Abstract

Many authors have presented methods of analyzing and history matching well production data. These methods include analytic models, finite difference models, convolution schemes, and mixed solution models. These methods typically yield reservoir and well characterization parameters.

Ideally, any production data analysis approach will provide methods to deal with variable rate and variable pressure data, allow determination of the flow regimes present in a given data set, allow various wellbore geometries, and incorporate fluid properties over the pressure depletion performance of the reservoir. The reality is that each model has individual strengths and weaknesses so analyses results may vary simply because of the model selected to interpret a particular data set. Combining several production analysis methods in one application allows an analyst to build on the strengths of several models to obtain a unique interpretation of a given data set.

This paper presents one approach to combining various single well reservoir simulation models, and demonstrates their use in analyzing and production data.

Introduction

A plethora of single well production data analysis models, ranging from empirical methods to analytic solutions of the diffusivity equation, are available to the practicing analyst. In an ideal world, one method would provide the "correct" results no matter what reservoir we are investigating or what characteristics we are attempting to quantify. In practice, we find that different analysis models frequently produce different results from the same dataset even though each analysis model may have a solid theoretical basis. As practicing analysts' we can take advantage of the strengths of each model to help us interpret a dataset. Use of several models should help us find a unique interpretation and to quantify the uncertainty we have in those results.

In addition to the underlying production model, the setting we use to examine the production data and select the appropriate history match can have significant influence on the interpretation results. Our analysis plots should help us include both the production rate and pressure data in the interpretation. It is also beneficial if the plots help us identify changes in well conditions or reservoir parameters.

Flow regime identification may be accomplished by combining the observed pressure draw-down and production rate into a single term (the pressure-rate ratio) and plotting this observed data versus time in semi-log, log-log and Cartesian scales. The plotting method is similar to and directly analogous to well test interpretation plots where only the pressure data is plotted. Once the flow regimes have been identified, reservoir and well parameters may be selected using the appropriate plot: the semi-log is used to pick permeability and skin; and the Cartesian plot is used to pick the onset of bounded reservoir behavior and hydrocarbon volume.

Once the reservoir and well parameters have been selected, single well models are used to history match the observed data in the pressure-rate ratio settings and in the more common production plots: semi-log producing rate and Cartesian producing pressure versus time.

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