Well Production Potential Evaluation for Hydraulic Refracturing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Many hydraulically fractured shale wells experience initial high production followed by a sharp decline, with a significant portion not contributing to production, and changes in the shale reservoir due to hydrocarbon depletion affecting fracture orientations and propagation, leading to missed hydrocarbon volumes.
Innovation Solution
A computer-implemented method and system for identifying candidate wells for hydraulic refracturing by generating model outputs, plotting them as a probability distribution function, and applying categorization rules to determine well production potential, optimizing hydraulic fracture design using data-driven analytics and predictive modeling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If hydraulic fracturing is performed on multiple stages to fully exploit shale reservoir potential, then hydrocarbon production is improved, but the complexity of the operation increases and many stages do not contribute to production
Solution Approach 1:
The system performs preliminary analysis before hydraulic fracturing operations by evaluating geological data, well completion data, and production data to predict which stages are likely to be non-productive. This allows operators to plan and optimize fracturing operations in advance, avoiding unnecessary stages and reducing operational complexity while maintaining productivity.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring production data from completed stages and using this information to adjust and optimize subsequent fracturing operations. The system learns from actual production outcomes to improve predictions and recommendations for remaining stages, enabling data-driven decision-making that reduces complexity while maintaining high productivity.
2Loss of substance
If hydraulic fracturing operations are revisited at previously drilled well sites to access missed hydrocarbon volumes, then resource recovery is improved, but the reliability of accessing new reservoirs is reduced due to modified stresses from hydrocarbon depletion
Solution Approach 1:
The system performs preliminary stress analysis and modeling before refracturing operations to predict how depleted reservoir stresses will affect fracture propagation. By analyzing geological data, production history, and stress field modifications, the system can pre-plan refracturing strategies that account for altered stress conditions, improving the reliability of accessing missed hydrocarbon volumes.
Solution Approach 2:
The system creates detailed digital models and simulations of the depleted reservoir conditions, copying the actual stress state and geological characteristics. These virtual models allow operators to test and optimize refracturing designs before actual field operations, reducing uncertainty and improving reliability of hydrocarbon recovery from refractured wells.
3Productivity
If data-driven analytics and predictive modeling are used to identify candidate wells and optimize fracture design, then productivity and resource recovery are improved, but the device complexity and computational requirements increase
Solution Approach 1:
The system integrates multiple functions into a unified platform that combines data collection, predictive modeling, well evaluation, and fracture design optimization. By creating a multi-functional system that handles various analytical tasks through integrated algorithms and workflows, the complexity is managed systematically while delivering comprehensive productivity improvements across the entire well evaluation and design process.
Data Source
AI summary
For each well of the plurality of wells, a plurality of model outputs are generated for the well. A range of productivity is determined for the well by plotting the plurality of model outputs in the form of a probability distribution function, projecting an actual production indicator result for the well onto the probability distribution function, comparing the actual production indicator result and the probability distribution function to identify a well production potential for the well, and determining a quality of the well production potential based on a plurality of categorization rules including a lowest quality category and a highest quality category. All of the wells in the plurality of wells are analyzed to identify wells in the highest quality category based on the determined quality of well production potentials for the plurality of wells for hydraulic refracturing.