A favorable reservoir identification method based on double occlusion

Abstract

The present invention provides a favorable reservoir identification method based on double occlusion, including: obtaining logging curves, performing outlier processing and normalization processing on them, and conducting histogram analysis to obtain the histogram distribution ranges of sandstone, mudstone, coal seam and limestone. Conducting histogram analysis on the natural gamma logging curve, combining the logging curve with seismic data, and performing waveform indication inversion to obtain a longitudinal wave impedance inversion body. Based on the natural gamma logging curve and the post-stack seismic data volume, performing waveform indication simulation to obtain a GR data volume, combining the waveform indication to invert the longitudinal wave group antibody, performing lithology occlusion processing on the GR data volume to obtain a sand-mudstone lithology body, obtaining a porosity body, and occluding the porosity body through the sand-mudstone lithology body to obtain a favorable reservoir body with high porosity. The favorable reservoir identification method based on double occlusion provided by the present invention improves the accuracy of lithology prediction effect and can provide a reliable geological basis for tight gas exploration.

Description

Technical Field

[0001] The present invention relates to the technical field of tight gas reservoir exploration, and particularly to a method for identifying favorable reservoirs based on double occlusion. Background Art

[0002] The Ordos Basin is a basin with overlapping coal, oil, and gas vertically. In recent years, the exploration and development of tight gas reservoirs have gradually shown strong strength. The overall sedimentary characteristics within the basin are that the coal seam sedimentary thickness is large, while the sandstone and mudstone are mostly developed in thin interbeds. On seismic profiles, due to extremely low impedance, the coal seam can form a strong reflection interface with the overlying rock formation, often showing as a bright spot, with strong continuity of the in-phase axis and large amplitude values. The tight sandstone reservoir body near the coal seam, due to its sedimentary characteristics mostly in the form of thin interbeds and small wave impedance difference with the surrounding mudstone, is difficult to form a good reflection in-phase axis and is often annihilated or shielded by the coal seam, bringing great challenges to the prediction of sweet spots in tight gas reservoirs. Therefore, many methods have emerged to remove the strong reflection coal seam on seismic profiles, but while removing the coal seam, these methods also greatly damage the seismic signals of tight gas reservoirs, resulting in poor subsequent lithology prediction effects. Therefore, it is very necessary to design a method for identifying favorable reservoirs based on double occlusion. Summary of the Invention

[0003] The purpose of the present invention is to provide a method for identifying favorable reservoirs based on double occlusion, which can solve the problem in the prior art that the seismic signals of tight gas reservoirs are damaged when removing the coal seam, improve the accuracy of lithology prediction effects, and can provide a reliable geological basis for tight gas exploration.

[0004] To achieve the above purpose, the present invention provides the following scheme:

[0005] A method for identifying favorable reservoirs based on double occlusion includes the following steps:

[0006] Step 1: Obtain well logging curves and perform outlier processing on them;

[0007] Step 2: Perform standardization processing on the well logging curves after outlier processing;

[0008] Step 3: Perform histogram analysis on the longitudinal wave velocity and density well logging curves after standardization processing to obtain the histogram distribution ranges of sandstone, mudstone, coal seam, and limestone;

[0009] Step 4: Perform histogram analysis on the natural gamma well logging curve after standardization processing, and distinguish sandstone and mudstone according to the analysis results;

[0010] Step 5: Combine well logging curves with seismic data to perform waveform indication inversion to obtain a longitudinal wave impedance inversion body, and distinguish coal seam and limestone;

[0011] Step 6: Based on the natural gamma logging curve and the post-stack seismic data volume obtained by prestack time migration of seismic data, perform waveform-indicated simulation to obtain the GR data volume, and distinguish mudstone and sandstone;

[0012] Step 7: According to the histogram distribution ranges of sandstone, mudstone, coal seam and limestone, jointly perform waveform-indicated inversion of P-wave impedance body, and perform lithology masking processing on the GR data volume to remove coal seam and limestone, so as to obtain the sand-mudstone lithology body;

[0013] Step 8: Obtain the target geological body, perform porosity waveform-indicated simulation on the target geological body to obtain the porosity volume, and mask the porosity volume with the sand-mudstone lithology body obtained in Step 7 to obtain the favorable reservoir body with high porosity.

[0014] Optionally, in Step 1, obtain the logging curve and perform outlier processing on it, specifically:

[0015] Obtain the logging curve, and for the outliers appearing on the logging curve, borrow the normal values around the upper and lower strata to replace the outliers to complete the outlier processing, where the logging curve includes density logging curve, acoustic logging curve and natural gamma logging curve.

[0016] Optionally, in Step 2, perform standardization processing on the logging curve after outlier processing, specifically:

[0017] Adopt the method of frequency histogram, select appropriate standard layers for the density logging curve, acoustic logging curve and natural gamma logging curve after outlier processing respectively, perform batch standardization processing, and perform fine standardization processing on the curves with poor standardization effect.

[0018] According to the specific embodiments provided by the present invention, the following technical effects are disclosed: The favorable reservoir identification method based on double occlusion provided by the present invention includes obtaining logging curves and performing outlier processing on them, performing standardization processing on the logging curves after outlier processing, performing histogram analysis on the longitudinal wave velocity and density logging curves after standardization processing to obtain the histogram distribution ranges of sandstone, mudstone, coal seam, and limestone, performing histogram analysis on the natural gamma logging curve after standardization processing, differentiating sandstone and mudstone according to the analysis results, combining the logging curves and seismic data for waveform-indicated inversion to obtain the longitudinal wave impedance inversion body, differentiating coal seam and limestone, performing waveform-indicated simulation based on the natural gamma logging curve and the post-stack seismic data volume obtained by prestack time migration of seismic data to obtain the GR data volume, differentiating mudstone and sandstone, combining the waveform-indicated inversion longitudinal wave group antibody according to the histogram distribution ranges of sandstone, mudstone, coal seam, and limestone, performing lithology occlusion processing on the GR data volume to remove coal seam and limestone to obtain the sand-mudstone lithology body, obtaining the target geological body, performing porosity waveform-indicated simulation on the target geological body to obtain the porosity body, and occluding the porosity body with the sand-mudstone lithology body obtained in step 7 to obtain the favorable reservoir body with high porosity; this method can solve the problem in the prior art that the seismic signal of the tight gas reservoir is damaged when removing the coal seam, improve the accuracy of lithology prediction effect, and can provide a reliable geological basis for tight gas exploration. Brief Description of the Drawings

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0020] Figure 1 Schematic flow chart of the favorable reservoir identification method based on double occlusion in the embodiment of the present invention;

[0021] Figure 2 Comparison chart of logging curves before and after removing outliers;

[0022] Figure 3a1 Schematic diagram before standardizing the natural gamma curve;

[0023] Figure 3a2 Schematic diagram after standardizing the natural gamma curve;

[0024] Figure 3b1 Schematic diagram before standardizing the density curve;

[0025] Figure 3b2 Schematic diagram after standardizing the density curve;

[0026] Figure 3c1 Schematic diagram before the normalization of the longitudinal wave velocity curve;

[0027] Figure 3c2 Schematic diagram after the normalization of the longitudinal wave velocity curve;

[0028] Figure 4 Longitudinal wave impedance histograms of sandstone, mudstone, coal seam and limestone;

[0029] Figure 5 Natural gamma histograms of sandstone and mudstone;

[0030] Figure 6 Wave impedance inversion profile;

[0031] Figure 7 Natural gamma waveform indication simulation profile;

[0032] Figure 8 Lithology occlusion waveform indication simulation profile;

[0033] Figure 9 Favorable reservoir body simulation profile. Detailed implementation manners

[0034] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0035] The purpose of the present invention is to provide a method for identifying favorable reservoirs based on double occlusion, which can solve the problem that the seismic signals of tight gas reservoirs are damaged when removing coal seams in the prior art, improve the accuracy of lithology prediction effect, and provide a reliable geological basis for tight gas exploration.

[0036] To make the above objects, features and advantages of the present invention more obvious and understandable, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific implementation manners.

[0037] As Figure 1 shown, the method for identifying favorable reservoirs based on double occlusion provided by the embodiments of the present invention includes the following steps:

[0038] Step 1: Obtain well logging curves and perform outlier processing on them;

[0039] Obtain well logging curves. For the outliers that appear on the well logging curves, use the normal values around the upper and lower strata to replace the outliers to complete the outlier processing. Among them, the well logging curves include density well logging curves, acoustic well logging curves, and natural gamma well logging curves. Among them, the comparison chart of the well logging curves before and after removing the outliers is as Figure 2 shown;

[0040] Step 2: Standardize the well logging curves after outlier processing;

[0041] Using the method of frequency histogram, for the density well logging curve, acoustic well logging curve, and natural gamma well logging curve after outlier processing, select appropriate standard layers and perform batch standardization processing respectively. For the curves with poor standardization effect, perform fine standardization processing. Among them, the comparison charts before and after standardization processing are as Figure 3a1 , Figure 3a2 , Figure 3b1 , Figure 3b2 , Figure 3c1 and Figure 3c2 shown;

[0042] Step 3: Perform histogram analysis on the longitudinal wave velocity and density well logging curves after standardization processing to obtain the histogram distribution ranges of sandstone, mudstone, coal seam, and limestone;

[0043] As Figure 4 shown, it can be seen from the histogram that the longitudinal wave impedance value of the coal seam is usually a minimum value. For example, in the example, it is less than 8000 g / cm3*m / s; the longitudinal wave impedance of limestone is usually a maximum value. For example, in the example, it is greater than 14000 g / cm3*m / s, and the longitudinal wave impedance of sandstone and mudstone is between that of the coal seam and limestone, that is, 8000 - 14000 g / cm3*m / s, but it is difficult to distinguish between sandstone and mudstone;

[0044] Step 4: Perform histogram analysis on the natural gamma well logging curve after standardization processing, and distinguish sandstone and mudstone according to the analysis results;

[0045] As Figure 5 shown, the GR value of sandstone is less than 105 API, while the GR value of mudstone is greater than 105 API. Sandstone and mudstone can be well distinguished through the natural gamma well logging curve;

[0046] Step 5: As Figure 6 shown, combine the well logging curves with seismic data to perform waveform-indicated inversion to obtain the longitudinal wave impedance inversion body and distinguish the coal seam and limestone;

[0047] This inversion method is a geostatistical inversion with the characteristics of low-frequency determination and high-frequency randomness. For the inverted profile, according to the indication of the histogram, the coal seam and limestone can be distinguished;

[0048] Step 6: As Figure 7 shown, based on the natural gamma logging curve and the post-stack seismic data volume obtained by prestack time migration of seismic data, perform waveform indicator simulation to obtain a GR data volume, and distinguish shale and sandstone;

[0049] On this data volume, shale can be well distinguished, but it is difficult to distinguish coal seams, limestone and sandstone;

[0050] Step 7: As Figure 8 shown, according to the histogram distribution ranges of sandstone, shale, coal seam and limestone, jointly perform waveform indicator inversion of the P-wave impedance body, perform lithology masking processing on the GR data volume, remove coal seams and limestone, and obtain a sand-shale lithology body;

[0051] Jointly perform waveform indicator inversion of the P-wave impedance body, perform lithology masking processing on the waveform indicator simulated GR body, and keep the value range of the P-wave impedance body between 8000 and 14000. In this way, it is equivalent to removing coal seams and limestone. Finally, what remains is the sand-shale body. And from Figure 3c1 and Figure 3c2 it can be known that the boundary of the GR values of sandstone and shale is 105 API, thus well identifying the tight sandstone reservoir;

[0052] By using the method of lithology masking to process the natural gamma body, the coal seams and limestone in the coal-bearing strata can be removed, leaving sandstone and shale. Moreover, the GR value of shale is high and the GR value of sandstone is low, thus obtaining a tight sandstone reservoir. This method is simple, fast and effective.

[0053] Step 8: Obtain the target geological body, perform porosity waveform indicator simulation on the target geological body to obtain a porosity body, and mask the porosity body with the sand-shale lithology body obtained in Step 7. As Figure 9 shown, thus obtaining a favorable reservoir body with high porosity, providing a geological basis for predicting geological sweet spots.

[0054] The favorable reservoir identification method based on double occlusion provided by the present invention includes obtaining logging curves and performing outlier processing on them, performing standardization processing on the logging curves after outlier processing, performing histogram analysis on the P-wave velocity and density logging curves after standardization processing to obtain the histogram distribution ranges of sandstone, mudstone, coal seam, and limestone, performing histogram analysis on the natural gamma logging curve after standardization processing, distinguishing sandstone and mudstone according to the analysis results, combining logging curves and seismic data to perform waveform-indicated inversion to obtain a P-wave impedance inversion body, distinguishing coal seam and limestone, performing waveform-indicated simulation based on the natural gamma logging curve and the post-stack seismic data volume obtained by prestack time migration of seismic data to obtain a GR data volume, distinguishing mudstone and sandstone, combining the waveform-indicated inversion P-wave group antibody according to the histogram distribution ranges of sandstone, mudstone, coal seam, and limestone, performing lithology occlusion processing on the GR data volume to remove coal seam and limestone to obtain a sand-mudstone lithology body, obtaining a target geological body, performing porosity waveform-indicated simulation on the target geological body to obtain a porosity body, and occluding the porosity body with the sand-mudstone lithology body obtained in step 7 to obtain a favorable reservoir body with high porosity; this method can solve the problem in the prior art that the seismic signal of a tight gas reservoir is damaged when removing the coal seam, improve the accuracy of lithology prediction effect, and can provide a reliable geological basis for tight gas exploration.

[0055] In this article, specific examples are used to elaborate on the principle and implementation mode of the present invention. The description of the above embodiments is only used to help understand the method and its core idea of the present invention; at the same time, for those of ordinary skill in the art, according to the idea of the present invention, there will be changes in the specific implementation mode and application scope. In summary, the content of this specification should not be construed as a limitation to the present invention.

Claims

1. A favorable reservoir identification method based on double occlusion, characterized in that It includes the following steps: Step 1: Obtain well logging curves and process their outliers; Step 2: Standardize the well logging curves after outlier processing; Step 3: Conduct histogram analysis on the P-wave velocity and density well logging curves after standardization processing to obtain the histogram distribution ranges of sandstone, mudstone, coal seam and limestone; Step 4: Conduct histogram analysis on the natural gamma well logging curve after standardization processing and distinguish sandstone and mudstone according to the analysis results; Step 5: Combine well logging curves with seismic data to perform waveform-indicated inversion to obtain a P-wave impedance inversion body and distinguish coal seam and limestone; Step 6: Based on the post-stack seismic data volume obtained from the pre-stack time migration of the natural gamma well logging curve and seismic data, perform waveform-indicated simulation to obtain a GR data volume and distinguish mudstone and sandstone; Step 7: According to the histogram distribution ranges of sandstone, mudstone, coal seam and limestone, combine the waveform-indicated inversion P-wave group antibody, perform lithology masking processing on the GR data volume, remove coal seam and limestone, and obtain a sand-mudstone lithology body; Step 8: Obtain the target geological body, perform porosity waveform-indicated simulation on the target geological body to obtain a porosity body, and mask the porosity body with the sand-mudstone lithology body obtained in Step 7 to obtain a favorable reservoir body with high porosity.

2. The favorable reservoir identification method based on double occlusion according to claim 1, wherein In Step 1, obtain well logging curves and process their outliers. Specifically: Obtain well logging curves. For the outliers that appear on the well logging curves, use the normal values around the upper and lower strata to replace the outliers to complete the outlier processing. Among them, the well logging curves include density well logging curves, acoustic well logging curves and natural gamma well logging curves.

3. The favorable reservoir identification method based on double occlusion according to claim 2, wherein In Step 2, standardize the well logging curves after outlier processing. Specifically: Adopt the method of frequency histogram. For the density well logging curve, acoustic well logging curve and natural gamma well logging curve after outlier processing, select appropriate standard layers and perform batch standardization processing respectively. For the curves with poor standardization effect, perform fine standardization processing.

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