CHEMICAL ENGINEERING TRANSACTIONS VOL. 33, 2013 A publication of The Italian Association of Chemical Engineering Online at: www.aidic.it/cet Guest Editors: Enrico Zio, Piero Baraldi Copyright © 2013, AIDIC Servizi S.r.l., ISBN 978-88-95608-24-2; ISSN 1974-9791 Optimization Models and Prediction of Drilling Rate (ROP) for the Brazilian Pre-Salt Layer Carlos M. C. Jacinto a , Paulo J. Freitas Filho* ,b , Sílvia M. Nassar b , Mauro Roisenberg b , Diego G. Rodrigues b , Mariana D. C. Lima b a CENPES – PETROBRAS RESEARCH CENTER/PDGP/PCP - Rio de Janeiro, RJ, 21941-915, BRAZIL b Dept. of Informatics and Statistics - PPGCC-Federal University of Santa Catarina P.O.Box 476, INE – CTC – UFSC. Florianópolis, SC, 88040-900, BRAZIL freitas@inf.ufsc.br This article presents an on-going research that addresses the optimization of the cost of drilling wells in environments of high complexity and risk such as those related to the pre-salt region offshore Brazil. The minimization of these costs is directly related to the maximization of ROP (Rate of Penetration). The metric cost, i.e., the cost per meter of drilled not rely solely on the ROP. It directly involves minimization of two major components of that cost: the cost of drills and the operations cost. In short, the better combination number of bits used (generally smaller is better) versus meters drilled, increased ROP and reduced the cost per meter drilled. Finding the best combination is a difficult task. The ideal way to this nirvana is a good planning well and good control of the operation during the process. In such scenarios, it is essential to utilize software tools able to predict and improve the rate of penetration. Such tools must consider both extrinsic drilling skills training, as well as the effects of drilling parameters and drill wear. Such computer systems may, for instance, come to provide reliable alternatives to drilling plans and/or considering alternative plans presented, confirming them or rejecting them based on the information available. The process of creation and implementation of computational models capable of predicting and optimizing the rate of penetration (ROP) in the pre-salt wells is not trivial process. In this research the following techniques are investigated: a Bayesian inference approach for targeting the elicitation process and subsequent combination of models; and a Dynamic Evolving Neural-Fuzzy Inference System (DENFIS). We present the results of this investigation to date, the relevance of the proposed approach and the future prospects of their use for the delivery of viable solutions to the problem. 1. Introduction In order to reduce costs it is necessary to accurately plan offshore oil drilling operations. The time required to successfully drill a well has to be estimated fairly precisely, since most of the costs associated are tied to the rental of equipment required for the operation as reported by Gandelman (2012); however, each operation has unique properties that make this task highly difficult. Many properties vary, such as rock type, rock porosity, gas presence, pressure, drill bit wear rate among others. All these properties affect the ROP, as well as many other parameters which are controlled by a drilling operator: weight on bit(WOB), revolutions per minute(RPM), bit type, bit diameter, bit wear rate, hydraulic horsepower per square inch(HSI). Most of the work in the planning phase is restricted to adjusting bit type and diameter, RPM and WOB in order to achieve an acceptable ROP. To optimize this work many systems using artificial neural networks (ANN) were proposed in the past to predict the rate of penetration (ROP) for the project planning phase such as Bilgesu et al. (1997) and even choose automatically some parameters such as RPM and WOB in Fonseca et al. (2006). Unfortunately for the available data on the Brazilian pre-salt layer these systems did not achieve a reliable result due to the poor quality and scarcity of data. To overcome these problems we investigated two alternative approaches: a Bayesian Network (BN) inference approach for targeting the DOI: 10.3303/CET1333138 Please cite this article as: Couto Jacinto C., Freitas Filho P.J., Nassar S.M., Rosenberg M., Rodrigues D.G., Lima M.D.C., 2013, Optimization models and prediction of drilling rate (rop) for the brazilian pre-salt layer, Chemical Engineering Transactions, 33, 823-828 DOI: 10.3303/CET1333138 823