Software Agents—An Emergent Software Technology That Enables Us To Build More Dynamic, Adaptable, and Robust Systems

Landre, Einar; Waersland, Geir Owe; Olmheim, Jorn; Rønneberg, Harald

doi:10.2118/103354-ms

Cited by 5 publications

(1 citation statement)

References 2 publications

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“…Common applications are distributed decision-making systems and distributed control systems. In the E&P industry, there are few references about applications of multiagent systems; three examples are the agent-based information-management system for oil dispatch and sales workflows (Ølmheim et al 2008), the application of multiagent systems in subsea-facility (Landre et al 2006) modeling, and the use of agents in reservoir-simulation history matching (Zangl et al 2011). Nevertheless, the application of intelligent agents in the industry is being actively explored.…”

Section: Aipa State-of-the-art Reviewmentioning

confidence: 99%

State of the Art of Artificial Intelligence and Predictive Analytics in the E&P Industry: A Technology Survey

Bravo

Saputelli²,

Rivas³

et al. 2013

SPE Journal

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Artificial intelligence (AI) has been used for more than 2 decades as a development tool for solutions in several areas of the exploration and production (E&P) industry: virtual sensing, production control and optimization, forecasting, and simulation, among many others. Nevertheless, AI applications have not been consolidated as standard solutions in the industry, and most common applications of AI still are case studies and pilot projects.In this work, an analysis of a survey conducted on a broad group of professionals related to several E&P operations and service companies is presented. This survey captures the level of AI knowledge in the industry, the most common application areas, and the expectations of the users from AI-based solutions. It also includes a literature review of technical papers related to AI applications and trends in the market and in research and development.The survey helped to verify that (a) data mining and neural networks are by far the most popular AI technologies used in the industry; (b) approximately 50% of respondents declared they were somehow engaged in applying workflow automation, automatic process control, rule-based case reasoning, data mining, proxy models, and virtual environments; (c) production is the area most affected by the applications of AI technologies; (d) the perceived level of available literature and public knowledge of AI technologies is generally low; and (e) although availability of information is generally low, it is not perceived equally among different roles.This work aims to be a guide for personnel responsible for production and asset management on how AI-based applications can add more value and improve their decision making. The results of the survey offer a guideline on which tools to consider for each particular oil and gas challenge. It also illustrates how AI techniques will play an important role in future developments of information-technology (IT) solutions in the E&P industry. IntroductionAlthough there is hardly a rigorous definition of the term "artificial intelligence" that is unequivocally accepted, the tools of AI and its intended uses have been well studied for decades and many applications have appeared. Loosely speaking, AI is the capability of machines (usually in the form of computer hardware and software) to mimic or exceed human intelligence in everyday engineering and scientific tasks associated with perceiving, reasoning, and acting. Because human intelligence is multifaceted, so is AI, comprising goals that range from knowledge representation and reasoning to learning, visual perception, and language understanding (Winston 1992). AI techniques have been present in the E&P industry for many years. A quick literature search reveals application of AI in SPE scientific and engineering papers as early as in the 1970s. There are numerous references about the

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Section: Aipa State-of-the-art Reviewmentioning

confidence: 99%

State of the Art of Artificial Intelligence and Predictive Analytics in the E&P Industry: A Technology Survey

Bravo

Saputelli²,

Rivas³

et al. 2013

SPE Journal

View full text Add to dashboard Cite

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The Goal Oriented Teams (GORITE) Framework

Rönnquist¹

Lecture Notes in Computer Science

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Use of Agent Structures for Event Detection: Identification of Wells Watering Out on Troll

et al. 2008

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The industry-wide focus on Intelligent Energy in recent years has lead to more and better data being available, both onshore and offshore. Regrettably, processing tools have not seen a proportional improvement, and therefore engineers experience a data overload. Fairly simple alarms along with manual routine checks using independent, "offline" tools are dominating the daily tasks in production optimisation, and more intelligence and flexibility is thus needed. In this paper we suggest an agent structure allowing online, real-time event detection. In this work producing oil wells watering out on Trollwere used as an example. The detection algorithm was designed to search for significant drops in the wellhead pressure. By detection of such trends at an early stage, a successful correction and restabilisation is more likely. Introduction In recent years development in computer technology has lead to a considerable increase in availability, quality and quantity of data. More and more data is made available both onshore and offshore, and the acquisition frequency and storage possibilities have been improved considerably. This development has offered a new range of possibilities for improving operations, which is widely known as IO (Integrated Operations), e-operation or i-field. However, the handling capacity in the organisations has not been able to follow the vast improvement in data availability. A variety of analysis and visualisation programs exist, but data retrieval, preparation and configuration may be time-consuming and complex. This leads to unreleased potential, especially related to real-time and online applications. The amounts of data available and the possibilities this offers largely "overpowers" the capacities of the current tools and methods used, thus leaving what can be called a capacity gap between data availability and data handling. To keep up with the amounts of data, data-driven workflowscan be a useful tool, with a central element being automatic event-detection. By data-driven workflows we understand workflows where tasks, analyses and adjustments to be performed are triggered by events detected in the real-time data stream, as opposed to being part of predefined, scheduled routines. In certain contexts and areas this is partly done today, but many alarms and detections used today are not "smart" enough, resulting in both false detections and missed events. Although the equipment in the industry is becoming more automated, with less manual operations related to daily operation, there will still be tasks that may be too risky, too complex, or simply too expensive to automate, either due to the nature of the task or the state/properties of available equipment. The solution(s) presented here are mainly intended as a tool to help filling this gap, by assisting production/process monitoring and by highlighting irregularities. Nevertheless, a natural expansion of the system would involve more complex algorithms simulating more "intelligence", and could also include automation of tasks based on detections.

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Software Agents—An Emergent Software Technology That Enables Us To Build More Dynamic, Adaptable, and Robust Systems

Cited by 5 publications

References 2 publications

State of the Art of Artificial Intelligence and Predictive Analytics in the E&P Industry: A Technology Survey

State of the Art of Artificial Intelligence and Predictive Analytics in the E&P Industry: A Technology Survey

The Goal Oriented Teams (GORITE) Framework

Use of Agent Structures for Event Detection: Identification of Wells Watering Out on Troll

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