Machine Learning-aided Causal Inference for Unraveling Chemical Dispersant and Salinity Effects on Crude Oil Biodegradation

2021 ◽  
pp. 126468
Author(s):  
Yiqi Cao ◽  
Qiao Kang ◽  
Baiyu Zhang ◽  
Zhiwen Zhu ◽  
Guihua Dong ◽  
...  
Keyword(s):  
Machine Learning ◽  
Crude Oil ◽  
Causal Inference ◽  
Salinity Effects ◽  
Chemical Dispersant ◽  
Oil Biodegradation

scholarly journals Effects of Dispersants and Biosurfactants on Crude-Oil Biodegradation and Bacterial Community Succession

2021 ◽  
Vol 9 (6) ◽  
pp. 1200
Author(s):  
Gareth E. Thomas ◽  
Jan L. Brant ◽  
Pablo Campo ◽  
Dave R. Clark ◽  
Frederic Coulon ◽  
...  
Keyword(s):  
Crude Oil ◽  
Early Stage ◽  
Niche Partitioning ◽  
Hydrocarbon Biodegradation ◽  
Hydrocarbonoclastic Bacteria ◽  
Bacterial Response ◽  
Oil Biodegradation ◽  
Interfacial Surface ◽  
Degrading Bacteria ◽  
Oil Spill Response

This study evaluated the effects of three commercial dispersants (Finasol OSR 52, Slickgone NS, Superdispersant 25) and three biosurfactants (rhamnolipid, trehalolipid, sophorolipid) in crude-oil seawater microcosms. We analysed the crucial early bacterial response (1 and 3 days). In contrast, most analyses miss this key period and instead focus on later time points after oil and dispersant addition. By focusing on the early stage, we show that dispersants and biosurfactants, which reduce the interfacial surface tension of oil and water, significantly increase the abundance of hydrocarbon-degrading bacteria, and the rate of hydrocarbon biodegradation, within 24 h. A succession of obligate hydrocarbonoclastic bacteria (OHCB), driven by metabolite niche partitioning, is demonstrated. Importantly, this succession has revealed how the OHCB Oleispira, hitherto considered to be a psychrophile, can dominate in the early stages of oil-spill response (1 and 3 days), outcompeting all other OHCB, at the relatively high temperature of 16 °C. Additionally, we demonstrate how some dispersants or biosurfactants can select for specific bacterial genera, especially the biosurfactant rhamnolipid, which appears to provide an advantageous compatibility with Pseudomonas, a genus in which some species synthesize rhamnolipid in the presence of hydrocarbons.

scholarly journals Classical and machine learning modeling of crude oil production in Nigeria: Identification of an eminent model for application

2021 ◽  
Vol 7 ◽  
pp. 3497-3505
Author(s):  
Chukwudi Paul Obite ◽  
Angela Chukwu ◽  
Desmond Chekwube Bartholomew ◽  
Ugochinyere Ihuoma Nwosu ◽  
Gladys Ezenwanyi Esiaba
Keyword(s):  
Machine Learning ◽  
Crude Oil ◽  
Oil Production ◽  
Crude Oil Production

Crude oil biodegradation potential of biosurfactant-producing Pseudomonas aeruginosa and Meyerozyma sp.

2021 ◽  
pp. 126276
Author(s):  
Ramla Rehman ◽  
Muhammad Ishtiaq Ali ◽  
Naeem Ali ◽  
Malik Badshah ◽  
Mazhar Iqbal ◽  
...  
Keyword(s):  
Pseudomonas Aeruginosa ◽  
Crude Oil ◽  
Oil Biodegradation

Causal Inference Meets Machine Learning

2020 ◽  
Author(s):  
Peng Cui ◽  
Zheyan Shen ◽  
Sheng Li ◽  
Liuyi Yao ◽  
Yaliang Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Causal Inference

A Survey on Causal Inference

2021 ◽  
Vol 15 (5) ◽  
pp. 1-46
Author(s):  
Liuyi Yao ◽  
Zhixuan Chu ◽  
Sheng Li ◽  
Yaliang Li ◽  
Jing Gao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Causal Inference ◽  
Observational Data ◽  
Causal Effect ◽  
Research Direction ◽  
Estimation Methods ◽  
Potential Outcome ◽  
Outcome Framework ◽  
Benchmark Datasets ◽  
Inference Methods

Causal inference is a critical research topic across many domains, such as statistics, computer science, education, public policy, and economics, for decades. Nowadays, estimating causal effect from observational data has become an appealing research direction owing to the large amount of available data and low budget requirement, compared with randomized controlled trials. Embraced with the rapidly developed machine learning area, various causal effect estimation methods for observational data have sprung up. In this survey, we provide a comprehensive review of causal inference methods under the potential outcome framework, one of the well-known causal inference frameworks. The methods are divided into two categories depending on whether they require all three assumptions of the potential outcome framework or not. For each category, both the traditional statistical methods and the recent machine learning enhanced methods are discussed and compared. The plausible applications of these methods are also presented, including the applications in advertising, recommendation, medicine, and so on. Moreover, the commonly used benchmark datasets as well as the open-source codes are also summarized, which facilitate researchers and practitioners to explore, evaluate and apply the causal inference methods.

Effects of Nutrient Source and Supply on Crude Oil Biodegradation in Continuous-Flow Beach Microcosms

2006 ◽  
Vol 132 (1) ◽  
pp. 75-84 ◽  
Author(s):  
Brian A. Wrenn ◽  
Kathryn L. Sarnecki ◽  
Eugene S. Kohar ◽  
Kenneth Lee ◽  
Albert D. Venosa
Keyword(s):  
Crude Oil ◽  
Continuous Flow ◽  
Nutrient Source ◽  
Oil Biodegradation

Application of Machine Learning in Predicting Crude Oil Production Volume

2021 ◽  
Author(s):  
Okechukwu Prince Innocent
Keyword(s):  
Machine Learning ◽  
Crude Oil ◽  
Predictor Variable ◽  
Oil Production ◽  
Water Volume ◽  
Production Volume ◽  
Affecting Factors ◽  
Factors Affecting ◽  
Crude Oil Production ◽  
The Relationship

Abstract The production of oil is of great and immense significance as a source of energy worldwide. The major factors affecting the production volume of oil is classified into two groups namely the geological and the human factor. Each group comprises of factors affecting oilfield production volume. The challenge in this project is to find the variable for the crude oil production volume in an oilfield because there are numerous factors affecting the crude oil production volume in an oilfield. The objective of this paper is to provide a more accurate and efficient solution on how to predict the oil production volume. Furthermore, Machine Learning algorithm called Multiple Linear Regression was developed using Python programming Language to predict the production volume of oil in an oilfield. The model was developed and fitted to train and test the factors that affect and influence the oil production volume. After a several studies have been made, the affecting factors were provided from the oilfield which would be trained and tested in order to model the relationship between predictor variable and response variable which are the significant affecting factors and the oil production volume respectively. The predictor variables are the startup number of wells, the recovery percent of previous year, the injected water volume of previous year and the oil moisture content of previous year. The predictor variable is the oil production volume. Moreover, the model was found to possess greater utility in predicting the production volume of oil as it yielded an oil production volume output with an accuracy of 98 percent. The relationship between oil production volume and the affecting factors was observed and drawn to a perfect conclusion. This model can be of immense value in the oil and gas industry if implemented because of its ability to predict oilfield output more accurately. It is an invaluable and very efficient model for the oilfield manager and oil production manager.

scholarly journals SOCIAL AND BEHAVIORAL FACTORS IN COGNITIVE AGING: APPLYING THE CAUSAL INFERENCE FRAMEWORK IN OBSERVATIONAL STUDIES

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S323-S323
Author(s):  
Anja K Leist
Keyword(s):  
Machine Learning ◽  
Cognitive Decline ◽  
Causal Inference ◽  
Cognitive Aging ◽  
Behavioral Interventions ◽  
Observational Studies ◽  
Intervention Research ◽  
Learning Approaches ◽  
Behavioral Factors

Abstract Rationale: There is an urgent need to better understand how to maintain cognitive functioning at older ages with social and behavioral interventions, given that there is currently no medical cure available to prevent, halt or reverse the progression of cognitive decline and dementia. However, in current models, it is still not well established which factors (e.g. education, BMI, physical activity, sleep, depression) matter most at which ages, and which behavioral profiles are most protective against cognitive decline. In the last years, advances in the fields of causal inference and machine learning have equipped epidemiology and social sciences with methods and models to approach causal questions in observational studies. Method: The presentation will give an overview of the causal inference framework and different machine learning approaches to investigate cognitive aging. First, we will present relevant research questions on the role of social and behavioral factors in cognitive aging in observational studies. Second, we will introduce the causal inference framework and recent methods to visualize and compute the strength of causal paths. Third, promising machine learning approaches to arrive at robust predictions are presented. The 13-year follow-up from the European SHARE survey that employs well-established cognitive performance tests is used to demonstrate the usefulness of the approach. Discussion: The causal inference framework, combined with recent machine learning approaches and applied in observational studies, provides a robust alternative to intervention research. Advantages for investigations under the new framework, e.g., fewer ethical considerations compared to intervention research, as well as limitations are discussed.

scholarly journals Performance Evaluation in Machine Learning: The Good, the Bad, the Ugly, and the Way Forward

2019 ◽  
Vol 33 ◽  
pp. 9808-9814 ◽  
Author(s):  
Peter Flach
Keyword(s):  
Machine Learning ◽  
Performance Evaluation ◽  
Causal Inference ◽  
Latent Variable ◽  
Measurement Theory ◽  
Latent Variable Models ◽  
Data Set ◽  
Evaluation Measures ◽  
Classifier Evaluation ◽  
The Way

This paper gives an overview of some ways in which our understanding of performance evaluation measures for machine-learned classifiers has improved over the last twenty years. I also highlight a range of areas where this understanding is still lacking, leading to ill-advised practices in classifier evaluation. This suggests that in order to make further progress we need to develop a proper measurement theory of machine learning. I then demonstrate by example what such a measurement theory might look like and what kinds of new results it would entail. Finally, I argue that key properties such as classification ability and data set difficulty are unlikely to be directly observable, suggesting the need for latent-variable models and causal inference.

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