Abstract

In this paper methodologies for reservoir characterization during underbalanced drilling is presented. In these methodologies we are using a transient wellflow model coupled to a transient reservoir model, and use estimation techniques to estimate reservoir properties. Our focus is to estimate the permeability and reservoir pressure along the well, using measured data usually available while drilling. The measured data are outlet rates, pump pressure and downhole pressure. The liquid injection and gas injection rates are used as input to the model. The methodologies are applied to synthetic cases.

Introduction

Underbalanced drilling is becoming increasingly popular. During an underbalanced drilling operation the well pressure should be kept below the formation pressure at all times. Since the well is producing while drilling, the well may be tested real time. Estimation of near wellbore characteristics of the formation gives important information when using smart completions since highly productive zones can be located.

Several recent papers1–4 have addressed well-testing during underbalanced drilling. Methodologies have been presented in these papers where the permeability profile in the near wellbore region is estimated based on the assumption that the total flow rate from the reservoir is known and that the reservoir pressure is assumed to be constant and known. An alternative technique1 has been presented where the assumption of known and constant reservoir pressure is not needed, but known total flow rate from the reservoir is still needed. The total flow rate from the reservoir is however not usually measured during an underbalanced drilling operation. As it is pointed out in Ref. 1 it is not straightforward to determine the total flow rate from the reservoir on the basis of the surface flow measurements. Compressibility of the fluids in the system may lead to loading and unloading in the wellbore, and mass flow rate at the surface is therefore affected by production, injection, and the change in mass stored in the wellbore as a function of time. It is therefore a need to develop methodology for reservoir characterization during underbalanced drilling that applies only data measured during the operations.

Some recent papers5,6 have addressed the challenge of calibrating a well flow model real time according to measured data. The advantage of having such a calibrated well flow model is that reliable predictions for the well conditions (like bottomhole pressure) can be given at all times. A limitation of these papers is that no reservoir models have been included.

In a previous paper7 a least square methodology to estimate near-well reservoir properties was presented. The present paper is an extension of this work. In the present paper the ensemble Kalman filter is introduced as an alternative method to estimate reservoir properties during underbalanced drilling. The importance of active tests during the drilling operation is also investigated.

The outline of the paper is as follows. The well flow model and the reservoir model are first described. Then two methodology for interpreting the reservoir properties from the measured data are described. The described methodologies are then applied to synthetic cases.

The description of the well flow model, reservoir model as well as the estimation methodologies, closely follows the presentations in Refs. 5–7, but are included here for the convenience of the reader.

Dynamic Well Flow Model

A dynamic model for describing the transient behavior of the two-phase flow conditions in LHD and UBD operations can be expressed with basis in the drift-flux formulation of the two-phase flow conservation laws8. Due to the complexity of the model, a numerical solution strategy is required.

The numerical scheme solves a set of three conservation equations, one for the mass of each phase and one for the mixture momentum. The mixture energy equation is not taken into account. Instead a fixed temperature profile in the well is used, which can be calculated in advance or provided by the data acquisition system.

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