Drillbit Optimization System: Real-Time Approach to Enhance Rate of Penetration and Bit Wear Monitoring

2015 ◽  
Author(s):  
Alexey Valisevich ◽  
Alexey Ruzhnikov ◽  
Ivan Bebeshko ◽  
Ricardo Moreno ◽  
Maxim Zhentichka ◽  
...  
Keyword(s):  
Real Time ◽  
Rate Of Penetration ◽  
Optimization System ◽  
Bit Wear ◽  
Wear Monitoring

Drillbit Optimization System: Real-Time Approach to Enhance Rate of Penetration and Bit Wear Monitoring (Russian)

2015 ◽  
Author(s):  
Alexey Valisevich ◽  
Alexey Ruzhnikov ◽  
Ivan Bebeshko ◽  
Ricardo Moreno ◽  
Maxim Zhentichka ◽  
...  
Keyword(s):  
Real Time ◽  
Rate Of Penetration ◽  
Optimization System ◽  
Bit Wear ◽  
Wear Monitoring

scholarly journals Real-Time Measurement of Drilling Fluid Rheological Properties: A Review

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3592
Author(s):  
Naipeng Liu ◽  
Di Zhang ◽  
Hui Gao ◽  
Yule Hu ◽  
Longchen Duan
Keyword(s):  
Decision Making ◽  
Rheological Properties ◽  
Real Time ◽  
Drilling Fluid ◽  
Time Measurement ◽  
Decision Making Process ◽  
Rate Of Penetration ◽  
The Real ◽  
Real Time Measurement ◽  
Fluid Properties

The accurate and frequent measurement of the drilling fluid’s rheological properties is essential for proper hydraulic management. It is also important for intelligent drilling, providing drilling fluid data to establish the optimization model of the rate of penetration. Appropriate drilling fluid properties can improve drilling efficiency and prevent accidents. However, the drilling fluid properties are mainly measured in the laboratory. This hinders the real-time optimization of drilling fluid performance and the decision-making process. If the drilling fluid’s properties cannot be detected and the decision-making process does not respond in time, the rate of penetration will slow, potentially causing accidents and serious economic losses. Therefore, it is important to measure the drilling fluid’s properties for drilling engineering in real time. This paper summarizes the real-time measurement methods for rheological properties. The main methods include the following four types: an online rotational Couette viscometer, pipe viscometer, mathematical and physical model or artificial intelligence model based on a Marsh funnel, and acoustic technology. This paper elaborates on the principle, advantages, limitations, and usage of each method. It prospects the real-time measurement of drilling fluid rheological properties and promotes the development of the real-time measurement of drilling rheological properties.

New Approach in Real-Time Bit Wear Prediction

2010 ◽  
Author(s):  
Behrad Rashidi ◽  
Geir Hareland ◽  
Andrew Wu
Keyword(s):  
Real Time ◽  
Wear Prediction ◽  
New Approach ◽  
Bit Wear

scholarly journals Drilling Efficiency Improvement and Rate of Penetration Optimization by Machine Learning and Data Analytics

2020 ◽  
Vol 5 (3) ◽  
pp. 381-394
Author(s):  
Sridharan Chandrasekaran ◽  
G. Suresh Kumar
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Data Storage ◽  
Predictive Accuracy ◽  
Empirical Models ◽  
Operational Parameters ◽  
Time Data ◽  
Bottom Line ◽  
Rate Of Penetration ◽  
Real Time Data

Rate of Penetration (ROP) is one of the important factors influencing the drilling efficiency. Since cost recovery is an important bottom line in the drilling industry, optimizing ROP is essential to minimize the drilling operational cost and capital cost. Traditional the empirical models are not adaptive to new lithology changes and hence the predictive accuracy is low and subjective. With advancement in big data technologies, real- time data storage cost is lowered, and the availability of real-time data is enhanced. In this study, it is shown that optimization methods together with data models has immense potential in predicting ROP based on real time measurements on the rig. A machine learning based data model is developed by utilizing the offset vertical wells’ real time operational parameters while drilling. Data pre-processing methods and feature engineering methods modify the raw data into a processed data so that the model learns effectively from the inputs. A multi – layer back propagation neural network is developed, cross-validated and compared with field measurements and empirical models.

A real-time process optimization system for injection molding

2009 ◽  
Vol 49 (10) ◽  
pp. 2031-2040 ◽  
Author(s):  
Dequn Li ◽  
Huamin Zhou ◽  
Peng Zhao ◽  
Yang Li
Keyword(s):  
Injection Molding ◽  
Real Time ◽  
Process Optimization ◽  
Time Process ◽  
Optimization System
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