Quantitative Evaluation Method and Device for Improving Seismic Inversion Effect Based on Powell

Abstract

The present disclosure relates to a method and device for quantitatively evaluating the seismic inversion effect based on improved Powell, including: obtaining well logging data, drilling data and seismic data of a work area; determining well logging factors according to the well logging data, determining seismic factors according to the seismic data, and determining well location factors according to the drilling data; the well logging factors, seismic factors and well location factors are main control factors, sorting the main control factors and determining their corresponding weight coefficients; according to the sorted main control factors and their corresponding weight coefficients, using the improved Powell method to respectively discriminate each main control factor of different geological horizons in the work area, determining the comprehensive judgment confidence of each main control factor, and completing the quantitative evaluation of the seismic inversion effect in the work area. To solve the problems that the conventional seismic inversion evaluation results in the past are not intuitive and objective, and cannot be quantitatively characterized on the plane.

Description

Technical Field

[0001] The present disclosure relates to the technical field of oil and gas reservoir prediction, and in particular to a method and device for quantitatively evaluating seismic inversion effects based on an improved Powell. Background Art

[0002] The seismic inversion method is currently the only means of quantitative reservoir prediction, but the seismic inversion results have multiple solutions. The quality of seismic inversion directly affects the accuracy of reservoir prediction and development and application effects. Therefore, the evaluation of seismic inversion results is extremely important.

[0003] At present, there are three main methods for evaluating the effect of seismic inversion: one is the forward modeling residual quality control, in which the sand body model obtained by inversion is forward modeled through deconvolution operation to obtain a synthetic seismic record, and then subtracted from the original seismic record to obtain the residual. The smaller the residual, the more consistent the inversion result is with the earthquake trend. The ratio of the residual to the original seismic is less than 10% as the standard for inversion passing. The smaller this ratio is, the better the seismic inversion effect is. The advantage of this method is that the evaluation results are more intuitive. The disadvantage is that the seismic inversion results are reversed back to the forward modeling results, and the process is cumbersome, with many intermediate quality control links and many influencing factors. In addition, this method does not comprehensively consider the impact of well data and geological data on seismic inversion. The second is the quality control of seismic waveform and amplitude changes. The inversion results are compared with the seismic waveform. The changes in the seismic waveform in the horizontal direction are used to reflect the changes in the lithology of the formation and compare them with the seismic inversion results. The advantages of this method are fast and convenient, and the sand bodies with obvious impedance differences between the sand body and the surrounding rock are easy to identify; the disadvantages are that the resolution of seismic data is limited, and thin interlayer sand bodies are difficult to identify directly on the seismic waveform profile. The seismic waveform and the inversion results cannot correspond one to one, and the evaluation results rely on human experience, and the evaluation results are not objective and quantitative. The third is the quality control of the post-test well, that is, using the reserved wells that do not participate in the seismic inversion operation to test the prediction accuracy of the sand body at the well point of the seismic inversion results; the advantage of this method is that the inversion accuracy can be evaluated intuitively and quantitatively, but the disadvantage is that the evaluation accuracy is controlled by the density and method of the post-test well reservation, so the evaluation results are still not objective enough. Although the above methods can reflect the prediction accuracy of the seismic inversion results, the evaluation results are one-sided, the evaluation parameters considered are not objective and comprehensive, and the evaluation results are difficult to be qualitative and quantitative. How to conduct quantitative evaluation of seismic inversion results for all locations and different layers in the study area has become a major issue affecting the application of seismic inversion technology in dense well network areas. Summary of the invention

[0004] The present invention proposes a quantitative evaluation method and device based on an improved Powell seismic inversion effect, so as to solve the problem that conventional seismic inversion evaluation results are not intuitive and objective and cannot be quantitatively represented on a plane.

[0005] According to one aspect of the present disclosure, a quantitative evaluation method for improving the seismic inversion effect based on Powell is provided, including:

[0006] Obtain well logging data, drilling data, and seismic data in the work area;

[0007] Determine logging factors based on the well logging data, seismic factors based on the seismic data, and well location factors based on the drilling data;

[0008] The logging factors, seismic factors, and well location factors are the main control factors. Sort the main control factors and determine their corresponding weight coefficients;

[0009] According to the sorted main control factors and their corresponding weight coefficients, use the improved Powell method to discriminate each main control factor of different geological horizons in the work area respectively, determine the comprehensive judgment confidence of the main control factors of different geological horizons, and complete the quantitative evaluation of the seismic inversion effect in the work area.

[0010] Preferably, the logging factors include: sand body thickness and interbed thickness;

[0011] The method for determining logging factors based on well logging data includes: interpreting the well logging data to obtain the sand body thickness and interbed thickness.

[0012] Preferably, the seismic factors include: seismic dominant frequency and signal-to-noise ratio;

[0013] The method for determining seismic factors based on seismic data includes: performing quality analysis on the seismic data to obtain the seismic dominant frequency and signal-to-noise ratio.

[0014] Preferably, the well location factors include: well pattern density;

[0015] The method for determining well location factors based on drilling data includes: obtaining the number of wells and the area of the wells in the work area;

[0016] Determine the well spacing according to the number of wells and the area, and determine the well pattern density according to the well spacing.

[0017] Preferably, the method for using the improved Powell method to discriminate each main control factor of different geological horizons in the work area respectively according to the sorted main control factors and their corresponding weight coefficients, and determining the comprehensive judgment confidence of the main control factors of different geological horizons includes:

[0018] According to the sorted main control factors, use the improved Powell method to discriminate each main control factor of different geological horizons in the work area respectively, and obtain the confidence corresponding to each main control factor;

[0019] Multiply the confidence level corresponding to each main control factor by the weight coefficient corresponding to the main control factor and then sum them up to finally obtain the comprehensive judgment confidence level of the main control factors in different geological horizons.

[0020] Preferably, the method for discriminating each main control factor of different geological horizons in the work area by using the improved Powell method according to the sorted main control factors to obtain the confidence level corresponding to each main control factor includes:

[0021] Step 10: Set the starting data point S0 on the seismic inversion prediction plan, set the control error ε and m search directions;

[0022] Step 11: Starting from the initial data point S0, perform one-dimensional searches along m different search directions in turn on the seismic inversion prediction plan for the searched data point S i , and then perform one-dimensional searches along m different search directions in turn until the last data point S n is reached, where i = 0, 1,..., n;

[0023] Step 12: Except for the initial data point S0, determine the step size λ i between each searched data point S i and its previous data point, and the direction p i between the data point S i and its previous data point;

[0024] Step 13: According to the main control factor X i corresponding to the searched data point S i , the step size λ i and the direction p i , determine the fitting function f(X i + λ i p i ) of this data point S i ;

[0025] Step 14: According to the average value λ of the step sizes of all the searched data points, determine the minimum value min f(X i + λp i ) in the fitting functions of all the data points, satisfying f(X i + λ i p i ) = min f(X i + λp i ), (i = 0, 1,... n);

[0026] Step 15: Search until i = n, satisfying f(X n + λ n p n ) = min f(Xn +λp n ), determine whether |X n - X0| is less than the control error ε. If so, then f(X n +λ n p n ) is the confidence level of the main control factor;

[0027] Step 16: Otherwise, return to Step 10, reset the starting data point S0 and / or the control error ε and / or the number of search directions until |X n - X0| < ε among all the searched data points.

[0028] According to one aspect of the present disclosure, there is provided a device for quantitatively evaluating the seismic inversion effect based on Powell, including:

[0029] An acquisition unit for acquiring logging data, drilling data and seismic data of the work area;

[0030] A main control factor determination unit for determining logging factors according to the logging data, seismic factors according to the seismic data, and well location factors according to the drilling data;

[0031] A weight coefficient determination unit for taking the logging factor, seismic factor and well location factor as main control factors, sorting the main control factors, and determining their corresponding weight coefficients;

[0032] A quantitative evaluation unit for respectively discriminating each main control factor of different geological horizons in the work area by using the improved Powell method according to the sorted main control factors and their corresponding weight coefficients, determining the comprehensive judgment confidence level of the main control factors of different geological horizons, and completing the quantitative evaluation of the seismic inversion effect in the work area.

[0033] The present invention has at least the following beneficial effects:

[0034] The present disclosure proposes a method and device for quantitatively evaluating the seismic inversion effect based on improved Powell. By determining the main control factors according to logging data, seismic data and drilling data, and using the Powell method, a quantitative evaluation is carried out on each main control factor to obtain a reliable area of the seismic inversion prediction result and quantify the applicable range of the seismic inversion data volume. Description of the Drawings

[0035] The drawings here are incorporated into the specification and form a part of this specification. These drawings show embodiments in accordance with the present disclosure and, together with the specification, are used to explain the technical solutions of the present disclosure.

[0036] Figure 1Shows a flowchart of a method for quantitatively evaluating the improved Powell seismic inversion effect according to an embodiment of the present disclosure;

[0037] Figure 2 Shows an analysis diagram of the seismic dominant frequency in the study area according to an embodiment of the present disclosure;

[0038] Figure 3 Shows an analysis diagram of the seismic signal-to-noise ratio in the study area according to an embodiment of the present disclosure;

[0039] Figure 4 Shows an analysis diagram of the well pattern density in the study area according to an embodiment of the present disclosure;

[0040] Figure 5 Shows a prediction map of the thickness of the SⅡ7+8 sandstone in the study area according to an embodiment of the present disclosure;

[0041] Figure 6 Shows a prediction map of the thickness of the SⅡ7+8 interlayer in the study area according to an embodiment of the present disclosure;

[0042] Figure 7 Shows an analysis and evaluation map of the inversion confidence degree of the SⅡ7+8 in the study area according to an embodiment of the present disclosure;

[0043] Figure 8 Shows an evaluation map of the preferred inversion horizons in the study area according to an embodiment of the present disclosure. Detailed implementation manners

[0044] Various exemplary embodiments, features and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. The same reference numerals in the drawings denote elements having the same or similar functions. Although various aspects of the embodiments are shown in the drawings, the drawings are not necessarily drawn to scale unless otherwise specified.

[0045] The special term "exemplary" herein means "serving as an example, embodiment or illustration". Any embodiment described as "exemplary" herein is not necessarily to be construed as superior to or better than other embodiments.

[0046] The term "and / or" herein merely describes an association relationship of associated objects and indicates that three relationships may exist. For example, A and / or B may represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the term "at least one" herein means any one of a plurality or any combination of at least two of a plurality. For example, including at least one of A, B, and C may represent including any one or more elements selected from the set composed of A, B, and C.

[0047] In addition, for a better illustration of the present disclosure, numerous specific details are given in the following specific embodiments. Those skilled in the art should understand that the present disclosure can also be implemented without some specific details. In some instances, methods, means, elements, and circuits well-known to those skilled in the art are not described in detail so as to highlight the gist of the present disclosure.

[0048] Figure 1 The flowchart showing the quantitative evaluation method for improving the Powell seismic inversion effect according to an embodiment of the present disclosure; Figure 2 The seismic dominant frequency analysis diagram of the study area according to an embodiment of the present disclosure; Figure 3 The seismic signal-to-noise ratio analysis diagram of the study area according to an embodiment of the present disclosure; Figure 4 The well pattern density analysis diagram of the study area according to an embodiment of the present disclosure; Figure 5 The predicted thickness diagram of SⅡ7+8 sandstone in the study area according to an embodiment of the present disclosure; Figure 6 The predicted thickness diagram of the SⅡ7+8 interlayer in the study area according to an embodiment of the present disclosure; Figure 7 The analysis and evaluation diagram of the inversion confidence of SⅡ7+8 in the study area according to an embodiment of the present disclosure; Figure 8 The evaluation diagram of the preferred inversion horizon in the study area according to an embodiment of the present disclosure. As Figures 1-8 shown, the quantitative evaluation method for improving the Powell seismic inversion effect includes: Step S01: Obtain well logging data, drilling data, and seismic data of the work area; Step S02: Determine well logging factors according to the well logging data, seismic factors according to the seismic data, and well position factors according to the drilling data; Step S03: Use the well logging factors, seismic factors, and well position factors as the main control factors, sort the main control factors, and determine their corresponding weight coefficients; Step S04: According to the sorted main control factors and their corresponding weight coefficients, use the improved Powell method to discriminate each main control factor of different geological horizons in the work area, and determine the comprehensive judgment confidence of the main control factors of different geological horizons, thereby completing the quantitative evaluation of the seismic inversion effect in the work area.

[0049] The quantitative evaluation method for improving the Powell seismic inversion effect provided by the embodiment of the present invention specifically includes the following steps:

[0050] Step S01: Obtain well logging data, drilling data, and seismic data of the work area.

[0051] Step S02: Determine well logging factors according to the well logging data, seismic factors according to the seismic data, and well position factors according to the drilling data.

[0052] In the present disclosure, the logging factors include: sand body thickness and interbed thickness; the method for determining logging factors based on logging data includes: interpreting the logging data to obtain the sand body thickness and interbed thickness.

[0053] In the present disclosure, the seismic factors include: seismic dominant frequency and signal-to-noise ratio; the method for determining seismic factors based on seismic data includes: performing quality analysis on the seismic data to obtain the seismic dominant frequency and signal-to-noise ratio.

[0054] In the present disclosure, the well location factors include: well pattern density; the method for determining well location factors based on drilling data includes: obtaining the number of wells and the area of the drilling in the work area; determining the well spacing based on the number of wells and the area, and determining the well pattern density based on the well spacing.

[0055] In an embodiment of the present disclosure, drilling and logging are carried out in the target work area to obtain logging data, and seismic acquisition is carried out in this target work area to obtain a three-dimensional seismic data volume; geological horizons, sand body thickness, and interbed thickness data can be obtained through logging data interpretation; quality analysis is performed on the collected three-dimensional seismic data volume, specifically including analysis of seismic dominant frequency, signal-to-noise ratio, and consistency of seismic wavelets near the well; analysis is performed on the number of wells and the area of the drilling, and the two are divided to obtain the well spacing, so that the well pattern density can be obtained.

[0056] Among them, the seismic dominant frequency refers to the pulsed vibrations of different frequencies generated by the seismic source during seismic exploration, and the main frequency concentrated by these pulses is the seismic dominant frequency; the signal-to-noise ratio refers to the ratio of the voltage of the output signal of the amplifier to the noise voltage output at the same time, and the seismic signal-to-noise ratio refers to the ratio of the signal to the noise; analysis of the consistency of seismic wavelets near the well: the theoretical wavelet is the Ricker wavelet, which has only one main peak, while the actual seismic data often lacks low-frequency components and will generate small peaks beside the main peak, that is, the sidelobe effect, and the sidelobe effect will reduce the accuracy of seismic imaging.

[0057] Step S03: The logging factors, seismic factors, and well location factors are the main control factors, sort the main control factors, and determine their corresponding weight coefficients.

[0058] In an embodiment of the present disclosure, among the geological horizons obtained through logging data interpretation, select one horizon for which the confidence level of the seismic inversion result is to be determined, and sort the corresponding main control factors on this geological horizon according to the influence on the inversion effect. Among them, the sorted main control factors are arranged from the main to the secondary as: sand body thickness, interlayer thickness, seismic dominant frequency, seismic signal-to-noise ratio, and well pattern density. All the main control factors form a data set X = {x1, x2,..., x z}, where z is the total number of main control factors; and determine the weight coefficient set V = {v1, v2,..., v z}, and v1 + v2 +... + v z = 1.

[0059] Step S04: According to the sorted main control factors and their corresponding weight coefficients, use the improved Powell method to discriminate each main control factor of different geological horizons in the work area respectively, determine the comprehensive judgment confidence of the main control factors of different geological horizons, and complete the quantitative evaluation of the seismic inversion effect in the work area.

[0060] In the present disclosure, the method of using the improved Powell method to discriminate each main control factor of different geological horizons in the work area respectively according to the sorted main control factors and their corresponding weight coefficients, and determining the comprehensive judgment confidence of the main control factors of different geological horizons includes: According to the sorted main control factors, use the improved Powell method to discriminate each main control factor of different geological horizons in the work area respectively to obtain the confidence corresponding to each main control factor; Multiply the confidence corresponding to each main control factor by the weight coefficient corresponding to this main control factor and then sum, and finally obtain the comprehensive judgment confidence of the main control factors of different geological horizons.

[0061] In the embodiments of the present disclosure, the confidence is a statement method of probability and also an interval estimation method in mathematical statistics, that is, within a certain allowable error range between the estimated value and the population parameter, how large is the corresponding probability, and this corresponding probability is called the confidence.

[0062] In the present disclosure, the method of using the improved Powell method to discriminate each main control factor of different geological horizons in the work area respectively according to the sorted main control factors to obtain the confidence corresponding to each main control factor includes:

[0063] Step 10: Set the starting data point S0 on the seismic inversion prediction plan view, set the control error ε and m search directions.

[0064] In the embodiments of the present disclosure, the value range of the control error is ε > 0, and preferably: between 0 - 0.1.

[0065] The seismic inversion prediction plan view is obtained by performing inversion using seismic data. Seismic inversion uses the logging data and seismic data of the target work area, establishes the time-depth correspondence relationship between the logging data and the seismic data through well-seismic matching analysis, performs seismic inversion using different algorithms, and extracts the seismic inversion prediction plan view of each geological horizon according to the seismic inversion results.

[0066] Step 11: Starting from the initial data point S0, perform one-dimensional search on the seismic inversion prediction plan view along m different search directions in sequence, for the searched data point S i , and then perform one-dimensional search along m different search directions in sequence until the last data point S is searched.n , where i = 0, 1, ..., n.

[0067] In the embodiments of the present disclosure, on the seismic inversion prediction plan view, starting from the initial data point S0, one-dimensional searches are performed along m different search directions that are not specified. When a next data point is searched in any search direction, starting from the next data point, searches in three directions are performed again, and so on, until no data points can be searched in different search directions and then it ends. At this time, the total number of all the searched data points is n.

[0068] Step 12: Except for the initial data point S0, determine the step size λ i between each searched data point S i and its previous data point i , and the direction p i between the data point S

[0069] In the embodiments of the present disclosure, if there are 3 search directions, starting from the initial data point S0, when searching in three directions, if a next data point is searched, then the searched data point is S1; determine the previous data point passed through in the search direction of the searched data point S1, that is, the step size between S0 and this data point S i is λ1, and record the search direction from the previous data point to this data point S1 as p1.

[0070] Step 13: According to the main control factor X i corresponding to the searched data point S i , the step size λ i , and the direction p i , determine the fitting function f(X i +λ i p i ) of this data point S i .

[0071] In the embodiments of the present disclosure, each searched data point S i corresponds to 5 main control factors X i , namely the sand body thickness, the interbed thickness, the seismic dominant frequency, the seismic signal-to-noise ratio, and the well pattern density. Substitute the values of the 5 main control factors, the step size, and the direction corresponding to the searched data point S i into the fitting function f(X i +λ i p i ), so as to determine the fitting functions f(X i ) respectively corresponding to the 5 main control factors of each searched data point S i +λ i p i .

[0072] Step 14: Based on all data points S i with step size λ i and the average value λ of all data points S i determine the minimum value min f(X i + λp i ) in the fitting function of all data points S i + λ i p i ) such that f(X i + λp i ) = min f(X i + λp i ), (i = 0, 1,... n).

[0073] In the embodiment of the present disclosure, find the sum of the step sizes λ i between all the searched data points S i and the previous data point, and then divide the sum of the step sizes λ i by the total number n of the searched data points to obtain the average step size λ. According to the fitting function f(X i + λ i p i ) corresponding to the 5 main control factors of each searched data point S i make the fitting function satisfy f(X i + λ i p i ) = min(X i + λp i ). This process is the convergence process.

[0074] Step 15: Search until i = n, satisfying f(X n + λ n p n ) = min f(X n + λp n ). Determine whether |X n - X0| is less than the control error ε. If so, then f(X n + λ n p n ) is the confidence level of this main control factor.

[0075] In the embodiment of the present disclosure, determine whether the absolute value of the difference between the 5 main control factors X n of the last searched data point S n and the 5 main control factors X0 of the corresponding initial point is less than the set control error. If so, then the fitting function corresponding to the 5 main control factors X = {x1, x2, x3, x4, x5} of the last searched data point S n is the confidence level U = {u1, u2, u3, u4, u5} of the main control factors of this geological horizon.

[0076] Step 16: Otherwise, go back to Step 10 to reset the starting data point S0 and / or the control error ε and / or the number of search directions until all the data points S i found satisfy |X n - X0| < ε.

[0077] In the embodiments of the present disclosure, when re - determining the confidence level, the starting data point, the size of the control error, and the number of search directions can be adjusted simultaneously or separately until the confidence level of the main control factors of the geological horizon of this layer is determined.

[0078] Multiply the determined confidence level of the main control factors of the geological horizon of this layer by the corresponding weight coefficients to obtain the confidence levels of the five main control factors of the geological horizon of this layer, that is, W = U * V = {u1v1, u2v2, u3v3, u4v4, u5v5}. After summing, the comprehensive judgment confidence level of the geological horizon of this layer is obtained.

[0079] Repeat the above Steps S03 and S04 for other geological horizons in the work area respectively, and the comprehensive judgment confidence levels of the seismic inversion results of different geological horizons can be obtained.

[0080] In the embodiments of the present disclosure, taking the study area of the SII oil reservoir group in the North No.2 West Area of the Daqing Oilfield as an example, for the upper - return development of the second - type oil layers in the SII oil reservoir group in the North No.2 West Area, it is necessary to distinguish the trend of the inter - well channel sand bodies for fine development adjustment. For this purpose, it is necessary to quantitatively evaluate the confidence level of the channel sand bodies predicted by seismic inversion to provide a sufficient geological basis and theoretical basis for fine development adjustment.

[0081] The quality of the seismic data in the study area includes the analysis of the main frequency and the signal - to - noise ratio. The SII oil reservoir group in the North No.2 West Area is composed of sand body types such as meandering rivers with interbedded sandstone and mudstone, and the total formation thickness is about 50 - 60 m. A stable negative reflection coefficient interface T1 is formed on the top surface of the SII section. Figure 2 is the analysis diagram of the seismic main frequency in the study area, as Figure 2 shown, the main frequency of the seismic data is about 45 Hz; Figure 3 is the analysis diagram of the seismic signal - to - noise ratio in the study area, as Figure 3 shown, the signal - to - noise ratio is greatly affected by the ground conditions and the values are different in different regions; for the above parameters, the higher the main frequency and the higher the seismic signal - to - noise ratio, the better the seismic quality.

[0082] Figure 4 is the analysis diagram of the well pattern density in the study area, as Figure 4 shown, by statistically analyzing the SⅡ oil reservoir group in the study area under 7 kinds of well spacings, the thicknesses of the sand bodies with different thicknesses predicted by inversion and the thicknesses of the sand bodies in the posterior wells are calculated, and the coincidence rate of the inversion sand body prediction is obtained. It can be seen that: first, the greater the reservoir thickness, the higher its prediction accuracy, and with the increase of the well pattern density, the prediction accuracy shows an increasing trend; second, when the well spacing is less than 200 m, the prediction accuracy of the sand body does not increase significantly with the increase of the well pattern density.

[0083] Figure 5 It is the predicted map of seismic inversion sand body thickness of the SⅡ7+8A sub-layer in the study area. Figure 6 It is the predicted map of the thickness of interbeds and interlayers of the SⅡ7+8A sub-layer in the study area. The thickness of the interlayer is greater than 1m, and the inversion accuracy is above 60%. When it is greater than 1.7m, the inversion accuracy is greater than 80%. Therefore, 1m of the interlayer is the current lower limit of inversion.

[0084] Figure 7 It is the analysis and evaluation map of the inversion confidence of SⅡ7+8 in the study area. As Figure 7 shown, the Powell method is used to conduct discriminant analysis on the inversion plane confidence of the SⅡ7+8 sub-layer in the study area. Figure 8 It is the evaluation map for optimizing the inversion horizons in the study area. As Figure 8 shown, by applying the method of the present disclosure, the evaluation map of the seismic and seismic inversion reservoir prediction effects of 17 sub-layers in the North Second West Area is established ( Figure 8 ), which can evaluate the reliable area of the channel prediction results and quantify the applicable range of the seismic inversion data volume.

[0085] Using the method of the present disclosure to guide the fine tapping of remaining oil between wells in the North Second West Area, 214 potential locations have been initially determined in the SⅡ oil reservoir group. 12 wells have implemented fracturing and perforation compensation measures, with a cumulative oil increment of 10,300 tons.

[0086] It can be understood that the above-mentioned various method embodiments mentioned in the present disclosure can be combined with each other to form a combined embodiment without violating the principle logic. Due to space limitations, the present disclosure will not elaborate further.

[0087] The execution subject of the quantitative evaluation method for the improved Powell seismic inversion effect can be a quantitative evaluation device for the Powell seismic inversion effect. For example, the quantitative evaluation method for the improved Powell seismic inversion effect can be executed by a terminal device, a server, or other processing devices. Among them, the terminal device can be a user equipment (UE), a mobile device, a user terminal, a terminal, a cellular phone, a cordless phone, a personal digital assistant (PDA), a handheld device, a computing device, a vehicle-mounted device, a wearable device, etc. In some possible implementation manners, the quantitative evaluation method for the improved Powell seismic inversion effect can be implemented by a processor calling computer-readable instructions stored in a memory.

[0088] Those skilled in the art can understand that in the above method of the specific implementation manner, the writing order of each step does not mean a strict execution order and does not constitute any limitation to the implementation process. The specific execution order of each step should be determined according to its function and possible internal logic.

[0089] The present disclosure also provides a device for quantitatively evaluating the seismic inversion effect based on Powell, including: an acquisition unit for acquiring well logging data, drilling data, and seismic data of a work area; a main control factor determination unit for determining well logging factors based on the well logging data, seismic factors based on the seismic data, and well position factors based on the drilling data; a weight coefficient determination unit for taking the well logging factors, seismic factors, and well position factors as main control factors, sorting the main control factors, and determining their corresponding weight coefficients; and a quantitative evaluation unit for, according to the sorted main control factors and their corresponding weight coefficients, using the improved Powell method to respectively discriminate each main control factor of different geological horizons in the work area, determining the comprehensive judgment confidence of the main control factors of different geological horizons, and completing the quantitative evaluation of the seismic inversion effect in the work area.

[0090] In some embodiments, the functions or modules included in the device provided by the embodiments of the present disclosure can be used to execute the methods described in the above method embodiments. The specific implementation can refer to the description of the above method embodiments. For the sake of brevity, it will not be repeated here.

[0091] The present disclosure solves the problems that the results of conventional seismic inversion evaluation are not intuitive, not objective, and cannot be quantitatively characterized on a plane. Specifically, the following beneficial effects are included: (1) Quantifying the credibility of seismic inversion results on a plane: quantitatively evaluating the seismic inversion prediction plan view, obtaining the reliable area of the seismic inversion prediction results, and quantifying the applicable range of the seismic inversion data volume. (2) Optimizing the applicable horizons of seismic inversion technology vertically: quantitatively evaluating the seismic inversion prediction plan view can obtain the average seismic inversion confidence of this geological horizon, and then evaluating different geological horizons to obtain the average seismic inversion confidence of each horizon. By comparing the confidences of each geological horizon vertically, it can be obtained that the geological horizon with a higher value is more suitable for the application of seismic inversion technology.

[0092] The present disclosure can intuitively and quantitatively evaluate the credibility of the seismic inversion reservoir prediction results, providing an intuitive and reliable reference basis for the deployment of exploration and evaluation decisions, the fine adjustment of development, and the tapping of potential through measures.

[0093] The above has described the embodiments of the present disclosure. The above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations are obvious to those of ordinary skill in the art in the technical field without departing from the scope and spirit of the described embodiments. The selection of the terms used herein is intended to best explain the principles of the embodiments, the practical application, or the improvement of the technology in the market, or to enable other ordinary skill in the art in the technical field to understand the disclosed embodiments.

Claims

1. A quantitative evaluation method for improving the seismic inversion effect based on Powell, characterized in that Including: Obtaining well logging data, drilling data and seismic data of the work area; Determining well logging factors according to the well logging data, determining seismic factors according to the seismic data, and determining well position factors according to the drilling data; Taking the well logging factors, seismic factors and well position factors as main control factors, sorting the main control factors, and determining their corresponding weight coefficients; According to the sorted main control factors and their corresponding weight coefficients, using the improved Powell method to respectively judge each main control factor of different geological horizons in the work area, and determining the comprehensive judgment confidence of the main control factors of different geological horizons. The method includes: according to the sorted main control factors, using the improved Powell method to respectively judge each main control factor of different geological horizons in the work area to obtain the confidence corresponding to each main control factor. Specifically, it includes: Step 10: Set the starting data point S0 on the seismic inversion prediction plan view, set the control error ε and m search directions; Step 11: Starting from the initial data point S0, perform one-dimensional searches on the seismic inversion prediction plan view along m different search directions in sequence. For the searched data point S i , then perform one-dimensional searches along m different search directions in sequence until the last data point S n is searched, where i = 0, 1,..., n; Step 12: Except for the initial data point S0, determine the step size λ i between each data point S found and its previous data point i , and the direction p i between the data point S and its previous data point i ; Step 13: Based on the searched data point S i corresponding master control factor X i , step size λ i and direction p i , determine the fitting function f(X i + λ i p i ) of this data point S i ; Step 14: Determine the minimum value min f(X i +λp i ) in the fitting function of all data points according to the average value λ of the step sizes of all the searched data points, satisfying f(X i +λ i p i ) = min f(X i +λp i ), i = 0, 1, … n; Step 15: Search until i = n, satisfying f(X n +λ n p n ) = min f(X n +λp n ). Determine whether |X n - X0| is less than the control error ε. If so, then f(X n +λ n p n ) is the confidence level of this main control factor; Step 16: Otherwise, go back to Step 10 and reset the starting data point S0 and / or the control error ε and / or the number of search directions until |X n - X0| < ε is satisfied for all the data points searched; Multiplying the confidence corresponding to each main control factor by the weight coefficient corresponding to the main control factor and then summing them to finally obtain the comprehensive judgment confidence of the main control factors of different geological horizons, and completing the quantitative evaluation of the seismic inversion effect in the work area.

2. The quantitative evaluation method for improving the Powell seismic inversion effect according to claim 1, characterized in that The well logging factors include: sand body thickness and interbed thickness; The method for determining well logging factors according to well logging data includes: interpreting the well logging data to obtain the sand body thickness and interbed thickness.

3. The quantitative evaluation method for improving the Powell seismic inversion effect according to claim 1, characterized in that, The seismic factors include: seismic dominant frequency and signal-to-noise ratio; The method for determining seismic factors according to seismic data includes: performing quality analysis on the seismic data to obtain the seismic dominant frequency and signal-to-noise ratio.

4. The quantitative evaluation method for improving the Powell seismic inversion effect according to claim 1, characterized in that The well position factors include: well pattern density; The method for determining well position factors according to drilling data includes: obtaining the number of wells and area of the wells drilled in the work area; Determining the well spacing according to the number of wells and area, and determining the well pattern density according to the well spacing.

5. A quantitative evaluation device for seismic inversion effect based on improved Powell, characterized in that, Including: An acquisition unit for acquiring well logging data, drilling data and seismic data of the work area; A main control factor determination unit for determining well logging factors according to the well logging data, determining seismic factors according to the seismic data, and determining well position factors according to the drilling data; A weight coefficient determination unit for taking the well logging factors, seismic factors and well position factors as main control factors, sorting the main control factors, and determining their corresponding weight coefficients; A quantitative evaluation unit for, according to the sorted main control factors and their corresponding weight coefficients, using the improved Powell method to respectively judge each main control factor of different geological horizons in the work area, and determining the comprehensive judgment confidence of the main control factors of different geological horizons. The method includes: according to the sorted main control factors, using the improved Powell method to respectively judge each main control factor of different geological horizons in the work area to obtain the confidence corresponding to each main control factor. Specifically, it includes: Step 10: Set the starting data point S0 on the seismic inversion prediction plan view, set the control error ε and m search directions; Step 11: Starting from the initial data point S0, perform one-dimensional searches on the seismic inversion prediction plan view along m different search directions in sequence. For the searched data point S i , then perform one-dimensional searches along m different search directions in sequence until the last data point S n is searched, where i = 0, 1,..., n; Step 12: Except for the initial data point S0, determine the step size λ between each data point S found and its previous data point i , and the direction p between the data point S i and its previous data point i ; i ; Step 13: Based on the searched data point S i corresponding main control factor X i , step size λ i and direction p i , determine the fitting function f(X i + λ i p i ) of this data point S i ); Step 14: Determine the minimum value min f(X i +λp i ) in the fitting function of all data points according to the average value λ of the step sizes of all the searched data points, satisfying f(X i +λ i p i ) = min f(X i +λp i ), i = 0, 1, … n; Step 15: Search until i = n, satisfying f(X n +λ n p n ) = min f(X n +λp n ), and judge whether |X n - X0| is less than the control error ε. If so, then f(X n +λ n p n ) is the confidence level of this main control factor; Step 16: Otherwise, go back to Step 10 and reset the starting data point S0 and / or the control error ε and / or the number of search directions until |X n - X0| < ε is satisfied for all the data points searched; Multiply the confidence level corresponding to each main control factor by the weight coefficient corresponding to the main control factor and then sum them up to finally obtain the comprehensive judgment confidence level of the main control factors in different geological horizons, thus completing the quantitative evaluation of the seismic inversion effect in the work area.

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