Neural Network Well Planning for Drilling Deviation Adaptation
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Solution Overview
Problem
Existing well planning systems struggle to adapt to deviations during drilling operations, leading to inefficiencies and potential negative outcomes, as they lack effective mechanisms for real-time adjustment and learning from drilling data.
Innovation Solution
A system and method that utilizes a neural network trained on drilling data to adjust digital well plans in response to deviations, classifying outcomes as positive or negative, and optimizing future drilling operations based on machine learning algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional well planning systems are used without real-time adaptation, then the system complexity remains low, but the reliability of drilling operations deteriorates due to inability to respond to deviations
Solution Approach 1:
The system implements a closed-loop feedback mechanism where drilling data is continuously acquired, compared against the digital well plan, and used to trigger database searches for similar deviations. The outcomes of these searches feed back into the system to inform future drilling decisions, creating a self-learning loop that improves reliability without requiring complete system redesign
Solution Approach 2:
The system performs self-service through automated database searching and outcome classification. When a deviation is detected, the system automatically searches the database for similar historical deviations, classifies outcomes as positive or negative, and uses this information to suggest corrections, reducing the need for constant human intervention while maintaining high reliability
2Adaptability or versatility
If real-time database searching and machine learning are implemented, then the adaptability to drilling deviations improves, but the loss of time for processing and analyzing data increases
Solution Approach 1:
The system performs preliminary action by pre-organizing drilling data into a searchable database structure before deviations occur. Historical drilling data, including deviations and their outcomes, are预先 categorized and stored, enabling rapid retrieval and comparison when real-time deviations are detected, thus reducing processing time while maintaining high adaptability
Solution Approach 2:
The system segments the data processing task into distinct modules: data acquisition, deviation detection, database searching, outcome classification, and plan adjustment. This segmentation allows each module to operate independently and efficiently, reducing overall processing time while maintaining comprehensive adaptability across all drilling scenarios
3Loss of information
If acquired drilling information is systematically analyzed and stored, then the loss of information from drilling operations is reduced, but the device complexity for data management increases
Solution Approach 1:
The database system serves multiple functions: storing historical drilling data, enabling deviation comparison, facilitating outcome classification, and supporting future plan generation. This multi-functionality reduces the need for separate specialized systems, managing information comprehensively without proportionally increasing complexity
Data Source
AI summary
A system and method that include receiving a digital well plan and issuing drilling instructions for drilling a well based at least in part on the digital well plan. The system and method also include comparing acquired information associated with drilling of the well with well plan information of the digital well plan to determine if there is at least one deviation from the digital well plan. The system and method additionally include performing a search of a database upon determining that there is the at least one deviation, wherein the search generates results that comprise at least one outcome that is classified as being a positive outcome or a negative outcome. The system and method further include training a neural network as a machine learning model based on the results to electronically adjust the digital well plan to increase a likelihood of at least one positive outcome.