Proposal

Generally, today's oil and gas projects are complex, high-risk, multidisciplinary ventures that require careful planning and precise implementation. A large number of interrelated factors and unforeseen (operational, technical and/or financial) events determine a drilling, completion, or work-over project's economic feasibility and ultimate success. Fundamental to the success of all project management is extensive economic modeling and risk analysis, since economic drivers and operational methods are developed from these processes.

This paper will discuss the development of a new analytical technique that will systematically and intuitively overcome the hurdles mentioned previously while not compromising result sophistication. A variety of underbalanced drilling examples will illustrate its efficiency. This method will allow the end user, whether novice or expert, to quickly and effectively assess economic feasibility and risks.

Introduction

The scope and task of integrating a comprehensive set of various inputs must follow the model of a high quality Management System, one that is designed to meet Operations, Quality, and Health Safety & Environmental Management Systems needs. The basic premise of the management system model is to embed business practices in the way work is done. The project manager must have a process that links these fundamental requirements with their analytical consideration for financial risk1.

Today, project managers are faced with a large amount of information from which to evaluate future prospects. The days of drilling a simple well have come and gone. Operators face tougher challenges and more uncertainties that can create more financial risk in the investments associated with new exploration. However, through the correct implementation of new technologies (i.e., Underbalanced Applications, Expandables, Rotary Steerables, etc.) greater opportunities exist to improve the return on investment. With these new technologies come greater risks in deciding which technology is best and which technology has the greatest potential for success.

Project managers have relied on risk analysis to gain insight into potential hazards and lost benefits2,3. To date, the typical approach for handling such complexity has been 1) to abstract details by dividing the problem into a number of simplified components, and/or 2), to analyze these (usually univariate) models in relative isolation. Unfortunately, such an approach usually fails to effectively model real-life interactions. Industry analytical methodology traditionally follows a standard path of evaluating cash flow (i.e., tax calculations, inflation affects, multi-method depreciation and loan repayments), generating economic indicators (i.e., the process of transforming cash flow into single-quantity variables that capture economic worth, and include net present values, profit investment ratios, internal rate of return and pay-back periods), analyzing risk/uncertainty (i.e., applying discount rates, expected values, decision trees and probabilistic techniques such as Monte Carlo simulations), and finally, incorporating the affects of regional fiscal regimes.

Previously, this has been difficult and necessitated individual analyses. Furthermore, the software tools were found to be difficult, inefficient, and cumbersome to the point that experts in the field of statistics and probabilistic modeling were required to interpret the complex and multivariate relationships developed during stochastic modeling. Often, the software tools were so problematic and inconsistent that the end user spent more time setting up and learning how to operate the software than using it, and generally, had to hire an expert to operate the software. It is generally accepted that these tools should help the project manager maximize the potential reward for the risked investment by using consistent mathematical solutions in an expeditious manner. An informed decision maker will more than likely make the right decision. Arguably, the benefit greatly outweighs the effort generally required to perform these analysis. However, managers still avoid the use of these tools.

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