Downhole Fluid Analysis Tool Using Neural Network Ensemble
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Solution Overview
Problem
Current fluid analysis methods in oil and gas operations rely on post-processing of data collected by optical sensors, which can be time-consuming and may not provide real-time, accurate predictions of downhole fluid properties, especially for characterizing formation fluids.
Innovation Solution
Implementing a fluid analysis tool with a neural network ensemble and an adaptive neuro-fuzzy inference system (ANFIS) that uses real-time measurements from optical sensors to generate predictions, enhancing the characterization of downhole fluids without the need for post-processing by utilizing a database of optical-PVT data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If post-processing of optical sensor data is performed off-site, then measurement precision can be improved, but loss of time increases and productivity decreases
Solution Approach 1:
The patent applies preliminary action by performing data processing and prediction models directly in the wellbore environment before the data needs to be used for decision-making. The optical sensor data is processed immediately using neural network ensembles and adaptive neuro-fuzzy inference systems, eliminating the need for subsequent off-site post-processing and enabling real-time predictions of formation fluid properties.
Solution Approach 2:
The patent replaces the traditional mechanical process of physically transporting data off-site for processing with an electronic/computational approach. Instead of relying on physical retrieval and manual or batch processing, the system uses computational models (neural networks and ANFIS) that can process data electronically in real-time within the wellbore environment.
2Productivity
If real-time predictions are generated using neural network ensembles and ANFIS, then productivity improves, but device complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the complex prediction system into multiple independent components: optical sensors for data collection, neural network ensembles for initial processing, and adaptive neuro-fuzzy inference systems for refinement. This modular approach allows each component to be optimized independently while working together to achieve real-time predictions.
Solution Approach 2:
The patent uses an intermediary processing layer in the form of computational models (neural networks and ANFIS) that mediate between the raw optical sensor data and the final predictions. This intermediary layer transforms the complex relationship between sensor measurements and fluid properties into a manageable processing pipeline that can operate in real-time.
3Measurement precision
If optical sensors are used to determine fluid properties, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent applies universality by using optical sensors that can simultaneously measure multiple fluid properties (density, gas:oil ratio, saturates, and hydrocarbon concentrations) with a single measurement system. This multi-functional approach eliminates the need for separate sensors for each property, reducing overall system complexity while maintaining comprehensive measurement capability.
Data Source
AI summary
A method includes obtaining a measurement of one or more properties of a downhole fluid using a fluid analysis tool. The fluid analysis tool includes fluid sensors and one or more processors coupled with the fluid sensors. A first prediction is generated using the measurement from the fluid sensors. A second prediction is generated using an adaptive neuro-fuzzy inference system based on the first prediction of the properties.