I n recent years, knowledge management has referred to efforts to capture, store, and deploy knowledge using a combination of information technology and business processes. 1-3 More specifically, organizations aim to acquire knowledge from valued individuals and to analyze business activities to learn from successes and failures. Such 42 computer.org/intelligent IEEE INTELLIGENT SYSTEMS K n o w l e d g e M a n a g e m e n t T h e A u t h o r s Alun Preece has worked in the area of knowledge-based systems for 15 years, the last six of which he has spent at the University of Aberdeen, Scotland. His current research interests are in distributed knowledge-based systems and industrial knowledge management. He received his PhD from the University of Wales, Swansea, developing decision support systems for health care planning. He is a member of the IEEE Computer Society, the AAAI, and the British Computer Society Specialist Group on Knowledge-Based Systems and Applied AI. Contact him at
fax 01-972-952-9435. AbstractDrilling knowledge management applications are often criticized as lacking in structure and providing little value return to users due to lack of relevant content and difficulty of use. The drilling optimization group of a major service company identified knowledge management as one of the enabling technologies for service delivery and embarked on an initiative to create a knowledge management system that addressed these key weaknesses.Previous collaboration with a major operator led to the development of a generic best practice system for drilling performance optimization. We have now supplemented that system with a separate but linked knowledge tool that allows us to capture and share the specific technical lessons learned by our engineers in their project work. This new tool promotes sharing of new knowledge amongst our optimization engineers.It gives company-wide access to the new knowledge they create. And the new knowledge it captures is used to update the generic best practice system.Proven knowledge engineering techniques were used to create an ontology of the drilling optimization domain, which was in turn used as the basis for the new knowledge tool's structure. The knowledge tool is used to capture all projects undertaken by the group and the main technical and process lessons learned in those projects. It is therefore a repository for case-based drilling performance knowledge. Entries created by applications engineers are processed and reviewed by technical experts prior to publication to the global community. The knowledge is structured according to problem, operating practice and the applicable phase of the project. A key element is the physical description of the downhole environment in which each lesson was learned. A structured search facility allows engineers to locate lessons learned and performance achieved in drilling environments that are physically equivalent but geographically distinct to those of the well they are studying.This paper shows how knowledge engineering and knowledge management techniques have been adopted to structure drilling knowledge, increasing its quality and ability to be re-used. We show how the new knowledge tool aids rapid location of relevant knowledge and further how the use of structured knowledge facilitates integration of different knowledge tools and updating of best practices. Several case histories are presented that show how use of the system has delivered real drilling performance improvements by communicating learning across geographic boundaries.
A rule based drill bit selection expert software system and Rate of Penetration (ROP) prediction algorithm has been recently applied in the optimization process of a 4500 m vertical foothills well in Western Canada. Post well analysis shows that when the expert system recommendations were followed by the operator, increases in ROP and run length over the local pacesetter well were experienced in each hole section. ROP increases of 15% in the 311.1mm section, 52% in the 215.9mm section and 60% in the 142.9mm section were achieved, as well as bit life increases up to 33% with TCI bits. Although the operator did not follow all of the expert system recommendations through the entire well, these increases did contribute to savings in drilling time below AFE of 15 days over the entire well. Comparison with the actual drilling performance showed close agreement in trend to the predicted ROP through most lithological intervals, which helped to confirm the accuracy of the process of geological / pore pressure predictions and the ROP prediction algorithm. The expert system is a rule based bit selection system that uses a detailed description of the drilling environment, including meter based lithology, synthetic wireline logs, predicted pore pressures and anticipated operating parameters of the well or hole interval being analyzed to produce a bit selection recommendation including IADC bit type and bit features. The ROP algorithm has been developed as a drilling optimization tool and attempts to model the technical limit ROP that can be expected through a given hole interval. The ROP algorithm uses as its inputs detailed lithological descriptions of the anticipated formations, hole size, mud weight, predicted pore pressure, bit type, and anticipated operating parameters to calculate an accurate meter based ROP prediction. The ROP algorithm has been applied in several drilling environments worldwide and comparisons with actual drilling performance have been used to modify the calculations and improve predictions. The ROP algorithm improves drilling decisions, and provides performance analysis while guiding financial planning. The ROP algorithm can be applied in the planning phase of a project to develop time curves based on expected performance and to compare and contrast potential bit/BHA types based on performance predictions. Furthermore, the ROP algorithm can be used in post-well analysis to identify areas where potential drilling performance was not achieved, and help in identifying improvements for future projects. Introduction Expert System Development An expert system for drill bit selection1,2 has been in development for over ten years. This development has utilized knowledge extraction and engineering techniques to encode bit design and application knowledge from experts in the developer's various research and application departments over a number of years. This process has resulted in a highly complex set of rules which model expert understanding governing the selection of drill-bit features according to the physical properties of the drilling environment under study. Rule-bases have been developed which separately deal with Impregnated, PDC, Steel-Tooth and Tungsten Carbide Insert (TCI) bits. Each rule base represents generically the major component features of the drill bit (cutting structure, bearing type, seal type, gauge enhancements etc.) and our understanding of the effect a range of rock and environmental properties have over their selection. Such environmental factors represented include, but are by no means limited to unconfined compressive strength, interfacial severity3, bit run length, BHA type etc... Statistical analyses of the rock properties within an application are included in the derivation of other attributes (e.g. abrasivity4, hardness meterage etc.) which are accumulated over the entire bit run length. This analytical approach allows the system to make decisions on bit selection and drillability in both homogeneous and inhomogeneous drilling applications5.
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