An Empirical Correlation to Predict Diamond Bit Drilling Rates

Abstract

Diamond bit drilling tests have been performed at the Drilling Research Laboratory (DRL) in Salt Lake City, Utah. These tests were sponsored and supervised by the Shell Development Company. The tests simulated down hole drilling pressures on a typical diamond drill bit and oilfield formation. Variations of typical field operating variables were tested for penetration rate response. An empirical equation has penetration rate response. An empirical equation has been derived from the test results to predict diamond bit drilling rates. Introduction Oil well profitability is determined by drilling costs. Penetration rate is a substantial factor in determining well drilling costs. Diamond drill bits are used to economic advantage in many drilling situations. Design, performance, and application of diamond bits has been greatly improved the past 10 to 15 years. However, only a small percentage of oil well footage is drilled by diamond bits. If the penetration rate could be easily and accurately predicted, penetration rate could be easily and accurately predicted, more economic applications for diamond bits may be found. Existing methods for predicting diamond bit performance range from simple charts to sophisticated performance range from simple charts to sophisticated computer analysis. Simpler methods, as cited by Striegler, do not account for all the pertinent drilling variables and have been found to error up to 200%. Sophisticated methods, developed by Appl and Walker, consider all the drilling and bit design variables. Accurate prediction of penetration rate can be obtained using sophisticated computer programs, if appropriate bit design and field data are available. A new empirical correlation to predict diamond bit drill rates has been derived from full sophisticated results at simulated down hole conditions. The derived empirical equation involves most of the pertinent drilling and bit design variables. The variables pertinent drilling and bit design variables. The variables have been formulated to extend the correlation to each unique set of drilling conditions and various bit designs and sizes. The empirical equation can be easily applied to most field operations, with reasonable accuracy. TEST FACILITY Drilling tests were conducted in the DRL well bore simulator. DRL test facility details and capabilities have been described previously by Tibbitts. Essentially, the DRL well bore simulator accomodates a sizable rock specimen under simulated down hole pressures for full scale drilling tests. Authentically pressures for full scale drilling tests. Authentically reproduced overburden, confining, and bore hole pressures or stresses are the key features of the pressures or stresses are the key features of the facility. Although the overburden and confining pressures have only a weak relationship to drillability, pressures have only a weak relationship to drillability, the capability to vary the bore hole pressure is of upmost importance to penetration rate response. The DRL facility is capable of utilizing any mud system, at full scale flow rates. A computer retrieval system allows for an accurate and instantaneous record of all the pertinent test variables. TEST PROCEDURE Three rock specimens of Mancos shale were used in the drilling tests. The rock properties and description are presented in Table 1. The same diamond drill bit was used on each rock sample. Diamond bit design and specifications are detailed in Table 2. Each rock specimen was drilled using a different field typical mud system. Mud system description and properties are given in Table 3. properties are given in Table 3.Two different values of weight on bit, rotary speed, flow rate, and bore hole pressure were tested on each rock specimen and mud system. These values of the test variables were combined to provide sixteen unique test conditions or data sets.

A review on half a century of experience in rate of penetration management: Application of analytical, semi-analytical and empirical models

“…Several ROP correlations have been proposed in the literature throughout the history of ROP management. Firstly, Duklet and Bates (1980)…”

“…Besides of the studies which have used the analytical and semi-analytical ROP models for the purpose of ROP management, lots of studies used their own ROP models for ROP management to test their applications (Galle and Woods, 1963;Eckel, 1967;Bourgoyne and Young, 1974;Peterson, 1976;Duklet and Bates, 1980;Warren, 1981Warren, , 1987Chia and Smith, 1986;Walker et al, 1986;Winters et al, 1987;Wojtanowicz and Kuru, 1987;Maidla and Ohara, 1991;Hareland and Hoberock, 1993;Hareland and Rampersad, 1994;Caicedo et al, 2005;Hareland and Nygaard, 2007;Osgouei andÖzbayoǧlu, 2007;Shirkavand et al, 2009;Hareland et al, 2010;Motahhari et al, 2010;Seifabad and Ehteshami, 2013;Kerkar et al, 2014;Bezminabadi et al, 2017;Wiktorski et al, 2017;Al-AbdulJabbar et al, 2019;Elkatatny, 2019;Al-AbdulJabbar et al, 2020;Etesami et al, 2021). Table 6 shows a brief review on research studies which have used different ROP models and empirical correlations as the main approach for ROP management.…”

Effect of Rock Properties on ROP Modeling Using Statistical and Intelligent Methods: A Case Study of an Oil Well in Southwest of Iran

“…Due to lack of access to database in these studies, often less attention have been paid to the rock properties. Studies indicated that among the operational parameters, weight on bit, rotational speed, bit wear, wellbore depth, mud weight and flow rate were crucial parameters (Bielstein, 1950;Cunningham and Eenink, 1959;Duklet & Bates, 1980;Paiaman et al, 2009). In this line, it was found that ROP increased with increasing weight on bit, rotational speed and mud flow rate.…”