Fluid Digital Twin Control for Autonomous Drilling Fluid Management
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Solution Overview
Problem
Hydrocarbon exploration and recovery operations face significant challenges in inventory management, with traditional automated fluid management systems requiring substantial space and manpower, and being inefficient in terms of workflow and capital investment.
Innovation Solution
The implementation of an autonomous fluid management system (AFMS) that utilizes a digital twin to make decisions, coupled with drone, collaborative robot, and warehouse system controls, to optimize drilling and completion fluid properties using real-time and historical data, thereby reducing manual labor and increasing productivity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional automated fluid management systems are used, then fluid properties can be managed, but significant space and manpower are consumed
Solution Approach 1:
The patent creates a digital twin (virtual copy) of the fluid management system that replicates all fluid dynamics, equipment states, and operational parameters in a simulated environment. This digital replica enables complete fluid management functionality without requiring physical infrastructure, thereby eliminating the 85% space consumption associated with traditional systems while maintaining full operational capability
Solution Approach 2:
The patent replaces the physical mechanical fluid management infrastructure with a computational model running on remote servers. The digital twin uses algorithms and data processing to simulate and control fluid properties, substituting the need for physical warehouses, equipment housing, and manual monitoring systems with virtual representations that require minimal physical space
2Ease of operation
If traditional automated fluid management systems are used, then fluid properties can be managed, but significant manpower is required
Solution Approach 1:
The digital twin system operates autonomously by continuously ingesting real-time data from sensors and historical databases, automatically simulating fluid behavior, predicting property changes, and generating management decisions without human intervention. The system serves itself by maintaining its own operational state through automated data processing and decision-making algorithms, eliminating the need for significant manpower while maintaining fluid management capability
Solution Approach 2:
The patent implements continuous feedback loops where the digital twin receives real-time operational data, processes it through simulation models, and automatically adjusts fluid management decisions based on predicted outcomes. This closed-loop automated feedback system replaces manual monitoring and adjustment processes, significantly reducing manpower requirements while improving response time and accuracy
3Productivity
If traditional fluid management operations are conducted, then productivity can be maintained, but workflow efficiency is reduced
Solution Approach 1:
The digital twin performs preliminary simulations and predictions of fluid behavior under various operational scenarios before actual operations occur. By pre-calculating optimal fluid properties, predicting potential issues, and preparing management decisions in advance, the system eliminates time-consuming trial-and-error processes and manual analysis, thereby improving workflow efficiency while maintaining or increasing productivity
Solution Approach 2:
The patent enables continuous operation of the digital twin system without interruption, processing fluid management data and generating decisions in real-time as operations proceed. This continuous computational action replaces discontinuous manual workflows, eliminating idle time and ensuring constant optimization of fluid properties throughout the operational cycle, thereby improving both efficiency and productivity
Data Source
AI summary
Examples described herein provide a computer-implemented method that includes determining, using a digital twin, a task to be performed based at least in part on real-time data. The method further includes initiating at least one of a drone, a collaborative robot, or a warehouse system to perform the task.