A theoretical framework for data-driven artificial intelligence decision making for enhancing the asset integrity management system in the oil & gas sector

2021 ◽  
pp. 104648
Author(s):  
Fereshteh Sattari ◽  
Lianne Lefsrud ◽  
Daniel Kurian ◽  
Renato Macciotta
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Management System ◽  
Theoretical Framework ◽  
Data Driven ◽  
Integrity Management ◽  
Oil Gas

scholarly journals Intelligent E-Vendor Relationship Management for Enhancing Global B2C E-Commerce Ecosystems

2021 ◽  
Vol 29 (3) ◽  
pp. 1-25
Author(s):  
H. Y. Lam ◽  
Y. P. Tsang ◽  
C. H. Wu ◽  
C. Y. Chan
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Management System ◽  
Management Strategies ◽  
Relationship Management ◽  
Data Driven ◽  
Missing Link ◽  
Product Range ◽  
Artificial Intelligence Techniques ◽  
Vendor Management

Recently, global e-commerce businesses have been blooming due to the convenience they offer, their product range, and the individualized products and services they offer. To maintain an entire ecosystem, effective platform-vendor relationships should be considered, through which e-commerce platforms can provide collaborative packages to vendors. E-vendor relationship management (eVRM) should then be developed to identify, attract, retain, and develop existing and new vendors so that groups of loyal vendors can be managed. However, eVRM in e-commerce is an area that has received less attention. This paper proposes an adaptive e-vendor relationship-management system (AVRMS) to provide decision-making support for the formulation of vendor management strategies. The contribution of this study is that it addresses the missing link of platform-vendor relationship management in global e-commerce environments, while integrating data-driven approaches and artificial intelligence techniques to generate a new synergy for the facilitation of eVRM.

Technology Focus: Field Development (September 2021)

2021 ◽  
Vol 73 (09) ◽  
pp. 43-43
Author(s):  
Reza Garmeh
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Data Analytics ◽  
Learning Algorithms ◽  
Development Strategies ◽  
Digital Transformation ◽  
Data Driven ◽  
Development Work ◽  
Field Development

The digital transformation that began several years ago continues to grow and evolve. With new advancements in data analytics and machine-learning algorithms, field developers today see more benefits to upgrading their traditional development work flows to automated artificial-intelligence work flows. The transformation has helped develop more-efficient and truly integrated development approaches. Many development scenarios can be automatically generated, examined, and updated very quickly. These approaches become more valuable when coupled with physics-based integrated asset models that are kept close to actual field performance to reduce uncertainty for reactive decision making. In unconventional basins with enormous completion and production databases, data-driven decisions powered by machine-learning techniques are increasing in popularity to solve field development challenges and optimize cube development. Finding a trend within massive amounts of data requires an augmented artificial intelligence where machine learning and human expertise are coupled. With slowed activity and uncertainty in the oil and gas industry from the COVID-19 pandemic and growing pressure for cleaner energy and environmental regulations, operators had to shift economic modeling for environmental considerations, predicting operational hazards and planning mitigations. This has enlightened the value of field development optimization, shifting from traditional workflow iterations on data assimilation and sequential decision making to deep reinforcement learning algorithms to find the best well placement and well type for the next producer or injector. Operators are trying to adapt with the new environment and enhance their capabilities to efficiently plan, execute, and operate field development plans. Collaboration between different disciplines and integrated analyses are key to the success of optimized development strategies. These selected papers and the suggested additional reading provide a good view of what is evolving with field development work flows using data analytics and machine learning in the era of digital transformation. Recommended additional reading at OnePetro: www.onepetro.org. SPE 203073 - Data-Driven and AI Methods To Enhance Collaborative Well Planning and Drilling-Risk Prediction by Richard Mohan, ADNOC, et al. SPE 200895 - Novel Approach To Enhance the Field Development Planning Process and Reservoir Management To Maximize the Recovery Factor of Gas Condensate Reservoirs Through Integrated Asset Modeling by Oswaldo Espinola Gonzalez, Schlumberger, et al. SPE 202373 - Efficient Optimization and Uncertainty Analysis of Field Development Strategies by Incorporating Economic Decisions in Reservoir Simulation Models by James Browning, Texas Tech University, et al.

scholarly journals From Poisons to Antidotes: Algorithms as Democracy Boosters

2022 ◽  
pp. 1-25
Author(s):  
Paolo Cavaliere ◽  
Graziella Romeo
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Political Institutions ◽  
Theoretical Framework ◽  
Traditional Authority ◽  
Political Processes ◽  
Viable Solution ◽  
Decision Making Processes ◽  
Automated Decision Making ◽  
Public Decision

Abstract Under what conditions can artificial intelligence contribute to political processes without undermining their legitimacy? Thanks to the ever-growing availability of data and the increasing power of decision-making algorithms, the future of political institutions is unlikely to be anything similar to what we have known throughout the last century, possibly with parliaments deprived of their traditional authority and public decision-making processes largely unaccountable. This paper discusses and challenges these concerns by suggesting a theoretical framework under which algorithmic decision-making is compatible with democracy and, most relevantly, can offer a viable solution to counter the rise of populist rhetoric in the governance arena. Such a framework is based on three pillars: (1) understanding the civic issues that are subjected to automated decision-making; (2) controlling the issues that are assigned to AI; and (3) evaluating and challenging the outputs of algorithmic decision-making.

Roadmap for Digitalized Asset Integrity Management System

2021 ◽  
Author(s):  
Asad Ali ◽  
Kevin Maley ◽  
Seonyeob Li ◽  
Ahmed Al Owaid ◽  
Abdulla Al Shehhi
Keyword(s):  
Artificial Intelligence ◽  
Best Practices ◽  
Management System ◽  
Cost Effective ◽  
Notification System ◽  
Integrity Management ◽  
Selection Decision ◽  
Time Required ◽  
The Cost ◽  
A Company

Abstract Asset integrity management system (AIMS) consisting of risk based inspection (RBI) and inspection management system (IMS) coupled with digitized equipment records and use of inspection tablets/mobiles will make paperless system for fast and timely decisions & actions. This paper provides a roadmap for implementation of an efficient and cost effective asset integrity management system that will increase the plant reliability & availability, decrease the time and efforts required for inspection, thus ultimately reducing the associated costs of operations. In this paper, the focus is towards digitalized AIMS that should make a company move to digital transformation and enabling it to adapt to industry 4.0 technologies such as artificial intelligence, augmented reality, data analytics, machine learning etc. First step is to perform a gap assessment of existing system to compare what is currently available within organization and what is required for going fully digital for AIM. Next step is to identify software features that are required for AIM digitalization and establish them as point based rating system which are used for rating best suitable software available in the market. Unique features for RBI module, inspection management module and field interface (tablet) module are identified with appropriate weightage to influence the software selection decision. Finally, an estimation of required resources, manpower timeline is provided that will guide in all phases of the implementation. Return on investment on such projects is manifolds. The digitalized AIM will greatly reduce the cost of day to to asset integrity management operations as it will no longer be needed to use multiple paper based reports and separate systems for RBI and IMS functions. Use of field tablet/mobile with possibility of artificial intelligence tools, will significantly reduce the time required for inspectors to do the on site inspection/testing & reporting. Interfacing of digitalized system with ERP/CMMS will automate the work order/notification system. Thus it will reduce an overall effort both in terms of time & money. The roadmap for digitalization of AIMS system will help any organization to make its AIMS digital and achieve the benefits of such system. The methodology provided is unique and can be adopted as best practices by the industry for digitally transforming the AIMS.

scholarly journals Artificial Intelligence and Its Application in Optimization under Uncertainty

2021 ◽  
Author(s):  
Saeid Sadeghi ◽  
Maghsoud Amiri ◽  
Farzaneh Mansoori Mooseloo
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Optimization Under Uncertainty ◽  
Data Driven ◽  
Future Research ◽  
Intelligent Decision Support Systems ◽  
Data Driven Approach ◽  
Managerial Implications ◽  
Optimization Methodology ◽  
Conventional Optimization

Nowadays, the increase in data acquisition and availability and complexity around optimization make it imperative to jointly use artificial intelligence (AI) and optimization for devising data-driven and intelligent decision support systems (DSS). A DSS can be successful if large amounts of interactive data proceed fast and robustly and extract useful information and knowledge to help decision-making. In this context, the data-driven approach has gained prominence due to its provision of insights for decision-making and easy implementation. The data-driven approach can discover various database patterns without relying on prior knowledge while also handling flexible objectives and multiple scenarios. This chapter reviews recent advances in data-driven optimization, highlighting the promise of data-driven optimization that integrates mathematical programming and machine learning (ML) for decision-making under uncertainty and identifies potential research opportunities. This chapter provides guidelines and implications for researchers, managers, and practitioners in operations research who want to advance their decision-making capabilities under uncertainty concerning data-driven optimization. Then, a comprehensive review and classification of the relevant publications on the data-driven stochastic program, data-driven robust optimization, and data-driven chance-constrained are presented. This chapter also identifies fertile avenues for future research that focus on deep-data-driven optimization, deep data-driven models, as well as online learning-based data-driven optimization. Perspectives on reinforcement learning (RL)-based data-driven optimization and deep RL for solving NP-hard problems are discussed. We investigate the application of data-driven optimization in different case studies to demonstrate improvements in operational performance over conventional optimization methodology. Finally, some managerial implications and some future directions are provided.

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