Artificial intelligence (AI) has been used for more than two decades as a development tool for solutions in several areas of the 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 R&D. 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 impacted 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 IT solutions in the E&P industry.

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

This paper describes a new method to continuously monitor and diagnose the condition of wells producing via continuous gas lift. The paper describes the application of this system in a mature onshore gas lift field in the Western United States and the results obtained therein. A central problem related to the operation of gas lift wells is the ability to identify underperforming wells and to address the underlying issues appropriately and in a timely manner. This problem is compounded by the trend toward leaner operations and relative scarcity of application specific domain knowledge. The purpose of this method is to address these issues by leveraging real time data, gas lift domain expertise and proven steady state analysis techniques in a desktop software application. This system performs four key functions: monitoring the wells' condition by collecting data; assessing the meaning of this data; recommending actions for correcting problems and responding to threats; and explaining their recommendations. The performance of the system has met initial expectations and provided additional unforeseen benefits. This paper sites specific cases which compare agent predictions to expert diagnoses and quantify the benefits of taking the recommended actions. What was found was that while the correct diagnoses of well performance issues was beneficial, the real benefit was in allowing production engineers to analyze a greater number of wells in far less time. To that end, the paper will discuss the role of this system as it relates to the overall production management workflow. The success of this project has demonstrated that intelligent agents can be used to effectively perform functions which were historically performed by a handful of experts. The paper will discuss key system design features which enable this level of functionality as well as other potential areas where the technology can be extended in the future. Introduction One of the current challenges facing the upstream E&P industry is the growing scarcity of specialist domain expertise and trained personnel needed to efficiently operate oil and gas assets. In cases where these resources are limited or unavailable, automation technology has often been touted as a solution. While the introduction of such technology has delivered numerous improvements in operational efficiency, it has also introduced new challenges. One such challenge involves the introduction of vast quantities of data that results in minimal actionable information 1,2. Operators are faced not only with the information technology task of managing this data, but also with the business challenge of leveraging the data to improve their profitability. In response to this new challenge, a growing number of projects are being initiated to help close this gap between data and information. This paper discusses one such effort. In this project, new technology has been developed to assist production engineers in the well-by-well optimization of gas lift systems. Well-by-well optimization has long been recognized as having value 3, but has often proven impractical to carry out on a routine basis due to the labor-intensive nature of the work and the limited number of individuals with the required level of expertise to perform it. This project sought to solve this problem by developing a system of intelligent agents which leverage both real time data and gas lift domain knowledge to assist engineers in these well-by-well optimization tasks.

In efforts to reduce carbon dioxide emissions from fossil fuel combustion, public funding for wind and solar alternative energy resources has enabled their evolution toward cost competitiveness with coal and natural gas options for electric power generation. To address combustion emissions from the transportation sector, the European Commission has committed to electrifying transportation, but this solution will not address transportation by air or by sea. Nor does it address continued production of petrochemical products that only require a small fraction of produced hydrocarbons. This study investigates the cost competitiveness of an alternative strategy to market crude oil priced to cover the cost of removing an amount of carbon dioxide equal to that produced through combustion of transportation fuels to be refined from it. This strategy enables continued use of fossil fuel for all transportation modes. The cost comparison considers life cycle carbon dioxide emissions and does not address other externalities related to materials or batteries employed in renewable energy options. Rather, we report known costs for carbon capture, use, and storage (CCUS) with consideration of both nature and technology based carbon capture with focus mainly on geologic storage and utilization. Because road and rail transportation can be electrified, of particular interest is the levelized cost comparison between carbon neutral fuel and electrified transportation, the latter including infrastructure implementation costs. The resulting cost comparison informs investment decisions and justifies marketing fossil fuels on a carbon neutral basis.

This paper describes a new method to continuously monitor and diagnose the condition of wells producing via continuous gas lift. The paper describes the application of this system in a mature onshore gas lift field in the Western United States and the results obtained therein. A central problem related to the operation of gas lift wells is the ability to identify underperforming wells and to address the underlying issues appropriately and in a timely manner. This problem is compounded by the trend toward leaner operations and relative scarcity of application specific domain knowledge. The purpose of this method is to address these issues by leveraging real time data, gas lift domain expertise and proven steady state analysis techniques in a desktop software application.This system performs four key functions: monitoring the wells' condition by collecting data; assessing the meaning of this data; recommending actions for correcting problems and responding to threats; and explaining their recommendations.The performance of the system has met initial expectations and provided additional unforeseen benefits. This paper sites specific cases which compare agent predictions to expert diagnoses and quantify the benefits of taking the recommended actions. What was found was that while the correct diagnoses of well performance issues was beneficial, the real benefit was in allowing production engineers to analyze a greater number of wells in far less time. To that end, the paper will discuss the role of this system as it relates to the overall production management workflow.The success of this project has demonstrated that intelligent agents can be used to effectively perform functions which were historically performed by a handful of experts. The paper will discuss key system design features which enable this level of functionality as well as other potential areas where the technology can be extended in the future.