AI-Driven Infill Well Placement Optimization
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Solution Overview
Problem
Conventional horizontal infill wellbore development in tight oil basins often results in excessive drilling costs and poor production due to a uniform, repetitive approach that fails to optimize well placement, leading to unnecessary wellbores and inefficient hydrocarbon extraction.
Innovation Solution
A method and system utilizing machine learning and artificial intelligence to analyze data from parent and infill wells, identifying quantifiable causes of pressure communication events to determine optimal locations and trajectories for infill wellbores, minimizing overlap and maximizing hydrocarbon production by strategically placing wells based on fracturing data and pressure communication analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a uniform geometric approach is used for infill wellbore development, then the development process is simple and consistent, but the number of wellbores required increases excessively leading to higher costs and poor production
Solution Approach 1:
The patent applies local quality by transitioning from a uniform geometric approach to a data-driven approach where wellbore placement is optimized based on local reservoir characteristics, pressure communication events, and fracturing data. Each infill wellbore location is determined by analyzing specific local conditions rather than applying a blanket uniform pattern, thereby reducing the total number of wellbores needed while maintaining development effectiveness.
Solution Approach 2:
The patent implements feedback by using pressure communication event data and fracturing operation results to inform subsequent wellbore placement decisions. The system continuously learns from pressure responses and communication events between wellbores to optimize the location of infill wellbores, adjusting the development strategy based on actual reservoir response rather than relying solely on predetermined geometric patterns.
2Ease of manufacture
If a uniform geometric approach is used for infill wellbore development, then the development process is straightforward, but production efficiency decreases due to unnecessary wellbores and poor placement
Solution Approach 1:
The patent replaces the mechanical/geometric approach with an information-based approach. Instead of using predetermined geometric patterns to determine wellbore locations, the system uses machine learning algorithms that process pressure communication event data, fracturing operation data, and reservoir characteristics to automatically optimize wellbore placement, thereby improving production efficiency while reducing the number of unnecessary wellbores.
Solution Approach 2:
The patent applies parameter changes by transforming the wellbore development approach from fixed geometric parameters (constant spacing and orientation) to dynamic, data-driven parameters. The system uses pressure communication event identification, feature extraction from operational data, and machine learning to determine optimal wellbore locations and trajectories, allowing the development strategy to adapt to actual reservoir conditions and maximize hydrocarbon production.
3Productivity
If infill wellbores are placed close together to maximize reservoir coverage, then reservoir exploitation is more intensive, but pressure communication events increase leading to reduced effectiveness
Solution Approach 1:
The patent uses feedback from pressure communication event monitoring to optimize infill wellbore placement. By analyzing pressure responses and communication events between wellbores, the system identifies optimal spacing and positioning that maximizes reservoir coverage while minimizing harmful pressure interactions. This feedback-driven approach allows the system to learn from actual pressure behavior and adjust wellbore placement to maintain reliable pressure communication patterns.
Solution Approach 2:
The patent applies dynamics by making wellbore placement strategies adaptive rather than static. The system continuously monitors pressure communication events and adjusts infill wellbore positioning based on real-time pressure responses. This dynamic approach allows the development strategy to respond to changing reservoir conditions and pressure interactions, optimizing the balance between intensive reservoir exploitation and maintaining stable pressure communication patterns.
Data Source
AI summary
A method for determining a location and trajectory for a new wellbore relative to an adjacent wellbore includes: receiving controllable variable data and uncontrollable variable data related to fracturing a formation by a stimulation operation in a first wellbore penetrating the formation; receiving pressure communication event or pressure non-communication event identification data related to identification of a pressure communication event or pressure non-communication event in a second wellbore penetrating the formation in response to the fracturing; extracting features from the controllable and uncontrollable variable data to provide extracted features; detecting a pressure communication event using the extracted features and the pressure communication event or pressure non-communication event identification data using an analytic technique; identifying one or more quantified causes of the detected pressure communication event using an artificial intelligence technique; and determining the location and trajectory of the new wellbore using the one or more quantified causes.