Dynamic and static combined reservoir description method for dense well pattern
By introducing the effective coefficient K and correlation coefficient α of injection-production streamlines in dense well network areas, the high-precision post-stack inversion results of geostatistics are corrected, solving the problem of multiple solutions in reservoir description between wells in dense well networks and achieving higher-precision reservoir description.
Patent Information
- Application Number
- CN202410722541.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-06-05
- Publication Date
- 2025-12-05
AI Technical Summary
Under conditions of dense well networks, there is a discrepancy between dynamic and static descriptions of reservoirs between wells, and the high-precision post-stack inversion results of geostatistics have multiple interpretations, making it difficult to accurately describe the reservoir conditions between wells.
By introducing the effective coefficient K and correlation coefficient α of injection-production streamlines from dynamic production data, the high-precision post-stack inversion results of geostatistics are corrected. Numerical simulation is used to quantify inter-well connectivity. Combined with static reservoir drilling data, ambiguity is reduced and reservoir description accuracy is improved.
It effectively overcomes the multiple interpretations of geostatistical inversion, improves the accuracy and precision of reservoir description in dense well networks, and simplifies the operation process.
Smart Images
Figure CN121069470A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of dense well network reservoir description technology, and provides a reservoir description method that combines dynamic and static methods for dense well networks. Background Technology
[0002] Reservoir characterization is one of the core tasks of geological research in oil and gas field development. The accuracy of reservoir characterization greatly affects the development effect of oil fields. Under the condition of dense well network, the well network has a high degree of control over the reservoir. However, in the process of oil field development, there is still a phenomenon of discrepancy between dynamic and static data, and there is still a lot of uncertainty in the reservoir characterization between wells.
[0003] Reservoir description is a technique that comprehensively applies seismic, geological, and drilling data to track and predict the distribution and morphology of underground reservoirs. Currently, reservoir description methods both domestically and internationally generally include seismic inversion methods, seismic attribute analysis, and AVO technology. These methods have yielded numerous research and application results. Among them, well-controlled geostatistical high-precision post-stack inversion is recognized as one of the most accurate reservoir description methods, but it still has shortcomings. The main shortcoming is that geostatistical high-precision post-stack inversion involves uncertainty, resulting in multiple interpretations, making it difficult to select the appropriate reservoir description results in practical applications. This invention proposes a new reservoir description method based on actual dynamic production data from densely networked well areas. It uses numerical simulation to quantify the connectivity between wells and corrects the geostatistical high-precision post-stack inversion results. Its theoretical basis is to fully consider dynamic data based on static reservoir drilling data, and to reduce the uncertainty and multiple interpretations of geostatistical high-precision post-stack inversion through reasonable matching of static and dynamic data, thus more accurately describing the reservoir conditions between wells. Summary of the Invention
[0004] The purpose of this invention is to overcome the shortcomings of existing technologies by proposing a reservoir description method that combines dynamic and static approaches for dense well networks. For dense well network reservoir description, high-precision post-stack inversion data from geostatistics is selected. Based on the streamlines of injection and production wells in dynamic production data, an effective coefficient K for injection and production streamlines is introduced to characterize the reservoir connectivity between wells in the dense well network area. By calculating the correlation between the K-value plane map and the inversion plane map, the correlation coefficient α between the K-value of any grid point and the equally probable inversion data value is obtained. The α value is then used to correct the inversion results. Applying this invention can effectively overcome the ambiguity of geostatistical inversion, improve the accuracy of reservoir description, and is simple and easy to operate.
[0005] To achieve the above objectives, the present invention adopts the following technical solution:
[0006] A reservoir description method combining dynamic and static analysis of dense well networks includes:
[0007] Selection of dense well network area;
[0008] Multiple reservoir inversion bodies with equal probability were obtained by inverting geological data of the selected dense well network area;
[0009] Attribute calculations yield multiple equally probable inversion planar graphs;
[0010] Multiple equally probable inversion planar graph corrections;
[0011] The reservoir characterization was completed by fusing multiple corrected equal-probability inversion planar maps.
[0012] Furthermore, step S1 obtains multiple equally probable reservoir inversion bodies through high-precision post-stack inversion.
[0013] Furthermore, step S4 includes:
[0014] Numerical simulation was used to clarify the injection-production streamlines between wells, and the effective coefficient K of the injection-production streamlines was introduced to obtain a K-value planar map characterizing the degree of reservoir connectivity between wells in a dense well network area.
[0015] Multiple equally probable inversion planar maps and K-value planar maps are gridded to obtain planar grid data with Xn*Ym grid numbers. Correlation analysis is then used to calculate the correlation between the K-value of the same grid and the inversion data.
[0016] A correlation coefficient α is introduced to characterize the correlation between the K value and the inverted data, and an α-value plane plot is established;
[0017] Multiple corrected equal-probability inversion plane diagrams are obtained by calculating the α-value plane diagram and the corresponding equal-probability inversion plane diagram.
[0018] Furthermore, the dense well network area must meet the following conditions:
[0019] ① The well network in the target formation controls more than 90% of the total geological reserves in the target formation, and it is developed using water injection.
[0020] ② The reservoir connectivity of production wells is sensitive to production dynamic data, and the injection-production flow lines can accurately reflect the injection-production correspondence of the target layer.
[0021] Furthermore, the injection-production streamline effectiveness coefficient K in step S4 refers to the degree of effectiveness between injection and production wells.
[0022] Furthermore, the value of K ranges from 0 to 1. When K = 1, it indicates that the reservoir is connected between the injection well and the production well. When K = 0, it indicates that the reservoir is not connected between the injection well and the production well. When K is between 0 and 1, it indicates that the reservoir is partially connected and the larger the value of K, the better the connectivity.
[0023] Furthermore, step S42 includes:
[0024] ① Planar grid data for the K value is obtained through numerical simulation based on injection and mining data;
[0025] ②The K-value planar plot is obtained from the planar grid data of the K-value using a bilinear interpolation algorithm.
[0026] Furthermore, in step S43, a correlation coefficient α is introduced to characterize the correlation between the K value and the inversion data, and an α-value plane plot β is established.
[0027] Furthermore, step S43 includes:
[0028] ① Perform correlation matrix calculation on the K-value planar grid data and multiple equally probable inversion planar grid data to obtain the correlation coefficient α between the K value of any grid point and the equally probable inversion data value;
[0029] ②The α-valued planar plot β is obtained from the planar grid data of the α-values using a bilinear interpolation algorithm.
[0030] Furthermore, step S44 includes:
[0031] ① The N equally probable inversion plane mesh diagrams are represented as Fn (n inversion plane diagrams F1 to Fn), and the corrected inversion plane mesh diagrams are represented as Wn (n corrected inversion plane diagrams W1 to Wn). The corrected mesh point Fn(Xa, Yb) is represented as Wn(Xa, Yb):
[0032] Wn(Xa,Yb)=αn(Xa,Yb)*Wn(Xa,Yb);
[0033] ② The final reservoir description result is obtained by fusing multiple corrected equal probability inversion planar diagrams.
[0034]
[0035] The beneficial effects of this invention are as follows:
[0036] Based on the well-controlled geostatistical high-precision post-stack inversion reservoir description method, this paper utilizes abundant dynamic production data from dense well network areas to obtain planar grid data characterizing the inter-well reservoir connectivity within these areas. Correlation is established between this data and the geostatistical high-precision post-stack inversion results, thereby correcting the reservoir inversion results, reducing uncertainty, and improving reservoir description accuracy. A search of currently published patents and literature reveals that none of the reservoir description methods based on geostatistical high-precision post-stack inversion technology involve using dynamic data to correct the inversion results, demonstrating strong innovation. Attached Figure Description
[0037] Figure 1 This is a flowchart illustrating a method for describing a dense well network reservoir according to an embodiment of the present invention;
[0038] Figure 2for Figure 1 In one specific embodiment, a high-precision post-stack inversion planar map of the target layer based on geostatistics is shown.
[0039] Figure 3 for Figure 1 In one specific embodiment, the inter-well injection-production streamline diagram obtained from numerical simulation of the target layer is shown.
[0040] Figure 4 for Figure 1 A K-value planar diagram obtained based on the inter-well injection-production streamline in the embodiment;
[0041] Figure 5 for Figure 1 α-value planar grid diagram in the embodiment;
[0042] Figure 6 for Figure 1 The corrected target section reservoir description diagram in the embodiment. Detailed Implementation
[0043] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.
[0044] Example 1:
[0045] A reservoir description method combining dynamic and static analysis of dense well networks includes the following steps:
[0046] Step 1: Select a dense well network area, obtain geological data for the dense well network area, and obtain multiple equal probability reservoir inversion bodies through high-precision post-stack inversion;
[0047] Step 2: Through attribute calculation, obtain multiple equally probable inversion planar maps that characterize the reservoir development degree of the target section;
[0048] Step 3: Determine the injection-production streamlines between wells through numerical simulation, introduce the effective coefficient K of the injection-production streamlines, and obtain a K-value planar map that characterizes the degree of reservoir connectivity between wells in the dense well network area;
[0049] Step 4: Grid the multiple equally probable inversion planar maps and K-value planar maps to obtain planar grid data with Xn*Ym grid numbers. Calculate the correlation between the K-value of the same grid and the inversion data through correlation analysis.
[0050] Step 5: Introduce the correlation coefficient α to characterize the correlation between the K value and the inverted data, and establish an α value plane plot;
[0051] Step 6: The α-value plane map and the corresponding equal probability inversion plane map are used to calculate multiple corrected equal probability inversion plane maps. The multiple corrected equal probability inversion plane maps are then fused to complete the reservoir characterization.
[0052] The dense well network area must meet the following conditions:
[0053] ① The well network in the target formation controls more than 90% of the total geological reserves in the target formation, and it is developed using water injection.
[0054] ② The reservoir connectivity of production wells is sensitive to production dynamic data, and the injection-production flow lines can accurately reflect the injection-production correspondence of the target layer.
[0055] High-precision post-stack inversion includes:
[0056] 1. Inversion steps:
[0057] ① Establish an initial model;
[0058] ② Randomly select a grid point between wells;
[0059] ③ Estimate the conditional probability density function (pdf) at this point using ordinary kriging techniques;
[0060] ④ Randomly select a value from the probability density function, calculate the reflection coefficient, and convolve it with the wavelet to obtain the synthetic seismic record;
[0061] ⑤ If this value increases the match between the synthetic seismic record and the actual seismic record, then accept this value; otherwise, accept this value with a certain probability, the probability distribution of which is determined by the Boltzmann function. If rejected, return to the previous step.
[0062] ⑥ Lower the simulated annealing temperature;
[0063] ⑦ Repeat steps ② to ⑥ until the ideal synthetic seismic record matches the actual seismic record.
[0064] 2. Basic Methods
[0065] 1. Time-depth conversion: Through layer calibration, the logging curves in the depth domain are converted into the time domain and made consistent with the seismic layers.
[0066] 2. Structural Model: Based on the detailed interpretation of the seismic data, a three-dimensional structural model of the study area is established using deterministic modeling methods, based on the interpreted stratigraphic and fault files.
[0067] 3. Stratigraphic Model: Based on the structural model, high-resolution sequence stratigraphy and stratigraphic correlation are conducted using well core sampling, well logging data, and 3D seismic data to establish a high-precision isochronous stratigraphic model. Then, wave impedance simulation is performed under the constraints of this stratigraphic model.
[0068] 4. Deterministic Inversion: Also known as the sparse pulse inversion method, this approach assumes a sparse distribution of strong subsurface reflection coefficients. Based on the principle of sparseness, reflection coefficients are extracted from seismic traces and convolved with wavelets to generate synthetic seismic records. The residuals between the synthetic seismic records and the original seismic traces are used to modify the reflection coefficients, resulting in a new reflection coefficient sequence. This process is repeated until a reflection coefficient sequence that best approximates the seismic trace is obtained. The relative acoustic impedance data volume can then be calculated from the reflection coefficient sequence. Sparse pulse constrained inversion increases the trend and range of acoustic impedance control by geological models and well constraints. Since seismic data is band-limited, the obtained data lacks low-frequency information. Low-frequency compensation through the establishment of a geological model is necessary to obtain a full-band absolute acoustic impedance data volume, preparing for subsequent initial models.
[0069] 5. Geostatistical Inversion:
[0070] (1) Well logging data preprocessing: Since mud soaking and wellbore factors will affect the well logging data, environmental correction of the well logging data is required. One or more standard layers near the target layer are selected and the trend surface method is used for correction. Finally, the sonic and density curves obtained by correction are combined into a wave impedance curve.
[0071] (2) Statistical analysis of logging impedance data: Since sequential Gaussian processing requires data to follow a Gaussian distribution, data that does not follow a Gaussian distribution should be converted before conversion.
[0072] (3) Establish the initial model: Use the deterministic inversion results as the initial model.
[0073] (4) Fitting the variogram: Obtaining the spatial variogram is one of the key steps in geostatistical inversion. In conventional stochastic modeling, well point data is generally used. However, well point data is only densely packed in the vertical direction, resulting in high accuracy of the variogram. In the horizontal direction, however, the available data points for fitting are sparse, with large point spacing, leading to low accuracy of the fitted variogram. In geostatistical inversion, the vertical variogram can be obtained from well data, while the horizontal variogram can be calculated using sparse pulse constraint inversion impedance data, fully leveraging the advantage of the dense horizontal seismic impedance data. By scanning and calculating in each direction, the variogram in each direction is obtained, fully characterizing the anisotropy of the spatial distribution of reservoir properties.
[0074] (5) Determine control parameters: For the simulated annealing algorithm, the cooling schedule includes four parameters: ① The starting temperature is generally required to be large enough to ensure jumping out of local minima, but an overly large initial temperature will increase the computational workload without reason; ② The temperature reduction parameter. This parameter mainly ensures that the temperature can drop slowly to prevent quenching. There are various temperature reduction functions, and a commonly used one is an intuitive linear reduction function $T_{k + 1}=AT_{k}$, where $0\lt A\lt1$ (the closer $A$ is to 1, the slower the temperature drops. If the temperature drops too slowly, the computational workload will increase; if it drops too fast, the optimal solution may be missed); ③ Termination criterion. Determine a very small value close to 0 as the computational termination condition to ensure that the calculation can converge to the optimal solution; ④ The number of loop iterations. In theory, the more iterations, the more the system can tend to thermal equilibrium, and the more the synthetic seismic data can match the original seismic data optimally, but this will increase the computational workload.
[0075] Example 2:
[0076] Referring to Figure 1 As shown, the present invention provides a method for reservoir description of a dense well pattern, including the following steps:
[0077] Step 110, conduct high-precision post-stack inversion of geostatistics for the dense well pattern study area to obtain multiple equiprobability reservoir inversion bodies.
[0078] This method starts from existing seismic data, drilling data, and logging data, and completes high-precision post-stack inversion of geostatistics through commercial software to obtain multiple equiprobability inversion data bodies.
[0079] Step 102, through attribute calculation, obtain multiple equiprobability inversion plane maps characterizing the reservoir development degree of the target interval. Figure 2 One of the inversion plane maps is shown as follows.
[0080] Determine the top and bottom surfaces of the target interval through the existing seismic interpretation results of the target interval, and calculate through commercial software to obtain the inversion plane map of the target interval corresponding to each equiprobability inversion data body.
[0081] Step 103, clarify the injection-production flow lines between wells based on production dynamic analysis, and introduce the injection-production flow line effectiveness coefficient $K$ to characterize the connectivity of the reservoir between wells in the dense well pattern area.
[0082] First, statistically analyze the dynamic production data of each production well to clarify the production dynamics of the target interval; secondly, through numerical simulation methods, establish the injection-production flow lines between wells to obtain an injection-production flow line map as shown in Figure 3 As shown; thirdly, introduce the injection-production flow line effectiveness coefficient $K$. The value of $K$ represents the density of the injection-production flow lines between wells per unit area. The greater the density, the better the injection-production effectiveness, and the better the connectivity of the reservoir between the corresponding wells. For the convenience of subsequent calculations, through normalization, the value of $K$ is defined between 0 and 1.
[0083] Step 104: Grid the K-value planar graph. Figure 4 As shown, the K-value planar grid diagram is obtained;
[0084] Step 105: Introduce the correlation coefficient α to characterize the correlation between the K value and the inversion data, and establish an α-value plane plot.
[0085] First, the correlation matrix (default Pearson correlation coefficient) is calculated between the K-value planar grid map and each inverted planar grid map. This step is performed using the R function. Second, a correlation coefficient α is introduced, representing the correlation between the K-value planar grid map and the inverted planar grid map at each grid. Third, the α-value planar grid data is interpolated using a bilinear interpolation algorithm to obtain the α-value planar map β, as shown below. Figure 5 As shown.
[0086] Step 106: The α-value plane map β and the corresponding equal probability inversion plane map are used to calculate multiple corrected equal probability inversion plane maps. The multiple corrected equal probability inversion plane maps are then fused to complete the reservoir characterization.
[0087] First, multiply the N α-valued plane plots β with their corresponding inversion plane mesh plots to obtain N corrected inversion plane plots. Second, fuse multiple corrected, equally probable inversion plane plots, and then take the arithmetic mean of the N corrected inversion plane plots and divide it by the average probability Δβ to obtain the following... Figure 6 The final reservoir description diagram shown is shown.
[0088] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.
Claims
1. A method for reservoir description in a dense well pattern combining dynamic and static data, characterized in that, The method comprises the following steps: S1, selecting a dense well pattern area; S2, obtaining a plurality of equal-probability reservoir inversion bodies according to geological data inversion of the selected dense well pattern area; S3, calculating a plurality of equal-probability inversion plane maps; S4, correcting the plurality of equal-probability inversion plane maps; S5, completing reservoir description by fusing the corrected plurality of equal-probability inversion plane maps.
2. The method according to claim 1, wherein, The step S1 obtains a plurality of equal-probability reservoir inversion bodies through high-precision post-stack inversion.
3. The method according to claim 2, wherein, The step S4 comprises: S41, obtaining a K-value plane map representing the interwell reservoir connectivity degree in the dense well pattern area by introducing an injection-production flow line effectiveness coefficient K through numerical simulation to determine the injection-production flow line between the wells; S42, gridizing the plurality of equal-probability inversion plane maps and the K-value plane map to obtain Xn*Ym grid plane data, and calculating the correlation between the K-value and the inversion data in the same grid through correlation analysis; S43, introducing a correlation coefficient a to represent the correlation between the K-value and the inversion data, and establishing an a-value plane map; S44, calculating the corrected plurality of equal-probability inversion plane maps from the a-value plane map and the corresponding equal-probability inversion plane map.
4. The method according to any one of claims 1 to 3, wherein, The dense well pattern area needs to meet the following conditions: ①The well pattern control reserves of the target interval account for more than 90% of the total geological reserves of the target interval, and the target interval is developed by water injection; ②The reservoir connectivity of the production well is sensitive to the production dynamic data, and the injection-production flow line can accurately represent the injection-production correspondence of the target interval.
5. The method according to claim 4, wherein, The injection-production flow line effectiveness coefficient K in the step S4 refers to the effectiveness degree between the injection well and the production well.
6. The method according to claim 5, wherein, The K value is 0 to 1, when K=1, it indicates that the reservoir is connected between the injection well and the production well, when K=0, it indicates that the reservoir is not connected between the injection well and the production well, and when K is between 0 and 1, it indicates that the reservoir is partially connected, and the greater the K value, the better the connectivity.
7. The method according to claim 6, wherein, The step S42 comprises: ①Obtaining K-value plane grid data through numerical simulation based on injection-production data; ②The K-value plane map is obtained from the K-value plane grid data through a bilinear interpolation algorithm.
8. The method according to claim 7, wherein, In the step S43, a correlation coefficient a is introduced to represent the correlation between the K-value and the inversion data, and an a-value plane map β is established.
9. The method according to claim 8, wherein, The step S43 comprises: ①Calculating the correlation matrix of the K-value plane grid data and the plurality of equal-probability inversion plane grid data to obtain the correlation coefficient a value of the K-value and the equal-probability inversion data value of any grid point; ②The a-value plane map β is obtained from the a-value plane grid data through a bilinear interpolation algorithm.
10. The method according to claim 9, wherein, The step S44 comprises: ①The N equal-probability inversion plane grid maps are represented as Fn (n inversion plane maps F1 to Fn), the corrected inversion plane grid maps are represented as Wn (n inversion plane correction maps W1 to Wn), and the grid point Fn (Xa, Yb) is represented as Wn (Xa, Yb) after correction: Wn (Xa, Yb) = an (Xa, Yb) * Wn (Xa, Yb); ②Fusing the plurality of corrected equal-probability inversion plane maps to obtain the final reservoir description result.