Using Marketing Technology in Reservoir Engineering: The Application of Data Driven Predictive Analysis on a Mature Oil Field

Abstract

Mature fields allow for a novel approach to generate forecasts for infill drilling, borrowing techniques developed in marketing. Fields with a long production history usually have an abundance of both basic data and production data, which can be linked and utilised to obtain reliable forecasts. This takes away the need to build a time-consuming, full field simulation model. The forecasting method is referred to as Data Driven Predictive Analysis. This document focuses on an application of Data Driven Predictive Analysis on a mature oil field in the Sultanate of Oman. A Data Mining Tool is used to find correlations between well performance and reservoir data. These correlations are investigated in order to construct the optimal Bayesian network to describe the link between basic field data and infill well performance. The relevant data from the Data Mining Tool are used to train the Bayesian network. The trained network predicts the performance of new infill wells based on their expected properties, which are derived from a static model. A new feature presented in this paper is the generation of a 2D grid of infill well forecasts including uncertainty ranges directly from the static model, by importing geological property grids into the Bayesian network. Water coning is taken into account by including perforation standoff as a function of distance around producing wells, as derived from radial single well models. TX 75083-3836, U.S.A., fax +1-972-952-9435