Advanced collaborative environments (ACEs) were planned as an integral part of the operator's new Aberdeen office. The move in early 2008 coincided with a major business reorganization that prompted a critical reassessment of their scope and functionality. The selected approach is well suited to operations in a mature area. During the pilot project, three ACE implementation types were tested. One of these provided dedicated data feeds and visualization tools within the normal work area, operating only during normal working hours. The other two were (1) an asset-based, on-site, round-the-clock type and (2) an outsourced model, each representing a business-model extreme. The selected configuration was closest to the first of these models, with careful provision for future growth.The results of the pilot studies supported the business case but suggested that the realization of many of the benefits would hinge on attitudes, behaviors, and working practices. It was concluded that steady growth, based on demand and keeping pace with the changing habits, would give the best return. Detailed plans were created for each of the people, process, technology, environment, and organization components of the operator's ACE model. The new building design and layout has greatly enhanced the overall working environment. Other activities included the upgrading of fiber links, the introduction of an operability laboratory to test the compatibility of data feeds and systems, extensive human-factor studies, contract reassessment, and the introduction of a dedicated ACE support team. By June 2008, seven rigs were being supported simultaneously, including monitoring the progress of a wired-pipe trial in Trinidad. All this has been achieved during a period of outstanding drilling-and-completions (D&Cs) performance. This measured approach has delivered results, helping to win hearts and minds while avoiding overstretching resources. The paper includes details of the pilot project, implementation, and the progress and challenges to date.
Summary The paper discusses how data quality influences workflows and decision making in drilling and completions and examines the use of semiautomated processes for quality assurance. With poor data, additional steps are required and workflows must be repeated. In even relatively simple situations, controlled tests suggest that small changes or omissions may have a significant influence on the work efficiency or outcome. In earlier work, the quality of any data stream has been described in terms of identity, presence, measurement frequency, accuracy, continuity, units, and associated metadata. For some of these, a degree of self-checking is possible, applying simple algorithms to the data stream to detect presence and bounds, with alarms to alert the operator if these are transgressed. In other cases, such as the change in drag and torque with depth, the stream must be checked against a trend, called a pseudolog, determined from the physics. These calculations are performed by "smart agents" directly in real time on the wellsite information transfer standard markup language (WITSML) data feed from the rig. The paper describes the early work in developing smart agents to address data quality and structure of the associated tool kit that can be used to construct more- complex agents from a wider selection of data sources, including system-generated ones. The computational resources required are also discussed. The increase in digital data and skills shortage makes manual assurance of all the data streams neither practical nor cost effective. Because current applications are not tolerant of errors and omissions, a step change in data quality is needed if more-automated workflows are to be achieved. Greater assurance of the data at source and an improved understanding of the workflows will help.
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