Kick Detection and Influx Size Estimation during Offshore Drilling Operations using Deep Learning

2019 ◽  
Author(s):  
Andreas K. Fjetland ◽  
Jing Zhou ◽  
Darshana Abeyrathna ◽  
Jan Einar Gravdal
Keyword(s):  
Deep Learning ◽  
Size Estimation ◽  
Offshore Drilling ◽  
Drilling Operations

AUTOMATED POLYP SIZE ESTIMATION WITH DEEP LEARNING REDUCES OVERESTIMATION BIAS

2020 ◽  
Author(s):  
J Suykens ◽  
T Eelbode ◽  
J Daenen ◽  
P Suetens ◽  
F Maes ◽  
...  
Keyword(s):  
Deep Learning ◽  
Size Estimation ◽  
Polyp Size

A Study on the Main Parameters That Affects the Reliability of Fatigue Failure in a Marine Drilling Riser

2021 ◽  
Author(s):  
Bruno Luiz Barbosa das Chagas ◽  
Celso Kazuyuki Morooka
Keyword(s):  
Fatigue Damage ◽  
Fatigue Failure ◽  
Petroleum Reservoirs ◽  
Offshore Drilling ◽  
Deepwater Drilling ◽  
Sea Bottom ◽  
Reliability Method ◽  
Drilling Operations ◽  
Maritime Operations ◽  
Sea Currents

Abstract Advances in subsea exploration in the oceans to discover new petroleum reservoirs and sometimes different kind of minerals at the seabed in ultra deepwater, continuously introduce new challenges in offshore drilling operations. This motivates the development of increasingly safe maritime operations. In offshore petroleum, a marine drilling riser is the pipe that connects a wellhead at the sea bottom to a drillship at the sea surface, as an access to the wellbore. It serves as a guide for the drilling column with the drill bit and conductor to carry cuttings of rock coming from the wellbore drilling and its construction. Drilling riser is constantly exposed to adversity from the environment, such as waves, sea currents and platform motions induced by waves. These elements of the environment are prevailing factors that can cause a riser failure during deepwater drilling operations with undesirable consequences for the environment. In the present work, key parameters that influence the probability of fatigue failure in a marine drilling riser are identified, and a parametric evaluation with those parameters are carried out. Dynamic behavior of a riser is previously calculated and fatigue damage is estimated. Afterwards, the First Order Reliability Method (FORM) is applied to determine the probability of fatigue failure on the riser. Fundamentals of the procedure are described, and results are illustrated through the analysis for a typical riser in deepwater drilling operation. Parametric evaluations are done observing points considered as critical along the riser length, and looking to the sensitivity of key parameters in the process. For this study, the SN curve from API guidelines is applied and accumulated fatigue damage is estimated from simulations of the stress time series and applying the Palmgren-Miner’s rule. Finally, the influence of each parameter in the reliability of fatigue failure is verified and discussions given.

scholarly journals Particle Size Estimation in Mixed Commercial Waste Images Using Deep Learning

2021 ◽  
Author(s):  
Phongsathorn Kittiworapanya ◽  
Kitsuchart Pasupa ◽  
Peter Auer
Keyword(s):  
Computer Vision ◽  
Particle Size ◽  
Deep Learning ◽  
Waste Management ◽  
State Of The Art ◽  
Learning Algorithms ◽  
Input Image ◽  
Size Estimation ◽  
Waste Particles ◽  
Set Up

<div>We assessed several state-of-the-art deep learning algorithms and computer vision techniques for estimating the particle size of mixed commercial waste from images. In waste management, the first step is often coarse shredding, using the particle size to set up the shredder machine. The difficulty is separating the waste particles in an image, which can not be performed well. This work focused on estimating size by using the texture from the input image, captured at a fixed height from the camera lens to the ground. We found that EfficientNet achieved the best performance of 0.72 on F1-Score and 75.89% on accuracy.<br></div>

scholarly journals Speciation and ultra trace determination of mercury in produced waters from offshore drilling operations using portable instrumentation and matrix-matching calibration

2019 ◽  
Vol 146 ◽  
pp. 1072-1082 ◽  
Author(s):  
Jarol R. Miranda-Andrades ◽  
Sarzamin Khan ◽  
Carlos A.T. Toloza ◽  
Roberta M. Maciel ◽  
Rainério Escalfoni ◽  
...  
Keyword(s):  
Trace Determination ◽  
Offshore Drilling ◽  
Portable Instrumentation ◽  
Drilling Operations ◽  
Produced Waters ◽  
Ultra Trace

scholarly journals Intact Detection of Highly Occluded Immature Tomatoes on Plants Using Deep Learning Techniques

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2984
Author(s):  
Yue Mu ◽  
Tai-Shen Chen ◽  
Seishi Ninomiya ◽  
Wei Guo
Keyword(s):  
Deep Learning ◽  
Low Cost ◽  
Yield Prediction ◽  
Learning Approaches ◽  
Size Estimation ◽  
Detection Model ◽  
Lighting Conditions ◽  
Learning Techniques ◽  
Location Map ◽  
Tomato Detection

Automatic detection of intact tomatoes on plants is highly expected for low-cost and optimal management in tomato farming. Mature tomato detection has been wildly studied, while immature tomato detection, especially when occluded with leaves, is difficult to perform using traditional image analysis, which is more important for long-term yield prediction. Therefore, tomato detection that can generalize well in real tomato cultivation scenes and is robust to issues such as fruit occlusion and variable lighting conditions is highly desired. In this study, we build a tomato detection model to automatically detect intact green tomatoes regardless of occlusions or fruit growth stage using deep learning approaches. The tomato detection model used faster region-based convolutional neural network (R-CNN) with Resnet-101 and transfer learned from the Common Objects in Context (COCO) dataset. The detection on test dataset achieved high average precision of 87.83% (intersection over union ≥ 0.5) and showed a high accuracy of tomato counting (R2 = 0.87). In addition, all the detected boxes were merged into one image to compile the tomato location map and estimate their size along one row in the greenhouse. By tomato detection, counting, location and size estimation, this method shows great potential for ripeness and yield prediction.

Mechanical Property Analysis of Materials and Application of Buttress Thread Buckle Marine Conductor

2011 ◽  
Vol 233-235 ◽  
pp. 2043-2046
Author(s):  
Zhong Li ◽  
Lai Bin Zhang ◽  
Fan Luo ◽  
Bai Ling Zhang ◽  
Shu Ying Tan
Keyword(s):  
Electrical Resistance ◽  
Simulation Analysis ◽  
Offshore Drilling ◽  
Mechanical Experiment ◽  
Drilling Operations ◽  
Drilling Cost ◽  
Design Requirements ◽  
Resistance Weld ◽  
Offshore Oil ◽  
Working Efficiency

At present, offshore drilling operations often use buttress thread casing as surface casing. The design conception of buttress thread casing comes from the offshore drilling’s demands and this kind of casing is mainly used as surface casing. This paper has taken material mechanical experiment, numerical simulation analysis and field test, the research results show that the various parameters of buttress thread casing fully complies with the drilling design requirements and the offshore oilfield production demands. This product can reduce drilling cost effectively, improve working efficiency and safety, and realize manufacture domestically. Meanwhile, the development of this project will fill the blank of the ERW (Electrical Resistance Weld) casing in CNOOC (China National Offshore Oil Corporation), and have a broad prospect of application.

Study of Nonlinear Internal Waves and Impact on Offshore Drilling Units

2011 ◽  
Author(s):  
Nishu V. Kurup ◽  
Shan Shi ◽  
Zhongmin Shi ◽  
Wenju Miao ◽  
Lei Jiang
Keyword(s):  
Numerical Model ◽  
South China Sea ◽  
Internal Wave ◽  
Internal Waves ◽  
South China ◽  
Andaman Sea ◽  
Offshore Drilling ◽  
Nonlinear Internal Waves ◽  
Drilling Operations ◽  
Drilling System

Internal waves near the ocean surface have been observed in many parts of the world including the Andaman Sea, Sulu Sea and South China Sea among others. The factors that cause and propagate these large amplitude waves include bathymetry, density stratification and ocean currents. Although their effects on floating drilling platforms and its riser systems have not been extensively studied, these waves have in the past seriously disrupted offshore exploration and drilling operations. In particular a drill pipe was ripped from the BOP and lost during drilling operations in the Andaman sea. Drilling riser damages were also reported from the south China Sea among other places. The purpose of this paper is to present a valid numerical model conforming to the physics of weakly nonlinear internal waves and to study the effects on offshore drilling semisubmersibles and riser systems. The pertinent differential equation that captures the physics is the Korteweg-de Vries (KdV) equation which has a general solution involving Jacobian elliptical functions. The solution of the Taylor Goldstein equation captures the effects of the pycnocline. Internal wave packets with decayed oscillations as observed from satellite pictures are specifically modeled. The nonlinear internal waves are characterized by wave amplitudes that can exceed 50 ms and the present of shearing currents near the layer of pycnocline. The offshore drilling system is exposed to these current shears and the associated movements of large volumes of water. The effect of internal waves on drilling systems is studied through nonlinear fully coupled time domain analysis. The numerical model is implemented in a coupled analysis program where the hull, moorings and riser are considered as an integrated system. The program is then utilized to study the effects of the internal wave on the platform global motions and drilling system integrity. The study could be useful for future guidance on offshore exploration and drilling operations in areas where the internal wave phenomenon is prominent.

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