two-dimensional spectra of multiple fluid components and a mixing matrix reflecting the component ratio;

[0012] Further judgment is made on the two-dimensional spectral features obtained by blind source separation. If a certain two-dimensional spectrum still contains multiple fluid components, hierarchical clustering is performed on the points in the two-dimensional spectrum with non-zero amplitude to obtain a two-dimensional spectrum containing only a single fluid component. Based on the relaxation characteristics of the fluid and the position of the spectral peak, the fluid characteristic spectrum of each fluid is determined.

[0013] The determined fluid characteristic spectrum and the mixing matrix are processed to calculate the volume content of each fluid to achieve reservoir fluid evaluation.

[0014] As a further aspect of the present invention, the two-dimensional nuclear magnetic resonance logging two-dimensional spectrum is the nuclear magnetic resonance signal acquired from the reservoir, which is the superposition of the characteristic spectra of different fluids in the reservoir. The number of unknown fluid components in the reservoir is called blind source. The acquired nuclear magnetic resonance signal covers the lateral and longitudinal relaxation time ranges to obtain a complete two-dimensional relaxation spectrum. The process of separating the fluid components from the acquired nuclear magnetic resonance signal to obtain the source signal is called blind source separation.

[0015] As a further aspect of the present invention, when performing blind source separation on the two-dimensional spectrum of two-dimensional nuclear magnetic resonance logging, it is assumed that the depth of a certain formation point is m, there are r unknown fluid components, and the number of points in the two-dimensional spectrum is n = n1 × n2, where n1 and n2 are the number of points for the transverse relaxation time T2 and the longitudinal relaxation time T1, respectively; let the two-dimensional spectrum data be: X m×n =[x1,x2,...,x m ] T x m For the two-dimensional spectral data of the m-th depth point, x m Let S be an n×1 column vector, and T denote the transpose of the matrix; let the two-dimensional spectrum of the fluid component be: S r×n =[s1,s2,...,s r ] T s r Let A be the two-dimensional spectrum of the r-th fluid component, and let A be an n×1 column vector; then let A be the mixing matrix. m×r , so that:

[0016] X m×n =A m× r·S r×n

[0017] In the formula, the number of fluid components r is unknown and is called a blind source; the mixing matrix A is solved simultaneously from the acquired signal X to obtain the "source" signal S, which is the blind source separation.

[0018] As a further aspect of the present invention, when performing blind source separation on the two-dimensional spectrum of two-dimensional nuclear magnetic resonance logging, a non-negative matrix factorization (NMF) method is used for blind source separation, and the objective function is set as follows:

[0019]

[0020] In the formula, A ik ≥0, S kj ≥0; X ij Let A be the value of the j-th placement point at the i-th depth point; ik S represents the coefficient of the k-th component at the i-th depth point; kj Let i be the value of the j-th point for the k-th component; i = 1, 2, ..., m; j = 1, 2, ..., n; k = 1, 2, ..., r.

[0021] The unconstrained optimization problem derived from the Lagrange multiplier method is as follows:

[0022]

[0023] In the formula, min L means minimizing the objective function L in the solution of the optimization problem; λ and v are Lagrange multipliers, matrices of size m×r and r×n, respectively;

[0024] Taking the partial derivative with respect to L and setting it to zero, we have:

[0025]

[0026]

[0027] According to the Karush-Kurth-Tucker (KKT) conditional relaxation, we can obtain:

[0028] λ ik A ik =0, v kj S kj =0

[0029]

[0030]

[0031] Then we have the update iterative formulas for the mixing matrix A and the source signal S:

[0032]

[0033]

[0034] The initial mixing matrix and source signals are updated iteratively until the residual D is less than a set value, where D is:

[0035]

[0036] In the formula, |||| F It is the F-norm.

[0037] As a further aspect of the present invention, if a two-dimensional spectrum still contains multiple fluid components, then hierarchical clustering is performed on the points in the two-dimensional spectrum whose amplitude is not zero, including the following steps:

[0038] Suppose there are n points (sample points) in the two-dimensional spectrum with non-zero amplitude, and the two-dimensional coordinate matrix is ​​P. n×2 Then the Euclidean distance between the i-th and j-th sample points is:

[0039]

[0040] A bottom-up hierarchical clustering strategy is adopted, treating each sample point as a cluster, i.e., the number of clusters is n. The distance between each cluster C is calculated, and the maximum distance between clusters is calculated according to the full-linkage algorithm. That is, the distance between clusters C1 and C2 is determined by the farthest sample.

[0041]

[0042] Based on the distance between each cluster, the two clusters with the smallest distance are merged into one cluster, and the distance between the merged n-1 clusters is recalculated and merged again until all clusters are merged into one cluster. Then it can be split into the required number of clusters. That is, if a certain separated two-dimensional spectrum contains k fluid component response features, it is split into k clusters to obtain the subdivided two-dimensional spectrum.

[0043] As a further aspect of the present invention, when calculating the volume content of each fluid, if a total of r' two-dimensional spectra are subdivided, the fluid component type corresponding to each two-dimensional spectra is determined based on the position of the spectral peaks in each subdivided component's two-dimensional spectra. The two-dimensional spectra of the same fluid type are combined, and the fluid volume of each fluid type can be obtained from the mixing matrix A.

[0044] Secondly, the present invention also provides a two-dimensional nuclear magnetic resonance logging two-dimensional spectrum reservoir fluid evaluation system, comprising the following modules:

[0045] Data acquisition module: used to acquire the two-dimensional nuclear magnetic resonance logging two-dimensional spectrum of the reservoir;

[0046] Blind source separation module: used to perform blind source separation on two-dimensional NMR logging spectra and output two-dimensional spectra of various fluid components;

[0047] Feature identification and clustering module: used to identify the two-dimensional spectral features obtained by blind source separation. For two-dimensional spectra containing multiple fluid components, this module performs hierarchical clustering and outputs two-dimensional spectra containing only a single fluid component.

[0048] Fluid Characteristic Spectrum Determination Module: Used to determine the fluid characteristic spectrum of various fluids based on the relaxation characteristics and peak positions of the fluids;

[0049] Volume content calculation module: used to perform calculations on the determined fluid characteristic spectrum and the mixing matrix to calculate the volume content of each fluid, thereby realizing reservoir fluid evaluation.

[0050] As a further aspect of the present invention, the blind source separation module uses the nonnegative matrix factorization (NMF) method for blind source separation.

[0051] As a further aspect of the present invention, the feature judgment and clustering module uses a bottom-up aggregation strategy to cluster points in the two-dimensional spectrum with non-zero amplitude, and distinguishes different fluid components.

[0052] As a further aspect of the present invention, the volume content calculation module calculates the volume content of each fluid by performing calculations on the two-dimensional spectrum of the single fluid component obtained by clustering and the mixing matrix based on the mixing matrix obtained by the blind source separation module.

[0053] As a further aspect of the present invention, the reservoir fluid evaluation system further includes a user interface module for displaying the volume content of each fluid component and providing visualization of the reservoir fluid evaluation results.

[0054] Thirdly, the present invention also includes a computer device comprising: at least one processor and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to cause the at least one processor to perform the two-dimensional nuclear magnetic resonance logging two-dimensional spectrum reservoir fluid evaluation method.

[0055] Fourthly, the present invention also includes a computer-readable storage medium storing computer instructions for causing the computer to execute the two-dimensional nuclear magnetic resonance logging two-dimensional spectrum reservoir fluid evaluation method.

[0056] Compared with existing technologies, the two-dimensional nuclear magnetic resonance logging two-dimensional spectrum reservoir fluid evaluation method and system provided by this invention have the following advantages:

[0057] 1. Accuracy and Precision: This invention can accurately classify the content of various fluid components in a reservoir. Through the application of blind source separation and hierarchical clustering algorithms, it can effectively identify and quantitatively evaluate the presence of multiple fluids. Through multiple clustering operations, the number of fluid components and their characteristic spectra can be accurately determined. This precision helps engineers to better understand the fluid distribution within the reservoir, providing an important reference for subsequent oil and gas development.

[0058] 2. Identification and Analysis of Multiple Fluid Components: Traditional logging methods have limitations when dealing with complex reservoir fluids, while the method of this invention can accurately identify and analyze multiple fluid components in the reservoir. Therefore, engineers can gain a more comprehensive understanding of the content and distribution of different fluid components within the reservoir, providing more detailed information for oil and gas exploration and development.

[0059] 3. Practical Application Verification Results: The method of this invention was practically applied and verified in the tight sandstone reservoir of Well Z in a certain basin, and achieved good results. The consistency with the oil testing results proves the effectiveness and practicality of the method, providing reliable technical support for oilfield exploration and development.

[0060] 4. Breaking through the limitations of traditional technologies: Traditional two-dimensional nuclear magnetic resonance logging technology has certain limitations when dealing with complex fluids. However, the method of this invention overcomes the limitations of traditional technologies through novel means such as blind source separation and hierarchical clustering, bringing new breakthroughs and progress to reservoir fluid evaluation.

[0061] In summary, the two-dimensional nuclear magnetic resonance logging two-dimensional spectrum reservoir fluid evaluation method and system of the present invention can provide reliable technical support and data basis for oil and gas exploration and development in terms of accuracy, identification and analysis capabilities of multiple fluid components, practical application verification results, and breakthroughs in traditional technical limitations.

[0062] These or other aspects of the invention will become more apparent from the following description of embodiments. It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the invention. Attached Figure Description

[0063] To more clearly illustrate the technical solutions in the embodiments of the present invention or related technologies, the accompanying drawings used in the description of the exemplary embodiments or related technologies will be briefly introduced below. The drawings are used to provide a further understanding of the present invention and constitute a part of the specification. They are used together with the embodiments of the present invention to explain the present invention and do not constitute a limitation thereof. In the drawings:

[0064] Figure 1 This is a flowchart illustrating the data processing of the two-dimensional nuclear magnetic resonance logging two-dimensional spectrum reservoir fluid evaluation method according to an embodiment of the present invention.

[0065] Figure 2 This is a schematic diagram of the T2-T1 two-dimensional spectra of six fluids in the two-dimensional nuclear magnetic resonance logging two-dimensional spectrum reservoir fluid evaluation method of this invention.

[0066] Figure 3 This is a schematic diagram of a 50m layer containing six fluid components constructed in the two-dimensional nuclear magnetic resonance logging two-dimensional spectrum reservoir fluid evaluation method of the present invention.

[0067] Figure 4 This is a schematic diagram of the two-dimensional spectrum subdivision results of the hierarchical clustering method in the two-dimensional nuclear magnetic resonance logging two-dimensional spectrum reservoir fluid evaluation method of this invention.

[0068] Figure 5 This is a cross-plot of the calculated fluid component volume and model volume in the two-dimensional nuclear magnetic resonance logging two-dimensional spectrum reservoir fluid evaluation method of this invention.

[0069] Figure 6 This is a schematic diagram of the subdivided component two-dimensional spectrum in the two-dimensional nuclear magnetic resonance logging two-dimensional spectrum reservoir fluid evaluation method of this invention.

[0070] Figure 7 This is a schematic diagram of the quantitative evaluation results of fluid components in well Z using the two-dimensional nuclear magnetic resonance logging method for reservoir fluid evaluation in an embodiment of the present invention. Detailed Implementation

[0071] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0072] In some of the processes described in the specification, claims, and accompanying drawings of this invention, multiple operations appearing in a specific order are included. However, it should be clearly understood that these operations may not be executed in the order they appear herein, or may be executed in parallel. The operation numbers, such as 101, 102, etc., are merely used to distinguish different operations and do not represent any execution order. Furthermore, these processes may include more or fewer operations, and these operations may be executed sequentially or in parallel. It should be noted that the descriptions such as "first," "second," etc., in this document are used to distinguish different messages, devices, modules, etc., and do not represent a sequential order, nor do they limit "first" and "second" to different types.

[0073] The technical solutions in the exemplary embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described exemplary embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0074] Due to the low signal-to-noise ratio of 2D NMR logging signals in current tight oil and gas reservoir exploration, the complex fluid composition, and the overlapping of 2D spectra of different fluid components, it is difficult to determine fluid boundaries, separate components, and calculate the content of each component accurately. Therefore, this invention provides a 2D NMR logging method and system for reservoir fluid evaluation, capable of accurately identifying and quantitatively evaluating all fluid components. By deriving the equations for the 2D NMR logging spectral and component "sources," a relatively large number of fluid components is set, and a non-negative matrix factorization (NMF) algorithm is used for blind source separation to obtain the 2D spectra of multiple "sources" and the mixing matrix. Then, using a hierarchical clustering algorithm based on maximum distance (Complete), the number of fluid components is automatically determined based on the fluid's relaxation characteristics and spectral peak positions. Characteristic spectra of components such as clay-bound water, capillary-bound water, and movable water are identified. Through multiple clustering operations, the accurate determination of the number of fluid components and their characteristic spectra is achieved. Finally, these characteristic spectra and the mixing matrix are processed to calculate the volume content of each fluid, thereby achieving reservoir fluid evaluation.

[0075] The technical solution of the present invention will be further described below with reference to specific embodiments:

[0076] See Figure 1 As shown, Figure 1 This invention provides a flowchart of a two-dimensional nuclear magnetic resonance (NMR) logging method for evaluating reservoir fluids using a two-dimensional spectrum. An embodiment of this invention provides a two-dimensional nuclear magnetic resonance (NMR) logging method for evaluating reservoir fluids using a two-dimensional spectrum, which includes the following steps:

[0077] Step S10: Set the fluid component number r, perform blind source separation on the two-dimensional nuclear magnetic resonance logging two-dimensional spectrum, and obtain the two-dimensional spectrum of multiple fluid components and the mixing matrix reflecting the component ratio;

[0078] Step S20: Further judge the two-dimensional spectral features obtained by blind source separation. If a certain two-dimensional spectrum still contains multiple fluid components, perform hierarchical clustering on the points in the two-dimensional spectrum where the amplitude is not zero to obtain a two-dimensional spectrum containing only a single fluid component. Based on the relaxation characteristics of the fluid and the position of the spectral peak, determine the fluid characteristic spectrum of each fluid.

[0079] Step S30: Perform calculations on the determined fluid characteristic spectrum and the mixing matrix to calculate the volume content of each fluid to achieve reservoir fluid evaluation.

[0080] In an embodiment of the present invention, the two-dimensional nuclear magnetic resonance logging two-dimensional spectrum is the nuclear magnetic resonance signal acquired from the reservoir, which is the superposition of the characteristic spectra of different fluids in the reservoir. The number of unknown fluid components in the reservoir is called blind source. The acquired nuclear magnetic resonance signal covers the lateral and longitudinal relaxation time ranges to obtain a complete two-dimensional relaxation spectrum. The process of separating the fluid components from the acquired nuclear magnetic resonance signal to obtain the source signal is called blind source separation.

[0081] In an embodiment of the present invention, when performing blind source separation on a two-dimensional nuclear magnetic resonance (NMR) logging two-dimensional spectrum, the two-dimensional NMR logging two-dimensional spectrum is a superposition of characteristic spectra of different fluids in the reservoir. Assuming a formation depth of m, r unknown fluid components, and the number of points for the two-dimensional spectrum is n = n1 × n2, where n1 and n2 are the number of points for the transverse relaxation time T2 and the longitudinal relaxation time T1, respectively; let the two-dimensional spectrum data be: X m×n =[x1,x2,...,x m ] T x m For the two-dimensional spectral data of the m-th depth point, x m Let S be an n×1 column vector, and T denote the transpose of the matrix; let the two-dimensional spectrum of the fluid component be: S r×n =[s1,s2,...,s r ] T s r Let A be the two-dimensional spectrum of the r-th fluid component, and let A be an n×1 column vector; then let A be the mixing matrix. m×r , so that:

[0082] X m×n =A m× r·S r×n

[0083] In the formula, the number of fluid components r is unknown and is called a blind source; the mixing matrix A is solved simultaneously from the acquired signal X to obtain the "source" signal S, which is the blind source separation.

[0084] Blind source separation is performed using a nonnegative matrix factorization method, with the objective function set as follows:

[0085]

[0086] In the formula, A ik ≥0, S kj ≥0; X ij Let A be the value of the j-th placement point at the i-th depth point; ik S represents the coefficient of the k-th component at the i-th depth point; kj This represents the value of the j-th distribution point for the k-th component; i = 1, 2, ..., m; j = 1, 2, ..., n; k = 1, 2, ..., r;

[0087] The unconstrained optimization problem derived from the Lagrange multiplier method is as follows:

[0088]

[0089] In the formula, min L means minimizing the objective function L in the solution of the optimization problem; λ and v are Lagrange multipliers, matrices of size m×r and r×n, respectively;

[0090] Taking the partial derivative with respect to L and setting it to zero, we have:

[0091]

[0092]

[0093] According to the KKT conditional relaxation, we can obtain:

[0094] λ ik A ik =0, v kj S kj =0

[0095]

[0096]

[0097] Then we have the update iterative formulas for the mixing matrix A and the source signal S:

[0098]

[0099]

[0100] The initial mixing matrix and source signals are updated iteratively until the residual D is less than a set value, where D is:

[0101]

[0102] In the formula, |||| F It is the F-norm.

[0103] In an embodiment of the present invention, if a two-dimensional spectrum still contains multiple fluid components, hierarchical clustering is performed on the points in the two-dimensional spectrum whose amplitude is not zero, including the following steps:

[0104] Suppose there are n points (sample points) in the two-dimensional spectrum with non-zero amplitude, and the two-dimensional coordinate matrix is ​​P. n×2 Then the Euclidean distance between the i-th and j-th sample points is:

[0105]

[0106] A bottom-up hierarchical clustering strategy is adopted, treating each sample point as a cluster, i.e., the number of clusters is n. The distance between each cluster C is calculated, and the maximum distance between clusters is calculated according to the full-linkage algorithm. That is, the distance between clusters C1 and C2 is determined by the farthest sample.

[0107]

[0108] Based on the distance between each cluster, the two clusters with the smallest distance are merged into one cluster, and the distance between the merged n-1 clusters is recalculated and merged again until all clusters are merged into one cluster. Then it can be split into the required number of clusters. That is, if a certain separated two-dimensional spectrum contains k fluid component response features, it is split into k clusters to obtain the subdivided two-dimensional spectrum.

[0109] Assuming a total of r' two-dimensional spectra are subdivided, the fluid component type corresponding to each two-dimensional spectrum is determined based on the position of the spectral peaks in each subdivided two-dimensional spectrum. By combining two-dimensional spectra of the same fluid type, the fluid volume of each fluid type can be obtained from the mixing matrix A.

[0110] To verify the accuracy of the two-dimensional nuclear magnetic resonance logging two-dimensional spectrum reservoir fluid evaluation method in this embodiment of the invention, six different fluid types with T2-T1 two-dimensional spectra were set up, such as... Figure 2 As shown, the six different fluid types in (a) to (e) are clay-bound water, capillary-bound water, mobile water, asphaltene, bound oil, and mobile oil, respectively. Their peak values ​​T2 are 1 ms, 10 ms, 100 ms, 1 ms, 15 ms, and 150 ms, respectively; their peak values ​​T1 are 1 ms, 10 ms, 100 ms, 25 ms, 75 ms, and 750 ms, respectively; and the volume of each component is 4 pu.

[0111] Based on the constructed two-dimensional spectral models of six fluids, two-dimensional nuclear magnetic resonance (NMR) logging data for a 50m depth interval were randomly generated. The weights of the six fluids at each depth point were randomly generated, and the porosity at each depth point was 24%. Echo data were obtained through forward modeling using the Schlumberger CMR-NG two-dimensional NMR logging acquisition mode. Noise was added to obtain echo data with a signal-to-noise ratio of 20, and the BRD method was used for inversion to obtain two-dimensional NMR logging data containing noise. Figure 3 As shown, the first channel is the depth channel, the second channel is the noise-free model, the third to eighth channels are the weighted fluid T2-T1 two-dimensional spectra, and the ninth channel is the two-dimensional spectrum model after adding noise for inversion.

[0112] A relatively large fluid component number of 8 was set, and blind source separation was performed on the constructed two-dimensional nuclear magnetic resonance logging model containing noise. T2-T1 two-dimensional spectra of 8 fluid components were obtained. Hierarchical clustering was then used to further subdivide the fluid components obtained from blind source separation, resulting in 9 subdivided fluid components, such as... Figure 4 As shown in Table 1, the peak positions of the subdivided fluid components are as follows. Components 1 and 6 have a transverse relaxation time T2 of approximately 1 ms, less than 3 ms, and T1 / T2 is less than 4; therefore, these two fluid components are clay-bound water. Components 4 and 8 have a transverse relaxation time T2 of approximately 10 ms, greater than 3 ms, less than 33 ms, and T1 / T2 is less than 4; therefore, these two fluid components are capillary-bound water. Component 7 has a transverse relaxation time T2 of 103.9 ms, greater than 33 ms, and T1 / T2 is less than 4; therefore, this fluid component is mobile water. Component 5 has a transverse relaxation time T2 less than 3 ms, and T1 / T2 is greater than 4; therefore, this fluid component is asphaltene. Component 9 has a transverse relaxation time T2 greater than 3 ms, less than 33 ms, and T1 / T2 is greater than 4; therefore, this component is bound oil. Components 2 and 3 have a transverse relaxation time T2 greater than 33 ms, and T1 / T2 is greater than 4; therefore, these two fluid components are mobile oil.

[0113] Table 1. Hierarchical clustering method for refining the peak positions of two-dimensional spectra.

[0114] Components 1 2 3 4 5 6 7 8 9 <![CDATA[T2(ms)]]> 0.9 161.2 77.6 11.6 1.3 1.1 103.9 10.1 15.5 <![CDATA[T1(ms)]]> 0.3 804.8 804.8 15.5 24.1 1.5 120.3 8.7 89.8 <![CDATA[T1 / T2]]> 0.3 4.9 10.4 1.3 18.6 1.3 1.2 0.9 5.8

[0115] Based on the two-dimensional spectra of the six identified fluid components, the volume fraction of each fluid component at each depth point was calculated. The calculated fluid volumes were compared with the model volume fractions, and cross-plots were plotted accordingly. Figure 5 As shown, each fluid component is consistent with the model. The minimum absolute error of the volume content of the six fluids is less than 0.02 pu, the maximum absolute error is less than 4 pu, and the average absolute error is less than 1 pu. The relative error is small, indicating that the formed two-dimensional nuclear magnetic resonance logging blind source separation fluid identification and quantitative evaluation method is relatively accurate and effective.

[0116] This invention is applied to the tight sandstone reservoir of Well Z in a basin in western China. An exemplary application example is provided: Well Z is a key well in a tight sandstone oil and gas reservoir in a basin in western China. The target layer has low porosity, generally less than 15%. The two-dimensional nuclear magnetic resonance (NMR) logging data for this well was acquired using a Schlumberger CMR-NG instrument in T2-T1 mode. This mode has a low signal-to-noise ratio, and the inverted two-dimensional NMR spectrum shows overlapping signals from different fluids. The boundaries between different fluids in the separated two-dimensional spectral feature chart are difficult to determine, resulting in poor fluid identification accuracy and inaccurate volume calculations. Therefore, this invention is used to process and evaluate this well.

[0117] Using this invention to process the well, characteristic two-dimensional spectra of eight fluid components were obtained, such as... Figure 6 As shown in Table 2, the positions of the spectral peaks are also shown. Experiments show that the cutoff value between clay-bound water and capillary-bound water in this region is 3 ms, and the cutoff value between capillary-bound water and movable water is 8 ms. Components 1, 3, and 7 have a transverse relaxation time T2 less than 3 ms and T1 / T2 less than 4, and are classified as clay-bound water; component 8 has a transverse relaxation time T2 greater than 3 ms and less than 8 ms, and T1 / T2 less than 4, and is classified as capillary-bound water; components 4 and 6 have a transverse relaxation time T2 greater than 8 ms and T1 / T2 less than 4, and are classified as movable water; component 5 has a transverse relaxation time T2 less than 8 ms and T1 / T2 greater than 4, and is classified as bound oil; component 2 has a transverse relaxation time T2 greater than 8 ms and T1 / T2 greater than 4, and is classified as movable oil. The volume content of the five fluid components (clay-bound water, capillary-bound water, movable water, bound oil, and movable oil) was calculated, and the water and oil volumes were also calculated. The processing results are shown in the figure below. Figure 7 As shown.

[0118] Table 2. Detailed peak positions in the two-dimensional spectrum

[0119] Components 1 2 3 4 5 6 7 8 <![CDATA[T2(ms)]]> 1.5 57.9 0.8 8.65 2.3 17.9 0.5 4.1 <![CDATA[T1(ms)]]> 0.7 289.2 3.1 15.5 27.8 57.9 0.3 4.8 <![CDATA[T1 / T2]]> 0.5 4.9 3.7 1.7 12.0 3.2 0.5 1.2

[0120] Figure 7 In the analysis, the first channel represents natural gamma, the second depth channel, the third and fourth channels represent T2 and T1 distributions respectively, the fifth channel represents the T2-T1 distribution, the sixth channel represents the fluid volume profile, the seventh channel represents effective porosity and total porosity, the eighth channel represents the water-bearing volume, and the ninth channel represents the oil-bearing volume. The 13m section from 2221 to 2234m has an average total porosity of 0.1, an average water-bearing volume of 0.03, an average oil-bearing volume of 0.05, and an average oil saturation of approximately 50%. Therefore, this section is interpreted as a co-oil and water layer. Oil testing results show that this section produces 8.4t of oil and 7.6t of water per day, confirming its co-oil and water characteristics. The interpretation results are consistent with the oil testing results, indicating that the method is effective in practical applications.

[0121] It should be understood that although the above description follows a certain order, these steps are not necessarily executed in that order. Unless otherwise expressly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, some steps in this embodiment may include multiple steps or multiple stages, which are not necessarily completed at the same time, but may be executed at different times. The execution order of these steps or stages is not necessarily sequential, but may be performed alternately or in turn with other steps or at least a portion of the steps or stages in other steps.

[0122] In one embodiment, the present invention provides a two-dimensional nuclear magnetic resonance logging two-dimensional spectrum reservoir fluid evaluation system for performing the above-described two-dimensional nuclear magnetic resonance logging two-dimensional spectrum reservoir fluid evaluation method, the system comprising:

[0123] Data acquisition module: used to acquire the two-dimensional nuclear magnetic resonance logging two-dimensional spectrum of the reservoir.

[0124] Blind source separation module: used to perform blind source separation on the two-dimensional spectrum of the two-dimensional nuclear magnetic resonance logging and output two-dimensional spectra of various fluid components; the blind source separation module uses the non-negative matrix factorization (NMF) method for blind source separation.

[0125] Feature identification and clustering module: This module is used to identify the two-dimensional spectral features obtained from blind source separation. For two-dimensional spectra containing multiple fluid components, this module performs hierarchical clustering and outputs two-dimensional spectra containing only a single fluid component. Specifically, the feature identification and clustering module uses a bottom-up aggregation strategy to cluster points with non-zero amplitudes in the two-dimensional spectrum and distinguish different fluid components. Through multiple clustering operations, the number of fluid components and the feature spectrum are accurately determined.

[0126] Fluid Characteristic Spectrum Determination Module: Used to determine the fluid characteristic spectrum of various fluids based on the relaxation characteristics and peak positions of the fluid.

[0127] The volumetric content calculation module is used to perform calculations on the determined fluid characteristic spectrum and the mixing matrix to calculate the volumetric content of each fluid, thereby achieving reservoir fluid evaluation. Specifically, the volumetric content calculation module uses the mixing matrix obtained from the blind source separation module to perform calculations on the two-dimensional spectra of the clustered single fluid components and the mixing matrix to calculate the volumetric content of each fluid.

[0128] The reservoir fluid evaluation system also includes a user interface module for displaying the volume content of each fluid component and providing visualization of the reservoir fluid evaluation results.

[0129] This invention relates to a two-dimensional nuclear magnetic resonance (NMR) logging system for evaluating reservoir fluids. By deriving equations relating the two-dimensional NMR logging spectrum to the component "sources," and setting a relatively large number of fluid components, the system uses a non-negative matrix factorization (NMF) algorithm for blind source separation, obtaining two-dimensional spectra of multiple "sources" and a mixing matrix. Then, using a hierarchical clustering algorithm based on maximum distance (Complete), the system automatically determines the number of fluid components and identifies the characteristic spectra of components such as clay-bound water, capillary-bound water, and mobile water based on the fluid's relaxation characteristics and spectral peak positions. Through multiple clustering operations, the system accurately determines the number of fluid components and their characteristic spectra. Finally, these characteristic spectra are calculated in conjunction with the mixing matrix to determine the volumetric content of each fluid, thus achieving reservoir fluid evaluation.

[0130] In this embodiment, the two-dimensional nuclear magnetic resonance logging two-dimensional spectrum reservoir fluid evaluation system adopts the steps of a two-dimensional nuclear magnetic resonance logging two-dimensional spectrum reservoir fluid evaluation method as described above. Therefore, the operation process of the two-dimensional nuclear magnetic resonance logging two-dimensional spectrum reservoir fluid evaluation system will not be described in detail in this embodiment.

[0131] In one embodiment, a computer device is also provided in the present invention, including at least one processor and a memory communicatively connected to the at least one processor, the memory storing instructions executable by the at least one processor, the instructions being executed by the at least one processor to cause the at least one processor to perform the steps of the two-dimensional nuclear magnetic resonance logging two-dimensional spectrum reservoir fluid evaluation method.

[0132] In one embodiment, the present invention also provides a computer-readable storage medium storing computer instructions for causing the computer to perform the steps of the two-dimensional nuclear magnetic resonance logging two-dimensional spectrum reservoir fluid evaluation method.

[0133] Those skilled in the art will understand that implementing all or part of the processes in the above embodiments can be accomplished by a computer program, characterized by computer instructions, instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Furthermore, any references to memory, storage, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory.

[0134] Non-volatile memory may include read-only memory, magnetic tape, floppy disk, flash memory, or optical storage. Volatile memory may include random access memory or external cache memory. By way of illustration and not limitation, RAM can take many forms, such as static random access memory or dynamic random access memory.

[0135] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A two-dimensional nuclear magnetic resonance logging method for evaluating reservoir fluids using two-dimensional spectra, characterized in that, The method includes the following steps: Set fluid composition Blind source separation was performed on the two-dimensional spectrum of two-dimensional nuclear magnetic resonance logging to obtain two-dimensional spectra of various fluid components and a mixing matrix reflecting the component ratio; Further analysis of the two-dimensional spectral features obtained from blind source separation: if a certain two-dimensional spectrum still contains multiple fluid components, hierarchical clustering is performed on the points in the two-dimensional spectrum with non-zero amplitude to obtain a two-dimensional spectrum containing only a single fluid component. Based on the relaxation characteristics of the fluid and the position of the spectral peak, the fluid characteristic spectra of various fluids are determined. The determined fluid characteristic spectrum and the mixing matrix are processed to calculate the volume content of each fluid to achieve reservoir fluid evaluation. In the process of blind source separation of two-dimensional nuclear magnetic resonance logging two-dimensional spectra, it is assumed that a certain formation depth point is... Unknown fluid components The number of points in the two-dimensional spectrum is [number missing]. , Where n1 and n2 are the number of points for the transverse relaxation time T2 and the longitudinal relaxation time T1, respectively; let the two-dimensional spectral data be: , For the first Two-dimensional spectral data at a depth point For n A column vector of 1, where T denotes the transpose of the matrix; let the two-dimensional spectrum of the fluid component be: , For the first Two-dimensional spectra of each fluid component, for n A column vector of 1; with a mixed matrix , so that: In the formula, the number of fluid components Unknown sources are called blind sources; from the acquired signals Simultaneously solve the mixture matrix The "source" signal was obtained. This is known as blind source separation; In the blind source separation of two-dimensional nuclear magnetic resonance logging two-dimensional spectra, a non-negative matrix factorization-based method is adopted, and the objective function is set as follows: In the formula, , ;X ij Let A be the value of the j-th placement point at the i-th depth point; ik S represents the coefficient of the k-th component at the i-th depth point; kj Let i be the value of the j-th point in the k-th component; i = 1, 2, ..., m; j = 1, 2, ..., n; ; The unconstrained optimization problem derived from the Lagrange multiplier method is as follows: In the formula, This indicates that solving the optimization problem involves setting the objective function... Minimum; and Let be the Lagrange multipliers, and let be matrices of size m×r and r×n, respectively; right Taking the partial derivative and setting it to zero, we have: According to the KKT conditional relaxation, we can obtain: ， Then there is a mixture matrix and source signal The update and iteration formula: The initial mixing matrix and source signals are updated iteratively until the residuals are reached. Less than the set value, where for: In the formula, It is the F-norm.

2. The two-dimensional nuclear magnetic resonance logging method for evaluating reservoir fluids according to claim 1, characterized in that, Two-dimensional nuclear magnetic resonance (NMR) logging uses two-dimensional spectra to acquire NMR signals from reservoirs. These spectra are superimposed from the characteristic spectra of different fluids in the reservoir. The number of unknown fluid components in the reservoir is called blind source. The acquired NMR signals cover both lateral and longitudinal relaxation time ranges to obtain a complete two-dimensional relaxation spectrum. The process of separating fluid components from the acquired NMR signals to obtain source signals is called blind source separation.

3. The two-dimensional nuclear magnetic resonance logging method for evaluating reservoir fluids according to claim 2, characterized in that, If a two-dimensional spectrum still contains multiple fluid components, then hierarchical clustering should be performed on the points in the two-dimensional spectrum where the amplitude is not zero. Includes the following steps: Suppose that the points in the two-dimensional spectrum with non-zero amplitude, i.e., the sample points, have There are , and the two-dimensional coordinate matrix is Then the first and The Euclidean distance between the sample points is: A bottom-up hierarchical clustering strategy is adopted, treating each sample point as a cluster, i.e., the number of clusters is... Calculate each cluster The distance between clusters is calculated using the fully linked algorithm, which determines the maximum distance between clusters. and The distance is determined by the farthest sample: Based on the distance between each cluster, the two clusters with the smallest distance are merged into one cluster, and the result of the merge is recalculated. The distance between clusters is calculated, and the clusters are merged again until all clusters are merged into one cluster. This allows the desired number of clusters to be determined, i.e., if a separated two-dimensional spectrum contains... The response characteristics of each fluid component are then broken down into: Each cluster yields a subdivided two-dimensional spectrum.

4. The two-dimensional nuclear magnetic resonance logging method for evaluating reservoir fluids according to claim 3, characterized in that, When calculating the volume content of each fluid, if it is subdivided into... Each two-dimensional spectrum is analyzed. Based on the position of the peaks in the two-dimensional spectrum of each sub-component, the fluid component type corresponding to each two-dimensional spectrum is determined. Two-dimensional spectra of the same fluid type are then combined using a mixing matrix. This allows you to obtain the fluid volume for each fluid type.

5. A two-dimensional nuclear magnetic resonance logging two-dimensional spectrum reservoir fluid evaluation system, characterized in that, For performing the two-dimensional nuclear magnetic resonance logging two-dimensional spectrum reservoir fluid evaluation method according to any one of claims 1-4, the two-dimensional nuclear magnetic resonance logging two-dimensional spectrum reservoir fluid evaluation system comprises: Data acquisition module: used to acquire the two-dimensional nuclear magnetic resonance logging two-dimensional spectrum of the reservoir; Blind source separation module: used to perform blind source separation on two-dimensional NMR logging spectra and output two-dimensional spectra of various fluid components; Feature identification and clustering module: used to identify the two-dimensional spectral features obtained by blind source separation. For two-dimensional spectra containing multiple fluid components, it is also used to perform hierarchical clustering and output two-dimensional spectra containing only a single fluid component. Fluid Characteristic Spectrum Determination Module: Used to determine the fluid characteristic spectrum of various fluids based on the relaxation characteristics and peak positions of the fluids; Volume content calculation module: used to perform calculations on the determined fluid characteristic spectrum and the mixing matrix to calculate the volume content of each fluid, thereby realizing reservoir fluid evaluation.

6. The two-dimensional nuclear magnetic resonance logging two-dimensional spectrum reservoir fluid evaluation system as described in claim 5, characterized in that, The blind source separation module uses a non-negative matrix factorization method for blind source separation.

7. The two-dimensional nuclear magnetic resonance logging two-dimensional spectrum reservoir fluid evaluation system as described in claim 6, characterized in that, The feature judgment and clustering module uses a bottom-up aggregation strategy to cluster points with non-zero amplitudes in the two-dimensional spectrum and distinguish different fluid components.

8. The two-dimensional nuclear magnetic resonance logging two-dimensional spectrum reservoir fluid evaluation system as described in claim 7, characterized in that, The volume content calculation module calculates the volume content of each fluid by performing operations on the two-dimensional spectrum of the single fluid component obtained by clustering and the mixing matrix based on the mixing matrix obtained by the blind source separation module.

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