Data Analytics of Wettability Alteration with Surfactants in Carbonate Reservoirs

Wettability alteration with surfactants has been widely applied to improve oil recovery in carbonate reservoirs. An effective surfactant huff-puff application design requires comprehensive guidelines about where, how, and when this method could be applied. Besides, traditional methods to design an effective application and optimize surfactant performance are dependent on extensive experiments and are time-consuming. This study is to assist in the design of surfactant huff-puff via statistical methods and machine learning techniques. In this study, a dataset including 402 effective surfactant imbibition tests is established by collecting information from nearly 50 publications. The dataset provides a foundation for further data analysis. Descriptive statistical analysis methods are used to establish comprehensive application guidelines for surfactant huff-puff treatments. A Random Forest (RF) model was built to predict surfactant performance. Based on RF model, Shapley additive explanations approach was applied to obtain new insights to promote the understanding of wettability alteration with surfactants. Also, a new procedure integrating RF model with Powell’s method was built to optimize surfactant performance. Besides, principal component analysis and hierarchical clustering algorithm was applied to uncover the hidden pattens embedded in global surfactant huff-puff treatments. The analog reasoning was utilized to provide valuable experiences for designing surfactant treatments and predicting performance for new candidate projects. Finally, an adaptive, offline, and friendly desktop application was developed to facilitate data analysis and support decision making --Abstract, p. i

An Overview of PET Radiopharmaceuticals in Clinical Use: Regulatory, Quality and Pharmacopeia Monographs of the United States and Europe

Since 1976, more and more PET radiopharmaceuticals have been developed as the clinical introduction of [18F]FDG for various medical applications. However, few of them could be involved in routinely clinical use in hospitals partly because of restrictions in regulatory and facilities. This chapter aims to provide an overview of PET radiopharmaceuticals that are common manufactured (or prepared) in industry (or hospitals) about regulatory and quality aspects, and further summarize pharmacopeia-listed PET radiopharmaceuticals and their clinical usefulness herein. Particularly, PET radiopharmaceuticals listed in latest United States Pharmacopeia (USP) and/or European Pharmacopeia (EP) are included for this chapter. Finally, this chapter would be helpful in the basic understanding of clinical PET radiopharmaceuticals for physicians or technologists

Understanding VAEs in Fisher-Shannon Plane

In information theory, Fisher information and Shannon information (entropy) are respectively used to quantify the uncertainty associated with the distribution modeling and the uncertainty in specifying the outcome of given variables. These two quantities are complementary and are jointly applied to information behavior analysis in most cases. The uncertainty property in information asserts a fundamental trade-off between Fisher information and Shannon information, which enlightens us the relationship between the encoder and the decoder in variational auto-encoders (VAEs). In this paper, we investigate VAEs in the Fisher-Shannon plane and demonstrate that the representation learning and the log-likelihood estimation are intrinsically related to these two information quantities. Through extensive qualitative and quantitative experiments, we provide with a better comprehension of VAEs in tasks such as high-resolution reconstruction, and representation learning in the perspective of Fisher information and Shannon information. We further propose a variant of VAEs, termed as Fisher auto-encoder (FAE), for practical needs to balance Fisher information and Shannon information. Our experimental results have demonstrated its promise in improving the reconstruction accuracy and avoiding the non-informative latent code as occurred in previous works