Deep Reservoir Fracture Prediction Using Thickness Optimization
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Solution Overview
Problem
Current methods for predicting deep reservoir structural fractures are inadequate as they fail to account for reservoir thickness changes, leading to inaccurate predictions and low reliability, especially in unconventional energy sources requiring high precision.
Innovation Solution
A method that introduces a stress-based prediction formula group and a thickness-based optimization formula, classifying reservoir thickness units and using measurement and simulation data to calculate structural fracture linear density, with reliability analysis to improve prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If stress-based prediction formula group is used, then prediction method is established, but prediction accuracy is insufficient and does not account for thickness changes
Solution Approach 1:
The patent transforms the prediction approach by changing the parameter set from stress-only parameters to include thickness parameters. The optimization formula introduces thickness (h) as a key parameter, transforming the prediction from D′lf = f(σ, E, μ) to D′lf = f(σ, E, μ, h), thereby improving prediction precision while maintaining the stress-based foundation.
Solution Approach 2:
The patent adds a new dimension to the prediction model by incorporating thickness variation. Instead of considering only stress parameters in a two-dimensional parameter space, the model extends to three dimensions by including thickness as an additional parameter, enabling the prediction to account for vertical variations in reservoir geometry.
2Ease of operation
If conventional stress-based method is used, then prediction process is simple, but cannot explain correlation between thickness change and fracture distribution
Solution Approach 1:
The patent segments the reservoir into multiple thickness units (h1, h2, h3, ...) and applies the prediction formula to each segment independently. This segmentation allows the model to capture local thickness variations and their corresponding fracture density patterns, preserving the thickness-fracture correlation information that would be lost in a uniform thickness assumption.
Solution Approach 2:
The patent introduces dynamic adaptation by allowing the prediction model to adjust to varying thickness conditions. The optimization formula dynamically incorporates thickness parameter h, enabling the model to adapt its predictions based on the specific thickness characteristics of each reservoir segment, thereby maintaining operational simplicity while capturing thickness-fracture correlations.
3Productivity
If current prediction method is used, then prediction can be performed, but reliability of prediction results cannot be predicted and special lithology areas cannot be avoided
Solution Approach 1:
The patent performs preliminary classification of reservoir thickness units before applying the prediction formula. By pre-segmenting the reservoir into thickness units and identifying areas with significant thickness variations or special lithology, the model can apply appropriate prediction strategies to different zones, improving the reliability of prediction results while maintaining overall productivity.
Solution Approach 2:
The patent incorporates a feedback mechanism through the optimization process. The model uses measured data and simulation results to refine the thickness parameter h and improve prediction accuracy. This feedback loop allows the system to learn from prediction errors and adjust accordingly, enhancing reliability while preserving the core prediction capability.
Data Source
AI summary
A method for simulating and predicting deep reservoir structural fractures in consideration of thickness change is disclosed. The method firstly calculates a structural fracture apparent density by using a stress-based reservoir structural fracture prediction formula group, secondly obtains structural fracture linear density based on simulation experiment, thickness unit division and reservoir structural fracture prediction optimization formula, finally carries out a reliability judgment based on the structural fracture linear density, the apparent density and the measured inspection values by using parameter inspection and error analysis. The above method can effectively reduce the difficulty and cost of energy development.