Method and device for identifying gas-bearing high-quality reservoir of sandstone in thin-layer river channel

Abstract

The invention discloses a thin-layer river sandstone gas-bearing high-quality reservoir identification method and device. The method comprises the following steps: determining a quasi-longitudinal wave impedance sandstone identification threshold value, a longitudinal wave impedance effective sandstone identification threshold value and a gas-bearing high-quality sandstone identification threshold value; comparing the quasi-longitudinal wave impedance data body with a quasi-longitudinal wave impedance sandstone identification threshold value, and determining a sandstone distribution range; comparing the longitudinal wave impedance data volume with a longitudinal wave impedance effective sandstone identification threshold value, and determining an effective sandstone distribution range from the sandstone distribution range; comparing the longitudinal and transverse wave velocity ratio data volume with a gas-bearing high-quality sandstone recognition threshold value, and determining a gas-bearing high-quality sandstone distribution range from the effective sandstone distribution range; and based on the gas-bearing enrichment seismic indication factor, the gas-bearing high-quality reservoir is identified from the gas-bearing high-quality sandstone distribution range, and the gas-bearing enrichment seismic indication factor is determined according to the longitudinal and transverse wave velocity ratio data volume and the longitudinal wave impedance data volume. According to the method, the thin-layer river sandstone gas-bearing high-quality reservoir can be accurately identified, and a basis is provided for accurate deployment of a development well site.

Description

Technical Field

[0001] The invention relates to the technical field of oil and gas exploration and development, and in particular to a method and a device for identifying a thin-layer river channel sandstone gas-bearing high-quality reservoir. Background Art

[0002] With the continuous deepening of oil and gas exploration and development, thin-layer river channel sandstone has gradually become an important exploration target. Accurately and effectively predicting or identifying thin-layer river channel sandstone gas-bearing high-quality reservoirs is of great significance for the precise deployment of development wells.

[0003] However, due to the thin thickness of high-quality gas-bearing thin-layer channel sandstone, rapid phase change, complex oil-gas-water relationship, and small differences in the response characteristics of geophysical parameters between high-quality gas-bearing reservoirs and surrounding rocks, it is difficult to effectively predict using conventional geophysical methods, resulting in low prediction accuracy of thin-layer channel sandstone high-quality gas-bearing reservoirs, which in turn leads to a lack of reliable basis for the deployment of development wells and low oil and gas exploration efficiency.

[0004] To address the above problems, no effective solution has been proposed yet. Summary of the invention

[0005] The embodiments of this specification provide a method and device for identifying a thin-layer river channel sandstone gas-bearing high-quality reservoir to solve the problem that the prior art cannot accurately and effectively predict or identify a thin-layer river channel sandstone gas-bearing high-quality reservoir.

[0006] In a first aspect, the embodiments of this specification provide a method for identifying a thin-layer channel sandstone gas-bearing high-quality reservoir, the method comprising:

[0007] Determine the pseudo-P-wave impedance sandstone identification threshold, P-wave impedance effective sandstone identification threshold and gas-bearing high-quality sandstone identification threshold;

[0008] The pseudo-P-wave impedance data volume obtained based on post-stack inversion is compared with the pseudo-P-wave impedance sandstone identification threshold to determine the sandstone distribution range;

[0009] Comparing the P-wave impedance data volume obtained based on post-stack inversion with the P-wave impedance effective sandstone identification threshold to determine the effective sandstone distribution range from the sandstone distribution range;

[0010] Comparing the P-wave and S-wave velocity ratio data volume obtained based on prestack inversion with the gas-bearing high-quality sandstone identification threshold to determine the gas-bearing high-quality sandstone distribution range from the effective sandstone distribution range;

[0011] Based on the gas-enriched seismic indicator factor, the gas-bearing high-quality reservoir is identified from the distribution range of the gas-bearing high-quality sandstone, and the gas-enriched seismic indicator factor is determined according to the P-wave velocity ratio data volume and the P-wave impedance data volume.

[0012] In some embodiments, the method further comprises:

[0013] Acquire P-wave impedance logging data, natural gamma logging data, P-wave velocity ratio logging data and pseudo-P-wave impedance logging data, wherein the pseudo-P-wave impedance logging data is obtained by feature reconstruction based on the P-wave impedance logging data and the natural gamma logging data;

[0014] Filtering the P-wave impedance logging data, pseudo-P-wave impedance logging data, and P-wave and S-wave velocity ratio logging data;

[0015] A lithology and physical property identification scale is constructed with the P-wave impedance logging data after filtering as the horizontal axis and the pseudo-P-wave impedance logging data after filtering as the vertical axis;

[0016] A gas-bearing high-quality sandstone identification scale was constructed with the P-wave impedance logging data after filtering as the horizontal axis and the P-wave and S-wave velocity ratio logging data after filtering as the vertical axis.

[0017] In some embodiments, the determining of the pseudo-P wave impedance sandstone identification threshold, the P wave impedance effective sandstone identification threshold and the gas-bearing high-quality sandstone identification threshold comprises:

[0018] Determine the pseudo-P wave impedance sandstone identification threshold for distinguishing lithology and the P wave impedance effective sandstone identification threshold for distinguishing physical properties from the lithology and physical properties identification plate;

[0019] The identification threshold of high-quality gas-bearing sandstone for distinguishing gas-bearing properties is determined from the identification scale of high-quality gas-bearing sandstone.

[0020] In some embodiments, comparing the pseudo-P-wave impedance data volume obtained based on post-stack inversion with the pseudo-P-wave impedance sandstone identification threshold to determine the sandstone distribution range includes:

[0021] When the pseudo-P-wave impedance data volume obtained based on post-stack inversion is greater than the pseudo-P-wave impedance sandstone identification threshold, determining the sandstone distribution range;

[0022] The method of comparing the P-wave impedance data volume obtained based on post-stack inversion with the P-wave impedance effective sandstone identification threshold to determine the effective sandstone distribution range from the sandstone distribution range includes:

[0023] When the P-wave impedance data volume obtained based on post-stack inversion is smaller than the P-wave impedance effective sandstone identification threshold, determining the effective sandstone distribution range from the sandstone distribution range;

[0024] The method of comparing the P-wave and S-wave velocity ratio data volume obtained based on prestack inversion with the gas-bearing high-quality sandstone identification threshold to determine the gas-bearing high-quality sandstone distribution range from the effective sandstone distribution range includes:

[0025] When the P-wave and S-wave velocity ratio data volume obtained based on prestack inversion is smaller than the gas-bearing high-quality sandstone identification threshold, the gas-bearing high-quality sandstone distribution range is determined from the effective sandstone distribution range.

[0026] In some embodiments, the method further comprises:

[0027] According to the product of the P-wave impedance data volume and the P-S wave velocity ratio data volume, the seismic sensitivity quantitative index of high-quality gas-bearing sandstone is constructed;

[0028] According to the quantitative index of seismic sensitivity of high-quality gas-bearing sandstone, a prediction model of seismic indicator factors of gas-bearing enrichment is constructed according to the following formula:

[0029] F enrichment_Inversion =-a -10 ·F quantization_Inversion 2 +b -6 ·F quantization_Inversion +c

[0030] Among them, F enrichment_Inversion is the seismic indicator factor of gas enrichment, F quantization_Inversion is the quantitative index of seismic sensitivity of high-quality gas-bearing sandstone, and a, b, and c are constants.

[0031] In some embodiments, the gas-enrichment seismic indicator factor is determined based on a P-wave velocity ratio data volume and a P-wave impedance data volume, and includes:

[0032] The product of the P-wave velocity ratio data volume and the P-wave impedance data volume is input into the prediction model of the gas-enriched seismic indicator factor to obtain the gas-enriched seismic indicator factor.

[0033] In some embodiments, the method of identifying high-quality gas-bearing reservoirs from the distribution range of high-quality gas-bearing sandstones based on the gas-enrichment seismic indicator factor includes:

[0034] Compare the seismic indicator factors of gas enrichment in the distribution range of high-quality gas-bearing sandstones;

[0035] From the distribution range of high-quality gas-bearing sandstones, areas where the gas-enrichment seismic indicator factor is greater than the threshold of the gas-enrichment seismic indicator factor are identified as high-quality gas-bearing reservoirs.

[0036] In a second aspect, the embodiments of this specification also provide a device for identifying a thin-layer river channel sandstone gas-bearing high-quality reservoir, the device comprising:

[0037] A threshold determination module, used to determine the pseudo-P wave impedance sandstone identification threshold, the P wave impedance effective sandstone identification threshold and the gas-bearing high-quality sandstone identification threshold;

[0038] A first range determination module is used to compare the pseudo-P-wave impedance data volume obtained based on post-stack inversion with the pseudo-P-wave impedance sandstone identification threshold to determine the sandstone distribution range;

[0039] A second range determination module is used to compare the P-wave impedance data volume obtained based on post-stack inversion with the P-wave impedance effective sandstone identification threshold to determine the effective sandstone distribution range from the sandstone distribution range;

[0040] A third range determination module is used to compare the P-wave and S-wave velocity ratio data volume obtained based on prestack inversion with the gas-bearing high-quality sandstone identification threshold to determine the gas-bearing high-quality sandstone distribution range from the effective sandstone distribution range;

[0041] The identification module is used to identify high-quality gas-bearing reservoirs from the distribution range of high-quality gas-bearing sandstones based on gas-bearing enrichment seismic indicator factors, wherein the gas-bearing enrichment seismic indicator factors are determined based on a P-wave velocity ratio data body and a P-wave impedance data body.

[0042] In a third aspect, an embodiment of the present specification also provides a computer device, including a memory, a processor, and a computer program / instructions stored in the memory, wherein the processor executes the computer program / instructions to implement the steps of the above-mentioned method for identifying thin-layer river channel sandstone gas-bearing high-quality reservoirs.

[0043] In a fourth aspect, the embodiments of this specification also provide a computer-readable storage medium having a computer program / instruction stored thereon, which, when executed by a processor, implements the steps of the above-mentioned method for identifying thin-layer channel sandstone gas-bearing high-quality reservoirs.

[0044] The embodiments of this specification provide a method and device for identifying high-quality gas-bearing reservoirs in thin-layer river channel sandstones. First, the pseudo-P-wave impedance sandstone identification threshold, the P-wave impedance effective sandstone identification threshold, and the gas-bearing high-quality sandstone identification threshold are determined. Then, the pseudo-P-wave impedance data body obtained based on post-stack inversion is compared with the pseudo-P-wave impedance sandstone identification threshold to determine the sandstone distribution range. Then, the P-wave impedance data body obtained based on post-stack inversion is compared with the P-wave impedance effective sandstone identification threshold to determine the effective sandstone distribution range from the sandstone distribution range. Then, the P-wave velocity ratio data body obtained based on pre-stack inversion is compared with the gas-bearing high-quality sandstone identification threshold to determine the gas-bearing high-quality sandstone distribution range from the effective sandstone distribution range. Finally, based on the gas-enriched seismic indicator factor, the gas-bearing high-quality reservoir is identified from the gas-bearing high-quality sandstone distribution range, and the gas-enriched seismic indicator factor is determined according to the P-wave velocity ratio data body and the P-wave impedance data body. In the embodiments of this specification, the pseudo-P-wave impedance data volume, the P-wave impedance data volume, and the P-wave and S-wave velocity ratio data volume can be accurately obtained by using high-resolution pre-stack and post-stack inversion methods. On this basis, the idea of ​​"sandstone-effective sandstone-gas-bearing high-quality sandstone" hierarchical prediction and step-by-step constraint is adopted, that is, the pseudo-P-wave impedance data volume, the P-wave impedance data volume, and the P-wave and S-wave velocity ratio data volume are successively compared with the corresponding thresholds, and the effective sandstone distribution range is accurately determined under the constraint of the sandstone distribution range, and the gas-bearing high-quality sandstone distribution range is accurately determined under the constraint of the effective sandstone distribution range. Finally, the gas-enriched seismic indicator factor can be used to accurately identify the gas-bearing high-quality reservoir from the gas-bearing high-quality sandstone distribution range, and improve the identification accuracy of the thin-layer river sandstone gas-bearing high-quality reservoir, thereby providing a reliable basis for the precise deployment of well sites in the development stage. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0046] Figure 1 It is a flow chart of a method for identifying a thin-layer river channel sandstone gas-bearing high-quality reservoir provided in an embodiment of this specification;

[0047] Figure 2 It is the lithology physical property identification quantity version (lithology-effective sandstone rock physics seismic identification quantity version) provided in the embodiments of this specification;

[0048] Figure 3It is the gas-bearing high-quality sandstone identification quantity version (gas-bearing high-quality sandstone rock physics seismic identification quantity version) provided in the embodiments of this specification;

[0049] Figure 4 It is a cross-sectional diagram of the pseudo longitudinal impedance data volume inversion provided in the embodiment of this specification;

[0050] Figure 5 is a cross-sectional view of the longitudinal wave impedance data volume inversion provided in the embodiment of this specification;

[0051] Figure 6 is a cross-sectional diagram of effective sandstone indicator factors provided in the embodiments of this specification;

[0052] Figure 7 It is a profile diagram of the indicator factors of high-quality gas-bearing sandstone provided in the embodiments of this specification;

[0053] Figure 8 It is a prediction model analysis diagram of the gas enrichment logging indicator factor provided in the embodiment of this specification;

[0054] Fig. 9 It is a profile diagram of seismic indicator factors of gas enrichment degree provided in the embodiments of this specification;

[0055] Fig.10 This is a schematic diagram of the structure of a device for identifying a thin-layer river channel sandstone gas-bearing high-quality reservoir provided in an embodiment of this specification;

[0056] Fig.11 It is a schematic diagram of the structural composition of an electronic device provided in an embodiment of this specification. DETAILED DESCRIPTION

[0057] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments in this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this specification.

[0058] With the continuous deepening of oil and gas exploration and development, thin-layer river channel sandstone has gradually become an important exploration target. Accurately and effectively predicting or identifying thin-layer river channel sandstone gas-bearing high-quality reservoirs is of great significance for the precise deployment of development wells.

[0059] However, thin-layer channel sandstones are usually stacked in multiple stages vertically, thin in thickness, and have fast phase changes horizontally. The oil, gas and water relationship is complex, which leads to many challenges in oil and gas exploration in thin-layer channel sandstones. Conventional prediction methods cannot meet the requirements of oil and gas reservoir exploration and development. Generally, compared with the surrounding rock, the geophysical parameter response characteristics of thin-layer channel sandstones with high gas content are small, and the superposition of parameters such as wave impedance and longitudinal and transverse wave velocity ratio is serious. It is often difficult to effectively identify high-quality gas-bearing sandstones, dense dry sandstones and mudstones using a single parameter. As an effective reservoir prediction technology, seismic inversion is of great significance in oilfield exploration and development. However, under the condition that the thickness of thin river channel sandstone is less than 1 / 4 to 1 / 8 of the seismic wavelength, the seismic response characteristics of thin river channel sandstone are unclear due to the limitation of seismic resolution. Conventional seismic inversion methods usually use well logging data to interpolate to obtain a low-frequency initial model and merge it with seismic frequency band information, resulting in the seismic inversion results being unable to break through the effective frequency band of seismic data and lacking high-frequency information. As a result, conventional inversion results can only predict the macroscopic favorable phase belt distribution of sand bodies, and cannot accurately predict or identify thin river channel sandstone gas-bearing high-quality reservoirs (cannot accurately characterize the boundaries and vertical distribution characteristics of thin sand bodies). That is, it is difficult to effectively predict thin river channel sandstone gas-bearing high-quality reservoirs using conventional geophysical methods, resulting in low prediction accuracy of thin river channel sandstone gas-bearing high-quality reservoirs, which in turn leads to a lack of reliable basis for the deployment of development wells and low oil and gas exploration efficiency.

[0060] In view of the problem that the thin layer of high-quality gas-bearing river channel sandstone is thin in thickness, has fast phase change, complex oil-gas-water relationship, and small difference in geophysical parameter response characteristics between the high-quality gas-bearing reservoir and the surrounding rock, and is difficult to effectively predict using conventional geophysical methods, this application considers introducing a method and device for identifying thin layer of high-quality gas-bearing river channel sandstone reservoirs to improve the identification or prediction accuracy of thin layer of high-quality gas-bearing river channel sandstone reservoirs, thereby providing a reliable basis for the precise deployment of development wells.

[0061] Based on the above ideas, this specification proposes a method for identifying high-quality gas-bearing reservoirs in thin-layer river channel sandstones. First, the pseudo-P-wave impedance sandstone identification threshold, the P-wave impedance effective sandstone identification threshold, and the gas-bearing high-quality sandstone identification threshold are determined. Then, the pseudo-P-wave impedance data body obtained based on post-stack inversion is compared with the pseudo-P-wave impedance sandstone identification threshold to determine the sandstone distribution range. Then, the P-wave impedance data body obtained based on post-stack inversion is compared with the P-wave impedance effective sandstone identification threshold to determine the effective sandstone distribution range from the sandstone distribution range. Then, the P-wave velocity ratio data body obtained based on pre-stack inversion is compared with the gas-bearing high-quality sandstone identification threshold to determine the gas-bearing high-quality sandstone distribution range from the effective sandstone distribution range. Finally, based on the gas-enriched seismic indicator factor, the gas-bearing high-quality reservoir is identified from the gas-bearing high-quality sandstone distribution range, and the gas-enriched seismic indicator factor is determined according to the P-wave velocity ratio data body and the P-wave impedance data body.

[0062] Figure 1 It is a flow chart of a method for identifying high-quality gas-bearing reservoirs in thin-layer river channel sandstone provided in an embodiment of this specification. Although this specification provides method operation steps or device structures as shown in the following embodiments or drawings, the method or device may include more or fewer operation steps or module units after partial merger based on routine or no creative labor. In the steps or structures that do not logically have a necessary causal relationship, the execution order of these steps or the module structure of the device is not limited to the execution order or module structure shown in the embodiments or drawings of this specification. When the method or module structure described is applied in an actual device, server or terminal product, it can be executed sequentially or in parallel according to the method or module structure shown in the embodiments or drawings (for example, in a parallel processor or multi-threaded processing environment, or even including distributed processing, server cluster implementation environment). For specific implementation, refer to Figure 1 As shown, the method may include the following contents.

[0063] S101: Determine the pseudo-P-wave impedance sandstone identification threshold, the P-wave impedance effective sandstone identification threshold, and the gas-bearing high-quality sandstone identification threshold.

[0064] In some embodiments, before the above S101, the specific implementation may also include:

[0065] Acquire P-wave impedance logging data, natural gamma logging data, P-wave velocity ratio logging data and pseudo-P-wave impedance logging data, wherein the pseudo-P-wave impedance logging data is obtained by feature reconstruction based on the P-wave impedance logging data and the natural gamma logging data;

[0066] Filtering the P-wave impedance logging data, pseudo-P-wave impedance logging data, and P-wave and S-wave velocity ratio logging data;

[0067] A lithology and physical property identification scale is constructed with the P-wave impedance logging data after filtering as the horizontal axis and the pseudo-P-wave impedance logging data after filtering as the vertical axis;

[0068] A gas-bearing high-quality sandstone identification scale was constructed with the P-wave impedance logging data after filtering as the horizontal axis and the P-wave and S-wave velocity ratio logging data after filtering as the vertical axis.

[0069] In some embodiments, the above-mentioned determination of the pseudo-P wave impedance sandstone identification threshold, the P wave impedance effective sandstone identification threshold and the gas-bearing high-quality sandstone identification threshold may include:

[0070] Determine the pseudo-P wave impedance sandstone identification threshold for distinguishing lithology and the P wave impedance effective sandstone identification threshold for distinguishing physical properties from the lithology and physical properties identification plate;

[0071] The identification threshold of high-quality gas-bearing sandstone for distinguishing gas-bearing properties is determined from the identification scale of high-quality gas-bearing sandstone.

[0072] In some embodiments, the above-mentioned P-wave impedance logging data, natural gamma logging data, and P-wave velocity ratio logging data may be logging data that has been pre-processed and then optimized. The pre-processing may include environmental correction and standardization processing, which is not specifically limited in the present application. Specifically, the original logging data (original logging data such as original acoustic time difference, shear wave time difference, density, natural gamma, etc.) may be obtained first, and then the original logging data may be environmentally corrected and standardized. Then, based on the sensitivity of the known logging data to lithology, physical properties, and gas content (such as the sensitivity of the logging curve to lithology, physical properties, and gas content), the logging data that is most sensitive to lithology, physical properties, and gas content is selected from the processed logging data, such as selecting the natural gamma logging curve data (i.e., the above-mentioned natural gamma logging data) as a lithology sensitive parameter, the P-wave impedance logging data as a physical property sensitive parameter, and the P-wave velocity ratio logging data as a gas content sensitive parameter. By prioritizing lithology sensitive parameters, physical property sensitive parameters and gas content sensitive parameters, the foundation can be laid for the subsequent spatial prediction of lithology, physical property and gas content (i.e. determining the sandstone distribution range, effective sandstone distribution range and gas-bearing high-quality sandstone distribution range).

[0073] In some embodiments, the pseudo-P-wave impedance logging data is obtained by feature reconstruction based on the P-wave impedance logging data and the natural gamma logging data. The specific feature reconstruction process is as follows: the medium and low frequency information in the P-wave impedance logging data can be retained, and the high frequency information in the natural gamma logging data can be integrated. Then, the characteristic curve is reconstructed in the frequency domain, and the reconstructed synthetic seismic record is kept consistent with the original one, and the seismic band inversion result is not affected. The pseudo-P-wave impedance logging data is constructed by numerically reversing the high frequency information of the natural gamma logging data. By integrating the high frequency information in the natural gamma logging data, the constructed pseudo-P-wave impedance data can have a good ability to distinguish sandstone from mudstone, and a high lithology differentiation ability. At the same time, it provides high-resolution sensitive characteristic parameters for the identification of thin-layer river channel sandstone gas-bearing high-quality reservoirs. The high-resolution sensitive characteristic parameters are the pseudo-P-wave impedance logging data.

[0074] In some embodiments, the above-mentioned filtering process may be a low-pass filtering process, and the low-pass filtering process may retain the seismic inversion frequency band information. A low-pass filter may be used to perform low-pass filtering on the P-wave impedance logging data, the pseudo-P-wave impedance logging data, and the P-wave velocity ratio logging data to obtain the P-wave impedance logging data, the pseudo-P-wave impedance logging data, and the P-wave velocity ratio logging data after filtering or low-pass filtering. After obtaining the P-wave impedance logging data, the pseudo-P-wave impedance logging data, and the P-wave velocity ratio logging data after filtering, a lithology-physical property identification plate (or lithology-effective sandstone rock physics seismic identification plate) and a gas-bearing high-quality sandstone identification plate (gas-bearing high-quality sandstone rock physics seismic identification plate) may be constructed. Specifically:

[0075] The above-mentioned construction uses the filtered P-wave impedance logging data as the horizontal axis and the filtered pseudo-P-wave impedance logging data as the vertical axis. The specific process is: the filtered P-wave impedance logging data is intersected with the filtered pseudo-P-wave impedance logging data, and the reservoir logging interpretation conclusion is used as the color code to establish the lithology-effective sandstone rock physics seismic identification scale (lithology-effective sandstone rock physics seismic identification scale). Among them, intersecting the filtered P-wave impedance logging data with the filtered pseudo-P-wave impedance logging data means establishing an interactive scatter plot of the two parameters, such as Figure 2 As shown, Figure 2 It is a lithology and physical property identification plate (lithology - effective sandstone rock physics and seismic identification plate). Figure 2 The horizontal axis in the figure is the P-wave impedance logging data after filtering (PI _logs_低通滤波 , unit is g / cm 3 *m / s, the vertical axis is the pseudo-P-wave impedance logging data after filtering (PI-GR _logs_低通滤波 , unit is g / cm 3 *m / s), Figure 2 The colors of the scattered points are based on the reservoir logging interpretation conclusions, which can determine the numerical distribution characteristics of different reservoir types and different attributes. Figure 2It can be seen from the lithology and physical property identification plate (lithology-effective sandstone rock physics seismic identification plate) that the pseudo-P-wave impedance (PI-GR) can better distinguish sandstone from mudstone due to the fusion of high-frequency information of natural gamma, but it cannot distinguish low-resistance effective sandstone from high-resistance dry sandstone. Although P-wave impedance (PI) cannot distinguish sandstone from mudstone, it can distinguish low-resistance effective sandstone from high-resistance dry sandstone. Therefore, in the lithology and physical property identification plate (lithology-effective sandstone rock physics seismic identification plate) established by pseudo-P-wave impedance and P-wave impedance, the numerical distribution range of effective sandstone, high-resistance dry sandstone, low-resistance mudstone, and high-resistance mudstone can be better divided. Among them, effective sandstone is sandstone with good reservoir properties, and high-resistance dry sandstone is sandstone with poor reservoir properties (micro-gas-bearing layer has poor reservoir properties). Based on the reservoir logging interpretation conclusion, the vertical and horizontal axis parameters (PI _logs_低通滤波 ,PI-GR _logs_低通滤波 )’s value distribution range.

[0076] The above-mentioned process of determining the pseudo-P-wave impedance sandstone identification threshold for distinguishing lithology and the P-wave impedance effective sandstone identification threshold for distinguishing physical properties from the lithology and physical properties identification plate is as follows: Figure 2 The scatter plot is based on the principle of differentiating different reservoir types. The vertical axis pseudo-P-wave impedance logging data (PI-GR _logs_低通滤波 ) to distinguish sandstone from mudstone, and determine the pseudo-P wave impedance sandstone identification threshold (T 砂岩 )(like Figure 2 The pseudo-P-wave impedance sandstone identification threshold (T 砂岩 ) is 13500g / cm 3 *m / s, namely, pseudo-compression wave impedance logging data (PI-GR _logs_低通滤波 ) can predict lithology and distinguish sandstone from mudstone (e.g., pseudo-P wave impedance data volume is larger than T 砂岩 (eg: 13500g / cm 3 *m / s) is sandstone, which is less than T 砂岩 (eg: 13500g / cm 3 *m / s) is mudstone). You can also Figure 2 The scatter plot of the well data is based on the principle of differentiating different reservoir types. _logs_低通滤波 ) to distinguish between dense dry sandstone (high-resistance dry sandstone) and effective sandstone (low-resistance effective sandstone), and determine the effective sandstone identification threshold (T 有效砂岩 )(like Figure 2 The effective sandstone identification threshold (T 有效砂岩 ) is 11500g / cm 3 *m / s, namely the P-wave impedance logging data (PI_logs_低通滤波 ) can predict physical properties and distinguish between dense dry sandstone (high-resistance dry sandstone) and effective sandstone (low-resistance effective sandstone) (e.g., the longitudinal wave impedance data volume is greater than T 有效砂岩 (eg: 11500g / cm 3 *m / s) is dense dry sandstone (high-resistance dry sandstone), which is less than T 有效砂岩 (eg: 11500g / cm 3 *m / s) is effective sandstone (low-resistance effective sandstone).

[0077] The above construction uses the filtered P-wave impedance logging data as the horizontal axis and the filtered P-wave velocity ratio logging data as the vertical axis. The specific process is: intersect the filtered P-wave impedance logging data with the filtered P-wave velocity ratio logging data, and use the reservoir logging interpretation conclusion as the color code to establish the gas-bearing high-quality sandstone identification plate (gas-bearing high-quality sandstone rock physics seismic identification plate). Among them, intersecting the filtered P-wave impedance logging data with the filtered P-wave velocity ratio logging data means establishing an interactive scatter plot of the two parameters, such as Figure 3 As shown, Figure 3 It is the identification plate of high-quality gas-bearing sandstone (rock physics and seismic identification plate of high-quality gas-bearing sandstone). Figure 3 The horizontal axis in the figure is the P-wave impedance logging data after filtering (PI _logs_低通滤波 , unit is g / cm 3 *m / s, the vertical axis is the P-wave velocity ratio logging data after filtering (Vp / Vs _logs_低通滤波) .from Figure 3 It can be seen from the identification plate of high-quality gas-bearing sandstone (rock physics seismic identification plate of high-quality gas-bearing sandstone) that high-quality gas-bearing sandstone has the characteristics of low P-wave velocity ratio and low P-wave impedance. Combining the P-wave velocity ratio (Vp / Vs) with P-wave impedance (PI) can better identify high-quality gas-bearing sandstone.

[0078] The above-mentioned gas-bearing high-quality sandstone identification threshold for distinguishing gas-bearing properties is determined from the gas-bearing high-quality sandstone identification quantity plate. The specific process is as follows: based on the gas-bearing high-quality sandstone identification quantity plate (gas-bearing high-quality sandstone rock physics seismic identification quantity plate), the gas-bearing high-quality sandstone numerical distribution range is divided to determine the gas-bearing high-quality sandstone identification threshold (T 含气优质砂岩 )(like Figure 3 The identification threshold of high-quality gas-bearing sandstone (T 含气优质砂岩 ) is 1.8) (e.g., the P-wave velocity ratio of the data volume is less than the identification threshold of gas-bearing high-quality sandstone (T 含气优质砂岩 )(e.g. 1.8) is gas-bearing high-quality sandstone, and is greater than the identification threshold of gas-bearing high-quality sandstone (T 含气优质砂岩 ) (such as: 1.8) is low-resistance effective sandstone).

[0079] By determining the above-mentioned pseudo-P-wave impedance sandstone identification threshold, P-wave impedance effective sandstone identification threshold and gas-bearing high-quality sandstone identification threshold, the foundation can be laid for qualitatively predicting the sandstone distribution range, effective sandstone distribution range and gas-bearing high-quality sandstone distribution range.

[0080] S102: Compare the pseudo-P-wave impedance data volume obtained based on post-stack inversion with the pseudo-P-wave impedance sandstone identification threshold to determine the sandstone distribution range.

[0081] S103: Compare the P-wave impedance data volume obtained based on post-stack inversion with the P-wave impedance effective sandstone identification threshold to determine the effective sandstone distribution range from the sandstone distribution range.

[0082] S104: Compare the P-wave and S-wave velocity ratio data volume obtained based on pre-stack inversion with the gas-bearing high-quality sandstone identification threshold to determine the gas-bearing high-quality sandstone distribution range from the effective sandstone distribution range.

[0083] In some embodiments, the above S102 compares the pseudo-P wave impedance data volume obtained based on post-stack inversion with the pseudo-P wave impedance sandstone identification threshold to determine the sandstone distribution range. In specific implementation, it may include:

[0084] When the pseudo-P-wave impedance data volume obtained based on post-stack inversion is greater than the pseudo-P-wave impedance sandstone identification threshold, determining the sandstone distribution range;

[0085] The above S103 compares the P-wave impedance data volume obtained based on post-stack inversion with the P-wave impedance effective sandstone identification threshold to determine the effective sandstone distribution range from the sandstone distribution range. In specific implementation, it may include:

[0086] When the P-wave impedance data volume obtained based on post-stack inversion is smaller than the P-wave impedance effective sandstone identification threshold, determining the effective sandstone distribution range from the sandstone distribution range;

[0087] The above S104 compares the P-wave and S-wave velocity ratio data volume obtained based on prestack inversion with the gas-bearing high-quality sandstone identification threshold to determine the gas-bearing high-quality sandstone distribution range from the effective sandstone distribution range. In specific implementation, it may include:

[0088] When the P-wave and S-wave velocity ratio data volume obtained based on prestack inversion is smaller than the gas-bearing high-quality sandstone identification threshold, the gas-bearing high-quality sandstone distribution range is determined from the effective sandstone distribution range.

[0089] In some embodiments, the pseudo-P-wave impedance data volume (PI-GR) can be inverted by performing post-stack high-resolution seismic phase-controlled waveform nonlinear chaotic inversion. _Inversion) and longitudinal wave impedance data volume (PI _Inversion ). See Figure 4 and Figure 5 As shown, Figure 4 The pseudo-longitudinal wave impedance data volume (PI-GR _Inversion ) Inversion profile, Figure 5 is the longitudinal wave impedance data volume (PI _Inversion )Inversion profile. Figure 4 , Figure 5 The well logging curves in are all natural gamma. Figure 4 It can be seen that the pseudo-longitudinal wave impedance data volume (PI-GR _Inversion ) The inversion results have good ability to distinguish sandstone from mudstone due to the integration of high-frequency information of natural gamma. At the same time, they have high vertical resolution, clear horizontal distribution characteristics, and reasonable pinch-out characteristics of each small layer of sand bodies. The inversion results are consistent with the logging interpretation conclusions of each well (such as Well A1, Well A2 and Well A3), and can accurately characterize each small layer of sand bodies. Figure 2 The lithology-effective sandstone rock physics seismic identification version is used as a guide. When the pseudo-P-wave impedance data volume (PI-GR _Inversion ) value is greater than 13500g / cm 3 *m / s (i.e. greater than the pseudo-P wave impedance sandstone identification threshold (T 砂岩 )), the distribution range of sandstone can be qualitatively predicted. The distribution range of sandstone can be referred to Figure 4 The area marked by the rectangular box. Figure 2 The lithology-effective sandstone rock physics seismic identification version is analyzed. Figure 5 It can be seen that the longitudinal wave impedance data volume (PI _Inversion ) is difficult to effectively distinguish sandstone and mudstone, so the low impedance area in the profile can be either effective low-resistance sandstone or low-resistance mudstone.

[0090] In some embodiments, the pseudo-P-I data volume (PI-GR) is inverted based on the high-resolution post-stack inversion method. _Inversion ), longitudinal wave impedance data volume (PI _Inversion ) can be compared with the pseudo-P-wave impedance data obtained based on post-stack inversion (PI-GR _Inversion ) and the pseudo-P-wave impedance sandstone identification threshold (T 砂岩 ), in the pseudo-longitudinal wave impedance data volume (PI-GR _Inversion ) is greater than the pseudo-P wave impedance sandstone identification threshold (T 砂岩 ), the distribution range of sandstone can be qualitatively predicted, that is, the distribution range of sandstone can be determined. The P-wave impedance data volume (PI _Inversion ) and the effective sandstone identification threshold of longitudinal wave impedance (T 有效砂岩), in the longitudinal wave impedance data volume (PI _Inversion ) is less than the effective sandstone identification threshold of longitudinal wave impedance (T 有效砂岩 ), the distribution range of the low-resistance effective sandstone can be qualitatively predicted under the constraint of the sandstone distribution range, that is, the effective sandstone distribution range can be determined from the sandstone distribution range.

[0091] Specifically, the lithology physical property identification quantity version (lithology-effective sandstone rock physics seismic identification quantity version) can be used. Figure 2 ) is used as a guide, when the pseudo-longitudinal wave impedance data volume (PI-GR _Inversion ) is greater than the pseudo-P wave impedance sandstone identification threshold (T 砂岩 ), the distribution range of sandstone can be qualitatively predicted.

[0092] It is also possible to construct an effective sandstone indicator factor (F effective )(as shown in formula (1)), by comparing the effective sandstone indicator factor (F effective ) and the effective sandstone identification threshold of longitudinal wave impedance (T 有效砂岩 ) to qualitatively predict the effective sandstone distribution range. Specifically, the lithology-effective sandstone rock physics seismic identification quantity version (lithology-effective sandstone rock physics seismic identification quantity version) can be used. Figure 2 ) as a guide, in the pseudo-longitudinal wave impedance data volume (PI-GR _Inversion ) is greater than the pseudo-P wave impedance sandstone identification threshold (T 砂岩 ),the effective sandstone indicator factor (F effective ) is less than the effective sandstone identification threshold value (T 有效砂岩 ), the distribution range of low-resistivity effective sandstone or effective sandstone distribution range can be qualitatively predicted. Among them, the constructed effective sandstone indicator factor (F effective ) (as shown in formula (1)), which can reflect the constraints on the distribution range of sandstone (i.e., PI-GR _Inversion >T 砂岩 The longitudinal wave impedance data volume (PI _Inversion ) and the pseudo-longitudinal impedance data volume (PI-GR _Inversion ) fusion, remove mud and retain sand (remove mudstone and retain sandstone, and construct an effective sandstone indicator factor (F effective ), as shown in the following formula (1):

[0093]

[0094] See also Figure 6 As shown, Figure 6 is the effective sandstone indicator factor (F effective ) cross-sectional view. Figure 6 It is based on the P-wave impedance data volume (PI _Inversion) and the pseudo-longitudinal impedance data volume (PI-GR _Inversion ) fusion, and construct the effective sandstone indicator factor (F effective )profile. Figure 6 In the pseudo longitudinal wave impedance data volume (PI-GR _Inversion ) predicted sandstone results (sandstone distribution range) as constraints, remove mud and retain sand, that is, when the pseudo-PW impedance data volume (PI-GR _Inversion )> Pseudo-P wave impedance sandstone identification threshold (T 砂岩 )(Example: 13500g / cm 3 *m / s), the effective sandstone indicator factor (F effective )Retain the longitudinal impedance data volume (PI _Inversion ) value, otherwise the effective sandstone indicator factor (F effective ) is set to an impedance value that is clearly distinguishable from effective sandstone (e.g., high-resistance mudstone impedance value). This example is preset to 13500 g / cm 3 *m / s. Figure 2 The lithology-effective sandstone rock physics seismic identification quantity version is used as a guide. When the effective sandstone indicator factor (F effective ) value < longitudinal wave impedance data volume greater than T 有效砂岩 (eg: 11500g / cm 3 *m / s), the distribution range of low-resistance effective sandstone can be qualitatively predicted. The distribution range of low-resistance effective sandstone or effective sandstone can be referred to Figure 6 The area marked by the rectangular box in the section.

[0095] In some embodiments, wide-angle adaptive prestack inversion can be performed based on amplitude-preserved prestack gather data to invert the P-wave and S-wave velocity ratio data volume (Vp / Vs _Inversion ). In the inverted data volume of the ratio of longitudinal and transverse wave velocities (Vp / Vs _Inversion ) can be compared with the longitudinal and transverse wave velocity ratio data (Vp / Vs _Inversion ) and the above-mentioned gas-bearing high-quality sandstone identification threshold (T 含气优质砂岩 ), in the longitudinal and transverse wave velocity ratio data volume (Vp / Vs _Inversion ) is less than the identification threshold of high-quality gas-bearing sandstone (T 含气优质砂岩 ), the distribution range of high-quality gas-bearing sandstone can be qualitatively predicted under the constraint of the effective sandstone distribution range, that is, the distribution range of high-quality gas-bearing sandstone can be determined from the effective sandstone distribution range.

[0096] It is also possible to construct an indicator factor for high-quality gas-bearing sandstone (F superior )(as shown in formula (2)), by comparing the indicator factor of high-quality gas-bearing sandstone (F superior ) and the identification threshold of high-quality gas-bearing sandstone (T 含气优质砂岩) to qualitatively predict the distribution range of high-quality gas-bearing sandstone. Specifically, the gas-bearing high-quality sandstone identification quantity version (gas-bearing high-quality sandstone rock physics seismic identification quantity version) ( Figure 3 ), in the effective sandstone indicator factor (F effective ) is less than the effective sandstone identification threshold value (T 有效砂岩 ) (It should be noted that the effective sandstone indicator factor (F effective ) is less than the effective sandstone identification threshold value (T 有效砂岩 ) is also in the pseudo-longitudinal wave impedance data volume (PI-GR _Inversion ) is greater than the pseudo-P wave impedance sandstone identification threshold (T 砂岩 ), the indicator factor of high-quality gas-bearing sandstone (F superior ) is less than the identification threshold of high-quality gas-bearing sandstone (T 含气优质砂岩 ), the distribution range of high-quality gas-bearing sandstone can be qualitatively predicted. Among them, the constructed indicator factor of high-quality gas-bearing sandstone (F superior )(as shown in formula (2)), which can reflect the constraints on the effective sandstone distribution range (i.e., F effective <T 有效砂岩 The constraint of F effective <T 有效砂岩 Again in PI-GR _Inversion >T 砂岩 The P-wave velocity ratio inversion data volume (Vp / Vs _Inversion ) and effective sandstone indicator factor (F effective ) to construct the indicator factor of high-quality gas-bearing sandstone (F superior ), as shown in the following formula (2):

[0097]

[0098] See also Figure 7 As shown, Figure 7 is the indicator factor of high-quality gas-bearing sandstone (F superior ) cross-sectional view. Figure 7 It is based on the prestack wide angle adaptive prestack inversion of the P-wave and S-wave velocity ratio data volume (Vp / Vs _Inversion ) and effective sandstone indicator factor (F effective ) and constructed the indicator factor of high-quality gas-bearing sandstone (F superior )profile. Figure 7 The effective sandstone indicator factor (F effective ) is a constraint on the predicted low-resistance effective sandstone results (effective sandstone distribution range), that is, when the effective sandstone indicator factor (F effective )<Longitudinal wave impedance data volume is greater than T 有效砂岩 (eg: 11500g / cm3 *m / s), the indicator factor of high-quality gas-bearing sandstone (F superior ) retains the longitudinal and transverse wave velocity ratio data volume (Vp / Vs _Inversion ) value, otherwise the gas-bearing high-quality sandstone indicator factor (F superior ) is set to a value of the ratio of the longitudinal and transverse wave velocities that is clearly distinguishable from gas-bearing high-quality sandstone (e.g., the Vp / Vs value of mudstone). In this example, the value is preset to 2.0. Figure 3 The gas-bearing high-quality sandstone identification quantity version (gas-bearing high-quality sandstone rock physics seismic identification quantity version) is used as a guide. When the gas-bearing high-quality sandstone indicator factor (F superior ) value < identification threshold of high-quality gas-bearing sandstone (T 含气优质砂岩 )(e.g. 1.8), the distribution range of high-quality gas-bearing sandstone can be qualitatively predicted. The distribution range of high-quality gas-bearing sandstone can be referred to Figure 7 The area marked by the rectangular box in the section.

[0099] By adopting high-resolution pre-stack and post-stack inversion methods, the pseudo-P-wave impedance data volume, P-wave impedance data volume and P-S wave velocity ratio data volume can be accurately obtained. By inverting the pseudo-P-wave impedance data volume, P-wave impedance data volume and P-S wave velocity ratio data volume, and then adopting the idea of ​​"sandstone-effective sandstone-gas-bearing high-quality sandstone" hierarchical prediction and step-by-step constraint, the effective sandstone distribution range can be accurately determined under the constraint of the sandstone distribution range, and the gas-bearing high-quality sandstone distribution range can be accurately determined under the constraint of the effective sandstone distribution range, so that the gas-bearing high-quality sandstone distribution range can be accurately predicted, laying the foundation for the subsequent accurate identification or prediction of thin-layer river sandstone gas-bearing high-quality reservoirs.

[0100] S105: Identify high-quality gas-bearing reservoirs from the distribution range of high-quality gas-bearing sandstones based on a gas-bearing enrichment seismic indicator factor, wherein the gas-bearing enrichment seismic indicator factor is determined based on a P-wave velocity ratio data volume and a P-wave impedance data volume.

[0101] In some embodiments, the above-mentioned gas enrichment seismic indicator factor may also be referred to as the gas enrichment degree seismic indicator factor (F enrichment_Inversion ). The seismic indicator factor of gas enrichment degree can be obtained through the pre-constructed prediction model of gas enrichment seismic indicator factor.

[0102] In some embodiments, before the above S105, in specific implementation, the following steps may also be included:

[0103] According to the product of the P-wave impedance data volume and the P-S wave velocity ratio data volume, the seismic sensitivity quantitative index of high-quality gas-bearing sandstone is constructed;

[0104] According to the quantitative index of seismic sensitivity of high-quality gas-bearing sandstone, a prediction model of seismic indicator factors of gas-bearing enrichment is constructed according to the following formula:

[0105] F enrichment_Inversion =-a -10 ·F quantization_Inversion 2 +b -6 ·F quantization_Inversion +c

[0106] Among them, F enrichment_Inversion is the seismic indicator factor of gas enrichment, F quantization_Inversion is the quantitative index of seismic sensitivity of high-quality gas-bearing sandstone, and a, b, and c are constants.

[0107] In some embodiments, the gas enrichment seismic indicator factor in S105 is determined based on the P-wave velocity ratio data volume and the P-wave impedance data volume, and in specific implementation, may include:

[0108] The product of the P-wave velocity ratio data volume and the P-wave impedance data volume is input into the prediction model of the gas-enriched seismic indicator factor to obtain the gas-enriched seismic indicator factor.

[0109] In some embodiments, the process of constructing the gas enrichment seismic indicator factor prediction model can be as follows:

[0110] We can first obtain the saturation curve (Sg _logs ) and the porosity curve interpreted by logging (Por _logs ) (predicted by the logging curve), and multiply the two to construct the gas enrichment logging indicator factor (F enrichment_logs )(as shown in formula (3)):

[0111] F enrichment_logs =Sg _logs ×Por _logs Formula (3)

[0112] Then the above filtered longitudinal and transverse wave velocity ratio logging data (Vp / Vs _logs_低通滤波 ) and the filtered P-wave impedance logging data (PI _logs_低通滤波 ) to construct the quantitative logging sensitivity index of high-quality gas-bearing sandstone (F quantization_logs )(as shown in formula (4)):

[0113] F quantization_logs =Vp / Vs _logs_低通滤波 ×PI _logs_低通滤波 Formula (4)

[0114] Then the gas enrichment logging indicator factor (F enrichment_logs ) and the quantitative index of well logging sensitivity of gas-bearing high-quality sandstone (F quantization_logs) are intersected (i.e., an interactive scatter plot of the two parameters is established), and the relationship between the two is fitted to establish the gas enrichment logging indicator factor (F enrichment_logs ) prediction model (as shown in formula (5)):

[0115] F enrichment_logs =f(F quantization_logs ) Formula (5)

[0116] See also Figure 8 As shown, Figure 8 This is the analysis diagram of the prediction model of the gas enrichment logging indicator factor. enrichment_logs ) and the quantitative index of well logging sensitivity of gas-bearing high-quality sandstone (F quantization_logs ) intersect, it can be seen that there is a good negative correlation between the two, and the quantitative index of well logging sensitivity of high-quality gas-bearing sandstone (F quantization_logs ) is lower, the gas enrichment logging indicator factor (F enrichment_logs ) is higher, the better the reservoir physical properties and gas content are. Fitting the relationship between the two, the well logging indicator factor (F enrichment_logs ) prediction model, the specific formula is F enrichment =-2.841 -10 ·F quantization 2 +4.265 -6 ·F quantization +0.0858, the correlation coefficient is R=0.78.

[0117] Afterwards, the longitudinal impedance data volume (PI _Inversion ) and the longitudinal and transverse wave velocity ratio data volume (Vp / Vs _Inversion ) to construct the quantitative seismic sensitivity index of gas-bearing high-quality sandstone (F quantization_Inversion )(as shown in formula (6)):

[0118] F quantization_Inversion =Vp / Vs _Inversion ×PI _Inversi on Formula (6)

[0119] Since the gas enrichment logging indicator factor (F enrichment_logs ) prediction model (Formula (5), specifically Figure 8 The formula shown in FIG. 1 is obtained by fitting the logging data after low-pass filtering. Therefore, it is assumed that the model is applicable to the frequency band range of the seismic inversion data volume. The prediction model of the gas enrichment logging indicator factor is the same as the prediction model of the gas enrichment seismic indicator factor (the prediction model of the gas enrichment seismic indicator factor). Therefore, the prediction model of the gas enrichment seismic indicator factor is:

[0120] F enrichment_Inversion =-a-10 ·F quantization_Inversion 2 +b -6 ·F quantization_Inversion +c

[0121] Among them, a can be 2.841, b can be 4.265, and c can be 0.0858.

[0122] In some embodiments, the product of the P-wave velocity ratio data volume and the P-wave impedance data volume can be obtained and input into the gas enrichment seismic indicator factor prediction model constructed above to output the gas enrichment seismic indicator factor (F enrichment_Inversion ). Therefore, it is possible to use the gas enrichment seismic indicator factor (F enrichment_Inversion ), under the constraints of the distribution range of gas-bearing high-quality sandstone (i.e., F superior <T 含气优质砂岩 Under the constraint of superior <T 含气优质砂岩 Again in F effective <T 有效砂岩 Under the constraint, F effective <T 有效砂岩 Again in PI-GR _Inversion >T 砂岩 Under the constraints of (i.e., step-by-step constraints and graded prediction), the thin-layer channel sandstone gas-bearing high-quality reservoirs are identified, that is, the gas-bearing high-quality reservoirs are identified from the distribution range of the gas-bearing high-quality sandstone.

[0123] Specifically, the prediction results of high-quality gas-bearing sandstone (distribution range of high-quality gas-bearing sandstone) can be used as constraints to quantify the seismic sensitivity index of high-quality gas-bearing sandstone (F quantization_Inversion ) and gas-bearing high-quality sandstone indicator factor (F superior ) to construct the seismic indicator factor of gas enrichment degree (F enrichment_Inversion )(as shown in formula (7)), f in formula (7) takes the formula form of formula (5):

[0124]

[0125] See also Fig. 9 As shown, Fig. 9 The seismic indicator factor profile of gas enrichment degree. First, the P-wave velocity ratio data volume (Vp / Vs _Inversion ) and longitudinal wave impedance data volume (PI _Inversion ) and substitute Figure 3 The established gas enrichment logging indicator factor prediction model (i.e., the product result is substituted into the model, the gas enrichment logging indicator factor prediction model is equivalent to the above-mentioned gas enrichment seismic indicator factor prediction model or called the gas enrichment seismic indicator factor prediction model), and the gas enrichment seismic indicator factor (Fenrichment_Inversion ). And the indicator factor of high-quality gas-bearing sandstone (F superior ) is used as a constraint to predict the distribution range of high-quality gas-bearing sandstone. When the indicator factor of high-quality gas-bearing sandstone (F superior ) value < identification threshold of high-quality gas-bearing sandstone (T 含气优质砂岩 )(e.g. 1.8), the seismic indicator factor of gas enrichment (F enrichment_Inversion ) value is retained, otherwise the seismic indicator factor of gas enrichment degree (F enrichment_Inversion ) is set to a constant of 0. Using the seismic indicator factor of gas enrichment (F enrichment_Inversion ) can quantitatively analyze the quality of thin-layer river channel sandstone. The higher the value, the higher the porosity of the thin-layer river channel sandstone and the better the gas content.

[0126] In some embodiments, the above S105, based on the gas-enriched seismic indicator factor, identifies the gas-bearing high-quality reservoir from the distribution range of the gas-bearing high-quality sandstone. When implemented specifically, it may include:

[0127] Compare the seismic indicator factors of gas enrichment in the distribution range of high-quality gas-bearing sandstones;

[0128] From the distribution range of high-quality gas-bearing sandstones, areas where the gas-enrichment seismic indicator factor is greater than the threshold of the gas-enrichment seismic indicator factor are identified as high-quality gas-bearing reservoirs.

[0129] In some embodiments, the above-mentioned gas-enriched seismic indicator factor is positively correlated with the porosity and gas content of thin-layer river channel sandstone, that is, the higher the value of the gas-enriched seismic indicator factor, the higher the porosity and the better the gas content of the thin-layer river channel sandstone. The gas-enriched seismic indicator factor can quantitatively analyze the quality of the thin-layer river channel sandstone. The area with higher porosity and better gas content of the thin-layer river channel sandstone is a high-quality gas-bearing reservoir of the thin-layer river channel sandstone.

[0130] Specifically, the gas-enriched seismic indicator factors in the distribution range of high-quality gas-bearing sandstones can be compared, and then the areas where the gas-enriched seismic indicator factors are greater than the threshold of the gas-enriched seismic indicator factors can be identified from the distribution range of high-quality gas-bearing sandstones as high-quality gas-bearing reservoirs. The areas where the gas-enriched seismic indicator factors are greater than the threshold of the gas-enriched seismic indicator factors indicate the areas with the highest gas-enriched seismic indicator factor values, and such areas are thin-layer river sandstone high-quality gas-bearing reservoirs.

[0131] By accurately identifying or predicting thin-layer river channel sandstone gas-bearing high-quality reservoirs, a reliable basis can be provided for the precise deployment of development wells, solving the problem that existing technologies are unable to accurately and effectively identify thin-layer river channel sandstone gas-bearing high-quality reservoirs, restricting the deployment of oilfield development wells, and thus affecting the efficiency of oil and gas exploration and development.

[0132] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. For details, please refer to the description of the above-mentioned related processing related embodiments, and no further description is given here.

[0133] The above is an explanation of the present invention, however, it is worth noting that the specific embodiment is only for the purpose of better illustrating the present application and describing a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0134] The above method is described below in conjunction with a specific embodiment. However, it should be noted that this specific embodiment is only for better illustrating the present application and does not constitute an improper limitation on the present application.

[0135] Before specific implementation, the original logging data can be preprocessed (such as environmental correction and standardization), and then the processed logging data can be optimized, that is, the parameters sensitive to lithology, physical properties and gas content can be selected, such as natural gamma logging data can be selected as lithology sensitive parameters, P-wave impedance logging data as physical property sensitive parameters, and P-wave velocity ratio logging data as gas content sensitive parameters. Then retain the medium and low frequency information in the P-wave impedance logging data, integrate the high frequency information in the natural gamma logging data, reconstruct the characteristic curve in the frequency domain, and keep the reconstructed synthetic seismic record consistent with the original one, without affecting the seismic band inversion result. The pseudo-P-wave impedance logging data is constructed by numerically inverting the high frequency information of natural gamma. Then the P-wave impedance logging data, pseudo-P-wave impedance logging data, and P-wave velocity ratio logging data are low-pass filtered. Finally, a lithology and physical property identification scale can be constructed with the P-wave impedance logging data after filtering as the horizontal axis and the pseudo-P-wave impedance logging data after filtering as the vertical axis. A gas-bearing high-quality sandstone identification scale can also be constructed with the P-wave impedance logging data after filtering as the horizontal axis and the P-S wave velocity ratio logging data after filtering as the vertical axis. A prediction model for gas-enriched seismic indicator factors can also be constructed in advance, which is used to predict gas-enriched seismic indicator factors.

[0136] In specific implementation, the pseudo-P-wave impedance sandstone identification threshold for distinguishing lithology and the P-wave impedance effective sandstone identification threshold for distinguishing physical properties can be first determined from the lithology and physical property identification quantity plate, and the gas-bearing high-quality sandstone identification threshold for distinguishing gas-bearing properties can be determined from the gas-bearing high-quality sandstone identification quantity plate. Then, the pseudo-P-wave impedance data body obtained based on post-stack inversion is compared with the pseudo-P-wave impedance sandstone identification threshold to determine the sandstone distribution range. The P-wave impedance data body obtained based on post-stack inversion is then compared with the P-wave impedance effective sandstone identification threshold to determine the effective sandstone distribution range from the sandstone distribution range. The P-wave velocity ratio data body obtained based on pre-stack inversion is then compared with the gas-bearing high-quality sandstone identification threshold to determine the gas-bearing high-quality sandstone distribution range from the effective sandstone distribution range. Finally, the gas-bearing high-quality reservoir can be identified from the gas-bearing high-quality sandstone distribution range based on the gas-bearing enrichment seismic indicator factor, and the gas-bearing enrichment seismic indicator factor is determined according to the P-wave velocity ratio data body and the P-wave impedance data body.

[0137] The above method adopts the idea of ​​hierarchical prediction and step-by-step constraint of "sandstone-effective sandstone-gas-bearing high-quality sandstone". The constructed gas-enriched seismic indicator factor can quantitatively analyze the quality of thin-layer river channel sandstone and improve the accuracy of gas-bearing prediction of thin-layer river channel sandstone, thereby providing a basis for well site deployment in the development stage.

[0138] In a specific implementation scenario, the process of the method for identifying a thin-layer channel sandstone gas-bearing high-quality reservoir is described, and the steps may be as follows:

[0139] Step 1: Perform environmental correction and standardization on the original acoustic wave time difference, shear wave time difference, density, natural gamma and other logging data, and perform rock physical analysis based on the processed logging data. It is preferred that the natural gamma logging curve is the lithology sensitive parameter, the longitudinal wave impedance is the physical property sensitive parameter, and the longitudinal and shear wave velocity ratio is the gas content sensitive parameter.

[0140] Step 2: Keep the P-wave impedance logging data (PI _logs ) and the low- and medium-frequency information, combined with natural gamma logging data (GR _logs ) high-frequency information, reconstruct the characteristic curve in the frequency domain, and make the reconstructed synthetic seismic record consistent with the original one, without affecting the seismic frequency band inversion result, and adopt the method of numerical inversion of natural gamma high-frequency information to construct pseudo-P-wave impedance logging data (PI-GR _logs ), improve the ability to distinguish lithology and provide high-resolution sensitive characteristic parameters for predicting thin-layer river channel sandstone.

[0141] Step 3: Pseudo-Pulsational Impedance Logging Data (PI-GR _logs ) and P-wave impedance logging data (PI _logs) is low-pass filtered to retain the seismic inversion frequency band information. _logs_低通滤波 ) and low-pass filtered P-wave impedance logging data (PI _logs_低通滤波 ) are intersected, and the reservoir logging interpretation conclusion is used as a color code to establish a lithology-effective sandstone rock physics seismic identification scale, divide the numerical distribution range of low-resistance effective sandstone, high-resistance dry sandstone, low-resistance mudstone, and high-resistance mudstone, and determine the pseudo-P-wave impedance sandstone identification threshold (T 砂岩 ), and determine the effective sandstone identification threshold (T 有效砂岩 ).

[0142] Step 4: Perform post-stack high-resolution seismic phase-controlled waveform nonlinear chaotic inversion to invert the pseudo-P-wave impedance data volume (PI-GR _Inversion ). Guided by the lithology-effective sandstone rock physics seismic identification data set, when the pseudo-P-wave impedance data set (PI-GR _Inversion ) is greater than the pseudo-P wave impedance sandstone identification threshold (T 砂岩 ), the distribution range of sandstone can be qualitatively predicted.

[0143] Step 5: Perform post-stack high-resolution seismic phase-controlled waveform nonlinear chaotic inversion to invert the P-wave impedance data volume (PI _Inversion ). Taking the sandstone prediction results of step 3 as constraints, the longitudinal wave impedance data volume (PI _Inversion ) and the pseudo-longitudinal impedance data volume (PI-GR _Inversion ) fusion, removing mud and retaining sand, and constructing an effective sandstone indicator factor (F effective )(Formula (1)). Guided by the lithology-effective sandstone rock physics seismic identification scale, when the effective sandstone indicator factor (F effective ) is less than the effective sandstone identification threshold value (T 有效砂岩 ), the distribution range of the low-resistivity effective sandstone can be qualitatively predicted.

[0144]

[0145] Step 6: Compare the P-wave velocity ratio logging data (Vp / Vs _logs ) to perform low-pass filtering and retain the seismic inversion frequency band information. The P-wave velocity ratio logging data (Vp / Vs _logs_低通滤波 ) and the low-pass filtered P-wave impedance logging data (PI _logs_低通滤波 ) are intersected, and the reservoir logging interpretation conclusion is used as a color code to establish a rock physics seismic identification plate for high-quality gas-bearing sandstones, divide the numerical distribution range of high-quality gas-bearing sandstones, and determine the identification threshold of high-quality gas-bearing sandstones (T 含气优质砂岩 ).

[0146] Step 7: Based on the amplitude-preserved prestack gather data, wide-angle adaptive prestack inversion is performed to invert the P-wave and S-wave velocity ratio data volume (Vp / Vs _Inversion ). Using the prediction results of the effective low-resistance sandstone in step 4 as constraints, the P-wave velocity ratio inversion data volume (Vp / Vs _Inversion ) and effective sandstone indicator factor (F effective ) to construct the indicator factor of high-quality gas-bearing sandstone (F superior )(Formula (2)). Guided by the rock physics seismic identification scale for high-quality gas-bearing sandstone, when the indicator factor of high-quality gas-bearing sandstone (F superior ) value is less than the identification threshold of gas-bearing high-quality sandstone (T 含气优质砂岩 ), the distribution range of high-quality gas-bearing sandstone can be qualitatively predicted.

[0147]

[0148] Step 8: The saturation curve (Sg _logs ) and the porosity curve interpreted by logging (Por _logs ) to construct the gas enrichment logging indicator factor (F enrichment_logs ) (formula (3)).

[0149] F enrichment_logs =Sg _logs ×Por _logs Formula (3)

[0150] The low-pass filtered P-wave velocity ratio logging curve (Vp / Vs _logs_低通滤波 ) and low-pass filtered longitudinal impedance logging curve (PI _logs_低通滤波 ) to construct the quantitative logging sensitivity index of high-quality gas-bearing sandstone (F quantization_logs )(Formula (4)).

[0151] F quantization_logs =Vp / Vs _logs_低通滤波 ×PI _logs_低通滤波 Formula (4)

[0152] The gas enrichment logging indicator factor (F enrichment_logs ) and the quantitative index of well logging sensitivity of gas-bearing high-quality sandstone (F quantization_logs ) and fit the relationship between them to establish the gas enrichment logging indicator factor (F enrichment_logs ) prediction model (Formula (5)).

[0153] F enrichment_logs =f(F quantization_logs ) Formula (5)

[0154] Step 9: Invert the data volume of the P-wave velocity ratio (Vp / Vs _Inversion ) and the longitudinal impedance inversion data volume (PI _Inversion ) to construct the quantitative seismic sensitivity index of gas-bearing high-quality sandstone (F quantization_Inversion )(Formula (6)).

[0155] F quantization_Inversion =Vp / Vs _Inversion ×PI _Inversion Formula (6)

[0156] Since the gas enrichment logging indicator factor (F enrichment_logs ) prediction model (Formula (5)) is obtained by fitting the logging data after low-pass filtering. Therefore, it is assumed that the model is applicable to the frequency band range of the seismic inversion data volume. The prediction model of the logging indicator factor of gas enrichment is the same as the prediction model of the seismic indicator factor of gas enrichment.

[0157] Taking the prediction results of high-quality gas-bearing sandstone in step 7 as constraints, the seismic sensitivity quantitative index of high-quality gas-bearing sandstone (F quantization_Inversion ) and gas-bearing high-quality sandstone indicator factor (F superior ) to construct the seismic indicator factor of gas enrichment degree (F enrichment_Inversion )(Formula (7)). f in Formula (7) takes the formula form of Formula (5).

[0158]

[0159] Step 10: Based on the constructed seismic indicator factor of gas enrichment (F enrichment Inversion ) can quantitatively analyze the quality of thin-layer river channel sandstone. The higher the value, the higher the porosity and the better the gas content of the thin-layer river channel sandstone. The area with higher porosity and better gas content of thin-layer river channel sandstone is the high-quality gas reservoir of thin-layer river channel sandstone. That is, the gas-enriched seismic indicator factor in the distribution range of high-quality gas-containing sandstone can be compared, and then the area with the gas-enriched seismic indicator factor greater than the threshold of the gas-enriched seismic indicator factor can be identified from the distribution range of high-quality gas-containing sandstone as the high-quality gas reservoir.

[0160] The present invention can be guided by the lithology-physical property identification version (lithology-effective sandstone rock physics seismic identification version) and the gas-bearing high-quality sandstone identification version (gas-bearing high-quality sandstone rock physics seismic identification version), based on high-resolution pre-stack and post-stack inversion results (i.e., high-resolution seismic phase-controlled waveform nonlinear chaotic post-stack inversion and pre-stack wide-angle adaptive pre-stack inversion results), adopts the idea of ​​"sandstone-effective sandstone-gas-bearing high-quality sandstone" hierarchical prediction and step-by-step constraint, and gradually constructs effective sandstone indicator factors, gas-bearing high-quality sandstone indicator factors, and gas enrichment degree indicator factors, thereby realizing quantitative evaluation of the physical properties and gas content of thin-layer river channel sandstone, and can accurately identify thin-layer river channel sandstone gas-bearing high-quality reservoirs, providing a reliable basis for well site deployment in the development stage.

[0161] Although this specification provides examples such as the following embodiments or the attached Fig.10 The method operation steps or device structure shown in the figure, but based on routine or no creative labor, the method or device may include more or fewer operation steps or module units after partial combination. In the steps or structures that do not have a necessary causal relationship logically, the execution order of these steps or the module structure of the device is not limited to the execution order or module structure shown in the embodiments of this specification or the drawings. When the method or module structure is applied to an actual device, server or terminal product, it can be executed sequentially or in parallel according to the method or module structure shown in the embodiments or drawings (for example, in a parallel processor or multi-threaded processing environment, or even in a distributed processing or server cluster implementation environment). Based on the above-mentioned method for identifying a thin-layer river channel sandstone gas-bearing high-quality reservoir, the embodiments of this specification also propose an embodiment of a device for identifying a thin-layer river channel sandstone gas-bearing high-quality reservoir. As Fig.10 As shown, the device may specifically include the following modules:

[0162] The threshold determination module 1001 can be used to determine the pseudo-P wave impedance sandstone identification threshold, the P wave impedance effective sandstone identification threshold and the gas-bearing high-quality sandstone identification threshold;

[0163] The first range determination module 1002 may be used to compare the pseudo-P-wave impedance data volume obtained based on post-stack inversion with the pseudo-P-wave impedance sandstone identification threshold to determine the sandstone distribution range;

[0164] The second range determination module 1003 may be used to compare the P-wave impedance data volume obtained based on post-stack inversion with the P-wave impedance effective sandstone identification threshold to determine the effective sandstone distribution range from the sandstone distribution range;

[0165] The third range determination module 1004 may be used to compare the P-wave and S-wave velocity ratio data volume obtained based on pre-stack inversion with the gas-bearing high-quality sandstone identification threshold to determine the gas-bearing high-quality sandstone distribution range from the effective sandstone distribution range;

[0166] The identification module 1005 can be used to identify high-quality gas-bearing reservoirs from the distribution range of high-quality gas-bearing sandstones based on gas-bearing enrichment seismic indicator factors, wherein the gas-bearing enrichment seismic indicator factors are determined based on a P-wave velocity ratio data volume and a P-wave impedance data volume.

[0167] In some embodiments, the threshold determination module 1001 can be used to obtain P-wave impedance logging data, natural gamma logging data, P-wave velocity ratio logging data and pseudo-P-wave impedance logging data, wherein the pseudo-P-wave impedance logging data is obtained by feature reconstruction based on the P-wave impedance logging data and the natural gamma logging data; the P-wave impedance logging data, the pseudo-P-wave impedance logging data and the P-wave velocity ratio logging data are filtered; a lithology and physical property identification scale is constructed with the P-wave impedance logging data after filtering as the horizontal axis and the pseudo-P-wave impedance logging data after filtering as the vertical axis; a gas-bearing high-quality sandstone identification scale is constructed with the P-wave impedance logging data after filtering as the horizontal axis and the P-wave velocity ratio logging data after filtering as the vertical axis.

[0168] In some embodiments, the threshold determination module 1001 can be specifically used to determine the pseudo-PW impedance sandstone identification threshold for distinguishing lithology and the PW impedance effective sandstone identification threshold for distinguishing physical properties from the lithology and physical property identification plate; and to determine the gas-bearing high-quality sandstone identification threshold for distinguishing gas-bearing properties from the gas-bearing high-quality sandstone identification plate.

[0169] In some embodiments, the first range determination module 1002 can be specifically used to determine the sandstone distribution range when the pseudo-P-wave impedance data volume obtained based on post-stack inversion is greater than the pseudo-P-wave impedance sandstone identification threshold; the second range determination module 1003 can be specifically used to determine the effective sandstone distribution range from the sandstone distribution range when the P-wave impedance data volume obtained based on post-stack inversion is less than the P-wave impedance effective sandstone identification threshold; the third range determination module 1004 can be specifically used to determine the gas-bearing high-quality sandstone distribution range from the effective sandstone distribution range when the P-wave and S-wave velocity ratio data volume obtained based on pre-stack inversion is less than the gas-bearing high-quality sandstone identification threshold.

[0170] In some embodiments, the identification module 1005 can be used to construct a quantitative seismic sensitivity index of high-quality gas-bearing sandstone according to the product result of the P-wave impedance data volume and the P-S wave velocity ratio data volume;

[0171] According to the quantitative index of seismic sensitivity of high-quality gas-bearing sandstone, a prediction model of seismic indicator factors of gas-bearing enrichment is constructed according to the following formula:

[0172] F enrichment_Inversion =-a -10 ·F quantization_Inversion2 +b -6 ·F quantization_Inversion +c

[0173] Among them, F enrichment_Inversion is the seismic indicator factor of gas enrichment, F quantization_Inversion is the quantitative index of seismic sensitivity of high-quality gas-bearing sandstone, and a, b, and c are constants.

[0174] In some embodiments, the identification module 1005 can be specifically used to input the product of the P-wave velocity ratio data volume and the P-wave impedance data volume into a gas enrichment seismic indicator factor prediction model to obtain a gas enrichment seismic indicator factor.

[0175] In some embodiments, the above-mentioned identification module 1005 can be specifically used to compare the gas-enriched seismic indicator factor in the distribution range of gas-bearing high-quality sandstone; identify the area where the gas-enriched seismic indicator factor is greater than the gas-enriched seismic indicator factor threshold from the distribution range of gas-bearing high-quality sandstone as a gas-bearing high-quality reservoir.

[0176] It can be seen from the above that a device for identifying thin-layer river channel sandstone gas-bearing high-quality reservoirs provided in an embodiment of this specification adopts the idea of ​​"sandstone-effective sandstone-gas-bearing high-quality sandstone" graded prediction and step-by-step constraint. The gas-enriched seismic indicator factor constructed can quantitatively analyze the quality of thin-layer river channel sandstone, improve the identification accuracy of thin-layer river channel sandstone gas-bearing high-quality reservoirs, and thus provide a reliable basis for well site deployment in the development stage.

[0177] The embodiment of the present specification also provides an electronic device based on the above-mentioned thin-layer river channel sandstone gas-bearing high-quality reservoir identification method, including a processor and a memory for storing processor executable programs / instructions, wherein the processor can perform the following steps according to the programs / instructions when implemented: determining a pseudo-P-wave impedance sandstone identification threshold, a P-wave impedance effective sandstone identification threshold, and a gas-bearing high-quality sandstone identification threshold; comparing the pseudo-P-wave impedance data body obtained based on post-stack inversion with the pseudo-P-wave impedance sandstone identification threshold to determine the sandstone distribution range; comparing the P-wave impedance data body obtained based on post-stack inversion with the P-wave impedance effective sandstone identification threshold to determine the effective sandstone distribution range from the sandstone distribution range; comparing the P-wave velocity ratio data body obtained based on pre-stack inversion with the gas-bearing high-quality sandstone identification threshold to determine the gas-bearing high-quality sandstone distribution range from the effective sandstone distribution range; identifying the gas-bearing high-quality reservoir from the gas-bearing high-quality sandstone distribution range based on the gas-enriched seismic indicator factor, wherein the gas-enriched seismic indicator factor is determined based on the P-wave velocity ratio data body and the P-wave impedance data body.

[0178] In order to complete the above instructions more accurately, refer to Fig.11As shown, the embodiment of this specification also provides another specific electronic device, wherein the electronic device includes a network communication port 1101, a processor 1102 and a memory 1103, and the above structures are connected through internal cables so that each structure can perform specific data interaction.

[0179] The network communication port 1101 can be used to determine the pseudo-P wave impedance sandstone identification threshold, the P wave impedance effective sandstone identification threshold and the gas-bearing high-quality sandstone identification threshold;

[0180] The processor 1102 may be specifically used to compare the pseudo-P-wave impedance data volume obtained based on post-stack inversion with the pseudo-P-wave impedance sandstone identification threshold to determine the sandstone distribution range; compare the P-wave impedance data volume obtained based on post-stack inversion with the P-wave impedance effective sandstone identification threshold to determine the effective sandstone distribution range from the sandstone distribution range; compare the P-wave velocity ratio data volume obtained based on pre-stack inversion with the gas-bearing high-quality sandstone identification threshold to determine the gas-bearing high-quality sandstone distribution range from the effective sandstone distribution range; identify the gas-bearing high-quality reservoir from the gas-bearing high-quality sandstone distribution range based on the gas-bearing enrichment seismic indicator factor, wherein the gas-bearing enrichment seismic indicator factor is determined according to the P-wave velocity ratio data volume and the P-wave impedance data volume;

[0181] The memory 1103 may be specifically used to store corresponding instruction programs.

[0182] In this embodiment, the network communication port 1101 can be a virtual port that is bound to different communication protocols so that different data can be sent or received. For example, the network communication port can be a port responsible for web data communication, a port responsible for FTP data communication, or a port responsible for email data communication. In addition, the network communication port can also be a physical communication interface or communication chip. For example, it can be a wireless mobile network communication chip, such as GSM, CDMA, etc.; it can also be a Wifi chip; it can also be a Bluetooth chip.

[0183] In this embodiment, the processor 1102 may be implemented in any appropriate manner. For example, the processor may take the form of a microprocessor or processor and a computer-readable medium storing a computer-readable program code (such as software or firmware) executable by the (micro)processor, a logic gate, a switch, an application specific integrated circuit (ASIC), a programmable logic controller, and an embedded microcontroller, etc. This specification does not limit this.

[0184] In this embodiment, the memory 1103 may include multiple levels. In a digital system, anything that can store binary data can be a memory; in an integrated circuit, a circuit with a storage function that has no physical form is also called a memory, such as RAM, FIFO, etc.; in a system, a storage device with a physical form is also called a memory, such as a memory stick, TF card, etc.

[0185] The embodiment of the present specification also provides a computer storage medium based on the above-mentioned thin-layer channel sandstone gas-bearing high-quality reservoir identification method, the computer storage medium stores a computer program / instruction, and when the computer program / instruction is executed, it realizes: determining a pseudo-P-wave impedance sandstone identification threshold, a P-wave impedance effective sandstone identification threshold, and a gas-bearing high-quality sandstone identification threshold; comparing the pseudo-P-wave impedance data body obtained based on post-stack inversion with the pseudo-P-wave impedance sandstone identification threshold to determine the sandstone distribution range; comparing the P-wave impedance data body obtained based on post-stack inversion with the P-wave impedance effective sandstone identification threshold to determine the effective sandstone distribution range from the sandstone distribution range; comparing the P-wave velocity ratio data body obtained based on pre-stack inversion with the gas-bearing high-quality sandstone identification threshold to determine the gas-bearing high-quality sandstone distribution range from the effective sandstone distribution range; identifying the gas-bearing high-quality reservoir from the gas-bearing high-quality sandstone distribution range based on the gas-enriched seismic indicator factor, the gas-enriched seismic indicator factor being determined according to the P-wave velocity ratio data body and the P-wave impedance data body.

[0186] In this embodiment, the storage medium includes, but is not limited to, a random access memory (RAM), a read-only memory (ROM), a cache, a hard disk (HDD), or a memory card. The memory may be used to store computer program instructions. The network communication unit may be an interface for network connection communication set in accordance with the standard specified by the communication protocol.

[0187] In this embodiment, the functions and effects specifically implemented by the program instructions stored in the computer storage medium can be explained in comparison with other implementations and will not be described in detail here.

[0188] Although the present specification provides method operation steps as described in the embodiments or flow charts, more or less operation steps may be included based on conventional or non-creative means. The order of steps listed in the embodiments is only one way of executing the order of many steps, and does not represent a unique execution order. When the device or client product in practice is executed, it can be executed in sequence or in parallel according to the method shown in the embodiments or the drawings (for example, a parallel processor or a multi-threaded processing environment, or even a distributed data processing environment). The term "include", "include" or any other variant thereof is intended to cover non-exclusive inclusion, so that the process, method, product or device including a series of elements includes not only those elements, but also includes other elements that are not explicitly listed, or also includes elements inherent to such a process, method, product or device. In the absence of more restrictions, it is not excluded that there are other identical or equivalent elements in the process, method, product or device including the elements. The first, second, etc. words are used to represent the name, and do not represent any particular order.

[0189] Those skilled in the art also know that, in addition to implementing the controller in a purely computer-readable program code, the controller can be made to implement the same function in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers by logically programming the method steps. Therefore, such a controller can be considered as a hardware component, and the devices for implementing various functions included therein can also be considered as structures within the hardware component. Or even, the devices for implementing various functions can be considered as both software modules for implementing the method and structures within the hardware component.

[0190] This specification may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, classes, etc. that perform specific tasks or implement specific abstract data types. This specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media including storage devices.

[0191] Through the description of the above embodiments, it can be known that those skilled in the art can clearly understand that the present specification can be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present specification can essentially be embodied in the form of a software product, which can be stored in a storage medium such as ROM / RAM, a disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a mobile terminal, a server, or a network device, etc.) to execute the methods described in each embodiment of the present specification or some parts of the embodiments.

[0192] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. This specification can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld devices or portable devices, tablet devices, multi-processor systems, microprocessor-based systems, set-top boxes, programmable electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc.

[0193] Although the present specification is described through embodiments, persons skilled in the art will appreciate that there are many variations of the present specification without departing from the spirit of the present specification, and it is intended that the appended claims include these variations without departing from the spirit of the present specification.

Claims

1. A method for identifying high-quality gas-bearing reservoirs in thin river channel sandstones, characterized in that: include: Determine the pseudo-P-wave impedance sandstone identification threshold, the P-wave impedance effective sandstone identification threshold, and the gas-bearing high-quality sandstone identification threshold; Comparing the pseudo-P-wave impedance data volume obtained based on post-stack inversion with the pseudo-P-wave impedance sandstone identification threshold to determine the sandstone distribution range; Comparing the P-wave impedance data volume obtained based on post-stack inversion with the P-wave impedance effective sandstone identification threshold to determine the effective sandstone distribution range from the sandstone distribution range; Comparing the P-wave and S-wave velocity ratio data obtained based on prestack inversion with the gas-bearing high-quality sandstone identification threshold to determine the gas-bearing high-quality sandstone distribution range from the effective sandstone distribution range; Based on the gas-enrichment seismic indicator factor, high-quality gas-bearing reservoirs are identified from the distribution range of high-quality gas-bearing sandstones. The gas-enrichment seismic indicator factor is determined according to the P-wave velocity ratio data volume and the P-wave impedance data volume.

2. The method according to claim 1, characterized in that The method further comprises: Acquiring compressional wave impedance logging data, natural gamma ray logging data, compressional and shear wave velocity ratio logging data, and pseudo-compressional wave impedance logging data, wherein the pseudo-compressional wave impedance logging data is obtained by feature reconstruction based on the compressional wave impedance logging data and the natural gamma ray logging data; Filtering the P-wave impedance logging data, pseudo-P-wave impedance logging data, and P-wave and S-wave velocity ratio logging data; Construct a lithologic property identification scale with the filtered P-wave impedance logging data as the horizontal axis and the filtered pseudo-P-wave impedance logging data as the vertical axis; A gas-bearing high-quality sandstone identification scale was constructed with the filtered P-wave impedance logging data as the horizontal axis and the filtered P-wave and S-wave velocity ratio logging data as the vertical axis.

3. The method according to claim 2, characterized in that The determination of the pseudo-P-wave impedance sandstone identification threshold, the P-wave impedance effective sandstone identification threshold, and the gas-bearing high-quality sandstone identification threshold includes: Determine the pseudo-P-wave impedance sandstone identification threshold for distinguishing lithology and the effective P-wave impedance sandstone identification threshold for distinguishing physical properties from the lithology and physical property identification plate; The identification threshold of high-quality gas-bearing sandstones for distinguishing gas-bearing properties is determined from the identification scale of high-quality gas-bearing sandstones.

4. The method according to claim 1, wherein The step of comparing the pseudo-P-wave impedance data volume obtained based on post-stack inversion with the pseudo-P-wave impedance sandstone identification threshold to determine the sandstone distribution range includes: When the pseudo-P-wave impedance data volume obtained based on post-stack inversion is greater than the pseudo-P-wave impedance sandstone identification threshold, determining the sandstone distribution range; The comparing the P-wave impedance data volume obtained based on post-stack inversion with the P-wave impedance effective sandstone identification threshold to determine the effective sandstone distribution range from the sandstone distribution range includes: When the P-wave impedance data volume obtained based on post-stack inversion is smaller than the P-wave impedance effective sandstone identification threshold, determining the effective sandstone distribution range from the sandstone distribution range; The method of comparing the P-wave and S-wave velocity ratio data volume obtained based on prestack inversion with the gas-bearing high-quality sandstone identification threshold to determine the gas-bearing high-quality sandstone distribution range from the effective sandstone distribution range includes: When the P-wave and S-wave velocity ratio data volume obtained based on prestack inversion is smaller than the gas-bearing high-quality sandstone identification threshold, the gas-bearing high-quality sandstone distribution range is determined from the effective sandstone distribution range.

5. The method according to claim 1, wherein The method further comprises: Based on the product of the P-wave impedance data volume and the P-wave velocity ratio data volume, a quantitative seismic sensitivity index of high-quality gas-bearing sandstone is constructed; Based on the quantitative seismic sensitivity index of high-quality gas-bearing sandstone, a prediction model for the seismic indicator factor of gas enrichment is constructed according to the following formula: F enrichment_Inversion =-a -10 ·F quantization_Inversion 2 +b -6 ·F quantization_Inversion +c Among them, F enrichment_Inversion is the seismic indicator factor for gas enrichment, F quantization_Inversion is the quantitative index of seismic sensitivity of high-quality gas-bearing sandstone, and a, b, and c are constants.

6. The method according to claim 5, characterized in that The gas enrichment seismic indicator factor is determined based on a P-wave velocity ratio data volume and a P-wave impedance data volume, and includes: The product of the P-wave and S-wave velocity ratio data volume and the P-wave impedance data volume is input into the gas enrichment seismic indicator factor prediction model to obtain the gas enrichment seismic indicator factor.

7. The method according to claim 1, characterized in that The method of identifying high-quality gas-bearing reservoirs from the distribution range of high-quality gas-bearing sandstones based on gas-enrichment seismic indicator factors includes: Compare the seismic indicator factors of gas enrichment in the distribution range of high-quality gas-bearing sandstones; From the distribution range of high-quality gas-bearing sandstones, areas where the gas-enrichment seismic indicator factor is greater than the gas-enrichment seismic indicator factor threshold are identified as high-quality gas reservoirs.

8. A device for identifying high-quality gas-bearing reservoirs in thin river channel sandstone, characterized in that: include: A threshold determination module, used to determine the pseudo-P-wave impedance sandstone identification threshold, the P-wave impedance effective sandstone identification threshold, and the gas-bearing high-quality sandstone identification threshold; A first range determination module is configured to compare the pseudo-P-wave impedance data volume obtained based on post-stack inversion with the pseudo-P-wave impedance sandstone identification threshold to determine the sandstone distribution range; A second range determination module is configured to compare the P-wave impedance data volume obtained based on post-stack inversion with the P-wave impedance effective sandstone identification threshold to determine the effective sandstone distribution range from the sandstone distribution range; a third range determination module, configured to compare the P-wave and S-wave velocity ratio data volume obtained based on prestack inversion with the gas-bearing high-quality sandstone identification threshold to determine the gas-bearing high-quality sandstone distribution range from the effective sandstone distribution range; The identification module is used to identify high-quality gas-bearing reservoirs from the distribution range of high-quality gas-bearing sandstones based on gas-bearing enrichment seismic indicator factors, wherein the gas-bearing enrichment seismic indicator factors are determined based on the P-wave velocity ratio data volume and the P-wave impedance data volume.

9. A computer device comprising a memory, a processor, and a computer program / instruction stored in the memory, characterized in that: The processor executes the computer program / instructions to implement the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program / instruction stored thereon, characterized in that: When the computer program / instructions are executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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