Overestimation in Operators Budgets and Long-Term Forecasting; A Non-Operator Perspective

Abstract

AbstractEBN is the Dutch state energy company that is a large non-operating partner of over 10 different operators that produce from more than 200 on- and offshore assets with more than 850 projects defined on them. Estimating budget production, medium and long-term forecasts and its associated operating and capital expenditures are of vital importance to EBN. Larger companies with many assets and even more projects, at varying degree of maturity, have great difficulty to reliably predict an aggregated forecast.Historically, EBN would copy and risk operator data, which led to continuous overestimation of both budget production and longterm forecasts. A straightforward correction method was developed; that consists of two parts: firstly, the budget production is set for all producing assets and projects by assessing technical, subsurface, infrastructural and human factors on the operator's fields and projects performance. Secondly, the medium and longterm forecast is delayed with 1 to 4 years for respective SPE PRMS resource classes "justified for development" to "project unviable" and the associated project forecasts are risked with a chance of development according to their subclasses of the contingent resource classes.Data analytics on almost 10 years of reserve reporting according to SPE PRMS standards led to a straightforward solution to reduce short and medium-term forecasting error. The short-term absolute average error used to be 8%. Through the implementation of the new method, 7 years ago, the absolute average short-term forecasting error dropped to 4%. The long-term aggregated forecast, obtained by simply copying the operator data, resulted in an overestimation of up to 50% 5 years ahead. The overestimation was reduced to an absolute average error of 23% by an earlier correction method, which only used risking factors on contingent projects, but no time delay. This paper presents a new method, that uses both risking factors and time delays on the realization of projects. The method reduced the error in the long-term forecast to an uncertainty band of a few percent.Various causes for the overestimation were identified. The budget production errors were primarily attributed to wrong uptime predictions. Longterm forecast errors are impacted by the overestimation of the number of executed projects, while the timing and performance of new projects affects both the short and middle term forecasts.The solution presented is the first methodology for EBN that is able to predict aggregated forecasts of hundreds of projects of several operators with an accuracy within a 5% margin over a lengthy period. The described risking factors described, and delay times, are dependent on the portfolio maturity and investment climate. Historic data has to be utilized to determine these factors for your portfolio.