A 3D phase-controlled inversion modeling method

Abstract

A method for 3D phase-controlled inversion modeling. It includes the steps of selecting the root mean square amplitude as the sedimentary phase sensitive seismic attribute; fusing the target layer sedimentary phase and the root mean square amplitude to obtain a two-dimensional plane seismic phase; stereoizing the two-dimensional plane seismic phase; and obtaining a 3D phase-controlled inversion model. The present invention fuses the various sedimentary phase sensitive seismic attributes with the target layer sedimentary phase into a new plane attribute, namely the two-dimensional plane seismic phase, which not only gives the seismic attribute a geological significance, but also highlights the attributes of the dominant reservoir, and can achieve a fine characterization of the distribution range of the dominant reservoir. The two-dimensional plane seismic phase is converted into a three-dimensional space seismic phase body, and finally added to the inversion spectrum as a low-frequency phase-controlled factor, giving the inversion prediction result a low-frequency geological significance, effectively improving the reservoir prediction accuracy. The prediction result obtained by this method greatly improves the plane coincidence rate while maintaining the high resolution of the geostatistical inversion in the vertical direction, effectively improving the prediction accuracy of lithologic oil and gas reservoirs.

Description

Technical Field

[0001] The invention belongs to the technical field of petroleum geophysical exploration reservoir prediction, and in particular relates to a 3D phase-controlled inversion modeling method. Background Art

[0002] As oil exploration and development continue to deepen, oil and gas exploration becomes increasingly complex, and corresponding exploration technologies are constantly improving. Therefore, identifying hidden oil and gas reservoirs, thin interbedded oil and gas reservoirs, and carefully characterizing the underground distribution of reservoirs are becoming increasingly important. Seismic reservoir prediction technology, based on seismic, geological, and well logging information and supported by advanced computer technology, studies the changes in reservoir distribution characteristics during different stages of exploration and development. As a comprehensive and highly practical exploration technology, the application of reservoir prediction has greatly improved the predictive capabilities of oil explorers and developers. However, due to limitations in data multi-resolution and resolution, its prediction accuracy is still not very high.

[0003] As the difficulty and risk of oil and gas exploration increase, my country's eastern region has gradually entered the stage of detailed exploration of lithologic oil and gas reservoirs. One of the main challenges facing oil workers is how to find new oil fields and oil and gas reservoirs around or within known oil fields, how to predict and evaluate reservoir quality, improve reservoir development efficiency, and further improve the accuracy of reservoir prediction.

[0004] Seismic inversion is an important method for obtaining lithology in geophysics, and there are many different types of this method. Compared with deterministic inversion, geostatistical inversion can fully utilize geological, seismic, and well logging information to effectively improve the accuracy of seismic reservoir prediction. Therefore, it has become popular among reservoir prediction researchers in recent years. With the deepening of research and application of geostatistical inversion, it has been found that its inversion results have high resolution in the vertical direction, which can meet the needs of tracking thin sand bodies in lithologic oil and gas reservoirs. However, the wave impedance attributes extracted in the plane are poorly consistent with geological laws. The reason is that seismic information lacks low-frequency and high-frequency data. High-frequency data can be supplemented by inversion algorithms, while low-frequency data is often supplemented by simple well interpolation models. However, the low-frequency data obtained in this way often does not conform to geological laws in space, making the inversion results unable to meet the needs of detailed lithologic exploration and development in the plane. Therefore, a 3D phase-controlled inversion modeling method is urgently needed. Summary of the Invention

[0005] In order to solve the above problems, the present invention aims to provide a 3D phase-controlled inversion modeling method.

[0006] To achieve the above-mentioned object, the 3D phase-controlled inversion modeling method provided by the present invention comprises the following steps performed in sequence:

[0007] 1) Obtain data including amplitude, frequency, waveform, phase, power spectrum, and energy of the target layer in the predicted area. Based on the sedimentary facies and paleomorphology of the target layer, select the root mean square amplitude that best matches the target layer sedimentation from the above data as the sedimentary facies sensitive seismic attribute;

[0008] 2) The sedimentary facies of the target layer are gridded according to the 25mx25m bins of the seismic data to obtain the gridded sedimentary facies S(x,y) of the target layer, and then the sedimentary facies of the target layer are classified according to the reservoir development. At the same time, the root mean square amplitude obtained in step 1), i.e., the sedimentary facies sensitive seismic attribute, is also gridded according to the 25mx25m bins, and the value range of the gridded root mean square amplitude is normalized to between 0 and 1 to obtain the normalized root mean square amplitude A(x,y). The root mean square amplitude is then classified according to its ability to reflect reservoir development. The gridded sedimentary facies S(x,y) of the target layer and the normalized root mean square amplitude A(x,y) are then fused to obtain the two-dimensional plane seismic facies f(x,y);

[0009] 3) converting the two-dimensional plane seismic phase into a range of seismic wave impedance; and then 3D-converting the two-dimensional plane seismic phase after the range conversion using the horizon of three-dimensional seismic tracing;

[0010] 4) On the basis of conventional geostatistical inversion, the above three-dimensional spatial seismic phase volume is added to the inversion spectrum in the frequency domain as a low-frequency phase control factor to obtain a 3D phase control inversion data volume.

[0011] In step 2), the target layer sedimentary phase is divided into three categories: "excellent", "good" and "poor"; the root mean square amplitude is divided into three categories: 0-0.5, 0.5-0.8 and 0.8-1; the gridded target layer sedimentary phase S(x, y) and the normalized root mean square amplitude A(x, y) are merged to obtain the two-dimensional plane seismic phase f(x, y): when a certain gridded target layer sedimentary phase S(x i ,y j ) is “excellent”, its two-dimensional plane seismic phase f(x i ,y j )=1; When the target layer sedimentary phase S(x i ,y j ) is “poor”, its two-dimensional plane seismic phase f(x i ,y j )=0; When the target layer sedimentary phase S(x i ,y j ) is “good”, its two-dimensional plane seismic phase Where A(x i ,y j ) is the normalized RMS amplitude of the category.

[0012] In step 3), the formula for converting the two-dimensional plane seismic phase to the value range of seismic wave impedance is F(x i ,y j )=f(x i ,y j )×(P max -P min )+P min , where F(x i ,y j ) is the two-dimensional plane seismic phase after range conversion, P max is the maximum value of seismic wave impedance, P min is the minimum value of seismic wave impedance; the method of converting the two-dimensional plane seismic phase after the above value range into three-dimensional by using the three-dimensional seismic tracking layer is: taking the top and bottom layers of the three-dimensional space as constraints through the formula Interpolation is performed to transform it into a three-dimensional seismic phase body, where F(x i ,y j ,t) is a certain type of three-dimensional earthquake phase body, F(x i ,y j ) is the two-dimensional plane seismic phase after the value range of a certain category is converted, C1 is the top level constraint, and C2 is the bottom level constraint.

[0013] The 3D phase-controlled inversion modeling method provided by the present invention has the following beneficial effects:

[0014] 1. The sensitive seismic attributes of various sedimentary facies are integrated with the sedimentary facies of the target layer into a new plane attribute, namely the two-dimensional plane seismic facies. This not only gives the seismic attributes geological significance, but also highlights the attributes of the dominant reservoir, and can achieve a detailed description of the distribution range of the dominant reservoir.

[0015] 2. Convert the two-dimensional plane seismic phase into a three-dimensional spatial seismic phase body, and finally add it to the inversion spectrum as a low-frequency phase control factor, giving the inversion prediction results low-frequency geological significance and effectively improving the accuracy of reservoir prediction.

[0016] 3. The prediction results of the 3D phase-controlled inversion model maintain the high vertical resolution of geostatistical inversion while significantly improving the plane coincidence rate, effectively improving the prediction accuracy of lithologic oil and gas reservoirs. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is the sedimentary facies map of the target layer in the area to be predicted.

[0018] Figure 2 A sensitive seismic attribute map optimized for the target layer sedimentary facies and paleo-geomorphology.

[0019] Figure 3A two-dimensional plane seismic facies map is obtained by integrating the sedimentary facies-sensitive seismic attributes with the sedimentary facies of the target layer.

[0020] Figure 4 This is a schematic diagram of the top and bottom constrained layers in the conversion of two-dimensional plane seismic phases into three-dimensional spatial seismic phase bodies.

[0021] Figure 5 It is the three-dimensional seismic phase stereogram after conversion.

[0022] Figure 6 The target layer wave impedance plane attribute map extracted from the conventional geostatistical inversion results.

[0023] Figure 7 In order to add the three-dimensional spatial seismic phase body as a low-frequency band to the results of geostatistical inversion, a 3D phase-controlled inversion data body is formed, and the plane attributes of the target layer are extracted on this data body.

[0024] Figure 8 The sedimentary microfacies map of the target layer was redrawn after adding new wells to the well network. DETAILED DESCRIPTION

[0025] The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0026] like Figure 1-Figure 3 As shown, the 3D phase-controlled inversion modeling method provided by the present invention includes the following steps performed in sequence:

[0027] 1) Obtain data including amplitude, frequency, waveform, phase, power spectrum and energy of the target layer in the area to be predicted, and select the relevant data from the above data based on the sedimentary facies and paleogeography of the target layer. Figure 1 The RMS amplitude with the highest degree of consistency with the sedimentary phase of the target layer C1 is used as the sedimentary phase sensitive seismic attribute. Figure 2 The rms amplitude shown is the same as Figure 1 The target layer C1 sedimentary facies shown in the figure shows that the amplitude is strong where the reservoir is well developed, and gradually weakens as the sand body thickness decreases. The strength of the root mean square amplitude is positively correlated with the reservoir thickness, which can roughly reflect the sedimentary characteristics of the reservoir.

[0028] 2) The sedimentary facies of the target layer are gridded according to the 25mx25m bin of the seismic data to obtain the gridded sedimentary facies S(x,y) of the target layer, and then the sedimentary facies of the target layer are classified into three categories according to the reservoir development, namely, "excellent", "good" and "poor". At the same time, the root mean square amplitude obtained in step 1), i.e., the sensitive seismic attribute of the sedimentary facies, is also gridded according to the 25mx25m bin, and the value range of the gridded root mean square amplitude is normalized to 0 to 1, the normalized root mean square amplitude A(x, y) is obtained, and then the root mean square amplitude is classified according to the ability of the root mean square amplitude to reflect the development of the reservoir. The present invention divides it into three categories, namely, the root mean square amplitude values ​​​​are 0-0.5, 0.5-0.8, and 0.8-1. Then, the gridded target layer sedimentary phase S(x, y) and the normalized root mean square amplitude A(x, y) are fused to obtain the two-dimensional plane seismic phase f(x, y); when a certain gridded target layer sedimentary phase S(x i ,y j ) is “excellent”, its two-dimensional plane seismic phase f(x i ,y j )=1; When the target layer sedimentary phase S(x i ,y j ) is “poor”, its two-dimensional plane seismic phase f(x i ,y j )=0; When the target layer sedimentary phase S(x i ,y j ) is “good”, its two-dimensional plane seismic phase Where A(x i ,y j ) is the normalized root mean square amplitude of the category. Figure 2 、 Figure 3 Respectively Figure 1 By comparison, it can be seen that the two-dimensional plane seismic phase is Figure 1 The sedimentary facies of the target layer C1 shown in the figure are more consistent. The two-dimensional plane seismic facies obtained after fusion further expand the dominant reservoir attributes while suppressing the inferior reservoir attributes.

[0029] 3) Convert the above two-dimensional plane seismic phase to the value range of seismic wave impedance, that is, F(x i ,y j )=f(x i ,y j )×(P max -P min )+P min , where F(x i ,y j ) is the two-dimensional plane seismic phase after range conversion, P max is the maximum value of seismic wave impedance, Pmin The maximum value of seismic wave impedance in the present invention is P max The minimum value of seismic wave impedance P is 8000. min is 13000. Then, the 3D seismic tracing horizon is used to stereotune the 2D plane seismic phase after the above range conversion, that is, the top and bottom layers of the 3D space are constrained by the formula Interpolation is performed to transform it into a three-dimensional seismic phase body, such as Figure 4 、 Figure 5 As shown, where F(x i ,y j ,t) is a certain type of three-dimensional earthquake phase body, F(x i ,y j ) is the two-dimensional plane seismic phase after the value range of a certain category is converted, C1 is the top level constraint, and C2 is the bottom level constraint.

[0030] 4) On the basis of conventional geostatistical inversion, the above three-dimensional spatial seismic phase volume is added to the inversion spectrum in the frequency domain as a low-frequency phase control factor to obtain a 3D phase control inversion data volume.

[0031] By using the 3D phase-controlled inversion modeling method provided by the present invention, the target layer plane attributes extracted from the obtained 3D phase-controlled inversion data volume are as follows: Figure 7 The plane attributes of the target layer extracted by conventional geostatistical inversion are shown as follows. Figure 6 As shown, Figure 8 It is the sedimentary microfacies map of the target layer drawn after the well network is infilled with new wells. Figure 6 、 Figure 7 Respectively Figure 8 The comparative analysis shows that the prediction results obtained by the 3D phase-controlled inversion modeling method provided by the present invention are consistent with those in the plane. Figure 8 The consistency of the sedimentary microfacies shown is greatly improved.

[0032] The inventors have experimented and applied the method of the present invention in region X. Compared with other algorithms, the prediction results obtained by the 3D phased inversion modeling method maintain high vertical resolution while significantly improving the planar coincidence rate. The combination of theory and practice has achieved good results.

Claims

1. A method for 3D phase-controlled inversion modeling, comprising the following steps performed in sequence: 1) Obtain data including amplitude, frequency, waveform, phase, power spectrum, and energy of the target layer in the predicted area. Based on the sedimentary facies and paleomorphology of the target layer, select the root mean square amplitude that best matches the target layer sedimentation from the above data as the sedimentary facies sensitive seismic attribute; 2) The sedimentary facies of the target layer are gridded according to the 25mx25m bins of the seismic data to obtain the gridded sedimentary facies S(x,y) of the target layer, and then the sedimentary facies of the target layer are classified according to the reservoir development. At the same time, the root mean square amplitude obtained in step 1), i.e., the sedimentary facies sensitive seismic attribute, is also gridded according to the 25mx25m bins, and the value range of the gridded root mean square amplitude is normalized to between 0 and 1 to obtain the normalized root mean square amplitude A(x,y). The root mean square amplitude is then classified according to its ability to reflect reservoir development. The gridded sedimentary facies S(x,y) of the target layer and the normalized root mean square amplitude A(x,y) are then fused to obtain the two-dimensional plane seismic facies f(x,y); 3) converting the above-mentioned two-dimensional plane seismic phase into a range of seismic wave impedance; then using the horizon of three-dimensional seismic tracking to stereoscopicize the two-dimensional plane seismic phase after the range conversion; 4) Based on conventional geostatistical inversion, the three-dimensional seismic phase volume is added to the inversion spectrum in the frequency domain as a low-frequency phase control factor to obtain a 3D phase control inversion data volume; Its characteristics are: In step 2), the target layer sedimentary facies are divided into three categories: "excellent", "good", and "poor"; the root mean square amplitude is divided into three categories: 0-0.5, 0.5-0.8, and 0.8-1; the gridded target layer sedimentary facies S(x, y) and the normalized root mean square amplitude A(x, y) are merged to obtain the two-dimensional plane seismic facies f(x, y): when a certain gridded target layer sedimentary facies S(x i ,y j ) is "excellent", its two-dimensional plane seismic phase f(x i ,y j )=1; When the target layer sedimentary phase S(x i ,y j ) is "poor", its two-dimensional plane seismic phase f(x i ,y j )=0; When the target layer sedimentary phase S(x i ,y j ) is "good", its two-dimensional plane seismic phase Where A(x i ,y j ) is the normalized RMS amplitude of the category.

2. The 3D phase-controlled inversion modeling method according to claim 1, characterized in that: In step 3), the formula for converting the two-dimensional plane seismic phase to the value range of seismic wave impedance is F(x i ,y j )=f(x i ,y j )×(P max -P min )+P min , where F(x i ,y j ) is the two-dimensional plane seismic phase after range conversion, P max is the maximum value of seismic wave impedance, P min is the minimum value of seismic wave impedance; the method of converting the two-dimensional plane seismic phase after the above value range into three-dimensional by using the three-dimensional seismic tracking layer is: taking the top and bottom layers of the three-dimensional space as constraints through the formula Interpolation is performed to transform it into a three-dimensional seismic phase body, where F(x i ,y j ,t) is a certain type of three-dimensional earthquake phase body, F(x i ,y j ) is the two-dimensional plane seismic phase after the value range of a certain category is converted, C1 is the top level constraint, and C2 is the bottom level constraint.

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