Abstract

This paper presents a practical method for bit selection through modeling ROP and Bit-lift from the conventional and routinely available bit record and logging data. This method employs simple statistical analysis to quantify the effects of individual parameters on bit performance and then identify the most significant parameters from the uncountable variables associated with drilling processes. The parameters incorporated in the model can be demonstrated to be statistically significant, physically interpretable and dynamically controllable. Based on the model of ROP and Bit-life, the value of cost-per-footage can be normalized and therefore bit selection can be conducted on a scientific basis. In addition, the method for developing model to optimize drilling parameters for individual bit runs and for dynamic drilling conditions is illustrated. Also the application of the model in ROP prediction, in diagnosing drilling problem is discussed briefly. This method unlocks a whole plethora of methods to model bit performance and has the potential to be automated for routine application.

Introduction

When drilling a well, bit related costs account for a colossal value. It follows that even a small percentage of savings in it can yield significant cost-reduction. Quantitatively the formula below has long been recognized as a tool to measure bit performance:

Equation (1) (Available in full paper)

where CPUD stands for Cost-Per-Unit-Depth, C for the cost of bit, Cr for the operating cost of the rig per unit time; τb, for the sum of the actual drilling time; τt for tripping and connection time; ΔD for the given interval of depth. Because ΔD is the product of ROP (Rate Of Penetration)

and τb, Eq.1 can be re-written as:

Equation (1.1) (Available in full paper>

According to Eq. 1.1 the following observations can be made:

  • To any bit run, CPUD is effectively determined by ROP and τb because bit cost (Cb) and rig rate (Cr) are constant and trip time (τt) vary little; to any well, the sum of trip times depend on the number of bit runs and the number of bit runs are determined by both τb, and ROP. In short, improving bit performance is the direct and efficient approach to drilling cost-reduction.

  • To individual bit run, changes in ROP have greater impacts on CPUD. It can be seen from Eq.l.l, ROP appears in all the 3 terms while τb, appears in 2 terms, Cr in 2 terms and Cb in 1 term. Furthermore partial derivative of Eq.l.l can explain the impacts of individual components on CPUD analytically. However, it should be noted that both ROP and τb may be themselves correlated and, to a well, the sum of trip time (τt) depend on the number of bit runs and the number of bit runs are determined by τb and ROP as well.

Apparently Eq.l.l has been straightforward, but to apply it practically is difficult. For instance, there are two bits, bit No.16 and bit No.74, with the same name and same size while the value of CPUD for No.16 is 61unit and for No.74 is 189unti (Table 1a).

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