scholarly journals Experiencing ProvLake to Manage the Data Lineage of AI Workflows

2020 ◽  
Author(s):  
Leonardo Guerreiro Azevedo ◽  
Renan Souza ◽  
Raphael Melo Thiago ◽  
Elton Soares ◽  
Marcio Moreno
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Management ◽  
Oil And Gas ◽  
Core Concept ◽  
Data Lineage ◽  
Oil And Gas Exploration ◽  
Provenance Data ◽  
Management Techniques ◽  
Artificial Intelligence Systems

Machine Learning (ML) is a core concept behind Artificial Intelligence systems, which work driven by data and generate ML models. These models are used for decision making, and it is crucial to trust their outputs by, e.g., understanding the process that derives them. One way to explain the derivation of ML models is by tracking the whole ML lifecycle, generating its data lineage, which may be accomplished by provenance data management techniques. In this work, we present the use of ProvLake tool for ML provenance data management in the ML lifecycle for Well Top Picking, an essential process in Oil and Gas exploration. We show how ProvLake supported the validation of ML models, the understanding of whether the ML models generalize respecting the domain characteristics, and their derivation.

Application of machine learning algorithms to predict permeability in tight sandstone formations

Nafta-Gaz ◽  
2021 ◽  
Vol 77 (5) ◽  
pp. 283-292
Author(s):  
Tomasz Topór ◽  
Keyword(s):  
Machine Learning ◽  
Oil And Gas ◽  
Learning Algorithms ◽  
Confining Pressure ◽  
Machine Learning Algorithms ◽  
Core Material ◽  
Confining Stress ◽  
Tight Sandstone ◽  
Oil And Gas Exploration ◽  
Better Than

The application of machine learning algorithms in petroleum geology has opened a new chapter in oil and gas exploration. Machine learning algorithms have been successfully used to predict crucial petrophysical properties when characterizing reservoirs. This study utilizes the concept of machine learning to predict permeability under confining stress conditions for samples from tight sandstone formations. The models were constructed using two machine learning algorithms of varying complexity (multiple linear regression [MLR] and random forests [RF]) and trained on a dataset that combined basic well information, basic petrophysical data, and rock type from a visual inspection of the core material. The RF algorithm underwent feature engineering to increase the number of predictors in the models. In order to check the training models’ robustness, 10-fold cross-validation was performed. The MLR and RF applications demonstrated that both algorithms can accurately predict permeability under constant confining pressure (R2 0.800 vs. 0.834). The RF accuracy was about 3% better than that of the MLR and about 6% better than the linear reference regression (LR) that utilized only porosity. Porosity was the most influential feature of the models’ performance. In the case of RF, the depth was also significant in the permeability predictions, which could be evidence of hidden interactions between the variables of porosity and depth. The local interpretation revealed the common features among outliers. Both the training and testing sets had moderate-low porosity (3–10%) and a lack of fractures. In the test set, calcite or quartz cementation also led to poor permeability predictions. The workflow that utilizes the tidymodels concept will be further applied in more complex examples to predict spatial petrophysical features from seismic attributes using various machine learning algorithms.

scholarly journals Modelling of Traffic Flows and Supply Chains Based on Geospatial Knowledge

2021 ◽  
Vol 2068 (1) ◽  
pp. 012042
Author(s):  
A Kolesnikov ◽  
P Kikin ◽  
E Panidi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Supply Chains ◽  
Inventory Management ◽  
Traffic Flows ◽  
Machine Learning Methods ◽  
Customer Interaction ◽  
Long Term Impact ◽  
Artificial Intelligence Systems

Abstract The field of logistics and transport operates with large amounts of data. The transformation of such arrays into knowledge and processing using machine learning methods will help to find additional reserves for optimizing transport and logistics processes and supply chains. This article analyses the possibilities and prospects for the application of machine learning and geospatial knowledge in the field of logistics and transport using specific examples. The long-term impact of geospatial-based artificial intelligence systems on such processes as procurement, delivery, inventory management, maintenance, customer interaction is considered.

Machine-Learning in Oil and Gas Exploration: A New Approach to Geological Risk Assessment

2019 ◽  
Author(s):  
F. Silva ◽  
S. Fernandes ◽  
J. Casacão ◽  
C. Libório ◽  
J. Almeida ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Assessment ◽  
Oil And Gas ◽  
New Approach ◽  
Oil And Gas Exploration

scholarly journals Entropy of Artificial Intelligence

Universe ◽  
2022 ◽  
Vol 8 (1) ◽  
pp. 53
Author(s):  
T. S. Biró ◽  
Antal Jakovác
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Learning Process ◽  
Probability Space ◽  
Natural Processes ◽  
Artificial Intelligence Systems

We describe a model of artificial intelligence systems based on the dimension of the probability space of the input set available for recognition. In this scenario, we can understand a subset, which means that we can decide whether an object is an element of a given subset or not in an efficient way. In the machine learning (ML) process we define appropriate features, in this way shrinking the defining bit-length of classified sets during the learning process. This can also be described in the language of entropy: while natural processes tend to increase the disorder, that is, increase the entropy, learning creates order, and we expect that it decreases a properly defined entropy.

Artificial Intelligence AI Assisted Thermography to Detect Corrosion Under Insulation CUI

2021 ◽  
Author(s):  
Ayman Amer ◽  
Ali Alshehri ◽  
Hamad Saiari ◽  
Ali Meshaikhis ◽  
Abdulaziz Alshamrany
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Oil And Gas ◽  
Assessment Tool ◽  
Oil And Gas Industry ◽  
Real Field ◽  
Learning Approach ◽  
Real Time System ◽  
Gas Industry ◽  
Corrosion Under Insulation

Abstract Corrosion under insulation (CUI) is a critical challenge that affects the integrity of assets where the oil and gas industry is not immune. Its severity arises due to its hidden nature as it can often times go unnoticed. CUI is stimulated, in principle, by moisture ingress through the insulation layers to the surface of the pipeline. This Artificial Intelligence (AI)-powered detection technology stemmed from an urgent need to detect the presence of these corrosion types. The new approach is based on a Cyber Physical (CP) system that maximizes the potential of thermographic imaging by using a Machine Learning application of Artificial Intelligence. In this work, we describe how common image processing techniques from infra-red images of assets can be enhanced using a machine learning approach allowing the detection of locations highly vulnerable to corrosion through pinpointing locations of CUI anomalies and areas of concern. The machine learning is examining the progression of thermal images, captured over time, corrosion and factors that cause this degradation are predicted by extracting thermal anomaly features and correlating them with corrosion and irregularities in the structural integrity of assets verified visually during the initial learning phase of the ML algorithm. The ML classifier has shown outstanding results in predicting CUI anomalies with a predictive accuracy in the range of 85 – 90% projected from 185 real field assets. Also, IR imaging by itself is subjective and operator dependent, however with this cyber physical transfer learning approach, such dependency has been eliminated. The results and conclusions of this work on real field assets in operation demonstrate the feasibility of this technique to predict and detect thermal anomalies directly correlated to CUI. This innovative work has led to the development of a cyber-physical that meets the demands of inspection units across the oil and gas industry, providing a real-time system and online assessment tool to monitor the presence of CUI enhancing the output from thermography technologies, using Artificial Intelligence (AI) and machine learning technology. Additional benefits of this approach include safety enhancement through non-contact online inspection and cost savings by reducing the associated scaffolding and downtime.

scholarly journals Towards Autonomous Driving Using Vision Based Intelligent Systems

2021 ◽  
Vol 8 (2) ◽  
pp. 1-2
Author(s):  
Julkar Nine
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Image Processing ◽  
Deep Learning ◽  
Intelligent Systems ◽  
Autonomous Driving ◽  
Current Generation ◽  
Transfer Data ◽  
Safety Features ◽  
Artificial Intelligence Systems

Vision Based systems have become an integral part when it comes to autonomous driving. The autonomous industry has seen a made large progress in the perception of environment as a result of the improvements done towards vision based systems. As the industry moves up the ladder of automation, safety features are coming more and more into the focus. Different safety measurements have to be taken into consideration based on different driving situations. One of the major concerns of the highest level of autonomy is to obtain the ability of understanding both internal and external situations. Most of the research made on vision based systems are focused on image processing and artificial intelligence systems like machine learning and deep learning. Due to the current generation of technology being the generation of “Connected World”, there is no lack of data any more. As a result of the introduction of internet of things, most of these connected devices are able to share and transfer data. Vision based techniques are techniques that are hugely depended on these vision based data.

scholarly journals Data management challenges for artificial intelligence in plant and agricultural research

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 324
Author(s):  
Hugh F. Williamson ◽  
Julia Brettschneider ◽  
Mario Caccamo ◽  
Robert P. Davey ◽  
Carole Goble ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Management ◽  
Data Science ◽  
Agricultural Research ◽  
Plant Science ◽  
Agricultural Technologies ◽  
Novel Food ◽  
Different Sources

Artificial Intelligence (AI) is increasingly used within plant science, yet it is far from being routinely and effectively implemented in this domain. Particularly relevant to the development of novel food and agricultural technologies is the development of validated, meaningful and usable ways to integrate, compare and visualise large, multi-dimensional datasets from different sources and scientific approaches. After a brief summary of the reasons for the interest in data science and AI within plant science, the paper identifies and discusses eight key challenges in data management that must be addressed to further unlock the potential of AI in crop and agronomic research, and particularly the application of Machine Learning (AI) which holds much promise for this domain.

scholarly journals AI Accidents: An Emerging Threat

2021 ◽  
Author(s):  
Zachary Arnold ◽  
◽  
Helen Toner
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Learning Systems ◽  
Wide Range ◽  
Policy Suggestions ◽  
The Future ◽  
Modern Machine ◽  
Artificial Intelligence Systems

As modern machine learning systems become more widely used, the potential costs of malfunctions grow. This policy brief describes how trends we already see today—both in newly deployed artificial intelligence systems and in older technologies—show how damaging the AI accidents of the future could be. It describes a wide range of hypothetical but realistic scenarios to illustrate the risks of AI accidents and offers concrete policy suggestions to reduce these risks.

Machine-driven earth exploration: Artificial intelligence in oil and gas

2021 ◽  
Vol 40 (4) ◽  
pp. 298-301
Author(s):  
Tariq Alkhalifah ◽  
Ali Almomin ◽  
Ali Naamani
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Fault Detection ◽  
Oil And Gas ◽  
Image Resolution ◽  
The Earth ◽  
Recent Advances ◽  
Potential Applications ◽  
Boundary Mapping

Artificial intelligence (AI), specifically machine learning (ML), has emerged as a powerful tool to address many of the challenges we face as we try to illuminate the earth and make the proper prediction of its content. From fault detection, to salt boundary mapping, to image resolution enhancements, the quest to teach our computing devices how to perform these tasks accurately, as well as quantify the accuracy, has become a feasible and sought-after objective. Recent advances in ML algorithms and availability of the modules to apply such algorithms enabled geoscientists to focus on potential applications of such tools. As a result, we held the virtual workshop, Artificially Intelligent Earth Exploration Workshop: Teaching the Machine How to Characterize the Subsurface, 23–26 November 2020.

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