Systeme und Verfahren zur Verbesserung der Modellierung von Drahtgebundener Förderung bei der Drahtgebundenen Förderung unter Verwendung eines Physik-Informierten Neuronalen Netzwerks

Details

Veröffentlichungs-Nr.

EP4737679

Anmeldetag

29. Oktober 2025

Veröffentlichung

6. Mai 2026

Rechtsraum

EP

IPC

E21B23/14, G06N3/0464

Abstract

Systems and methods for enhancing wireline conveyance modeling in wireline conveyance using a physics-informed neural network (PINN) are provided. A method includes: receiving historical job data from at least one past wireline conveyance operation, the historical job data including: static input data, and timeseries input data, processing the historical job data through a neural network (NN) configured to learn the relationships between input features and target variables including: an input layer, a plurality of hidden layers, and an output layer, integrating the neural network with a numerical physics-based wireline conveyance model to form a physics-informed neural network (PINN), training the PINN using the historical job data and estimation results of the physical model to enhance prediction accuracy, utilizing the trained PINN to predict at least one physical parameter for a current wireline conveyance operation, and outputting the predicted at least one physical parameter for use in the wireline conveyance operation.

Anmelder

Firmen

Services Pétroliers Schlumberger
Schlumberger Technology B.V.

Land

🇫🇷 Frankreich