UBCTD Geosteering Advisory for Real-Time Well Path Adjustment
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Solution Overview
Problem
Existing underbalanced coiled tubing drilling (UBCTD) technologies face challenges in maintaining the wellbore within the target producing zone due to the delay in geological marker registration, leading to inefficient reservoir coverage and production optimization.
Innovation Solution
A real-time advisory system using a hybrid machine-learning architecture, combining k-nearest neighbor models and artificial neural networks, to predict and adjust drilling paths based on historical and real-time data, enabling geosteering adjustments to optimize well placement and reservoir contact.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real-time machine learning prediction is implemented to improve well path accuracy, then well path prediction accuracy is improved, but system complexity increases
Solution Approach 1:
The system segments the well path prediction task into multiple components: a planning stage that generates initial well path predictions, and a real-time stage that processes drilling parameters and adjusts predictions. This segmentation allows complex machine learning models to be deployed in a modular fashion, improving well path accuracy while managing system complexity through staged processing.
Solution Approach 2:
The system performs preliminary well path prediction during the planning stage before actual drilling begins. This preliminary action establishes baseline predictions that can be compared against real-time data, enabling the system to detect deviations and make adjustments without requiring all computational complexity to be present during real-time operations.
2Manufacturing precision
If real-time data processing is used to improve drilling operation accuracy, then drilling operation accuracy is improved, but processing time increases
Solution Approach 1:
The system performs preliminary processing of drilling parameters and well path predictions during the planning stage, before real-time drilling operations begin. This allows the system to pre-compute baseline predictions and establish performance thresholds, so that real-time operations only require comparing current readings against pre-computed values, significantly reducing processing time while maintaining high drilling operation accuracy.
3Productivity
If automated geosteering adjustment is implemented to improve reservoir contact, then productivity is improved, but automation extent increases
Solution Approach 1:
The system implements feedback by continuously comparing real-time drilling parameter data against machine learning predicted values. When deviations exceed predetermined thresholds, the system automatically generates geosteering adjustment recommendations. This feedback mechanism enables automated geosteering adjustment that improves reservoir contact while maintaining a balanced level of automation that works alongside human operators rather than fully replacing them.
Data Source
AI summary
A method that may include obtaining well path data describing a first well path through one or more formations. The method may include obtaining first acquired drilling parameter data in real-time during a drilling operation for a predetermined well, wherein the drilling operation corresponds to the first well path. The method may include determining, by a computer processor, first predicted drilling data using a machine-learning model, the well path data, and the first acquired drilling parameter data, wherein the first predicted drilling data may include a predicted well path. The method may include determining whether the first predicted drilling data satisfies a predetermined criterion. The method may include determining, in response to determining that the first predicted drilling data fails to satisfy the predetermined criterion, an adjusted well path. The method may include transmitting a first command to update the drilling operation to implement the adjusted well path.