Improving Real-Time Image-Data Quality With A Telemetry Model

Abstract

Summary The introduction of new measurements constantly increases the amount of real-time data from logging-while-drilling (LWD) services, providing better information for informed decision making. This is matched by telemetry technologies that provide data rates from bits per second to megabits per second. During a deployment, the bandwidth is shared between several services requiring different data rates, leaving the user with the task of aligning telemetry technology and shared data rates to the application. High-volume services often allow for data compression. Lossless or near-lossless compression provides the best data quality but requires large bandwidth. Lossy compression reduces bandwidth usage dramatically, but may result in degraded data quality. Thus, telemetry planning must be tied closely to service objectives and requires expert advice. We present a planning approach in which we model the realtime-data compression of an LWD high-resolution imaging tool that uses a flexible compression algorithm. This system allows lossy compression for telemetry rates as low as 1 bps, while at the same time delivering memory-data quality using high-speed-telemetry technology. The compression rate determines the image resolution and must be adjusted to the detail required in the transmitted image. Using offset-well data or simulated memory data, the expected real-time image is simulated and its level of detail quantified. For instance, the presence and appearance of fractures in real time can be forecasted. This enables us to optimize telemetry usage and define compression and drilling parameters for a successful deployment of the imaging service. This paper will outline the methods and benchmark the technology of the imaging tool, using real-time data measured during field deployments. We will then use parameter variations to show how increasing net bandwidth improves the amount of detail in the images, and how these are used for different levels of realtime-data interpretation.