Abstract

The concept of decision and risk analysis for economic and engineering applications has become widely accepted in the petroleum industry. However, the actual use of decision and risk analysis tools remains limited, due mostly to complexity of these tools. Moreover, the professional use of these tools requires advanced knowledge of decision and risk analysis theory. The solution is to integrate decision and risk analysis tools into petroleum economic and engineering applications using developer's kits or toolsets. Advanced visualization techniques should be applied to provide the visual aids to input data with uncertainties and simplify the analysis of the results.

Introduction

Decision and risk analysis can be integrated with a wide variety of engineering and economic applications in the oil and gas industry including economic evaluation of oil and gas reserves, reservoir modeling and simulation, seismic interpretation, petrophysical analysis, and others. Recent studies show that corporate financial performance can be improved by integration of risk and decision analysis with a company's workflows1,2. The probabilistic methods include Monte Carlo simulations, sensitivity analysis, decision analysis using decision trees, forecasting, real options, and others3,4,5. (Optimization and portfolio management are outside of the scope of this paper). However, only few software applications in oil and gas industry integrate risk and decision analysis functionalities. Other applications provide only deterministic analysis.

If the application is capable of providing only deterministic analysis and there is requirement to provide risk analysis, a few solutions are possible:

  1. Integrate risk and decision analysis functionalities with the current user application. This may be the best solution from the user perspective since he/she would have the information displayed in one location, but predominantly requires custom programming implemented by software developers. This strategy is much simpler if risk and decision analysis functionalities are envisioned at the stage of application design.

  2. Manually execute the calculations in the user application multiple times. This would simulate the functions that decision and risk analysis tools perform. This process can be partially implemented without special tools by entering data to the application. However, this analysis is very limited and requires user understanding of each calculation. This option would not provide visualization of the decision being made and requires specialist knowledge from the user.

  3. Export data to another application that contains decision and risk analysis functionalities and can statistically analyze results. For example, data can be exported to a spreadsheet application with risk and decision analysis add-ins. This may be appropriate for simple problems and/or simple models. However, in most cases, this approach cannot support a complete workflow process: input data change, calculate input data, and visually present analysis results. Spreadsheets also have the hidden risk of version control and are prone to user adaptations that can unknowingly alter the results.

This paper presents a practical approach on how to use decision and risk analysis toolsets, including: a short overview of these toolsets, the application requirements for calculation engines, and the proper selection criteria for integrating these tools into software applications. Finally a case study is presented on how decision and risk analysis tools can be implemented in an economic evaluation application for the petroleum industry.

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