scholarly journals Risk Analysis of DP Incidents During Drilling Operations

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Zaloa Sanchez-Varela ◽  
David Boullosa-Falces ◽  
Juan Luis Larrabe-Barrena ◽  
Miguel Angel Gomez-Solaeche
Keyword(s):  
Risk Analysis ◽  
Significant Risk ◽  
Dynamic Positioning ◽  
Traditional Classification ◽  
Drilling Operations ◽  
Type Of Failure ◽  
Financial Losses

This paper aims to present a method to determine the type of dynamic positioning (DP) incidents that have a more significant risk during drilling operations in the period 2007-2015, according to the element or the type of failure that causes the DP system to fail. Two different classifications are made: 1) according to the element that produces the incident (which has been the traditional classification in the industry) and 2) according to the type of error that arises, the latter being an alternative classification proposed in this paper. The predictable financial losses for each level of severity are used to define the resulting consequences for each case. A risk analysis is performed with the data obtained, showing the potentially more dangerous incidents, either because of their higher number of occurrences or because their consequences are remarkable. According to the classification proposed, the main causes with the higher risk results were power and environmental, according to the traditional classification, and fault/failure. Thus, the power segment’s combination of failures is the riskiest cause during the DP drilling operations.

scholarly journals Performance of an Advanced Intelligent Control Strategy in a Dynamic Positioning (DP) System Applied to a Semisubmersible Drilling Platform

2021 ◽  
Vol 9 (4) ◽  
pp. 399
Author(s):  
Mohamad Alremeihi ◽  
Rosemary Norman ◽  
Kayvan Pazouki ◽  
Arun Dev ◽  
Musa Bashir
Keyword(s):  
Oil And Gas ◽  
Weather Conditions ◽  
Dynamic Positioning ◽  
System Failures ◽  
Oil Drilling ◽  
System A ◽  
Advanced Control ◽  
Safety And Reliability ◽  
Drilling Operations ◽  
Shallow Seas

Oil drilling and extraction platforms are currently being used in many offshore areas around the world. Whilst those operating in shallow seas are secured to the seabed, for deeper water operations, Dynamic Positioning (DP) is essential for the platforms to maintain their position within a safe zone. Operating DP requires intelligent and reliable control systems. Nearly all DP accidents have been caused by a combination of technical and human failures; however, according to the International Marine Contractors Association (IMCA) DP Incidents Analysis, DP control and thruster system failures have been the leading causes of incidents over the last ten years. This paper will investigate potential operational improvements for DP system accuracy by adding a Predictive Neural Network (PNN) control algorithm in the thruster allocation along with a nonlinear Proportional Integral derivative (PID) motion control system. A DP system’s performance on a drilling platform in oil and gas deep-water fields and subject to real weather conditions is simulated with these advanced control methods. The techniques are developed for enhancing the safety and reliability of DP operations to improve the positioning accuracy, which may allow faster response to a critical situation during DP drilling operations. The semisubmersible drilling platform’s simulation results using the PNN strategy show improved control of the platform’s positioning.

Optimising Well Times Through Drilling Connection Practices

2021 ◽  
Author(s):  
Faisal Mohd Mazlan ◽  
Ahmad Zhafran Ahmad Redzuan ◽  
Mohd Idzwan Amiruddin ◽  
Ahmad Faizal Ramli ◽  
Pete Slagel ◽  
...  
Keyword(s):  
Best Practices ◽  
Significant Risk ◽  
Data Driven ◽  
Drilling Performance ◽  
Change In Practice ◽  
Data Driven Approach ◽  
Drilling Operations ◽  
Historical Databases ◽  
Additional Costs ◽  
Time Savings

Abstract From an operator's perspective, many operational instructions are written implicitly that are not sufficiently detailed to optimize drilling efficiency. Upon a review of several partner operators’ drilling performance, it was noticed that there was a significant focus on the following aspects of technical limit drilling: ROP, tripping speeds, offline activities and connection times. One operator specifically reviewed Gulf-of-Thailand best practices and implemented them in Malaysia. One of the significant areas of improvement includes drilling connections. In the previous version, PETRONAS Malaysia Drilling Operations follows a conservative ERD connection method requiring to ream a single/stand, take a good survey a minimum 10m off bottom prior to making a connection and applied to all wells regardless of inclination or complexity. This was in response to risk of stuck pipe incidents happening during these critical static periods. A comparison of the connection times after their change in practice compared to PCSB practices given the same tools and well complexity indicated massive potential time savings with no additional costs. A change in the drilling connection practices could easily save almost half of this particular "flat time" with no significant risk, amounting to a possible saving of almost 26 hours in a well of around 3000m MDDF. This also led to a better understanding of the impacts of certain "rule-of-thumb" practices that needed to be questioned from time to time. This comparison coupled with many existing literatures available allowed a data-driven approach to improving well times. Some of this information is easily glossed over considering the only time-based data most wells refer to would be the Daily Drilling Report. This paper also emphasizes the importance of data collection and usage of historical databases to search for more opportunities in terms of safety, cost and time.

A New Method for Well-Specific-Risk Analysis in Drilling Operations

1998 ◽  
Author(s):  
Lasse Berg Andersen ◽  
Gunnar Veire ◽  
Borgar Rokke
Keyword(s):  
Risk Analysis ◽  
New Method ◽  
Specific Risk ◽  
Drilling Operations

scholarly journals Prediction of Loss of Position during Dynamic Positioning Drilling Operations Using Binary Logistic Regression Modeling

2021 ◽  
Vol 9 (2) ◽  
pp. 139
Author(s):  
Zaloa Sanchez-Varela ◽  
David Boullosa-Falces ◽  
Juan Luis Larrabe Barrena ◽  
Miguel A. Gomez-Solaeche
Keyword(s):  
Logistic Regression ◽  
Human Error ◽  
Binary Logistic Regression ◽  
Weather Conditions ◽  
Dynamic Positioning ◽  
Dynamic Positioning System ◽  
Logistic Regression Models ◽  
Adverse Weather ◽  
Drilling Operations ◽  
Offshore Industry

The prediction of loss of position in the offshore industry would allow optimization of dynamic positioning drilling operations, reducing the number and severity of potential accidents. In this paper, the probability of an excursion is determined by developing binary logistic regression models based on a database of 42 incidents which took place between 2011 and 2015. For each case, variables describing the configuration of the dynamic positioning system, weather conditions, and water depth are considered. We demonstrate that loss of position is significantly more likely to occur when there is a higher usage of generators, and the drilling takes place in shallower waters along with adverse weather conditions; this model has very good results when applied to the sample. The same method is then applied for obtaining a binary regression model for incidents not attributable to human error, showing that it is a function of the percentage of generators in use, wind force, and wave height. Applying these results to the risk management of drilling operations may help focus our attention on the factors that most strongly affect loss of position, thereby improving safety during these operations.

Recommendation of Counteractions for Prevention of Critical Events in Sub-Surface Drilling Environments

2013 ◽  
Vol 5 (3) ◽  
pp. 12-30
Author(s):  
Martin Winter ◽  
Felix Riedel ◽  
Felix Lee ◽  
Rudolf K. Fruhwirth ◽  
Florian Kronsteiner ◽  
...  
Keyword(s):  
Decision Support ◽  
User Interfaces ◽  
Preventive Measures ◽  
Machine Learning Algorithms ◽  
Sensor Data ◽  
Data Detection ◽  
Critical Events ◽  
Drilling Operations ◽  
Gas Bearing ◽  
Financial Losses

Sub-Surface Drilling is the process of making boreholes into the Earth, which can reach depths of many kilometers. One of the major purposes of such boreholes is the exploration of oil or gas bearing formations with the goal to recover the content of such reservoirs. Problems in drilling operations pose serious risks for the crew and the environment and can cause significant financial losses. Critical events usually do not arise abruptly, but develop over time before they escalate. In this work, the authors present a system that integrates sensor data and machine learning algorithms into a decision support system (DSS), thus helping to avoid critical events by monitoring and recommending preventive measures. The authors describe how the DSS is implemented as a distributed system and how data-driven decision support processes are implemented and integrated into the system. The DSS detects drilling operations by recognizing temporal patterns in the sensor data and uses a combination of detected operational rig-states and sensor data to predict and recommend preventive measures for the stuck pipe problem. The sensor data, detection results and predictions are distributed to all stakeholders and displayed in appropriate user interfaces.

On the Reliability and Risk Analysis of Subsea Blowouts Preventers With Focused Attention on DP Rigs

2005 ◽  
Author(s):  
Nilo de Moura Jorge
Keyword(s):  
Risk Analysis ◽  
Water Depth ◽  
Rio De Janeiro ◽  
Safety Margin ◽  
Focused Attention ◽  
Dynamic Positioning ◽  
Years Of Experience ◽  
Main Factors ◽  
Operational Practices ◽  
Selection Of

This paper provides a selection of findings on the reliability and risk analysis of submarine blowout preventers (BOPs) achieved in recent studies, which the author has participated among technicians in Petrobras and Rio de Janeiro Federal University - UFRJ. Petrobras is a deepwater E&P leading company and special attention has been given on the deepwater and ultra deepwater scenarios. Particular factors on the BOP for dynamic positioning (DP) rigs need to be accounted as far as risks on safety and downtime are concerned. The analyses have considered a comprehensive BOP reliability database that covers more than 12 years of experience, as well as, a collection of DP experience has been accessed during work. The riser safety margin tends to be unavailable as the water depth becomes deeper, and, so, in an emergency disconnection, the safety isolation of the well tends to be relied on the BOP mechanical barriers only. In this context, risks on different configurations of the BOP are compared and their results discussed with focused attention on main factors of risk. Finally, there has been visible progress on BOP reliability within recent years and a number of good operational practices in place are also mentioned in the paper.

Application of a Deepwater Riser Risk Analysis to Drilling Operations and Riser Design

2001 ◽  
Author(s):  
Billy D. Ambrose ◽  
Matthew S. Childs ◽  
Steven A. Leppard ◽  
Russell L. Krohn
Keyword(s):  
Risk Analysis ◽  
Drilling Operations ◽  
Riser Design ◽  
Deepwater Riser

scholarly journals Loss Assessment for Sustainable Industrial Infrastructure: Focusing on Bridge Construction and Financial Losses

2020 ◽  
Vol 12 (13) ◽  
pp. 5316 ◽  
Author(s):  
Ji-Myong Kim ◽  
Taehui Kim ◽  
Sungjin Ahn
Keyword(s):  
Risk Factors ◽  
Construction Projects ◽  
New Technologies ◽  
Rapid Development ◽  
Significant Risk ◽  
Sustainable Construction ◽  
Insurance Company ◽  
Estimation Model ◽  
Bridge Construction ◽  
Financial Losses

Bridges are important infrastructures for urban growth and the economic development of a country, because bridges allow a large volume of logistics and transportation by connecting rivers, canyons, islands and lands. As such, massive resources including financial, material and human resources are invested for bridge construction and management. However, although the latest bridge construction is undergoing rapid development of new technologies and designs, the management and prevention of risks still tend to rely on qualitative practices, which, as a result, calls for more quantified and systematic measurement and, thus, more sustainable management of potential risks. As part of efforts in managing risks to achieve quantitative risk management, this study aimed to predict losses of financial resources by identifying statistically significant risk factors based on the past record of insurance claim payouts (compensation for a loss that occurred as a result of a material damage in bridge construction projects) from a major insurance company in Korea, and conducted a multiple regression analysis to identify the loss indicators and to develop a loss estimation model. The statistical analysis confirmed that superstructure types, superstructure construction methods, and construction duration are the three significant risk factors that affects financial losses of bridge construction projects among the seven variables adopted as independent variables, which included the superstructure type, maximum span length, superstructure construction method, foundation type, floods, typhoons, and construction duration. Such findings, and the consequentially developed risk prediction model of this study, will contribute to sustainable construction management through cost reduction by predicting and preventing the future financial loss factors of bridge construction.

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