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Log Data Acquisition and Quality Control
While the first well logs recorded seventy years ago had no provision for data quality control, the development of increasingly sophisticated logging techniques has led to the introduction of a large number of tests to validate acquired data. Log quality can be assured by stringent control of depth, calibrations, signal processing and operating procedures. This work gives a thorough description of these features. The meaningful interpretation of well logs depends on valid input. An understanding of log acquisition, and the performance of rigorous quality checks are the prerequisites for an accurate evaluation of a formation. These elements also enable log users to make decisions based on cal...
Quest for Quality Data
The Digital Oilfield is the buzzword of the oil industry in these early years of the 21st century.Data swiftly flows to databases, moves around the world at the speed of light and can be exchanged seamlessly between all stakeholders. From time to time, for good housekeeping, data is cleansed. Many people are involved in these intermediate or final steps.But, who cares about the real sources of oilfield data, seismic profiles, wirelines and LWD logs, drilling data and core measurements? This book expresses the real concerns about input data. It explains the inherent weaknesses of the oilfield data acquisition processes and gives recommendations on how to improve them. This quest goes through the paths of uncertainty management and elucidates the important role of the field engineers.
Geoscience Data and Collections
Geoscience data and collections (such as, rock and sediment cores, geophysical data, engineering records, and fossils) are necessary for industries to discover and develop domestic natural resources to fulfill the nation's energy and mineral requirements and to improve the prediction of immediate and long term hazards, such as land slides, volcanic eruptions and global climate change. While the nation has assembled a wealth of geoscience data and collections, their utility remains incompletely tapped. Many could act as invaluable resources in the future but immediate action is needed if they are to remain available. Housing of and access to geoscience data and collections have become critica...
Petrophysics
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The Log Analyst
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Hart's E&P.
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Machine Learning and Knowledge Discovery in Databases
The three volume proceedings LNAI 11051 – 11053 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2018, held in Dublin, Ireland, in September 2018. The total of 131 regular papers presented in part I and part II was carefully reviewed and selected from 535 submissions; there are 52 papers in the applied data science, nectar and demo track. The contributions were organized in topical sections named as follows: Part I: adversarial learning; anomaly and outlier detection; applications; classification; clustering and unsupervised learning; deep learning; ensemble methods; and evaluation. Part II: graphs; kernel methods; learning paradigms; matrix and tensor analysis; online and active learning; pattern and sequence mining; probabilistic models and statistical methods; recommender systems; and transfer learning. Part III: ADS data science applications; ADS e-commerce; ADS engineering and design; ADS financial and security; ADS health; ADS sensing and positioning; nectar track; and demo track.
