This paper describes the authors' experiences as a multidisciplinary team within a national science research organization, the Commonwealth Scientific and Industrial Research Organisation (CSIRO), in building a web-based knowledge portal to support water quality management in an Australian wet tropical region, the Douglas Shire. Their initial assumption was that a knowledge-sharing tool developed through community participation would enhance efforts towards sustainable development in this predominantly sugar cane growing area of far north Queensland. After presenting the general context of the study and a description of the web portal developed, we discuss three sociotechnical challenges faced: the question of value, i.e. understanding what motivates members of a community to become involved in co-design of technology; the problem of translation, i.e. how to develop common understandings and shared visions, given the often differing epistemologies in the so-called lay–expert knowledge divide and between the different discipline areas; and the paradox of meta-design, i.e. the difficulty of asking people to commit to a project of collaborative technology development at a stage when – by the very nature of co-design – that technology is still undefined and emergent. The article ends by offering some tentative conclusions on the authors' experience.

An important aspect of online learning community development is to evaluate whether or not a particular online learning community is successful. This paper presents preliminary findings in developing and evaluating an online learning community using Preece's framework for designing and evaluating the success of online communities. The community consisted of CSIRO staff interested in what constitutes values in water use in Australia and how to account for such values. About 400 staff participated in the discussion, that was limited to two weeks. Daily observations of the content and participation were made, and participants were surveyed after the discussion had closed. A total of about 17,000 words of discussion data have been analyzed using purpose-built software. Initial findings using Preece's criteria indicate that the online learning community was successful from a sociability perspective. We found that the number of participants was higher than expected, the volume of emails was high, participants referred to each other quite regularly, the discussion stayed on topic most of the time, the participants saw value in the discussion (even the lurkers), and participants were satisfied with the social interaction within the community.

Abstract Although knowledge is predicted to surpass oil and gas reserves as the most important asset in 21st century oil and service companies, engineers remain cynical about the benefits of current knowledge management initiatives. This paper discusses problems such as the strong bias of existing knowledge management systems in favour of document searching and focuses on how overlooked areas can be addressed. Results of a collaborative project with 5 major oil companies aimed at overcoming some important limitations of current knowledge systems are discussed. A key feature of the initiative is software to integrate knowledge capture and reuse into normal work processes. The software uses well data and stored drilling experiences, including problems and solutions, from a global drilling and completions database provided by the member oil companies. Experiences with deployment of this software into oil companies will be covered as well as the results of research projects on automated learning and case based reasoning to enhance the company knowledge base for optimal design of new wells. The study of overall well quality has also been included in the project. By defining well quality metrics and then examining them collectively, we can better assess drilling performance in a given field, and ascertain if the company is using its knowledge to improve new well production and cut costs.

A case base system for a complex problem like oil field design needs to be richer than the usual case based reasoning system. The system described in this paper contains large heterogeneous cases with metalevel knowledge. A multi level indexing scheme with both preallocated and dynamically computed indexing capability has been implemented. A user interface allows dynamic creation of similarity measures based on modelling of the user’s intentions. Both user aiding and problem solution facilities are supported, a novel feature is that risk estimates are also provided. Performance testing indicates that the case base produces on average, better predictions for new well developments than company experts. Early versions of the system have been deployed into oil companies in 6 countries around the world and research is continuing on refining the system in response to industry feedback.