Oil-gas-water interface prediction method and system based on time-lapse earthquake

Abstract

The invention discloses an oil-gas-water interface prediction method and system based on a time-lapse earthquake, and belongs to the technical field of development-earthquake processing interpretation. The prediction method comprises the following steps: constructing a three-dimensional porosity model; establishing an original water saturation model, and determining an original oil-gas-water interface height; determining rock mechanical parameters; based on the three-dimensional porosity model, the original water saturation model and the rock mechanical parameters in combination with a time-lapse seismic amplitude difference formula, calculating a residual value of a model time-lapse seismic amplitude difference and an observation time-lapse seismic amplitude difference, and searching for an optimal oil-gas-water interface height change and a porosity value which enable the residual value to be minimum; according to the optimal oil-gas-water interface height change, the porosity value and the original oil-gas-water interface height, current water saturation change is predicted, and remaining oil-gas distribution is obtained. By inverting the change of the height of the oil-gas-water interface before and after the time-lapse earthquake, the water saturation change condition of the target horizon can be obtained at the same time, and the problems that the nonlinear inversion data calculation amount is large and the result is not convergent are effectively solved.

Description

Technical Field

[0001] This invention belongs to the field of development-seismic processing and interpretation technology, specifically relating to a method and system for predicting oil-gas-water interfaces based on time-shifted seismic data. Background Technology

[0002] In the mid-to-late stages of oil and gas field development, it is necessary to identify the remaining oil-rich areas after multiple waterflooding operations in order to proactively adjust the development plan for the next stage. Conventional methods for predicting remaining oil use historical data on single-well production and pressure to update pre-established reservoir attribute models, thereby predicting the distribution of remaining oil.

[0003] Time-shifted seismic monitoring, also known as 4D seismic monitoring, uses two or more 3D seismic data volumes acquired at different times to evaluate changes in reservoir fluid saturation and pore pressure. The physical basis of 4D seismic monitoring is that as oil and gas fields are developed, fluid saturation and pore pressure gradually decrease, resulting in observable differences in seismic amplitude.

[0004] In existing technologies, Landro (2001) introduced a method for predicting remaining oil based on pre-stack time-shift seismic inversion. This method predicts the changes in reservoir water saturation and pore pressure after development by inverting the amplitude difference between two three-dimensional seismic events. Dadashpour et al. (2007) integrated the Hertz-mindlin pore model based on Landro (2001) and used the Gauss-Newton method for iterative inversion to obtain the optimal water saturation model after development. Buland and Ouair (2006) introduced a method for predicting remaining oil based on multivariate Bayesian statistical inversion. Their method establishes attribute probability distribution models and similarity models from well logging curves to predict the distribution model of demand attributes in seismic data. Grana and Mukerji (2015) established similarity functions between reservoir petrophysical parameters and remaining oil distribution and used a complete Bayesian inversion method to jointly predict reservoir petrophysical parameters and remaining oil distribution. Zhong et al. (2020) used CycleGAN (Recurrent Generative Adversarial Network) analysis to predict the distribution of remaining oil in reservoirs. This method differs from traditional Bayesian inversion in that CycleGAN automatically learns and generates a distribution model of demand attributes in seismic data. The methods used by Landro (2001) and Dadashpour et al. (2007) to predict remaining oil are collectively referred to as deterministic seismic inversion. This method has the advantage of being computationally fast and usually converges to a deterministic solution. However, due to the constraint of the longitudinal resolution of seismic data, it often cannot predict the distribution of remaining oil in reservoirs with a thickness less than 1 / 4 of a seismic wavelength. Methods using Bayesian inference frameworks (Buland and Ouair (2006), Grana and Mukerji (2015), and Zhong (2022)) are collectively referred to as stochastic inversion. These inversion methods have the advantage of providing richer high-frequency information and being able to quantitatively describe the uncertainty of the inversion parameters (remaining oil distribution). However, these methods often require a large amount of computation time, and the algorithm often fails to converge without a guaranteed model similarity function.

[0005] Finding a new method that can effectively avoid the problem of large computational load in stochastic inversion is an important technical problem in the field of remaining oil prediction in current oil and gas field development. Summary of the Invention

[0006] In order to overcome the shortcomings of the prior art, the present invention aims to provide a method and system for predicting oil-gas-water interface based on time-shifted earthquakes. By inverting the change in the height of the oil-gas-water interface before and after the time-shifted earthquake, the water saturation change of the target layer can be obtained at the same time, effectively solving the problems of large computational load and non-convergence of nonlinear inversion data.

[0007] To achieve the above objectives, the present invention employs the following technical solution:

[0008] This invention provides a method for predicting oil, gas, and water interfaces based on time-shifted seismic data, comprising the following steps:

[0009] Construct a three-dimensional porosity model; establish an original water saturation model to determine the original oil-gas-water interface height; determine the rock mechanical parameters;

[0010] Based on the three-dimensional porosity model, the original water saturation model, and rock mechanics parameters combined with the time-lapse earthquake amplitude difference formula, the residual values ​​of the model time-lapse earthquake amplitude difference and the observed time-lapse earthquake amplitude difference are calculated, and the optimal oil-gas-water interface height change and porosity value that minimize the residual value are searched.

[0011] The remaining oil and gas distribution is obtained by predicting the current water saturation change based on the optimal oil-gas-water interface height change and porosity value, as well as the original oil-gas-water interface height.

[0012] In one embodiment, the process of constructing the three-dimensional porosity model is as follows:

[0013] Seismic multi-attribute analysis was used to train well logging porosity data and seismic data, and a set of optimal seismic attributes was selected to predict the three-dimensional porosity model, thus completing the establishment of the three-dimensional porosity model.

[0014] In one embodiment, the process of establishing the original water saturation model is as follows:

[0015] The original water saturation model was obtained by using a three-dimensional porosity model and the relationship between the original water saturation, porosity, and oil-gas-water interface of the reservoir.

[0016] In one embodiment, the relationship between the initial water saturation, porosity, and oil-gas-water interface of the reservoir is as follows:

[0017]

[0018] Among them, S w It is the water saturation level. It is porosity, H is the height of the reservoir from the interface between the two fluids, A and b are empirical coefficients, and H b It is the original oil-gas-water interface height.

[0019] In one embodiment, the empirical coefficients A and b in the relationship between the original water saturation, porosity, and oil-gas-water interface of the reservoir are determined by cross-analysis of the water saturation and porosity logging curves.

[0020] In one embodiment, the process of determining the rock mechanical parameters is as follows:

[0021] Pressure-directed Gassmann equation simulations were performed to calculate rock mechanical parameters under different water saturation levels.

[0022] By combining changes in water saturation with rock mechanical parameters, regression analysis was performed to determine the rock mechanical parameters.

[0023] In one embodiment, the expression for the time-shifted seismic amplitude difference formula is as follows:

[0024]

[0025] Where ΔH,φ,H b These represent the height of the oil-gas-water interface, porosity, and the original oil-gas-water interface height, respectively. m1, m2, and m3 are rock mechanics parameters, and ρ... w , ρ ma and Δρ g These represent formation water density, hydrocarbon density under monitored earthquake, original pore fluid density, reservoir framework density, and density difference before and after fluid development, respectively; Δs is the time-lapsed earthquake amplitude difference, ΔS w It is the degree of change in water saturation; It represents the water saturation at the baseline earthquake.

[0026] In one embodiment, the formula for the residual values ​​of the time-shifted seismic amplitude difference in the calculation model and the observed time-shifted seismic amplitude difference is as follows:

[0027] F(ΔH)=‖Δs r -Δs m ||2

[0028] Where, Δs m It is the time-shifted earthquake amplitude difference in the model, Δs r F(ΔH) is the observed time-lapsed earthquake amplitude difference, and F(ΔH) is the model time-lapsed earthquake amplitude difference Δs. m and the difference in earthquake amplitude Δs over time observation r The residual value.

[0029] In one embodiment, the search for the optimal oil-gas-water interface height and porosity value that minimizes the residual value is performed using the Monto Carlo method.

[0030] The present invention also provides an oil-gas-water interface prediction system based on time-shifted seismic data, including a three-dimensional porosity construction module, an original water saturation and interface height determination module, a rock mechanics parameter determination module, an optimal oil-gas-water interface height change and porosity value determination module, and a prediction module.

[0031] The 3D porosity construction module is used to build 3D porosity models.

[0032] The module for determining the initial water saturation and interface height is used to establish an initial water saturation model and determine the initial oil-gas-water interface height.

[0033] The rock mechanics parameter determination module is used to determine rock mechanics parameters;

[0034] The module for determining the optimal oil-gas-water interface height variation and porosity value is based on a three-dimensional porosity model, the original water saturation model, and rock mechanics parameters combined with the time-lapse earthquake amplitude difference formula. It calculates the residual values ​​of the time-lapse earthquake amplitude difference in the model and the time-lapse earthquake amplitude difference in the observation, and searches for the optimal oil-gas-water interface height variation and porosity value that minimizes the residual values.

[0035] The prediction module is used to predict the current water saturation change and obtain the remaining oil and gas distribution based on the optimal oil-gas-water interface height change, porosity value, and original oil-gas-water interface height.

[0036] Compared with the prior art, the present invention has the following beneficial effects:

[0037] This invention provides a method for predicting the oil-gas-water interface based on time-lapse seismic data. Traditional nonlinear inversion methods often suffer from high computational cost, non-convergence, and susceptibility to local minima. This invention optimizes the search process for oil-gas-water interface height changes and porosity by constructing a three-dimensional porosity model and using the time-lapse seismic amplitude difference formula, effectively reducing computational cost. Under the constraints of actual reservoir geological conditions, it avoids non-convergence and local minima to the greatest extent possible. Through the analysis of time-lapse seismic data, changes in the oil-gas-water interface can be captured more precisely. Attached Figure Description

[0038] Figure 1 This is a schematic diagram of the process of a time-shifted seismic-based oil-gas-water interface prediction method provided in an embodiment of the present invention;

[0039] Figure 2 This is a diagram of the original wedge-shaped water saturation model used for testing the algorithm in one embodiment of the present invention;

[0040] Figure 3 Figure (a) is a water saturation profile obtained by inversion using the method of the present invention in one embodiment of the present invention, and Figure (b) is a detail view;

[0041] Figure 4 This is a specific implementation diagram of the oil-gas-water interface prediction method based on time-shifted seismic data in one embodiment of the present invention;

[0042] Figure 5 This is a cross-sectional diagram of rock mechanical parameters with water saturation under different pressures in block A according to an embodiment of the present invention;

[0043] Figure 6In one embodiment of the present invention, a three-dimensional porosity model of block A is established through machine learning multi-attribute seismic analysis;

[0044] Figure 7 Figure (a) shows the water saturation model of Block A established by combining water saturation logging curves, porosity logging curves, and a three-dimensional porosity model in one embodiment of the present invention; Figure (b) shows the corresponding three-dimensional porosity model.

[0045] Figure 8 Figure (a) is an original water saturation profile of wells #1 and #2 in Block A according to an embodiment of the present invention; Figure (b) is a current water saturation profile of wells #1 and #2 in Block A.

[0046] Figure 9 Figure (a) is a diagram of the original gas-water interface height in Block A in one embodiment of the present invention; Figure (b) is a diagram of the predicted current gas-water interface height in Block A.

[0047] Figure 10 Figure (a) is a profile of the original water saturation of well #1 in one embodiment of the present invention; Figure (b) is a profile of the current water saturation of well #1.

[0048] Figure 11 This is a water saturation logging profile of well #1 in one embodiment of the present invention. Detailed Implementation

[0049] To enable those skilled in the art to better understand the present invention, 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. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0050] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0051] likeFigure 1 As shown, an embodiment of the present invention provides a method for predicting the oil-gas-water interface based on time-shifted seismic data, comprising the following steps:

[0052] S1: Construct a three-dimensional porosity model; establish an original water saturation model; determine the original oil-gas-water interface height; determine rock mechanical parameters;

[0053] S2: Based on the three-dimensional porosity model, the original water saturation model, and rock mechanics parameters combined with the time-lapse earthquake amplitude difference formula, calculate the residual values ​​of the model time-lapse earthquake amplitude difference and the observed time-lapse earthquake amplitude difference, and search for the optimal oil-gas-water interface height change and porosity value that minimize the residual values;

[0054] S3: Based on the optimal oil-gas-water interface height change and porosity value, and the original oil-gas-water interface height, predict the current water saturation change to obtain the remaining oil and gas distribution.

[0055] Compared with traditional nonlinear inversion, this invention firstly uses multi-attribute analysis to establish an initial three-dimensional porosity model, which reduces the uncertainty of nonlinear inversion to a certain extent and significantly reduces the computing time requirement. In addition, this invention directly inverts the oil-gas-water interface and porosity, which are important parameters for oil and gas reservoir numerical simulation and can improve the accuracy of reservoir numerical modeling.

[0056] Specifically, in S1, the process of constructing the three-dimensional porosity model is as follows:

[0057] Seismic multi-attribute analysis was used to train well logging porosity data and seismic data, and a set of optimal seismic attributes was selected to predict the three-dimensional porosity model, thus completing the establishment of the three-dimensional porosity model.

[0058] Seismic multi-attribute analysis can correlate various seismic attributes with well logging porosity data to find the combination of seismic attributes that best reflects porosity changes. This multi-attribute analysis comprehensively considers multiple characteristics of seismic data, improving the accuracy and reliability of porosity prediction. The constructed three-dimensional porosity model not only provides the distribution of porosity in a two-dimensional plane but also considers its continuity in three-dimensional space. This helps to gain a more comprehensive understanding of reservoir porosity characteristics, providing important basis for subsequent oil and gas resource assessment and development strategies. In complex geological structures, porosity distribution often exhibits high heterogeneity. By constructing a three-dimensional porosity model, the changes in porosity in different geological structures can be more intuitively displayed, providing more detailed information for geological interpretation and oil and gas resource assessment. Using seismic multi-attribute analysis to construct a three-dimensional porosity model can automate the processing of large amounts of seismic and well logging data, improving data processing efficiency. Furthermore, this method has high repeatability, facilitating similar porosity predictions at different time periods or in different blocks.

[0059] The process of establishing the original water saturation model is as follows:

[0060] The original water saturation model was obtained by using a three-dimensional porosity model and the relationship between the original water saturation, porosity, and oil-gas-water interface of the reservoir.

[0061] The relationship between the initial water saturation, porosity, and oil-gas-water interface of the above reservoir is as follows:

[0062]

[0063] Where S w It is the water saturation level. H is porosity, H is the height of the reservoir from the interface between the two fluids, and A and b are empirical coefficients determined by cross-analysis of water saturation and porosity logging curves. b It is the original oil-gas-water interface height.

[0064] The original water saturation model is calculated using a three-dimensional porosity model and the relationship between the original reservoir water saturation, porosity, and the oil-gas-water interface. This method comprehensively considers the influence of porosity and reservoir distance (the height of the fluid interface) on water saturation. This method is more accurate than using well logging or seismic data alone because it combines information from multiple data sources, resulting in a water saturation model that more closely reflects real geological conditions. The three-dimensional porosity model provides a continuous distribution of porosity in three-dimensional space, and the water saturation model is calculated based on this continuous porosity distribution. Therefore, the established original water saturation model also has good spatial continuity and can more realistically reflect the distribution of water saturation in the reservoir. The established original water saturation model provides fundamental data for subsequent analysis and applications. In complex geological structures, the distribution of water saturation often exhibits high heterogeneity. By establishing a three-dimensional original water saturation model, the variation of water saturation in different geological structures can be more intuitively displayed, providing more detailed information for geological interpretation and oil and gas resource assessment.

[0065] The process of determining rock mechanical parameters is as follows:

[0066] Pressure-directed Gassmann equation simulations were performed to calculate rock mechanical parameters under different water saturation levels.

[0067] By combining changes in water saturation with rock mechanical parameters, regression analysis was performed to determine the rock mechanical parameters.

[0068] By performing pressure-directed Gassmann equation simulations, rock mechanical parameters under different reservoir pressures can be calculated. This method considers the influence of water saturation on rock mechanical properties under varying reservoir pressures, thus reflecting the actual situation more accurately. Furthermore, by cross-referencing water saturation variations with rock mechanical parameters and performing regression analysis, errors can be further reduced, improving parameter accuracy. Rock mechanical parameters determined through this method more accurately reflect the actual reservoir conditions, thereby enhancing the applicability of relevant models. This helps to more accurately assess the reservoir's exploitation potential and formulate more rational exploitation strategies.

[0069] The expression for the time-shifted earthquake amplitude difference formula is as follows:

[0070]

[0071] Where ΔH,φ,H b These represent the height of the oil-gas-water interface, porosity, and the original oil-gas-water interface height, respectively. m1, m2, and m3 are rock mechanics parameters, and ρ... w , ρ ma and Δρ g These represent formation water density, hydrocarbon density under monitored earthquake, original pore fluid density, reservoir framework density, and density difference before and after fluid development, respectively; Δs is the time-lapsed earthquake amplitude difference, ΔS w It is the degree of change in water saturation; It represents the water saturation at the baseline earthquake.

[0072] The formulas for calculating the residual values ​​of the time-lapsed earthquake amplitude difference in the model and the time-lapsed earthquake amplitude difference in the observation are as follows:

[0073] F(ΔH)=‖Δs r -Δs m ||2

[0074] Where, Δs m It is the time-shifted earthquake amplitude difference in the model, Δs r F(ΔH) is the observed time-lapsed earthquake amplitude difference, and F(ΔH) is the model time-lapsed earthquake amplitude difference Δs. m and the difference in earthquake amplitude Δs over time observation r The residual value.

[0075] By using the Monto Carlo search to obtain the oil-gas-water interface height variation value and porosity that minimizes the residual, the advantage of the Monto Carlo search method is that it does not require the calculation of analytical solutions to complex nonlinear equations. At the same time, this invention uses a porosity model predicted by multiple attribute parameters as the initial model, which greatly reduces the time required for calculation.

[0076] This embodiment provides a method for predicting oil-gas-water interfaces based on time-shifted seismic data.

[0077] The specific steps described above are as follows:

[0078] Step 1: Determine the rock mechanical parameters, perform pressure-directed Gassmann equation simulation, calculate the rock mechanical parameters under different water saturation, combine the water saturation changes and rock mechanical parameters, and obtain the final rock mechanical parameters m1, m2, m3 through regression analysis.

[0079] Step 2, calculation of porosity model: use seismic multi-attribute analysis to train well logging porosity and seismic data to obtain a set of optimal seismic attributes to predict the three-dimensional porosity model.

[0080] Step 3, Calculation of the original water saturation model. According to the formula of Cuddy et al. (1993), the relationship between the original water saturation, porosity, and oil-gas-water interface of the reservoir can be written as:

[0081]

[0082] Among them, S w It is the water saturation level. It is porosity, H is the height of the reservoir from the interface between the two fluids, A and b are empirical coefficients obtained from cross-logging curves, and H b It is the original oil-gas-water interface height.

[0083] Specifically, the empirical coefficients in formula (2) are first determined by combining the water saturation and porosity logging curves, and then the obtained three-dimensional porosity model and formula (2) are used to calculate the three-dimensional original water saturation model.

[0084] Step 4: Search for the optimal ΔH and φ using the Monto Carlo method. Substitute the porosity model, water saturation model, and rock mechanics parameters obtained in steps 1, 2, and 3 into the time-shifted earthquake amplitude difference formula (1).

[0085] Among them, the time-shifted earthquake amplitude difference Δs in the model m and the difference in earthquake amplitude ΔS over time observation r The residual value can be written as a function of ΔH:

[0086] F(ΔH)=‖Δs r -Δs m ‖twenty three)

[0087] Given an initial water saturation change value ΔH 0 Through continuous iteration, F(ΔH) gradually approaches its minimum value, and finally the actual water saturation change ΔH is obtained. rThis is the minimum value of the function F(ΔH).

[0088] Step 5: Current water saturation prediction. Add the water saturation change ΔH obtained in Step 4 back to the original oil-gas-water interface height H obtained in Step 2. b The current water saturation model can be obtained by calculating using formula (2).

[0089] The time-shifted earthquake amplitude difference Δs can be expressed as:

[0090]

[0091] Where ΔH,φ,H b These represent the height of the oil-gas-water interface, porosity, and the original oil-gas-water interface height, respectively. m1, m2, and m3 are rock mechanics parameters, and ρ... w , ρ ma and Δρ g These represent formation water density, hydrocarbon density under monitored earthquake, original pore fluid density, reservoir framework density, and density difference before and after fluid development, respectively; Δs is the time-lapsed earthquake amplitude difference, ΔS w It is the degree of change in water saturation; It represents the water saturation at the baseline earthquake.

[0092] The present invention will now be described in further detail with reference to the accompanying drawings:

[0093] See Figure 4 This embodiment provides a method for predicting the oil-gas-water interface based on time-shifted seismic data, including the following steps:

[0094] Step 1: Determine the rock mechanics parameters in the time-shifted earthquake amplitude difference formula (1).

[0095] Step 2: Establish a porosity model through seismic multi-attribute analysis.

[0096] Step 3: Read in the original oil-gas-water interface height H from the original hydrocarbon-water interface diagram. b .

[0097] Step 4: Determine the coefficients A and b in formula (2) for the relationship between the original water saturation, porosity, and oil-gas-water interface of the reservoir by combining the water saturation and porosity logging curves.

[0098] Step 5: Calculate the time-lapse seismic amplitude difference ΔS in the model based on the time-lapse seismic amplitude difference formula (1) and the relationship formula (2) between the original water saturation, porosity, and oil-gas-water interface of the reservoir. m .

[0099] Step 6: Calculate the residual amount according to the formula (3) for the residual value.

[0100] Step 7: Search for the optimal ΔH, φ using the Monto Carlo method.

[0101] Step 8: Calculate the current water saturation using formula (2).

[0102] This embodiment applies the described water saturation method to a gas field block for which time-shifted seismic data has been acquired. This block has two main gas-producing layers, Upper C1 and Lower C1. This block will be referred to as Block A below.

[0103] like Figure 2 As shown, from left to right, the gas-water interface of each model gradually increases, and the thickness of the gas-bearing layer gradually decreases. Figure 2 In the wedge-shaped model, the gas-water interface rises continuously from left to right, and the thickness of the gas-bearing layer in each model channel gradually decreases. Figure 3 The model used is a three-dimensional porosity model at the seismic scale. Figure 3 Each model track is made of Figure 2 Each model trace in the model is inverted using the method provided in this embodiment. It can be seen that some thinner gas layers are not well inverted; this is because a seismic-scale three-dimensional porosity model was used for the inversion.

[0104] according to Figure 4 In the first step, P-wave and S-wave logging curves, water saturation logging curves, density curves, and pressure curves are used to calculate rock mechanical parameters under different pressures and water saturations. Cross-sectional water saturation regression analysis is then performed to obtain the final rock mechanical parameters m1, m2, and m3. Figure 5 This is a cross-sectional diagram of water saturation and rock mechanical parameters in Block A.

[0105] In step two, multi-attribute regression analysis is used to calculate the reservoir porosity model. Figure 6 It is a three-dimensional porosity model of block A established through machine learning multi-attribute seismic analysis of block A.

[0106] In step three, the original oil-gas-water interface height map is read. Figure 9 (a) is the original oil-gas-water interface height diagram of Block A.

[0107] In step four, the water saturation and porosity logging curves are intersected to obtain the Leverette-J function. The three-dimensional porosity model obtained in step two is then substituted into the obtained J function equation to establish the three-dimensional original water saturation model. Figure 7 It is block A based on Figure 6 The original water saturation model was established.

[0108] In steps five, six, and seven, the three-dimensional porosity model of block A obtained in steps two and four, the original oil-gas-water interface height map, and the rock mechanics parameters are substituted into the time-shifted seismic amplitude difference formula (1) and the residual value formula (3), and the optimal ΔH and φ are found using a MontoCarlo search.

[0109] In step eight, the ΔH calculated in steps five, six, and seven is added to the original oil-gas-water interface height map in step three to predict the current oil-gas-water interface height map. Then, the current oil-gas-water interface height map is substituted into the formula (2) relating the original water saturation, porosity, and oil-gas-water interface of the reservoir to calculate the current water saturation profile. Figure 8 and Figure 10 These are the current water saturation profiles of wells #1 and #2, respectively. Figure 9 (b) is a map showing the predicted current oil-gas-water interface height.

[0110] Figure 11 This is the water saturation logging profile of well #1, with data collected in 2001 for seismic monitoring. (Comparison) Figure 10 and Figure 11 It can be observed that, Figure 10 In the current water saturation profile of well #1, it is indicated that the Lower C1 reservoir at the well's location is completely water-flooded, with only a small amount of thin gas-bearing layer remaining at the very top of the Upper C1 reservoir. This is consistent with... Figure 11 The results match the 2001 water saturation profile, demonstrating the accuracy of the water saturation prediction method of this invention.

[0111] In another embodiment, a time-shifted seismic-based oil-gas-water interface prediction system is also provided, including a three-dimensional porosity construction module, an original water saturation and interface height determination module, a rock mechanics parameter determination module, an optimal oil-gas-water interface height change and porosity value determination module, and a prediction module.

[0112] The 3D porosity construction module is used to build 3D porosity models.

[0113] The module for determining the initial water saturation and interface height is used to establish an initial water saturation model and determine the initial oil-gas-water interface height.

[0114] The rock mechanics parameter determination module is used to determine rock mechanics parameters;

[0115] The module for determining the optimal oil-gas-water interface height variation and porosity value is based on a three-dimensional porosity model, the original water saturation model, and rock mechanics parameters combined with the time-lapse earthquake amplitude difference formula. It calculates the residual values ​​of the time-lapse earthquake amplitude difference in the model and the time-lapse earthquake amplitude difference in the observation, and searches for the optimal oil-gas-water interface height variation and porosity value that minimizes the residual values.

[0116] The prediction module is used to predict the current water saturation change and obtain the remaining oil and gas distribution based on the optimal oil-gas-water interface height change, porosity value, and original oil-gas-water interface height.

[0117] This invention aims to enhance the application of seismic data in remaining oil prediction by providing a deterministic inversion method using post-stack time-lapse seismic data to predict the distribution of remaining oil. The invention integrates the pressure-directed Gassmann equation and the Leverette-J equation based on well logging curves to obtain the analytical expression of the theoretical model. An iterative method is used to find the minimum difference between the 4D seismic amplitude and the theoretical model, thereby predicting changes in the oil and gas interface during seismic acquisition and ultimately predicting changes in water saturation to obtain the distribution of remaining oil and gas. This method is applied to post-stack time-lapse seismic difference volumes, featuring fast computation speed, improving the application of seismic data in oil and gas development, and can be extended to all basins with time-lapse seismic data.

[0118] The above content is only for illustrating the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made to the technical solution based on the technical concept proposed in this invention shall fall within the scope of protection of the claims of this invention.

Claims

1. A method for predicting oil-gas-water interfaces based on time-shifted seismic data, characterized in that, Includes the following steps: Construct a three-dimensional porosity model; establish an original water saturation model and determine the original oil-gas-water interface height; Determine the rock mechanical parameters; Based on the three-dimensional porosity model, the original water saturation model, and rock mechanics parameters combined with the time-lapse earthquake amplitude difference formula, the residual values ​​of the model time-lapse earthquake amplitude difference and the observed time-lapse earthquake amplitude difference are calculated, and the optimal oil-gas-water interface height change and porosity value that minimize the residual value are searched. The remaining oil and gas distribution is obtained by predicting the current water saturation change based on the optimal oil-gas-water interface height change and porosity value, as well as the original oil-gas-water interface height.

2. The method for predicting the oil-gas-water interface based on time-shifted seismic data according to claim 1, characterized in that, The process of constructing the three-dimensional porosity model is as follows: Seismic multi-attribute analysis was used to train well logging porosity data and seismic data, and a set of optimal seismic attributes was selected to predict the three-dimensional porosity model, thus completing the establishment of the three-dimensional porosity model.

3. The method for predicting the oil-gas-water interface based on time-shifted seismic data according to claim 1, characterized in that, The process of establishing the original water saturation model is as follows: The original water saturation model was obtained by using a three-dimensional porosity model and the relationship between the original water saturation, porosity, and oil-gas-water interface of the reservoir.

4. The method for predicting the oil-gas-water interface based on time-shifted seismic data according to claim 3, characterized in that, The relationship between the original water saturation of the reservoir, porosity, and oil-gas-water interface is as follows: Among them, S w It is the water saturation level. It is porosity, H is the height of the reservoir from the interface between the two fluids, A and b are empirical coefficients, and H b It is the original oil-gas-water interface height.

5. The method for predicting the oil-gas-water interface based on time-shifted seismic data according to claim 4, characterized in that, In the formula relating the original water saturation, porosity, and oil-gas-water interface of the reservoir, the empirical coefficients A and b are determined by cross-analysis of the water saturation and porosity logging curves.

6. The method for predicting the oil-gas-water interface based on time-shifted seismic data according to claim 1, characterized in that, The process for determining the rock mechanical parameters is as follows: Pressure-directed Gassmann equation simulations were performed to calculate rock mechanical parameters under different water saturation levels. By combining changes in water saturation with rock mechanical parameters, regression analysis was performed to determine the rock mechanical parameters.

7. The method for predicting the oil-gas-water interface based on time-shifted seismic data according to claim 1, characterized in that, The expression for the time-shifted seismic amplitude difference formula is as follows: Where ΔH,φ,H b These represent the height of the oil-gas-water interface, porosity, and the original oil-gas-water interface height, respectively. m1, m2, and m3 are rock mechanics parameters, and ρ... w , ρ ma and Δρ g These represent formation water density, hydrocarbon density under monitored earthquake, original pore fluid density, reservoir framework density, and density difference before and after fluid development, respectively; Δs is the time-lapsed earthquake amplitude difference, ΔS w It is the degree of change in water saturation; It represents the water saturation at the baseline earthquake.

8. The method for predicting the oil-gas-water interface based on time-shifted seismic data according to claim 1, characterized in that, The formulas for the residual values ​​of the time-shifted earthquake amplitude difference in the calculation model and the observed time-shifted earthquake amplitude difference are as follows: F(ΔH)=‖Δs r -Δs m ‖2 Where, Δs m It is the time-shifted earthquake amplitude difference in the model, Δs r F(ΔH) is the observed time-lapsed earthquake amplitude difference, and F(ΔH) is the model time-lapsed earthquake amplitude difference Δs. m The difference in earthquake amplitude Δs over time observation r The residual value.

9. The method for predicting the oil-gas-water interface based on time-shifted seismic data according to claim 1, characterized in that, The search for the optimal oil-gas-water interface height and porosity value that minimizes the residual value is performed using the Monto Carlo method.

10. A prediction system for oil-gas-water interface based on time-shifted seismic data, characterized in that, It includes a three-dimensional porosity construction module, a module for determining the original water saturation and interface height, a module for determining rock mechanics parameters, a module for determining the optimal oil-gas-water interface height variation and porosity value, and a prediction module; The 3D porosity construction module is used to build 3D porosity models. The module for determining the initial water saturation and interface height is used to establish an initial water saturation model and determine the initial oil-gas-water interface height. The rock mechanics parameter determination module is used to determine rock mechanics parameters; The module for determining the optimal oil-gas-water interface height variation and porosity value is based on a three-dimensional porosity model, the original water saturation model, and rock mechanics parameters combined with the time-lapse earthquake amplitude difference formula. It calculates the residual values ​​of the time-lapse earthquake amplitude difference in the model and the time-lapse earthquake amplitude difference in the observation, and searches for the optimal oil-gas-water interface height variation and porosity value that minimizes the residual values. The prediction module is used to predict the current water saturation change and obtain the remaining oil and gas distribution based on the optimal oil-gas-water interface height change, porosity value, and original oil-gas-water interface height.

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