A Statistical Model To Evaluate A Hydrocarbon Miscible Flood In An Upper Devonian Field In Central Alberta
Authors: Asgarpour Sohell , Papst Wolfgang 
Published: March 2010

The estimation of profitability indicators for an enhanced oil recovery scheme depends on several parameters such as incremental oil recovery, production profile, price forecast, and costs. Unfortunately, for most of these parameters, only a range may be available.

This paper presents a mathematical model based on the Monte Carlo technique to predict the profitability indicators of a hydrocarbon miscible flood in an Upper Devonian field in central Alberta. Based on a detailed geological study different reservoir types were identified in this field. A distribution of incremental oil was obtained from a wide range of geological and reservoir data. Simulation studies were performed to generate production forecasts for each reservoir type. This data was required for a Monte Carlo simulation study. Continuation of the Monte Corlo technique into the net present value analysis was also developed. Special consideration was given to test all relevant variables for dependency. The proposed model predicts the profitability indicators of the hydrocarbon miscible flood accounting for-the technical and economic risks associated with the miscible flood and the uncertainty of the oil price.

Introduction

Accurate economic evaluation of a hydrocarbon miscible flood (HCMF) where major capital costs and operating expenses have to be committed early in the life of the project is essential to the decision maker. The economic evaluation of a HCMF depends on several parameters such as production forecast, future oil and gas prices as well as capital and operating costs. The production forecast is usually obtained by a simulation study. For large pools a pseudo-miscible or a compositional model of the entire pool is not cost-effective because of the large numbers of grid blocks required for the simulation model of these pools. Therefore, the results of pattern elements representing geological prototypes are scaled up to generate the production forecast for the entire pool.

This forecast depends on several parameters such as original-oil-in-place (OOIP), residual oil saturation, relative permeabilities and mixing factor. In most cases these parameters have a wide range of possible values.

In a conventional economic analysis the uncertainty associated with production forecasts and other parameters is not taken into account. Sensitivity analyses may consider some degree of risk but the probability of occurrence of all possible levels of profitability cannot be displayed and the judgment of the evaluator usually influences the results.

To properly describe the risk and uncertainty associated with the implementation of a proposed major hydrocarbon miscible flood in central Alberta, the Monte Carlo simulation technique was used. The distribution of all possible values of the production forecast, oil and gas price forecast, and operating and capital investment were input into the Monte Carlo model to yielda distribution of all possible level of profitability indicators for this hydrocarbon miscible flood.

The Monte Carlo Model

The Monte Carlo Model developed for this hydrocarbon miscible flood follows the principles outlined in the literature(1,2). Input variables are entered in the form of distributions of all probable values.