Valuation of Supplemental and Enhanced Oil Recovery Projects With Risk Analysis
Authors: MacDonald R.C. , Campbell J.E. 
Published: December 2007

Advanced statistical techniques of risk analysis are applied to the valuation of proposed supplemental and EOR projects to assess the need for additional technical data and to improve the decision-making process. Financial evaluation must consider the technical complexities of the recovery process. To rely solely on historical production performance to assess the value of a given producing property is not practical. More reliance must be placed on the recovery-process models for reasonable projections of production profiles that will be the basis of financial projections of production profiles that will be the basis of financial evaluations. Uncertainties about the basic reservoir parameters in prediction models create uncertainties in the resulting financial prediction models create uncertainties in the resulting financial evaluations. Also, there are often substantial uncertainties in the financial factors used. Degree of uncertainty in a financial valuation is as important to the decision-making process as the estimate of a project's value. This paper presents techniques for determining the degree of uncertainty in a project valuation and which parameters contribute most to this uncertainty. We used a computer model to predict the future production profile. Uncertainty in model input is addressed by assigning appropriate distributions to input parameters. Model output uncertainty is assessed with a series of calculations with input values obtained by sampling the input distributions. The new aspect of this study is the use of the recently developed Latin hypercube sampling (LHS). LHS, more efficient than simple Monte Carlo sampling, readily allows important correlations between the input parameters considered, and permits the use of more sophisticated and appropriate recovery process models.

Introduction

Screening and ranking EOR projects must be based on data that are normally available through public records or other open sources. The additional oil recovered or incremental profitability that results from an EOR project is a function of several physical properties that describe the reservoir prospect. Screening criteria that are applied simply to the basic reservoir properties will not suffice in identifying promising EOR prospects. Instead, it is necessary to combine the reservoir properties and the tertiary oil recovery mechanics in a predictive model to estimate a production profile for each project. The resulting production profile is used with economic factors to assess incremental project profitability. A simple model for predicting the production profile of a five-spot CO2 flood was developed for this study. An important consideration in the screening process is the proper assessment of uncertainty in the outcomes predicted by the screening model. The degree of risk predicted by the screening model. The degree of risk associated with the application of a given process to a reservoir is as important as the most likely outcome. If the uncertainty in the basic data for the screening assessment is such that the range of possible outcomes is very broad, then EOR project selection and ranking is tentative at best. In this case, it would be valuable to identify those basic parameters that have the greatest influence on the parameters that have the greatest influence on the uncertainty of the outcome. A determination of the most important properties that affect the uncertainty of the evaluation would result in more definitive decisions in selecting candidates for application of EOR methods. In this paper, we used recently developed statistical techniques to assess the risk associated with a tertiary oil recovery CO2 project. The effect of uncertainties in the basic parameters on the final probability distributions of the outcomes is presented. The effect of correlations among basic properties is also examined. The risk analysis methods described in this paper are independent of the screening model. More sophisticated CO2, flood models, or models that describe other EOR processes, can be used with these techniques. All the calculations were performed on a commercially available microcomputer. There were no programming difficulties encountered because of either the size limitations or the computational speed of these relatively small machines.

Screening Model

The model in this study is based on the model reported by Claridge. The model may be separated into two distinct parts. The first part computes the production/ injection rate profile. The second part of the algorithm is an economic analysis of the production profile, and it results in a single profitability value for the cases studied. In this work, we have taken the net present value as the measure of project profitability. We assumed that the CO2 flood is carried out on a five-spot flood pattern.

JPT

P. 57