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.

scholarly journals Advanced Intelligent Control Strategy in Dynamic Positioning (DP) System Applied to a Semi-Submersible Drilling Platform in the North Sea

2021 ◽  
Author(s):  
Mohamad Alremeihi ◽  
Rosemary Norman ◽  
Kayvan Pazouki ◽  
Arun Dev ◽  
Musa Bashir
Keyword(s):  
North Sea ◽  
Oil And Gas ◽  
Weather Conditions ◽  
Dynamic Positioning ◽  
Gas Drilling ◽  
The North ◽  
Drilling Operations ◽  
The North Sea ◽  
And Performance ◽  
Wind Currents

Abstract Dynamic Positioning (DP) systems play a crucial role in oil and gas drilling and production floaters used globally for deep-water operations. Drilling operations need to maintain automatic positioning of the platform in the horizontal-plane within the safe zone. Operating DP systems typically require highly responsive control systems when encountering prevailing weather conditions. However, DP incident analysis demonstrates that control and thruster failures have been the leading causes of accidents for the past two decades, according to the International Marine Contractors Association (IMCA). In this paper, a Predictive Neural Network (PNN) strategy is proposed for thruster allocation on a platform; it has been developed by predicting the platform response and training the network to transform the required force commands from a nonlinear Proportional Integral Derivative (PID) motion controller for each thruster. The strategy is developed for increasing safety and zone keeping of DP-assisted-drilling operations in harsh weather. This is done by allowing the platform to recover the position more rapidly whilst decreasing the risk of losing the platform position and heading, which can lead to catastrophic damage. The operational performance of the DP system on a drilling platform subjected to the North Sea real environmental conditions of wind, currents and waves, is simulated with the model incorporating the PNN control algorithm, which deals with dynamic uncertainties, into the unstable conventional PID control system for a current drilling semi-submersible model. The simulation results demonstrate the improvement in DP accuracy and robustness for the semi-submersible drilling platform positioning and performance using the PNN strategy.

Reliability Analysis of Redundant Dynamic Positioning Control System With Human Factor Involved

2016 ◽  
Author(s):  
Fang Wang ◽  
Yong Bai ◽  
Feng Xu
Keyword(s):  
Control System ◽  
Markov Process ◽  
Reliability Analysis ◽  
Oil And Gas ◽  
Dynamic Positioning ◽  
Positioning Control ◽  
Safety And Reliability ◽  
Long Time ◽  
Component Failure Rates ◽  
Technical Failures

Deepwater oil and gas explorations bring more safety and reliability problems for the dynamically positioned vessels. With the demands for the safety of vessel crew and onboard device increasing, the single control architecture of dynamic positioning (DP) system can not guarantee the long-time faultless operation for deeper waters, which calls for much more reliable control architectures, such as the Class 2 and Class 3 system, which can tolerate a single failure of system according to International Maritime Organization’s (IMO) DP classification. The reliability analysis of the main control station of DP Class 3 system is proposed from a general technical prospective. The fault transitions of the triple-redundant DP control system are modeled by Markov process. The effects of variation in component failure rates on the system reliability are investigated. Considering the DP operation involved a human-machine system, the DP operator factors are taken into account, and the human operation error failures together with technical failures are incorporated to the Markov process to predict the reliability of the DP control system.

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.

scholarly journals INVENTORY CONTROL MANAGEMENT AND REVENUE GENERATING CAPABILITIES OF OIL AND GAS DRILLING FIRMS IN NIGERIA

10.26458/1947 ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 75-95
Author(s):  
Fineboy Ikechi JOSEPH ◽  
Cordelia Onyinyechi OMODERO ◽  
Uzochi Chinkata OKEZIE
Keyword(s):  
Inventory Control ◽  
Correlation Coefficient ◽  
Inventory Management ◽  
Oil And Gas ◽  
P Value ◽  
Oil Drilling ◽  
Gas Drilling ◽  
Drilling Operations ◽  
Oil And Gas Drilling ◽  
Control Management

Abstract The critical role of effective inventory control has been much emphasized in the oil and gas industry which is subdivided into upstream and downstream sectors with drilling activities falling within the upstream subsector. In light of this development, this study investigates effective inventory control and effective drilling activities of oil and gas drilling firms as well as its relationship with revenue generating capabilities of oil drilling firms in Nigeria. Simple random sampling technique was adopted. Presentation and analyses of primary data collected with questionnaire and testing of the hypotheses were done using percentage and Spearman’s Rank correlation coefficient. The results from the tests with the use SPSS show positive and significant correlation between ineffective inventory management and downtime in the operations of oil and gas drilling with a correlation value of 0.682 with p-value = 0.001< 0.05 which implies that there is 68% relationship between ineffective management and downtime drilling.  There is a significant correlation between incessant downtime in operations of oil and gas drilling firms and their income level owing to poorly managed inventory control with a correlation coefficient value of 0.788 with p-value = 0.000< 0.05 which implies that there is 79% relationship between income (profit) level and downtime in drilling operations. Incessant downtime in drilling operations of oil and gas firms as a result of poor inventory control management has significant difference with termination of contract of oil drilling firms with the result the F-cal value as 344.632 while F tabulated value as 3.901 leading to rejection of hull hypothesis.  Based on the findings, it was the recommended that oil drilling firms should strengthen their inventory management system for effective and timely work delivery in order to avert downtime, loss of income and termination of contracts. Finally, members of staff of an oil and gas drilling firms in inventory unit should be trained and retrained on regular basis to embrace technological changes in inventory management to improve their performances which would in turn strengthen the inventory management of such firms.

AUSTRALIAN OFFSHORE DRILLING: PAST, PRESENT AND FUTURE

1975 ◽  
Vol 15 (1) ◽  
pp. 141
Author(s):  
J. A. W. White
Keyword(s):  
Weather Conditions ◽  
Dynamic Positioning ◽  
Offshore Drilling ◽  
Gas Field ◽  
Adverse Weather Conditions ◽  
Adverse Weather ◽  
Drilling Operations ◽  
Sea Mud ◽  
Under Construction ◽  
Foam Drilling

The Australian offshore drilling industry is now ten years old. In late 1964 the Global Marine drill-ship "Glomar III" spudded Esso's Gippsland Shelf No. 1 (later re-named Barra-conta-1); the discovery well of the Barracouta gas field. Fourteen other mobile offshore rigs have drilled wells in Australian waters, including one jack-up, four semi-submersibles and two drill-barges. Five production platforms have been built and now supply Australia with a large proportion of her oil requirements.Water depths have ranged from 8 m (Ripple Shoals No. 1) to 388 metres (East Mermaid No. 1) and distances offshore from the mainland from 5 km (Golden Beach No. 1/1A) to 400 km (Troubadour No. 1). Wells have been drilled to depths of over 4,500 metres.Several new techniques have been introduced including turret mooring (Discoverer II), foam drilling (Glomar Tasman) and dynamic positioning (Sedco 445). New drilling vessels under construction in Australia will provide additional offshore drilling capacity.For the future we can expect to see larger drill-ships and semi-submersibles which will be able to continue drilling operations in adverse weather conditions. Dynamic positioning and improved conventional anchoring systems will enable the deeper waters to be explored. New equipment and techniques will probably include buoyant marine risers, sub-sea mud discharge pumps and electro-hydraulic preventer actuated systems.

scholarly journals Determining the likelihood of incidents caused by human error during dynamic positioning drilling operations

2021 ◽  
pp. 1-13
Author(s):  
Zaloa Sanchez-Varela ◽  
David Boullosa-Falces ◽  
Juan L. Larrabe-Barrena ◽  
Miguel A. Gomez-Solaeche
Keyword(s):  
Logistic Regression ◽  
Water Depth ◽  
Regression Models ◽  
Human Error ◽  
Logistic Regression Model ◽  
Binary Logistic Regression ◽  
Weather Conditions ◽  
Dynamic Positioning ◽  
Logistic Regression Models ◽  
Drilling Operations

Abstract The probability of a human-caused incident occurring during dynamic positioning (DP) drilling operations is determined in this paper using binary logistic regression models built with data on 42 incidents that took place during the period 2011–2015. For each case, a range of variables characterising the configuration of the DP system, weather conditions and water depth are taken into account. These variables are taken into account to develop a logistic regression model that shows the likelihood of an incident being caused by human error. The results obtained show that human-based incidents are significantly more likely to occur when there is a lower usage of thrusters. These results are useful for focusing our attention on variables that may be associated with incidents attributable to human error, as well as for setting operational limits that could help to prevent these incidents and improve safety during these operations.

Modeling of Multi-Agent System for Drilling Simulation

2011 ◽  
Vol 383-390 ◽  
pp. 1555-1561
Author(s):  
Wu Li Wang ◽  
Yan Jiang Wang
Keyword(s):  
Simulation System ◽  
Drilling Process ◽  
Multi Agent System ◽  
Agent System ◽  
Oil Drilling ◽  
Well Control ◽  
Existing Problems ◽  
System A ◽  
Multi Agent ◽  
Drilling Simulation

In view of the characteristics of the oil drilling process and the existing problems of traditional simulation system, a new distributed drilling simulation model was established based on Multi-Agent system (MAS) technology. By means of autonomous, cooperative and reactive characteristic of Agent, the drilling laws and phenomenon can be reflected promptly and accurately under any circumstances. The MAS modeling for oil drilling simulation, the structure and knowledge representation of each Agent and the communication among Agents are described in detail. Finally, an Agent-based normal drilling well control simulation training example was given. The simulation results show that the simulator based on Multi-Agent system has better performances than traditional drilling simulators, and enhances the integrated training function of the drilling simulation system.

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