Offshore exploration requires the evaluation of hydrocarbon presence, estimation of volumes in place, and flow potential. To this capacity, formation testers are widely used to determine static data such as reservoir fluid gradients and reservoir pressure, obtain fluid samples, and to assess reservoir connectivity. Dynamic data, acquired with interval pressure transient testing and well testing techniques, are used to assess reserves and productivity. However, these evaluation techniques provide dynamic data at different resolution and length scales, and with different environmental footprint, cost, and operational constraints.

A new wireline formation testing technique known as deep transient testing (DTT) has been introduced, which combines high-resolution measurements, higher flow rates, and longer test durations to perform transient tests in higher permeability, thicker formation, and at greater depth of investigation than with previous formation testers - without flaring and at a low carbon footprint. The platform combines advanced metrology with extensive automation to generate unique, real-time reservoir insights.

Traditionally, pressure transient analysis and well deliverability predictions were produced through an analytical framework. Today, deep transient testing measurements are interpreted, and placed in reservoir context, in real-time by integration with geological and reservoir models. These steps can be performed from any wellsite utilizing cloud-based resources. Products such as reservoir fluid compressibility, saturation pressure, equation of state (EOS) models, well productivity, or minimum connected volumes are integrated in real-time interpretation utilizing numerical analysis. The digital infrastructure enables key reservoir insights to be shared between all stakeholders in a transparent and collaborative environment for both operational control and rapid decision making.

This paper presents a case study where the new DTT technique was combined with numerical analysis and real-time integrated workflows to characterize a multilayer reservoir in a recent discovery in deepwater Mexico. During the drawdown phase of the DTT operation, real-time downhole fluid analysis was used to determine the fluid composition, density, viscosity, compressibility, and saturation pressure. These fluid properties were then used to generate and tune an EOS model. Accurate drawdown flow rate measurements and the subsequent pressure transients were combined with the fluid model and geologic model to enable integrated pressure transient history matching. The resulting calibrated numerical model honors the fluid measurements and geologic model and was used to predict the permeability profile, zonal producibility, and the volume of influence of the test.

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