A method and apparatus for tight reservoir sweet spot prediction based on sediment transport system theory

Abstract

The present application relates to a kind of compact reservoir dessert prediction method and device based on sediment transport system theory, comprising the following steps: constructing statistical model, including constructing coarse-grained sediment statistical parameter model and fine-grained sediment statistical parameter model according to different particle size range;Forward fitting correction statistical model, including correcting the coarse-grained sediment statistical parameter model and fine-grained sediment statistical parameter model based on sediment forward numerical simulation method;Correction chart is further corrected statistical model, including the formula correction and experience chart correction of the porosity and permeability value of coarse-grained sediment statistical parameter model and fine-grained sediment statistical parameter model;Instance analysis.The method uses the reservoir parameter gradation law of clastic particle transport process, quantifies the control action research of sediment model and diagenesis to dessert, determines the temporal and spatial variation law of offshore tight reservoir dessert boundary under different sedimentary conditions and different confidence intervals, improves the accuracy of offshore tight oil and gas field development.

Description

Technical Field

[0001] This invention relates to a method, apparatus, and medium for predicting sweet spots in tight reservoirs based on sediment transport system theory, belonging to the field of petroleum exploration. Background Technology

[0002] There are two main challenges in evaluating and predicting tight reservoirs at sea based on sparse well networks: first, the well-seismic response of sand bodies under strong compaction conditions is unclear, making it difficult to accurately characterize sand body boundaries; second, the amount of diagenetic analysis data is limited, making it difficult to predict sweet spots. Traditional methods for characterizing sand bodies based on a combination of well and seismic analysis have high ambiguity, making it difficult to improve the accuracy of sweet spot prediction for tight reservoirs at sea.

[0003] Sweet spot prediction is a key technology in the field of oil and gas geology. Traditional characterization methods are mainly based on geophysical responses and lack sedimentary model guidance, resulting in many ambiguities. In addition, there is a lack of differentiated and quantitative characterization methods for sweet spot prediction of reservoirs with different sedimentary backgrounds, which cannot provide sweet spot boundaries with different confidence intervals to guide the scientific and efficient development of offshore tight reservoir oil and gas resources. Therefore, it is particularly necessary to establish a quantitative sweet spot prediction method that integrates sedimentary models. Summary of the Invention

[0004] To address the aforementioned problems, the purpose of this invention is to provide a method for predicting sweet spots in tight reservoirs based on sediment transport system theory. Based on the progressive laws governing marine clastic particle transport, this method utilizes sediment transport system theory and starts from a statistical function model of particle transport. A cutoff function is used to divide the process into levels to determine the confidence interval of the sweet spot boundary. Simultaneously, an adjustment function is established by combining diagenetic stages and infill components to correct the sweet spot boundary.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] This invention provides a method for predicting sweet spots in tight reservoirs based on sediment transport system theory, comprising the following steps:

[0007] Construct statistical models, including constructing statistical parameter models for coarse-grained sedimentation and statistical parameter models for fine-grained sedimentation according to different grain size ranges;

[0008] The forward model fitting and correction statistical model includes setting simulation parameters with the same depositional background based on the forward modeling numerical simulation method, performing depositional simulation through the finite element mesh difference method, and using reservoir parameter data in the simulation results to correct the coarse-grained depositional statistical parameter model and the fine-grained depositional statistical parameter model.

[0009] A calibration chart was constructed to further calibrate the statistical model, including formula calibration and empirical chart calibration of the porosity and permeability values ​​of the coarse-grained sedimentation statistical parameter model and the fine-grained sedimentation statistical parameter model;

[0010] Based on the corrected statistical model, parameter input, model establishment, normal transformation, and confidence interval setting are performed to finally obtain the distribution range of sweet spots in marine tight reservoirs corresponding to different confidence intervals.

[0011] Furthermore, the construction of the statistical parameter model for the coarse-grained sedimentary system includes:

[0012] When the average particle size is greater than 2 mm, based on the theory of sediment transport systems, a measurement point m is set at a transport distance of m from the initial transport point m0. i m i =m0+i×m, establish m i Point-level granularity probability cumulative distribution function As shown in equation (1), its distribution conforms to the Pareto distribution:

[0013]

[0014] In the formula, Kp represents the measurement point m. i The minimum particle size is measured in mm, and α is the morphological index in the Pareto distribution, reflecting the amplitude of the cumulative probability curve.

[0015] Calculate the cumulative probability of granularity percentages x1% and x2% respectively. and Substituting into formula (1) yields the cumulative probability morphological index.

[0016]

[0017] Cumulative probability morphological parameters in the same sedimentary system and sediment transport distance m i The relationship is linear, and the slope τ can be solved using the least squares method:

[0018]

[0019] Combining formulas (1) to (3), and solving them together, we set the initial minimum particle size at the initial transport point m0 ​​as Kp0, then the average particle size... The unit is mm, referring to the transport distance in meters. i The calculation formula (4) is as follows:

[0020]

[0021] ω is the correction value for the fitting parameters, which is related to the sediment supply rate and topographic slope.

[0022] Furthermore, the construction of the statistical parameter model for the coarse-grained sedimentary system also includes:

[0023] Based on the detrital particle filling process, the proportion of interstitial material or matrix is ​​determined by the particle size. cumulative distribution function If the proportion of interstitial material or matrix at the initial transport point m0 ​​is set to C0, then m i The proportion of interstitial material or matrix at the location C i For example, calculation formula (5):

[0024]

[0025] Furthermore, the construction of the statistical parameter model for the fine-grained deposition system includes:

[0026] When the average particle size is greater than 2 mm, based on the theory of sediment transport systems, a measurement point m is set at a distance m from the initial transport point m0. i m i =m0+i×m, establish m i Point-level granularity probability cumulative distribution function Based on statistical experience, their distributions conform to the Weibull distribution and the log-normal distribution, respectively:

[0027] For sediments in fine-grained sedimentary systems that have traveled long distances and have a large depositional range, the cumulative statistical model of the Weibull distribution is applicable, as shown in equation (6):

[0028]

[0029] in Let be the average particle size, and β be the morphological index in the Weibull distribution, reflecting the amplitude of the cumulative probability curve. The cumulative probabilities of particle sizes at x1% and x2% are calculated respectively. and Substituting into formula (6) yields the morphological index.

[0030]

[0031] cumulative probability curve morphology parameters in the same sedimentary system and sediment transport distance m i The relationship is linear, and the slope τ can be solved using the least squares method:

[0032]

[0033] Combining (6) to (8), we solve the problem together. We set the initial average particle size at the initial transport point m0 ​​as M0, and consider the relationship between particle size and transport distance m. iThe calculation formula (9) and the calculation formula (10) for the proportion of interstitial material or matrix are as follows:

[0034]

[0035]

[0036] ω is the correction value for the fitting parameters, which is related to the sediment supply rate and topographic slope.

[0037] Furthermore, the construction of the statistical parameter model for the fine-grained deposition system includes:

[0038] For fine-grained sedimentary systems with short transport distances and limited depositional extents, the cumulative probability statistical model with a log-normal distribution is suitable.

[0039]

[0040] erfc is the residual error function, x* = ln(x), and σ* is the standard deviation calculated using the following formula:

[0041]

[0042] μ* is the mean, and its calculation formula is shown in equation (13):

[0043]

[0044] Its probability function can represent the shape of the cumulative probability statistical model:

[0045]

[0046] Within the same sedimentary system, it also exhibits linear characteristics, namely:

[0047]

[0048] τ can be obtained by fitting data at different granularities at different locations;

[0049] Combining (11) to (15), we solve the problem together. We set the initial average particle size at the initial transport point m0 ​​as M0. The particle size and transport distance mi are calculated using formula (16) and the proportion of interstitial material or matrix is ​​calculated using formula (17).

[0050]

[0051] C i =C0-τ·i·m-ω (17)

[0052] ω is the correction value for the fitting parameters, which is related to the sediment supply rate and topographic slope.

[0053] Furthermore, the sedimentation simulation was conducted using finite element software, including:

[0054] Boundary conditions were set according to the principle of constant Fr number, including terrain pattern, velocity inlet, pressure outlet, symmetrical top interface, and smooth wall. Initial condition parameters were set, and finite element differential numerical simulation was performed.

[0055] The sedimentation process is simulated by adjusting the hydrodynamic parameters of the model under the principle of controlling variables, and then conducting numerical simulation of sedimentation.

[0056] Using the KS test, the ω value was adjusted to meet the goodness-of-fit test.

[0057] Furthermore, the hydrodynamic parameters include topographic slope, simulation duration, water flow direction, sediment supply rate, sea level change curve, and critical angle for instability and landslide.

[0058] The terrain slope was obtained after interpreting the three-dimensional seismic data and then correcting for decompaction and deerosion.

[0059] The sediment supply rate is calculated based on the formation thickness and depositional age of a single well.

[0060] The direction of water flow and the critical angle for instability and collapse are set separately based on the sedimentary background.

[0061] Furthermore, the porosity and permeability values ​​of the coarse-grained sedimentary statistical parameter model and the fine-grained sedimentary statistical parameter model are corrected using formulas and empirical charts, specifically as follows:

[0062] The formula correction for the statistical parameter model of coarse-grained sedimentation is performed using correction formula (18):

[0063]

[0064] In the formula, OP represents the primary porosity, IGV represents the intergranular porosity, COPL represents the porosity reduction rate caused by compaction, and CEPL represents the porosity reduction rate caused by bonding.

[0065] The empirical chart correction is used to further combine formulas (4) and (5) to establish the porosity and transport distance m after compaction. i Quantitative relationship:

[0066]

[0067] In the formula, δ is an adjustment parameter that can be adjusted by fitting based on the drilling data;

[0068] For the statistical parameter model of fine-grained sedimentation, the correction formula (16) is:

[0069]

[0070] The empirical chart correction further combines formulas (9)~(10) and formulas (16)~(17) to establish the porosity and transport distance m after compaction. i Quantitative relationship:

[0071]

[0072] log-normal: φ i =δ×CEPL×(1-M) i )=δ×CEPL×(1-τ·i·m-ω) (22).

[0073] This invention also provides a tight reservoir sweet spot prediction device based on sediment transport system theory, comprising:

[0074] The first processing unit is used to construct statistical models, including constructing coarse-grained sedimentary statistical parameter models and fine-grained sedimentary statistical parameter models according to different grain size ranges.

[0075] The second processing unit is used for forward modeling fitting and correction of statistical models, including setting simulation parameters with the same depositional background based on the depositional forward modeling numerical simulation method, performing depositional simulation through the finite element mesh difference method, and using reservoir parameter data in the simulation results to correct the coarse-grained depositional statistical parameter model and the fine-grained depositional statistical parameter model.

[0076] The third processing unit is used to construct calibration charts to further calibrate statistical models, including formula calibration and empirical chart calibration of porosity and permeability values ​​for coarse-grained sedimentation statistical parameter models and fine-grained sedimentation statistical parameter models.

[0077] The fourth processing unit is used to input parameters, establish the model, perform normal transformation and set confidence intervals based on the corrected statistical model, and finally obtain the distribution range of sweet spots in marine tight reservoirs corresponding to different confidence intervals.

[0078] The present invention also provides a computer-readable storage medium storing computer instructions, which, when executed by a processor, implement the described method for predicting sweet spots in tight reservoirs based on sediment transport system theory.

[0079] The present invention has the following advantages due to the adoption of the above technical solutions:

[0080] By utilizing the variation law of reservoir parameters during the clastic particle transport process, the control effect of sedimentary model and diagenesis on sweet spots can be quantified, clarifying the spatiotemporal variation law of sweet spot boundaries in offshore tight reservoirs under different sedimentary conditions and confidence intervals, thereby improving the accuracy of offshore tight oil and gas field development. Attached Figure Description

[0081] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts. In the drawings:

[0082] Figure 1 This is a flowchart illustrating the workflow of a method for predicting sweet spots in marine tight reservoirs based on sediment transport system theory.

[0083] Figure 2 This is a schematic diagram showing the statistical model establishment process, calculation formulas, and corresponding statistical curve styles for two sedimentary systems (coarse-grained sedimentary system and fine-grained sedimentary system) based on the sediment transport system theory for predicting sweet spots in marine tight reservoirs.

[0084] Figure 3 This presents the forward modeling results and fitting method for predicting sweet spots in marine tight reservoirs based on sediment transport system theory.

[0085] Figure 4 This is an empirically corrected chart of the porosity-permeability relationship for a method of predicting sweet spots in marine tight reservoirs based on sediment transport system theory.

[0086] Figure 5 This is an example of a method for predicting sweet spots in marine tight reservoirs based on sediment transport system theory, which involves the correction of the coordinate system of the computational model and the distribution range of sweet spots in tight reservoirs based on different confidence zones.

[0087] Parameter descriptions in the attached diagram: Initial transport point m0 ​​(unit: km); Sediment transport distance m i (unit: km), Kp measurement point (m) i Minimum particle size (in mm); The average particle size (in mm); the proportion of interstitial material or matrix at the initial transport point m0 ​​is C0 (dimensionless); cumulative curve morphology parameters. (Dimensionless); Amplitude of the cumulative probability curve and sediment transport distance m i The slope of the fitted curve τ (dimensionless); the original porosity OP (dimensionless), the intergranular porosity IGV (dimensionless), the porosity reduction COPL caused by compaction (dimensionless), the porosity reduction CEPL caused by cementation (dimensionless), and the porosity correction parameter δ (dimensionless). Detailed Implementation

[0088] Exemplary embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the invention and to fully convey the scope of the invention to those skilled in the art.

[0089] This invention provides a method for predicting sweet spots in tight reservoirs based on sediment transport system theory, comprising the following steps: constructing a statistical model, including constructing coarse-grained sedimentary statistical parameter models and fine-grained sedimentary statistical parameter models according to different grain size ranges; forward fitting and correction of the statistical model, including setting simulation parameters with the same sedimentary background based on sedimentary forward numerical simulation method, performing sedimentary simulation through finite element mesh difference method, and correcting the coarse-grained sedimentary statistical parameter models and fine-grained sedimentary statistical parameter models using reservoir parameter data in the simulation results; constructing a correction chart to further correct the statistical model, including formula correction and empirical chart correction of the porosity and permeability values ​​of the coarse-grained sedimentary statistical parameter models and fine-grained sedimentary statistical parameter models; and case analysis, including parameter input, model establishment, normal transformation, and confidence interval setting based on the corrected statistical model, and finally obtaining the distribution range of sweet spots in marine tight reservoirs corresponding to different confidence intervals. The method utilizes the variation law of reservoir parameters during the clastic particle transport process to quantify the control effect of sedimentary mode and diagenesis on sweet spots, clarify the spatiotemporal variation law of sweet spot boundaries in offshore tight reservoirs under different sedimentary conditions and confidence intervals, and improve the accuracy of offshore tight oil and gas field development.

[0090] like Figure 1 As shown, embodiments of the present invention provide a method for predicting sweet spots in tight reservoirs based on sediment transport system theory, including the following steps:

[0091] S1. Constructing a statistical model

[0092] During the transport of clastic sediments, reservoir parameters (including grain size, interstitial material or matrix content, etc.) gradually decrease with increasing transport distance. However, the corresponding patterns differ for sedimentary systems with different grain size ranges and tectonic settings (topographic slope). Therefore, this invention classifies sedimentary systems into two types based on different grain size ranges: coarse-grained and fine-grained. ① According to the theory of sedimentary transport systems, the cumulative grain size probability curve within a single sampling point in a coarse-grained sedimentary system conforms to a Pareto distribution, and the transport distance is directly proportional to the morphological parameters of the cumulative probability distribution curve. ② According to the theory of sedimentary transport systems, the cumulative grain size probability curve within a single sampling point in a fine-grained sedimentary system conforms to a Weibull distribution or a Log-normal distribution, and the transport distance is directly proportional to the morphological parameters of the cumulative probability distribution curve. Based on this, quantitative calculation formulas for grain size, interstitial material or matrix content, and transport distance are established, see [link to relevant documentation]. Figure 2 .

[0093] Clastic tight reservoirs are classified into two types based on their average grain size range: coarse-grained sedimentary systems (including alluvial fans, fan deltas, braided rivers, etc.) and fine-grained sedimentary systems (meandering rivers, deltas, and their fine-grained sediments). A statistical calculation model is established based on the variation of reservoir parameters (including grain size, interstitial material or matrix content, etc.) with transport distance in modern sediments.

[0094] S1-1. Constructing a statistical parameter model for coarse-grained sedimentary reservoirs.

[0095] When the average particle size is greater than 2 mm, based on the theory of sediment transport systems, a measurement point m is set at a distance of m (in km) from the initial transport point m0. i (m i =m0 + i×m, sampling points are visible Figure 2 ), respectively establish m i Point-level granularity probability cumulative distribution function Based on statistical experience, its distribution conforms to the Pareto distribution:

[0096]

[0097] In the formula, Kp represents the measurement point m. i The minimum particle size is given in mm, and α is the morphological index in the Pareto distribution, reflecting the amplitude of the cumulative probability curve. The cumulative probabilities of particle sizes at x1% and x2% are calculated respectively. and Substituting into formula (1) yields the morphological index.

[0098]

[0099] Cumulative probability morphological parameters in the same sedimentary system and sediment transport distance m i The relationship is linear, and the slope τ can be solved using the least squares method:

[0100]

[0101] Combining formulas (1) to (3), and solving them together, we set the initial minimum particle size at the initial transport point m0 ​​as Kp0, then the average particle size... (Unit: mm) Regarding the transport distance (m) i The calculation formula (4) is as follows:

[0102]

[0103] ω is the correction value for the fitting parameters, which is related to the sediment supply rate and topographic slope.

[0104] Furthermore, based on the detrital particle filling process, the proportion of interstitial material or matrix is ​​determined by the particle size. cumulative distribution function If the proportion of interstitial material or matrix at the initial transport point m0 ​​is set to C0, then m i Formula (5) for calculating the proportion of interstitial material or matrix at the location:

[0105]

[0106] S1-2, Constructing a statistical parameter model for fine-grained sedimentary reservoirs

[0107] When the average particle size is greater than 2 mm, based on the theory of sediment transport systems, a measurement point m is set at a distance m from the initial transport point m0. i (m i =m0 + i×m, sampling points are visible Figure 2 ), respectively establish m i Point-level granularity probability cumulative distribution function Based on statistical experience, its distribution conforms to the Weibull distribution and the log-normal distribution.

[0108] S1-2-1. For fine-grained sedimentary systems with long transport distances and large depositional extents, such as fluvial and deltaic deposits, the cumulative statistical model of the Weibull distribution is suitable:

[0109]

[0110] in Let be the average particle size, and β be the morphological index in the Weibull distribution, reflecting the amplitude of the cumulative probability curve. The cumulative probabilities of particle sizes at x1% and x2% are calculated respectively. and Substituting into formula (6) yields the morphological index.

[0111]

[0112] cumulative probability curve morphology parameters in the same sedimentary system and sediment transport distance m i The relationship is linear, and the slope τ can be solved using the least squares method:

[0113]

[0114] Combining (6) to (8), we solve the problem together, setting the initial average particle size at the initial transport point m0 ​​as M0. Regarding the relationship between particle size and transport distance m... i The calculation formula (9) and the calculation formula (10) for the proportion of interstitial material or matrix are as follows:

[0115]

[0116]

[0117] ω is the correction value for the fitting parameters, which is related to the sediment supply rate and topographic slope.

[0118] S1-2-2. For fine-grained sedimentary systems with short transport distances and limited depositional ranges, such as deep-water turbidity currents, the cumulative probability statistical model with a log-normal distribution is suitable:

[0119]

[0120] erfc is the residual error function, x* = ln(x), and σ* is the standard deviation calculated using the following formula:

[0121]

[0122] μ* is the mean, and its calculation formula is:

[0123]

[0124] Its probability function can represent the shape of the cumulative probability statistical model:

[0125]

[0126] Within the same sedimentary system, it also exhibits linear characteristics, namely:

[0127]

[0128] τ can be obtained by fitting data at different granularities at different locations.

[0129] Combining (11) to (15), we solve the problem together, setting the initial average particle size at the initial transport point m0 ​​as M0. Regarding the relationship between particle size and transport distance m... i The calculation formula (16) and the calculation formula (17) for the proportion of interstitial material or matrix are as follows:

[0130]

[0131] C i =C0-τ·i·m-ω (17)

[0132] ω is the correction value for the fitting parameters, which is related to the sediment supply rate and topographic slope.

[0133] S2, Forward Fit Correction Statistical Model

[0134] Under different tectonic and sediment supply backgrounds, the fit of the KS test varies greatly, so it is necessary to combine the sedimentary forward modeling to correct the quantitative statistical model.

[0135] The statistical parameter models (4)~(5), (9)~(10) and (16)~(17) have correction values ​​for the fitting parameters. This invention uses a sedimentary forward modeling method for fitting. The simulation results under the same sedimentary background are used to fit the ω value.

[0136] Forward modeling selects numerical simulation methods; the simulation methods and results are described in [link to documentation]. Figure 3 The forward modeling conditions were set in accordance with the actual research area, and the sedimentary simulation parameters involved are shown in Table 1. In the simulation process, the terrain slope can be obtained by interpreting the 3D seismic data and then correcting for decompaction and deerosion; the sediment supply rate can be calculated based on the single-well formation thickness and sedimentary age; the water flow direction and the critical angle for instability and collapse are set separately according to the sedimentary background.

[0137] Table 1. Parameters for Numerical Simulation of Sedimentation

[0138]

[0139] Deposition simulations can be performed using finite element method (FEM) software, including:

[0140] ① Boundary condition setting: Based on the principle of constant Fr number, boundary conditions such as terrain pattern, velocity inlet, pressure outlet, symmetrical top interface, and smooth wall are set, and initial condition parameters are set to perform finite element differential numerical simulation.

[0141] ② Sedimentation process simulation: Under the principle of controlling variables, the hydrodynamic parameters of the model (parameter types include topographic slope, simulation duration, water flow direction, sediment supply rate, sea level change curve, and critical angle of instability and collapse) are adjusted to conduct sedimentation numerical simulation.

[0142] ③ Use the KS test and adjust the ω value to make it meet the goodness-of-fit test.

[0143] S3. Construct a calibration chart to further calibrate the statistical model.

[0144] Based on the compaction origin, coarse-grained sedimentary systems and fine-grained sedimentary systems were sequentially calibrated using plates ( Figure 4 Formulas (18) and (20) determine the degree of compaction and cementation, and substitute them into formulas (19), (21), and (22) respectively to calculate the porosity and permeability parameters. Among them, the porosity reduction rate (COPL) caused by compaction and the porosity reduction rate (CEPL) caused by cementation can be obtained from the observation and statistics of thin sections from the core well. The permeability is obtained by solving the porosity and permeability fitting formula in the work area.

[0145] ① In coarse-grained sedimentary systems, the densification of tight reservoirs is mostly caused by the reduction of porosity due to cementation after sedimentary particles are filled with matrix, thus leading to densification. The correction formula (18) (cited from Houseknecht, 1987) is:

[0146]

[0147] Using primary porosity (OP) and intergranular porosity (IGV), combined with formulas (4) and (5), the porosity after compaction and the transport distance m are established. i Quantitative relationship:

[0148]

[0149] δ is an adjustment parameter that can be adjusted based on the data from the drilled wells.

[0150] ② In fine-grained sedimentary systems, the densification of tight reservoirs is mostly caused by the reduction of porosity due to the compaction of fine-grained sedimentary particles, thus leading to densification. The correction formula (16) (cited from Houseknecht, 1987) is:

[0151]

[0152] By combining formulas (9) to (10) and formulas (16) to (17), the porosity after compaction and the transport distance m are established. i Quantitative relationship:

[0153]

[0154] log-normal: φ i =δ×CEPL×(1-M) i )=δ×CEPL×(1-τ·i·m-ω) (22).

[0155] S4. Case analysis, including parameter input, model building, normal variation and confidence interval setting based on the corrected statistical model, and finally obtaining the distribution range of sweet spots of marine tight reservoirs corresponding to different confidence intervals.

[0156] Specific application examples:

[0157] Sweet spot prediction for tight reservoirs was conducted based on data from two wells, A and B. Figure 5 The abscissa of the above calculation model is subjected to normal transformation; then its confidence curve is determined, and the calculation parameters are input. The input parameters for the calculation are shown in Table 2.

[0158] Table 2 Reservoir Evaluation Parameters

[0159]

[0160] Based on the current lower limit of offshore tight reservoir development (permeability 5mD), sweet spot boundary locations with confidence levels of 50% and 80% were plotted. Figure 5 The method for determining boundary stage values ​​using confidence intervals is described in [link to documentation]. Figure 5 Taking layered edge-water oil (gas) reservoirs as an example, this method can increase the proportion of recoverable controlled reserves by 15%.

[0161] In summary, this invention provides a method for predicting sweet spots in offshore tight reservoirs based on sediment transport system theory. It can predict sweet spots in offshore tight reservoirs using limited single-well data (including grain size and thin sections) based on sediment transport system theory. This invention helps improve the accuracy of sweet spot boundary prediction, reduce the risk of recoverable reserves, and enhance the reliability of offshore tight gas development.

[0162] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for predicting sweet spots in tight reservoirs based on sediment transport system theory, characterized in that, Including the following steps: Construct statistical models, including constructing statistical parameter models for coarse-grained sedimentation and statistical parameter models for fine-grained sedimentation according to different grain size ranges; The forward model fitting and correction statistical model includes setting simulation parameters with the same depositional background based on the forward modeling numerical simulation method, performing depositional simulation through the finite element mesh difference method, and using reservoir parameter data in the simulation results to correct the coarse-grained depositional statistical parameter model and the fine-grained depositional statistical parameter model. A calibration chart was constructed to further calibrate the statistical model, including formula calibration and empirical chart calibration of the porosity and permeability values ​​for both coarse-grained and fine-grained sedimentary statistical parameter models. Specifically: The formula correction for the statistical parameter model of coarse-grained sedimentation is performed using the correction formula (18): （18） In the formula, OP represents the primary porosity, IGV represents the intergranular porosity, and COPL represents the porosity reduction rate caused by compaction. The empirical chart correction is used to further combine formulas (4) and (5) to establish the porosity and transport distance m after compaction. i Quantitative relationship: （19） δ is an adjustment parameter, which is adjusted based on the drilling data. The expressions for formulas (4) and (5) are as follows: （4） （5） In the formula, It is a cumulative probability morphological index; The sediment transport distance in meters (m) i Minimum particle size; p0 is the initial minimum particle size at the initial transport point m0; τ is the amplitude of the cumulative probability curve. , and sediment transport distance m i The slope of the fitted curve; ω is the correction factor for the fitted parameters, which is related to the sediment supply rate and the topographic slope; The sediment transport distance in meters (m) i The proportion of interstitial material or matrix at the location; The percentage of interstitial material or matrix at the initial transport point m0; For the statistical parameter model of fine-grained sedimentation, the correction formula (20) is: （20） The empirical chart correction further combines formulas (9)~(10) and formulas (16)~(17) to establish the porosity and transport distance m after compaction. i Quantitative relationship: Weibull: (21) log-normal: (22) The expressions for formulas (9) to (10) and formulas (16) to (17) are as follows: （9） （10） （16） （17） In the formula, The cumulative curve morphology parameters are: CEPL is the porosity reduction rate caused by cementation; M0 is the initial average particle size at the initial transport point m0. Based on the corrected statistical model, parameter input, model establishment, normal transformation, and confidence interval setting are performed to finally obtain the distribution range of sweet spots in marine tight reservoirs corresponding to different confidence intervals.

2. The method for predicting sweet spots in tight reservoirs based on sediment transport system theory according to claim 1, characterized in that, The construction of a statistical parameter model for coarse-grained sedimentation includes: When the average particle size is greater than 2 mm, based on the theory of sediment transport systems, a measurement point m is set at a transport distance of m from the initial transport point m0. i m i= m0+i×m, establish m i Point-level granularity probability cumulative distribution function As shown in equation (1), its distribution conforms to the Pareto distribution: （1） In the formula, Kp represents the measurement point m. i The minimum particle size is measured in mm, and α is the morphological index in the Pareto distribution, reflecting the amplitude of the cumulative probability curve. Calculate the cumulative probability of granularity percentages x1% and x2% respectively. and Substituting into formula (1), we obtain the cumulative probability morphological index. : （2） Cumulative probability morphological parameters in the same sedimentary system and sediment transport distance m i The relationship is linear, and the slope τ can be solved using the least squares method: （3） Combining formulas (1) to (3), and solving them together, the initial minimum particle size at the initial transport point m0 ​​is set as follows: p0, then the average particle size The unit is mm, and the distance is in meters (m). i The calculation formula (4): （4） ω is the correction value for the fitting parameters, which is related to the sediment supply rate and topographic slope.

3. The method for predicting sweet spots in tight reservoirs based on sediment transport system theory according to claim 2, characterized in that, The construction of statistical parameter models for coarse-grained sedimentation also includes: Based on the detrital particle filling process, the proportion of interstitial material or matrix is ​​given by particle size x < cumulative distribution function If the proportion of interstitial material or matrix at the initial transport point m0 ​​is set to C0, then m i The proportion of interstitial material or matrix at the location As shown in calculation formula (5): （5）。 4. The method for predicting sweet spots in tight reservoirs based on sediment transport system theory according to claim 3, characterized in that, The construction of a statistical parameter model for fine-grained sedimentation includes: When the average particle size is less than 2 mm, based on the theory of sediment transport systems, a measurement point m is set at a distance m from the initial transport point m0. i m i= m0+i×m, establish m i Point-level granularity probability cumulative distribution function Based on statistical experience, their distributions conform to the Weibull distribution and the log-normal distribution, respectively: For sediments in fine-grained sedimentary systems that have traveled long distances and have a large depositional range, the cumulative statistical model of the Weibull distribution is applicable, as shown in equation (6): （6） in Let be the average particle size, and β be the morphological index in the Weibull distribution, reflecting the amplitude of the cumulative probability curve. The cumulative probabilities of particle sizes at x1% and x2% are calculated respectively. and Substituting into formula (6) yields the morphology index. : （7） cumulative probability curve morphology parameters in the same sedimentary system and sediment transport distance m i The relationship is linear, and the slope τ can be solved using the least squares method: （8） Combining (6) to (8), we solve the problem together, setting the initial average particle size at the initial transport point m0 ​​as M0, and considering the relationship between particle size and transport distance m. i The calculation formula (9) and the calculation formula (10) for the proportion of interstitial material or matrix are as follows: （9） （10） ω is the correction value for the fitting parameters, which is related to the sediment supply rate and topographic slope.

5. The method for predicting sweet spots in tight reservoirs based on sediment transport system theory according to claim 3, characterized in that, The construction of a statistical parameter model for fine-grained sedimentation includes: For fine-grained sedimentary systems with short transport distances and limited depositional extents, the cumulative probability statistical model with a log-normal distribution is suitable. （11） erfc is the residual error function. =ln(x), The formula for calculating the standard deviation is: （12） The average is calculated using the formula shown in equation (13): （13） Its probability function can represent the shape of the cumulative probability statistical model: （14） Within the same sedimentary system, it also exhibits linear characteristics, namely: （15） Obtained by fitting granular data at different locations; Combining (11) to (15), we solve the problem together. We set the initial average particle size at the initial transport point m0 ​​as M0. The particle size and transport distance mi are calculated using formula (16) and the proportion of interstitial material or matrix is ​​calculated using formula (17). （16） （17） ω is the correction value for the fitting parameters, which is related to the sediment supply rate and topographic slope.

6. The method for predicting sweet spots in tight reservoirs based on sediment transport system theory according to claim 2, characterized in that, The deposition simulation was conducted using finite element method software, including: Boundary conditions were set according to the principle of constant Fr number, including terrain pattern, velocity inlet, pressure outlet, symmetrical top interface, and smooth wall. Initial condition parameters were set, and finite element differential numerical simulation was performed. The sedimentation process is simulated by adjusting the hydrodynamic parameters of the model under the principle of controlling variables, and then conducting numerical simulation of sedimentation. Using the KS test, the ω value was adjusted to meet the goodness-of-fit test.

7. The method for predicting sweet spots in tight reservoirs based on sediment transport system theory according to claim 6, characterized in that, The hydrodynamic parameters include topographic slope, simulation duration, water flow direction, sediment supply rate, sea level change curve, and critical angle for instability and landslide. The terrain slope was obtained after interpreting the three-dimensional seismic data and then correcting for decompaction and deerosion. The sediment supply rate is calculated based on the formation thickness and depositional age of a single well. The direction of water flow and the critical angle for instability and collapse are set separately based on the sedimentary background.

8. A device for predicting sweet spots in tight reservoirs based on sediment transport system theory, used to implement the method for predicting sweet spots in tight reservoirs based on sediment transport system theory as described in any one of claims 1 to 7, characterized in that, include: The first processing unit is used to construct statistical models, including constructing coarse-grained sedimentary statistical parameter models and fine-grained sedimentary statistical parameter models according to different grain size ranges. The second processing unit is used for forward modeling fitting and correction of statistical models, including setting simulation parameters with the same depositional background based on the depositional forward modeling numerical simulation method, performing depositional simulation through the finite element mesh difference method, and using reservoir parameter data in the simulation results to correct the coarse-grained depositional statistical parameter model and the fine-grained depositional statistical parameter model. The third processing unit is used to construct calibration charts to further calibrate statistical models, including formula calibration and empirical chart calibration of porosity and permeability values ​​for coarse-grained sedimentation statistical parameter models and fine-grained sedimentation statistical parameter models. The fourth processing unit is used to input parameters, establish the model, perform normal transformation and set confidence intervals based on the corrected statistical model, and finally obtain the distribution range of sweet spots in marine tight reservoirs corresponding to different confidence intervals.

9. A computer-readable storage medium storing computer instructions, said computer instructions being executed by a processor to implement the tight reservoir sweet spot prediction method based on sediment transport system theory as described in any one of claims 1 to 7.

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