Cesar Bravo

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

Artificial intelligence (AI) has been used for more than two decades as a development tool for solutions in several areas of the EP (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. Introduction While there is hardly a rigorous definition of the term artificial intelligence (AI) 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. Since human intelligence is multifaceted, so is AI, comprising goals that range from knowledge representation and reasoning, to learning, to 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 applications of neural networks, fuzzy logic, genetic algorithms, expert systems, and other artificial techniques in the resolution of problems in diverse areas, such as reservoir simulation, production optimization, process control, and fault detection and diagnosis, among many others. AI is an area of great interest in the E&P industry, mainly in applications related to production control and optimization, proxy model simulation, and virtual sensing. The most popular techniques are artificial neural networks, fuzzy logic, and genetic algorithms, with interesting developments in hybrid and nontraditional techniques. There has been recent increase in such AI-based commercial applications for production management. While the full impact of such applications is still being realized, there are already solutions in the market with a positive impact in the E&P industry.

Debilitating cutaneous poxvirus infection in a Hodgson's grandala (Grandala coelicolor).

A case of cutaneous avian pox infection in a Hodgsons grandala (Grandala coelicolor) is described. The bird was emaciated and had nodules on the eyelids, bill, neck, legs, and toes. Eosinophilic intracytoplasmic inclusion bodies were visualized by light microscopy in epithelial cells of the cutaneous nodules. Electron microscopy revealed numerous pox virions in the inclusion bodies. This is the first report of cutaneous poxvirus infection in a Hodgsons grandala.

Managing The Production Lifecycle: A Framework For Scalable Digital Oilfield Implementations

This paper describes the expanded value of the digital oilfield (DOF) that is made possible by a flexible IT architecture. The architecture is designed for smarter E&P solutions and can cater to an ever-growing appetite for quick, efficient, and proven solutions to diverse productions and operations industry challenges throughout the production lifecycle. Investments made in early DOF projects have shown an individualistic flavor with unique architecture, approach, tools, standards, and practices without due consideration for scalability and extensibility. Such customized solutions slow down the adoption of good features from one project to other. Typical examples include customized workflows, a lack of open and service-oriented architecture, and a nonstandards-based database or application interface. Partly, this could arise because of lack of experienced and skilled resources, gaps in technology, or the absence of any direct or related analogues to the specific problems of the project. The following IT architectural components are necessary to enable the flexibility that modern DOF projects need to drive returns:

An intelligent operating system for oilfield processing

This paper discusses the implementation of an intelligent operating system for oilfield processing. The infrastructure of this intelligent software system, from the database backend to the graphical user interface (GUI) frontend, is examined step by step. Oilfield processing, in general, involves the measurement and analysis of reservoirs, controlling oil or gas production flow through the wellbore, risers and manifolds operation, and wellhead operations, then proceeds to the flare point or transportation out from the production facilities. This paper describes how the system can be applied to upper stream oil and gas production management and forecasting. A Halliburton R5000 desktop for production system (DSP) optimization software system was used for the research discussed in this paper. Several sample workflows were implemented for the proof of concepts. The study results indicate that the selected system met all requirements and proved that an integrated and completed system can reduce human error as well as operating costs. The system is also flexible enough to apply to other industrial applications, such as assembly and manufacturing processes.