Summary
A new approach to drilling cost estimation is presented that establishes a
general framework for cost estimation using a formalized systems perspective. A
generalized functional approach is developed that combines regression-based
techniques, as used in the Joint Association Survey (JAS), with the
multidimensional attributes of drilling, as incorporated in the Mechanical Risk
Index (MRI), Directional Difficulty Index (DDI), and Difficulty Index (DFI)
models. A generalized methodology for framing the drilling cost estimation
problem is presented and illustrated using a stylized example.
Introduction
The objective of drilling a hydrocarbon well is to make hole as quickly as
possible subject to the technological, operational, quality, and safety
constraints associated with the process. These objectives are frequently
conflicting and depend on factors that interrelate, that vary with respect to
time, location, and personnel, and that are subject to significant intrinsic
and market uncertainty. Drilling rates are often constrained by factors that
the driller does not control and in ways that cannot be documented. In many
situations, the causes of dysfunction are complex, occur simultaneously, and
lack effective solutions.
The evaluation of drilling performance commands a high degree of visibility
in oil and gas companies, and over the past few decades, various methods have
been proposed to evaluate drilling cost and complexity. To understand the
drilling process, it is necessary to isolate the factors affecting drilling and
to quantify their interaction (Brett and Millheim 1986; Schreuder and Sharpe
1999; Mannon 2001; Jones and Poupet 2000). There is no way to identify all the
characteristics of drilling that might be important, but many characteristics
of the process can be observed, and in practice it is necessary to consider
only a set of factors that adequately represent drilling conditions. Well
characteristics are measured directly, while operator experience and wellbore
quality frequently need to be represented by proxy variables. Many unobservable
factors also impact drilling performance, such as well planning and
preparation, project management skills, communication skills, and training
(Rowe et al. 2000; Holland et al. 2003; Cayeux et al. 2001; Kaminski et al.
2002; Ursem et al. 2003; Harris 1999).
The premise of a systems approach to cost estimation is that examination of
process characteristics from a large collection of wells will allow the factors
that influence drilling cost to be discovered. The outline of the paper is as
follows. Drilling performance benchmarks and estimation techniques that are
commonly used in industry are first summarized. The main factors that
characterize the drilling process are then formally defined. The generalized
functional approach is specified and calibrated, and an example is used to
illustrate the procedure. In the Appendix, a formulation of the JAS and the MRI
model is presented.
Drilling Performance Benchmarks
Two methods are commonly used to benchmark drilling performance. The first
method is based on experimental design and controlled field studies. Typically,
one or more parameters of the drilling process are varied, and the impact of
the variable(s) on output measures such as the rate of penetration (ROP) or
cost per foot (CPF) is examined (Bourgoyne et al. 2003). Controlled field
studies are often the best way to understand the relationships between drilling
factors under a set of conditions that are tightly controlled. The analytic
results that are derived are often based on engineering and scientific
fundamentals specific to the wellbore conditions, experimental design,
equipment, and contractor, and therefore the ability to generalize the results
to other wells and locations may be limited.
The second method of studying factor effects is based on an aggregate
assessment of well data. In this method, data that characterize a set of wells
are collected and relationships are established between the variables using
empirical modeling techniques (Iyoho et al. 2005; Bond et al. 1996; Saifaldeen
et al. 1997; Noerager et al. 1987). The aggregate approach to analysis uses a
set of drilling data and seeks to discover relationships between various
drilling factors and the cost and complexity of the wellbore. In this approach,
wells drilled under a wide variety of conditions provide the raw data to
explore the manner in which (measurable) factors contribute to drilling cost.
This allows researchers to compare and contrast a variety of factors that
impact drilling and to develop models that describe the average behavior of the
performance metrics.
In the Gulf of Mexico, the JAS and the MRI are popular methods used to
evaluate drilling cost and complexity. The JAS estimates drilling cost using
survey data and quadratic regression models constructed from four descriptor
variables (API 2002). The MRI is a risk index that uses six primary variables
and 14 qualitative indicators to characterize wellbore complexity (Dodson and
Dodson 2003). In the Appendix, the formulation of these models is provided. A
DDI and a DFI have been introduced to characterize the complexity of drilling
directional and extended-reach wells (Oag and Williams 2000; Shirley 2003), and
recently Mechanical Specific Energy (MSE) surveillance has been used to improve
bit efficiency and performance and obtain a more objective assessment of
drilling efficiency (Curry et al. 2005; Dupriest and Koederitz 2005; Dupriest
2006).
© 2007. Society of Petroleum Engineers
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History
- Original manuscript received:
2 June 2005
- Revised manuscript received:
27 January 2007
- Manuscript approved:
27 February 2007
- Version of record:
20 June 2007
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