Cloud Computing and Big Data for Oil and Gas Industry Application in China

The oil and gas industry is a complex data-driven industry with compute-intensive, data-intensive and business-intensive features. Cloud computing and big data have a broad application prospect in the oil and gas industry. This research aims to highlight the cloud computing and big data issues and challenges from the informatization in oil and gas industry. In this paper, the distributed cloud storage architecture and its applications for seismic data of oil and gas industry are focused on first. Then,cloud desktop for oil and gas industry applications are also introduced in terms of efficiency, security and usability. Finally, big data architecture and security issues of oil and gas industry are analyzed. Cloud computing and big data architectures have advantages in many aspects, such as system scalability, reliability, and serviceability. This paper also provides a brief description for the future development of Cloud computing and big data in oil and gas industry. Cloud computing and big data can provide convenient information sharing and high quality service for oil and gas industry

What attracts vehicle consumers’ buying:A Saaty scale-based VIKOR (SSC-VIKOR) approach from after-sales textual perspective?

Purpose: The increasingly booming e-commerce development has stimulated vehicle consumers to express individual reviews through online forum. The purpose of this paper is to probe into the vehicle consumer consumption behavior and make recommendations for potential consumers from textual comments viewpoint. Design/methodology/approach: A big data analytic-based approach is designed to discover vehicle consumer consumption behavior from online perspective. To reduce subjectivity of expert-based approaches, a parallel Naïve Bayes approach is designed to analyze the sentiment analysis, and the Saaty scale-based (SSC) scoring rule is employed to obtain specific sentimental value of attribute class, contributing to the multi-grade sentiment classification. To achieve the intelligent recommendation for potential vehicle customers, a novel SSC-VIKOR approach is developed to prioritize vehicle brand candidates from a big data analytical viewpoint. Findings: The big data analytics argue that “cost-effectiveness” characteristic is the most important factor that vehicle consumers care, and the data mining results enable automakers to better understand consumer consumption behavior. Research limitations/implications: The case study illustrates the effectiveness of the integrated method, contributing to much more precise operations management on marketing strategy, quality improvement and intelligent recommendation. Originality/value: Researches of consumer consumption behavior are usually based on survey-based methods, and mostly previous studies about comments analysis focus on binary analysis. The hybrid SSC-VIKOR approach is developed to fill the gap from the big data perspective

Economic Value of the Oil and Gas Resources on the Outer Continential Shelf

A theoretical framework for estimating the economic value of the federal government's offshore oil and gas resources is developed. This framework is then applied to geological and economic data generated by the Minerals Management Service in support of their five-year leasing plan. With an 8 percent real discount rate and a 1 percent real price growth rate, the remaining economic rent as of 1987 on the reserves plus the undiscovered offshore oil and gas resources is estimated at $118.6 billion (1987 dollars). The present value of the government's receipts from cash bonus and royalty payments on these deposits is estimated at$37.2 billion. Over 80 percent of the remaining economic rent is derived from developed reserve deposits located in the Gulf of Mexico. The private sector has previously paid cash bonuses for the leases located on those deposits and financed the installation of the development platforms. Because of this, the government will collect only a small portion, approximately 22 percent, of the rent remaining on those reserves.Environmental Economics and Policy, Resource /Energy Economics and Policy,

Off-line computing for experimental high-energy physics

The needs of experimental high-energy physics for large-scale computing and data handling are explained in terms of the complexity of individual collisions and the need for high statistics to study quantum mechanical processes. The prevalence of university-dominated collaborations adds a requirement for high-performance wide-area networks. The data handling and computational needs of the different types of large experiment, now running or under construction, are evaluated. Software for experimental high-energy physics is reviewed briefly with particular attention to the success of packages written within the discipline. It is argued that workstations and graphics are important in ensuring that analysis codes are correct, and the worldwide networks which support the involvement of remote physicists are described. Computing and data handling are reviewed showing how workstations and RISC processors are rising in importance but have not supplanted traditional mainframe processing. Examples of computing systems constructed within high-energy physics are examined and evaluated

Electromagnetic imaging and deep learning for transition to renewable energies: a technology review

Electromagnetic imaging is a technique that has been employed and perfected to investigate the Earth subsurface over the past three decades. Besides the traditional geophysical surveys (e.g., hydrocarbon exploration, geological mapping), several new applications have appeared (e.g., characterization of geothermal energy reservoirs, capture and storage of carbon dioxide, water prospecting, and monitoring of hazardous-waste deposits). The development of new numerical schemes, algorithms, and easy access to supercomputers have supported innovation throughout the geo-electromagnetic community. In particular, deep learning solutions have taken electromagnetic imaging technology to a different level. These emerging deep learning tools have significantly contributed to data processing for enhanced electromagnetic imaging of the Earth. Herein, we review innovative electromagnetic imaging technologies and deep learning solutions and their role in better understanding useful resources for the energy transition path. To better understand this landscape, we describe the physics behind electromagnetic imaging, current trends in its numerical modeling, development of computational tools (traditional approaches and emerging deep learning schemes), and discuss some key applications for the energy transition. We focus on the need to explore all the alternatives of technologies and expertise transfer to propel the energy landscape forward. We hope this review may be useful for the entire geo-electromagnetic community and inspire and drive the further development of innovative electromagnetic imaging technologies to power a safer future based on energy sources.This work was supported by the European Union’s Horizon 2020 research and innovation programme under grant agreements No. 955606 (DEEP-SEA) and No. 777778 (MATHROCKS). Furthermore, the research leading of this study has received funding from the Ministerio de Educación y Ciencia (Spain) under Project TED2021-131882B-C42.Peer ReviewedPostprint (published version

An Independent Review of USGS Circular 1370: An Evaluation of the Science Needs to Inform Decisions on Outer Continental Shelf Energy Development in the Chukchi and Beaufort Seas, Alaska

Reviews the U.S. Geological Survey's findings and recommendations on Alaska's Arctic Ocean, including geology, ecology and subsistence, effect of climate change on, and impact of oil spills. Makes recommendations for data management and other issues

Deterring and Compensating Oil Spill Catastrophes: The Need for Strict and Two-Tier Liability

The BP Deepwater Horizon oil spill highlighted the glaring weakness in the current liability and regulatory regime for oil spills and for environmental catastrophes more broadly. This article proposes a new liability structure for deep sea oil drilling and for catastrophic risks generally. It delineates a two-tier system of liability. The first tier would impose strict liability up to the firm's financial resources plus insurance coverage. The second tier would be an annual tax equal to the expected costs in the coming year beyond this damages amount. A single firm will be identified as responsible for generating the risk. It would be required to demonstrate substantial ability to pay in the first tier before being permitted to engage in the risky activity. This structure provides for efficient deterrence for environmental catastrophes, since the responsible party is bearing in expectation the risks it is imposing. It also addresses the challenges posed by the fat-tailed distributions of catastrophic environmental risks and provides for more assured and adequate compensation of potential losses than current liability and regulatory arrangements.