Intelligent Real-Time Risk Evaluation and Drilling Parameter Optimization for Enhanced Safety in Deep-Well Operations

This paper presents an integrated downhole risk prevention and control system designed to enhance safety, efficiency and sustainability in deep-well drilling operations. The system incorporates advanced measurement processing, risk evaluation, and intelligent data transmission technologies for real-time monitoring of nine key drilling parameters, such as downhole drilling pressure, bending moment, and torque, etc. Bench tests and field trials demonstrated the system’s reliability in accurately capturing and transmitting data under high-pressure, high-temperature conditions. For instance, it successfully monitored bottom-hole pressure up to 61.4 MPa and temperature to 120.8 °C, allowing for early detection of abnormal events such as pressure kicks and torsional stick-slip. The system was laboratory-tested to withstand bottom-hole pressures up to 61.4 MPa and temperatures of 120.8 °C. During field trials, the tool operated safely under actual downhole conditions of approximately 59.2 MPa and 115 °C, which are within its rated limits. The system also facilitated automated controlled actions, including mud weight and pump rate control, to prevent incidents. These results underscore the system’s potential to significantly improve real-time and intelligent process control, minimize operational risks, and advancing the sustainability of drilling practices. The approach marks a step forward in intelligent drilling technologies, supporting proactive decision-making in energy extraction. Future work will extend this system to ultra-deep and high-temperature wells while integrating advanced AI-based analytics for further optimization.

Authors

Institutions

Publication Details

Journal
Processes
Published
2025-09-28
DOI
https://doi.org/10.3390/pr13103102
Citations
2
Primary Topic
Drilling and Well Engineering
Type
article
Field-Weighted Citation Impact
2.29

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Intelligent Real-Time Risk Evaluation and Drilling Parameter Optimization for Enhanced Safety in Deep-Well Operations

Di Wang, Zhengchang Yi, Ming Yi, Zhenbao Li et al.
2 citations
Processes
Drilling and Well Engineering
2.29
article

Intelligent Real-Time Risk Evaluation and Drilling Parameter Optimization for Enhanced Safety in Deep-Well Operations

Di Wang, Zhengchang Yi, Ming Yi, Zhenbao Li, Ping Cheng
article en
2 citations

Abstract

This paper presents an integrated downhole risk prevention and control system designed to enhance safety, efficiency and sustainability in deep-well drilling operations. The system incorporates advanced measurement processing, risk evaluation, and intelligent data transmission technologies for real-time monitoring of nine key drilling parameters, such as downhole drilling pressure, bending moment, and torque, etc. Bench tests and field trials demonstrated the system’s reliability in accurately capturing and transmitting data under high-pressure, high-temperature conditions. For instance, it successfully monitored bottom-hole pressure up to 61.4 MPa and temperature to 120.8 °C, allowing for early detection of abnormal events such as pressure kicks and torsional stick-slip. The system was laboratory-tested to withstand bottom-hole pressures up to 61.4 MPa and temperatures of 120.8 °C. During field trials, the tool operated safely under actual downhole conditions of approximately 59.2 MPa and 115 °C, which are within its rated limits. The system also facilitated automated controlled actions, including mud weight and pump rate control, to prevent incidents. These results underscore the system’s potential to significantly improve real-time and intelligent process control, minimize operational risks, and advancing the sustainability of drilling practices. The approach marks a step forward in intelligent drilling technologies, supporting proactive decision-making in energy extraction. Future work will extend this system to ultra-deep and high-temperature wells while integrating advanced AI-based analytics for further optimization.

ProcessesVol. 13(10)
China University of Petroleum, Beijing (CN), ILMA University (PK)
China National Petroleum Corporation
Openalex Percentile: Top 11%
Drilling and Well Engineering
2.29
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.