Using Marketing Technology in Reservoir Engineering: The Application of Data Driven Predictive Analysis on a Mature Oil Field
Authors: Zijlstra Ellen Bertina , Riethmuller Gael , Schaeftlein Susanne , Al Mahruqi Salima Saif , Naamani Ali , Wolters Frank Leonce 
Published: May 2016

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.

Introduction

The conventional method to generate infill well forecasts including uncertainties is by using full field 3D simulation models, which are time consuming to construct and maintain. The global trend in the petroleum industry is that hydrocarbon reservoirs become more and more mature with increasing datasets. Infill drilling opportunities become less economic, but require more simulation study effort, because there are more well data to calibrate models to and there are more basic data to include.

Therefore the question arises whether we can convert this "time accumulation effect" from a problem into a solution and find a faster method to produce infill well forecasts, without sacrificing reliability. This is indeed possible, by following a totally different, top-down forecasting method, which is referred to as Data Driven Predictive Analysis (DDPA).

Data Driven Predictive Analysis

DDPA has recently been developed and introduces marketing technology such as data mining (Coste, 2002) and Bayesian networks into the petroleum industry. Marketeers are used to handle huge amounts of data, which have been collected on customer bonus cards and which are used to find statistical correlations. The analogy is that mature oil fields often have an abundance of both basic data and production data, which can similarly be linked and utilised to obtain reliable forecasts including uncertainty ranges, in an extremely fast manner. The methodology will be illustrated in this paper by an application on the Najma field (code name) in the Sultanate of Oman. The Najma field is a heterogenous, high viscosity oil field with strong water drive and reservoir thickness up to 90 m. Production started in 1986. In total 83 producer wells have been drilled, both vertical and horizontal. The challenge addressed is to find optimal well locations for the next drilling campaign and generate reliable oil production forecasts in a short timeframe. The nomenclature in this paper is aligned with (Stone, 2008), the workflow steps are taken over from (Mohaghegh, 2003) and the practical execution method is similar to (Al Kinani, 2009). A new element is the strong integration with a full field static model. In another approach (Gomez, 2009) a top-down model is reconstructed completely from production and log data.