Streamlined Production Workflows and Integrated Data Management: A Leap Forward in Managing CSG Assets

Biniwale, Shripad; Trivedi, Rajesh; Delaney, Scott David; White, Christopher Paul; Blair, James; Zangl, Georg

doi:10.2118/133831-ms

Cited by 5 publications

(3 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the E&P industry, WFA has become a major driver of the DOF. Because processes in the E&P industry are complex and require the use of several applications and the access to multiple and diverse information sources, WFA is currently one of the areas of major interest in the industry (Szatny 2007;Sankaran et al 2009;Biniwale et al 2010;Moridis et al 2011). …”

Section: Theme No 5: Workflow Automationmentioning

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

Self Cite

View full text Add to dashboard Cite

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

show abstract

Section: Theme No 5: Workflow Automationmentioning

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

Self Cite

View full text Add to dashboard Cite

show abstract

“…It is responsible for comprehensive data management throughout the asset's life, providing the basis for the numerous workflows that exist within production operations (Trivedi & Biniwale, 2011). The Application and Model Integration tier seamlessly integrates various tools and applications within different domains from data acquisition, allocation, reporting, and analysis processes up to the asset optimization (Biniwale, et al, 2010). The Monitoring & Surveillance tier is the basis for subsequent engineering analysis, problem diagnosis, and modeling.…”

Section: Fig 3-solution Framework For Remote Field Management Of Thementioning

confidence: 99%

Remote Field Management of Greenfield Assets in Australia and East Timor: Delivering Value by Automated Production Surveillance and Optimization Platform

Biniwale

Trivedi

Tran

et al. 2013

Day 2 Tue, October 29, 2013

Self Cite

View full text Add to dashboard Cite

Eni Australia is operating the Blacktip gas field offshore Australia and Kitan oil field offshore East Timor. The main challenge was to manage these green fields remotely, with lean resources and strict deadlines covering tasks like data capture and acquisition, data validation, application integration and field analysis efficiently. The implications of the decisions made during the development of a green field are imperative to the optimization of reservoir production. Therefore, an integrated production decision support system was required for performance monitoring of asset health and optimizing production to meet the targets. This paper describes how these challenges were overcome at Eni Australia by centralizing the operations of both fields into a standardized production platform, providing a seamless data transfer from offshore assets to the onshore technical office and establishing the foundation towards a digital oilfield. The platform framework automates the process of acquisition, transfer, conditioning, aggregation and storage of production and operations data into a secure centralized data repository with an auditable amendment history. The production platform provides improved operational and reservoir visibility, which forms the basis for all subsequent analysis and underpins the engineering justification to support operational decisions.The current solution framework provides a multifunctioning system that can be expanded for future assets and fields as they come online by leveraging the investment in the existing system. All the domain experts, from production operations to reservoir engineering, will have the required information at their fingertips for collaborative decision making. Thus, a centralized production platform provides engineers with an improved field visibility for remote field management, proactive decision making and help them to strategically develop fields by optimizing production. Introduction:

show abstract

“…These factors can lead to uncertainty in production forecasting, field development planning, and LNG plant deliverability. Getting a more accurate picture of production forecasts for existing wells can optimize the number of new wells that will have to be drilled to meet the targets for future LNG trains (Biniwale, Trivedi, Zangl, White, Delaney, & Blair, 2010). This paper addresses the challenges of GSC production and provides an innovative approach and methodology to handle the uncertainties in forecasting its potential and deliverability.…”

Section: Forecasting Challenges: Uncertaintymentioning

confidence: 99%

Managing LNG Deliverability: An Innovative Approach Using Neural Network and Proxy Modeling for Australian CSG Assets

Biniwale

Trivedi

2012

Abu Dhabi International Petroleum Conference and Exhibition

Self Cite

View full text Add to dashboard Cite

Accurate prediction of individual well potential and estimation of field capacity are the key for managing Coal Seam Gas (CSG) wells and its deliverability to Liquefied Natural Gas (LNG) plant. Because there are no downhole gauges in these wells there is limited reservoir data. The associated uncertainty, the absence of fast predictive wellbore models and challenges in generating accurate well performance predictions add to the deliverability challenge.

show abstract

Streamlined Production Workflows and Integrated Data Management: A Leap Forward in Managing CSG Assets

Cited by 5 publications

References 14 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

Remote Field Management of Greenfield Assets in Australia and East Timor: Delivering Value by Automated Production Surveillance and Optimization Platform

Managing LNG Deliverability: An Innovative Approach Using Neural Network and Proxy Modeling for Australian CSG Assets

Contact Info

Product

Resources

About