Method and device for predicting metamorphic rock buried hill fracture development degree
By establishing seismic waveform characteristics and fracture development models, combining nonlinear neural network algorithms and multi-source data analysis, the problem of fracture distribution and reservoir feature prediction in metamorphic rock subsidence mountain reservoirs is solved, and high-precision reservoir distribution prediction and improvement of exploration and development efficiency is achieved.
Patent Information
- Application Number
- CN202311573747.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2023-11-23
- Publication Date
- 2025-05-23
AI Technical Summary
The prior art is difficult to effectively predict the fracture distribution and reservoir characteristics in metamorphic rock subsidence oil reservoirs, resulting in lower accuracy of exploration and development.
By establishing seismic waveform characteristics and fracture development models, combining nonlinear neural network algorithms and multi-source data analysis, the development degree and reservoir distribution characteristics of metamorphic rock latent mountain fractures are predicted.
The precise characterization of the development of metamorphic rock sublime mountain fissures has been achieved, the accuracy of prediction of reservoir distribution characteristics has been improved, and the efficiency of exploration and development has been enhanced.
Smart Images
Figure CN120028839A_ABST
Abstract
Claims
1. A method for predicting the degree of fracture development in metamorphic rock buried hills. It is characterized in that The specific steps are as follows: Step 1: Conduct core observation and logging data analysis in the target area to determine the fracture development characteristics of the target area; Step 2: Use forward modeling software to analyze the seismic reflection characteristics of the fracture development characteristics; Step 3, establishing the relationship between seismic reflection characteristics and oil well production and geological model through nonlinear neural network algorithm; Step 4: Establish a lithofacies model based on the lithology distribution and profile division of the target area; Step 5, determining the distribution range of different seismic wave group characteristics through seismic wave group characteristic analysis; Step six, combine the distribution range of seismic wave group characteristics with the lithology of the formation, establish a fracture development distribution model, and analyze the accuracy of the prediction.
2. A method for predicting the development degree of metamorphic rock buried hill fractures according to claim 1, It is characterized in that Step 2 specifically includes: using GMAX forward modeling software, coring and logging analysis of wells in the target area, inputting the characteristics of the block of fractures into the GMAX forward modeling software, and analyzing the response characteristics of the fractures as fracture zones in seismic performance.
3. A method for predicting the development degree of metamorphic rock buried hill fractures according to claim 2, It is characterized in that Step three specifically includes: using refract software to establish a fracture wave group feature module, inputting seismic data, recorded drilling data, lithology observation data, logging data and single well production of produced oil wells in the target area, and establishing a fracture wave group feature module by computer to match the well data in the target area with the seismic wave group characteristics, and finally establishing a relationship model between single well production characteristics, geological models and seismic wave group characteristics.
4. A method for predicting the development degree of metamorphic rock buried hill fractures according to claim 3, It is characterized in that Step 4, the lithology characteristics of the well are replayed through the well logging curve data of the known well by Gxplorer software, the lithology distribution of the well is determined, and the results are summarized to form a regional lithology distribution map and establish a regional lithology distribution model.
5. A method for predicting the development degree of metamorphic rock buried hill fractures according to claim 4, It is characterized in that Step five, with the help of seisware software, the distribution range of different seismic wave group characteristics is determined through the crack analysis module.
6. A method for predicting the development degree of metamorphic rock buried hill fractures according to claim 5, It is characterized in that Step six is to calculate and determine the matching relationship between the distribution range of different seismic wave group characteristics and the drilling data and single oil well production in the known area, analyze the accuracy of the prediction, and superimpose it with the results of the buried hill lithology analysis to finally establish a fracture development distribution model.
7. A method for predicting the development degree of metamorphic rock buried hill fractures according to claim 1, It is characterized in that Step 6: When analyzing the accuracy of the prediction, the standard for reliable and accurate prediction is that the actual production is compared with the fracture prediction production results by analyzing the horizontal and vertical production characteristics of the wells in the known blocks, and the matching rate is at least 80%.
8. A device for predicting the development degree of fractures in metamorphic rock buried hills, It is characterized in that It includes fracture intensity digitization module, data input module, seismic reflection characteristic analysis module, seismic wave group characteristic establishment module, lithofacies model establishment module, fracture development characteristic establishment module; Fracture strength digitization module, used to analyze core and logging data in the target area, determine fracture development characteristics, and digitize fracture development characteristics; Data input module, used to input conventional logging data, drilling data, logging data, core data, mud loss volume and loss velocity data into the computer; The seismic reflection characteristic analysis module is used to establish the relationship between the fracture dip, fracture spacing, fracture direction and the seismic isotropic axis; through seismic interpretation, the areas with more chaotic seismic waves are fracture development areas; The seismic wave group characteristic establishment module is used to combine the input statistical drilling data, single well production of oil wells, and seismic reflection relationship analysis to establish a seismic wave group characteristic model; the seismic wave group characteristic model is to classify different seismic waves through the reflection of seismic waves in seismic interpretation; The lithofacies model building module is used to analyze core observation and logging data, determine the lithology distribution characteristics of the target area, and combine the lithology distribution characteristics of the target area and the profile division data to establish a lithofacies model; The fracture development characteristic establishment module is used to analyze and combine the calculated fracture spatial distribution characteristics, lithofacies model, and seismic wave group characteristic model to establish a fracture development characteristic model; based on the seismic wave group characteristic model and the fracture development model, the metamorphic rock buried hill oil and gas reservoir in the target area is predicted to obtain different types of seismic waves.