Reservoir prediction method and device for eliminating strong reflection influence by layers

Abstract

The invention relates to a reservoir prediction method and device for eliminating strong reflection influence in a layered manner, and the method comprises the following steps: carrying out the precise calibration of a well seismic, determining the seismic reflection characteristics corresponding to a reservoir in a research region, and recognizing a reflection layer S corresponding to the reservoir and a reflection layer C corresponding to an underlying stratum; performing forward modeling simulation analysis to obtain strong reflection influence factors; according to the strong reflection influence factors, constructing a reservoir prediction model for eliminating strong reflection influence in a layered manner; and calculating according to the reservoir prediction model to obtain reservoir indication attributes for eliminating strong reflection influence in a layered manner, and predicting reservoir distribution. According to the reservoir prediction method and device provided by the invention, influence factors of seismic strong reflection are obtained through forward modeling analysis, strong reflection influence caused by non-reservoir factors is eliminated through a layered suppression thought, reservoir information is highlighted, and the reservoir prediction precision is effectively improved.

Description

Technical Field

[0001] This invention relates to the field of geophysical exploration technology, specifically to a reservoir prediction method and apparatus for layered elimination of strong reflection effects. Background Technology

[0002] In the field of seismic exploration, interference from strong reflection signals has always been a challenge in reservoir prediction. To better identify and characterize reservoir structures, existing research has developed various techniques to eliminate or suppress the effects of strong reflections, including multi-wavelet decomposition, matched pursuit, adaptive operators, and mode decomposition.

[0003] Among these methods, multi-wavelet decomposition primarily decomposes seismic signals into multiple wavelets, enabling better identification and processing of different frequency components in seismic data. Matching pursuit algorithms improve accuracy by iteratively selecting the optimal atom to approximate the original signal. Adaptive operator methods dynamically adjust processing parameters based on the characteristics of seismic data to better suppress strong reflections and extract weak reflection signals. Mode decomposition can decompose complex seismic signals into a series of intrinsic mode functions, helping to distinguish between strong and weak reflection signals. However, these methods mainly rely on direct suppression and reduction of strong reflections, aiming to eliminate them as completely as possible. They lack analysis of the factors influencing strong reflections, mixing multiple influences together for unified elimination. When reservoirs affect strong reflections, this can easily lead to the loss of valuable reservoir information.

[0004] Therefore, how to effectively and accurately eliminate the strong reflection effects caused by various non-reservoir factors, highlight reservoir information, and improve the prediction accuracy of reservoirs is a technical problem that needs to be solved in this field. Summary of the Invention

[0005] To address the above problems, the present invention aims to provide a reservoir prediction method and apparatus for layered elimination of the influence of strong reflections. Compared with the prior art, the reservoir prediction method and apparatus provided by the present invention obtains the influencing factors of strong seismic reflections through forward modeling analysis, and eliminates the influence of strong reflections caused by non-reservoir factors through layered suppression, highlighting reservoir information and effectively improving the accuracy of reservoir prediction.

[0006] To achieve this objective, the present invention adopts the following technical solution:

[0007] In a first aspect, the present invention provides a reservoir prediction method for stratified elimination of the influence of strong reflections, the reservoir prediction method comprising the following steps:

[0008] The well-seismic calibration was accurate to determine the seismic reflection characteristics of the reservoirs in the study area and to identify the reflection horizon S corresponding to the reservoirs and the reflection horizon C corresponding to the underlying strata.

[0009] Forward modeling analysis yielded the factors influencing strong reflection.

[0010] Based on the aforementioned strong reflection influencing factors, a reservoir prediction model is constructed to eliminate the influence of strong reflection in a stratified manner;

[0011] Based on the reservoir prediction model, reservoir indicator attributes that eliminate the influence of strong reflections are calculated to predict reservoir distribution.

[0012] This invention addresses the problems of low accuracy and multiple solutions in existing reservoir prediction methods under strong reflection conditions by providing a layered method for eliminating the influence of strong reflection. First, the seismic reflection characteristics corresponding to the reservoir are determined through precise well-seismic calibration. Then, the propagation process of seismic waves in the subsurface medium is analyzed through forward modeling to identify key factors affecting strong reflection. Next, based on the factors influencing strong reflection obtained from the forward modeling analysis, a layered reservoir prediction model is constructed to eliminate the influence of strong reflection. Finally, using the constructed reservoir prediction model, the reservoir indicator attributes after eliminating the influence of strong reflection are calculated, thereby predicting the reservoir distribution. Through this process, this invention can effectively eliminate strong reflection interference caused by non-reservoir factors, thus clearly revealing reservoir information and improving the accuracy of reservoir prediction under strong reflection conditions.

[0013] Preferably, the process of accurate well-seismic calibration includes: performing lithological logging interpretation on the drilled wells in the study area to obtain lithological types and lithological combinations.

[0014] Preferably, the lithology types include background lithology, reservoir lithology, and overlying lithology.

[0015] Preferably, the background lithology includes coal and rock.

[0016] Preferably, the reservoir lithology includes sandstone.

[0017] Preferably, the underlying lithology includes coal and / or limestone.

[0018] Preferably, the lithological combination includes any one or at least two of sandy mud ash, sandy mud fly ash, peat ash, or mud ash.

[0019] Preferably, the process of accurate well seismic calibration further includes: taking drilled wells with sand-mud-coal-ash lithological combinations as typical wells, performing well seismic composite record calibration, determining the seismic reflection characteristics corresponding to the reservoir, and identifying the reflection horizon S corresponding to the reservoir and the reflection horizon C corresponding to the underlying strata.

[0020] Preferably, the forward modeling analysis process includes: performing seismic forward modeling on the geological model of the drilled wells in the study area, and analyzing the factors influencing strong reflection by combining actual seismic logging data.

[0021] In this invention, the method of seismic forward modeling based on a geological model is a conventional method in the field. The geological model can be constructed using conventional methods in the field, such as statistically analyzing the P-wave and S-wave velocities and density parameters of key geological strata based on typical wells, and then constructing the geological model. The forward modeling method can also employ conventional methods in the field, such as using the Zoeppritz equation to calculate gathers and then stacking them into a post-stack data volume. In this invention, forward modeling is performed on geological models with different lithological combinations, and the data obtained from the forward modeling is compared with actual seismic well logging data to analyze the factors influencing strong reflections.

[0022] Preferably, the strong reflection influencing factors include the reflection of the top boundary wave crest of the coal seam and the reflection of the side lobe wave crest of the interface of the underlying limestone layer.

[0023] In this invention, the strong reflection influencing factors generally also include the reflection of the bottom boundary wave crest of the sandstone reservoir. The reflection of the bottom boundary wave crest of the sandstone reservoir is not a non-reservoir influencing factor and generally does not need to be eliminated.

[0024] Preferably, the effect of the side lobe peak reflection at the interface of the underlying limestone layer is denoted as Amp(S1), where Amp(S1) is a fixed value.

[0025] Preferably, the method for obtaining Amp(S1) is as follows: performing seismic forward modeling on the drilled wells of the marl lithological combination, and obtaining the amplitude attribute of the sidelobe of the underlying limestone layer interface is Amp(S1).

[0026] Preferably, the influence of the wave peak reflection at the top boundary of the coal seam is denoted as |Amp(C)|-|Amp(C1)|, where Amp(C) represents the amplitude attribute of the reflection layer C corresponding to the typical underground overburden of the sand-mud-coal-ash lithological combination, and Amp(C1) is a fixed value.

[0027] In this invention, the amplitude attribute of the reflection layer C corresponding to the overlying strata is used to calculate the influence of the coal seam top boundary wave crest reflection, instead of using the amplitude attribute of the reflection layer S corresponding to the reservoir. This is because the reflection axis of the overlying limestone layer is relatively stable in actual seismic data and easy to extract. Furthermore, the limestone interface is relatively close to the coal seam and relatively far from the sand layer, containing more coal seam information, and can more accurately reflect the influence of the coal seam top boundary wave crest reflection.

[0028] Preferably, the method for obtaining Amp(C1) is as follows: performing seismic forward modeling on the drilled wells of the sand-mud-lime lithology combination, and the resulting amplitude attribute of the limestone layer interface is Amp(C1).

[0029] Preferably, the reservoir prediction model for eliminating the influence of strong reflections by layering is expressed as: Amp(E)=[Amp(S)-Amp(S1)]-n×[|Amp(C)|-|Amp(C1)|], where Amp(E) represents the sandstone reservoir indicator attribute, Amp(S) represents the amplitude attribute of the reflection layer S corresponding to the typical well reservoir of the sand-mud-coal-ash lithological combination, and n represents the adjustment coefficient, which takes a value of 0-1.

[0030] In this invention, n is an adjustment coefficient obtained by analyzing the drilling results of the entire study area. The Amp(S) and Amp(C) of the drilled wells in the study area can be obtained from actual seismic data, and Amp(S1) and Amp(C1) can be obtained from seismic forward modeling. Substituting these values ​​into the reservoir prediction model, the specific value of n is obtained.

[0031] Preferably, the calculation process of the reservoir indicator attribute includes: obtaining the maximum plane amplitude attribute of the reflecting layer S through actual seismic data as the amplitude attribute Amp(S) of the reflecting layer S, and obtaining the maximum plane amplitude attribute of the reflecting layer C through actual seismic data as the amplitude attribute Amp(C) of the reflecting layer C, and substituting Amp(S), Amp(C), Amp(S1) and Amp(C1) into the reservoir prediction model to calculate the reservoir indicator attribute.

[0032] As a preferred embodiment of the first aspect of the present invention, the reservoir prediction method includes the following steps:

[0033] For accurate well-seismic calibration, firstly, lithological logging interpretation is performed on the drilled wells in the study area to obtain lithological types and lithological combinations. The lithological combinations include any one or at least two of sandy mud-lime, sandy mud-fly ash, peat ash, or mud. Then, wells with the sandy mud-fly ash lithological combination are used as typical wells for well-seismic synthetic record calibration to determine the seismic reflection characteristics corresponding to the reservoir and to identify the reflection horizon S corresponding to the reservoir and the reflection horizon C corresponding to the underlying strata.

[0034] Seismic forward modeling was performed on the geological models of drilled wells in the study area. Combined with actual seismic logging data, the influencing factors of strong reflection were analyzed. These factors include wave crest reflection at the coal seam top boundary and sidelobe wave crest reflection at the interface of the underlying limestone layer. The influence of the sidelobe wave crest reflection at the interface of the underlying limestone layer is denoted as Amp(S1), which is a fixed value. Amp(S1) is obtained by performing seismic forward modeling on drilled wells with marl lithological assemblages, and obtaining the underlying limestone layer... The amplitude attribute of the interface sidelobe is Amp(S1), and the influence of the reflection of the top boundary wave crest of the coal seam is denoted as |Amp(C)|-|Amp(C1)|, where Amp(C) represents the amplitude attribute of the reflection layer C corresponding to the typical underground overburden of the sand-mud-coal-ash lithological combination, and Amp(C1) is a fixed value. The method for obtaining Amp(C1) is as follows: Seismic forward modeling is performed on the drilled wells of the sand-mud-coal-ash lithological combination, and the amplitude attribute of the limestone layer interface obtained is Amp(C1).

[0035] Based on the factors influencing strong reflection, a reservoir prediction model that eliminates the influence of strong reflection in layers is constructed, expressed as: Amp(E)=[Amp(S)-Amp(S1)]-n×[|Amp(C)|-|Amp(C1)|], where Amp(E) represents the indicator attribute of sandstone reservoir, Amp(S) represents the amplitude attribute of the reflection layer S corresponding to the typical well reservoir of sandstone-mud-coal ash lithology combination, and n represents the adjustment coefficient, which takes a value of 0-1;

[0036] The maximum planar amplitude attribute of the reflecting layer S is obtained from actual seismic data as the amplitude attribute Amp(S) of the reflecting layer S, and the maximum planar amplitude attribute of the reflecting layer C is obtained from actual seismic data as the amplitude attribute Amp(C) of the reflecting layer C. Substituting Amp(S), Amp(C), Amp(S1), and Amp(C1) into the reservoir prediction model, the reservoir indicator attributes are obtained, and the reservoir distribution is predicted.

[0037] Secondly, the present invention provides a reservoir prediction device for stratified removal of strong reflection effects, the reservoir prediction device comprising:

[0038] The acquisition module is used to determine the seismic reflection characteristics of the reservoir in the study area through accurate well-seismic calibration, and to identify the reflection horizon S corresponding to the reservoir and the reflection horizon C corresponding to the underlying strata.

[0039] The forward modeling analysis module is used to obtain the influencing factors of strong reflection through forward modeling analysis;

[0040] The reservoir prediction model construction module is used to construct a reservoir prediction model that eliminates the influence of strong reflection based on the factors affecting strong reflection.

[0041] The reservoir indicator attribute calculation module is used to calculate the reservoir indicator attributes after removing the strong reflection influence based on the reservoir prediction model, and to predict the reservoir distribution.

[0042] Compared with the prior art, the present invention has the following beneficial effects:

[0043] The reservoir prediction method and apparatus provided by this invention obtain the influencing factors of strong seismic reflection through forward modeling analysis, and eliminate the influence of strong reflection caused by non-reservoir factors by using the idea of ​​layered suppression, highlighting reservoir information and effectively improving the accuracy of reservoir prediction. Attached Figure Description

[0044] Figure 1 Flowcharts of the reservoir prediction methods provided in Embodiments 1 and 2 of the present invention;

[0045] Figure 2 This is a schematic diagram of the main lithological combination of the target strata in the study area in Embodiment 2 of the present invention;

[0046] Figure 3 This is a schematic diagram comparing the reflection characteristics after layering and eliminating factors affecting strong reflection in Embodiment 2 of the present invention. Figure 3 In the middle: (a) represents the reflection characteristics of the reflection horizon S corresponding to the reservoir in the sand-mud-coal-ash lithological assemblage; (b) represents the reflection characteristics of the side lobe S1 corresponding to the interface of the overlying limestone layer in the marl-lime lithological assemblage; (c) represents the reflection characteristics of the sand-coal lithological assemblage obtained after removing the influence of (b) from (a); (d) represents the reflection characteristics of the reflection horizon C corresponding to the overlying strata in the sand-mud-coal-ash lithological assemblage; (e) represents the reflection characteristics of the interface C1 of the limestone layer in the sand-mud-lime lithological assemblage; (f) represents the reflection characteristics of the coal seam obtained after removing the influence of (e) from (d); and (g) represents the reflection characteristics of the sand layer E obtained after removing the influence of (f) from (c).

[0047] Figure 4 This is the Amp(S) diagram of the maximum planar amplitude property of layer S in Embodiment 2 of the present invention;

[0048] Figure 5 This is the Amp(E) diagram of the sandstone reservoir indicator properties after layering and removing the influence of strong reflection in Embodiment 2 of the present invention;

[0049] Figure 6 This is a schematic diagram of the reservoir prediction device provided in Embodiment 3 of the present invention. Detailed Implementation

[0050] The technical solution of the present invention will be further illustrated below through specific embodiments. Those skilled in the art should understand that the embodiments described are merely illustrative of the present invention and should not be construed as limiting the invention in any way.

[0051] Example 1

[0052] This embodiment provides a reservoir prediction method that eliminates the influence of strong reflections by layering. The reservoir prediction method obtains the influencing factors of strong seismic reflections through forward modeling analysis, and eliminates the strong reflection influences caused by non-reservoir factors by layering suppression, highlighting reservoir information and thus predicting reservoir distribution. This reservoir prediction method can be executed by a reservoir prediction device that eliminates the influence of strong reflections by layering, which can be implemented in hardware and / or software.

[0053] like Figure 1 As shown, the reservoir prediction method provided in this embodiment includes:

[0054] S101, well-seismic precise calibration, to determine the seismic reflection characteristics of the reservoir in the study area, and to identify the reflection horizon S corresponding to the reservoir and the reflection horizon C corresponding to the underlying strata;

[0055] The process of precise well-seismic calibration includes: performing lithological logging interpretation on the drilled wells in the study area to obtain lithological types and lithological combinations;

[0056] Specifically, the lithological types include background lithology, reservoir lithology, and overlying lithology;

[0057] Specifically, the background lithology includes coal and rock;

[0058] Specifically, the reservoir lithology includes sandstone;

[0059] Specifically, the underlying lithology includes coal and / or limestone;

[0060] Specifically, the lithological assemblage includes any one or at least two of sandy mud ash, sandy mud fly ash, fly ash, or mud ash;

[0061] The process of accurate well-seismic calibration also includes: taking drilled wells with sand-mud-coal-ash lithological combinations as typical wells, performing well-seismic synthetic record calibration, determining the seismic reflection characteristics corresponding to the reservoir, and identifying the reflection horizon S corresponding to the reservoir and the reflection horizon C corresponding to the underlying strata.

[0062] S102, forward modeling analysis, yields the influencing factors of strong reflection;

[0063] The forward modeling analysis process includes: performing seismic forward modeling on the geological models of the drilled wells in the study area, and combining the actual seismic logging data to analyze the factors influencing strong reflections;

[0064] Specifically, the factors influencing strong reflection include the reflection of the top boundary wave peak of the coal seam and the reflection of the side lobe wave peak of the interface of the underlying limestone layer;

[0065] Specifically, the influence of the sidelobe peak reflection at the interface of the underlying limestone layer is denoted as Amp(S1), and Amp(S1) is a fixed value. Specifically, the method for obtaining Amp(S1) is as follows: performing seismic forward modeling on the drilled wells of the marl lithological combination, and the amplitude attribute of the sidelobe at the interface of the underlying limestone layer is Amp(S1).

[0066] Specifically, the influence of the reflection of the top boundary wave peak of the coal seam is denoted as |Amp(C)|-|Amp(C1)|, where Amp(C) represents the amplitude attribute of the reflection layer C corresponding to the typical underground overburden of the sand-mud-coal-ash lithological combination, and Amp(C1) is a fixed value;

[0067] Specifically, the method for obtaining Amp(C1) is as follows: Seismic forward modeling is performed on the drilled wells of the sand-mud-lime lithology combination, and the amplitude attribute of the limestone layer interface obtained is Amp(C1).

[0068] S103, Based on the strong reflection influencing factors, construct a reservoir prediction model that eliminates the influence of strong reflection in a layered manner;

[0069] The reservoir prediction model for eliminating the influence of strong reflections by layering is expressed as: Amp(E)=[Amp(S)-Amp(S1)]-n×[|Amp(C)|-|Amp(C1)|], where Amp(E) represents the sandstone reservoir indicator attribute, Amp(S) represents the amplitude attribute of the reflection layer S corresponding to the typical well reservoir of the sand-mud-coal-ash lithological combination, and n represents the adjustment coefficient, which takes a value of 0-1.

[0070] S104, Calculate the reservoir indication attributes by removing strong reflection effects based on the reservoir prediction model, and predict the reservoir distribution.

[0071] The calculation process of the reservoir indicator attributes includes: obtaining the maximum plane amplitude attribute of the reflecting layer S through actual seismic data as the amplitude attribute Amp(S) of the reflecting layer S, and obtaining the maximum plane amplitude attribute of the reflecting layer C through actual seismic data as the amplitude attribute Amp(C) of the reflecting layer C. Substituting Amp(S), Amp(C), Amp(S1), and Amp(C1) into the reservoir prediction model to calculate the reservoir indicator attributes.

[0072] Example 2

[0073] This embodiment provides a reservoir prediction method for layered elimination of strong reflection effects. Wells A1, A2, B, and C have been drilled in the study area. Wells A1 and A2 are sand-mud-fly ash combinations, with sandstone and coal generally developed. Well B is a mud-fly ash combination, with sandstone and coal developed. Well C is a mud-ash combination, with neither sandstone nor coal developed. The specific technical solution is explained below.

[0074] like Figure 1 As shown, the reservoir prediction method provided in this embodiment includes the following steps:

[0075] S101, precise well-seismic calibration, determines the seismic reflection characteristics corresponding to the reservoirs in the study area, and identifies the reflection horizon S corresponding to the reservoir and the reflection horizon C corresponding to the underlying strata. Specifically, it involves first performing lithological logging interpretation on the drilled wells in the study area to obtain the lithological type and lithological combination, such as... Figure 2 As shown, the lithological assemblage includes sandy mudstone, sandy mudstone, fly ash, peat, and mudstone. Then, taking the drilled wells with the sandy mudstone lithological assemblage as typical wells, well seismic composite records are calibrated to determine the seismic reflection characteristics corresponding to the reservoir and identify the reflection horizon S corresponding to the reservoir and the reflection horizon C corresponding to the underlying strata. In this embodiment, the reflection horizon S corresponding to the reservoir is affected by the side lobes of the reservoir bottom boundary, coal seam, and underlying limestone interface, forming superimposed wave crests with strong reflections at S.

[0076] S102, Forward modeling analysis to obtain the influencing factors of strong reflection, specifically: Seismic forward modeling is performed on the geological models of drilled wells in the study area. Combined with actual seismic logging data, the influencing factors of strong reflection are analyzed. These factors include wave crest reflection at the top boundary of the coal seam and side lobe wave crest reflection at the interface of the underlying limestone layer. The influence of the side lobe wave crest reflection at the interface of the underlying limestone layer is denoted as Amp(S1), which is a fixed value. The method for obtaining Amp(S1) is as follows: Seismic forward modeling is performed on the geological models of drilled wells with marl lithological assemblages. The amplitude attribute of the sidelobe of the underlying limestone layer interface obtained by seismic forward modeling is Amp(S1). The influence of the wave peak reflection at the top boundary of the coal seam is denoted as |Amp(C)|-|Amp(C1)|, where Amp(C) represents the amplitude attribute of the reflection layer C corresponding to the typical underlying strata of the sand-mud-coal-lime lithological combination. Amp(C1) is a fixed value. The method for obtaining Amp(C1) is as follows: Seismic forward modeling is performed on the drilled wells of the sand-mud-lime lithological combination, and the amplitude attribute of the limestone layer interface obtained is Amp(C1).

[0077] In this invention, for the strong reflection influencing factors, the influence of the side lobe peak reflection at the interface of the underlying limestone layer with a large influence, Amp(S1), is generally removed first, and then the influence of the peak reflection at the top boundary of the coal seam, |Amp(C)|-|Amp(C1)|, is removed. Alternatively, a reservoir prediction model can be used to remove them directly.

[0078] S103. Based on the factors affecting strong reflection, a reservoir prediction model that eliminates the influence of strong reflection in layers is constructed, which is expressed as: Amp(E)=[Amp(S)-Amp(S1)]-n×[|Amp(C)|-|Amp(C1)|], where Amp(E) represents the sandstone reservoir indicator attribute, Amp(S) represents the amplitude attribute of the reflection layer S corresponding to the typical well reservoir of the sand-mud-coal-ash lithological combination, and n represents the adjustment coefficient, which takes a value of 0-1;

[0079] The reservoir prediction model first eliminates the side lobe peak reflections at the interface of the overlying limestone layer, i.e., Amp(S)-Amp(S1), due to the influence of strong reflection factors. Figure 3 As shown in the diagram, the principle of removing the strong reflection influencing factor Amp(S)-Amp(S1) in the reservoir prediction model is illustrated by comparing reflection characteristics. Figure 3 Figure (a) shows the reflection characteristics of the reflection horizon S corresponding to the reservoir in the sand-mud-coal-ash lithological assemblage, and (b) shows the reflection characteristics of the side lobe S1 corresponding to the interface of the overlying limestone layer in the marl lithological assemblage. Therefore, by removing the influence of the side lobe of the interface of the overlying limestone layer in the marl lithological assemblage from the reflection characteristics of the reservoir in the sand-mud-coal-ash lithological assemblage, the reflection characteristics of sand and coal can be reflected more accurately, as shown in Figure (c).

[0080] The reservoir prediction model also calculates the reflection of the top boundary wave peak, a strong reflection factor, in the reservoir prediction model, i.e., |Amp(C)|-|Amp(C1)|, as shown below. Figure 3 As shown in the diagram, the principle of |Amp(C)|-|Amp(C1)| in the reservoir prediction model is illustrated using a contrast diagram of reflection features. Figure 3 In Figure (d), the reflection characteristics of the underlying strata C in the sand-mud-coal-ash lithological assemblage are shown. In Figure (e), the reflection characteristics of the limestone layer interface C1 in the sand-mud-coal-ash lithological assemblage are shown. Therefore, by removing the reflection characteristics of the underlying strata in the sand-mud-coal-ash lithological assemblage from the reflection characteristics of the limestone layer interface in the sand-mud-coal-ash lithological assemblage, the reflection characteristics of coal can be partially reflected, as shown in Figure (f).

[0081] Finally, the reservoir prediction model uses the reflection characteristics of sand and coal to eliminate the reflection characteristics of coal and then predicts the reflection characteristics of sand, such as... Figure 3 As shown in Figure (c), by removing the influence of the reflection characteristics of coal (f) from the reflection characteristic diagram of sand and coal, the reflection characteristics of sand reservoirs can be reflected, as shown in Figure (g).

[0082] It should be noted that this invention utilizes the underlying strata to calculate the influence of the wave peak reflection at the top boundary of the coal seam. It can take advantage of the fact that the reflection axis of the underlying limestone layer is relatively stable in actual seismic data, easy to extract, and contains a lot of coal seam information. However, since the underlying strata only contain part of the coal seam information, |Amp(C)|-|Amp(C1)| can only approximate the coal seam information. Therefore, in this invention, an adjustment coefficient n is set to adjust the model so as to remove the reflection characteristics of coal more accurately in practical applications, thus obtaining a more accurate reservoir prediction model, expressed as: Amp(E)=[Amp(S)-Amp(S1)]-n×[|Amp(C)|-|Amp(C1)|];

[0083] S104, based on the reservoir prediction model, calculate the reservoir indicator attributes by removing the influence of strong reflections through stratification, and predict the reservoir distribution, specifically as follows: Figure 4 As shown, the maximum planar amplitude attribute of the reflecting layer S is obtained from actual seismic data as the amplitude attribute Amp(S) of the reflecting layer S, and the maximum planar amplitude attribute of the reflecting layer C is obtained from actual seismic data as the amplitude attribute Amp(C) of the reflecting layer C. Substituting Amp(S), Amp(C), Amp(S1), and Amp(C1) into the reservoir prediction model, the reservoir indicator attributes are obtained, such as... Figure 5 As shown, the reservoir distribution is predicted;

[0084] Figure 4 The Amp(S) plot represents the maximum planar amplitude attribute of layer S. Combined with the actual drilling results from wells A1, A2, B, and C, it can be seen that... Figure 4 It cannot accurately reflect the distribution of sandstone reservoirs. Figure 5 To remove the strong reflection effects from the sandstone reservoir indicator property Amp(E) map, from... Figure 5 As can be seen from the data, wells A1 and A2 are located in the sandstone development zone, while wells B and C are located in the sandstone non-development zone. The predicted results of the four wells are completely consistent with the actual drilling results.

[0085] Therefore, the reservoir prediction method provided by this invention can reduce the ambiguity of sandstone reservoir prediction caused by coal seams and underlying limestone layers, and improve the accuracy of reservoir prediction under strong reflection influence.

[0086] Example 3

[0087] This embodiment provides a reservoir prediction device for layered elimination of strong reflection effects, such as... Figure 6 As shown, the reservoir prediction device includes an acquisition module 110, a forward simulation analysis module 120, a reservoir prediction model construction module 130, and a reservoir indicator attribute calculation module 140. Wherein:

[0088] The acquisition module 110 is used to determine the seismic reflection characteristics of the reservoir in the study area through accurate well-seismic calibration, and to identify the reflection horizon S corresponding to the reservoir and the reflection horizon C corresponding to the underlying strata.

[0089] The forward modeling analysis module 120 is used to obtain the influencing factors of strong reflection through forward modeling analysis;

[0090] The reservoir prediction model construction module 130 is used to construct a reservoir prediction model that eliminates the influence of strong reflection based on the factors affecting strong reflection.

[0091] The reservoir indicator attribute calculation module 140 is used to calculate the reservoir indicator attributes after removing the strong reflection influence based on the reservoir prediction model, and to predict the reservoir distribution.

[0092] In the acquisition module 110, the process of precise well-seismic calibration includes: performing lithological logging interpretation on the drilled wells in the study area to obtain lithological types and lithological combinations. The lithological combination includes any one or at least two of sand-mud-lime, sand-mud-fly ash, peat-fly ash, or mud-lime. The process of precise well-seismic calibration also includes: using the drilled wells with the sand-mud-fly ash lithological combination as typical wells, performing well-seismic synthetic record calibration to determine the seismic reflection characteristics corresponding to the reservoir, and identifying the reflection horizon S corresponding to the reservoir and the reflection horizon C corresponding to the underlying strata.

[0093] In the forward modeling analysis module 120, the forward modeling analysis process includes: performing seismic forward modeling on the geological models of the drilled wells in the study area, and combining actual seismic logging data to analyze and obtain the strong reflection influencing factors. The strong reflection influencing factors include the wave crest reflection at the top boundary of the coal seam and the side lobe wave crest reflection at the interface of the underlying limestone layer. The influence of the side lobe wave crest reflection at the interface of the underlying limestone layer is denoted as Amp(S1), and Amp(S1) is a fixed value. The method for obtaining Amp(S1) is: performing seismic forward modeling on the drilled wells of the marl lithological combination. The amplitude attribute of the sidelobe of the underlying limestone layer interface obtained from the seismic forward modeling is Amp(S1). The influence of the wave crest reflection at the top boundary of the coal seam is denoted as |Amp(C)|-|Amp(C1)|, where Amp(C) represents the amplitude attribute of the reflection layer C corresponding to the typical underlying strata of the sand-mud-coal-lime lithological combination. Amp(C1) is a fixed value. The method for obtaining Amp(C1) is as follows: Seismic forward modeling is performed on the drilled wells of the sand-mud-lime lithological combination, and the amplitude attribute of the limestone layer interface obtained is Amp(C1).

[0094] In the reservoir prediction model construction module 130, the reservoir prediction model that eliminates the strong reflection effect by layering is expressed as: Amp(E)=[Amp(S)-Amp(S1)]-n×[|Amp(C)|-|Amp(C1)|], where Amp(E) represents the sandstone reservoir indicator attribute, Amp(S) represents the amplitude attribute of the reflection layer S corresponding to the typical well reservoir of the sand-mud-coal-ash lithological combination, and n represents the adjustment coefficient, which takes a value of 0-1.

[0095] In the reservoir indicator attribute calculation module 140, the calculation process of the reservoir indicator attribute includes: obtaining the maximum plane amplitude attribute of the reflecting layer S through actual seismic data as the amplitude attribute Amp(S) of the reflecting layer S, and obtaining the maximum plane amplitude attribute of the reflecting layer C through actual seismic data as the amplitude attribute Amp(C) of the reflecting layer C, and substituting Amp(S), Amp(C), Amp(S1) and Amp(C1) into the reservoir prediction model to calculate and obtain the reservoir indicator attribute.

[0096] The apparatus provided in this embodiment can execute the reservoir prediction method for layered removal of strong reflection effects provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the method.

[0097] The applicant declares that the above description is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Those skilled in the art should understand that any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention fall within the protection and disclosure scope of the present invention.

Claims

1. A reservoir prediction method for layered elimination of strong reflection effects, characterized in that, The reservoir prediction method includes the following steps: The well-seismic calibration was accurate to determine the seismic reflection characteristics of the reservoirs in the study area and to identify the reflection horizon S corresponding to the reservoirs and the reflection horizon C corresponding to the underlying strata. Forward modeling analysis yielded the factors influencing strong reflection. Based on the aforementioned strong reflection influencing factors, a reservoir prediction model is constructed to eliminate the influence of strong reflection in a stratified manner; Based on the reservoir prediction model, reservoir indicator attributes that eliminate the influence of strong reflections are calculated to predict reservoir distribution.

2. The reservoir prediction method according to claim 1, characterized in that, The process of accurate well-seismic calibration includes: performing lithological logging interpretation on the drilled wells in the study area to obtain lithological types and lithological combinations.

3. The reservoir prediction method according to claim 2, characterized in that, The lithological types include background lithology, reservoir lithology, and overlying lithology; Preferably, the background lithology includes coal and rock; Preferably, the reservoir lithology includes sandstone; Preferably, the underlying lithology includes coal and / or limestone; Preferably, the lithological combination includes any one or at least two of sandy mud ash, sandy mud fly ash, peat ash, or mud ash.

4. The reservoir prediction method according to claim 3, characterized in that, The process of accurate well-seismic calibration also includes: taking drilled wells with sand-mud-coal-ash lithological combinations as typical wells, performing well-seismic synthetic record calibration, determining the seismic reflection characteristics corresponding to the reservoir, and identifying the reflection horizon S corresponding to the reservoir and the reflection horizon C corresponding to the underlying strata.

5. The reservoir prediction method according to any one of claims 1-4, characterized in that, The forward modeling analysis process includes: performing seismic forward modeling on the geological models of the drilled wells in the study area, and combining the actual seismic logging data to analyze the factors influencing strong reflection.

6. The reservoir prediction method according to claim 5, characterized in that, The factors influencing strong reflection include the reflection of the top boundary wave peak of the coal seam and the reflection of the side lobe wave peak of the interface of the underlying limestone layer. Preferably, the effect of the side lobe peak reflection at the interface of the underlying limestone layer is denoted as Amp(S1), where Amp(S1) is a fixed value. Preferably, the method for obtaining Amp(S1) is as follows: performing seismic forward modeling on the drilled wells of the marl lithological combination, and the amplitude attribute of the sidelobe of the underlying limestone layer interface obtained is Amp(S1). Preferably, the influence of the wave peak reflection at the top boundary of the coal seam is denoted as |Amp(C)|-|Amp(C1)|, where Amp(C) represents the amplitude attribute of the reflection layer C corresponding to the typical underground overburden of the sand-mud-coal-ash lithological combination, and Amp(C1) is a fixed value; Preferably, the method for obtaining Amp(C1) is as follows: performing seismic forward modeling on the drilled wells of the sand-mud-lime lithology combination, and the resulting amplitude attribute of the limestone layer interface is Amp(C1).

7. The reservoir prediction method according to claim 6, characterized in that, The reservoir prediction model for eliminating the influence of strong reflections by layering is expressed as: Amp(E)=[Amp(S)-Amp(S1)]-n×[|Amp(C)|-|Amp(C1)|], where Amp(E) represents the sandstone reservoir indicator attribute, Amp(S) represents the amplitude attribute of the reflection layer S corresponding to the typical well reservoir of the sand-mud-coal-ash lithological combination, and n represents the adjustment coefficient, which takes a value of 0-1.

8. The reservoir prediction method according to claim 7, characterized in that, The calculation process of the reservoir indicator attributes includes: obtaining the maximum plane amplitude attribute of the reflecting layer S through actual seismic data as the amplitude attribute Amp(S) of the reflecting layer S, and obtaining the maximum plane amplitude attribute of the reflecting layer C through actual seismic data as the amplitude attribute Amp(C) of the reflecting layer C. Substituting Amp(S), Amp(C), Amp(S1), and Amp(C1) into the reservoir prediction model to calculate the reservoir indicator attributes.

9. The reservoir prediction method according to any one of claims 1-8, characterized in that, The reservoir prediction method includes the following steps: For accurate well-seismic calibration, firstly, lithological logging interpretation is performed on the drilled wells in the study area to obtain lithological types and lithological combinations. The lithological combinations include any one or at least two of sandy mud-lime, sandy mud-fly ash, peat ash, or mud. Then, wells with the sandy mud-fly ash lithological combination are used as typical wells for well-seismic synthetic record calibration to determine the seismic reflection characteristics corresponding to the reservoir and to identify the reflection horizon S corresponding to the reservoir and the reflection horizon C corresponding to the underlying strata. Seismic forward modeling was performed on the geological models of drilled wells in the study area. Combined with actual seismic logging data, the influencing factors of strong reflection were analyzed. These factors include wave crest reflection at the coal seam top boundary and sidelobe wave crest reflection at the interface of the underlying limestone layer. The influence of the sidelobe wave crest reflection at the interface of the underlying limestone layer is denoted as Amp(S1), which is a fixed value. Amp(S1) is obtained by performing seismic forward modeling on drilled wells with marl lithological assemblages, and obtaining the underlying limestone layer... The amplitude attribute of the interface sidelobe is Amp(S1), and the influence of the reflection of the top boundary wave crest of the coal seam is denoted as |Amp(C)|-|Amp(C1)|, where Amp(C) represents the amplitude attribute of the reflection layer C corresponding to the typical underground overburden of the sand-mud-coal-ash lithological combination, and Amp(C1) is a fixed value. The method for obtaining Amp(C1) is as follows: Seismic forward modeling is performed on the drilled wells of the sand-mud-coal-ash lithological combination, and the amplitude attribute of the limestone layer interface obtained is Amp(C1). Based on the factors influencing strong reflection, a reservoir prediction model that eliminates the influence of strong reflection in layers is constructed, expressed as: Amp(E)=[Amp(S)-Amp(S1)]-n×[|Amp(C)|-|Amp(C1)|], where Amp(E) represents the indicator attribute of sandstone reservoir, Amp(S) represents the amplitude attribute of the reflection layer S corresponding to the typical well reservoir of sandstone-mud-coal ash lithology combination, and n represents the adjustment coefficient, which takes a value of 0-1; The maximum planar amplitude attribute of the reflecting layer S is obtained from actual seismic data as the amplitude attribute Amp(S) of the reflecting layer S, and the maximum planar amplitude attribute of the reflecting layer C is obtained from actual seismic data as the amplitude attribute Amp(C) of the reflecting layer C. Substituting Amp(S), Amp(C), Amp(S1), and Amp(C1) into the reservoir prediction model, the reservoir indicator attributes are obtained, and the reservoir distribution is predicted.

10. A reservoir prediction device for stratified elimination of strong reflection effects, characterized in that, The reservoir prediction device includes: The acquisition module is used to determine the seismic reflection characteristics of the reservoir in the study area through accurate well-seismic calibration, and to identify the reflection horizon S corresponding to the reservoir and the reflection horizon C corresponding to the underlying strata. The forward modeling analysis module is used to obtain the influencing factors of strong reflection through forward modeling analysis; The reservoir prediction model construction module is used to construct a reservoir prediction model that eliminates the influence of strong reflection based on the factors affecting strong reflection. The reservoir indicator attribute calculation module is used to calculate the reservoir indicator attributes after removing the strong reflection influence based on the reservoir prediction model, and to predict the reservoir distribution.

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