Predicting Waterflood Responses With Decision Trees
Authors: Fedenczuk L. , Hoffmann K. , Fedenczuk T. 
Published: March 2010

Adding new wells and new production in existing fields under Enhanced Oil Recovery (EOR) is particularly important in mature fields that are characterized by a long history of field activity. Different drilling programs, a variety of field treatments, well conversions and new injectors add many layers of complexity and uncertainty to the existing effects of geological, completion and production factors.

Surveillance and prediction of responses caused by injected fluids in fields with dozens of patterns and hundreds of wells calls for computer-based systems that estimate responses based on numerical and statistical solutions. This is especially important when geological understanding is very weak (i.e. no core, no log data).

This paper shows how results from EOR surveillance programs can be integrated with geological data. Furthermore, this paper shows how to build predictive models for production estimates based on injection responses and geology. These models support a two to three times more accurate selection of wells with high oil production during EOR than historically implemented selections.

Included in the paper are practical tips on how to select the best model and derive solutions with decision trees that are equivalent to sets of English-based rules. Solutions from decision trees are compared with solutions from logistic regression and neural networks. This comparison deals with the statistical accuracy of model predictions, interpretation ability and assisting in applying these models to support field decisions.

Introduction

The main goal of the study was to develop and test a model that would predict production performance during waterfloods. In predictive modelling, regression is traditionally applied to calculate continuous target variables(1). Models that predict binary response variables use logistic regression(2). A binary target variable is characterized by two events. They can be of a numerical nature (0 and 1) where zero represents a non-event and one represents an event. Alternatively, a character string with two outcomes (e.g. No and Yes) is often applied.

In the case of a continuous target variable, we may predict the fluid rates of oil, water, gas or the total fluids. The binary 0 or 1 target variable can represent a low or high production output, respectively. A production cutoff can be based on economical or engineering criteria applied to the actual rates or volumes in a specific time period.

This paper presents the principles of a numerical model development for predicting well performance during waterflood in the Pekisko B Field. The production performance predictions were based on geological and injection response parameters. The injection response parameter definition and non-numerical integration with geological data was presented in an earlier paper(3).

In this paper, the performance predictions were done for a binary target variable that identified wells with good performance. The performance was based on the actual normalized volume of production. A well's normalized production was defined as a ratio of the well production in a specific time period, to the total field production in the same time period. A binary target variable (indicator) of two levels (0 and 1) was assigned based on the normal-ized production.