Using Data-Mining CRISP-DM Methodology to Predict Drilling Troubles in Real-Time

2021 ◽  
Author(s):  
Salem Al Gharbi ◽  
Abdulaziz Al-Majed ◽  
Abdulazeez Abdulraheem ◽  
Shirish Patil ◽  
Salaheldin Elkatatny
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Data Processing ◽  
Real Time ◽  
Cost Savings ◽  
Clear Understanding ◽  
Time Data ◽  
Artificial Intelligent ◽  
Practice Parameters ◽  
Intelligent Technology

Abstract Drilling is considered one of the most challenging and costly operations in the oil and gas industry. Several initiatives were applied to reduce the cost and increase the effectiveness of drilling operations. One of the frequent difficulties that faces these operations is unexpected drilling troubles that take place and stops the operation, resulting in losing a lot of time and money, and could lead to safety issues culminating in a fatality situation. For that, the industry is in continues efforts to prevent drilling troubles. Part of these efforts is utilizing the artificial intelligence (AI) technologies to identify troubles in advance and prevent them before maturing to a serious situation. Multiple approaches were tried; however, errors and significant deviation were observed when comparing the prediction results to the actual drilling data. This could be due to the improper design of the artificial intelligent technology or inappropriate data processing. Therefore, searching for dynamic and adequate artificial intelligent technology and encapsulated data processing model is very essential. This paper presents an effective data-mining methodology to determine the most efficient artificial intelligent technology and the applicable data processing techniques, to identify the early symptoms of drilling troubles in real-time. This methodology is CRISP-DM that stands for Cross Industry Standard Process for Data Mining. This methodology consists of the following phases: Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation and Deployment. During these phases, multiple data-quality techniques were applied to improve the reliability of the real-time data. The developed model presented a significant improvement in identifying the drilling troubles in advance, compared to the current practice. Parameters such as hook-load and bit-depth, were studied. Actual data from several oil fields were used to develop and validate this smart model. This model provided the drilling engineers and operation crew with bigger window to mitigate the situation and resolve it, prevent the occurrence of several drilling troubles, result in big time and cost savings. In addition to the time and cost savings, CRISP-DM provided the artificial intelligent experts and the drilling domain experts with a framework to exchange knowledge and sharply increase the synergy between the two domains, which lead to a common and clear understanding, and long-term successful drilling and AI teams collaboration. The novelty of this paper is the introduction of data-mining CRIPS methodology for the first time in the prediction of drilling troubles. It enabled the development of a successful artificial intelligence model that outperformed other drilling troubles prediction practices.

scholarly journals The real-time big data processing method based on LSTM or GRU for the smart job shop production process

2020 ◽  
Vol 14 ◽  
pp. 174830262096239 ◽  
Author(s):  
Chuang Wang ◽  
Wenbo Du ◽  
Zhixiang Zhu ◽  
Zhifeng Yue
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Production Process ◽  
Real Time ◽  
Job Shop ◽  
Production Data ◽  
Time Data ◽  
Big Data Processing ◽  
The Real ◽  
Data Processing Method

With the wide application of intelligent sensors and internet of things (IoT) in the smart job shop, a large number of real-time production data is collected. Accurate analysis of the collected data can help producers to make effective decisions. Compared with the traditional data processing methods, artificial intelligence, as the main big data analysis method, is more and more applied to the manufacturing industry. However, the ability of different AI models to process real-time data of smart job shop production is also different. Based on this, a real-time big data processing method for the job shop production process based on Long Short-Term Memory (LSTM) and Gate Recurrent Unit (GRU) is proposed. This method uses the historical production data extracted by the IoT job shop as the original data set, and after data preprocessing, uses the LSTM and GRU model to train and predict the real-time data of the job shop. Through the description and implementation of the model, it is compared with KNN, DT and traditional neural network model. The results show that in the real-time big data processing of production process, the performance of the LSTM and GRU models is superior to the traditional neural network, K nearest neighbor (KNN), decision tree (DT). When the performance is similar to LSTM, the training time of GRU is much lower than LSTM model.

A New Approach of Expanding Data Processing Ability for Configuration Monitoring Software MCGS Based on OLE

2011 ◽  
Vol 65 ◽  
pp. 295-298 ◽  
Author(s):  
Fan Yang ◽  
Cai Li Zhang
Keyword(s):  
Data Processing ◽  
Real Time ◽  
Monitoring System ◽  
Programming Language ◽  
Configuration Software ◽  
Time Data ◽  
Software Configuration ◽  
Processing Ability ◽  
Significant Performance ◽  
Monitoring Software

Considering the insufficient ability of data processing existed in configuration software, a scheme integrated both advantages of advanced programming language and configuration software is provided. In this scheme real-time data acquisition and complex processing are achieved by advanced programming language, the human-computer interface and other functions of the monitoring system are achieved by configuration software. Configuration software achieves the purpose of expanding data processing ability by data communications between advanced programming language and configuration software based on OLE technology. The practical application result indicates that the data processing ability of configuration software can be effectively expanded based on OLE technology, which has well stability and real-time, and can play significant performance in complex parameters and data processing related monitoring system.

A Novel Well Test Data Analyzer and Process Optimizer Using Artificial Intelligence and Machine Learning Techniques

2021 ◽  
Author(s):  
Nagaraju Reddicharla ◽  
Subba Ramarao Rachapudi ◽  
Indra Utama ◽  
Furqan Ahmed Khan ◽  
Prabhker Reddy Vanam ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Real Time ◽  
Test Data ◽  
Oil Field ◽  
Well Testing ◽  
Well Test ◽  
Time Data ◽  
Real Time Data ◽  
Well Tests

Abstract Well testing is one of the vital process as part of reservoir performance monitoring. As field matures with increase in number of well stock, testing becomes tedious job in terms of resources (MPFM and test separators) and this affect the production quota delivery. In addition, the test data validation and approval follow a business process that needs up to 10 days before to accept or reject the well tests. The volume of well tests conducted were almost 10,000 and out of them around 10 To 15 % of tests were rejected statistically per year. The objective of the paper is to develop a methodology to reduce well test rejections and timely raising the flag for operator intervention to recommence the well test. This case study was applied in a mature field, which is producing for 40 years that has good volume of historical well test data is available. This paper discusses the development of a data driven Well test data analyzer and Optimizer supported by artificial intelligence (AI) for wells being tested using MPFM in two staged approach. The motivating idea is to ingest historical, real-time data, well model performance curve and prescribe the quality of the well test data to provide flag to operator on real time. The ML prediction results helps testing operations and can reduce the test acceptance turnaround timing drastically from 10 days to hours. In Second layer, an unsupervised model with historical data is helping to identify the parameters that affecting for rejection of the well test example duration of testing, choke size, GOR etc. The outcome from the modeling will be incorporated in updating the well test procedure and testing Philosophy. This approach is being under evaluation stage in one of the asset in ADNOC Onshore. The results are expected to be reducing the well test rejection by at least 5 % that further optimize the resources required and improve the back allocation process. Furthermore, real time flagging of the test Quality will help in reduction of validation cycle from 10 days hours to improve the well testing cycle process. This methodology improves integrated reservoir management compliance of well testing requirements in asset where resources are limited. This methodology is envisioned to be integrated with full field digital oil field Implementation. This is a novel approach to apply machine learning and artificial intelligence application to well testing. It maximizes the utilization of real-time data for creating advisory system that improve test data quality monitoring and timely decision-making to reduce the well test rejection.

Early Warning of Power Plant Equipment Based on Massive Real-Time Data Mining Technology

2014 ◽  
Vol 599-601 ◽  
pp. 1487-1490 ◽  
Author(s):  
Li Kun Zheng ◽  
Kun Feng ◽  
Xiao Qing Xiao ◽  
Wei Qiao Song
Keyword(s):  
Data Mining ◽  
Power Plant ◽  
Real Time ◽  
Early Warning ◽  
Time Data ◽  
Mining Technology ◽  
Equipment Safety ◽  
State Evaluation ◽  
Power Plant Equipment ◽  
Real Time Data

This paper mainly discusses the application of the mass real-time data mining technology in equipment safety state evaluation in the power plant and the realization of the equipment comprehensive quantitative assessment and early warning of potential failure by mining analysis and modeling massive amounts of real-time data the power equipment. In addition to the foundational technology introduced in this paper, the technology is also verified by the application case in the power supply side remote diagnosis center of Guangdong electric institute.

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