Well Placement Using Insertion Point Drivers for Reservoir Modeling
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Solution Overview
Problem
The existing methods for determining well positions in a field require significant human effort and computational time, and existing software solutions still involve a large number of variables, making the process costly and inefficient.
Innovation Solution
A process that positions wells one after another by calculating fluid property and distance maximization insertion point drivers, using a combined insertion point driver to determine the optimal well location, reducing the number of variables and improving computational efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional software products are used to position wells based on geographic coordinates optimization, then well positioning accuracy is improved, but the number of parameters to optimize increases significantly (6 parameters per well, 90 parameters for 15 wells), making the process costly and time-consuming
Solution Approach 1:
The patent extracts and removes unnecessary parameters from the optimization process by transitioning from geographic coordinate-based optimization (6 parameters per well) to a simplified driver-based approach. The method extracts only the essential factors needed for well positioning by using insertion point drivers that directly evaluate well performance potential, thereby eliminating redundant parameters while maintaining positioning accuracy.
Solution Approach 2:
The patent fundamentally changes the parameter space by replacing traditional geographic coordinates (x, y, z positions) with insertion point drivers that evaluate reservoir quality metrics. This parameter transformation reduces the complexity from optimizing 6 geometric parameters per well to evaluating a smaller set of reservoir-specific drivers, thereby simplifying the optimization process while improving relevance to actual well performance.
2Productivity
If the number of parameters to optimize is reduced to decrease computational cost and time, then computational efficiency is improved, but the reliability and accuracy of well positioning may deteriorate
Solution Approach 1:
The patent changes the nature of parameters from geometric coordinates to reservoir-quality-driven insertion point drivers. This transformation maintains reliability by using parameters that directly reflect reservoir characteristics and well performance potential, rather than purely geometric parameters. The insertion point drivers incorporate reservoir quality metrics that ensure the simplified optimization process still yields reliable well positioning results.
Solution Approach 2:
The method enables the system to automatically evaluate well positioning quality through insertion point drivers that self-assess reservoir suitability without requiring extensive manual parameter adjustment or complex optimization algorithms. The drivers inherently capture the essential factors for reliable positioning, allowing the system to maintain accuracy while reducing computational burden.
Data Source
AI summary
The process comprises positioning wells one after another in a group of potential cells of a geocellular model, each positioning of a well comprises: calculating for each cell of the group of potential cells, a fluid property insertion point driver (DFP1) representative of a fluid property maximization; calculating for each cell of the group of potential cells, a maximized distance insertion point driver (DMD1) representative of a maximization of a distance to another cell or group of cells having at least an undesired property; calculating for each cell of the group of potential cells a combined insertion point driver based on the fluid property insertion point driver (DFP1) and the maximized distance insertion point driver (DMD1); defining a well insertion point of the well being positioned at the cell having a maximal combined insertion point driver.