Real Time Seismic Monitoring Of Drilling Operations

2018 ◽  
Author(s):  
J.E. Lindgard ◽  
T. Thiem ◽  
E.V. Bergfjord ◽  
R.S. Agersborg
Keyword(s):  
Real Time ◽  
Seismic Monitoring ◽  
Drilling Operations

Turbodrill Bottomhole Assembly and Real-Time Data Improve Through-Tubing Coiled-Tubing Drilling Operations in Sour Gas Well

2015 ◽  
Author(s):  
A. Ebrahimi ◽  
P. J. Schermer ◽  
W. Jelinek ◽  
D. Pommier ◽  
S. Pfeil ◽  
...  
Keyword(s):  
Real Time ◽  
Time Data ◽  
Gas Well ◽  
Coiled Tubing ◽  
Real Time Data ◽  
Drilling Operations ◽  
Sour Gas

Improving Real-Time Drilling Data Quality Using Artificial Intelligence and Machine Learning Techniques

2021 ◽  
Author(s):  
S. H. Al Gharbi ◽  
A. A. Al-Majed ◽  
A. Abdulraheem ◽  
S. Patil ◽  
S. M. Elkatatny
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Quality ◽  
Real Time ◽  
Input Data ◽  
Support Vector ◽  
The Real ◽  
Drilling Data ◽  
Drilling Operations

Abstract Due to high demand for energy, oil and gas companies started to drill wells in remote areas and unconventional environments. This raised the complexity of drilling operations, which were already challenging and complex. To adapt, drilling companies expanded their use of the real-time operation center (RTOC) concept, in which real-time drilling data are transmitted from remote sites to companies’ headquarters. In RTOC, groups of subject matter experts monitor the drilling live and provide real-time advice to improve operations. With the increase of drilling operations, processing the volume of generated data is beyond a human's capability, limiting the RTOC impact on certain components of drilling operations. To overcome this limitation, artificial intelligence and machine learning (AI/ML) technologies were introduced to monitor and analyze the real-time drilling data, discover hidden patterns, and provide fast decision-support responses. AI/ML technologies are data-driven technologies, and their quality relies on the quality of the input data: if the quality of the input data is good, the generated output will be good; if not, the generated output will be bad. Unfortunately, due to the harsh environments of drilling sites and the transmission setups, not all of the drilling data is good, which negatively affects the AI/ML results. The objective of this paper is to utilize AI/ML technologies to improve the quality of real-time drilling data. The paper fed a large real-time drilling dataset, consisting of over 150,000 raw data points, into Artificial Neural Network (ANN), Support Vector Machine (SVM) and Decision Tree (DT) models. The models were trained on the valid and not-valid datapoints. The confusion matrix was used to evaluate the different AI/ML models including different internal architectures. Despite the slowness of ANN, it achieved the best result with an accuracy of 78%, compared to 73% and 41% for DT and SVM, respectively. The paper concludes by presenting a process for using AI technology to improve real-time drilling data quality. To the author's knowledge based on literature in the public domain, this paper is one of the first to compare the use of multiple AI/ML techniques for quality improvement of real-time drilling data. The paper provides a guide for improving the quality of real-time drilling data.

Impact of Data Quality on Well Operations: Case Study of Work-over Operations

2016 ◽  
Author(s):  
Alfred Enyekwe ◽  
Osahon Urubusi ◽  
Raufu Yekini ◽  
Iorkam Azoom ◽  
Oloruntoba Isehunwa
Keyword(s):  
Data Quality ◽  
Data Management ◽  
Real Time ◽  
Data Management System ◽  
Quality Data ◽  
Process Safety ◽  
Well Control ◽  
Drilling Operations ◽  
Short Time

ABSTRACT Significant emphasis on data quality is placed on real-time drilling data for the optimization of drilling operations and on logging data for quality lithological and petrophysical description of a field. This is evidenced by huge sums spent on real time MWD/LWD tools, broadband services, wireline logging tools, etc. However, a lot more needs to be done to harness quality data for future workover and or abandonment operations where data being relied on is data that must have been entered decades ago and costs and time spent are critically linked to already known and certified information. In some cases, data relied on has been migrated across different data management platforms, during which relevant data might have been lost, mis-interpreted or mis-placed. Another common cause of wrong data is improperly documented well intervention operations which have been done in such a short time, that there is no pressure to document the operation properly. This leads to confusion over simple issues such as what depth a plug was set, or what junk was left in hole. The relative lack of emphasis on this type of data quality has led to high costs of workover and abandonment operations. In some cases, well control incidents and process safety incidents have arisen. This paper looks at over 20 workover operations carried out in a span of 10 years. An analysis is done on the wells’ original timeline of operation. The data management system is generally analyzed and a categorization of issues experienced during the workover operations is outlined. Bottlenecks in data management are defined and solutions currently being implemented to manage these problems are listed as recommended good practices.

Real‐Time Seismic Monitoring in Haiti and Some Applications

2018 ◽  
Vol 89 (2A) ◽  
pp. 407-415 ◽  
Author(s):  
Allison L. Bent ◽  
John Cassidy ◽  
Claude Prépetit ◽  
Maurice Lamontagne ◽  
Sophia Ulysse
Keyword(s):  
Real Time ◽  
Seismic Monitoring

Real-Time Seismic Monitoring of the New Cape Girardeau Bridge and Preliminary Analyses of Recorded Data: An Overview

2006 ◽  
Vol 22 (3) ◽  
pp. 609-630 ◽  
Author(s):  
Mehmet Çelebi
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Mississippi River ◽  
Free Field ◽  
Seismic Monitoring ◽  
Ambient Vibration ◽  
Design Parameters ◽  
Specific Response ◽  
Response Characteristics ◽  
Broadband Network

This paper introduces the state-of-the-art seismic monitoring system implemented for the 1,206-m-long (3,956 ft) cable-stayed Bill Emerson Memorial Bridge in Cape Girardeau (Missouri), a new Mississippi River crossing, approximately 80 km from the epicentral region of the 1811 and 1812 New Madrid earthquakes. The real-time seismic monitoring system for the bridge includes a broadband network consisting of superstructure and free-field arrays and comprises a total of 84 channels of accelerometers deployed on the superstructure (towers and deck), pier foundations (caisson tops and bents), and in the vicinity of the bridge (e.g., free-field, both surface and downhole). The paper also introduces the high-quality response data obtained from the broadband network that otherwise would not have been possible with older instruments. Such data is aimed to be used by the owner, researchers, and engineers to (1) assess the performance of the bridge, (2) check design parameters, including the comparison of dynamic characteristics with actual response, and (3) better design future similar bridges. Preliminary spectral analyses of low-amplitude ambient vibration data and that from a small earthquake reveal specific response characteristics of this new bridge and the free-field in its proximity. There is coherent tower-cable-deck interaction that sometimes results in amplified ambient motions. Also, while the motions at the lowest (triaxial) downhole accelerometers on both Missouri and Illinois sides are practically free from any feedback of motions of the bridge, the motions at the middle downhole and surface accelerometers are influenced significantly even by amplified ambient motions of the bridge.

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