Wireline Formation Testing – Networking a Globally Distributed Team for Optimal Reservoir Characterization

Abstract

Abstract Accurate characterization of fluid and pressure regimes within a reservoir is a crucial part of the overall reservoir description process. In many offshore exploration and appraisal wells this task is performed using data acquired with wireline formation testers (WFT). Significant planning and simulation is required to properly design a WFT program. This is especially true when the anticipated environment is different from that normally encountered in the local region. Collaboration with a wider group of experts is required. Secondly, an iterative approach to the acquisition of WFT data is required, in which the results of the previous step determine the next step. This paper describes the process for WFT data acquisition within a major service company. We focus on two key aspects. First is the networked collaboration of domain experts around the world who are used as a resource for any given situation. Communication tools, sharing models, and networking options are discussed. Secondly, we examine the processes for real-time interactive communication with the wellsite and relevant stakeholders. Data transmission, real-time integration into reservoir models, and tools for operational interface are discussed. Finally we show field examples of these processes in use. In one example we document the gathering of best practices for sampling heavy oil in West Africa (heavy oil is a relatively rare occurrence in this region and sampling required the input of expertise from other regions). In a second example, we show the use of collaborative tools to optimize data acquisition for accurate reservoir characterization in the case of an exploratory well that encountered fluids very different from predrill expectations. And finally we show an example of using Interval Pressure Transient Testing (IPTT) and Vertical Interference Testing (VIT) data to economically pre-empt the acquisition of expensive DST data.