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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2025 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.