Applications of Reservoir Sensitivity Prediction Based on Quantum Neural Networks

Wei Zhang; Jiao Wang; Jian Bo Xie; Fei Teng

doi:10.4028/www.scientific.net/AMR.712-715.2428

Paper Titles

A New Gradual Segmentation Algorithm of Fingerprint Image
p.2407

Local Piecewise-Linearity Prediction Method of Chaotic Time Series
p.2415

INS/GPS Integrated Navigation Method Based on Neural Network
p.2419

A Resilient Particle Swarm Optimization Algorithm with Dynamically Changing Inertia Weight
p.2423

Applications of Reservoir Sensitivity Prediction Based on Quantum Neural Networks
p.2428

Study on Application of Fast Intrinsic Mode Decomposition to Short-Term Load Forecasting
p.2432

Load Forecasting by Considering Wind Power Based on Sequential Time Classification LSSVM Model
p.2437

An Intelligent Algorithm Method of Element Layout Priority Sequence on Consoles Human Machine Interface
p.2441

A Hybrid Intelligence Algorithm for No-Wait Flow Shop Scheduling
p.2447

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 712-715Applications of Reservoir Sensitivity Prediction...

Applications of Reservoir Sensitivity Prediction Based on Quantum Neural Networks

Abstract:

The problem of diagnosing and forecasting reservoir sensitivity damage timely and accurately is always an important field of the reservoir protection research, this paper based on collecting data from core analysis .the main factors affecting reservoir sensitivity are obtained with single variable regression method, established a model of reservoir sensitivity prediction by applying an approach based on multi-level transfer function quantum neural networks. It effectively improved networks convergence and prediction accuracy. The analysis indicates that the model needs fewer parameters, has wider applicability and reliable results (the coincidence rate attains over 91%).and quantitatively reflect reservoir potential sensitivity, thus it can provide reliable basis to reservoir protection.