AVO inversion oil gas prediction method based on rock physical analysis

Abstract

The invention discloses an AVO inversion oil and gas prediction method based on rock physical analysis. The problems that an existing AVO inversion method is complex in solution, poor in relevance with reservoir attributes and low in oil and gas detection precision are solved. The method comprises the following steps: S1, obtaining the correlation of logging data and seismic data in a research area to a reservoir through rock physics research; a rock physical model is established based on the correlation of the logging data and the seismic data to the reservoir to serve as the basis of seismic inversion result interpretation; s2, on the basis of the geological framework model, logging and seismic data, stochastic simulation inversion of a reservoir is carried out by taking a layer as a unit, and a seismic inversion body is established; and S3, based on the rock physical model in the step S1 and the seismic inversion body established in the step S2, drawing an oil and gas distribution diagram, and determining the distribution range of oil and gas contained in the research area. The AVO inversion oil and gas prediction method based on rock physical analysis is high in relevance with reservoir attributes, and the oil and gas detection precision is improved.

Description

Technical Field:

[0001] The present invention relates to the technical field of reservoir engineering geology, and particularly relates to an AVO inversion oil and gas prediction method based on rock physics analysis. Background Art:

[0002] Since the AVO method was proposed in 1984, which can help depict the fluid content of reservoirs, it has laid the foundation for oil and gas prediction technologies based on AVO attributes. Geophysicists have simplified the Zoeppritz equation from different perspectives to establish different forms of AVO equations, realizing AVO attribute inversion and fluid detection. Since directly solving the Zoeppritz equation on which AVO attributes are based is extremely complex and it is difficult to give a clear physical concept, most AVO approximate equations are established on a series of assumptions and have a poor correlation with the actual geological situation. Therefore, it affects the accuracy of oil and gas detection using AVO attributes.

[0003] With the continuous deepening of oil and gas exploration and development, oil and gas reservoirs are becoming more and more complex, and the exploration focus has shifted from the original structural oil and gas reservoirs to lithologic oil and gas reservoirs and subtle oil and gas reservoirs. On the one hand, due to the gradually deeper, smaller and more complex exploration targets, the exploration difficulty has increased, and the accuracy requirements for reservoir prediction have also become higher and higher. On the other hand, in addition to determining the existence of reservoirs and predicting the development degree of reservoirs, the ultimate goal of reservoir prediction also requires determining the properties of fluids contained in the reservoirs. Fluid identification has become a new challenge and bottleneck problem in complex reservoir prediction. Summary of the Invention:

[0004] The present invention aims at the problems in the background art that the existing AVO inversion methods are complex to solve, have a poor correlation with reservoir properties, and have low oil and gas detection accuracy, and provides an AVO inversion oil and gas prediction method based on rock physics analysis. This AVO inversion oil and gas prediction method based on rock physics analysis has a high correlation with reservoir properties and improves the accuracy of oil and gas detection.

[0005] The present invention can achieve the solution to its problems through the following technical solutions: This AVO inversion oil and gas prediction method based on rock physics analysis includes the following steps:

[0006] S1. Obtain the correlation of well logging data and seismic data with the reservoir in a study area through rock physics research; based on the correlation of well logging data and seismic data with the reservoir, establish a rock physics model as the basis for interpreting seismic inversion results;

[0007] S2. Based on the geological framework model, well logging and seismic data, conduct stochastic simulation inversion of the reservoir in units of layers to establish a seismic inversion body;

[0008] S3. Based on the rock physics model in step S1 and the inversion body established in step S2, draw an oil and gas distribution map to clarify the distribution range of oil and gas in the study area.

[0009] Furthermore, the step S1 establishes a rock physics model based on the correlation between the well logging data and the seismic data to the reservoir as a basis for interpreting the seismic inversion results; the specific method includes:

[0010] 1.1. Standardize the logging curves, analyze the characteristics of different logging curves in different lithofacies, select logging curves in a targeted manner, and perform reservoir prediction;

[0011] 1.2. Make a template for oil saturation and find out the well logging curves and reservoir parameters with high correlation and good discrimination;

[0012] 1.3. Use the well logging curves and reservoir parameters with high correlation and good discrimination to establish an intersection diagram; find the threshold of the oil-bearing reservoir through the intersection diagram; and determine the oil-bearing reservoir within the threshold range.

[0013] Furthermore, the logging curve includes: gamma GR; spontaneous potential SP; density DEN; resistivity (RMN, RMG).

[0014] Furthermore, reservoir parameters include: reservoir thickness, porosity, permeability and reservoir density.

[0015] Furthermore, the method for making a template for oil saturation is: using two selected logging curves as x-coordinates and y-coordinates respectively, and the oil saturation values ​​corresponding to the two curves to make the template for oil saturation.

[0016] Furthermore, the method of establishing the seismic inversion volume in S2 includes:

[0017] 2.1. Create an initial geological model;

[0018] 2.2. Combine well data with seismic data, estimate seismic wavelets, and calibrate synthetic seismic records;

[0019] 2.3. Based on the initial geological model in step 2.1, the synthetic seismic record calibration in step 2.2 is used to convert from the time domain to the depth domain. Combined with the seismic information and logging data, random simulation iterative inversion is performed. When the optimal match is reached, the seismic inversion volume is output.

[0020] Furthermore, the method for creating an initial geological model includes:

[0021] Combining stratigraphic data and geological parameters, a stratigraphic framework is established. By analyzing and fitting the histogram and variogram distribution of reservoir physical characteristics and rock properties, their characteristic values ​​are obtained, a mathematical model is established, and an initial geological model is created.

[0022] Furthermore, the stochastic simulation iterative inversion includes sequential Gaussian simulation and lithology indication simulation techniques.

[0023] Furthermore, the lithology indication simulation technique includes simulated annealing stochastic inversion and waveform indication inversion.

[0024] Furthermore, the method for drawing the oil and gas distribution map in step S3 is as follows:

[0025] Based on the rock physics model in step S1 and the seismic inversion volume established in step S2, use the rock physics template to calibrate the seismic inversion volume, find out the areas with oil and gas, and draw the oil and gas distribution map.

[0026] The theoretical basis for establishing the multiphase fluid rock physics model is:

[0027] The elastic parameters that have a direct impact on the seismic waves propagating in the underground medium are the longitudinal wave velocity, transverse wave velocity, and medium density of the rocks on both sides of the interface. Research in rock physics shows that the above three elastic parameters are closely related to rock properties and fluid types. These elastic parameters can be obtained through prestack elastic parameter inversion, which is the most important physical basis for using prestack seismic data for elastic parameter inversion.

[0028] Based on the geological framework model, logging, and seismic data, the theoretical basis for performing stochastic simulation inversion of the reservoir in units of layers is:

[0029] Geostatistics is based on the theory of regionalized variables and uses the variogram (or variance) function as the basic tool to study natural phenomena that are distributed in space and exhibit certain structural and random characteristics. The spatial distribution problem of reservoir parameters exactly conforms to this natural phenomenon. Geostatistics is used to characterize the variation laws of reservoir parameters of various sedimentary types and make predictions about the spatial distribution of reservoir parameters in the unknown areas between wells using the known laws.

[0030] Stochastic simulation inversion is a simulation method based on a geological model. JASON has developed stochastic simulation into a stochastic simulation inversion of the reservoir in units of layers based on the geological framework model, logging, and seismic data. The commonly used stochastic simulations are sequential Gaussian simulation technology and lithology indication simulation technology.

[0031] The key technology of stochastic simulation is to analyze and fit the histograms and variogram distributions of reservoir physical properties and rock attributes, calculate their characteristic values, establish a mathematical model, and perform stochastic modeling and inversion for different variable types (continuous variables are porosity and permeability; discrete variables are lithology) using different methods (kriging and co-kriging, sequential Gaussian conditional simulation, sequential indicator simulation).

[0032] Stochastic simulation describes the relationship between data in the spatial data field by establishing a variogram, thereby achieving the purpose of establishing a statistical correlation function between spatial reservoir parameter points.

[0033] Compared with the above background technology, the present invention has the following beneficial effects:

[0034] The AVO inversion oil and gas prediction method based on rock physical analysis of the present invention has a high correlation with reservoir properties and improves the accuracy of oil and gas detection.

[0035] In field applications, the northern transition zone of the Sabei Development Area is located at the edge of the oil-bearing area, with a low well network density, high water content, serious ineffective and inefficient circulation, insufficient understanding of the distribution of sand bodies between wells, and difficulty in identifying residual oil. The application of the technology of the present invention can achieve fine characterization of sand bodies between wells, accurate prediction of interwell fluids and residual oil evaluation, guide the precise adjustment of old wells, and further explore potential areas of high-efficiency wells. Description of the drawings:

[0036] Figure 1 is a flow chart of the method of the present invention;

[0037] Figure 2 The embodiment of the present invention contains the distribution characteristics of the longitudinal and transverse wave velocity ratios of different fluid facies;

[0038] Figure 3 It is an AVO inversion attribute profile diagram based on rock physics analysis according to an embodiment of the present invention. Specific implementation method:

[0039] In order to make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0040] like Figure 1 As shown, an AVO inversion oil and gas prediction method based on rock physical analysis includes the following steps:

[0041] S1. Obtain the correlation between logging data and seismic data and reservoirs in a study area through rock physics research; establish a rock physics model based on the correlation between logging data and seismic data and reservoirs as the basis for interpreting seismic inversion results; specific methods include:

[0042] 1.1. Standardize the logging curves, analyze the characteristics of different logging curves in different lithofacies, select logging curves in a targeted manner, and perform reservoir prediction; the logging curves include: gamma GR; natural potential SP; density DEN; resistivity (RMN, RMG); reservoir parameters include: reservoir thickness, porosity, permeability and reservoir density, etc.

[0043] 1.2. Make a template for oil saturation, and find out well logging curves and reservoir parameters with high correlation and good discrimination.

[0044] The method for making the template for oil saturation is as follows: Take two selected well logging curves as the x - coordinate and y - coordinate respectively, and make a template for oil saturation with the oil saturation values corresponding to the two curves.

[0045] 1.3. Use the well logging curves and reservoir parameters with high correlation and good discrimination to establish a cross - plot; find out the threshold of the oil - bearing reservoir through the cross - plot; determine the oil - bearing reservoir within the threshold range.

[0046] S2. Based on the geological framework model, well logging and seismic data, conduct stochastic simulation inversion of the reservoir layer by layer to establish a seismic inversion body; the specific method includes:

[0047] 2.1. Create an initial geological model.

[0048] Combined with horizon data and geological parameters, establish a stratigraphic framework. By analyzing and fitting the histograms and variogram distributions of reservoir physical properties and rock attributes, obtain their characteristic values, establish a mathematical model, and create an initial geological model.

[0049] 2.2. Conduct well - seismic combination, estimate seismic wavelets, and calibrate synthetic seismic records.

[0050] 2.3. On the basis of the initial geological model in step 2.1, use the synthetic seismic records calibrated in step 2.2 to convert from the time domain to the depth domain. Combine seismic information and well logging data, and conduct stochastic simulation iterative inversion. When the optimal match is reached, output the seismic inversion body.

[0051] The stochastic simulation iterative inversion includes sequential Gaussian simulation and lithology - indicator simulation techniques; the lithology - indicator simulation technique includes simulated annealing stochastic inversion and waveform - indicator inversion.

[0052] S3. Based on the rock physics model in step S1 and the seismic inversion body established in step S2, draw an oil - gas distribution map to clarify the distribution range of oil - gas in the study area. The specific method includes:

[0053] Based on the rock physics model and the seismic inversion body, use the rock physics template to calibrate the seismic inversion body, find out the areas with oil - gas, and draw an oil - gas distribution map.

[0054] Example 1

[0055] Using the AVO inversion oil - gas prediction method based on rock physics analysis of the present invention, re - recognize the dominant sand bodies in the extended area of the northern transition zone of the Saertu Oilfield, including the following steps:

[0056] In the outer expansion area of the northern transition zone of the Sartu Oilfield, 8.5 square kilometers of seismic data were loaded, noise suppressed, gather flattened, amplitude compensated and other optimization processing and analysis were carried out; the logging data of 255 wells were sorted out, normalized processing and sensitive elastic parameter analysis were carried out, and the fine calibration of 255 wells was further completed by using the matching relationship between the accurate seismic interpretation horizons and the seismic data; through pre-stack inversion, the prediction accuracy of inter-well sand bodies exceeded 85%.

[0057] I. Establish a multiphase fluid rock physics model:

[0058] By establishing a direct interpretation relationship between logging data and the lithology, physical properties, oil-bearing properties, etc. of the reservoir, an oil-gas-water rock physics model was established. The crossplot analysis of elastic and physical parameters in the study area was completed, and a rock physics template of two parameters, namely the P-wave to S-wave velocity ratio and the P-wave impedance, was completed; it provided a standard for subsequent reservoir and remaining oil analysis.

[0059] Appendix Figure 2 is the crossplot of the P-wave to S-wave velocity ratio and gamma. From the appendix Figure 2 it can be seen that the gamma curve and the P-wave to S-wave velocity ratio can distinguish most of the oil-bearing reservoirs. Therefore, the P-wave to S-wave velocity ratio less than 1.95 and the gamma curve less than 100 are selected as the thresholds for oil-bearing reservoirs.

[0060] II. Complete AVO pre-stack seismic inversion for oil and gas identification and implement the scale of oil and gas distribution:

[0061] Based on parameter analysis, well-seismic calibration, and geological model establishment, AVO pre-stack seismic inversion for oil and gas identification was completed, and inversion data volumes of parameters such as P-wave impedance, S-wave impedance, P-wave to S-wave velocity ratio, and density were obtained.

[0062] III. Based on the rock physics model in step S1 and the inversion data volume established in step S2, use the previous rock physics template to calibrate the seismic inversion body to determine the location of oil and gas distribution; draw an oil and gas distribution map to clarify the oil and gas distribution range in the study area;

[0063] Combining the inversion results and the rock physics template, the gas distribution prediction of 3 oil reservoir groups between S01 and SII4 in the study area was completed, and the gas distribution range in the outer expansion area of the northern transition zone was clarified.

[0064] Appendix Figure 3 is the AVO inversion profile based on rock physics analysis. The figure shows the oil saturation profile, the dark color tone is the high saturation area, and the black curve is the oil saturation. From the oil saturation data volume obtained by using the inversion density body as the spatial constraint body, the inter-well prediction is consistent with the density data trend in space and has a high degree of conformity at the actual drilled wells.

[0065] Those of ordinary skill in the art will realize that the embodiments described herein are provided to assist the reader in understanding the implementation methods of the present invention, and it should be understood that the scope of protection of the present invention is not limited to such specific statements and embodiments. Those of ordinary skill in the art can make various other specific deformations and combinations that do not depart from the essence of the present invention based on these technical revelations disclosed in the present invention, and these deformations and combinations are still within the scope of protection of the present invention.

Claims

1. An AVO inversion oil and gas prediction method based on rock physics analysis, Features: The following steps are involved: S1. Obtain the correlation of well logging data and seismic data to reservoirs in a study area through rock physics research; Based on the correlation of well logging data and seismic data to the reservoir, a rock physics model is established as the basis for interpreting the seismic inversion results; S2. Based on the geological framework model, well logging and seismic data, random simulation inversion of the reservoir is performed layer by layer to establish a seismic inversion volume; S3. Based on the rock physics model in step S1 and the seismic inversion body established in step S2, a hydrocarbon distribution map is drawn to clarify the distribution range of hydrocarbons in the study area.

2. The AVO inversion oil and gas prediction method based on rock physical analysis according to claim 1, Features: The step S1 establishes a rock physics model based on the correlation between the well logging data and the seismic data to the reservoir as a basis for interpreting the seismic inversion results; the specific method includes: 1.

1. Standardize the logging curves, analyze the characteristics of different logging curves in different lithofacies, select logging curves in a targeted manner, and perform reservoir prediction; 1.

2. Make a template for oil saturation and find out the well logging curves and reservoir parameters with high correlation and good discrimination; 1.

3. Use the well logging curves and reservoir parameters with high correlation and good discrimination to establish an intersection diagram; find the threshold of the oil-bearing reservoir through the intersection diagram; and determine the oil-bearing reservoir within the threshold range.

3. The AVO inversion oil and gas prediction method based on rock physical analysis according to claim 2, Features: Well logging curves include: gamma curve, natural potential curve, density curve and resistivity curve.

4. The AVO inversion oil and gas prediction method based on rock physical analysis according to claim 2, Features: Reservoir parameters include: reservoir thickness, porosity, permeability, and reservoir density.

5. The AVO inversion oil and gas prediction method based on rock physical analysis according to claim 2, Features: The method for making the oil saturation template is: using two selected logging curves as x-coordinates and y-coordinates respectively, and making the oil saturation template with the oil saturation values ​​corresponding to the two curves.

6. The AVO inversion oil and gas prediction method based on rock physical analysis according to claim 2, Features: S2 method for establishing seismic inversion volume, including: 2.

1. Create an initial geological model; 2.

2. Combine well data with seismic data, estimate seismic wavelets, and calibrate synthetic seismic records; 2.

3. Based on the initial geological model in step 2.1, the synthetic seismic record calibration in step 2.2 is used to convert from the time domain to the depth domain. Combined with the seismic information and logging data, random simulation iterative inversion is performed. When the optimal match is reached, the seismic inversion volume is output.

7. The AVO inversion oil and gas prediction method based on rock physical analysis according to claim 6, Features: The method for creating an initial geological model comprises: Combined with horizon data and geological parameters, a stratigraphic framework is established. By analyzing and fitting the histograms and variogram distributions of reservoir physical properties and rock attributes, their characteristic values are obtained, a mathematical model is established, and an initial geological model is created.

8. The AVO inversion oil and gas prediction method based on rock physics analysis according to claim 6, characterized in that: the stochastic simulation iterative inversion includes sequential Gaussian simulation and lithology indicator simulation techniques.

9. The AVO inversion oil and gas prediction method based on rock physics analysis according to claim 8, characterized in that: the lithology indicator simulation technique includes simulated annealing stochastic inversion and waveform indicator inversion.

10. The AVO inversion oil and gas prediction method based on rock physics analysis according to claim 1, characterized in that: the method for drawing the oil and gas distribution map in step S3 is: Based on the rock physics model in step S1 and the seismic inversion volume established in step S2, the seismic inversion volume is calibrated using the rock physics template, the areas with oil and gas are found, and the oil and gas distribution map is drawn.

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