Applications and Challenges of Deep Learning in Oil and Gas Field Development

Опубликована: Фев. 4, 2024

Under the background of global energy transformation and environmental protection, application artificial intelligence technology has become an important trend in oil gas field development industry. However, how to effectively utilize improve efficiency safety development, while addressing economic issues it brings, is a major question that researchers need consider. Based on actual needs exploitation, basic principles methods deep learning are studied, main models training introduced. The process described detail, realization steps depth optimization model for studied. challenges including data security, complexity, computing resource demand so on. results show as powerful tool, great potential security but still faces some challenges. Therefore, future research should pay more attention these problems promote development.

Язык: Английский

---

Predicting the rate of underground corrosion of steel pipelines: A review

International Journal of Corrosion and Scale Inhibition, Год журнала: 2024, Номер 13(1)

Опубликована: Март 30, 2024

Estimation of the probable rate corrosion underground steel pipelines has long been a challenging problem for engineers and researchers is still importance.The complexity solving this due to large number influencing factors (the composition ground electrolyte, gas solid phases soil), their constant daily seasonal changes, use cathodic protection protective polymer coatings.Another feature process its probabilistic nature.Due complex nature phenomenon, several different approaches have developed predict rate.This review deals with that affect formation development defects in various methods used pipelines.The basis predicting growth outer wall are pipe soils, which can be divided into qualitative quantitative ones.Qualitative mainly determine degree soil activity (scoring methods).Scoring create prerequisites quantifying steels soils.However, currently existing models take account no more than two factors.Due imperfection simulation based on statistical processing data obtained either full-scale tests samples or during pipeline inspection.Models types (deterministic, probabilistic, those created using machine learning) presented criteria applicability analyzed.

Язык: Английский

---

A Review of Deformations Prediction for Oil and Gas Pipelines Using Machine and Deep Learning

Studies in systems, decision and control, Год журнала: 2024, Номер unknown, С. 289 - 317

Опубликована: Янв. 1, 2024

Язык: Английский

---

DeepPipe: A Multi-Stage Knowledge-Enhanced Physics-Informed Neural Network for Hydraulic Transient Simulation of Multi-Product Pipeline

Journal of Industrial Information Integration, Год журнала: 2024, Номер 42, С. 100726 - 100726

Опубликована: Ноя. 1, 2024

Язык: Английский

---