Novel Application of Artificial Intelligence with Potential to Transform Well Planning Workflows on the Norwegian Continental Shelf

2021 ◽  
Author(s):  
Jon Gustav Vabø ◽  
Evan Thomas Delaney ◽  
Tom Savel ◽  
Norbert Dolle
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Continental Shelf ◽  
Data Science ◽  
Search Algorithm ◽  
Solution Space ◽  
Good Decision ◽  
Trade Offs ◽  
Well Trajectory ◽  
Norwegian Continental Shelf

Abstract This paper describes the transformational application of Artificial Intelligence (AI) in Equinor's annual well planning and maturation process. Well planning is a complex decision-making process, like many other processes in the industry. There are thousands of choices, conflicting business drivers, lots of uncertainty, and hidden bias. These complexities all add up, which makes good decision making very hard. In this application, AI has been used for automated and unbiased evaluation of the full solution space, with the objective to optimize the selection of drilling campaigns while taking into account complex issues such as anti-collision with existing wells, drilling hazards and trade-offs between cost, value and risk. Designing drillable well trajectories involves a sequence of decisions, which makes the process very suitable for AI algorithms. Different solver architectures, or algorithms, can be used to play this game. This is similar to how companies such as Google-owned DeepMind develop customized solvers for games such as Go and StarCraft. The chosen method is a Tree Search algorithm with an evolutionary layer on top, providing a good balance in terms of performance (i.e., speed) vs. exploration capability (i.e., it looks "wide" in the option space). The algorithm has been deployed in a full stack web-based application that allows users to follow an end-2-end workflow: from defining well trajectory design rules and constraints to running the AI engine and evaluating results to the optimization of multi-well drilling campaigns based on risk, value and cost objectives. The full-size paper describes different Norwegian Continental Shelf (NCS) use cases of this AI assisted well trajectory planning. Results to-date indicate significant CAPEX savings potential and step-change improvements in decision speed (months to days) compared to routine manual workflows. There are very limited real transformative examples of Artificial Intelligence in multi- disciplinary workflows. This paper therefore gives a unique insight how a combination of data science, domain expertise and end user feedback can lead to powerful and transformative AI solutions – implemented at scale within an existing organization.

scholarly journals The sovereignty of Artificial Intelligence over Human Ethics and Heedfulness

2021 ◽  
Vol 23 (08) ◽  
pp. 657-665
Author(s):  
Sunil Varma Mudundi ◽  
◽  
Tejaswi Pasumathy ◽  
Dr. Raul Villamarin Roudriguez ◽  
◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Computer Vision ◽  
Critical Thinking ◽  
Cognitive Abilities ◽  
Machine Learning Algorithms ◽  
Good Decision ◽  
Deep Dive ◽  
Detection Techniques ◽  
Ethics And Technology

Artificial Intelligence in present days is in extreme growth. We see AI in almost every field in work today. Artificial Intelligence is being introduced in crucial roles like recruiting, Law enforcement and in the Military. To be involved in such crucial roles, it needs lots of trusts and scientific evaluation. With the evolution of artificial intelligence, automatic machines are in a speed run in this decade. Developing a machine/robot with a set of tools/programs will technically sort of some of the challenges. But the problem arises when we completely depend on robots/machines. Artificial intelligence this fast-growing technology will be very helpful when we take help from it for just primary needs like face detection, sensor-controllers, bill counters…etc. But we face real challenges when we involve with decision making, critical thinking…etc. In mere future, automated machines are going to replace many positions of humans. Many firms from small to big are opting for Autonomous means just to make their work simpler and efficient. Using a machine gives more accurate results and outputs in simulated time. As technology is developing fast, they should be developed as per societal rules and conditions. Scientists and analysts predict that singularity in AI can be achieved by 2047. Ray Kurzweil, Director of Technology at Google predicted that AI may achieve singularity in 2047. We all saw the DRDO invention on autonomous fighting drones. They operate without any human assistance. They evaluate target type, its features and eliminate them based on edge detection techniques using computer vision. AI is also into recruiting people for companies. Some companies started using AI Recruiter to evaluate the big pool of applications and select efficient ones into the industry. This is possible through computer vision and machine learning algorithms. In recent times AI is being used as a suggestion tool for judgement too. Apart from all these advancements, some malicious scenarios may affect humankind. When AI is used in the wrong way many lives will fall in danger. Collecting all good and evil from past experiences is it possible to feed a machine to work autonomously. As many philosophers and educated people kept some set of guidelines in society is it practically possible to follow when AI achieves singularity and when we talk about the neural networking of human. They have good decision-making skills, critical thinking…etc. We will briefly discuss the ethics and AI robots / Machines that involve consciousness and cognitive abilities. In this upgrading technological world, AI is ruling a maximum number of operations. So, we will discuss how can ethics be followed. How can we balance ethics and technology in both phases.We will deep dive into some of these interesting areas in this article.

Intelligent Big Data Analytics

2019 ◽  
pp. 1-19 ◽  
Author(s):  
Zhaohao Sun
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Big Data ◽  
Data Analytics ◽  
Data Science ◽  
Big Data Analytics ◽  
Business Decision ◽  
Technology System ◽  
System Service ◽  
Managerial Perspective

Intelligent big data analytics is an emerging paradigm in the age of big data, analytics, and artificial intelligence (AI). This chapter explores intelligent big data analytics from a managerial perspective. More specifically, it first looks at the age of trinity and argues that intelligent big data analytics is at the center of the age of trinity. This chapter then proposes a managerial framework of intelligent big data analytics, which consists of intelligent big data analytics as a science, technology, system, service, and management for improving business decision making. Then it examines intelligent big data analytics for management taking into account four managerial functions: planning, organizing, leading, and controlling. The proposed approach in this chapter might facilitate the research and development of intelligent big data analytics, big data analytics, business intelligence, artificial intelligence, and data science.

Port Dada Integration

2021 ◽  
pp. 1-22
Author(s):  
José Luís Cacho ◽  
Adalberto Tokarski ◽  
Elizabete Thomas ◽  
Valentina Chkoniya
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Cloud Computing ◽  
Supply Chain ◽  
Data Integration ◽  
Data Science ◽  
Just In Time ◽  
Use Of Data ◽  
Digital Infrastructure ◽  
Digital Transition

The maritime supply chain is growing in complexity. Ports are at the crossroads of many activities, modes, and stakeholders, and are actively becoming digital hubs. Today, digital and physical connectivity go hand in hand. The port could benefit from taping the opportunities arising from digitalization and data integration since it helps to leverage external knowledge, engage stakeholders, create new decision-making anchors, lower the risk of certain investments, boost productivity and cut costs, and accelerate greening and digital transition, generating possibilities for just-in-time operations and optimizations. The chapter aims to apprehend the use of data science in the port sector. The state of the art in Brazil and Portugal are different. Even inside Brazil, there is no homogeneity of ports in the usage of digital infrastructure, cloud computing, or artificial intelligence. The existing inequalities hinder general cooperation between nations but, at the same time, reveal opportunities to approach specific nodes in the international supply chain.

Biased Data

2020 ◽  
pp. 106-122
Author(s):  
Abigail Christina Fernandez
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Data Analysis ◽  
Data Collection ◽  
Data Science ◽  
Analysis Data ◽  
Relevant Field

Data is just data if it is not put to proper comprehensive usage. Information is Knowledge and Knowledge gets upgraded to wisdom pertaining to insight in the relevant field of analysis. Data Science has become the key that unravels many pitches of interest in diversified fields of quest. It is of optimal stipulation that the solutions that the Artificial Intelligence Algorithms provide should do justice to the intent for which what it was built. But at times, inadvertently the word bias is declaimed, which has become an implicit or explicit inclusion in the Algorithms and the data collection methodologies incorporated. IT companies manoeuvring this technology need to treat this hushed underplay in prediction and decision making with top-notch priority to epitomise this imminent episode of Machine Learning in Data Analysis.

scholarly journals The Limitations of Decision-Making

Information ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 559
Author(s):  
Paul Walton
Keyword(s):  
Decision Making ◽  
Information Processing ◽  
Theoretical Basis ◽  
Quality Of Information ◽  
Collaborative Activity ◽  
Good Decision ◽  
Trade Offs ◽  
Evolution Characteristics ◽  
Required Quality

In a world faced with technological, health and environmental change and uncertainty, decision-making is challenging. In addition, decision-making itself is becoming a collaborative activity between people and artificial intelligence. This paper analyses decision-making as a form of information processing, using the ideas of information evolution. Information evolution studies the effect of selection pressures and change on information processing and the consequent limitations of that processing. The analysis identifies underlying information evolution factors that affect the quality of information used throughout decision-making and, hence, affect the quality of decisions. These factors imply a set of challenges in which the pressures that drive useful trade-offs in a static environment also hinder decision-making of the required quality in times of change. The analysis indicates the information evolution characteristics of a good decision-making approach and establishes the theoretical basis for tools to demonstrate the information evolution limitations of decision-making.

Artificial Intelligence in Utilitarian vs. Hedonic Contexts: The “Word-of-Machine” Effect

2020 ◽  
pp. 002224292095734
Author(s):  
Chiara Longoni ◽  
Luca Cian
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Natural Language Processing ◽  
Transaction Costs ◽  
Language Processing ◽  
Word Of Mouth ◽  
Rapid Development ◽  
Policy Makers ◽  
Trade Offs

Rapid development and adoption of AI, machine learning, and natural language processing applications challenge managers and policy makers to harness these transformative technologies. In this context, the authors provide evidence of a novel “word-of-machine” effect, the phenomenon by which utilitarian/hedonic attribute trade-offs determine preference for, or resistance to, AI-based recommendations compared with traditional word of mouth, or human-based recommendations. The word-of-machine effect stems from a lay belief that AI recommenders are more competent than human recommenders in the utilitarian realm and less competent than human recommenders in the hedonic realm. As a consequence, importance or salience of utilitarian attributes determine preference for AI recommenders over human ones, and importance or salience of hedonic attributes determine resistance to AI recommenders over human ones (Studies 1–4). The word-of machine effect is robust to attribute complexity, number of options considered, and transaction costs. The word-of-machine effect reverses for utilitarian goals if a recommendation needs matching to a person’s unique preferences (Study 5) and is eliminated in the case of human–AI hybrid decision making (i.e., augmented rather than artificial intelligence; Study 6). An intervention based on the consider-the-opposite protocol attenuates the word-of-machine effect (Studies 7a–b).

Motorcycle-Security using Position Searching Algorithm Based on Hybrid Fuzzy-Dijkstra

2016 ◽  
Vol 3 (2) ◽  
pp. 468 ◽  
Author(s):  
Rofiq Mubarok ◽  
Dwi Verdy Firmansyah ◽  
Dheny Haryanto ◽  
Noor Pratama Apriyanto ◽  
Umniyatul Mahmudah ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Shortest Path ◽  
Search Algorithm ◽  
Safety System ◽  
Dijkstra Algorithm ◽  
Shortest Path Algorithm ◽  
Position Information ◽  
Motorcycle Safety ◽  
Artificial Intelligence Algorithm

<strong>Motorcycle safety system has been provided by the manufacturer in the form of a handlebar lock and electrical key equipped with alarms. Keys provided by the manufacturers sometimes fail in securing a motorcycle. In addition the safety system does not provide position information of the stolen motorcycle to the owner. With these problems, the paper presents safety locked motorcycle equipped with artificial intelligence algorithms. Artificial intelligence algorithm is used to find and detect the location of the motorcycle using the shortest path algorithm. This paper applies search algorithm using Dijkstra algorithm where the algorithm is used to make the decision to get the location of the motorcycle. By using the algorithm, the location of the motorcycle can be detected but it is not able to find the shortest path needed. Therefore, this paper describes the modification of Dijkstra algorithm by adding a Fuzzy algorithm that is used for the weight values in decision making, so that it can pursue to find the shortest path.</strong>

Artificial Intelligence

2021 ◽  
pp. 30-48
Author(s):  
Mani Arora
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Decision Support ◽  
Support Systems ◽  
Bad News ◽  
Expert Advice ◽  
Complex World ◽  
Rational Decision ◽  
Sources Of Information ◽  
Good Decision

Virtually everyone has to make hundreds of decisions every day in his day-to-day life. “Good decision making” means we are informed and have relevant and appropriate information on which to base our choices among alternatives. Decision support systems are emerging as a very powerful tool for making rational decision based on various sources of information. In this chapter, the authors attempt to understand how the intelligent decisions are required for any successful endeavour. In this complex world where information explosion has good and bad news, both finding analysing the adequate information is a tedious task that always requires expert advice. In today's digitalised world, various programs are designed especially for the education sector, which helps both the learner and tutor. Technology can reduce time to make decisions for the fussy or confused learner, thereby providing assistance.

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